Sensors & Transducers
International Official Journal of the International
Frequency Sensor Association (IFSA) Devoted to
Research and Development of Sensors and Transducers
Volume 193, Issue 10, October 2015
Editor-in-Chief
Prof., Dr. Sergey Y. YURISH
IFSA Publishing: Barcelona  Toronto
Copyright  2015 IFSA Publishing, S. L. All rights reserved.
This journal and the individual contributions in it are protected under copyright by IFSA Publishing, and the
following terms and conditions apply to their use:
Photocopying: Single photocopies of single articles may be made for personal use as allowed by national
copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying,
including multiple or systematic copyright, copyright for advertising or promotional purposes, resale, and all
forms of document delivery.
Derivative Works: Subscribers may reproduce tables of contents or prepare list of articles including abstract for
internal circulation within their institutions. Permission of the Publisher is required for resale or distribution
outside the institution.
Permission of the Publisher is required for all other derivative works, including compilations and translations.
Authors' copies of Sensors & Transducers journal and articles published in it are for personal use only.
Address permissions requests to: IFSA Publisher by e-mail: editor@sensorsportal.com
Notice: No responsibility is assumed by the Publisher for any injury and/or damage to persons or property as a
matter of products liability, negligence or otherwise, or from any use or operation of any methods, products,
instructions or ideas contained in the material herein.
Printed in the USA.
Sensors & Transducers
Volume 193, Issue 10,
October 2015
www.sensorsportal.com
e-ISSN 1726-5479
ISSN 2306-8515
Editors-in-Chief: Professor, Dr. Sergey Y. Yurish, tel.: +34 93 4137941, e-mail: editor@sensorsportal.com
Editors for Western Europe
Editors South America
Meijer, Gerard C.M., Delft Univ. of Technology, The Netherlands
Ferrari, Vittorio, Universitá di Brescia, Italy
Mescheder, Ulrich, Univ. of Applied Sciences, Furtwangen, Germany
Costa-Felix, Rodrigo, Inmetro, Brazil
Walsoe de Reca, Noemi Elisabeth, CINSO-CITEDEF
UNIDEF (MINDEF-CONICET), Argentina
Editor for Eastern Europe
Editors for Asia
Sachenko, Anatoly, Ternopil National Economic University, Ukraine
Editors for North America
Katz, Evgeny, Clarkson University, USA
Datskos, Panos G., Oak Ridge National Laboratory, USA
Fabien, J. Josse, Marquette University, USA
Ohyama, Shinji, Tokyo Institute of Technology, Japan
Zhengbing, Hu, Huazhong Univ. of Science and Technol., China
Li, Gongfa, Wuhan Univ. of Science and Technology, China
Editor for Asia-Pacific
Mukhopadhyay, Subhas, Massey University, New Zealand
Editor for Africa
Maki K., Habib, American University in Cairo, Egypt
Editorial Board
Abdul Rahim, Ruzairi, Universiti Teknologi, Malaysia
Abramchuk, George, Measur. Tech. & Advanced Applications, Canada
Aluri, Geetha S., Globalfoundries, USA
Ascoli, Giorgio, George Mason University, USA
Atalay, Selcuk, Inonu University, Turkey
Atghiaee, Ahmad, University of Tehran, Iran
Augutis, Vygantas, Kaunas University of Technology, Lithuania
Ayesh, Aladdin, De Montfort University, UK
Baliga, Shankar, B., General Monitors, USA
Barlingay, Ravindra, Larsen & Toubro - Technology Services, India
Basu, Sukumar, Jadavpur University, India
Booranawong, Apidet, Prince of Songkla University, Thailand
Bousbia-Salah, Mounir, University of Annaba, Algeria
Bouvet, Marcel, University of Burgundy, France
Campanella, Luigi, University La Sapienza, Italy
Carvalho, Vitor, Minho University, Portugal
Changhai, Ru, Harbin Engineering University, China
Chen, Wei, Hefei University of Technology, China
Cheng-Ta, Chiang, National Chia-Yi University, Taiwan
Cherstvy, Andrey, University of Potsdam, Germany
Chung, Wen-Yaw, Chung Yuan Christian University, Taiwan
Cortes, Camilo A., Universidad Nacional de Colombia, Colombia
D'Amico, Arnaldo, Università di Tor Vergata, Italy
De Stefano, Luca, Institute for Microelectronics and Microsystem, Italy
Ding, Jianning, Changzhou University, China
Djordjevich, Alexandar, City University of Hong Kong, Hong Kong
Donato, Nicola, University of Messina, Italy
Dong, Feng, Tianjin University, China
Erkmen, Aydan M., Middle East Technical University, Turkey
Fezari, Mohamed, Badji Mokhtar Annaba University, Algeria
Gaura, Elena, Coventry University, UK
Gole, James, Georgia Institute of Technology, USA
Gong, Hao, National University of Singapore, Singapore
Gonzalez de la Rosa, Juan Jose, University of Cadiz, Spain
Goswami, Amarjyoti, Kaziranga University, India
Guillet, Bruno, University of Caen, France
Hadjiloucas, Sillas, The University of Reading, UK
Hao, Shiying, Michigan State University, USA
Hui, David, University of New Orleans, USA
Jaffrezic-Renault, Nicole, Claude Bernard University Lyon 1, France
Jamil, Mohammad, Qatar University, Qatar
Kaniusas, Eugenijus, Vienna University of Technology, Austria
Kim, Min Young, Kyungpook National University, Korea
Kumar, Arun, University of Delaware, USA
Lay-Ekuakille, Aime, University of Lecce, Italy
Li, Fengyuan, HARMAN International, USA
Li, Jingsong, Anhui University, China
Li, Si, GE Global Research Center, USA
Lin, Paul, Cleveland State University, USA
Liu, Aihua, Chinese Academy of Sciences, China
Liu, Chenglian, Long Yan University, China
Liu, Fei, City College of New York, USA
Mahadi, Muhammad, University Tun Hussein Onn Malaysia, Malaysia
Mansor, Muhammad Naufal, University Malaysia Perlis, Malaysia
Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados, Mexico
Mishra, Vivekanand, National Institute of Technology, India
Moghavvemi, Mahmoud, University of Malaya, Malaysia
Morello, Rosario, University "Mediterranea" of Reggio Calabria, Italy
Mulla, Imtiaz Sirajuddin, National Chemical Laboratory, Pune, India
Nabok, Aleksey, Sheffield Hallam University, UK
Neshkova, Milka, Bulgarian Academy of Sciences, Bulgaria
Passaro, Vittorio M. N., Politecnico di Bari, Italy
Patil, Devidas Ramrao, R. L. College, Parola, India
Penza, Michele, ENEA, Italy
Pereira, Jose Miguel, Instituto Politecnico de Setebal, Portugal
Pillarisetti, Anand, Sensata Technologies Inc, USA
Pogacnik, Lea, University of Ljubljana, Slovenia
Pullini, Daniele, Centro Ricerche FIAT, Italy
Qiu, Liang, Avago Technologies, USA
Reig, Candid, University of Valencia, Spain
Restivo, Maria Teresa, University of Porto, Portugal
Rodríguez Martínez, Angel, Universidad Politécnica de Cataluña, Spain
Sadana, Ajit, University of Mississippi, USA
Sadeghian Marnani, Hamed, TU Delft, The Netherlands
Sapozhnikova, Ksenia, D. I. Mendeleyev Institute for Metrology, Russia
Singhal, Subodh Kumar, National Physical Laboratory, India
Shah, Kriyang, La Trobe University, Australia
Shi, Wendian, California Institute of Technology, USA
Shmaliy, Yuriy, Guanajuato University, Mexico
Song, Xu, An Yang Normal University, China
Srivastava, Arvind K., Systron Donner Inertial, USA
Stefanescu, Dan Mihai, Romanian Measurement Society, Romania
Sumriddetchkajorn, Sarun, Nat. Electr. & Comp. Tech. Center, Thailand
Sun, Zhiqiang, Central South University, China
Sysoev, Victor, Saratov State Technical University, Russia
Thirunavukkarasu, I., Manipal University Karnataka, India
Thomas, Sadiq, Heriot Watt University, Edinburgh, UK
Tian, Lei, Xidian University, China
Tianxing, Chu, Research Center for Surveying & Mapping, Beijing, China
Vanga, Kumar L., ePack, Inc., USA
Vazquez, Carmen, Universidad Carlos III Madrid, Spain
Wang, Jiangping, Xian Shiyou University, China
Wang, Peng, Qualcomm Technologies, USA
Wang, Zongbo, University of Kansas, USA
Xu, Han, Measurement Specialties, Inc., USA
Xu, Weihe, Brookhaven National Lab, USA
Xue, Ning, Agiltron, Inc., USA
Yang, Dongfang, National Research Council, Canada
Yang, Shuang-Hua, Loughborough University, UK
Yaping Dan, Harvard University, USA
Yue, Xiao-Guang, Shanxi University of Chinese Traditional Medicine, China
Xiao-Guang, Yue, Wuhan University of Technology, China
Zakaria, Zulkarnay, University Malaysia Perlis, Malaysia
Zhang, Weiping, Shanghai Jiao Tong University, China
Zhang, Wenming, Shanghai Jiao Tong University, China
Zhang, Yudong, Nanjing Normal University China
Sensors & Transducers Journal is a peer review international journal published monthly by International Frequency Sensor Association (IFSA).
Available in both: print and electronic (printable pdf) formats. Copyright © 2015 by IFSA Publishing, S. L. All rights reserved.
Sensors & Transducers Journal
Contents
Volume 193
Issue 10
October 2015
www.sensorsportal.com
ISSN 2306-8515
e-ISSN 1726-5479
Research Articles
Smart and Customized Electrical Conductivity Sensorfor Measurements
of the Response Time from SprayersBased on Direct Injection
Heitor V. Mercaldi, Caio H. Fujiwara,Elmer A. G. Peñaloza, Vilma A. Oliveira,
Paulo E. Cruvinel ...............................................................................................................
1
Dynamical Capillary Rise Photonic Sensor for Testing of Diesel
and Biodiesel Fuel
Michal Borecki, Michael L. Korwin-Pawlowski, Mariusz Duk, Andrzej Kociubiński,
Jarosław Frydrych, Przemyslaw Prus, Jan Szmidt ............................................................
11
Novel Smart Glove Technology as a Biomechanical Monitoring Tool
Brendan O’Flynn, J. T. Sanchez, S. Tedesco, B. Downes, J. Connolly, J. Condell,
K. Curran ............................................................................................................................
23
Wide Spectral Sensitivity of Monolithic a-SiC:H pi’n/pin Photodiode Outside
the Visible Spectrum
Manuela Vieira, Manuel Augusto Vieira, Isabel Rodrigues,Vitor Silva, Paula Louro,
A. Fantoni ...........................................................................................................................
33
Sub-nanosecond Gating of Large CMOS Imagers
Octavian Maciu, Wilfried Uhring, Jean-Pierre Le Normand, Jean-Baptiste Kammerer,
Foudil Dadouche, Norbert Dumas......................................................................................
41
Superpixel Compressive Sensing Recovery of Spectral Images Sensed
by Multi-patterned Focal Plane Array Detectors
Yuri H. Mejia, Fernando A. Rojas, Henry Arguello.............................................................
50
Advanced Controlled Cryogenic Ablation Using Ultrasonic Sensing System
Assaf Sharon, Gabor Kosa ................................................................................................
57
Cavity Enhanced Absorption Spectroscopy in Air Pollution Monitoring
Janusz Mikołajczyk, Zbigniew Bielecki, Jacek Wojta, Sand Sylwester Chojnowski ..........
63
Design and Analysis of a Collision Detector for Hybrid Robotic Machine Tools
Dan Zhang, Bin Wei ...........................................................................................................
67
Numerical Signal Analysis of Thermo-Cyclically Operated MOG Gas Sensor
Arrays for Early Identification of Emissions from Overloaded Electric Cables
Rolf Seifert, Hubert B. Keller, Navas Illyaskutty, Jens Knoblauch and Heinz Kohler ........
74
Analysis of the Planar Electrode Morphology Applied to Zeolite Based
Chemical Sensors
Luiz Eduardo Bento Ribeiro, Glaucio Pedro de Alcântara, Cid Marcos Gonçalves
Andrade, Fabiano Fruett ....................................................................................................
80
An Empirical Study for Quantification of Carcinogenic Formaldehyde
by Integrating a Probabilistic Framework with Spike Latency Patterns
in an Electronic Nose
Muhammad Hassan, Amine Bermak, Amine Ait Si Ali and Abbes Amira ..........................
86
Alternative Processes for Manufacturing of Metal Oxide-based
Potentiometric Chemosensors
Winfried Vonau, Manfred Decker, Jens Zosel, Kristina Ahlborn, Frank Gerlach,
David Haldan and Steffen Weissmantel ...........................................................................
93
Improvement in Humidity Sensing of Graphene Oxide by Amide
Functionalization
Sumita Rani, Dinesh Kumar, Mukesh Kumar .................................................................... 100
PbS Infrared Detectors: Experiment and Simulation
S. Kouissa, A. Djemel, M. S. Aida, M. A. Djouadi .............................................................. 106
Amplitude to Phase Conversion Based on Analog Arcsine Synthesis
for Sine-cosine Position Sensors
Mohieddine Benammar, Antonio Jr. Gonzales .................................................................. 114
New Design-methodology of High-performance TDC on a Low Cost
FPGA Targets
Foudil Dadouche, Timothé Turko, Wilfried Uhring, Imane Malass, Norbert Dumas,
Jean-Pierre Le Normand .................................................................................................... 123
Experiences in Automation and Control in Engineering Education with Realworld Based Educational Kits
Filomena Soares, Celina Pinto Leão,José Machado and Vítor Carvalho .......................... 135
Improving Systems Dynamics by Means of Advanced Signal Processing –
Mathematical, Laboratorial and Clinical Evaluation of Propofol Monitoring
in Breathing Gas
Dammon Ziaian, Philipp Rostalski, Astrid Ellen Berggreen, Sebastian Brandt,
Martin Grossherr, Hartmut Gehring, Andreas Hengstenberg and Stefan Zimmermann ... 145
The Use of Gas-Sensor Arrays in the Detection of Bole and Root Decays
in Living Trees: Development of a New Non-invasive Method of Sampling
and Analysis
Manuela Baietto, Sofia Aquaro, A. Dan Wilson, Letizia Pozzi, Daniele Bassi ................... 154
Motor Bourn Magnetic Noise Filtering for Magnetometers
in Micro-Rotary Aerial Vehicles
Nathan J. Unwin, Adam J. Postula..................................................................................... 161
Reflection from Disordered Silver Nanoparticles on Multilayer Substrate
Victor Ovchinnikov ............................................................................................................. 170
Performance Analysis of Commercial Accelerometers: A Parameter Review
Stephan Elies ..................................................................................................................... 179
Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats
by e-mail: editor@sensorsportal.com. Please visit journal’s webpage with preparation instructions:
http://www.sensorsportal.com/HTML/DIGEST/Submition.htm
International Frequency Sensor Association (IFSA).
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Smart and Customized Electrical Conductivity Sensor
for Measurements of the Response Time from Sprayers
Based on Direct Injection
1, 2
1
2
Heitor V. MERCALDI, 1, 2 Caio H. FUJIWARA,
1, 2
Elmer A. G. PEÑALOZA, 2 Vilma A. OLIVEIRA,
1
Paulo E. CRUVINEL
Embrapa Instrumentação, Rua XV de Novembro 1452, São Carlos, SP, 13560-970, Brazil
Universidade de São Paulo, Av. Trabalhador Sãocarlense 400, São Carlos, SP, 13566-590, Brazil
1
Tel.: (+55)1621072800, fax: (+55)1621075754
1
E-mail: paulo.cruvinel@embrapa.br
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: In the application of herbicides on the basis of direct injection systems, spraying response time plays
an important role for the quality of spraying, particularly when operating in real time. The response time is
defined as the time elapsed from the time of injection until the concentration of the mixture (water mixed with
herbicide) reaches 95 % of its regime value in the sprayer nozzles. In the response time, the transport delay and
the rise time for achieving the desired concentration are considered. This paper describes an intelligent sensor
mounted near the sprayer nozzles to measure the concentration response time in an herbicide direct injection
system, which uses a highly stable sinusoidal excitation signal. The sensor calibration was performed with NaCl
solutions at concentrations similar to those found in actual application conditions. Using an integrated system
based on the Arduino platform, an algorithm was developed to relate the measurements to the response time.
The integrated system comprises the sensor with its own sensing hardware, A/D converter, processing and
storage capabilities, software drivers, self-assessment algorithms and communication protocols. The immediate
application of the integrated system is in the monitoring of the response time of a precision herbicide
application. The results point to the next generation of smart devices that have embedded intelligence to support
decision making in precision agriculture. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Intelligent sensor, Electrical conductivity, Direct injection, Response time.
1. Introduction
Brazil has experienced in the last two decades a
significant increase in the use of pesticides for
agricultural production. Despite the significant
growth of the area cultivated with transgenic seeds, a
technology that promises to reduce chemical use in
agricultural production, sales of these products
increased by over 72 % between 2006 and 2012 and
http://www.sensorsportal.com/HTML/DIGEST/P_2729.htm
is still rising up according to data from the Brazilian
National Union of the Industry of Agricultural
Defense Products [1], association which represents
the pesticide manufacturers in the country.
In In the same period, the cultivated area with
grains, fiber, coffee and sugar cane grew by less than
19 %, from 68.8 million to 81.7 million hectares,
according to the Brazilian National Company for
Supply [2]. This means that the average consumption
1
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
of pesticides, which was just over 7 kilograms per
hectare in 2005, rose to 10.1 kilograms in 2011, an
increase of 43.2 %. Although this amount
indicates more protection for products and higher
incomes, the uniform rate of application leads
to soil and water contamination. A key approach to
reduce environmental pollution is to use
variable-rate application.
An approach to develop variable-rate sprayer
technologies is to install automation and control
procedures in conventional sprayers. In order to
adjust the sprayer operation, reference for variables
such as working pressures, travelling speeds, and
spraying concentration rates can be selected to
achieve uniform drop size distribution.
The agricultural machinery and technologies
available today allow chemical application variable
rate based on prescription maps or sensors [3].
Variable-rate application can be performed by
varying the concentration of the chemical on-the-go
using a direct injection system [4]. The direct
injection system is an electronically controlled
system in which the chemical is injected into the
carrier stream. The direct injection system has
separated chemical and carrier reservoirs and the
chemical can be injected into the carrier stream in
different positions.
In the literature, reports of systems to inject
concentrated pesticides into the carrier stream began
to appear in the 70th decade [5]. In [6], Vidrine and
collaborators tested the feasibility of injecting
concentrated pesticides. In [7], Reichard and Ladd
developed a field sprayer which included injection of
pesticides at specific rates accounting for variations
in travel speed. In [8], Chi and collaborators
developed a flow rate control system which allowed
the measurements of concentrated pesticides. In [9],
Ghate and Perry developed a field sprayer based on
the use of a compressed air to inject chemical into the
carrier stream. In [10], Miller and Smith reported the
development of a direct injection system. In general,
during the spraying process errors can be observed.
Research works on the evaluation of the application
rate errors have shown that errors are not only due to
the deviations from the target flow rates but also due
to interaction between the dynamics of the systems
and sprayer response time. By now, is quite well
known that the direct injection system sprayer
response time depends on the sprayer dynamics and
on the transport delay [11].
The transport delay is due to flow rates and
distance of the nozzle from the injection point. The
farther from the injection point the nozzle is, the
larger the uniformity of the mixture, but the higher
the transport delay of the sprayer. Several studies on
the performance of direct injection sprayers and the
response time have appeared [12-20]. Therefore, the
conventional implements can be reorganized to
operate in variable-rate using control systems [21].
An advantage of the injection rate application
over pressure-based variable rate application is the
ability to change the herbicide type as well as to
2
perform on-line changes in the concentration [22].
The direct injection systems advantage is in the
mixing of the required amount of chemicals with
water, saving the excess amount for later use [23]. A
key indicator to determine the precision of a direct
injection sprayer is the control system response time.
For sprayers, how much shorter the response time,
much higher will be its field precision.
This paper presents the complete version of a
smart and customized conductivity sensor (SCCS) for
the evaluation of the response time of direct injection
sprayers based on the electrical conductivity
measurements. Previous discussions related to its
development were presented in [24], and [25]. With
the response time measurements in variable rate
sprayers, a looking-ahead approach, which is useful
to increase competitiveness and support sustainability
in agriculture can be performed.
After this introduction, this paper is organized as
follows. Section 2 presents the theoretical
background on electrical conductivity; Section 3
presents the materials and methods for the
development of the SCCS and the procedures for its
validation. Finally, the results and discussions are
presented in Section 4, followed by the conclusion
in Section 5.
2. Theoretical Background
The electrical conductivity, also called specific
conductance, is the ability of a solution to conduct an
electric current. The mechanism for the electrical
current conduction in electrolyte solutions is not the
same as for metals. In liquids, this process is based
on the movement of solvated ions, which are
attracted by an electrical field. Therefore, the
physical-chemical process is related to the occurrence
of combination between the molecules of a solvent
with molecules or ions of the dissolved substance. As
electrolyte solutions obey Ohm’s law in the same
way as the metallic conductors, when powered by
direct current passing through the body of the
solution, the conductance denoted G is defined as the
inverse of the resistance expressed in Ω −1 or Siemens
(S). The conductance G of a homogeneous body
having uniform section is proportional to the crosssectional area of the conductor A and inversely
proportional to the length of the conductor denoted
by l, that is:
G=
σA ,

(1)
where the proportionality constant σ is the electrical
conductivity given in S/m. The ratio l /A is called the
conductivity cell constant and depends on the
instrumentation used. The conductivity increases
with increasing temperature. Furthermore, the
conductivity of a solution depends on the number of
ions present and for this reason the most common is
the use of the molar conductivity defined as:
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
Λm =
σ ,
(2)
M
where Λm is the equivalent conductivity or the molar
conductivity in Sm2/mol and M is the molarity or
molar concentration in mol/L. The molar
conductivity varies with the concentration of the
electrolytes. The main reason for this effect is the
change in the number or mobility of the ions present.
The first case occurs in weak electrolytes, where the
dissociation of ions in a solution is not complete. The
second case occurs on strong electrolytes, where in
the solution the dissociation of the molecule into ions
is total, resulting in a very strong interaction between
the oppositely charged ions, and can reduce
its mobility.
The measurements of electrical conduction in
ionic solutions are useful for a quick and routine
analysis of solutions, since it is a simple measure
related to the properties of the solution. In this
context, the conductivity of a solution in a cell having
an arbitrary dimension can be obtained by measuring
the resistance of a solution of known concentration to
determine the cell constant. After the cell constant is
determined, the values of conductivities of different
solutions can be obtained from experimental
measurements data. For devices without automatic
temperature compensation, the conductivity must be
determined at the reference temperature.
The measurement of absolute values of
conductivity requires the use of linear temperature
compensation. Therefore, an electrical conductivity
measured at room temperature can be corrected to
one reference temperature, such per example, 25°C
as follows:
G25 =
Gθ
,
1 + (α 100 )(θ − 25)
Thus, turning the unit concentration mol/L to
mol/cm3, the equivalent conductivity Λm between two
electrodes spaced 1 cm away due to 1 mol of
substance may be given as:
Λm =
1000σ corrected
M
(5)
Then, for a parallel plate sensor, the conductance
G can be determined based on the molar conductivity
Λm. The corrected specific conductivity of the
electrolyte is then given in terms of the total ionic
concentration M (mol/cm3) of the substance in the
electrolyte solution and the equivalent conductivity.
Therefore, by using the Equation (1), the conductance
G can be found as:
Λ M
G ( A, , Λ m , M ) =  m
 1000
 
× A 
  
(6)
Peck and Roth defined response time (tT) as the
period from the instant the injection begins until the
chemical concentration rate reaches 95 % of the
equilibrium rate [27]. The rise time (tr) and
transport delay (td) characteristics of a sprayer
proposed by these authors are shown in Fig. 1.
A 95 % concentration rate corresponds to the
chemical concentration of the spraying, which is
necessary for satisfactory weed control [28].
(3)
where θ is the room temperature, Gθ is the
conductivity measured at room temperature and α is
the temperature coefficient of variation in %/°C.
Typical values for temperature coefficients are given
in Table 1 [26].
Table 1. Typical temperature coefficients of substances.
α (%/°C)
1.0 to 1.6
1.8 to 2.2
2.2 to 3.0
around 2.0
Substance
Acids
Bases
Salts
Potable water
In solutions, yet it is necessary to correct the
conductivity observed by subtracting the conductivity
of the solvent, to get the value of σcorrected. Therefore,
the molar conductivity Λm shall be written as:
Λm =
σ corrected
M
(4)
Fig. 1. Delay time (td), rise time (tr), and response time (tT)
of a typical injection system as described in [27]. The
dotted line indicates the time behavior of the concentrated
mixture (water-NaCl) as a response to an injection input.
The response illustrated in Fig. 1 can be identified
as a first order system plus delay time. The time
response is given by:
Tr = Td + 3Tc ,
(7)
where 3Tc is the requested time to reach 95 % in
concentration in relation of the steady state value,
i.e., after Td seconds [29].
3
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
3. Material and Methods
The injection sprayer systems can reduce
applicator exposure to chemicals during the mixing
and loading process. A direct injection sprayer has a
typical control loop as shown in Fig. 2. In this figure,
the upper blocks indicate the direct injection
components and corresponding variables qhref, Vh,
and qh, which represent the set point for the chemical
flow,
controlled,
and
measured
variables
respectively. In the lower blocks, at the some figure,
is possible to observe the sprayer components, which
are described as qfref, Vf, and qm, which represent the
set point for the mixture flow, controlled, and
measured variables respectively. In this type of direct
injection sprayer, the injection point is located
upstream from the sprayer pump as presented in [30],
and [31]. The water flow qW is dependent of both the
flow mixture qm and the injection flow qh. The
customized smart sensor is assembled at the nozzle in
the end of the boom to measure the flow mixture
concentration, which is proportional to its output
denoted vSCCS.
Fig. 2. Block diagram of herbicide and mixture control.
The components of the customized smart sensor
designed to measure the response time in spraying
systems using direct injection of pesticides are shown
in Fig. 3. For the implementation of the smart sensor,
voltage regulators, opto-isolators and filters, as well
as an integrated circuit for signal generator having
the capability of frequencies adjustment were used.
Active analogue filters, non-inverting amplifier
drives and isolators circuits were implemented with
operational amplifiers.
appropriate frequency and magnitude. Such module
was designed to produce signals with high stability
and accuracy in an operating frequency range of
0.01 Hz to 1 MHz (Fig. 5).
Fig. 4. Block diagram for the excitation circuitry.
Fig. 3. Block diagram of the SCCS for response time
measurements in spraying systems based on direct injection
of pesticides.
3.1. Excitation Circuits
An excitation circuitry (Fig. 4) for the SCCS was
implementing to provide a sinusoidal signal with
4
Fig. 5. The circuit of the excitation signal generator.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
For the generation of the sinusoidal signal, an
XR2206 integrated circuit, which produces sinusoidal
signals with considerably low harmonic distortion,
was used [32]. The oscillator frequency was set to
1.0684 kHz, which is suitable for the application and
reducing the electrolysis and the polarization of the
solution. In order to tailor the sinusoidal signal for
the sensor application considered, a high-pass
filtering and a signal amplification module were also
used. After conditioning, the signal was appropriate
for use presenting voltage limits as Vmax = 4.96 V and
Vmin = -4.92 V.
The output signals of each of the stages of the
excitation circuitry were analyzed using an
oscilloscope (model TDS2012B, Tektronix®) and
graphics were later built in Matlab® software.
3.2. Signal Conditioning
A signal conditioning circuitry was implemented
to obtain a precise continuous voltage level with
appropriate magnitude to be connected to an Arduino
Uno platform. The Arduino Uno is an electronic
prototyping platform, hardware open and single
board, designed with an Atmel AVR microcontroller
with built-in input-output support and a standard
programming language with origin in Wiring
projects, essentially based on C/C ++ [33]. The
circuitry comprises a precision rectifier, a second
order low-pass filter and an isolator buffer
circuit (Fig. 6).
Fig. 6. Block diagram of the signal conditioning circuitry.
For signal conditioning (Fig. 7), the operational
amplifier integrated circuit (model LF347), which
presents broad bandwidth range (4 MHz), high SlewRate (13 V/s), high impedance input (1012 Ω), and
fast settling time (2 µs) was used [34]. A buffer
circuit was implemented with a low voltage
operational amplifier (model OPA344) with an
output type Rail to Rail [35] to isolate and protect the
Arduino Uno platform against voltage surges or
malfunctioning of the designed circuits.
The output of the buffer circuit is limited to
voltages from 0 V to 5 V, safe input voltage range for
the analogue/digital converter (ADC) of the Arduino
Uno platform. This ADC has 6 channel 10-bit
resolution with absolute accuracy of ±2 LSB
(≈15 mV), and maximum sample rate of 15 kS/s.
The frequency response and impedance
characteristics of the buffer circuit were
analyzed through computer simulations performed in
LTspice® software.
Fig. 7. The circuit for signal conditioning.
3.3. Sensor Mounting and Calibration
Based on the theoretical backgrounds previously
presented, a set of parallel plates conductivity type
transducers was built and analyzed for measuring the
response time based on the electrical conductivity of
the mixed flow in an agricultural direct injection
spraying system. Two stainless steel electrodes with a
diameter of 5 mm were used. The transducer was
constructed with a polyacetal base and assembled
direct into the nozzle body equipped with a
diaphragm check valves. Fig. 8 illustrates the
positioning and location of the SCCS for response
time measurements.
To analyze the sensor with a static fluid, the
electrodes were coupled to the base and spaced at
three different distances chosen as 0.5 mm, 1.5 mm
and 1 mm, resulting in constant cells equal to
0.255 cm-1, 0.500 cm-1 and 0.764 cm-1, respectively.
The calibration was performed using a commercial
conductivity meter (model mCA150, Tecnopon),
with an operating range of 0 to 200 µS/cm, resolution
of 0.1 µS/cm, 2 % of full scale accuracy and 1 % of
full scale precision [37]. The measurements
performed with the commercial conductivity meter
were checked with a standard KCl solution
(0.02000 mol/L). Static tests were carried out for the
analysis of solutions consisting of water and NaCl.
The procedures were conducted for three different
cell constants at 25 oC.
3.4. Response Time Measurements
In order to validate the developed sensor, a real
experiment to measure the response time of a sprayer
system based on direct injection was performed. The
experiment was set for a cell constant of 0.500 cm-1.
For real time analysis, the conductivity was
measured and the results were processed via
LabVIEW® software, i.e., using the Arduino Uno
platform, which allows the computational processing
and intelligence aggregation [36].
The initial concentration of the injected NaCl
solution was 50 g of salt for 16 L of potable water.
This NaCl solution was injected upstream from the
5
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
sprayer pump at 2.3 L/min during 30 seconds. The
mainstream flow was regulated at 16 L/min and
23 L/min with pressure at 200 kPa and 400 kPa,
respectively. For each regulated flow, 3 repetitions of
the response time measurements were conducted.
To obtain a time constant Tc in a range of
variation lower than 5 %, the liquid temperature
could not vary more than ±2.5oC during the
conductivity acquisitions. This threshold was
determined based on simulation using the Equations
(3) and (7), as well as calibration curves. For
validation, and in order to consider a general
scenario, it was used a temperature coefficient of
variation (α) equal to 3.0 %/°C.
Fig. 8. Details of the customized smart sensor (SCCS) to measure the response time of direct injection sprayers.
The individual time responses and the transport
delay time for each repetition were analyzed in an
actual spraying system. Fig. 9 illustrates the sensor
location in the actual sprayer system.
Fig. 9. Instrumental arrangement for validation
of the intelligent sensor to measure response time
of a direct injection sprayer with TeeJet® QJS Multiple
Nozzle Bodies e-ChemSaver.
4. Results and Discussion
The computational processing of data and
analysis with the developed conductivity sensor was
performed using the LabVIEW® software, after
signal conditioning and the analogue/digital
6
conversion with the Arduino Uno platform. The use
of the electronic Arduino Uno platform and the
LabVIEW® interface allowed the aggregation of
intelligence for self-diagnostic of the SCCS,
as well as its self-assessment based on the use
of a specific algorithm (Fig. 10). The data valid
flag was determined experimentally based on the
dynamic range of the SCCS defined by
0.50 V ±2LSB < vSCCS ≤ 4.90 V ±2LSB, which is
related with the accuracy allowed by the internal
ADC of the Arduino Uno platform.
The calibration results identified the electrodes
faces distance for a better accuracy of the
conductivity sensor which is dependent on the
conductivity cell constant of the transducer. The
shorter the distance between the faces of the
electrodes for the same cross-sectional area, the
greater will be the accuracy of the measurements.
The calibration curves are shown in Fig. 11, Fig. 12
and Fig. 13 for constant cells given respectively by
0.255 cm-1, 0.500 cm-1 and 0.764 cm-1. Table 2 shows
the experimental values obtained for determining the
response time tT.
A first set of experiments were conducted and the
results are shown in Fig. 14. In this case, a waterNaCl solution flow of 16 L/min and pressure of
200 kPa were used. The dynamic responses obtained
for the three repetitions demonstrated the sensor
accuracy and reliability.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
Fig. 12. Calibration curves and comparison
of measurements of the electrical conductivity
from experimental solutions obtained with the SCCS
with cell constant of 0.500 cm-1.
Fig. 10. The flow-diagram of the algorithm
for self-assessment and self-diagnostic.
Fig. 13. Calibration curves and comparison
of measurements of the electrical conductivity
from experimental solutions obtained with the SCCS
with cell constant of 0.764 cm-1.
Table 2. Experimental values of delay and response times
(Mean is Average Values, Std is the Standard Deviation,
and CV is the Coefficient of Variation).
Fig. 11. Calibration curves and comparison
of measurements of the electrical conductivity
from experimental solutions obtained with the SCCS
with constant cell of 0.255 cm-1.
Repetitions
Flow
and
(L/min) statistical
analysis
1st
2nd
3rd
16
Mean
Std
CV
1st
2nd
3rd
23
Mean
Std
CV
Vmin
(0%)
Vmax
(100%)
td (s)
tT (s)
1.58
1.57
1.57
1.58
0.008
0.005
1.56
1.53
1.55
1.55
0.012
0.008
4.16
4.17
4.15
4.16
0.010
0.002
3.99
3.96
3.97
3.97
0.013
0.003
29.56
28.80
28.36
28.91
0.607
0.021
22.53
22.52
23.09
22.71
0.326
0.014
42.48
41.55
41.40
41.81
0.585
0.014
32.81
33.01
33.77
33.20
0.506
0,015
7
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
make integrated circuits more vulnerable to
electrical failure.
The integrated circuits used in the intelligent
sensor are new generations of electronic devices and
they provided good performance.
Furthermore, the use of the polyacetal base
was adequate and results have shown a robust
mechanical design.
The reliability assessments are crucial for the end
user as adjustments to electrical conditions and
thermal
management,
since
the
electrical
conductivity is dependent on the temperature of the
flows related to the mixture of water plus pesticide.
5. Conclusions
Fig. 14. Transients and transport delay times of the sprayer
with the SCCS assembled in an actual spraying system
with water-NaCl solution flow of 16 L/min and pressure
equals to 200 kPa.
For this experimental arrangement, the
average delay time was 28.91 s and the average
response time 41.81 s.
A second set of experiments were conducted and
the results are shown in Fig. 15. In this case, a waterNaCl solution flow of 23 L/min and pressure of
400 kPa were used. The results obtained for the
sprayer system having a flow rate equal to 23 L/min
and pressure of 400 kPa, as occurred previously, have
shown again, accuracy and reliability in the
measurements obtained with the developed sensor.
The average delay time was of 22.71 s and the
average response time 33.20 s.
Fig. 15. Transients and the transport delay times obtained
with the SCCS assembled in an actual spraying system
with water-NaCl solution flow of 23 L/min and pressure
equal to 400 kPa.
Also, it is important to notice that the use of faster
circuits implies higher current densities, lower
voltage tolerances and higher electric fields, which
8
A smart and customized sensor to measure the
response time of spray systems based on direct
injection was presented. The results have shown its
usability in real time applications. The decision to
embed the smart sensor directly in the sprayer nozzle
provides a scenario where the input data from the
physical sensor could be analyzed by various
knowledge-based routines. The sensor output could
be raw data or preprocessed information. This
information could be in the form of a flag, which
shows a confidence level of the response time for
pesticide applications.
The results based on the calibration curves for the
sensor in three different assemblies showed that the
accuracy of measurements depends directly on the
conductivity cell constant. However, to determine the
response time of a direct injection system of
pesticides, a customized sensor with shorter spacing
between its electrodes can provide an adequate
sensitivity for sensing the level of the concentration
in the mixture involving pesticide plus water.
The results of the sprayer system response time
with direct injection obtained in this research work
have shown that the smart sensor developed has good
repeatability, reliability and practicality. Furthermore,
the results show the decreasing of the response time
with the increasing of the flows in consequence of the
increased speed in which the mixture of water and
pesticide travels through the system.
The use of an intelligent sensor provides more
additional information than that of traditional sensors.
The information provided by an intelligent sensor can
include actual data, corrected data, validity of the
data, and reliability of the sensor.
Furthermore, such SCCS development meets
future prospects in practical applications, bringing
potential benefits for sustainability, as well as
precision agriculture processes.
Acknowledgements
The authors acknowledge the financing
support from the Brazilian Agricultural Research
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
Corporation (Embrapa Instrumentation, Process MP2
No. 02.11.07.0.25.00.00) as well as from the CNPq
Brazilian agency under grant 306477/2013-0.
References
[1]. SINDAG. SINDAG and Embrapa study agchem
aerial application in Brazil, Accessed at March 5,
2015. [Online]. Available:
http://news.agropages.com/News/NewsDetail—
13170.htm (2014).
[2]. CONAB, Monitoring of the Brazilian grain crops season 2013/14 (original in Portuguese), Brazilian
National Company for Supply, Tech. Rep., 2014.
[3]. M. Sökefeld, Variable rate technology for herbicide
application, in Precision Crop Protection - the
Challenge and Use of Heterogeneity. Springer
Verlag, 2010, pp. 335-347.
[4]. J. Vondrica, Study on the process of direct nozzle
injection for realtime site-specific pesticide
application, Ph.D. dissertation, Institut für
Landtechnik der Rheinschen Friedrich-WilhelmsUniversität, 2008.
[5]. K. L. Hughes, A. R. Frost, A review of agricultural
spray metering, Journal of Agricultural Engineering
Research, Vol. 32, Issue 3, 1985, pp. 197-207.
[6]. C. G. Vidrine, C. E. Goering, C. L. Day,
M. R. Gebhardt, D. B. Smith, A constant pesticide
application rate sprayer model, Transaction of the
ASAE, Vol. 18, Issue 3, 1975, pp. 439-443.
[7]. D. L. Reichard, T. L. Ladd, Pesticide injection and
transfer system for field sprayers, Transactions of the
ASAE, Vol. 26, Issue 3, 1983, pp. 683-686.
[8]. L. Chi, R. L. Kushawara, F. W. Gigsby, Chemical
flow rate control in injection-type sprayers, Canadian
Agricultural Engineering, Vol. 30, Issue 1, 1988,
pp. 19-26.
[9]. S. R. Ghate, C. D. Perry, Ground speed control of
pesticide application rates in a compressed air direct
injection sprayers, Transaction of the ASAE, Vol. 37,
Issue 1, 1994, pp. 33-38.
[10]. M. S. Miller, D. B. Smith, A review of application
error for sprayers, Transactions of the ASAE, Vol. 35,
Issue 3, 1992, pp. 787-791.
[11]. P. S. Lammers, J. Vondricka, Direct injection
sprayer, in Precision Crop Protection - the Challenge
and Use of Heterogeneity, Springer Verlag, 2010,
pp. 295-310.
[12]. R. S. Budwig, J. M. McDonald, T. J. Karsky,
Evaluation of chemical injection system for mobile
agricultural spray equipment, Transaction of the
ASAE, 1988, Paper No. 88-1592.
[13]. F. D. Tompkins, K. D. Howard, C. R. Mote,
R. S. Freeland, Boom flow characteristics with direct
chemical injection, Transaction of the ASAE, Vol. 33,
Issue 3, 1990, pp. 737-743.
[14]. K. A. Sudduth, S. C. Borgelt, J. Hou, Performance of
chemical injection sprayer system, Applied
Engineering in Agriculture, Vol. 11, Issue 3, 1995,
pp. 343-348.
[15]. Y. M. Koo, H. R. Sumner, Total flow control for a
direct injection sprayer, Applied Engineering in
Agriculture, Vol. 14, Issue 4, 1998, pp. 363-367.
[16]. H. Zhu, R. D. Fox, H. E. Ozkan, R. D. Brazee,
R. C. Derksen, A system to determine lag time and
mixture uniformity for inline injection sprayers,
[17].
[18].
[19].
[20].
[21].
[22].
[23].
[24].
[25].
[26].
[27].
[28].
[29].
[30].
Applied Engineering in Agriculture, Vol. 14, Issue 2,
1998, pp. 103-110.
E. A. Anglund and P. D. Ayers, Field evaluation of
response times for a variable rate (pressured-based
and injection) liquid chemical applicator, Applied
Engineering in Agriculture, Vol. 19, Issue 3, 2003,
pp. 273-282.
P. Hloben, Study on the response time of direct
injection systems for variable rate application of
herbicides. PhD dissertation, Universitäts und
Landesbibliothek Bonn, 2007.
A. EL Aissaoui, F. Lebeau, M.-F. Destain,
K. Houmy, Applying control volume finite element
for modeling direct injection boom spraying, in
Proceedings of the Congress 7th World Congress of
Computer in Agriculture, Reno, USA, 22-24 June
2009, pp. 190-198.
N. S. Hassen, C. S. Nor Azwadi, J. M. Sheriff,
Advanced techniques for reducing spray losses in
agrochemical application system, Life Science
Journal, Vol. 11, Issue 3, 2014, pp. 56-66.
M. Jafari, A. Hemmat, M. Sadeghi, Development and
performance assessment of a DC electric variablerate controller for use on grain drills, Computers and
Electronics in Agriculture, Vol. 73, Issue 1, 2010,
pp. 56-65.
M. L. Stone, D. K. Giles, K. J. Dieball, Distributed
network system for control of spray droplet size and
application rate for precision chemical application,
Transaction of the ASAE, Canada, 1999, Paper no.
99-3112.
A. Landers, The theory and constituent parts of a
direct injection sprayer, Accessed at January 27,
2015. [Online]. Available:
http://vivo.cornell.edu/individual/AI-24241250305
(1998).
H. V. Mercaldi, C. H. Fujiwara, E. A. G. Peñaloza,
V. A. Oliveira, P. E. Cruvinel, An intelligent and
customized electrical conductivity sensor to evaluate
the response time of a direct injection system, in
Proceedings of the 6th International Conference on
Sensor Device Technologies and Applications
(SENSORDEVICES’15), Venice, Italy, 23-28 August
2015, pp. 19-24.
C. H. Fujiwara, H. V. Mercaldi, E. A. G. Peñaloza,
P. E. Cruvinel, V. A. Oliveira, Direct injection
system response time determination using a
conductivity smart sensor directly coupled into
herbicide sprayer nozzle, Abstracts Proceedings of
the XLIV Brazilian Congress of Agricultural
Engineering (CONBEA’15), Sao Pedro-SP, Brazil,
13-17 September 2015, pp. 171.
A guide to conductivity measurement: Theory and
practice
of
conductivity
applications
(MELTER/TOLEDO), Accessed at September 27,
2015. [Online]. Available:
http://www.tau.ac.il/~chemlaba/Files/conductivity_gu
ide_EN%20(2).pdf (2013)
D. R. Peck, L. O. Roth, Field sprayer induction
system development and evaluation, Transaction of
the ASAE, 1975, Paper No. 75-1541.
K. A. Bennet, R. B. Brown, Direct nozzle injection
and precise metering for variable rate herbicide
application, Transaction of the ASAE, 1997, Paper
No. 97-1046.
K. Ogata, Modern Control Engineering, 4th ed.,
Prentice Hall PTR, 2001.
U. R. Antuniassi, P. Miller, M. Paice, Performance
evaluation of injection metering systems, Revista
9
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 1-10
Brasileira de Engenharia Agricola e Ambiental,
Vol. 6, Issue 1, 2002, pp. 159-165.
[31]. K. P. Gillis, D. K. Giles, D. C. Slaughter, D.
Downey, Injection mixing system for boomles,
target-activated herbicide spraying, Transaction of
the ASAE, Vol. 46, Issue 4, 2003, pp. 997-1008.
[32]. Exar Corporation, Datasheet XR2206, Accessed at
February 24, 2015. [Online]. Available:
https://www.sparkfun.com/datasheets/Kits/XR2206
104 020808.pdf (2008)
[33]. Arduino, Arduino Uno, Accessed at March 16, 2015.
[Online]. Available:
http://arduino.cc/en/Main/ArduinoBoardUno (2015)
[34]. Texas Instruments, Datasheet LF147, LF347-N.
Accessed at February 24, 2015. [Online]. Available:
http://www.ti.com/lit/ds/symlink/lf147.pdf (2013)
[35]. Texas Instruments, Datasheet OPA344. Accessed at
March 04, 2015. [Online]. Available:
www.ti.com/lit/ds/symlink/opa344.pdf (2008)
[36]. R. Taymanov, K. Sapozhnikova, Metrological selfcheck and evolution of metrology, Measurement,
Vol. 43, Issue 7, 2010, pp. 869-877.
[37]. Tecnopon, Instructions manual mCA150. Accessed at
February 24, 2015. [Online]. Available:
http://www.tecnopon.com.br (2014).
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
10
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Dynamical Capillary Rise Photonic Sensor
for Testing of Diesel and Biodiesel Fuel
1
Michal BORECKI, 2 Michael L. KORWIN-PAWLOWSKI,
3
Mariusz DUK, 3 Andrzej KOCIUBIŃSKI, 4 Jarosław FRYDRYCH,
5
Przemyslaw PRUS, 1 Jan SZMIDT
1
Institute of Microelectronics and Optoelectronics, Warsaw University of Technology,
75 Koszykowa Str., 00-662 Warsaw, Poland
2
Département d’informatique et d’ingénierie, Université du Québec en Outaouais,
101 rue Saint-Jean-Bosco, J8X 3X7 Gatineau Québec, Canada
3
Institute of Electronics and Information Technology, Lublin University of Technology, 38A
Nadbystrzycka Str., 20-618 Lublin, Poland
4
Automotive Industry Institute, 55 Jagiellońska Str., 03-301 Warsaw, Poland
5 Blue Oak Inventions, 31/101 Lwowska Str., 56-400 Oleśnica, Poland
1
Tel.: +48 22 234 77 49, fax: +48 22 234 60 63
E-mail: borecki@imio.pw.edu.pl
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: There are many fuel quality standards introduced by national organizations and fuel producers. Usual
techniques for measuring fuel parameters like cetane number, cetane index, fraction composition, viscosity,
density, and flash point, require relatively complex and expensive laboratory equipment. On the fuel user side,
fast and low cost sensing of useful state of biodiesel fuel is important. The main parameters of diesel fuel
compatibility are: density, viscosity and surface tension. These three parameters define indirectly the quality of
the fuel atomization process and the injected portion of energy that affect the quality of the fuel.
In the presented paper the purposefulness of fuel testing using measurements of separable parameters is
discussed. On this base, a sensor which enables the examination of relation of the mentioned parameters in one
arrangement is proposed, analyzed and tested. The sensor uses the dynamic capillary rise method with photonic
multichannel data reading in an inclined capillary. The principle of the sensor’s operation, the construction of the
sensor head, and the experimental results are presented. The capillary is a disposable element. The sensor testing
was performed with freshly prepared biodiesel fuels, and fuels stored for 2 years. We conclude that the proposed
construction may be in future the base of low cost commercially marketable instruments for basic fuel
classification: fit for use or not. That classification includes initial fuel composition and fuel parameters change
during storage. Therefore, the proposed sensor is intended to use in fuel buying/selling point rather than used as
part of a diesel engine automated system. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Biodiesel fuel, Diesel fuel, Fuel quality, Fuel storage, Viscosity, Density, Surface tension,
Multiparametric sensor, Capillary sensor, Capillary rise.
http://www.sensorsportal.com/HTML/DIGEST/P_2730.htm
11
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
1. Introduction
The paper consists of 5 sections. The first section
is the introduction, where the diesel fuel, diesel engine
properties as well as surface tension measurements
and known sensors for diesel fuel testing are
discussed. The second section describes the
monitoring of capillary dynamical rise as an idea of
multiparametric sensor for diesel fuel testing. The
construction of the developed sensor is presented in
section three. In section four the discussion of obtained
results of diesel fuels examination using the developed
sensor is presented and a method of fuels classification
is proposed. The conclusion is gathered in section five.
1.1. Diesel Fuel
Classical diesel fuels are made from distillated
products of crude oil with addition of improvers.
Biodiesel fuel is a mixture of classical diesel fuel, biocomponents and additives, which include, for
example, ignition improvers, injector cleanliness,
lubricants and antioxidants. Present-day biocomponents are obtained from vegetal oils, or the biomass. Important disadvantages of biodiesel fuel can be
overcome by fuel processing. A new generation of
biodiesel fuel can be made using bio-component
isomerization or hydrogenation.
The quality of biodiesel fuel is defined in relation
to four main processes: a) fuel storage stability, b) fuel
transfer dynamics from tank to injection unit, c)
combustion in the chamber, and d) gas emission.
Nowadays, producers define the useful state of
diesel fuel by several parameters: cetane number
(CN), cetane index, density, distillation parameters
and kinematic viscosity. One of the most important
diesel fuel quality parameters is its ignition quality.
The ignition quality depends on the molecular
composition of the fuel and is characterized by the
ignition delay time, which is the time between the start
of injection and the start of combustion. Other diesel
fuel parameters, not directly related to engine
performance, characterize its operability, as for
example: amount of carbon residue, water and
sediment, cloud point, conductivity, oxidation
stability, acidity, copper corrosion, flashpoint,
lubricity, appearance, and color [1].
1.2. Diesel Fuel and Engine
Diesel engine performance, fuel consumption, and
emitted pollutants result from the combustion process.
The environment of combustion, the injected fuel’s
form and the fuel quality play primary role in the
diesel combustion process [2]. Therefore,
contemporary diesel engines may be equipped with the
set of sensors that are aimed to monitor several
parameters: engine speed, fuel temperature, fuel
pressure at the injector/common rail and at the fuel
supply pump, oil pressure, oil temperature, air inlet
12
temperature, air flow/mass, coolant temperature, and
oil and fuel levels as well as exhaust gas – nitrogen
oxide presence. These sensors are often integrated
with on board diagnostic (OBD) bus.
The main challenge for diesel engine designers is
to adjust the combustion chamber size design
according to the diesel fuel injection characteristics
[3]. The diesel fuel must be introduced into the
combustion chamber, vaporize, and react with oxygen
at an assumed speed. The fuel properties that have the
greatest effect on the injection process are viscosity,
density and surface tension. If the injection is made at
a constant pressure, the viscosity affects the fuel spray
formation by limiting the speed of fuel flow. If the
injection system is designed to meter the volume of
injected fuel, the density of fuel defines the fuel
injected mass that is linked with the useable portion of
energy [4]. The surface tension is the one of main
factors that describes the fuel tendency to form drops,
known also as the fuel atomization process or spray
forming. Spray ignition phenomena are described by
dynamic spray characterization, as for example:
droplets concentrations, droplets diameters, droplets
velocities [5]. In standard diesel engines, the expected
injected droplet velocities are from 300 to 400 m/s,
while the expected droplet diameters are in the range
from 0 to 40 µm. Therefore, the costly diesel engines
sometimes are equipped with fluid condition sensors
that measure the density and the viscosity of fuel or oil
[6]. The most costly diesel engines, for example for
oceanic ships, sometimes are equipped with
combustion temperature or pressure sensor as well as
with spray form monitors which enables control of
advanced combustion strategies [7].
1.3. Measurement of Diesel Fuel Parameters
The standardization process of measurement of
diesel fuel parameters is still in progress [8].
Nowadays, the basic idea of fuel characterization is a
selective and specific measurement of sequent
parameters.
Diesel fuel operability parameters measurements
are not full standardized. The procedures and
measured equipment are set to a series of parameters,
for example:
- acid-number, used to describe the engine’s
corrosion susceptibility by the fuel acidity level
using chemical methods,
- flashpoint, used to describe the lowest
temperature at which the fuel is sufficiently
vaporized to form a flammable mixture with air
under standard conditions, can be measured using
the Pensky-Martens closed cup tester,
- lubricity of a substance is not a material property
and cannot be measured directly, but a standard
test method for evaluating the lubricity of diesel
fuels uses a high frequency reciprocating rig and
gives the value of wear trace diameter called WS
1.4 and expressed in [μm].
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
Measurements of ignition quality of fuel (CN)
have to be carried out in the Cooperative Fuel
Research Engine (CFR-5) or the Ignition Quality
Tester (IQT™). The distillation parameters may to be
measured using the distillation unit, the basic
components of which are: a distillation container, a
condenser and an associated cooling bath, a heater and
the temperature measuring device, as well as a
receiving cylinder, or may be measured with
automated distillation process analyzer [9]. The diesel
fuel kinematic viscosity is a measure of the time for a
fixed volume of liquid to flow by gravity through a
capillary. The kinematic viscosity is calculated from
calibrated device as
η = C ∙ t,
(1)
where: η is the viscosity, t is measured flow time, C is
the calibration constant.
In a European study, it was observed that using the
biodiesel fuel at low environment temperatures can
lead to the degeneration of the engine [10]. Therefore,
production standards for biodiesel fuel were
introduced: density at 15 °C (ISO3675) and lowtemperature fluidity for the transitional seasonal
periods and winter (DIN EN 116).
The basic disadvantage of such laboratory
approach to fuel measurements is the high cost of the
set of measurement devices and the complexity of the
procedures. For the ordinary fuel user, the analysis of
such collection of parameters is also too complex.
Moreover, the surface tension of diesel fuel
measurement is not standardized. Instead, the ASTM
D971 standard is set to determine the possible
contaminants of hydrocarbon fluids with water as the
purity of hydrocarbon fluids is important factor of
diesel fuels quality.
1.4. Surface Tension Measurement Methods
There are a few basic methods of surface tension
measurements: the capillary rise method, the drop
weight method, the ring or plate method, the
maximum bubble pressure method, the method of
analyzing the shape of the hanging liquid drop, and the
dynamic methods [11].
The capillary rise method is the oldest method used
for determination of the surface tension. To perform
the surface tension measurement, a capillary is first
dipped into the tested liquid. If the interaction forces
of the liquid with the capillary walls are stronger than
those between the liquid molecules, the liquid wets the
walls and rises in the capillary to a defined level. If the
cross-section area of the capillary is circular and its
radius is sufficiently small, then the meniscus is
semispherical. When the capillary is filled in the
vertical position, the surface tension may be calculated
with following equation:
γ≅
r∙g∙ h
where: γ is the surface tension, θ is the contact angle,
r is the inner radius of capillary tube, g is the gravity
constant, h is the height of meniscus, ρl is the density
of liquids, and ρv is the density of vapor [11]. For
standard temperatures, the density of liquid is much
greater than of the density of vapor, therefore the
presence of the vapor phase is ignored. The contact
angle may have different values in the static or
dynamic situations. Such situations for a capillary
being filled are presented in Fig. 1.
∙ ρ
ρ ,
(2)
Fig. 1. Static and advancing contact angle in capillary flow.
The advancing contact angle value may be
measured with analysis of the shape of the sessile drop
positioned on sloping surface [13]. In dynamic
situations, when the liquid is moving over the surface,
the velocity gradients in the fluid may be large and the
viscous forces may control the shape of the fluid
surface interface [14]. When the liquid is a multicomponent solution, the main component surface
tension is modified with the surface excess of other
components related to the effective surface
concentration, dependent on the nature of the solvents
and solutes [15]. Many additives of diesel fuel may be
considered as surfactant particles, which alter the
surface properties of fuel, even when present in small
quantities, Fig. 2.
Fig. 2. Surface tension in multi-component liquid.
13
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
The diffusion of surfactants to the surface
continues with a determined and limited velocity, until
equilibrium is established. The time to equilibrium
varies depending on surfactant concentrations and
type, and may range from 0.01 s to few hours.
Therefore, when the liquid flows up the capillary, the
surfactants particles also flow, but not necessary
maintaining the original surface concentrations,
resulting in modifications of the temporary surface
tension and the temporary advancing contact
angle (2) [16].
The simplest technique for measuring the capillary
rise is using a ruler with an optical reading, but its
automation requires a camera, a mechanical device as
a lift, or a liquid injector coupled with an additional
light source to improve the optical reading of the
meniscus position.
The readings of data in the capillary rise method to
measure the surface tension may be performed for
semi-transparent liquids also using the light
attenuation effect. The lensing effect of the meniscus
may be used for measurements of the contact angle
[17]. The principle of the measuring set-up for this
method is presented in Fig. 3. The lensing effect of the
meniscus is registered as dimensions in the camera
grid, while the capillary rise controls the amount of
registered light.
due to its wetting by liquid is measured. The ring
method is unsuitable for dynamic surface tension
measurements, but can be extended with captured
image analysis to simultaneous measurements of
surface tension and contact angle [18].
The drop weight method is one of the most
common methods used for surface tension automated
measurements. In this method, drops formed at the tip
of the glass capillary are weighted and counted. The
pendant drop at the tip starts to detach when its weight
reaches the value balancing the surface tension of the
liquid. The advantage of the method is the possibility
to measure surface tensions between liquids and other
than optically transparent materials [19]. The
disadvantage of this method is the necessity to
calculate the corrections for the capillary tip
parameters and the volume of the drop, which are
characteristic of a given device. Also evaporation of
the drops may be a problem for some measurements.
The shape analyzing method of liquid drop is based
on the effect of the liquid’s deformation caused by the
gravitation force action. The hanging drop shape can
be analyzed in set-up presented in Fig. 4.
Fig. 4. Pendant drop tensiometer.
Fig. 3. Principle of surface tension and contact angle
measurement lengthwise of capillary.
The ring method and the Wilhelmy plate method
are similar. In these methods an object is moved
perpendicularly into or out of the liquid. The plate is
moved towards the surface until the meniscus connects
with it, or the submerged ring is pulled out of the
liquid. The additional force acting on the plate or ring
14
When the drop lies on a plane, the surface area of
a drop is proportional to its squared radius, and the
gravitational deformation depends on its volume. This
deformation is proportional to the third power of the
drop radius and to the liquid’s surface tension as well
as on the liquid – plane contact angle, or the plane’s
surface energy [20].
In the maximum bubble pressure method, an air or
gas bubble is blown at constant rate through a capillary
submerged in the tested liquid. The maximum
measured pressure that is required to insert the bubble
of gas into the liquid may be used for surface tension
determinations.
The analysis of the shape of an oscillating liquid
jet method bases on optical observations of
characteristic shapes of the liquid that flushes out from
an elliptic orifice.
The methods that are suitable to measure the
dynamic surface tension are the maximum bubble
pressure and the oscillating liquid jet methods, but
they require more complex experimental laboratory
set-ups.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
1.5. Surface Tension and Advancing Angle
of Diesel Fuel
It has been proven that surface tension is one of the
most important of inhomogeneous fluid properties. It
reflects the range of interactions in a fluid more
directly than the bulk properties do [21]. The
relationship between the surface tensions of different
real vegetable oils and their fatty acid composition was
postulated in [22]. Predicting the surface tension of
biodiesel fuels from their fatty acid composition
showed that the differences in surface tension between
biodiesel types are not the only cause of the reported
differences in engine tests [23].
The surface tension and advancing angle are
interesting case in diesel fuel characterization. On one
hand the surface tension and ASTM D971 standard are
proposed as useful indicator of cetane number [24] and
fuel atomization process [25]. On the other hand, it
was postulated that the surface tension of most liquid
hydrocarbons is very similar. For example, for
HydroCal 300 - a hydrotreated naphthenic medium
grade lubricant oil and IFO-120, an intermediate fuel
oil, the measured surface tensions at 25 °C are the
same and equal 31.8 mN/m, while their viscosities
differ significantly: 162mPa·for HydroCal and 487
mPa for IFO-120 [26].
Results of investigation of surface tension values
of diesel fuels and their components, with static
capillary rinse method performed according (2), are
presented in Table 1, where EN-590 depicted premium
diesel fuel, FAME is fatty acids methyl ester - bio
component, x% is concentration of bio component in
fuel. For experiment VitroCom CV3040Q capillaries
of inner diameter equal 300 µm were used. Since a
film of oil remains on the inner surface of the capillary
after the receding phase, representing complete
wetting, the static contact angle in (2) was considered
to be equal to 0° [27].
Obtained data shows that commercial premium
diesel fuel, standard diesel fuel and 100 % biodiesel
component surface tension values are considerably
differ. It should be noted that contemporary diesel
fuels components are not only pure hydrocarbons, it
consist significant concentration of fatty acids esters.
Moreover FAME requires different fuel additives than
petrodiesel. Therefore, such changes in fuel
composition may significantly change surface tension,
contact angle and other diesel fuel parameters [28].
Table 1. Surface tension values of standard fuels and their
components, obtained with static capillary rinse method.
Temp
(°C)
10
20
30
Surface tension, (mN/m),
of fuel type
Premium,
Standard,
BioFuel,
EN-590,
EN-590,
EN 14214
FAME 0%
FAME 7% FAME 100 %
28.0
29.5
31.5
27.0
28.4
30.6
26.1
27.3
29.7
Advancing contact angles between a glass slide
and different hydrocarbon oils differ significantly
depending on the temperature, the speed of liquid
creep and the type of liquid hydrocarbon. For example,
measured at 25 °C and 264 µm/s the advancing angle
of HydroCal 300 is 36° while of IFO-120 is 54° [26].
1.6. Sensors for Diesel Fuel Testing
Generally, diesel fuel analysis has taken place in
certified laboratories. Though, there is a rising demand
for on-line and real-time diesel fuel monitoring. To
meet such requirements, series of different type
sensors are introduced, nowadays, into market.
Some sensors base on surface acoustic waves
examination [29]. They are claimed as liquid/oil
quality monitors, while they are used to measure
acoustic impedance at monitored liquid surface:
Z = ω ∙ ρ ∙ η,
(3)
where: ω is the radian frequency, ρ is the density, η is
the viscosity. Due to impedance dependence on
density and viscosity, the postulated diesel fuel quality
monitor has to consist of two sensor heads. But, the
measurement tricky of proposed construction is
determination of diesel fuel dynamical coupling to
examined surface, which depends on surfactants
properties. Therefore, mentioned sensors can be used
for monitoring simpler than diesel fuel liquids as for
example lubricant oil. Still the works on critical
coupling surface parameters improving are in progress
[30]. The density of fuel is measured with the use of
resonant frequency sensors [31] that are capable of
electronic integration [32].
Optical sensors are also popular in diesel fuel
examination. Infrared sensors are used for two
proposes, to measure the blend concentration in
blended fuel and to measure predict fuel quality [33].
Some smart ultraviolet sensors enable the examination
of a set of fuel parameters, but the possibility of easy
measurement comes with a high price of the devices
[34]. On the other hand, some proposed
multiparametric sensors may be low cost, but using
them requires trained operating personnel. The
possibility of diesel fuel quality testing using an
optoelectronic set-up implementing a multiparametric method was shown in [35] and [36]. The
components for such sensors are under technological
optimization [37] [38] [39].
2. Monitoring of the Capillary Dynamical
Rise as Idea of Multiparametric Sensor
for Diesel fuel Testing
Diesel fuel users require the simplest possible
answer to a question: is that fuel useful for my engine?
15
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
This answer may be deducted from capillary rinse
sensor depicted in [40].
The idea of the sensor was inspired by the need to
come up with a measurement method of a set of diesel
fuel parameters in one system that would require the
minimum of automated mechanical elements. The
aimed at set of diesel fuel parameters includes:
density, viscosity, surface tension and wetting
represented by the advancing contact angle between
fuel and glass. To obtain the dynamical data of
advancing contact angle one needs to measure the
capillary rise speed. An increase of the speed of the
speed of the liquid’s movement and its distance in the
capillary in order to improve the precision of the
measurement is possible by inclination of the
capillary. When the capillary’s axis is inclined at the α
angle to the horizontal, a liquid is drawn in by capillary
forces according to equation (4).
=
∙ ∙
∙∙
∙
∙
∙ ∙
,
(4)
where γ is the surface tension, θ is the advancing
contact angle, r is the inner radius of the capillary tube,
g is the gravity constant, l is the length coordinate of
meniscus at capillary axis, t is the time of
measurement, α is the angle of inclination, ρl is the
density of liquid, and η is its viscosity. In the general
case depicted by equation (4) the advancing contact
angle θ is a function of the liquid’s speed. This results
in an involved dependence for dynamical calculation
of liquid parameters. Assuming that the advancing
contact angle is characterized by semi constant values,
equation (4) may be solved for the dynamical diesel
fuel analysis in two ways.
The first solution of equation (4) is for local speed
measurement at a set length coordinate of meniscus
with the difference equation:
∙ ∙
=
∙ ∙
∙
,
∙ ∙
(5)
where: θs is the semi constant value of advancing
contact angle at coordinate ls, while ls and Δls are
presented in Fig 5.
The second solution of equation (4) is for average
speed measurement at a set distance and is based on
integration:
∙ ∙
∙ ∙
=
∙∙
∙
,
Fig. 6. Parameters position for local speed determination
with (6).
3. Sensor Construction
The sensor construction used in dynamical capillary
rise application analysis was firstly presented at the
SENSORDEVICES 2015 Conference [40].
3.1. Sensor Head
The sensor’s head consists of two functional
blocks: the base and the disposable capillary optrode,
shown on Fig. 7.
The base is used to integrate the removable vessel
for the examined liquid, the three optical paths of the
source and the receiver, as well as for positioning the
disposable capillary optrode at one of the three
possible inclination angles. The ramp geometry used
in presented experiments is shown in Fig. 8. The
capillary optrode is dipped for 3 mm into the liquid.
(6)
where: θ21 is the average and semi constant value of
advancing contact angle between coordinate l1 and l2
presented in Fig. 6. For the sensor application,
equation (6) may be solved numerically by inputting
in the measured time of rise t2-t1 at corresponding
coordinates of meniscus.
16
Fig. 5. Parameters position for local speed
determination with (5).
Fig. 7. The dynamical capillary rise sensor head.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
The disposable optrode is made from 15 cm
sections cut from the TSP 700-850 capillary from
Polymicro Inc. In the optical paths, large core optical
fibers BFH 37-800 from Thorlabs were used. They are
characterized by the core radius of 800 µm and the
hard clad outer diameter of 830 µm, which is similar
to the capillary’s outer diameter. The tips of the fibers
are positioned at the distance of 1mm, where in the
middle of the distance between the tips is positioned
the capillary (Fig. 9).
To operate the system, at the 0.1 s sampling rate, a
script in DasyLab 10 was designed. The script
algorithm is based on the signal filtration and signal
derivative analysis [42].
The raw data collected for acetone are presented in
Fig. 10. The initial values of signals for different paths
are off-set on Fig. 10 for clarity. Moreover, the
differences of the initial signal values do not matter, as
the sensor operation is based on the differences of the
measured time values. The measured time differences
for acetone increase with capillary length coordinate:
Δt1<Δt2<Δt3 and [(t2-t1) = 0.9 s] < [(t3-t2) = 2.6 s].
Therefore obtained results are in agreement with
theoretical model represented with equations (5)
and (6).
Fig. 8. Ramp geometry used in experiments.
Fig. 10. Acetone characteristics.
Fig. 9. Optical path.
The geometry of the optical path defines the value
of Δls, required in equation (3). The approximate value
of Δls may be interpreted as, visible by fiber, section
of capillary at its axis. Therefore it may be calculated
using numerical aperture of fiber and its core diameter.
As the optical paths are the same Δls=Δl1=Δl2=Δl3≈
1.2 mm.
The results of measurement of fresh PD presented
in Fig. 11 show that, contrary to acetone, the Δts are
measurable in each optical patch Δt1=0.2 s, Δt2=0.3 s,
Δt3=0.7 s, and that the time intervals
(t2-t1) = 6.5 s and (t3-t2) = 13 s are greater than for
acetone, as fuel viscosity is much greater than that of
acetone.
3.2. Optoelectronic Signal Processing
As light source, three fiber coupled LEDs with
three different emission wavelengths were used. The
lowest-power LEDs were selected from Thorlabs list
of high power devices: M490F2, blue with the 490 nm
wavelength; M565F1, green/yellow - 565 nm
wavelength; and M625F1, red - 625 nm wavelength.
The diodes were connected to DC2100 controllers
operating in the light modulation mode. The receiving
fibers were inserted into the optoelectronic detection
unit of our own design, presented previously in [41].
The optoelectronic unit was connected to a PC by an
analog input of IOtech Personal DAQ 3000 data
acquisition system. That system was also used to
monitor the ambient temperature with two LM35DT
circuits and control it with a radiant heater at 25 °C.
Fig. 11. Measurement signals of fresh premium quality
petrodiesel PD.
In the presented set-up, the t1 measurement value
is uncertain, as the filling of the vessel may not be
repeatable when a hand held pipetor is used.
17
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
4. Experimental Results
In this section are presented the diesel and
biodiesel fuels parameters examination as well as
experimental classification procedure results.
4.1. Diesel and Biodiesel Fuels Used
for Examination
The operation of the sensor was examined with
fuels that are mixtures prepared from components
according to the European Union standards. The fuels
were fresh, or stored in room condition for two years.
Base oil (BO) was prepared from crude oil
distillation products. The petrodiesel (PD) was
prepared with additives according to the EN-590
norm. Biodiesel fuels (BDx – x is the volume ratio of
the biocomponent) were prepared from PD with
addition of fatty acids methyl esters (FAME) and other
additives according to the EU standard. The lubricants
were added in different volumes, but in the standard
range, to different fuel mixtures. This enables testing
the lubricant effects on CI and the fuel classification.
The results of laboratory examination of prepared
fuels are presented in Table 2. The accuracy of the
density measurement was ±2.0 kg/m3 while the
resolution was ±0.1 kg/m3. Kinematic viscosity,
lubricity (ISO 12156) and oxidation stability (EN
16091) measurement accuracy was ±1 %. Kinematic
viscosity was measured at 40 °C, while the density at
15 ºC.
The density and kinematic viscosity increase
monotonic with FAME fuel component concentration
(Fig. 12), while cetane index decreases monotonically.
The fuels BO, BD30 BD50, BD70 and BD100 do not
meet the standards of density, viscosity and FAME
ratio, but meet the quality test of CN. In our opinion
(tests on CFR-5), fuels from PD up to BD30 can be
used without problems. The characteristics of lubricity
and oxidation stability of examined fuels are not
monotonic versus FAME fuel component
concentration, as depicted in Fig. 13.
The calculated values of surface tension of fuels,
basing on equation (6), as well as on the assumption
that average advancing contact angle is 0 and using
data collected in Table 2, are presented in Fig. 14. The
obtained results show that the assumption for the
advancing angle is not correct when absolute values
are under consideration. But experiment show, that
when fuel speed decreases, the calculated surface
tension increases and tends to the static measured
values presented in Table 1.
Fig. 12. Density and kinematic viscosity of examined
fuels vs. FAME component concentration.
Fig. 13. Lubricity and oxidation stability of examined
fuels vs. FAME component concentration.
Table 2. The parameters of investigated fuels.
18
Fuel
acronym
FAME [%]
Density [kg/m3]
BO
PD
BD02
BD04
BD06
BD08
BD10
BD30
BD50
BD70
BD100
0
0
2
4
5.8
7.8
9.7
28.8
48.9
68.6
100
805.0
832.6
833.6
834.5
835.4
836.4
837.4
847.0
857.4
867.3
883.2
Parameter
Lubricity WS
Kinematic
1.4
viscosity
[mm2/s]
[µm]
1.581
734
3.367
321
3.3825
209
3.394
202
3.401
215
3.413
207
3.432
214
3.595
210
3.813
216
4.058
209
4.509
196
Oxidation
stability
[min]
55.02
37.56
41.36
50.28
50.19
50.18
33.08
32.58
27.23
23.50
19.02
CN
49.8
59.6
58.4
58.3
58.6
57.3
57.3
54.9
53.6
53.7
51.2
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
The non-monotonic character of surface tension
versus FAME concentration is visible, but should not
disturb further considerations as the lubricity of
examined fuels also does not depend monotonically.
These results show also that the values of surface
tensions are probable and that the advancing contact
angles differ significantly for the analyzed fuels as
differs the lubricity.
by equations (5) and (6) in tX-tX-1 and Δt3 contain
information on surface tension dynamics, density and
viscosity.
The
difference
in
(t3-t2)
or
(t2-t1) times varies much more from one fuel to another
than the fuels densities or viscosities.
For the purpose of diesel fuel classification with
the use of dynamical capillary rise measurement,
freshly prepared fuels from PD to BD30 meet quality
requirements. On the base of oxidation stability
investigation, all fuels stored for two years in closed
dark tanks do not meet quality requirements. The
volumes of stored fuels seemed to be constant, no
presence of resins was observed, but the fuels were
more transparent, which indicated that chemical
reactions occurred in the fuels under storage.
Data of time intervals obtained for fresh fuels are
presented in Fig. 16. The amplitude difference of
(t3-t2) is greater than (t2-t1). Therefore, the allowed
range of (t3-t2) is set. It should be pointed that this
determination of fuel quality is not always accurate, as
BD50 biodiesel fuel looks here acceptable.
Fig. 14. Surface tensions of fresh fuels calculated from
equation (6).
The comparison of surface tension calculated with
above mentioned assumption for methods based on
local (5) and average measurement (6) is presented in
Fig. 15. Obtained results confirm previous conclusion
of the speed influence on calculated surface tensions.
It is thus evident that standardization of dynamic
surface tension measurements of diesel fuel is not a
trivial task.
Fig. 16. Measurement data of fresh diesel fuels related
for method described by (6) – time intervals.
The time interval values of fuels stored for two
years are presented in Fig. 17.
Fig. 15. Surface tensions of fresh fuels calculated from
equations (5) and (6).
4.2. Procedure of Diesel Fuel Classification
On the base of previous results, it is clear that the
direct calculation of physical values of fuel parameters
including dynamical values of surface tension is
methodologically difficult. But, methods represented
Fig. 17. Measurement data of stored diesel fuels related
for method described by (6) – time intervals.
19
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
The results presented in Fig. 17 in comparison to
the results seen in Fig. 16 indicate that the measured
times intervals increase for stored fuels, but not
monotonically. The proposed time difference range
discrimination is again not conclusive, as spoiled
BD02 is classified as fulfilling the quality
requirements.
The results of examination of time differences Δt3
for fresh and stored fuels are presented in Fig. 18. The
set range of allowed range of time difference to meet
proper quality fuel is again not conclusive as
according to it the stored base oil meets requirements.
Conclusive examination can be obtained with
simultaneous examination of time difference and time
interval data as presented in Table 3.
Therefore, on the base of data collected in the
experiments, the set of parameters determining the
useful state of biodiesel fuel should include the limits
of the time intervals t3-t2, the limits of time differences
Δt3, and the results of logical operator
[(t3-t2) and Δt3].
Fig. 18. Measurement data of fresh and stored diesel
fuels related for method described by (5) – time
differences.
Table 3. Comparison of fuels classification results.
Fuel
acronym
BO
PD
BD02
BD04
BD06
BD08
BD10
BD30
BD50
BD70
BD100
Fuel
condition
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Fresh
Old
Classification result of fuels fit for use
Time interval (Ti)
Time difference (Td)
Ti and Td
Assumed quality
N
N
Y
N
Y
Y
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
N
N
N
N
N
Y
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
N
N
Y
N
N
N
N
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
N
N
N
N
N
N
N
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
N
N
N
N
N
N
N – Wrong classification result; Y – Proper classification result.
5. Conclusions
We proposed a sensor working on the principle of
optical examination of diesel fuel under dynamical
capillary rise conditions. The presented device has
been tested with fuels of different compositions and
20
storage age, and it shows great promise for practical
implementation.
The experiment data indicate that stored fuels may
flow significantly slower than fresh ones, and their
flow speeds can be determined by measurements of
time intervals. The change in flow speed modifies the
conditions of fuel injection and spray formation. The
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
direct analysis of dynamical phenomenon of surface
tension of diesel fuel brings about interpretation
difficulties.
The analysis of the measured signals of diesel and
biodiesel fuels showed the relationship of the times of
fuel flow in the capillary with the useful state of diesel
fuels. Especially, the summarized results of the
experiments led us to the conclusion that Δt3 time
measurement data differences and t3-t2 time intervals
relate more clearly to the acceptable quality fuels than
independent set of surface tension, viscosity and
density data.
We showed that the information on useful state of
diesel fuel could be presented in the form of
recommended ranges of times of the fuel crossing
sections of an inclined capillary during the capillary
fill. Specific results can be obtained applying logical
and operator to the capillary fill analysis using two
methods: time difference and time interval. This
approach is not yet as accurate as other laboratory
methods, but its intrinsic advantages of simplicity of
design and low instrumentation costs could make the
technology viable in the near future.
Acknowledgements
This work was partially supported by the
NCBiR/PGNiG grant Polish Technology for Shell
Gas, task T3.1.
References
[1]. Department of Industry, Science and Resources,
Setting national fuel quality standards - Discussion
paper 4, in Operability fuel parameters (petrol and
diesel), Environment Australia, 2001.
[2]. E. Torres-Jimenez, M. Pilar-Dorado, B. Kegl,
Experimental investigation of injection characteristics
of bioethanol-diesel fuel and bioethanol-biodiesel
blends, Fuel, Vol. 90, 2011, pp. 1968-1979.
[3]. G. E. Totten ed., T. W. Rayan, Diesel Fuel
Combustion Characteristics, in Fuels and Lubricants
Handbook, properties, performance, and testing,
ASTM International, 2003, pp. 717–720.
[4]. Z, Hu, L. T. M. Somers, T. Davies, A. McDougall,
R. F. Cracknell, A study of liquid fuel injection and
combustion in a constant volume vessel at diesel
engine conditions, Fuel, Vol. 107, 2013, pp. 63-73.
[5]. S. K. Aggrawal, A review of spray ignition
phenomena: present status and future research,
Progress in Energy and Combustion Science, Vol. 24,
1998, pp. 565-600.
[6]. K. Durdag, Solid state acoustic wave sensors for real‐
time in‐line measurement of oil viscosity, Sensor
Review, Vol. 28, 2008, pp. 68 – 73.
[7]. R. A. Atkins, J. H. Gardner, W. N. Gibler, C. E. Lee,
M. D. Oakland, M. O. Spears, V. P. Swenson, H. F.
Taylor, J. J. McCoy, and G. Beshouri, Fiber-optic
pressure sensors for internal combustion engines,
Applied Optics, Vol. 33, 1994, pp. 1315-1320.
[8]. C. Schaschke, I. Fletcher, N. Glen, Density and
Viscosity Measurement of Diesel Fuels at Combined
High Pressure and Elevated Temperature, Processes,
Vol. 1, 2013, pp. 30-48.
[9]. G. Fannin, Distillation process analyser with ASTM
D86
Compliance,
Petro
Industry
News,
Vol. August/September, 2013, pp. 40-41.
[10]. M. Balat, H. Balat, A critical review of biodiesel as a
vehicular fuel, Energy Conversion and Management,
Vol. 49, 2008, pp. 2727–2741.
[11]. P. G. de Gennes, F. Brochard-Wyart, D. Quéré,
Capillary and Wetting Phenomena–Drops, Bubbles,
Pearls, Waves, Springer-Verlag, 2004.
[12]. D. L. Katz, W. Saltman, Surface Tension of
Hydrocarbons, Industrial Engineering Chemistry,
Vol. 31, 1, 1939, pp. 91–94.
[13]. Shi‐Yow Lin, Hong‐Chi Chang, Lung‐Wei Lin, Pao‐
Yao Huang, Measurement of dynamic advancing
receding contact angle by video‐enhanced sessile drop
tensiometry, Review of Scientific Instruments, Vol. 67,
1996, pp. 2852-2858.
[14]. Q. Chen, E. Ramé, S. Garoff, The breakdown of
asymptotic hydrodynamic models of liquid spreading
at increasing capillary number, Physics of Fluids,
Vol. 7, 1995, pp. 2631-2639.
[15]. D. O. Topping, G. B. McFiggans, G. Kiss, Z. Varga,
M. C. Facchini, S. Decesari, and M. Mircea, Surface
tensions of multi-component mixed inorganic organic
aqueous systems of atmospheric significance:
measurements, model predictions and importance for
cloud activation predictions, Atmospheric Chemistry
and Physics, Vol. 7, 2007, pp. 2371–2398.
[16]. J. D. Pandey, T. Srivastava, Surface tension - A
theoretical study of multicomponent solutions,
Journal of Molecular Liquids, Vol. 155, 1, 2010,
pp. 51–56.
[17]. M. G. Cottingham, C. D. Bain D. J. T. Vaux, Rapid
method for measurement of surface tension in
multiwell plates, Laboratory Investigation, Vol. 84,
2004, pp. 523–529.
[18]. Ruey‐Yug Tsay, Shaw‐Chern Yan, Shi‐Yow Lin, A
video‐enhanced plate method for simultaneous
measurements of surface tension and contact angle,
Review of Scientific Instruments, Vol. 66, 1995,
pp. 5065-5069.
[19]. M. Borecki and J. Kruszewski, Intelligent high
resolution sensor for detecting of liquid mediums,
Optica Applicata, Vol. 31, 2001, pp. 691–699.
[20]. K. D. Danov, et al., Capillary meniscus dynamometry
– Method for determining the surface tension of drops
and bubbles with isotropic and anisotropic surface
stress distributions, Journal of Colloid and Interface
Science, Vol. 440, 2015, pp. 168–178.
[21]. H. W. Xiang, Surface Tension, in The CorrespondingStates Principle and its Practice Thermodynamic,
Transport and Surface Properties of Fluids, Elsevier,
2005, pp. 215–228.
[22]. E. A. Melo-Espinosa, et al., Surface tension prediction
of vegetable oils using artificial neural networks and
multiple linear regression, Energy Procedia, Vol. 57,
2014, pp. 886 – 895.
[23]. C. A. W. Allen, K. C. Watts, R. G. Ackman, Predicting
the surface tension of biodiesel fuels from their fatty
acid composition, Journal of the American Oil
Chemists' Society, Vol. 76, 3, 1999, pp. 317–323.
[24]. Ö. L. Gülder, G. Moroz, Surface tension as an
indicator of cetane number of diesel fuels,
Warrendale, PA: Society of Automotive Engineers,
1986.
[25]. P. Krause, O. Klyus, Application of surface tension
parameter for estimation of fuel atomization process in
Diesel engines, Scientific Journals - Maritime
University of Szczecin, Vol. 36, 2013, pp. 98–101.
21
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 11-22
[26]. A. A. Keller, V. Broje, K. Setty, Effect of advancing
velocity and fluid viscosity on the dynamic contact
angle of petroleum hydrocarbons, Journal of
Petroleum Science and Engineering, Vol. 58, 2007,
pp. 201–206.
[27]. B. Jańczuk, E. Chibowski, T. Białopiotrowicz,
Influence of N-alkanes on contact angle and flotability
of quartz, Journal of Materials Science, Vol. 25, 1990,
pp. 1353–1356.
[28]. F. Wang, J. Wu, and Z. Liu, Surface Tensions of
Mixtures of Diesel Oil or Gasoline and
Dimethoxymethane, Dimethyl Carbonate, or Ethanol,
Energy Fuels, Vol. 20, 6, 2006, pp. 2471–2474.
[29]. K. Durdag, Solid state acoustic wave sensors for real‐
time in‐line measurement of oil viscosity, Sensor
Review, Vol. 28, 2008, pp. 68 – 73.
[30]. P. Firek, et al., Electric Characterization and Selective
Etching of Aluminum Oxide, Plasma Processes and
Polymers, Vol. 6, 2009, pp. 840-843.
[31]. D. Sparks, K. Kawaguchi, M. Yasuda, D. Riley,
V. Cruz, N. Tran, A. Chimbayo, N. Najafi, Embedded
MEMS‐based concentration sensor for fuel cell and
biofuel applications, Sensors and Actuators A,
Vol. 145‐146, 2008, pp. 9‐13.
[32]. S. Y. Yurish, N. V. Kirianaki, R. Pallàs‐Areny,
Universal frequency‐to‐digital converter for quasi‐
digital and smart sensors: specifications and
applications, Sensor Review, Vol. 25, 2, 2005,
pp. 92 – 99.
[33]. P. Baptista, P. Felizardo, J. C. Menezes, M. J. N.
Correia, Multivariate near infrared spectroscopy
models for predicting the methyl esters content in
biodiesel, Analytica Chimica Acta, Vol. 607, 2008,
pp. 153-159.
[34]. M. Wlodarski, A. Bombalska, M. Mularczyk-Oliwa,
M. Kaliszewski, and K. Kopczyński, Fluorimetric
techniques in analysis and classification of fuels, Proc.
SPIE of the 10th Symposium on Laser Technology,
Vol. 8703, 2012, pp. 87030B.
[35]. M. Borecki and M. L. Korwin-Pawlowski, Optical
capillary sensors for intelligent microfluidic sample
classification, in Nanosensors: Theory and
Applications in Industry, Healthcare and Defence, T.
C. Lim, Ed., CRC Press, 2011, pp. 215– 245.
[36]. M. Borecki, P. Doroz, P. Prus, P. Pszczółkowski, J.
Szmidt, M. L. Korwin-Pawlowski, J. Frydrych, A.
Kociubiński and M. Duk, Fiber Optic Capillary Sensor
[37].
[38].
[39].
[40].
[41].
[42].
with Smart Optode for Rapid Testing of the Quality of
Diesel and Biodiesel Fuel, International Journal of
Advances in Systems and Measurements, Vol. 7, 2014,
pp. 57–67.
A. Baranowska, P. Miluski, M. Kochanowicz, J.
Zmojda, D. Dorosz, Capillary optical fibre with Sm3+
doped ribbon core, Proc. SPIE of the Conference
Photonics
Applications
in
Astronomy,
Communications, Industry, and High-Energy Physics
Experiments, Vol. 9662, 2015, pp. 96620Z.
M. Geca, M. Borecki, M. L. Korwin-Pawlowski, A.
Kociubiński, Local liquid sample heating: integration
and isolation of a micro-heater, Proc. SPIE of the
Conference Photonics Applications in Astronomy,
Communications, Industry, and High-Energy Physics
Experiments, Vol. 9662, 2015, pp. 96620E.
P. Prus, M. Borecki, M. L. Korwin-Pawlowski, M.
Duk, Software detection of characteristics data of
optical signals received in multiparametric capillary
sensors of diesel fuel, Proc. SPIE of the Conference
Photonics
Applications
in
Astronomy,
Communications, Industry, and High-Energy Physics
Experiments, Vol. 9662, 2015, pp. 966209.
M. Borecki, J. Szmidt, M. L. Korwin-Pawlowski, A.
Kociubinski, M. Duk, J. Frydrych, P. Prus, Capillary
Rise Multiparametric Sensor for Testing of Diesel and
Biodiesel Fuel, in Proceedings of the 6th International
Conference on Sensor Devices Technologies and
Applications (SENSORDEVICES’15), Venice, Italy,
23-29 August 2015, pp. 37-43.
M. Borecki, J. Szmidt, T. Niemiec, M. Szmidt, K.
Urbańska, M. L. Korwin-Pawlowski, M. Duk, A.
Kociubiński, Sensing of Essential Amino Acids
Behaviour Under Fast Thermal Shocks in Liquid
Water Environment, in Proceedings of the 5th
International Conference on Sensor Devices
Technologies
and
Applications
(SENSORDEVICES’14), Lisbon, Portugal, 16–20
November, 2014, pp. 32–38.
P. Prus, M. Borecki, M. L. Korwin-Pawlowski, A.
Kociubiński, and M. Duk, Automatic detection of
characteristic points and form of optical signals in
multiparametric capillary sensors, Proc. SPIE of the
Conference Photonics Applications in Astronomy,
Communications, Industry, and High-Energy Physics
Experiments, Vol. 9290, 2014, pp. 929009.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved
(http://www.sensorsportal.com)
22
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Novel Smart Glove Technology
as a Biomechanical Monitoring Tool
1
Brendan O’FLYNN, 1 J. T. SANCHEZ, 1 S. TEDESCO, 2 B. DOWNES,
3
J. CONNOLLY, 3 J. CONDELL, 3 K. CURRAN
1
Tyndall National Institute, University College Cork, Cork, Ireland
2
Waterford Institute of Technology, Waterford, Ireland
3
Computing & Engineering, Magee College, Ulster University, Derry, N. Ireland
1
Tel.: +353 21 2346041, fax: +353 21 49004958
E-mail: brendan.oflynn@tyndall.ie
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Developments in Virtual Reality (VR) technology and its overall market have been occurring since the
1960s when Ivan Sutherland created the world’s first tracked head-mounted display (HMD) – a goggle type head
gear. In society today, consumers are expecting a more immersive experience and associated tools to bridge the
cyber-physical divide. This paper presents the development of a next generation smart glove microsystem to
facilitate Human Computer Interaction through the integration of sensors, processors and wireless technology.
The objective of the glove is to measure the range of hand joint movements, in real time and empirically in a
quantitative manner. This includes accurate measurement of flexion, extension, adduction and abduction of the
metacarpophalangeal (MCP), Proximal interphalangeal (PIP) and Distal interphalangeal (DIP) joints of the fingers
and thumb in degrees, together with thumb-index web space movement. This system enables full real-time
monitoring of complex hand movements. Commercially available gloves are not fitted with sufficient sensors for
full data capture, and require calibration for each glove wearer. Unlike these current state-of-the-art data gloves,
the UU / Tyndall Inertial Measurement Unit (IMU) glove uses a combination of novel stretchable substrate
material and 9 degree of freedom (DOF) inertial sensors in conjunction with complex data analytics to detect joint
movement. Our novel IMU data glove requires minimal calibration and is therefore particularly suited to multiple
application domains such as Human Computer interfacing, Virtual reality, the healthcare environment. Copyright
© 2015 IFSA Publishing, S. L.
Keywords: Data glove, IMU, Virtual reality, Arthritis, Joint Stiffness, Hand Monitoring.
1. Introduction
Data gloves contain strategically placed sensors
controlled by circuitry that communicates finger joint
movement to an end device. In recent years data
gloves have been evaluated by researchers as an
effective replacement for the universal goniometer
(UG) [12–17]. Results showed comparable
http://www.sensorsportal.com/HTML/DIGEST/P_2731.htm
repeatability to the UG with the added advantage of
simultaneous angular measurement and removal of
intra-tester and inter-tester reliability problems
associated with the UG. Data gloves however have
several drawbacks; they require laborious calibration,
are difficult to don and doff; and are designed to fit
specific hand sizes and so require small, medium and
large gloves to fit all hand variations. The first
23
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
iteration of our system was developed using a state-ofthe-art 5DT Ultra 14 data glove [18]. In this paper, our
inertial measurement unit (IMU) Smart Glove is
evaluated against this data glove for accuracy and
repeatability and further validated using the Vicon
motion capture system [19].
Virtual reality (VR) systems can be segmented into
one of three experiences: non-immersive, semiimmersive, and fully immersive. Non-immersive
systems would be those that can be visualized on a
desktop computer. Semi-immersive VR environments
incorporate images projected on the walls (e.g., cave
automatic virtual environment, better known by the
acronym CAVE). For a period of time, the user may
superficially succumb to the perception of “being
there”, but all the while still be aware of their real
world surroundings. Finally, there is fully-immersive
technology. In these systems, real-world visual and
auditory cues are completely blocked out and the user
has a sensory experience of being inside the computergenerated world. The experience is made ever more
real through the use of hand-held and/or wearable
devices that in some cases deliver haptic feedback
which invoke sensations of touch. To enable Human
Computer Interaction (HCI) in this immersive fashion,
high precision data acquisition systems need to be
developed which are accurate, require minimal
calibration and which provide real-time data streams
wirelessly. The development of such a glove based
system lends itself to multiple use cases including the
Gaming environment and hand healthcare (e.g.,
Rheumatoid Arthritis (RA) monitoring).
This paper is organized as follows. Section 2
describes the glove hardware. Section 3 addresses the
system implementation, the calibration of the glove
anddescribes the graphical user interface (GUI).
Section 4 describes the Data analytics and Post
processing while Section 5 gives an account of the
tests and results. Section 6 goes into the conclusions.
The acknowledgment section closes the paper.
1.1. Virtual Reality (VR)
To be compatible with the Virtual Reality use case,
it is important that any glove system developed for
HCI adheres to the requirements detailed below:
Accuracy & Precision. Accuracy is the degree of
closeness to a quantity's actual true value. Precision is
the degree to which repeated measurements give the
same quantity. Here, we define accuracy and precision
to consist of position and orientation. Different parts
of the hand should have priority for accuracy: a)
Mapping of the center of the virtual hand is most
important for many VR applications, b) The finger tips
are next most important for accuracy as these joints
can be estimated via inverse kinematics and other
constraints, c) The skeleton/joints of the hand are next
most important for accuracy.
Consistent recognition of gestures. Like speech
recognition, if a gesture recognition system
occasionally misinterprets signals then a break in
24
presence occurs and users can become frustrated.
Accidental gestures (known as false positives) are also
a problem (e.g., accidentally signaling a command
when unconsciously “talking with the hands”).
Low latency. The faster the response of the system,
then the more pleasant the user experience and the
more easily users can enter a state of flow.
Simulation of button presses. Some applications
will greatly benefit from simulation of button presses
that provide a sense of self-haptic feedback (e.g., by
touching two fingers together) and to control the game
and system.
1.2. Rheumatoid Arthritis Assessment
RA is an auto-immune disease which attacks the
synovial tissue lubricating skeletal joints and is
characterized by pain, swelling, stiffness and
deformity. This systemic condition affects the
musculoskeletal system including bones, joints,
muscles and tendons that contribute to loss of function
and Range of Motion (ROM). Early identification of
RA is important to initiate treatment, reduce disease
activity, restrict its progression and ultimately lead to
its remission. Clinical manifestations of RA can be
confused with similar unrelated musculo-skeletal and
muscular disorders. Identifying its tell-tale symptoms
for early diagnosis has been the long-term goal of
clinicians and researchers. Classifiers such as the
Disease Activity Score (DAS) and Health Assessment
Questionnaire (HAQ) provide an outcome
measurement that reflects a patient’s severity of RA
disease activity. Such measurements are subjective
and can be influenced by other factors such as
depression or unrelated non-inflammatory conditions.
Traditional objective measurement of RA using the
universal goniometer (UG) and visual examination of
the hands is labour intensive, open to inter rater and
intra-rater reliability problems.
The DAS and HAQ [2, 3] are commonly used to
measure disease onset and to assess disease status
during clinical assessment [1]. Joint Stiffness is a
common condition of RA that affects a patients’
ability to perform basic activities and daily functions
[4, 5]. Several objective measurement systems have
been devised by researchers and assessed in clinical
trials for effectiveness as a joint stiffness measurement
device [6–11].
2. Tyndall Glove HW Description
The objective of our IMU Smart Glove is to
quantitatively measure finger joint ROM including
flexion, extension, adduction and abduction of the
MCP, PIP and DIP joints of the fingers and thumb in
degrees, together with thumb-index web space, palmar
abduction to assist medical clinicians with the accurate
measurement of the common condition of loss of
movement in the human hand in patients with arthritis.
All Smart Glove functionality is maintained,
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
controlled and analyzed by our in-house developed
software system.
The described glove is a second generation
iteration of the system by the authors as described in
previous work [20].
another one on the palm of the hand), the relative
orientation of each IMU is calculated and used to
generate angular and velocity movement throughout
flexion and extension exercise of each finger joint and
to calculate splaying of each finger.
2.2. Microcontroller
The processor selected for use in the system is an
Atmel AVR32 UC3C 32 Bit Microcontroller. This is
a high performance, low power 32-bit AVR
microcontroller with built in single precision floating
point unit. It was selected to enable complex
embedded algorithms focused on motion analysis to
be developed for real time low power consumption
operation.
Fig. 1. The IMU Smart Glove rev 2.
2.1. System HW Description
The IMU glove, shown in Fig. 1, has been
manufactured using a mix of stretchable & flexible
technology. Stretchable areas of the device cross each
finger joint so they can conform to the human hand.
The glove includes 16 9-axes IMU’s (each
including 3-axis accelerometer, 3-axis gyroscope and
3-axis magnetometer) strategically placed to account
for the degrees of freedom (DOF) of each finger joint
of the hand. IMUs are positioned on the stretchable
interconnect and are located on the phalange of each
finger segment to measure orientation and
biomechanical parameters.
Each IMU provides 6 DOF motion information
(3 translational + 3 rotational) and 3D orientation
information. By placing an IMU at both sides of each
finger joint, (that is one per each finger bone and
2.3. Wireless Communication
The RS9110-N-11-22 [21] module shown in Fig. 2
is an IEEE 802.11b/g/n WLAN device that directly
provides a wireless interface to any equipment with a
UART or SPI interface for data transfer. It integrates a
MAC, baseband processor, RF transceiver with power
amplifier, a frequency reference, and an antenna in
hardware. It also provides all WLAN protocols and
configuration functionality. A networking stack in
embedded in the firmware to enable a fully selfcontained 802.11n WLAN solution for a variety of
applications.
The module incorporates a highly integrated
2.4 GHz transceiver and power amplifier with direct
conversion architecture, and an integrated frequency
reference antenna. The RS9110-N-11-22 comes with
flexible frameworks to enable usage in various
scenarios including high throughput networking
applications.
Fig. 2. RS9110-N-11-22 System Block Diagram.
The system operates according to a low complexity
standard 4-wire SPI interface with the capability of
operation up to a maximum clock speed of 25MHz.
The communications module conforms to IEEE
802.11b/g/n standards and includes hardware
accelerated implementation of WEP 64/128-bit and
AES in infrastructure and ad-hoc modes. Themodule
supports multiple security features such as
WPA/WPA2-PSK, WEP, TKIP which makes it
compatible with all medical ERP systems.
25
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
2.4. Sensors
The MPU-9150 [22] is a full three axis inertial
measurement system incorporating tri-axis angular
rate sensor (gyroscope) with sensitivity up to
131 LSBs/dps and a full-scale range of ±250, ±500,
±1000, and ±2000 dps, tri-axis accelerometer with a
programmable full scale range of ±2 g, ±4 g, ±8 g and
±16 g and a tri-axis compass with a full scale range of
±1200 μT. This module incorporates embedded
algorithms for run-time bias and compass calibration,
so no user intervention is required. The MPU-9150
features three 16-bit analog-to-digital converters
(ADCs) for digitizing gyroscope outputs, three 16-bit
ADCs for digitizing accelerometer outputs, and three
13-bit ADCs for digitizing magnetometer outputs. For
precision tracking of both fast and slow motions, the
module features a user programmable gyroscope fullscale range of ±250, ±500, ±1000, and ±2000°/sec
(dps), a user programmable accelerometer full-scale
range of ±2 g, ±4 g, ±8 g, and ±16 g, and a
magnetometer full-scale range of ±1200 μT.
2.5. Additional Features
To make the system adaptable in operation and
compatible with a wide range of use cases outside the
immediate application of RA monitoring, the IMU
Smart Glove system also incorporates such features as
optional storage via a micro SD card, battery
monitoring and recharge facility, a USB bootloader,
USB communication interface, and 15 Analogue
inputs for optional resistive sensors (e.g., bend sensors
or force sensors). The analogue front end is a buffered
voltage divider to enable additional sensing
functionality.
2.6. Flex Technology
The IMU Smart Glove PCB is a combination of
flexible and stretchable PCB technology [33]. The
stretchable material enables the microsystem to
closely replicate mechanical properties of the human
hand more accurately than standard flexible
technology. As shown in Fig. 3, stretchable PCB
sections are incorporated on hand areas crossing
several finger joints to enable flexion at the knuckles
and provide an interconnect mechanism between the
“islands” of rigid PCB substrates which incorporate
the WIMU technology.
The stretchable PCB technology is available from
the company “Q.P.I. Group”. The substrate material is
polyurethane. it is possible to obtain a stretch factor of
up to 30 % to enable wearable sensor system
interconnect, depending on the design implemented on
the copper pattern.
26
Fig. 3. PCB top view and flexible areas.
3. System Implementation
All the system embedded code is implemented
using the Atmel Studio 6 IDE. Currently the
implementation includes full application code that
continuously reads sensor outputs and wirelessly
transmits their data through a TCP socket.
The accuracy of IMU-based real time motion
tracking algorithms is highly influenced by sensor
sampling rate. Therefore a fundamental design
requirement of the IMU Smart Glove was high
application throughput to facilitate the development of
algorithms using suitable PC SW such as MATLAB,
C# and Unity. In addition, it was envisaged that once
the algorithms would have been fully developed and
tested, they would be fully implemented on the
embedded platform. This eliminates the requirement
for a high throughput device and allows for a low
power implementation for example using BLE in a
potential third generation of the glove.
To ensure maximum achievable sampling rates
and computation time are compatible with the
application scenario envisaged as specified in
conjunction with clinical partners regarding signal
temporal granularity, it was decided not to share the
I2C bus between each of the 16 MPU9150’s. Instead,
dedicated I2C lines are provided to each one of the
sensors and are driven in parallel. This provides the
added advantage of ensuring synchronization between
all IMU sensors.
3.1. Case 1. Raw Data Transmission
The embedded processor enables multiple modes
of operation depending on the use case and degree of
data granularity required. Having the wireless system
transmitting raw data at the highest achievable data
rate is desirable for the development of the analytics
as it is more practical to develop them using PC based
SW (real time or post processing) and then porting
them to the embedded system than develop them
directly within the embedded system.
3.2. Case 2. Transmission of Raw Data and
Information
The wireless system transmits raw data and
quaternions/rotation matrix (from gyros) at the highest
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
achievable data rate. Quaternions then will be subject
to drift/errors and the analytics to correct for this are
implemented within the controlling software. At this
stage we have a clear idea of the maximum processing
time that could be allocated in the embedding to this
task and that is taken into consideration when
designing these algorithms.
3.3. Case 3. Transmission of Processed Data
With the wireless system with full analytics
embedded, the internal sampling rate of the sensors
should be kept to a maximum achievable SR, the high
wireless data rate might no longer be required.
The processing time (per sample cycle) allocated
to the embedded tasks and estimated maximum
sampling rate / Application Throughput with the
microcontroller running at 48 MHz are shown in table
I Depending on the computational complexity of the
drift correction algorithm, (which are under
development at the Tyndall Institute), different
application data throughputs are achievable as shown
in Table 1.
Table 1. Processing time requirements for motion
data analysis.
Sensor
Sampling
(16 IMUs)
Wireless
Communicat
ions
Quaternions
/ Rotation
Matrix
Algorithm
Drift
correction
algorithms
Estimated
application
throughput
/data rate
Raw data
Raw Data +
Quaternions
Full
analytics
embedded
~ 0.9 ms
~ 0.9 ms
~ 0.9 ms
~0.3 ms
0.4-0.5 ms
~0.4-0.5 ms
None
~ 0.3-0.5 ms
~0.3-0.5 ms
None
None
~
8/3/2/1.33/0.
5 ms
~750 Hz /
2 Mbps
~500 Hz /
2 Mbps
100/200/250
/300/400 Hz
3.4. Calibration using Accelerometry and
Gyroscope Datasets
Data glove accuracy and repeatability is affected
by the non-linear nature of glove sensor output and any
misalignment between the wearers hand and data
glove sensor positioning. Data glove sensor
calibration improves sensor accuracy and matches the
boundaries of each sensor to those of each finger joint.
A calibration routine requires the glove wearer to
position groups of finger joints such as MCP’s and
PIP’s at specific poses. Each pose places a finger joint
group and relevant data glove sensors at their
minimum and maximum boundaries. The IMU Smart
Glove uses on-board sensors to automatically calibrate
each glove sensor, regardless of the wearer’s joint
flexibility. Each glove accelerometer sensor is
sampled when the hand is in a neutral position to
calculate finger joint thickness and slope offset, and
used during angular calculation. Accelerometers
placed on each one of the finger’s phalanges provide
information with regards to the inclination to gravity
of the phalanx. The output response of each sensor
provides information on the orientation of the sensor
to gravity as shown in Fig. 4. The orientation to gravity
of each one of the sensors placed on adjacent
phalanges can be used to estimate the flexion of the
finger.
Fig. 4. Output response vs. Orientation to gravity
For example, if the measured acceleration for a
specific finger from the medial phalanx accelerometer
is (Xout, Yout, Zout) = (-1,0,0) g and from the
proximal phalanx accelerometer is (Xout, Yout, Zout)
= (0,0,1) g, it indicates a flexion of the PIP joint of 90
degrees. The inclination to gravity is determined
according to the standard formulas (1), (2) and (3):

θ = tan −1 
AX ,OUT
2
 A2
 Y ,OUT + AZ ,OUT

ψ = tan −1 
AY ,OUT
2
2
 AX ,OUT + AZ ,OUT








2
 A2

X ,OUT + AY ,OUT 

AZ ,OUT


φ = tan −1 
(1)
(2)
(3)
where θ is the angle between the horizon and the
x-axis of the accelerometer, ψ is the angle between the
horizon and the y-axis of the accelerometer, and φ is
the angle between the gravity vector and the z-axis.
3.5. GUI/User Interface
Data is streamed in real-time according to the use
cases outlined above and post processed by our
controlling software. This software is called
27
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
‘DigitEase’ [34] shown in Fig. 5. A pivotal role of
DigitEase is its ability to encapsulate movement
associated with finger joints in real time. Fig. 5 shows
an example of DigitEase’s user interface.
Fig. 5. Angular output from the data glove is displayed in 3D.
Algorithms segment recorded data to extract
relevant flexion and extension movement information.
Each piece of sensor data is categorised into prerepetition, flexion, sustain, extension and postrepetition movement.
Fig. 6 demonstrates a typical flexion and extension
angular movement profile used by DigitEase.
Segmentation of finger joint movement is required to
isolate flexion and extension movement data from
unrequired pre-rep, port-rep and sustain time. Flexion
and extension movement is analysed for initial and
final angles. Both values represent minimum and
maximum ROM information for movement
repetitions and are indicators of completion and
initialisation of flexion and extension movement.
DigitEase’s data analysis dashboard presents postsegmented patient movement information. Fig. 7
shows an example of the dashboard.
The dashboard displays summary information on
patient-completed exercise routines. It details
individual repetitions and constituent sub-elements for
each exercise routine. Colour coding of each repetition
segment indicates performance information. Line
charts graphically depict velocity, angle-angle and
relative phase information for individually selected
repetitions.
Fig. 6. Chart demonstrating segments that characterise segmentation of finger joint movement.
28
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
Fig. 7. DigitEase data analysis dashboard.
4. Data Analytics and Post Processing
Each angular calculation is low-pass filtered to remove
sensor noise. A complementary filter with error
control is implemented to combine accelerometer
output with gyroscope rotation angle. Gyroscope
rotational angle is initially accurate and drifts over
time. Accelerometer angle cannot distinguish between
lateral acceleration and rotation. The complementary
filter acts as a high-pass and low-pass filter on both
signals. It combines estimated gyroscope rotation and
accelerometer angle to create an angular output.
they can be divided in three main approaches: a) the
deterministic approach (least-squares), b) the
frequency–based approach (Complementary Filter)
and c) the stochastic (Kalman filtering).
4.1. Algorithms for Joint Angle Estimation
Fig. 8 shows the joints of the hand along with their
number of degrees of freedom. The joint angles can be
calculated as the result of the relative orientation of
adjacent phalanges one to another so the algorithms to
estimate the joints angles from the IMUs are based on
the orientation estimation of the sensors themselves.
This orientation is commonly represented by
quaternions. Equations (4) - (7) represent the
orientation / quaternions of adjacent phalanges that are
linked by each joint.
q
,
q
(4)
,
(5)
,
(6)
,
(7)
Over the years, a variety of algorithms for the
estimation of orientation have been developed. The
majority of these are quaternion-based algorithm and
Fig. 8. Degrees of freedom of hand.
The deterministic approach was originally
introduced in 1965, in the so-called Wahba’s problem
[30], which is a constrained least-squares optimization
problem for finding the rotation matrix between two
coordinate systems from a set of weighted vector
measurements. Some of such algorithms are the
TRIAD (Tri-axial Attitude Determination), QUEST
(Quaternion ESTimator) and FQA (Factored
Quaternion Algorithm). These algorithms are based on
the concept of vector matching and require
measurements of constant reference vectors.
The frequency–based approach fuses the
orientation estimated from accelerometers and
29
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
magnetometers with the orientation estimated from
gyroscope with a complementary filter. This filter
blends the static low-frequency information provided
by accelerometers and magnetometers, and the
dynamic high frequency information provided by
gyroscopes. The aim of the complementary filter is to
ensure a compromise between the accuracy provided
by short-term integration of the gyroscope data and the
long-term measurements precision obtained by the
accelerometer and magnetometer [31].
Stochastic estimation algorithms use a dynamic
model for predicting aspects of the time behaviour of
a system and a measurement model in order to produce
the most accurate estimate possible of the system state.
Among all stochastic algorithms, the Kalman filter is
one of the most often used algorithms for tasks that
involve multisensory fusion, filtering and motion
prediction [32]. The filter works in a two-step process
consisting of a prediction step and an update step. In
the prediction step, Kalman filter produces estimates
of the current state variables, along with their
uncertainties. Once the outcome of the next
measurement (corrupted with error and noise) is
observed, these estimates are updated using a
weighted average. Because of the algorithm's
recursive nature, it can run in real time using only the
present input measurement and the previously
calculated state and its uncertainty matrix.
5. Testing Strategies and Results
Our new data glove was assessed for accuracy and
repeatability and was compared with the 5DT state-ofthe-art data glove. The Vicon MX motion capture
system was used during accuracy testing to
independently measure angular values generated at
each finger joint. Movement was recorded by Vicon
and simultaneously by DigitEase whilst each glove
was placed on blocks of wood cut to specific angles.
Angular readings were assessed using Root Mean
Square Error (RMS) to provide an indicator of the
variance between each estimated angular repetition
value and the expected true value influenced by the
angle on each block of wood. RMS error is influenced
by both positive and negative errors which are either
above or below the expected true value. Therefore
RMS output is a measure of the angular error.
Repeatability testing examined the ability of each data
glove to consistently replicate angular readings when
the subjects hand was held in a repeatable position.
Testing strategies were originally developed to assess
data glove suitability as a replacement for the UG.
Although no formal set of repeatability testing
strategies exist, the strategies used by [12] have been
adopted by subsequent research groups [13, 16, 23–
26] and are used in this study to allow comparison
between study results.
The ‘flat hand’ test examines each data glove’s
ability to maintain a minimum repeatable value after
full stretch of each data glove sensor. The plaster
mould test examines the ability of each data glove to
30
reproduce angular readings when positioned in a
repeatable position. In all tests, our data glove was not
calibrated for the subject, the 5DT data glove was
calibrated.
5.1. ‘Flat Hand’ Results
The ‘flat hand’ test results demonstrated in Table 2
show that the IMU data glove outperformed the 5DT
data glove. Mean MCP readings for the IMU glove
were near-perfect -0.38°, with PIP readings of -2.53°.
The 5DT produced readings of 4.17° for MCP and
2.27° for PIP. The results for our IMU glove are based
on a system which is not calibrated before use.
Table 2. Comparison of mean angular readings
recorded during ‘flat hand’ testing.
Index MCP
Index PIP
Middle MCP
Middle PIP
Ring MCP
Ring PIP
Little MCP
Little PIP
Mean MCP
Mean PIP
Overall mean
5DT
(Angle / SD)
2.34 (1.59)
2.04 (1.05)
5.9 (0.55)
3.27 (1.13)
5.14 (0.59)
1.02 (0.52)
3.32 (0.88)
2.76 (1.32)
4.17 (0.90)
2.27 (1.0)
3.22 (0.95)
IMU
(Angle / SD)
-0.59 (1.87)
-2.74 (0.90)
1.32 (2.26)
-2.94 (1.25)
-2.33 (1.21)
-2.7 (1.11)
0.07 (2.56)
-1.75 (1.31)
-0.38 (1.98)
-2.53 (1.14)
-1.46 (1.56)
5.2. Plaster Mould Test Results
Table 3 shows comparison results for plaster
mould testing for the 5DT and our IMU data glove.
Readings showed the IMU Smart Glove produced
better repeatability for MCP and PIP joints and better
overall repeatability as indicated by the lower mean
range angular reading. Comparison of mean range and
SD readings from plaster mould testing for each data
glove.
Table 3. Plaster mould test results.
Glove
5DT
IMU
MCP
Range
SD
8.85
2.13
5.99
1.89
PIP
Range
SD
6.23
2.09
5.10
1.58
Mean
Range
SD
7.54
2.11
5.55
1.74
5.3. Glove Finger Position Accuracy Results
Table 4 shows comparison of results for the 5DT
and our IMU Smart Glove compared with the Vicon
motion capture system and the UG.
Results showed the goniometer had greatest
overall accuracy of 93.23 % with overall RMS of
2.76°. This is in agreement with typical findings on
goniometric accuracy with 95 % of intratester
reliability within 5° of measurement and intertester
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
reliability in the range of 7° to 9° [27–29]. The Vicon
system provided mean accuracy of 89.33 % with RMS
of 5.19°. Mean accuracy percentage for each sensor
including mean error and overall accuracy percentage.
Table 4. Comparison between Tyndall WIMU Glove
and 5DT Glove.
Sensor
Index MCP
Index PIP
Middle MCP
Middle PIP
Ring MCP
Ring PIP
Little MCP
Little PIP
Overall
accuracy %
RMS
Vicon
93.31
91.23
91.46
84.08
87.20
86.99
86.14
94.23
5DT
94.20
92.01
79.66
74.97
70.46
91.99
85.83
74.56
Goniometer
97.95
90.75
95.83
88.96
97.37
90.70
91.28
93.03
IMU
89.57
91.47
82.40
77.29
82.02
89.51
83.38
86.27
89.33
82.96
93.23
85.24
5.19
7.15
2.76
5.95
the MCP joints. The IMU glove performed better than
all other data glove studies. Readings recorded by
earlier studies are averaged for several subjects. This
can hide higher inaccurate results for some subjects.
For example, [12] recorded range readings from 5
subjects that varied between 2.5° to 6.7°. Results were
averaged to 4.4°. Similarly, results from ‘flat hand’
testing from the study by [13] were summarised from
a group of 6 male and female participants. Mean male
range results went from 2.37° to 5.49° and mean
female from 3.90° to 4.75°.
6. Conclusions
This inaccuracy was most likely caused by noise,
marker occlusion, and distance of reflective markers
from Vicon cameras. Our IMU data glove provided
best accuracy measurement of both data gloves and
demonstrated similar accuracy to the Vicon
measurement system. RMS results show that readings
obtained from sensors contained approximately 5.95°
of error. Results shown in Table 3 indicate that all
sensors demonstrated accuracy between 82 % to 91 %
except for the Middle PIP sensor that had accuracy of
77.29 %. This decreased accuracy may have been
caused by slight stretch of sensor cable for this
particular sensor.
Data gloves have been proven to be a viable
replacement for the UG and can offer unbiased finger
joint ROM measurement. However their dependence
on calibration reduces their usefulness in the many
application spaces. The novel IMU based wireless
Smart Glove detailed in this paper removes the
requirement
for
sensor
calibration
using
accelerometers and gyroscopes teamed with
intelligent software techniques. Test results showed
our IMU data glove had comparable repeatability to
the UG with the added advantage of simultaneous
angular measurement and removal of intra-tester and
inter-tester reliability. Accuracy testing results showed
the IMU data glove provided better accuracy and less
overall error than the 5DT data glove with which it
was compared. Of note the IMU glove required no
calibration before use whilst maintaining results which
demonstrated it had similar accuracy to the Vicon
system.
5.4. Comparison with Previous Trials
Acknowledgements
The results shown in Table 5 compare ‘flat hand’
and plaster mould tests for the 5DT and our IMU data
glove with previous research studies involving data
gloves.
The support of Science Foundation Ireland (SFI)
as well as the National Access Program (NAP) support
provided by the Tyndall National Institute is gratefully
acknowledged. This work was also supported by
Department of Education and Learning (DEL).
Table 5. Comparison of ‘flat hand’ and plaster mould tests
with previous data glove studies.
Study
Wise et al. [12]
Dipietro et al. [13]
Simone et al. [15]
Gentner and
Classen [26]
5DT (this study)
IMU (this study)
Flat hand
test
(Range / SD)
4.4 (2.2)
3.84 (1.23)
1.49 (0.5)
Plaster mould
test
(Range / SD)
6.5 (2.6)
7.47 (2.44)
5.22 (1.61)
2.61 (0.86)
6.09 (1.94)
2.27 (0.995)
4.86 (1.56)
7.54 (2.11)
1.74)
The 5DT data glove demonstrated range readings
that out-performed data glove findings by [12] [13]
and were similar to [26]. The data glove examined by
[15] provided better results than all studies including
the 5DT and our IMU glove. However this glove
contained only 5 sensors that recorded movement of
References
[1]. D. M. van der Heijde, van ’t H. M, P. L. van Riel, and
L. B. van de Putte, Development of a disease activity
score based on judgment in clinical practice by
rheumatologists, J. Rheumatol., Vol. 20, No. 3, 1993,
pp. 579–81.
[2]. J. F. Fries, P. Spitz, R. G. Kraines, and H. R. Holman,
Measurement of patient outcome in arthritis, Arthritis
Rheum., Vol. 23, No. 2, 1980, pp. 137–145.
[3]. J. F. Fries, P. W. Spitz, and D. Y. Young, The
dimensions of health outcomes: the health assessment
questionnaire, disability and pain scales, J.
Rheumatol., Vol. 9, No. 5, 1982, p. 789-793.
[4]. P. Emery, F. C. Breedveld, M. Dougados, J. R.
Kalden, M. H. Schiff, and J. S. Smolen, Early referral
recommendation for newly diagnosed rheumatoid
arthritis: evidence based development of a clinical
guide, Ann. Rheum. Dis., Vol. 61, No. 4, Apr. 2002,
pp. 290–297.
31
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 23-32
[5]. F. C. Arnett, S. M. Edworthy, D. A. Bloch, D. J.
McShane, J. F. Fries, et al., The American
Rheumatism Association 1987 revised criteria for the
classification of rheumatoid arthritis, Arthritis and
Rheumatism, Vol. 31, No. 3, Mar-1988, pp. 315–324.
[6]. J. T. Scott, Morning stiffness in rheumatoid arthritis,
Ann. Rheum. Dis., Vol. 19, Dec. 1960, pp. 361–368.
[7]. V. Wright and R. J. Johns, Quantitative and qualitative
analysis of joint stiffness in normal subjects and in
patients with connective tissue diseases, Ann. Rheum.
Dis., Vol. 20, Mar. 1961, pp. 36–46.
[8]. M. L. Ingpen and P. H. Kendall, A simple apparatus
for assessment of stiffness, Ann. Phys. Med., Vol. 9,
No. 5, Feb. 1968, pp. 203–205.
[9]. a Unsworth, P. Yung, and I. Haslock, Measurement of
stiffness in the metacarpophalangeal joint: the
arthrograph, Clin. Phys. Physiol. Meas., Vol. 3, No. 4,
Nov. 1982, pp. 273–81.
[10]. A. Howe, D. Thompson, and V. Wright, Reference
values for metacarpophalangeal joint stiffness in
normals, Ann. Rheum. Dis., Vol. 44, No. 7, Jul. 1985,
pp. 469–76.
[11]. E. Dionysian, J. M. Kabo, F. J. Dorey, and R. a Meals,
Proximal interphalangeal joint stiffness: measurement
and analysis, J. Hand Surg. Am., Vol. 30, No. 3, May
2005, pp. 573–9.
[12]. S. Wise, W. Gardner, E. Sabelman, E. Valainis, Y.
Wong, et al., Evaluation of a fiber optic glove for
semi-automated goniometric measurements, J.
Rehabil. Res. Dev., Vol. 27, No. 4, 1990, p. 411.
[13]. L. Dipietro, A. M. Sabatini, and P. Dario, Evaluation
of an instrumented glove for hand-movement
acquisition, J. Rehabil. Res. Dev., Vol. 40, No. 2,
2003, pp. 179–189.
[14]. L. K. Simone and D. G. Kamper, Design
considerations for a wearable monitor to measure
finger posture, J. Neuroeng. Rehabil., Vol. 2, No. 1,
Mar. 2005, p. 5.
[15]. L. K. Simone, N. Sundarrajan, X. Luo, Y. Jia, and D.
G. Kamper, A low cost instrumented glove for
extended monitoring and functional hand assessment,
J. Neurosci. Methods, Vol. 160, No. 2, Mar. 2007,
pp. 335–348.
[16]. G. Saggio, S. Bocchetti, C. A. Pinto, G. Orengo, and
F. Giannini, A novel application method for wearable
bend sensors, in Proceedings of the 2nd International
Symposium on Applied Sciences in Biomedical and
Communication Technologies, Bratislava, Slovakia,
Nov-2009, pp. 1–3.
[17]. K. Li, I.-M. Chen, S. H. Yeo, and C. K. Lim,
Development of finger-motion capturing device based
on optical linear encoder, J. Rehabil. Res. Dev.,
Vol. 48, No. 1, 2011, p. 69.
[18]. 5DT, 5DT Data Glove 14 Ultra, 2011. [Online],
Available at:
http://www.5dt.com/products/pdataglove14.html
[retrieved: 01-2012].
[19]. Vicon Motion Systems, Vicon, 2013. [Online],
Available at: http://www.vicon.com/. [retrieved: 012012].
[20]. B. O’Flynn, J. Sanchez, P. Angrove, J. Connolly, J.
Condell, and K. Curran, Novel smart sensor glove for
arthritis rehabiliation, in Proceedings of the IEEE
International Conference on Body Sensor Networks,
(BSN 2013), May-2013, pp. 1–6.
[21]. Redpine Signals, RS9110-N-11-22: 802.11BGN
wireless device server, RS9110-N-11-22 Product
brief, 2008, [Online] Available at:
http://www.redpinesignals.com/pdfs/RS9110-N-1122 Wlan Module.pdf [retrieved: 01-2012].
[22]. MPU-9150 Product Specification Revision 4.0,
InvenSense, California, Vol. 1, No. 408, 2012,
pp. 1–52.
[23]. G. Kessler, N. Walker, and L. Hodges, Evaluation of
the CyberGlove (TM) as a whole hand input device,
GVU Technical Report, GIT-GVU-95-05, Georgia
Institute of Technology, 1995.
[24]. M. Mentzel, F. Hofmann, T. Ebinger, B. Jatzold, L.
Kinzl, and N. J. Wachter, Reproducibility of
measuring the finger joint angle with a sensory glove,
Handchir Mikrochir Plast Chir, Vol. 33, No. 1, 2001,
pp. 9–64.
[25]. N. W. Williams, J. M. T. Penrose, C. M. Caddy, E.
Barnes, D. R. Hose, and P. Harley, A goniometric
glove for clinical hand assessment, J Hand Surg Br.,
Vol. 25, No. 2, 2000, pp. 200–207.
[26]. R. Gentner and J. Classen, Development and
evaluation of a low-cost sensor glove for assessment
of human finger movements in neurophysiological
settings, J. Neurosci. Methods, Vol. 178, No. 1, Mar.
2009, pp. 138–47.
[27]. A. Hellebrandt, E. Duvall, and M. Moore, The
measurement of joint motion. Part III : Reliability of
goniometry, Phys Ther Rev, Vol. 29, No. 6, 1949,
pp. 302–307.
[28]. E. Lewis, L. Fors, and W. J. Tharion, Interrater and
intrarater
reliability
of
finger
goniometrie
measurements, Am. J. Occup. Ther., Vol. 64, 2010,
pp. 555–561.
[29]. N. B. Reese and W. D. Bandy, Measurement of Range
of motion and muscle length: background, history and
basic principles, Joint Range of Motion and Muscle
Length testing, 2nd ed., Saunders/Elsevier, St. Louis,
MO, 2010. pp. 3–29.
[30] G. Wahba, A least-square estimate of spacecraft
attitude, SIAM Rev., 8, 1965, p. 409.
[31] H. Fourati, N. Manamanni, Position Estimation
Approach by Complementary Filter-aided IMU for
Indoor Environment, in Proceedings of the 12th
biannual European Control Conference, Zurich,
Switzerland, July 2013.
[32] G. F. Welch, The use of the Kalman filter for human
motion tracking in virtual reality, Presence, Vol 18,
2009.
[33] Brendan O’Flynn, J. T. Sanchez, James. Connolly,
Joan. Condell, Kevin. Curran, Philip Gardiner, Barry
Downes, Integrated Smart Glove for Hand Motion
Monitoring, in Proceedings of the 9th International
Conference on Sensor Technologies and Applications
(SENSORCOMM’15), Venice, Italy, August 23 - 28,
2015, pp. 45-50.
[34] Connolly, J., Condell, J., Curran, K. and Gardiner, P.,
Towards Joint Stiffness measurement of Rheumatoid
Arthritis sufferers, in Proceedings of the 2nd European
Conference on Design 4 Health (Design4Health),
Sheffield Hallam University, UK, SheffieldPub, 2013,
pp. 150.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
32
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Wide Spectral Sensitivity of Monolithic a-SiC:H pi’n/pin
Photodiode Outside the Visible Spectrum
1, 2, 3
1
Manuela Vieira, 1, 2 Manuel Augusto Vieira, 1 Isabel Rodrigues,
1, 2
Vitor Silva, 1, 2 Paula Louro, 1, 2 A. Fantoni
Telecommunication and Computer Dept. ISEL, R. Conselheiro Emídio Navarro,
1959-007 Lisboa, Portugal
2
CTS-UNINOVA, Quinta da Torre, Monte da Caparica, 2829-516, Caparica, Portugal
3
DEE-FCT-UNL, Quinta da Torre, Monte da Caparica, 2829-516, Caparica, Portugal
1
Tel.: +351218317150, fax: +351218317144
1
E-mail: mv@isel.ipl.pt
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: In this paper, we experimentally demonstrate the use of near-ultraviolet steady state illumination to
increase the spectral sensitivity of a double a-SiC/Si pi’n/pin photodiode beyond the visible spectrum (400 nm880 nm). The concept is extended to implement a 1 by 4 wavelength division multiplexer with channel
separation in the visible/near infrared ranges. The device consists of a p-i'(a-SiC:H)-n/p-i(a-Si:H)-n
heterostructure, sandwiched between two transparent contacts. Optoelectronic characterization of the device is
presented and shows the feasibility of tailoring the wavelength and bandwidth of a polychromatic mixture of
different wavelengths. Results show that the spectral current under steady state ultraviolet irradiation depends
strongly on the wavelength of the impinging light, and on the background intensity and irradiation side allowing
controlled high-pass filtering properties. If several monochromatic pulsed lights, in the visible/near infrared
(VIS/NIR) range, separately or in a polychromatic mixture illuminate the device, data shows that, front
background enhances the light-to-dark sensitivity of the medium, long and infrared wavelength channels, and
quench strongly the low wavelengths channels. Back background has the opposite behavior; it enhances only
channel magnitude in short wavelength range and strongly reduces it in the long ones. This nonlinearity
provides the possibility for selective tuning of a specific wavelength. A capacitive optoelectronic model
supports the experimental results. A numerical simulation is presented. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Amorphous SiC technology, Optoelectronics, Spectral sensitivity, UV irradiation, Photodiode,
Multiplexer device, VIS/NIR decoding, Numerical simulation.
1. Introduction
The LED is a very effective lighting technology
due to its high brightness, long life, energy
efficiency, durability, affordable cost, optical
spectrum and its colour range for creative purposes.
Their application as communication device with a
http://www.sensorsportal.com/HTML/DIGEST/P_2732.htm
photodiode as receptor, has been used for many years
in hand held devices, to control televisions and other
media equipment, and with higher rates between
computational devices [1]. This communication path
has been employed in the near infra-red (NIR) range,
but due to the increasing LED lighting in homes and
offices, the idea to use them for visible light
33
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
communications (VLC) has come up recently. Newly
developed
technologies,
for
infrared
telecommunication systems, allow increase of
capacity, distance, and functionality, leading to the
design of new reconfigurable active filter [2-4]. To
enhance the transmission capacity and the application
flexibility of optical communication, efforts have to
be considered, namely the fundamentals of
Wavelength Division Multiplexer (WDM) based on
a-SiC:H light controlled filters, when different visible
signals are encoded in the same optical transmission
path [5]. They can be used to achieve different
filtering processes, such as: amplification, switching,
and wavelength conversion.
In this paper, it is demonstrated that the same
a-SiC:H device under front and back controlled near
ultraviolet optical bias acts as a reconfigurable active
filter in the visible and near infrared ranges, making
the bridge between the infrared and the red spectral
ranges. In consequence, bridging the visible spectrum
to the telecom gap offers the opportunity to provide
alternative and additional low cost services to
improve operative production processes in office,
home and automotive networks.
In Section 1, an introduction is given and in
Section 2, some experimental results are presented. In
Section 3, the bias controlled selector is analyzed and
in Section 4, the Wavelength Division Multiplexed
(WDM) based on SiC technology is described. In
Section 5, the optoelectronic model gives insight the
physics of the device, the decoding algorithm is
presented in Section 6, and finally, in Section 7, the
conclusions are presented.
2. Experimental Details
2.1. Device Configuration
The light tunable filter is realized by
using a double pi’n/pin a-SiC:H photodetector
produced by Plasma Enhanced Chemical Vapor
Deposition (PECVD).
The device has Transparent Conductive Oxide
(TCO) front and back biased optical gating elements
as depicted in Fig. 1.
Fig. 1. Device configuration and operation.
The active device consists of a p-i'(a-SiC:H)-n/pi(a-Si:H)-n heterostructure with low conductivity
doped layers. The deposition conditions and
optoelectronic characterization of the single layers
were described elsewhere [5].
The thicknesses and optical gap of the front
i'- (200 nm; 2.1 eV) and back i- (1000 nm; 1.8 eV)
layers are optimized for light absorption in the blue
and red ranges, respectively [6].
2.2. Device Operation
Monochromatic (infrared, red, green, blue and
violet; λIR,R,G,B,V) pulsed communication channels
(input channels) are combined together, each one
with a specific bit sequence, impinge on the device
and are absorbed accordingly to their wavelengths
(see arrow magnitudes in Fig. 1).
The combined optical signal (multiplexed signal;
MUX) is analyzed by reading out the generated
photocurrent under negative applied voltage (-8 V),
without and with near ultraviolet background
(λBackground=390 nm) and different intensities, applied
either from the front (λF) or the back (λB) sides. The
device operates within the visible range using as
input color channels the square wave modulated low
power light supplied by near-infrared/visible
(VIS/NIR) LEDs. In Fig. 2(a), the 524 nm input
channel is displayed under front, back and without
UV irradiation. The arrows indicate the enhancement
(solid line) or quenching (dot line) of the dark signal,
respectively under front and back irradiation.
λ input=524 nm
λ Background =390 nm
0,6
0,4
Dark
Back
0,2
0,0
0,0
0,5
1,0
1,5
Time (ms)
(a)
2,0
697 nm
524 nm
850 nm
400 nm
1,5
Front
Photocurrent (μA)
Photocurrent (μA)
0,8
2,5
Front
1,0
Back
0,5
0,0
0,0
0,5
1,0
1,5
2,0
Time (ms)
(b)
Fig. 2. (a) 524 nm input channel under front, back and without (dark) background irradiation;
(b) MUX signals and under front and back λ=390 nm irradiation and different bit sequences.
34
2,5
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
In Fig. 2 (b), the polychromatic mixture of four
different input channels (400 nm, 524 nm, 697 nm
and 850 nm) under front and back 2800 µWcm-2
irradiation, is displayed. At the top, the input
channels wavelengths and their bit sequences guide
the eyes.
3. Bias Controlled Selector
3.1. Optical Bias Controlled Filter
The spectral sensitivity was tested through
spectral response measurements [7] without and
under 390 nm front and back backgrounds of variable
intensities. The spectral gain (α), defined as the ratio
between the signal with and without irradiation
was inferred.
In Fig. 3, the spectral gain (α) is displayed under
steady state irradiations. In Fig. 3 (a), the light was
applied from the front (λF) and in Fig. 3 (b), the
irradiation occurs from the back side (λB). The
background intensity (φ) was changed between
5 µWcm-2 and 3800 µWcm-2.
Results show that, the optical gains have opposite
behaviors. Under front irradiation (Fig. 3(a)) and low
Gain (αF)
5
4
3
2
2
Φ (μW/cm )
3.0
λF=390nm
5
20
56
100
160
250
575
1270
2600
3800
3500 Hz
2.5
2.0
Φ
Gain (αB)
6
flux, the gain is high in the infrared region, presents a
well-defined peak at 725 nm and strongly quenches
in the visible range. As the power intensity increases,
the peak shifts to the visible range and can be
deconvoluted into two peaks, one in the red range
that slightly increases with the power intensity of the
background and another in the green range that
strongly increases with the intensity of the ultraviolet
(UV) radiation.
In the blue range, the gain is much lower. This
shows the controlled high-pass filtering properties of
the device under different background intensities.
Under back bias (Fig. 3(b)) the gain in the blue/violet
range has a maximum near 420 nm that quickly
increases with the intensity. Moreover, it strongly
lowers for wavelengths higher than 450 nm, acting as
a short-pass filter. Thus, back irradiation, tunes the
violet/blue region of the visible spectrum whatever
the flux intensity, while front irradiation, depending
on the background intensity, selects the infrared or
the visible spectral ranges. Here, low fluxes select the
near infrared region and cuts the visible one, the
reddish part of the spectrum is selected at medium
fluxes, and high fluxes tune the red/green ranges with
different gains.
λB=390 nm
2
Φ (μW/cm )
3500 Hz
Φ
1.5
1.0
5
20
55
100
160
250
575
1270
2600
3800
0.5
1
400 450 500 550 600 650 700 750 800
400 450 500 550 600 650 700 750 800
Wavelength (nm)
Wavelength (nm)
(a)
(b)
Fig. 3. Front (λF) and back (λB) spectral gains (αF,B) respectively, under λ=390 nm irradiations.
3.2. Nonlinear Spectral Gain
To analyze the effect of the background intensity
in the input channels, several monochromatic pulsed
lights separately (850 nm, 697 nm, 626 nm, 524 nm,
470 nm, 400 nm; input channels) or combined (MUX
signal) illuminated the device at 12000 bps [8].
Steady state optical bias with different intensities
was superimposed separately from the front and back
sides and the photocurrent measured. For each
individual channel the photocurrent gain under
irradiation was determined. In Fig. 4, these
gains are displayed as a function of the
background lighting under front (Fig. 4(a)) and back
(Fig. 4(b)) irradiation.
Results show that, even under transient conditions
and using commercial visible and NIR LEDs, the
background side and intensity alters the signal
magnitude of the input channels.
The gain depends mainly on the channel
wavelength and to some extent on the lighting
intensity. Even across narrow bandwidths, the
photocurrent gains are quite different. This
nonlinearity allows identification of the different
input channels in the visible/infrared ranges.
4. Wavelength Division Multiplexer
4.1. Input Channels
Four monochromatic pulsed lights with different
intensities, separately (400 nm, 470 nm, 697 nm and
850 nm; input channels) or combined (MUX signal)
illuminated the device at 12000 bps.
35
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
Steady state 390 nm front and back optical bias
with 2800 µWcm-2 intensity was superimposed
separately and the photocurrent was measured. In
Fig. 5 (a), the blue and violet transient signals are
presented under front and back irradiations while in
Fig. 5 (b), the red and infrared signals are displayed.
5 λFront=390 nm
2,0
λBack=390 nm
4
470 nm
524 nm
626 nm
697 nm
850 nm
1,5
αB
αF
3
470 nm
524 nm
626 nm
697 nm
850 nm
2
1
1
1,0x10
1,0x10
2
1,0
0,5
3
1
1,0x10
2
1,0x10
3
1,0x10
-2
1,0x10
-2
Φ (μWcm )
Φ (μWcm )
(a)
(b)
Fig. 4. Front (a) and back (b) optical gains as a function of the background intensity for different input wavelengths
in the VIS/NIR range.
λB=470 nm
Photocurrent (μΑ)
Back
Front
λIR=850 nm
λV=400 nm
Back
0.3
Front
0.0
0.0
0.5
1.0
1.5
λR=697 nm
0,2
Photocurrent (μΑ)
0.6
Front
Front
0,1
Back
Back
2.0
2.5
0,0
0,0
0,5
1,0
1,5
Time (ms)
Time (ms)
(a)
(b)
2,0
2,5
Fig. 5. Input signals under front and back 390 nm background irradiation;
a) violet and blue channels. b) red and infrared channels.
In Table 1, the measured optical gains for five
different input channels are displayed.
Table 1. Optical gains under 390 nm front (αFront)
and back (αBack) irradiations.
αBack
αFront
λ=400
nm
11.6
0.9
λ=470
nm
1.8
1.5
λ=524
nm
0.61
3.2
λ=697
nm
0.46
4.3
λ=850
nm
0.44
3.5
Back irradiation enhances, differently, the input
signals in the short wavelength range (Fig. 5(a))
while front irradiation increases them otherwise in
the long wavelength range (Fig. 5(b)). This side
dependent effect is used to enhance or to quench the
36
input signals allowing their recognition and providing
the possibility for selective tuning of the visible and
IR input channels.
4.2. MUX Signal
In Fig. 6, two MUX signals due to the input
signals of Fig. 2(a) and Fig. 5 are displayed without
(dark) and under front and back irradiation. On top,
the signals used to drive the input channels are shown
to guide the eyes into the on/off channel states.
Results show that, the background side alters the
form of the MUX signal, enhancing or quenching
different spectral ranges. In Fig. 6(a) all the on/off
states are possible so, without optical bias, 24 ordered
levels are detected and correspond to all the possible
combinations of the on/off states.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
697 nm
524 nm
470 nm
400 nm
1.2
Back
Dark
Front
697 nm
524 nm
850 nm
400 nm
1.0
Photocurrent (μA)
Photocurrent (μA)
1.5
0.9
0.6
0.3
0.8
Back
0.6
0.4
0.2
Front
0.0
0.0
0.5
1.0
1.5
2.0
0.0
0.0
2.5
0.5
1.0
Time (ms)
Time (ms)
(a)
(b)
1.5
Fig. 6. MUX signals: (a) without and under front and back λ=390 nm irradiation and different bit sequences.
(b) Front and back irradiation and two channels (400 nm and 697 nm) with the same bit sequence.
Under, either front or back irradiation, each of
those four channels, by turn, are enhanced or
quenched differently (Fig. (6), Table 1) resulting in
an increase magnitude of red/green under front
irradiation or of the blue/violet one, under back
lighting. Since the gain of the four input channels is
different (αF,B; Table 1) this nonlinearity allows
identifying the different input channels in a large
visible/infrared range. In Fig. 6 (b), both 400 nm and
697 nm channels have the same bit sequence which
corresponds to only 23 ordered levels, however once
the optical gains of both channels are quite different
under front and back irradiation (Table 1) it is
possible to identify them. Under back irradiation the
MUX signal receive its main contribution from the
400 nm channel while under front irradiation it is
mainly weighed buy the long wavelength channels.
By comparing front and back irradiation is possible
to decode the transmitted information.
Under front irradiation, near-UV radiation is
absorbed at the beginning of the front diode and, due
to the self-bias effect, increases the electric field at
the back diode where the red/infrared incoming
photons (see Fig. 1) are absorbed accordingly to their
wavelengths (see Fig. 3) resulting in an increased
collection. Under back irradiation the electric field
decreases mainly at the back i-n interface enhancing
the electric field at the front diode quenching it at the
back one. This leads to an increased collection of the
violet/blue input signals.
So, by switching between front to back irradiation
the photonic function is modified from a long- to a
short-pass filter allowing, alternately selecting the
red/infrared channels or the blue and violet ones,
thus, making the bridge between the visible and the
infrared regions.
was developed [5] and upgraded to include several
input channels. The ac circuit representation is
displayed in Fig. 7 (b) and is supported by the
complete dynamical large signal Ebers-Moll model
with series resistances and capacities.
Q1
Based on the experimental results and device
configuration a two connected phototransistors model
(Fig. 7 (a)), made out of a short- and a long-pass filter
i
np
IB,IG
n
Q2
(a)
I3
I1
C1
Q1
R1 (V)
I2
I4
C2 R2
Q2
(b)
.v1
α1/C1
λV
λB
+
i1(t)
+

+
λG
λR,IR
5. Optoelectronic Model
I IR, IR IG
p i´ np
i2(t) α /C
2 2
+
.

 dt
v1
-1/R1C1
1/R1C2

1/R1C1
v2
v2
 dt
+
-1/R1C2 -1/R2C2
i (t)
+
1/R2
(c)
Fig. 7. a) Two connected transistor model;
b) equivalent electric circuit; c) block diagram
of the optoelectronic state model.
37
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
The charge stored in the space-charge layers is
modelled by the capacitor C1 and C2. R1 and R2
model the dynamical resistances of the internal and
back junctions under different dc bias conditions. The
operation is based upon the following strategic
principle: the flow of current through the resistor
connecting the two transistor bases is proportional to
the difference in the voltages across both capacitors
(charge storage buckets). The modified electrical
model developed is the key of this strategic operation
principle. Two optical gate connections ascribed to
the different light penetration depths across the front
(Q1) and back (Q2) phototransistors were considered
to allow independent blue (I1), red/infrared (I2) and
green (I3, I4) channels transmission. Four squarewave current sources with different intensities are
used; two of them, I1 and I2, with different
frequencies to simulate the input blue and red
channels and the other two, I3 and I4, with the same
frequency but different intensities, to simulate the
green channel due to its asymmetrical absorption
across both front and back phototransistors.
In Fig. 7(c), the block diagram of the
optoelectronic state model is displayed. The resistors
(R1, R2) and capacitors (C1, C2) synthesize the desired
filter characteristics. The input signals, λIR,R,G,B,V
1,0
697 nm
5241.0
nm
850 nm
400 nm
Simulation
Experimental
0.8
Front
0,5
0,0
0,0
0,5
1,0
1,5
2,0
Simulation
Experimental
Back
Back
MUX signal (μA)
MUX signal (μA)
697 nm
524 nm
1,5
850 nm
400 nm
model the input channels and i(t) the output signal.
The amplifying elements, α1 and α2 are linear
combinations of the optical gains of each impinging
channel, respectively into the front and back
phototransistors and account for the enhancement or
quenching of the channels (Fig. 3) due to the steady
state irradiation. Under front irradiation we have:
α2>>α1 and under back irradiation α1>>α2. This
affects the reverse photo capacitances, (α1,2/C1,2) that
determine the influence of the system input on the
state change.
A graphics user interface computer program was
designed and programmed within the MATLAB®
programming language, to ease the task of numerical
simulation. This interface allows selecting model
parameters, along with the plotting of bit signals and
both simulated and experimental photocurrent results.
To simulate the input channels we have used the
individual magnitude of each input channel without
background lighting (Fig. 2 and Fig. 5), and the
corresponding gain at the simulated background
intensity (Table 1). Fig. 8, presents results of a
numerical simulation with 3000 µW/cm2 front and
back λ=390 nm irradiation and the experimental
outputs of Fig. 2(b) and Fig. 6(b), respectively.
2,5
Time (ms)
(a)
0.6
0.4
0.2
0.0
0.0
Front
0.5
1.0
1.5
2.0
2.5
Time (ms)
(b)
Fig. 8. Numerical simulation with front and back λ=390 nm irradiation, and different channel wavelength combinations
and bit sequences.
Values of R1=10 kΩ, R2=1 kΩ, C1=1000 pF,
C2=20000 pF were used during the simulation
process (Fig. 7(c)). On top of the figures, the drive
input LED signals guide the eyes into the different
on/off states and correspondent wavelengths
A good fitting between experimental and
simulated results was achieved. The plots show the
ability of the presented model to simulate the
sensitivity behavior of the proposed system in the
visible/infrared spectral ranges. The optoelectronic
model with light biasing control has proven to be a
good tool to design optical filters. Furthermore, this
model allows for extracting theoretical parameters by
fitting the model to the measured data (internal
resistors and capacitors). Under back irradiation
38
higher values of C2 were obtained confirming the
capacitive effect of the near-UV radiation on the
device that increases the charge stored in the space
charge layers of the back optical gate of Q2 modelled
by C2 [9].
6. Decoding Algorithm
Results show that the background side changes
the shape of the MUX signal, enhancing or
quenching different spectral ranges. In Fig. 8(a) all
the on/off states are possible so, 24 ordered levels are
detected and correspond to all possible combinations
of the on/off states. Under, either front or back
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
irradiation, each of those four channels, by turn, are
enhanced or quenched differently (Fig. 5, Table 1)
resulting in an increased magnitude of red/green
under front irradiation or of the blue/violet one, under
back lighting. Since the gain of the input channels is
different (αF,B; Table 1) this nonlinearity allows
identifying the different input channels in a large
visible/infrared range. Under front irradiation the
MUX signal presents sixteen separate levels each one
ascribed to one of the of the 24 possible combinations
of the on/off states and pondered by their optical
gains. So, by assigning each output level to a four
digit binary code weighted by the optical gain of the
each channel, the signal can be decoded. A
transmission capability of 15 kbps was achieved.
The decoding algorithm is based on a proximity
search [10]. Each time slot is translated to a vector in
multidimensional space. The vector components’ are
computed as a function of the sampled currents I1 and
I2, where I1 and I2 are the currents measured under
front and back optical bias in the respective time slot.
The result is then compared with all vectors obtained
from a calibration sequence. The color bits of the
nearest calibration point are assigned to the time slot.
An Eucledian metric is applied to measure distances.
We have used this simple algorithm to perform 1-to16 demultiplexer (DEMUX) function and to decode
the multiplex signals. As proof of concept the
decoding algorithm was implemented in Matlab [11]
and tested using different binary sequences. In Fig. 9
a random MUX signal under front and back
irradiation is displayed as well as the decoding
results. A good agreement between the signals used
to drive the LED’s and the decoded sequences is
achieved. In all tested sequences tested the RGBV
signals were correctly decoded.
Photocurrent (uA)
V [0100010100101111]
3.5
B [0101110011001010]
]
G [0101001001111100
3
R [0111010110010100]
respectively from a long-pass filter to pick the
red/infrared channels to a short-pass filter to select
the violet channel, giving a step reconfiguration of
the device. The green and blue channels are selected
by combining both active long- and short-pass filters
into a band-pass filter. In practice, the decoding
applications far outnumber those of demultiplexing.
Multilayer SiC/Si optical technology can provide a
smart solution to communication problem by
providing a possibility of optical bypass for the
transit traffic by dropping the fractional traffic that is
needed at a particular point.
7. Conclusions
We experimentally and theoretically demonstrate
the use of near-ultraviolet steady state illumination to
increase the spectral sensitivity of a double a-SiC/Si
pi’n/pin photodiode beyond the visible spectrum
(400 nm-880 nm). The concept is extended to
implement a 1 by 4 wavelength division multiplexer
with channel separation in the visible/near
infrared ranges.
Results show that, the pi´n/pin multilayered
structure becomes reconfigurable under front and
back irradiation, acting as data selector in the
VIS/NIR ranges. The device performs WDM
optoelectronic logic functions providing photonic
functions such as signal amplification, filtering and
switching. The opto-electrical model with light
biasing control has proven to be a good tool to design
optical filters in the VIS/NIR. An optoelectronic
model was presented and proven to be a good tool to
design optical filters in the VIS/NIR range. A
decoding algorithm to decode
Acknowledgements
This work was supported by FCT (CTS multi
annual funding) through the PIDDAC Program funds
(UID/EEA/00066/2013)
and
PTDC/EEAELC/120539/2010.
Front
2.5
2
1.5
References
1
0.5
Back
0
0
0.2
0.4
0.6
0.8
Time (ms)
1
1.2
-3
x 10
Fig. 9. DEMUX signals and decoded RGBV binary
bit sequences.
The DEMUX sends the input logic signal to one
of its 2n (n is the number of color channels) outputs,
according to the optoelectronic demux algorithm. So,
by means of optical control applied to the front or
back diodes, the photonic function is modified,
[1]. T. Komiyama, K. Kobayashi, K. Watanabe,
T. Ohkubo, Y. Kurihara, Study of visible light
communication system using RGB LED lights, in
Proceedings of the IEEE SICE Annual Conference
(SICE’11), 2011, pp. 1926-1928.
[2]. S. S. Djordjevic, et al., Fully Reconfigurable Silicon
Photonic Lattice Filters With Four Cascaded Unit
Cells, IEEE Photonics Technology Letters, Vol. 23,
No. 1, 1 January 2011, pp. 41-44.
[3]. P. P. Yupapin, P. Chunpang, An Experimental
Investigation of the Optical Switching Characteristics
Using Optical Sagnac Interferometer Incorporating
One and Two Resonators, Optics & Laser
Technology, Vol. 40, No. 2, 2008, pp. 273-277.
[4]. S. Ibrahim, et al., Fully Reconfigurable Silicon
39
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 33-40
[5].
[6].
[7].
[8].
Photonic Lattice Filters with Four Cascaded
Unit Cells, in Proceedings of the Optical Fibre
Communications Conference (OSA/OFC/NFOEC),
San Diego, 21 Mar. 2010, paper OWJ5.
M. Vieira, P. Louro, M. Fernandes, M. A. Vieira,
A. Fantoni, J. Costa, Three Transducers Embedded
into One Single SiC Photodetector: LSP Direct Image
Sensor, Optical Amplifier and Demux Device, in
Advances in Photodiodes, Chap. 19, InTech, 2011,
pp. 403-425.
M. A. Vieira, P. Louro, M. Vieira, A. Fantoni,
A. Steiger-Garção, Light-activated amplification in
Si-C tandem devices: A capacitive active filter
model, IEEE Sensor Jornal, Vol. 12, No. 6, 2012,
pp. 1755-1762.
M. A. Vieira, M. Vieira, P. Louro, V. Silva,
A. S. Garção, Photodetector with integrated optical
thin film filters, Journal of Physics: Conference
Series, Vol. 421, No. 1, 25 March 2013, 012011.
M. Vieira, M. A. Vieira, I. Rodrigues, V. Silva,
P. Louro, Tuning optical a-SiC/a-Si active filters by
UV bias light in the visible and infrared spectral
ranges, Phys. Status Solidi, Vol. 11, 2014, pp. 1-4.
[9]. M. Vieira, M. A. Vieira, I. Rodrigues, V. Silva,
P. Louro, A. Fantoni, UV Irradiation to Increase the
Spectral Sensitivity of a-SiC:H pi’n/pin Photodiode
Beyond the Visible Spectrum Light, in Proceedings
of the 6th International Conference on Sensor
Devices,
Technologies
and
Applications
(SENSORDEVICES’15), Venize, Italy, 2015,
pp. 44-49.
[10]. M. A. Vieira, M. Vieira, P. Louro, V. Silva, J. Costa,
A. Fantoni, SiC Multilayer Structures as Light
Controlled Photonic Active Filters, Plasmonics,
Vol. 8, No. 1, 2013, pp. 63-70.
[11]. M. A. Vieira, M. Vieira, J. Costa, P. Louro,
M. Fernandes, A. Fantoni, Double pin Photodiodes
with two Optical Gate Connections for Light
Triggering: A capacitive two-phototransistor model,
Sensors & Transducers, Vol. 10, Special Issue,
February 2011, pp. 96-120.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
40
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Sub-nanosecond Gating of Large CMOS Imagers
Octavian Maciu, Wilfried Uhring, Jean-Pierre Le Normand,
Jean-Baptiste Kammerer, Foudil Dadouche, Norbert Dumas
ICube, UMR 7357, University of Strasbourg and CNRS
23, rue du Loess BP 20, F-67037 Strasbourg Cedex 2, France
Tel.: +33 (0)3 88 10 68 27, fax: +33 (0) 3 88 10 65 48
E-mail: wilfried.uhring@unistra.fr
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Ultra-fast gating of large array imagers can be quite challenging to implement due to the distributed
RC (Resistance Capacitance) nature of the metal wires used in all ICs (Integrated Circuits) for electrical
connections. For the transmission of a signal across a long path, the metal line reduces the electrical bandwidth
and adds a delay. The behavior of these lines has been modeled and a new solution is presented to circumvent
these limitations. In this paper, we present an edge-based approach to the gating circuitry that allows sub
nanosecond gating with a very low skew across the whole imager. Simulation data shows that our solution is an
efficient way of reducing the effect of the distributed RC line delay with a small penalty on surface area and
consumption. Copyright © 2015 IFSA Publishing, S. L.
Keywords: SPAD, Ultra-fast gating, Edge-triggered, Fast pulse, Skewless.
1. Introduction
Over the last decade, ultra-fast imaging has been a
booming field with many considerable breakthroughs.
A key advancement to this technology has been the
ability to design image sensors with a sub-nanosecond
temporal resolution. These imagers could be
configured in either single-shot mode in [1] and [2], or
can be repetitive with photodiode or photogate in [3]
and [4] or based on Single-Photon Avalanche Diode
(SPAD) arrays rather than classical photodiodes in [5]
and [6]. In this case, the temporal resolution can be
even lower. Currently, integrated streak cameras
operate at the fastest frequency, therefore also
requiring robust acquisition signals. These fast signals
are used in order to have a sub-nanosecond shutter
speed. Depending on the design, the temporal
resolution of an integrated streak camera can vary
from a few hundred picoseconds to several
http://www.sensorsportal.com/HTML/DIGEST/P_2733.htm
nanoseconds. A 1D or 2D approach of integrated
streak camera solution can be found in [3], where a
delay generator based on the propagation delay of
logic gates is used for sub-nanosecond shuttering. The
delay could be customized using current starved
inverters. In [1], [4] and [7], we see the use of edgebased control signals for fast gating.
Similar to a clock signal in a synchronous
sequential circuit, the gating signals are distributed to
the entire imager array. Due to the distributed RC
(Resistance capacitance) line delay, the integrity of the
signal is compromised along the row of pixels. This
would degrade the performance as a pixel at the end of
the array would not perceive the same signal as
another near the beginning of the array. Moreover, it
would be completely unusable for ultra-fast gating
purposes as the rise time and fall time would be far
greater than the gating time. This is especially true for
ultra-fast image sensors as a signal commutes at the
41
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
order of 100 picoseconds. It behooves us then to
ensure that the signal is identical and useable for every
single pixel in the array. Therefore, a more reliable
approach would be to reduce the dependency on the
pulse width and have an edge triggered gating. This
solution was implemented in a 0.18 μm CMOS
(Complementary Metal Oxide Semiconductor)
process. These approaches have been applied for
smaller dimension image sensors and it is therefore
interesting to propose a solution that is extendable to
full resolution imagers.
interesting to study what the maximum row length for
which the pulse will still be useable. Firstly, we can
model this problem by looking at a single distributed
RC model. Fig. 1 shows the equivalent distributed line
representation of a row containing N pixels where RD
and CD are the resistance and the capacitance per
length unit of the metal line respectively. The localized
pixel input capacitances Cpixel act like a distributed
capacitance according to the pixel pitch CD where,
pixel
CD
Pixel
=
C pixel
(1)
Pixel Pitch
2. Theoretical Approach
The total distributed capacitance C Dtotal is thus given
As mentioned before, in an imager array, the gating
signal is being driven at the beginning of a row.
Therefore, the signal perceived at the end of the row
will no longer be a clean square pulse. Hence, it is
by the sum of the equivalent distributed input pixel
capacitance and the line distributed capacitance,
C D = CD + C D .
total
Pixel 1
Pixel 2
Cpixel
V1
RD
CD
RD
CD
pixel
Pixel N
Cpixel
RD
CD
RD
CD
Cpixel
RD
CD
RD
CD V2
Fig. 1. Distributed RC line representation of a sensor row including N Pixels.
The open-circuited Laplace transfer function from the
beginning of the line V1 to the end of the line V2 can
be written as [8, 9]:
H (s) =
V2
V1
=
time. The fastest rise time of V2 can thus be obtained
by the following expression:
(
)
TR = 0.35π ⋅ RD C D + C D pixel ⋅ l 2
1
cosh s ⋅ RD CD total ⋅ l
,
(2)
where s is the Laplace variable, and l is the length of
the row. The behavior of the line can be approximated
by the simplified circuit model depicted in Fig. 2 [8].
(3)
Equation (3) states that the rising time increases with
the square of the row length l and then can
dramatically reach a value that makes it impossible to
transport a nanosecond pulse across a large array
sensor.
2.1. Parameters Extraction
V1
R
C/2
C/2 V2
Fig. 2. Simplified circuit of entire line.
Where R = RD ⋅ l and C = C Dtotal ⋅ l are the total
resistance and capacitance of the line. The unit step
response gives us a clear indication that while not
perfect, this approximation is sufficient to illustrate the
distributed RC delay problem [8]. The distributed RC
effect acrosss the line therefore affects the driver rise
42
The parameters RD and CD can generally be found in
the design kit documentation. Otherwise, the unit line
and surface capacitance can be obtained by an analog
extracted view simulation. Based on variants (square
and rectangular) of the diagram in Fig. 3, theses
parameters can be deduced with two different sets of
equations (4).
Cline = 2 ⋅ Cedge ( l + w ) + Csurface ⋅ w ⋅ l ,
(4)
where w and l are the respectively the width and the
length of the line, Cline is the extracted line
capacitance, Cedge is the edge capacitance, given in
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
F/m, and Csurface the surface capacitance of the line,
given in F/m².
l
w
Fig. 3. Diagram of layout model variants used
(line and rectangle).
Then, the distributed capacitance for a long line
can be approximated by:
C D = 2 ⋅ Cedge + Csurface ⋅ w ,
(5)
Fig. 4. Illustration of lost signal integrity across
a row of pixels.
where w is the width of the considered line. This
capacitance did not take account of the parasitic
capacitance added by the neighboring wires. This last
cannot be easily determined a priori. A first rule of
thumbs should be to multiply about 2 or 3 times this
capacitance. A post extracted simulation, see
section 6, is mandatory to obtain an accurate value.
The resistivity can be extracted in the same way or
it can be obtained by process data, such as the metal
resistivity ρ and thickness T. Therefore, the resistance
per length unit RD of a line of width w is given by:
RD =
ρ
w ⋅T
(6)
3. Ultra-Fast Gating for Large Array
Sensor
Fig. 4 shows the shape of a 1 ns pulse propagating
in a sensor row at the beginning of the line, where the
pulse is applied, in the middle and at the end of the
line. If we assume the classic case where the logic of
the pixel reshapes the pulse with a threshold voltage of
half the power supply (solid line in Fig. 4), we clearly
see that after a certain length, the conventional gating
is no longer adequate.
Furthermore, it is interesting to create a model in
order to anticipate this signal distortion with respect to
the length of the sensor. Based on the distributed
model and a voltage threshold of half the power
supply, Fig. 5 compares the Full-Width at Half
Maximum (FWHM) ratio of the in-pixel reshaped
pulse versus the original pulse according to the
original pulse FWHM to the rise time. The effective
FWHM inside the pixel decreases as soon as the
original FWHM pulse is below the rising time and is
reduced to zero for a ratio of 30 %.
Fig. 5. Final to original pulse width ratio versus the original
pulse width to rising time ratio.
Using (3), the relationship between the increasing
rise time and the length of the array is shown in Fig. 6
for a metal line, level 2, of a typical 0.18 µm CMOS
process with a resistivity ρ of 2.65⋅10-8 Ω.m, a
thickness T of 425 nm, a pixel pitch of 35 µm, an input
pixel capacitance of Cpixel of 2 fF, a surface capacitance
Csurface of 0.015 fF/µm², an edge capacitance Cedge of
0.032 fF/µm and a width w of 3 µm. Sub-nanosecond
rise times are not allowed for sensor dimensions above
16 mm.
A way to enhance the line bandwidth is to increase
the line width w because it reduces the resistance of
the line. Fig. 7 shows the calculated rise time in the
middle of the previous line according to the metal
width w. Increasing the width of the metal track above
3 μm has minimal impact on rise time and thus
becomes irrelevant.
43
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
approach is needed. This approach consists of an
additional signal GATE_DELAY (cf. Fig. 8).
GATE_DELAY is delayed thus creating the effective
pulse of width d seen above through a logic gate. It can
therefore be extended to the Reset signal of the SPAD
as seen in Fig. 9. Hence, we are able to achieve gating
in the range of 100 ps up to 1 ns while maintaining
signal integrity throughout the SPAD array.
Quenching time is much longer than the reset and
gating signals and do not require an ultrafast signal
generation.
Required
Timing
Fig. 6. Rise Time of a signal seen across a pixel row
through a metal line of up to 20 mm and a pixel pitch of
35 µm with the following parameters: ρ = 2.65.10-8 Ω.m,
T = 425 nm, Cpixel = 2 fF, Csurface = 0.015 fF/µm²,
Cedge = 0.032 fF/µm and w = 3 µm.
Quench
Reset
Gating
GATE
GATE_DELAY
d
Fig. 8. Timing diagram of typical SPAD quench, reset
and gating with delayed (d) edge-based signals below.
VDD_SPAD
RQuench
SPAD_RST
SPAD_RST_DELAY
GATE
GATE_DELAY
Vref
+
-
SPAD OUT
Fig. 7. Rise Time with respect to metal track width
at midway along a row of pixels.
Therefore, generating 200 ps FWHM gating within
large sensor arrays over 10 mm is impossible with
pulse propagation techniques. To ensure signal
integrity, the use of edge-sensitive logic is mandatory.
4. Proposed Design of Edge-Triggered
Gating
4.1. Edge-based Circuit for Ultra-fast Gating
In SPAD array image sensors, for ultra-fast gating
mode there are three critical signals needed for optimal
operation: Quench, Reset and Gate. Moreover, SPADs
could be gated immediately after the reset to avoid the
detection of photons arriving before the investigation
time slot. While Quench is a much slower signal, the
Reset and Gate signals can be sub-nanosecond pulses.
Having shown clear limitations for ultra-fast
gating in large SPAD array image sensors, a different
44
SPAD
Quench
V_ANODE (-)
Fig. 9. SPAD Edge-triggered circuit with active quench,
reset and gating.
4.2. Edge-based Driver for Ultra-fast Gating
In Fig. 10, it can be seen that the GATE signal is
delayed to obtain a GATE_DELAY signal. In order to
generate the delay, we used a delay generator to drive
the signals. The delay generator is constructed using a
series of current starved double inverters as seen
in [10].
The delay of both signals (cf. Fig. 10) can be
determined by a modifiable control voltage (Vctrl). We
are able to modify the pulse width by splitting the
signal in two branches with one providing minimal
propagation delay and the other being delayed with
respect to the other. Both signals are then fed into an
in-pixel logic gate to obtain the desired pulse derived
from the delay between the two signals.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
VMIN_DELAY
IN
Vctrl
GATE
VMOD
GATE
IN
Vctrl
GATE_DELAY
Fig. 10. Delay generator block made up of double
current starved inverters used to create
GATE and GATE_DELAY signals.
4.3. Simulation of Edge-based Technique
The presented solution can be demonstrated and
validated through a Cadence simulation with two
buffered lines of 20 mm long and a width of 3 µm
based on the model presented in this paper in order to
obtain a 300 ps pulse through a logic gate.
In Fig. 11, the FWHMs of the two pulses at the
beginning (solid line) and at the end (dashed line) of a
20 mm length line are both equal to 300 ps. Our
solution performs well in a Cadence simulation using
the model presented in Fig. 3 and the defining
equations shown previously. Consequently, thanks to
the edge pulse technique generation, we can ensure
that all the pixels of the sensor exhibit the same
temporal gate width. However, a non-negligible skew
is still present.
will introduce a skew of over 1.5 ns. Hence, the edge
based technique has to be improved for large
dimensions if nanosecond or sub-nanosecond gating
are targeted.
In order to present a more robust approach on ultrafast gating, we proceed to eliminate the large skew
present across the array mentioned. This can be done
by introducing two skewed inverters on each branch
(GATE and GATE_DELAY) before the logic gate.
The threshold voltage of each inverter can be modified
by altering the beta ratio effects inside each inverter.
By compensating the voltage drop at a given time
along the sensor lines thanks to the inverted threshold
voltage, the skew across the entire sensor array can be
compensated.
Fig. 12 shows the simulation of needed VTH
variation across a 20 mm image sensor row using
above mentioned parameters. The threshold voltage at
the beginning of the sensor is arbitrary set at 1.1 V
(above VDD/2 where VDD = 1.8 V) in order to maintain
threshold levels across the array well above the |VT| of
each transistor. For a required voltage VTH, the inverter
should be adjusted by modifying the geometry of the
PMOS and NMOS transistors, thus changing
the beta ratio.
Fig. 12. Simulation of needed VTH variation
across a 20 mm image sensor row using above
mentioned parameters.
This can be obtained by solving the following
equation:
Fig. 11. Simulation of a 300 ps pulse created using the
edge-based technique (line: 20 mm length, 3 µm width).
Signals at the beginning of the line (solid line) and at the
end (dotted line).
Vin = Vth
I n = I p , for 
Vout = VDD 2
(7)
In this case, both the NMOS and the PMOS
transistor are in the saturated operation region, thus the
currents are given by:
5. Analysis and Elimination of Skew
In =
For large array ultra-fast image sensors, ensuring
signal integrity across the array is only half of the
challenge. As can be seen in Fig. 6, a 20 mm sensor
Ip =
1
2
2 
W 
V 
μ n Cox  n  (VTH − VT N )  1 + λ DD 
2 
L

 n
W 
μ p Cox  p  VDD − VTH − VTN
2
 Lp 
1
(
)
2
VDD 

1 + λ

2 

(8)
45
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
Replacing In and Ip in equation (7) leads to:
W 
W 
2
μ n  n  (VTH − VT ) = μ p  p  (VDD − VTH − VTp
 Ln 
 Lp 
)
2
(9)
skew seen in Fig. 11 is eliminated. The adjusted
threshold voltage are indicated with doted and dashed
line. This ensure that a sub-nanosecond gated
operation can be proceeded uniformly across the
whole sensor.
Hence, we obtain the ratio ρ:
 Wp 
 
2
Lp  μn
(VTH − VT )
=
ρ=
 Wn  μ p (VDD − VTH − VTp
L 
 n
)
2
(10)
Furthermore, in order to keep the best fill factor, the
inverter area A given by:
 Wn  2  Ln
 Lp +  W
 Ln 
 n
A = Wp L p + LnWn = ρ 
 2
 Wn

(11)
has to be kept as low as possible. For a threshold
voltage Vth above VDD/2, the PMOS transistor has to be
more conductive than the NMOS one, thus the ratio ρ
should be greater than one. For this, we can consider
using Wn=Wmin and Lp=Lmin as the best choice. The
inverter area becomes:
Fig. 13. Simulation of skewless pulse. Solid line: signal
at the beginning of the sensor, dotted line: signal
at the end of a 20 mm sensor.
5.1. Variable Slew Rate Driver
W
A= ρ n
 Ln
 2
 Lmin

L
+ n
 Wn
 2
1 2
2
 Wmin = ρα Lmin + Wmin (12)
α

Minimizing A according to the variable α leads to best
sizes of the MOS transistors for the smallest use of
surface area:
Wn
Ln
=
Wmin
ρ ⋅ Lmin
and
Wp
Lp
=
ρ ⋅ Wmin
(13)
Lmin
In a similar way, the optimal sizes of the transistors for
a threshold voltage VTH under VDD/2 can be computed
as:
Wn
Ln
=
ρ ⋅ Wmin
Lmin
and
Wp
Lp
=
Wmin
ρ ⋅ Lmin
(14)
VDD
In both case, the area of each threshold adjusted
inverter is given by:
A = Wmin Lmin 4 ρ
V Slew Rate
(15)
The more ratio ρ is distant from 1, the higher the
surface area.
It would be ideal to designate an area of a pixel for
the placement of automatically generated skewed
inverters based on their position in the sensor row.
By employing this technique, it can be seen in
Fig. 13 that the pulse at the beginning of the sensor
row and the pulse at the end are synchronized and the
46
To be able to apply the previously described
method to eliminate skew across the sensor, a custom
driver is used. This driver should have a variable slew
rate to further compensate skew and ensure that the
skew eliminating current starved inverters are
effective. This is due to the fact that when operating at
sub-nanosecond time intervals, the skew elimination is
only possible for a relatively slow driving signal as can
be seen in Fig. 13. Otherwise, it would not be possible
to compensate the threshold voltage across the sensor
array as the signal’s rise time would be too fast.
Furthermore, it is desirable to be able to control the
slew rate of the signal being driven across the sensor
array. This can be done using a voltage controlled
current starved inverter element as seen in Fig. 14.
IN
OUT
Fig. 14. Voltage-controlled slew driver made
up of a current starved inverter cell.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
As only the rise time of the edge signals is used to
generate the fast pulse generation, the current starved
inverter is degenerated only on the PMOS branch .A
symmetrical slow driver would create significant
limitations in operating frequency due to the slow fall
time. Hence it is best to design an asymmetrical driver
with a slow rise time and fast fall time. This can be
done by altering the geometry of the NMOS transistor
in Fig. 14. As can be seen in Fig. 15, the rise time is
just over 9 ns and the fall time is three times faster at
around 3 ns for a maximum period of 20 ns (50 MHz).
taking into account the aforementioned problems and
solutions that arise during the design process. This
new chip has a total row length of approximately
13 mm split into 10 sub-row. Each sub-row consists of
33 pixels (1.3 mm).
In Fig. 16, we have shown the layout for our SPAD
gating system and pixel. For this application, there are
three critical signals, therefore six lines needed for
edge-based pulse propagation (seen in cyan and red).
It can be seen that while they have a considerable
impact on the pixel fill factor, it remains reasonable
for pixel pitch of 35 µm.
6.1. Post Extracted Parameters (Additive
Parasitic Capacitances)
Fig. 15. Variable slew rate driver response
with a period of 20 ns.
6. Application to an Ultra-fast Pixel
This new concept has been applied to a new chip
designed to operate with a sub-nanosecond gating
For a more accurate picture of total parasitic
capacitance across a row of pixels, a post extracted
simulation is needed. This is due to the fact that there
are more Cedge parasitic capacitances in parallel than
originally accounted for in our theoretical model. In
the design presented in Fig. 16, these extra parasitic
elements triple the total parasitic capacitance for a line.
Therefore, the impact of neighbouring parasitic
elements inside the pixel are also very significant.
Thus, the adjustment of the threshold voltage of the
skew inverter has to be done after a post extracted
simulation. Even if the pixel capacitance remains low
compare to the line capacitance, altering the inverter’s
transistor geometry will have a little impact on the
distributed pixel capacitance and consequently, the
post extracted simulation has to be reiterate. A good
rule of thumbs for the first iteration is to considered
that the distributed capacitance is about 3 times the
single line capacitance described in section 2.
Fig. 16. Layout of SPAD pixel using ultra-fast technique.
47
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
6.2. Monte-Carlo Simulations
After performing a Monte-Carlo simulation on the
possible dispersion existing between the two most
spaced pulses within a single line (beginning and end
of line), we obtain a sigma of only 18.42 ps rms.
However, it is important to take into account the
possible dispersion found between lines, i.e. across the
entire sensor. By doing a similar simulation, we obtain
a sigma of 37.32 ps rms, i.e. a mismatch of less than
90 ps FWHM. This result is illustrated in the
histogram in Fig. 17.
In order to circumvent the inevitable mismatch, it
is conceivable to modify (post-fab) the supply
voltages of the inverters added in order to fine tune the
additional skew.
Fig. 17. Interline Monte Carlo simulation showing possible dispersion due to fabrication mismatches.
7. Conclusion
Acknowledgements
In the increasingly relevant field of ultra-fast
imaging, gating signals play a key role in assuring
optimal operation. When designing large resolution
sensors, acquisition pulses must be uniformly
distributed throughout the entire array. Due to the
delay effect of a distributed RC line, this becomes
troublesome. For this, a conventional driver at the
beginning of the line is no longer adequate and a more
robust solution needs to be implemented. This new
approach is independent to pulse-based gating and
instead uses an edge-triggered gating. Moreover, the
designer could place such edge-based drivers on either
side of the sensor to obtain a near-perfect gating signal
distribution. While this technique is still hindered by
signal skew, it can be eliminated using the method
presented in this paper. As mentioned earlier, these
effects can as much as triple the parasitic effects seen
by one line. Therefore, our approach is crucial in
ensuring the effective transmission of a subnanosecond pulse across a large sensor array. The
maximal pixel operating frequency can be in the range
of 50 MHz and the gating operation well below
1 nanosecond. The process mismatch show skew
spread of about 90 ps FWHM on the entire sensor.
We thank the French agency “Agence Nationale
de la Recherche” for financial support under the grant
ANR-14-CE26-0024-01.
48
References
[1]. F. Morel, J.-P. Le Normand, C.-V. Zint, W. Uhring, Y.
Hu, and D. Mathiot, A new spatiotemporal CMOS
imager with analog accumulation capability for
nanosecond low-power pulse detections, Sensors
Journal, Vol. 6, No. 5, 2006, pp. 1200-1208.
[2]. M. Zlatanski and W. Uhring, Streak-mode optical
sensor in standard BiCMOS technology, in
Proceedings of the IEEE Sensors, Limerick, Ireland,
2011, pp. 1604-1607.
[3]. M. Zlatanski, W. Uhring, J. Le Normand, C. Zint, and
D. Mathiot, Streak camera in standard (Bi)CMOS
(bipolar complementary metaloxide-semiconductor)
technology, Measurement Science and Technology,
Vol. 21, No. 11, 2010, p. 115203.
[4]. Keita Yasutomi, SangMan Han, Min-Woong Seo,
Taishi Takasawa, Keiichiro Kagawan, Shoji
Kawahito, A time-resolved image sensor for tubeless
streak cameras, Proc. SPIE, 9022, 2014, 902202.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 41-49
[5]. Maruyama, Y., Blacksberg, J., Charbon, E., A 1024 x
8, 700-ps Time-Gated SPAD Line Sensor for
Planetary Surface Exploration With Laser Raman
Spectroscopy and LIBS, IEEE Journal of Solid-State
Circuits, Vol. 49, No. 1, 2014, pp. 179-189.
[6]. L. Pancheri, N. Massari, F. Borghetti and D. Stoppa,
A 32x32SPAD pixel array with nanosecond gating and
analog readout, in Proceedings of the Int. Image
Sensor Workshop, 2011, R40.
[7]. Octavian Maciu, Wilfried Uhring, Jean-Pierre Le
Normand, Jean-Baptiste Kammerer and Foudil
Dadouche, Edge-Based Technique for Ultra-Fast
Gating of Large Array Imagers, in Proceedings of the
9th International Conference on Sensor Technologies
and Applications (SENSORCOMM ’15), Venice, Italy,
23-28 August 2015, pp. 24-28.
[8]. V. B. Rao, Delay Analysis of the Distributed RC Line,
in Proceedings of the 32nd Conference on Design
Automation (DAC '95), 1995, pp. 370-375.
[9]. Ogata, M., Okabe, Y., Nishi, T., Simple RC models of
distributed RC lines in consideration with the delay
time, in Proceedings of the International Symposium
on
Circuits
and
Systems
(ISCAS
'04),
Vol. 4, May 2004, pp. 23-26.
[10]. Zlatanski, M. Uhring, W. Le Normand, and J.-P. Zint,
V., A new high-resolution Time-to-Digital Converter
concept based on a 128 stage 0.35 µm CMOS delay
generator, in Proceedings of the Joint IEEE NorthEast Workshop on Circuits and Systems and TAISA
Conference (NEWCAS-TAISA '09), 2009, pp. 1-4.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
49
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Superpixel Compressive Sensing Recovery of Spectral
Images Sensed by Multi-patterned Focal Plane
Array Detectors
1
1
Yuri H. MEJIA, 2 Fernando A. ROJAS, 2 Henry ARGUELLO
Department of Electrical Engineering, Universidad Industrial de Santander, Colombia
2
Department of Systems Engineering and Computer Science,
Universidad Industrial de Santander, Colombia
Tel.: +57 7 6344000
E-mail: yuri.mejia@correo.uis.edu.co, frojas@uis.edu.co, henarfu@uis.edu.co
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Conventional spectral imaging systems capture spectral and spatial information from a scene to
produce a spectral data cube by scanning procedures. Photolithography technology development enables the
production of complex filters by combining patterning techniques with optical coatings. These filters can be
directly deposited onto detector arrays in order to measure spectral information with a unique snapshot.
Nevertheless, recovering the spectral image with traditional methods following a demosaicing approach is
impractical. State-of-the-art establishes multispectral demosaicing for recovering images with a specific spatiospectral resolution depending on the number of pixels in the detector and the filter mosaic. Recently
compressive sensing technique has been developed that allows recovering signals with few measurements than
the traditional methods by using the sparse representation of the underlying signal. The selection of superpixels
in the multi-patterned focal plane array detectors to calculate the spectral response of a single pixel in the
reconstructed spectral images could improve the reconstruction, based on exploiting the sparse representation of
the spectral images. This paper presents a model for spectral images recovering from superpixels formed with
multi-patterned focal plane array detectors measurements using the concept of compressive sensing. This model
selects subsets of the superpixels measurements following a downsampling matrix operation, therefore a
reconstruction model is formulated by directly reconstruct a spectral image with the spectral resolution given by
the number of filters. The superpixel size selection leads to a variable recovered spatial resolution preserving the
filters spectral resolution. Multi-patterned focal plane array detectors measurements for real spectral images are
simulated in order to verify the effectiveness of the recovery model. An ensemble of random dichroic and band
pass filters is used. The superpixel compressive sensing reconstruction approach and the demosaicing scheme
reconstruction are compared. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Compressive sensing, Spectral images, Multi-patterned focal plane array detectors, Superpixel.
1. Introduction
Spectral images applications cover ocean
research, food safety, geology, and medical demands.
50
For instance, the characterization of phytoplankton in
the ocean [1], quality evaluation in the area of food
safety
[2],
plant
stress
assessment
[3],
characterization of different bacterial colonies [4],
http://www.sensorsportal.com/HTML/DIGEST/P_2734.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
disease diagnosis, and image-guided surgery [5].
In some spectral imagers, the scene is beamsplit
into the wavelength components for example using a
prism assembly, and each of these images is captured
in a separate detector array. In this method the
sensing devices have significant size and weight
disadvantages [6]. One of the most intuitive
multispectral scanning techniques is the tunable
filter, where a complete spectral image is produced
after a sequence of exposures by capturing an image
of one spectral band at time. For instance, the
spectral image can be sensed by using a filter wheel
where some optical filters are installed in a rotatory
mechanical structure [7]. Most of the methods are
related to scanning operations where multiple
exposures are used causing motion artifacts.
On the contrary, some techniques use multipatterned filter arrays detectors and collect multiple
wavelength spectra from a single snapshot [8].
Nowadays, optical coatings technologies have been
miniaturized and optimized such that the creation of
multi-patterned arrays of different optical filters, with
traditional design and manufacturing methods, is
allowed [6]. The optical coatings production
methodology combines modern optical thin film
deposition techniques with microlithographic
procedures. This process enables micron-scale
precision patterning of optical thin film dichroic
coatings on a single substrate. A dichroic filter is an
accurate color filter used to selectively pass light of a
small range of wavelengths while reflecting
other wavelengths.
For example, Miao, et al. [9] generate a multipatterned filter arrays following a binary tree-based
method, which starts from a checkerboard pattern. By
recursively separating the original checkerboard, the
algorithm generates the multi-patterned filter arrays
given the number of spectral bands and the
probability of appearance of each band. Then, they
design a demosaicing algorithm based on the same
binary tree. Brauers and Aach [10] propose a multipatterned filter arrays that consists of color filter
blocks of the size 3×2 pixels, this configuration
allows to use a fast bilinear interpolation with a
reconstruction up to 6 spectral bands. Monno, et al.
[11] propose a five-band multi-patterned filter arrays.
In the pattern, the green-like channel is distributed as
in the Bayer color filter array (CFA), and other
channels are arranged following a binary-tree
approach. For demosaicing an adaptive kernel can be
estimated directly from the raw data. Common to
these systems is that the multi-patterned filter arrays
design is application and number of bands dependent,
which reach at most 6.
On the other hand, Compressive Sensing (CS) has
emerged as a rising research area that allows the
acquisition of signals at sampling rates below the
Nyquist-criterion. In CS traditional sampling is
substituted by measurements of random projections
of the signal. The signals are then reconstructed by
solving an l1 and l2 minimization problem in a basis
where admits sparse representations. CS exploits the
fact that hyperspectral images can be sparse in some
basis representation.
Mathematically,
a
multispectral
image
N ×N ×L
M
F ∈ℜ
in its vector representation f ∈ ℜ with
M=N2L, can be expressed as f=Ψθ, where θ is the
coefficients sequence of S elements that represents f,
with S<<M, and Ψ is a representation basis. Here,
N×N represents the spatial dimensions, and L the
number of spectral bands in the data cube.
Compressive sensing allows f recovering from m
random projections when m ≥ S log (M) << M.
Assuming that the multi-patterned filter arrays
detectors perform a linear measurement process that
calculate m<<M internal products between f and a
collection of vectors {Hi}j, as y i = f , H j , then
y=Hf, where the set of yi projections forms the vector
y of m elements, H is the measurement matrix with
dimensions m×M, with HjT rows, and f is the original
signal of size M. For recovering f from y, there exist
infinite solutions due to the size of y is much less
than the size of f.
Following the sparse representation of the signal
and the multi-patterned filter arrays detectors
measurements can be expressed as y=Hf =HΨθ=Aθ,
where A = HΨ ∈ ℜ m×M is the sensing matrix. This
underdetermined equation system can be solved if it
is satisfied that the measurement matrix H is
incoherent with the sparse transformation Ψ. It is
possible to exploit the capabilities of multi-patterned
filter selecting measurements subsets to form
superpixels that have spectral information of a single
reconstructed pixel. That is, spectral information of a
single pixel can be reconstructed based on superpixel
measurements. The superpixel size selection leads to
a variable reconstructed spatial resolution preserving
the filters spectral resolution, reconstructing a spatial
decimated data cube. This information can be used in
applications requiring higher spectral than spatial
image quality, also for a quick view of the scene, for
example for purposes of transmission and
communication applications, because increasing
superpixel side reduces the time of data
cube recovery.
This paper presents a model for spectral images
reconstruction from superpixels formed with multipatterned filter arrays detectors measurements using
the principle of compressive sensing. This model
selects subsets of the superpixels measurements
following a downsampling matrix operation,
therefore a reconstruction model is formulated by
directly reconstruct a spectral image of variable
spatial resolution. The maximum spatial resolution is
limited by the detector resolution. The number of
different filters limits the spectral resolution.
The data cube is then reconstructed as
~
f = Ψ (argmin y − H S Ψθ 2 + τ θ 1 ), where y is the
θ
measurement selection, HS is the measurement
matrix defined as H S = [( D 0 )T ...( D q −1 )T ]T H, where
2
51
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
D  is the 
th
downsampling matrix used for
measurement selection, θ is an S-sparse
representation of a low resolution version of f on the
basis Ψ, and τ is a regularization constant.
The rest of this paper is organized as follows.
Section 2 describes the mathematical model of the
spectral image acquisition system using multipatterned filter arrays detectors. Section 3 describes
the traditional demosaicing process. Section 4
addresses the superpixel CS reconstruction approach.
Section 5 describes the mathematical model of the
dichroic and band pass filters. Section 6 shows the
simulation results. The conclusion closes the article.
Let Ti,j,k ∈ {0,1} be the discretization of the multipatterned filter arrays.
Then, the discretized multi-patterned filter arrays
detectors measurements can be expressed as
L −1
Yi, j =
F
+ ω i, j ,
(2)
where Yi,j is the intensity at the (i, j)th position of the
detector, i, j=0, 1, …, N-1, and the dimensions of the
detector are N×N. F is an N×N×L spectral data cube,
and ωi, j is the white noise of the sensing system.
The measurements Yi,j in (2) can be written in
matrix notation as
2. Spectral Image Acquisition by Multipatterned Focal Plane Array Detectors
Fig. 1 shows the functions of the sensormultispectral filter array system following the
physical sensing phenomena for L=6 spectral bands
and focusing in the jth-slice.
i , j , k Ti , j , k
k =0
y=Hf+ω,
(3)
where y is the N2-long vector representation of Yi,j,
f=vect([f0, …, fL-1]) is the vector representation of the
data cube F where fk is the vectorization of the kth
spectral band.
The output y in (3) can be extended as
 f0 


f
y = [diag(t 0 )  diag(t L −1 )] 1  + ω ,


  


H
 f L −1 
(4)
where t k is the vectorization of the kth multi-patterned
filter arrays spectral plane, diag(tk) is an N2×N2
diagonal matrix whose entries are tk, more
(t )
Fig. 1. Sensing phenomena representation of the multipatterned focal plane array detectors. The jth slice of the
data cube is coded by a row of the multi-patterned filters
and then integrated onto the detectors.
For purposes of illustration the Fig. 1 shows two
optical elements separately, but the device is a focal
plane array covered by multi-patterned filters. First,
the multi-patterned filters T(x, y, λ) modulates the
spatial-spectral data cube f0(x, y, λ), resulting in the
coded field f1(x, y, λ), where (x, y) are the spatial
coordinates, and λ is the wavelength. Then the coded
density impacts on the sensor. Eq. (1) represents the
coded density integrated into the detector.
f 2 (x, y, λ) =
T(x' , y' , λ) f (x' , y' , λ)h(x'−x, y'−y)dx' dy',
1
Block Pass λ0 Pass λ1 Pass λ2 Pass λ3
N2 (1st band) N2 (2nd band) N2 (3rd band) N2 (4th band)
diag(t 0)
diag(t 1)
diag(t 2)
diag(t 3)
N2
Fig. 2. The matrix H is shown for N=6, and L=4. Colored
squares represent unblocking light elements related to a
specific wavelength.
(1)
where T ( x' , y ' , λ ) is the transmission function
representing the multi-patterned filters, and
h( x'− x, y '− y ) is the optical response of the system.
Each pixel in the sensor is a discretized
measurement. The source f0(x, y, λ) can be written in
discrete form as Fi, j, k where i and j index the spatial
coordinates, and k determines the kth spectral plane.
52
=T
specifically k i i / N ,i − i / N  N ,k for i=0, …, N2-1.
Fig. 3 depicts a random multi-patterned filter arrays
based matrix H for N=6, and L=4. Colored squares
represent unblocking light elements related to a
specific wavelength.
3. Traditional Demosaicing
Given the set of measurements y a traditional
demosaicing algorithm estimates for each
reconstructed pixel the intensities for all wavelength
components. Common approach minimizes the linear
mean square error between the measurements and the
vector estimation multiplied by the sensing matrix,
that is
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
~
f = arg min y - Hf
f
2
,
(5)


 iq 
1, if j = iq + (q − 1) N   +  +   ( N − q ),
Di, j = 
N
 
q
0,
otherwise,

(6)
where
A closed-form solution to (5) is given by
(
~
f = HTH
)
−1
H T y = H + y,
where H+ is known as the pseudoinverse of H, and
HT is its transpose. For comparison purpose, this
approach is implemented.
i = 0,1,  ,
q
q
2
2
j=0,1,…,N2-1,
− 1,
and
 = 0,1,, q 2 − 1. Fig. 6 depicts the downsampling
matrix D for q=2, N=6, and  = 0,1, 2, 3. The white
squares represent one-valued elements.
4. Recovery by Superpixel CS Approach
In this model, the superpixel definition is based on
the assumption that q×q neighboring pixels, in an
N×N×L spectral image, could have a similar spectral
response, Fig. 3 illustrates this premise. Then, the
superpixel, which is formed of q×q measurement
pixels in the sensor, is taken as the spectral response
of a single pixel for a decimated reconstruction [12].
For instance, Fig. 4 shows an example of the
measurement selection for a superpixel side size
of q=2. In total are taken q2 subsets of measurements
in a single shot of the multi-patterned filter
arrays detectors.
N
(8)
N
q
q
y10
y12
y11
y13
N
A macropixel
1st
subset of
measurements
2nd subset of
measurements
3rd subset of
measurements
4th subset of
measurements
q
y0
L
y1
y2
y3
Fig. 4. Example of a subset selection for a superpixel size
of q×q = 2×2 that forms 4 subsets of measurements
in a single shot of the multi-patterned filter arrays based sensor.
N
N
Fig. 3. Illustration of the assumption that for q×q
neighboring pixels, in a N×N×L spectral image, the spectral
response is simillar.
In the acquisition model, the matrix product
between a downsampling matrix and the total sensor
measurements forms each subset of measurements.
More specifically, each subset is given by
y  = D  Hf ,
(7)
where D does a downsampling in each q×q square
of pixels for taken q2 different subsets of the total
measurements, and y  is the  th subset of
2
measurements where  ∈ {0, q − 1}. Fig. 5 shows
an example of a subset measurement selection
for q=2. Precisely, the function of the decimation
matrix D is selecting in each q×q block of the
measurements the  th - element for forming
the  th -subset of measurements. The decimation
matrix element-by-element can be expressed as:
In this case, the complete set of measurements is
given by
 y0   D0 
 1   1 
y   D 
y=
Hf = H S f,
=
     
 q 2 −1   q 2 −1 

  D
 y
(9)
where subjacent data cube projection is reconstructed
solving an l1 and l2 minimization problem, where the
decimation process is taken into account. The
optimization
problem
is
given
by
~
f = Ψ (argmin y − H S Ψθ 2 + τ θ 1 ), where y is given
θ
by (10), HS is the measurement matrix defined as
2
H S = [(D0 )T ...(Dq −1 )T ]T H, θ is an S-sparse
representation of a low resolution version of f on the
basis Ψ, and τ is a regularization constant [13].
HS is the measurement matrix defined as
H
S
= [(D 0 )T ...( D
q2 −1 T T
) ] H, where D  is the  th
downsampling matrix used for measurement
selection, θ is an S-sparse representation of a low
53
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
resolution version of f on the basis Ψ, and τ is a
regularization constant.
Dichroic filters are a special case of band-pass
filters that let pass only one spectral band. Then the
spectral response of a (λiD ) dichroic filter pixel can
be defined as
1, if λi D = k ,
(t k ) i = 
0, otherwise,
(11)
for λiD ∈ {0,..., L − 1} , and i = 0,..., N 2 − 1 . For example,
Fig. 5. The downsampling matrix D  is shown for q=2,
N=6 and  = 0,1, 2, 3. White squares represent ones and the
black elements are zero.
5. Filters
6. Simulations and Results
For developing this work, two multi-patterned
filter arrays were selected. First, a spectral response
for pixel that can be selected randomly from a set of
band-pass filters; and second, dichroic filters, that is a
special case of random band-pass where its spectral
response let pass only one spectral band for each
sensor pixel.
The spectral response of a (λi L , λi H ) band-pass
filter can be defined as
1, if λi L ≤ k ≤ λ i H ,
(t k ) i = 
otherwise,
0,
for
k = 0,..., L − 1
L
if L=4, and λ5D = 3 , then the spectral response of the
position i = 5 in the vectorized filter array is
(t k )1 = [0 0 0 1] . Fig. 6(b) depicts an example of a
coded field column filtered by dichroic filter pixels,
with representations of the spectral response of three
dichroic filter pixels.
,
(10)
i = 0,..., N 2 − 1
H
,
To verify the multi-patterned filter array detectors
reconstructions, a set of compressive measurements
is simulated using the model of Eq. (2). These
measurements are constructed employing two
spectral images captured with a CCD camera Apogee
Alta U260 and a VariSpec liquid crystal tunable
filter, in the range of wavelength 400 nm-560 nm,
with steps of 10 nm [14]. The resulting test data
cubes have 512 × 512 pixels of spatial resolution and
L=16 spectral bands. The RGB images mapped
versions of the selected data cubes are shown
in Fig. 7.
and
L
λi ≤ λ i ∈ {0,..., L − 1} . For instance, L=4, λi = 2 ,
and λi H = 3 define the spectral response of the
position i = 1 , as (t k )1 = [0 0 1 1] . Fig. 6(a)
depicts an example of a coded field column filtered
by band-pass filters, with representations of the
spectral response of three band-pass filter pixels.
100%
Fig. 7. The RGB images mapped versions of the selected
data cubes (left) Feathers, and (right) Glass Tiles.
100%
λℒ1 λℋ1
!
λ1
100%
100%
λℒ5 λℋ5
!
λ5
100%
100%
ℒ ℋ
λ8 λ8
(a)
λ!8
(b)
Fig. 6. Band-pass and dichroic filters representations.
(a) Example of a coded field column filtered by band-pass
filter pixels, with the spectral response representations
of three band-pass filter pixels; (b) Example of a coded
field column filtered by dichroic filter pixels, with the
spectral response of three dichroic filters.
54
The experiments were carried out using the
images Feathers and Glass Tiles. Compressive
sensing reconstruction is realized using the GPSR
algorithm [15]. Simulations results are analyzed in
terms of PSNR (Peak-Signal-to-Noise-Ratio) of the
reconstructed images. The representation basis Ψ is
a Kronecker product Ψ = Ψ1 ⊗ Ψ2 , where Ψ1 is the
two-dimensional-wavelet Symmlet-8 basis and Ψ2 is
the cosine basis. The simulations are performed in a
desktop architecture with an Intel Core i7 3.6 GHz
processor, 32 GB RAM, and using Matlab R2014a.
Each experiment is repeated ten times and the
respective results are averaged.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
The measurements are simulated using the first L
spectral bands of the real data cubes. Ensembles of
dichroic and band pass filters based on a random
selection of spectral bands are used for obtaining
measurements for the compressive sensing approach.
Fig. 8 shows a comparison of the average PSNR
reconstruction for the Feathers data cube. Fig 9
shows similar results for PSNR reconstructions in the
Glass Tiles data cube.
(a)
(b)
(c)
(d)
Band pass) approaches have a better performance
than the demosaicing approach. For q>1 the Dichroic
random filters has a better performance than the
others. Finally, increasing spectral bands decreases
the PSNR for all reconstruction methods.
Furthermore, the simulation time is halved at each q
increase.
7. Conclusions
A model for CS recovery of spectral images
sensed by multi-patterned focal plane array detectors
using a superpixel approach is presented. The model
performs a selection of measurements subsets to form
superpixels that have spectral information of a single
recovery pixel. The CS reconstruction approaches
using dichroic and band pass filters are compared
with a traditional demosaicing reconstruction. For the
CS reconstruction, the PSNR increases with the
superpixel side size. For instance, the improvements
range from 0.5 dB to 4 dB with respect to the
traditional approach in real data cubes. Results show
that increasing spectral bands decreases the PSNR for
all reconstruction methods.
Acknowledgments
Fig. 8. Reconstructions of the Feathers data cube using
(a) q = 1 , (b) q = 2 , (c) q = 4 and (d) q = 8 .
The authors gratefully acknowledge to
Vicerrectoría de Investigación y Extension of the
Universidad Industrial de Santander for supporting
this research registered under the project title:
Detección y Clasificación en imágenes espectrales
obtenidas a través de un sistema de adquisición
compresivo con un detector de un solo pixel,
(VIE 1802 code).
References
(a)
(b)
(c)
(d)
Fig. 9. Reconstructions of the Glass Tiles data cube using
(a) q = 1 , (b) q = 2 , (c) q = 4 and (d) q = 8 .
For the model that perform CS reconstructions,
increasing the size of the superpixel q improves the
PSNR. Additionally, the reconstruction based on
superpixel measurements selection (Dichroic and
[1]. J. Ryan, C. Davis, N. Tufillaro, R. Kudela,
B.-C. Gao, Application of the Hyperspectral Imager
for the Coastal Ocean to Phytoplankton Ecology
Studies in Monterey Bay, CA, USA, Remote Sensing,
Vol. 6, No. 2, January 2014, pp. 1007-1025.
[2]. Z. Xiong, A. Xie, D.-W. Sun, X.-A. Zeng, D. Liu,
Applications of Hyperspectral Imaging in Chicken
Meat Safety and Quality Detection and Evaluation: A
Review, Critical Reviews in Food Science and
Nutrition, 55, 9, 2015, pp. 1287-1301.
[3]. G. J. Bellante, S. L. Powell, R. L. Lawrence,
K. S. Repasky, T. A. O. Dougher, Aerial detection of
a simulated CO2 leak from a geologic sequestration
site using hyperspectral imagery, International
Journal of Greenhouse Gas Control, Vol. 13, March
2013, pp. 124-137.
[4]. M. Mehrübeoglu, G. W. Buck, D. W. Livingston,
Differentiation of bacterial colonies and temporal
growth patterns using hyperspectral imaging, in SPIE
Optical Engineering + Applications, 2014, 922206.
[5]. G. Lu, B. Fei, Medical hyperspectral imaging: a
review, Journal of Biomedical Optics, Vol. 19, No. 1,
January 2014, 10901.
55
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 50-56
[6]. J. D. Barrie, K. A. Aitchison, G. S. Rossano,
M. H. Abraham, Patterning of multilayer dielectric
optical coatings for multispectral CCDs, Thin Solid
Films, Vol. 270, No. 1-2, December 1995, pp. 6-9.
[7]. Z. Frentress, L. C. Young, H. D. Edwards, Field
Photometer with Nine-Element Filter Wheel, Applied
Optics, Vol. 3, 1964, pp. 303-309.
[8]. P. J. Lapray, X. Wang, J. B. Thomas, P. Gouton,
Multispectral Filter Arrays: Recent Advances and
Practical Implementation, Sensors, Vol. 14, No. 11,
2014, pp. 21626-21659.
[9]. L. Miao, H. Qi, R. Ramanath, W. E. Snyder, Binary
tree-based generic demosaicking algorithm for
multispectral filter arrays, IEEE Transactions on
Image Processing, Vol. 15, No. 11, 2006,
pp. 3550-3558.
[10]. J. Brauers, T. Aach, A Color Filter Array Based
Multispectral Camera, in Proceeding of the 12th
Workshop Farbbildverarbeitung, 2006.
[11]. Y. Monno, M. Tanaka, M. Okutomi, Multispectral
Demosaicking Using Adaptive Kernel Upsampling,
in Proceedings of the 18th IEEE International
Conference on Image Processing (ICIP'11), 2011,
pp. 3218-3221.
[12]. Y. Mejia, F. Rojas, H. Arguello, Macropixel
Compressive Sensing Reconstruction of Spectral
Images Sensed by Multispectral Filter Array-based
Sensors, in Proceeding of the 6th International
Conference on Sensor Device Technologies and
Applications (SENSORDEVICES’15), Venice, Italy,
23-28 August 2015, pp.158-163.
[13]. H. Arguello, G. R. Arce, Colored coded aperture
design by concentration of measure in compressive
spectral imaging, IEEE Transactions on Image
Processing, Vol. 23, No. 4, April 2014,
pp. 1896-1908.
[14]. CAVE | Projects: Multispectral Image Database.
[Online]. Available at:
http://www.cs.columbia.edu/CAVE/databases/multis
pectral/ [Accessed: 24-Feb-2015].
[15]. M. A. Figueiredo, R. D. Nowak, S. J. Wright,
Gradient projection for sparse reconstruction:
Application to compressed sensing and other inverse
problems, Journal of Selected Topics in Signal
Processing, Vol. 1, No. 4, 2007, pp. 586-597.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
56
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 57-62
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Advanced Controlled Cryogenic Ablation
Using Ultrasonic Sensing System
*
Assaf Sharon, Gabor Kosa
Robots and BioMedical Micro Systems (RBM2S) research laboratory, School of Mechanical
Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
*
Tel.: + 972523404144, fax: 972-3-6407334
*
E-mail: assaf.a1@gmil.com
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Cryoablation process is one of the methods for treating various tissue abnormities. Cryoablation
devices are mostly minimally invasive and are used in open loop control, monitored by additional imaging
devices. In this study, we monitor the growth of the ablated area by using a miniature ultrasonic transducer that
is collocated with the tip of the cryogenic device. The 20 MHz ultrasonic sensor is capable of measuring the size
of the ice sphere that is created in front of the needle. In addition to real time monitoring of the ablation process,
the ultrasonic sensor will be able to determine the local thickness of the tissue prior to the treatment (thus
enabling the setting of the power of the ablation treatment). The combined device will shorten the ablation
treatment and will eliminate the need for additional ablation treatments or monitoring devices. The proof of
concept was done in water, ultrasonic gel and muscle tissue. In the experiments we found that, in the frequency
domain one can identify at 10-12 MHz the increase of the intensity of the returned echo in the ice and the
decrease of the signal after the ice-tissue boundary. One can correlate the increase of the intensity with the
growth of the ice sphere. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Cryogenic, Ablation, Control, Ultrasound, Piezo, Ice.
1. Introduction
Cryosurgery, also referred to as Cryotherapy or
Cryoablation, is a minimally invasive surgical
technique in which freezing is used to destroy
undesirable tissue. Cryoablative techniques have
persistently improved over the past forty years with
the development of successive generations of devices
including Cryoneedles, Cryoballoons, intraoperative
ultrasound and vast knowledge of the mechanisms by
which cells are affected by low temperature exposure
[2]. We now recognize two mechanisms causing cell
death following a freezing cycle: direct mechanism
adjacent to the ablating device of cell rupture due to
intracellular ice crystal formation and cellular
http://www.sensorsportal.com/HTML/DIGEST/P_2735.htm
dehydration with associated osmotic damage, and
indirect mechanism of ischemia and necrosis
throughout the tissue/tumor peripheral zone [3-4]. To
perform a cryosurgical procedure successfully, it is
important to monitor precisely and evaluate
accurately the extent of freezing. Failure to do so can
lead to either insufficient or excessive freezing, and
consequently, to recurrence of malignancies treated
by cryosurgery or to destruction of healthy tissues
[5]. Most of the Cryoablation devices are used in an
open loop control. The results of the treatment are
inspected by additional imaging devices such as
ultrasound, camera, temperature sensors and other
sensors according to the relevant application [2].
57
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 57-62
of the ice sphere outer boundary due to the
impedance differences between frozen and unfrozen
tissue. We expect to be able to distinguish the frozen
tissue’s returning echo’s (inside the ice sphere) from
the unfrozen tissue’s returning echo’s (at the outer
rim of the ice sphere).
In Section 2 (Method), we will present an
overview of the entire system with detailed
description of its two main components; Ablation
device and monitoring device, following with an
explanation of the monitoring method and the
analysis done. Section 3 (Results) will include a
summary of the results of different analysis steps and
our conclusions will be presented in Section 4.
In this study, we intend to add a miniature
ultrasonic sensor to a cryoablation device in order to
determine the treatment progress in real time,
observing the ice sphere’s boundary growth. This
capability allows controlling directly the ablation
process by closed loop control. Such a device, will be
able to determine much more effectively (faster,
more accurately and more precisely). The close loop
controlled cryogenic device will be more efficient
and safe than the current treatment. In addition to real
time monitoring of the ablation process, the
ultrasonic sensor will be able to determine the local
thickness of the target area before treatment and will
enable a more accurate setting of the device’s
parameters (freezing power and period). The system
will shorten the ablation treatment and eliminate the
need for reoccurrence treatments.
To the best of our knowledge, there is no
Cryoablation device with collocated sensor
that monitors in-situ the progress of the target
tissue’s freezing.
The purpose of the present study is to show
feasibility of detecting the ice sphere growth from
within the ice sphere during Cryoablative therapy
without using additional monitoring devices. We
assume that by transducing a high frequency ultrasound wave we will be able to determine the location
2. Method
2.1. System Overview
The controlled Cryoablation system that we
developed is a device for Cryoablation therapy with
an ultrasound transducer attached to it as shown in
Fig. 1. In the future the ultrasonic sensor will be
integrated into the cryogenic needle.
Ultrasound
Transducer
Ice here
Cryogenic
Needle
(a
(a)
(b)
(c)
Fig. 1. CryoProbe to sensor connection before (a) and after (b) freezing; Schematic overview of the system (c).
The main components of the system are:
1) A cryoablation device with a Cryoprobe
reaching extreme low temperatures (about -170 ºC) at
its tip. Acronymed as CAS.
58
2) A forward looking ultrasonic sensor that can
measure regular and frozen tissue up to 10 mm in
depth. Acronymed as USS.
The detailed description of the components is
described in the following sections.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 57-62
2.2. Ablation Device
We used the Cryo-ablative device designated as
IceSense3™ system (Fig. 2) manufactured by
IceCure Medical. IceCure Medical developed a
minimally invasive Cryo-ablation therapy for the
Women health market [6].
The transducer is a single element transducer with
outer dimensions of Ø2×5.5 mm and central
frequency of 18.5 MHz as shown in the frequency
response in Fig. 4.
Fig. 4. Vermon US sensor frequency response with central
frequency of 18.5 MHz.
Fig. 2. IceCure's IceSense3™ System (Left)
and its CryoProbes with an example of Ice Sphere
at their tip (Right).
The IceSense3™ system, provides a minimally
invasive, in-office, definitive treatment which uses
low temperatures (about -170°C) to destroy (ablate)
the targeted tissue in situ. The system uses a closed
loop cryogen which reaches a Cryoprobe tip at the
center of the ablate tissue and cooled to sub-zero
temperatures, removing heat from the targeted tissue
by conduction [7] (See Fig. 1(b) and Fig. 2). During
the Cryo-ablative procedure, an ice spheroid (for
convenience we regard to it as a sphere) is formed
around the Cryoprobe tip. The ice sphere size varies
in time and can reach a diameter of 40 mm and
length of around 55 mm after 10 minutes.
This frequency is equivalent to 0.126 mm axial
resolution [8] in water. The frequency response
(Fig. 4), has a central frequency of 18.5 MHz
(at -3 dB) bandwidth frequency of 8.7 MHz,
low cut frequency 14.2 MHz and high cut
frequency 22.9 MHz.
The ultrasound transducer time response in water
is noted in Fig. 5.
2.3. Monitoring Device
2.3.1. 18.5 MHz Transducer (matching
Water)
We have used an Ultrasound transducer
manufactured by Vermon, France as our monitoring
element (Fig. 3).
Fig. 5. USS time response.
The transducer is controlled by USBox system of
Lecoeur Electronique Company, transmitting 230 V
(1 volts step), square pulse. The monitoring was done
in A-mode, transmitting and receiving from the same
transducer. The returning Echo’s were monitored by
Matlab software, sampling in 80 MHz in order to
observe returning Echo’s of up to 40 MHz.
2.3.2. 11.5 MHz Transducer (matching ICE)
Fig. 3. Vermon ultrasonic sensor -Ø2×5.5 mm.
We have used PiezoCAD software [9], first to
compare our calculations to the current 18.5 MHZ
transducer (Fig. 6) and then, custom designed using
the same S/W an 11.5 MHz Ultrasound transducer
matching Ice which was later manufactured by
ImaSonic, France.
59
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 57-62
2.5. Data Analysis
750000
Po/Pi
mW/W
0.000
0.000
Frequency, MHz
40000
Fig. 6. 18.5 MHz sensor calculated Transmit efficiency
using PiezoCAD S/W.
The transducer is a single element transducer with
outer dimensions of Ø1.5×5.0 mm and central
frequency of 11.5 MHz with two matching layers
fitting Ice as our medium (Fig. 7).
Fig. 7. 11.5 MHz transducer, ImaSonic, France.
Looking at the time response of the echo (Fig. 8),
one can observe the large attenuation of the signal
in the ice.
The ice-water boundary, cannot be distinguished
from direct A-mode inspection of the ice sphere’s
outer contour.
In order to detect the boundary we transferred the
signal to the frequency domain using Matlab
software’s signal analysis toolbox. We are using the
following steps to estimate accurately the ice sphere’s
contour’s distance:
1) The sound velocity in ice is estimated by
correlating the signal response near the exit of the
transducer (called ringing in the US jargon) in water
and ice.
2) Using the sound velocity, the time response is
converted into distance and an A-mode US image is
derived (Fig. 8).
3) The time response is also converted to the
frequency domain using the short time Fourier
transformation. The representation of the data is in a
spectrogram that enables identification of significant
features in the image (See Fig. 9).
Expected icewater
boundary
2.4. Experimental Method
Our main objective, was to recognize the outer
contour of the ice sphere in real time (during
treatment) when our monitoring device is located at
the center of the freezing zone hence allowing us to
have both treating and monitoring elements in a
single device. The transducer was chosen to be
minimally in size to allow in the future positioning
inside the ablating element (less than Ø3 mm). In this
experiment, we connected it to the center of the
CryoProbe freezing zone with additional connector
placing the transducer adjacent to the CryoProbe
outer surface (Fig. 1). We have used US Parker gel
inside a Standard 1000 ml beaker as our tissue model
(ablated medium) and compared it to water and
chicken breast tissue.
Several cryogenic treatment simulations were
done. The freezing process duration was up to
10 minutes. The ice sphere size was measured using
an external camera in time periods of 30 seconds as a
reference to the transducer measurements. All
returning echoes were received in a time to voltage
raw data manner and several analyses were done in
order to observe the ice sphere growth as
detailed below.
60
Fig. 8. Returning US Echo of 5 mm thick frozen meat slice.
Fig. 9. The spectogram at 3 mm (a) and 4 mm (b) radius
Ice sphere (In Parker Gel). The area of interest
of 10-12 MHz is in the black frames. The intensity
of the echo is depicted by jet colormap.
4) In order to emphasize the growth of the ice
sphere we used a binary conversion. This calculation
is based on the reduction of the spectrogram
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 57-62
according to the derivative of the intensity in the
spectrogram (growing intensity black, and
vice versa).
5) We also calculated, the sum of the total
intensity at the targeted frequency range and found
that it is a good indicator of the growth of the
ice sphere.
sphere. This binary intensity contrast is easier to
detect using computerized models in real time.
3. Results
3.1. 18.5 MHz Transducer (matching Water)
Using the correlation analysis, we were able to
determine that the frozen gel used has a sound
velocity of 2520 m/s (the literature value of ice sound
velocity is 3600 m/s and water 1430 m/s) with this
property determine we were able to match the
returning Echo’s with their correct location.
The dynamic process of the growing ice sphere is
characterized by the increase of the intensity from the
ice layer and the reduction of the intensity of the echo
from the water or tissue layer beyond the ice sphere.
Fig. 9 demonstrates that this process is the most
apparent in the bandwidth of 10-12 MHz.
Focusing on the bandwidth we identified in Fig. 9
we are able to show a consistence advancing increase
of intensity of the echo in time as shown in Fig. 10
(red and yellow in the jet colormap).
Fig. 10. 10-12 MHz Range at different ice sphere sizes:
radius of – 0 mm (a), 2 mm (b), 3 mm (c), 4 mm (d)
and 5 mm (e) - (In Parker Gel The intensity of the echo is
depicted by jet colormap).
This dynamic process can be correlated to the
growth of the ice sphere. The increase in the echo
from the ice is more emphasized than the decrease in
the gel.
To better distinguish the intensity increments, we
did additional binary analysis (Fig. 11).
Each signal received at different ice sphere size
was compared to the base signal (prior to the freezing
cycle) – areas with increased intensity than the base
signal are shown in black and areas with lower
intensity than the base intensity are shown in white.
The advance of the boundary can be detected by the
increase of the black area with the growth of the ice
Fig. 11. 10-12 MHz Range Binary differences analysis
between the base line (0 Sec) and different ice sphere sizes:
radius of – 1 mm (a); 2 mm (b); 3 mm (c); 4 mm (d);
and 5 mm (e) - (In Parker Gel).
Finally, we calculated the total intensity of the
returning Echo’s of different ice sphere sizes during
its growth and received an increasing graph for each
experiment. We compared all experiments in gel and
water in a single graph and receive a linear trend line
with R-squared value of 0.9656 (Fig. 12).
Fig. 12. Retrieved signal Total Intensity Vs. different ice
sphere sizes – 10-12 MHz Range (All Experiments in water
or Parker Gel) with additional combined Linear Trendline
(in black).
One can see that the total intensity is a good
quantitative measure for the increase of the
ice sphere.
The results in the chicken breast tissue show
similar results to the gel. There were visible
increments with time of the returning Echo’s at 1012 MHz frequency range, better observed when
looking at the binary analysis (Fig. 13).
We also repeated the returning Echo’s total
intensity calculation of different ice sphere sizes
during its growth and received an increasing graph
similar in values to all other experiments (Fig. 14).
Combining all the information gathered from the
results, we receive a very good indication that
measuring the ice ball growth from inside the ice ball
is possible even in real time.
61
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 57-62
4. Conclusions
Fig. 13. 10-12 MHz Range Binary differences analysis
between the base line (0 Sec) and different ice sphere sizes:
radius of – 1 mm (a); 2 mm (b); 3 mm (c); 4 mm (d);
and 5 mm (e) - (In chicken breast).
Examining our results, using the 2nd phase
ultrasound transducer, we can clearly distinguish the
ice-water boundary in the time domain. With the 1st
phase transducer, we can clearly distinguish a
growing intensity of the signal with the ice sphere
growth. We also noticed, that our finding are similar
when using ultrasound gel and breast tissue as our
ablate model.
With further experiments (further using and
analyzing the improved ultrasound sensor), we will
be able to determine the ice sphere real size with high
accuracy and precision (both with the time domain
echo and the intensity correlation method).
This capability will allow us to monitor the
Cryoablation treatment from within the Cryo-ablative
device and in the future have a closed loop combined
device both treating and monitoring in real time.
References
Fig. 14. Retrieved signal Total Intensity Vs. different ice
sphere sizes – 10-12 MHz Range (In chicken breast).
3.2. 11.5 MHz Transducer (Matching ICE)
Using the new custom made transducer we can
observe the returning echo even in the time domain,
due to the lower frequency transmitted pulse, and the
Ice matching layers, both reducing the attenuation.
Thus the ice-water boundary, can be distinguished
from direct A-mode inspection of the ice sphere’s
outer contour. Using the sound velocity, the time
response is converted into distance and an A-mode
US image is derived (Fig. 15).
Real boundry
Returning echoes of
the ice-water
[1]. A. Sharon, G. Kosa, Controlled Cryogenic Ablation
Using Ultrasonic Sensing, in Proceeding of the of the
6th International Conference on Sensor Device
Technologies
and
Applications
(SENSORDEVICES’15), Venice, Italy, 23-28 August
2015, pp. 116-120.
[2]. B. Rubinsky, CRYOSURGERY, Annual Review of
Biomedical Engineering, Vol. 2, 2000, pp. 157-187.
[3]. A. A. Gage, J. G. Baust, Cryosurgery-a review of
recent advances and current issues, Cryoletters,
Vol. 23, No. 2, 2002, pp. 69-78.
[4]. M. Maccini, D. Sehrt, A. Pompeo, F. A. Chicoli, W.
R. Molina, F. J. Kim, Biophysiologic considerations
in cryoablation: a practical mechanistic molecular
review, International Braz J Urol, Vol. 37, No. 6,
2011, pp. 693-696.
[5]. J. G. Baust, A. A. Gage, A. T. Robilottto, J. M.
Baust, The pathophysiology of thermoablation:
optimizing cryoablation, Current Opinion in
Urology, Vol. 19, No. 2, 2009, pp. 127-132.
[6]. IceCureMedical Website, Available: http://icecuremedical.com/
[7]. J. Baust, A. Gage, T. B. Johansen, J. Baust,
Mechanisms of cryoablation: clinical consequences
on malignant tumors, Cryobiology, Vol. 68, No. 1,
2014, pp. 1-11.
[8]. D. Ensminger, L. J. Bond, Ultrasonics:
Fundamentals, Technologies, and Applications, CRC
Press, Taylor & Francis Group, 2011.
[9]. Sonic
Concepts
Website:
Available:
http://www.sonicconcepts.com/index.php/products1/piezocad
Fig. 15. Returning US Echo of 2 mm thick frozen Ice cube.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
62
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 63-66
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Cavity Enhanced Absorption Spectroscopy
in Air Pollution Monitoring
Janusz MIKOŁAJCZYK, Zbigniew BIELECKI, Jacek WOJTAS
and Sylwester CHOJNOWSKI
Institute of Optoelectronics, Military University of Technology,
2 Kaliskiego Str., 00908Warsaw, Poland
Tel./fax.: 0048261837943
E-mail: zbigniew.bielecki@wat.edu.pl
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: The paper presents some practical aspects of cavity enhanced absorption spectroscopy application in
detection of nitrogen dioxide (NO2), nitrous oxide (N2O), nitric oxide (NO) and carbon monoxide (CO). These
gases are very important for monitoring of environment. There are shown results of lab-setups for N2O, NO, CO
detection and portable sensor of NO2. The portable instrument operates in the UV spectral range and reaches a
level of single ppb. The lab–devices use high precision mid-infrared spectroscopy and they was demonstrated
during testing the laboratory air. These sensors are able to measure concentration at the ppb level using quantum
cascade lasers, high quality optical cavities and modern MCT detection modules. It makes it possible to apply
such sensors in monitoring the atmosphere quality. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Laser absorption spectroscopy, Cavity enhanced spectroscopy, CEAS, Gas sensors, QCL.
1. Introduction
Detection of various gases and measurement of
their concentration are very important for monitoring
of industrial processes and investigation of their
environmental impact. Nitrous oxide is so-called
greenhouse
gas
emitted
from
agriculture,
transportation, fossil fuel combustion, wastewater
management, and industrial processes agriculture.
This gas is naturally present in the atmosphere and
has a variety of natural sources e.g. plants, animals,
and microorganisms. Nitrous oxide is removed from
the atmosphere by absorption of some bacteria or by
ultraviolet radiation destruction or by chemical
reactions. However, N2O can stay in the atmosphere
for an average of 114 years before being “natural”
removed. In practice, it is observed ca. 300 times
stronger impact of N2O on warming the atmosphere
in comparison with carbon dioxide [1].
http://www.sensorsportal.com/HTML/DIGEST/P_2736.htm
Carbon monoxide and oxides of nitrogen are ones
of the most important air pollutants from petrol,
diesel,
and
alternative-fuel
engines.
The
concentration of these pollutants is regulated by the
Euro emissions standards. Modern cars are not
significant problem for air quality but their large
quantity. Carbon monoxide and oxides of nitrogen
are generally invisible, and in comparison to CO2
their concentrations are less dependent on fuel
consumption. Carbon monoxide reduces the blood’s
oxygen-carrying capacity and it can be fatal in the
case of extreme levels of exposure. At lower
concentrations CO may increase a health risk,
particularly to those suffering from heart disease.
Nitrogen oxide NO reacts in the atmosphere to
form nitrogen dioxide which can have adverse effects
on people health with respiratory illness. The gas
concentration have been linked with increased
hospital admissions due to respiratory problems and
63
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 63-66
the exposure time may affect lung function and
increase the response to allergens in sensitive people.
Within the last decades, many methods were
developed for detection of volatile substances. There
is a few classifications of gas sensors. For example,
they can be classified into semiconductor-type, solid
electrolyte-type, electrochemical-type and catalytic
combustion-type, capacitor and a simple NDIR type.
A separate group of gas sensors are gas analyzers. In
this group, the main roles play mass spectrometry and
gas chromatography. Their main inconveniences are
size, cost of the apparatus and the maintenance,
complicated exploitation. But the designing process
of sensitive pollution air sensors is very dynamic.
It is shown, that optoelectronic sensors employing
laser absorption spectroscopy are very useful in the
effort to minimize the level of the environment
contamination [2]. They use the phenomenon of
optical radiation interaction with specific compounds
to detect and to measure the concentrations of the
molecules, provide achieving low detection limits
and high selectivity. For this purpose, it is necessary
to apply radiation, the wavelength of which is
precisely tuned to the spectral range characterized by
strong absorption of the tested molecules. Such
sensors are more sensitive and selective than many
others [2].
2. Cavity Enhanced Absorption
Spectroscopy in Atmosphere
Monitoring
Cavity enhanced absorption spectroscopy (CEAS)
was proposed by R. Engeln in 1998. The principle of
its operation is very similar to cavity ring down
spectroscopy (CRDS). In both setups there is applied
an optical cavity with a high quality factor that is
made up of two concave mirrors with very high
reflectivity R. This results in a long optical path, even
up to several kilometers [5]. A pulse of optical
radiation is injected into the cavity through one of the
mirrors. Then inside the cavity multiple reflections
are observed. After each reflection, part of the
radiation exiting from the cavity is registered with a
photodetector. The output signal from the
photodetector determines the intensity of radiation
propagated inside the optical cavity. If the laser
wavelength is matched to the absorption spectra of
gas filling the cavity, the cavity quality decreases.
Thus, parameters of the photodetector signal are
changed. Thanks to this, the absorption coefficient
and concentration of gas can be determined.
The main difference between CEAS and CRDS
relates to the laser beam and the optical cavity
alignment. In CEAS the light is injected at a very
small angle in respect to the cavity axis (Fig. 1). As a
result, dense structure of weak radiation modes is
obtained or they overlap. Sometimes, a piezoelectricdriven mount that modulates the cavity length
(position of the output mirror) is used in order to
64
prevent the establishment of a constant mode
structure within the cavity [3].
Fig. 1. The scheme of CEAS setup.
The mode structure causes that the entire system
is much less sensitive to instability in the cavity and
to instability in laser frequencies. Additionally, due to
off-axis illumination of the front mirror, the source
interference by the optical feedback from the cavity
is eliminated. CEAS sensors attain the detection limit
of about 10-9cm-1 [5]. Therefore, this method creates
the best opportunity to develop a portable
optoelectronic sensor of nitrogen oxides.
In the applied methods, determination of the gas
concentration is performing by measuring the decay
time of the photodetector signal [3-4]. If the laser
pulse duration is negligibly short and only the main
transverse mode of the cavity is excited, then
exponential decay of radiation intensity can be
measured. The decay time of signal in the cavity (τ)
depends on the reflectivity of mirrors, diffraction
losses and the extinction coefficient α:
τ=
L
,
c(1 − R + αL )
(1)
where L is the length of the resonator, c is the speed
of light. Determination of the examined gas
concentration is a two-step process. First,
measurement of the decay time (τ0) of radiation in the
optical cavity without tested gas is performed. During
next step, the same measurements is made (decay
time τ) for the cavity filled with the gas. Knowing the
absorption cross section (σ) of the examined gas, its
concentration can be calculated from the formula
C=
where
1
cσ
 1 1 ,
 − 
 τ1 τ 0 
(2)
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 63-66
τ0 =
L
c(1 − R )
(3)
3. Laboratory Experiments
Nitrogen oxides are very important to control in
the air because they cause accelerate corrosion of
stone buildings and metal structures, threaten human
health, irritate the respiratory system and general
weaken the body's resistance to infectious diseases.
That is why, the first research was focused on the
development of nitrogen oxides sensors providing the
detection limit as low as possible. In our
experimental
setups,
visible
and
mid-IR
semiconductor lasers were applied. The first one was
constructed using a blue-violet semiconductor laser
(414 nm) developed at the Institute of High Pressure
of the Polish Academy of Sciences. For detection of
NO, N2O, the mid-IR lasers operating at the
wavelength located in the infrared region were
applied (Fig. 2) [6].
Fig. 2. The results of the calculations of the absorption
cross section for selected gases.
There were applied quantum cascade lasers
(4.53 µm and 5.27 µm) from Alpes Lasers
SA, Switzerland.
In the case of CO, the prototype quantum cascade
laser (4.78 µm) from the Institute of Electron
Technology, Poland was used.
The views of the constructed NO and NO2 sensors
are presented in Fig. 3.
(a)
(b)
Fig. 3. Photos of NO2 sensor (a) and prototype NO sensor (b).
During the sensor investigation, concentration
measurements of reference gas samples were carried
out. Reference samples were prepared using the
491M type gas standards generator from KIN-TEK
Laboratories, Inc. (La Marque, TX, USA, Fig. 4).
The achieved results are summarized in Table 1.
Table 1. Measured detection limit of the designed
sensors [7].
Type of
sensor
NO2
N2O
NO
CO
Operation
Wavelength
414 nm
4.53 µm
5.27 µm
4.78 µm
Detection Limit
1 ppb
45 ppb
70 ppb
Approx. 150 ppb
4. Outdoor Tests
Fig. 4. Photo of 491M type gas standards generator.
The outdoor tests were performed with portable
NO2 sensor. The measurements were made in the
65
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 63-66
neighborhood of the university camp for different
kind of environment. The results are listed in Table 2.
Table 2. The measured NO2 concentration
for environments.
Place of measurement
Wooded area
In the neighborhood
of garbage dumps
Intersection
Rondo with high traffic
Overpass the road S8
In the neighborhood
of the shooting range
Concentration [ppb]
Min.
Max.
Mean
5
30
14
23
35
30
32
65
30
55
87
57
42
75
44
28
51
35
The lowest concentration value was obtained for
forest area. This value increases with movement in
the more urban area. The highest level is observed for
area with large car traffic. That is why, the addition
tests were performed for direct diesel car exhaust.
The mean value was 980 ppb. These results shown
that the NO2 sensor can be applied in many scenarios
of environment monitoring.
5. Conclusions
CEAS sensors are able to measure concentration
of atmosphere gases at the ppb level. Their sensitivity
is comparable with the sensitivities of other detecting
instruments. But in comparison, they offer quick
response, no-impact on tested air samples, online
operation, high selectivity and ease of use.
It is observed, that detection of NO2 molecules
can be performed using broadband multimode lasers.
It is possible because a relatively large mean
absorption cross section within the range of
several nanometers resulting from analyses of
electronic transitions.
However, for other compounds (like N2O, NO
and CO) mid-IR absorption lines are very narrow.
That is why, these sensors require to apply precisely
defined lasers wavelengths. The presented results
shown, that application of mid-IR QCL lasers in such
sensors is very promising. Currently, several works
related to the development of these sensors is still
continuing.
Acknowledgements
The presented work was supported by The
National Centre for Research and Development in the
scope of Project ID: 179616.
References
[1]. Web Portal United States EPA, Overview of
Greenhouse
Gases,
(http://www3.epa.gov/
climatechange/ghgemissions/gases/n2o.html).
[2]. M. Miczuga,
K. Kopczyński,
J. Pietrzak,
R. Owczarek, Measuring system for detection and
identification of hazardous chemicals, SPIE,
Vol. 8703, 2012.
[3]. T. Stacewicz,
J. Wojtas,
Z. Bielecki,
M. Nowakowski, J. Mikołajczyk, R. Mędrzycki, B.
Rutecka, Cavity ring down spectroscopy: detection of
trace amounts of substance, Opto-Electron. Rev.,
Vol. 20, No. 1, 2012, pp. 34-41.
[4]. J. B. Paul, L. Lapson, J. G. Anderson, Ultrasensitive
absorption spectroscopy with a high-finesse optical
cavity and off-axis alignment, Applied Optics,
Vol. 40, Issue 27, 2001, pp. 4904-4910.
[5]. I. Courtillot, J. Morville, V. Motto-Ros, D. Romanini.
Sub-ppb NO2 detection by optical feedback cavityenhanced absorption spectroscopy with a blue diode
laser, Applied Physics: B, Vol. 85, Issue 2, 2006,
pp. 407-412.
[6]. J. Wojtas, J. Mikolajczyk, Z. Bielecki, Aspects of the
Application of Cavity Enhanced Spectroscopy to
Nitrogen Oxides Detection, Sensors, Vol. 13, No. 6,
2013, pp. 7570-7598.
[7]. Z. Bielecki,
J. Wojtas,
J. Mikolajczyk,
S. Chojnowski, Application of Cavity Enhanced
Absorption Spectroscopy in Detection of Selected
Gas Pollutants, in Proceedings of the 6th International
Conference on Sensor Technologies and Applications
(SENSORCOMM’15),
Venice,
Italy,
23-28
August 2015, pp. 83-84
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
66
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Design and Analysis of a Collision Detector
for Hybrid Robotic Machine Tools
Dan ZHANG, Bin WEI
University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa,
Ontario, L1H 7K4, Canada
Tel.: 905.721.8668 ext. 5721, fax: 905.721.3370
E-mail: Dan.Zhang@uoit.ca, Bin.Wei@uoit.ca
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Capacitive sensing depends on the physical parameter changing either the spacing between the two
plates or the dielectric constant. Based on this idea, a capacitive based collision detection sensor is proposed and
designed in this paper for the purpose of detecting any collision between the end effector and peripheral
equipment (e.g., fixture) for the three degrees of freedom hybrid robotic machine tools when it is in operation.
One side of the finger-like capacitor is attached to the moving platform of the hybrid robotic manipulator and
the other side of the finger-like capacitor is attached to the tool. When the tool accidently hits the peripheral
equipment, the vibration will make the distance of the capacitor change and therefore trigger the machine to
stop. The new design is illustrated and modelled. The capacitance, sensitivity and frequency response of the
detector are analyzed in detail, and finally, the fabrication process is presented. The proposed collision detector
can also be applied to other machine tools. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Machine tools, Collision detection, Sensor, Capacitance, Vibration, Modelling.
1. Introduction
A capacitor is defined as two conductors that can
hold opposite charges. If the distance and relative
position between two conductors change due to the
external force, the capacitance value will be changed.
This is the basic principle of capacitive sensing,
which belongs to electrostatic sensing. The major
advantages of electrostatic sensing can be concluded
as follows, firstly, it is simple. No special functional
materials (such as piezoresistive and piezoelectric
materials) are required. The sensing principle is easy
to implement, requiring only two conducting
surfaces. Secondly, it has the characteristic of fast
response. Capacitor-based sensing has high response
speed due to the fact that the transition speed is
http://www.sensorsportal.com/HTML/DIGEST/P_2737.htm
controlled by the charging and discharging time
constants that are small for good conductors [1-2].
There are two kinds of capacitive electrode
geometries: parallel plate capacitor and interdigitated
finger capacitor. For the interdigitated finger
capacitor, it can be regarded as many parallel plate
capacitors combining together. One side of the finger
is fixed and the other side is suspended and can move
in one or more axes.
Parallel mechanisms have been widely used in
different kinds of areas [3-4], such as machine tools
as shown in Fig. 1. Parallel robotics machine tools
are the new trend for the manufacturing and
automations. When machine tools are in operations, it
is sometimes unavoidable to hit the peripheral
equipment (e.g. fixture) by the tool, which can
67
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
damage cutting tools, clamps and fixtures, or cause
damage to the machine itself as shown in Fig. 2,
which undermines the performance of the whole
parallel robotic system and delays the productions.
Fig. 1. 3-DOF parallel robotic machine tool developed
in the R&A Lab in UOIT.
Fig. 2. Collision occurred during machining [10].
Therefore, how to prevent the collisions during
machining is very important for machining processes.
When the end-effector/tool of parallel robotic
machine tool accidently hits the peripheral
equipment, e.g. fixture, in a better case scenario, say
an operator finds it and hits the emergency stop
button, then sometimes it is too later. The machine
tool should ideally stop immediately when the tool
hits the peripheral equipment and stop causing
68
further damage [5]. Based on this need and
motivation, a sensor should be developed to address
this goal. Some computer aided manufacturing
software have the ability to perform a machine
collision check, but some machine tools do not have
this function. Most computer aided manufacturing
programs determine the cutter paths considering
sometimes just the tool. In machine tools, it is likely
to drive the end effector outside the bounds, resulting
in a collision with others. Many machine tools do not
aware about the surrounding environment. The end
effector just follows the code and it is totally
dependent on an operator to detect if a crash occurs.
Let us image the following, when the tool hits the
fixture, it is a collision, then there should be a
vibration produced, so how can we harness this
vibration and convert the vibration to an electrical
signal that can be recognized by a computer.
Capacitive based sensor depends on the physical
parameter changing, e.g. the space between the plates
and the dielectric constant between them, etc. For
example, in the vehicle air bag deployment system, a
crash acceleration makes one plate closer to the other
and therefore trigger the bag to deploy. Inspired by
this idea, we design a capacitance-based collision
detector/sensor that can sensor the vibration and
convert the vibration to an electrical signal.
In [6], vibration on a rotating spindle is generated
by the sum of the variations in weight distribution.
The corrective action is needed to have a force with
an equal but opposite direction to cancel the
imbalance condition. The first step to achieve this is
to measure the vibration. There, a vibration sensor is
installed in a grinding machine spindle, and the
vibration is measured using a vibration sensor that is
composed of a seismic mass that is connected to a
piezoelectric transducer which converts the vibration
into an electric signal. The above one is mainly
measure the amount of vibration that the rotating
spindle produced and it is not appropriate to use here
as a collision detector. In [7], the chatter vibration is
detected by using three different acceleration sensors
that attached to three different axis of machine tool.
In [8], a web learning tool with 3D simulation for
axial table collision detection was proposed, but no
device has been designed. In [9], a vibration
detection algorithm was proposed and a speed
regulator was designed for the backlash vibration of a
machine tool. In [10], a new approach was presented
to detect and avoid hard and soft collisions caused by
user errors, and a capacitance based sensor was
briefly mentioned for the collision of the machine
tools, but it did not explicitly design and propose the
capacitance based collision sensor. In [11], a sixdimensional wrist force/torque sensor based on
E-type membranes is designed and fabricated, and it
is applied onto the five-axis parallel machine tool to
measure the tool forces and torques, previous one is
force/torque sensor (used to detect forces and
moments), but that is not a vibration sensor.
In this paper, a collision detection sensor is
designed that can sense the vibration that the end-
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
effector caused when the tool accidently hits the
fixture. When the tool accidently hits the ground or
something, it will produce vibration, the vibration
will make the distance of the capacitor change and
therefore trigger the machine to stop immediately. No
one has ever designed the collision detector that gears
towards the situation that the machine tool should
stop immediately when the end-effector hits the
peripheral equipment.
The structure of the paper is as follows. In
Section 2, the new design will be illustrated. The
capacitance and sensitivity will be discussed in
Section 3. Section 4 analyzes the resonant frequency
of the detector, and finally fabrication process is
presented in Section 5.
2. New Design
3. Capacitance and Sensitivity
For a single fixed finger and its two neighboring
moving fingers. There are two capacitances
associated with each finger pair, one is the left-side
of the finger, denoted as Cl , and the other is the
right-side of the finger, denoted as Cr . When the tool
is not vibrating, the values of these two capacitance is
the same, i.e.:
where
When the tool accidently hits the fixture, the tool
will vibrate, the free finger will move by a distance,
say x, and then the capacitance values of these two
capacitors become the following:
ε 0lt
d
,
(1)
ε 0 is the permittivity of the vacuum, l is the
engaged overlapping distance of the fingers, t is the
thickness of the fingers, d is the distance between a
fixed-comb
finger
and
its
neighboring
movable finger.
ε 0lt
Cl =
(2)
d−x
and
ε 0lt
Cr =
(3)
d+x
The total value of capacitance is:
C = Cl + Cr =
Capacitive sensing depends on the physical
parameter changing either the spacing between the
two plates or the dielectric constant. Our vibration
sensing method is based on this idea. Capacitive
sensing needs external electronics to make the
changes in capacitance to an output voltage that can
be read by a computer.
One side of the finger-like capacitor is attached to
the moving platform of the hybrid robotics
manipulator and the other side of the finger-like
capacitor is attached to the tool. When the tool
accidently hits the ground or something, it will
vibrate and the distance between the fingers will
change and therefore, the capacitance will change
and trigger the machine to stop. The capacitance
between a pair of fingers is contributed by the surface
of fingers in the overlapped region. Capacitance
derived from multiple pairs are connected in parallel,
so the total capacitance is the summation of
capacitance contributed by neighboring fingers.
Cl = Cr =
3.1. Movement Along y Direction
ε 0lt
d−x
+
ε 0lt
2d ε 0lt
d + x d 2 − x2
=
(4)
For a case study, suppose there are 13 fingers,
which means there are 12 capacitors, so the
12 capacitors will contribute the total capacitance of
the device. So the above can be rewritten as follows:
C = 12
2d ε 0lt
d 2 − x2
(5)
This change can be transferred to the electrical
signal, and under a certain value, then it means the
machine tool is in the process of manufacturing, even
though there is small vibrations, the capacitance
change is under that value, the capacitance will not
trigger the electrical controller to stop the machine,
but when the capacitance change is very large, then
the capacitor will trigger the controller to stop the
machine immediately. This is under the condition
that when the pieces are softer than fixture. When the
pieces are stiffer than fixture, then we need to set the
condition that when the capacitance under that value,
then the capacitor needs to trigger the controller to
stop the machine, and when the vibration is above
that value, then let the capacitor not trigger the
controller to stop the machine. It can also be put as
this way, under certain value range, the capacitance
change is not sensitive (big) enough to trigger the
controller to stop the machine, which is under the
condition that the piece is softer than fixture. And
also that certain value needs to be determined by
experimentation.
If: C= 12
2d ε 0lt
< Value 1
d 2 − x2
Value 1 needs to be determined by experiment.
The capacitor will not trigger the controller to
stop the machine
If: C= 12
2d ε 0lt
> Value 1
d 2 − x2
69
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
The capacitor will trigger the controller to stop
the machine.
Above certain value range, the capacitance
change is not sensitive (small) enough to trigger the
controller to stop the machine, which is under the
condition that the piece is stiffer than fixture. And
also that certain value needs to be determined
by experimentation.
If: |C= 12
2d ε 0lt
|< |Value 1|
d 2 − x2
The capacitor will not trigger the controller to
stop the machine.
If: |C= 12
2d ε 0lt
|> |Value 1|
d 2 − x2
The capacitor will trigger the controller to stop
the machine.
Set the decision logic to certain value, decision
logic will receive a signal from the capacitor/sensor
to determine if it is actually manufacturing or
collision. We can set the logic to negative value when
the pieces are stiffer than fixture, it is like a
mathematic logic. The sensor will not decide if the
contact is the beginning of a collision or simply
defines the manufacturing pieces. This is the function
of the decision logic [6], and set certain value to the
logic, if the value larger or smaller than certain value
that was given to the logic, then the detector/sensor
will trigger the machine to stop or not to stop, it is
related to the decision logic module design, which
has been out of author’s research scope, and can be
done as a future work. This report is mainly proposes
the idea that use the capacitive principle based
method to design the collision sensor.
In terms of when the cutting tool breaks the
moment it hits the fixture, this must be the condition
that the fixture is harder than the piece, if the piece is
harder than the fixture, then the tool will also break
when manufacturing pieces, so it is not realistic at all,
so the pieces must be softer than fixture at than case.
If it is in that case, as being said above, i.e. under
certain value range, the capacitance change is not
sensitive (big) enough to trigger the controller to stop
the machine when the tool is in the process of
manufacturing. And also that certain value needs to
be determined by experiment. Ideally, when the tool
hit the fixture, the detector/sensor will trigger the
machine to stop immediately, so the tool will not
break. Say a worst case scenario, the tool breaks, the
capacitance will also change, so it will trigger the
machine to stop. Either way, no matter the tool
breaks or not, if the capacitance change is above that
value, then it will trigger the machine to stop.
However during motions, the rate of capacitance
change can be measured, this rate of change can also
be called the displacement sensitivity, it is obtained
by taking the derivative of C with respect to x, and
we can have the following,
70
∂C 48d ε 0ltx
=
∂x ( d 2 − x 2 ) 2
S(x) =
(6)
The above is under the movement along the y
direction (transverse). Transverse comb drive devices
are frequently used for sensing for the sensitivity and
they are ease of fabrication.
3.2. Movement Along x Direction
When the movement is along the x direction, we
have the following, please note that the movement
along z direction is very small or none because the
suspension beam is along the z direction which
blocks the movement along z direction. There are
13 fingers, which means there are 12 capacitors, so
the 12 capacitors will contribute the total capacitance
of the device. At rest, the total capacitance is:
C = 12
ε 0lt
d
(7)
When there is force in x direction, which will
make the fingers move in the x direction, and
therefore will cause the effective thickness t’ to
change. Suppose the change value is x, under the
above changed condition, the capacitance will change
to the following:
C = 12
ε 0l (t − x)
d
(8)
The relative change of capacitance w.r.t.
displacement x (i.e. displacement sensitivity, or the
change of capacitance as a function of applied
displacement) can be expressed as follows:
12ε 0l
∂C
=−
d
∂x
(9)
4. Frequency Response
The device can be seen as a fixed-free cantilever
beam, and the resonant frequency can be
expressed as:
f1 =
1.732
2π
I=
wt 3
12
EIg
Fl13
(10)
(11)
There are two suspension beams, so the force/spring
constant can be expressed as follows [1]:
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
F = k⋅x
k = 2×
Ew t 3
4l13
(12)
(13)
Plug in the above F and I , resonant frequency
can be finally derived as follows:
f1 =
1.732 EIg 1.732 g
,
=
2π
Fl13
2π
6x
where l1 is the suspension beam length.
Fig. 4. Dimensions of the detector.
(14)
E is the
Young’s modulus. w is the width of the finger, t is
the thickness of the finger. The resonant frequency
value is depend on the above parameters, different
parameter will result different values.
5. Fabrication
Silicon bulk micromachining is the process that
involve partial removal of bulk material in order to
create three dimensional structures or free suspended
devices. Etching a subtractive process that remove
materials. Etching can be divided into two categories,
one is wet etching and the other is dry etching. For
the wet etching, the liquid etchants can be acids and
hydroxides; for the dry etching, we have the physical
etching (impact of atoms/ions), reactive ions and
enhanced by RF energy. And also isotropic etching
can give rounded profiles and anisotropic etching can
yield flat surfaces.
Prototyping and fabrication processes are
illustrated in the Fig.3-7. If we draw a vertical line
that cuts across both sets of fingers in Fig. 3, we will
get the cross section as shown in Fig. 6. Here we only
drew two of the 13 fingers in the cross section for the
purpose of clearly illustrating the fabrication process.
These are the two floating rectangles in final step.
Fig. 5. Capacitance based collision detector.
Silicon substrate
Deposition
(a) Deposition of oxide
Buried oxide
Silicon substrate
Deposition
(b) Deposition of Si
Si
Buried oxide
Silicon substrate
Patterning
(c) Patterning
Si
Buried oxide
Silicon substrate
Fig. 3. Vibration detector/sensor used in 3-DOF hybrid
robotic manipulator.
Fig. 6. Fabrication process.
71
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
And
Fabrication method 2:
Glass substrate
Glass wafer
Deposition
Deposition
(d) Deposition of Epoxy
Epoxy
20 um epoxy
Glass substrate
Glass wafer
Patterning
Deposition
(e) Patterning
Epoxy
Glass substrate
20 um Si
20 um epoxy
Bond
Glass wafer
(f) Bond
Patterning
Silicon substrate
Buried oxide
Si
Epoxy
20 um Si
20 um epoxy
Glass substrate
Glass wafer
Etch in TMAH
(g) Etch
Glass substrate
The epoxy is etched in an oxygen plasma (dry
etching), which undercuts the silicon to free the
moving parts;
Epoxy
Dry etching
Si
Buried oxide
Silicon substrate
20 um Si
20 um epoxy
Glass wafer
(h) Etch off silicon substrate
Glass substrate
Epoxy
Si
Buried oxide
Etch off the oxide
(i) Etch off oxide
Si
Epoxy
Glass substrate
Fig. 6. Fabrication process (Continued).
72
Etch epoxy, undercutting Si
Fig. 7. Fabrication process.
Compared with parallel-plate capacitors, the
capacitance between two neighboring set of fingers
are relatively small. However, one can achieve large
capacitance and force by increasing the number of
comb pairs. The proposed collision detector can also
be used in other machine tools.
7. Conclusions
In this paper, a capacitance based collision
detector is proposed and designed for the purpose of
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 67-73
detecting any collision between tool and peripheral
equipment (e.g. fixture). One side of the finger-like
capacitor is attached to the moving platform of the
hybrid robotic manipulator and the other side of the
finger-like capacitor is attached to the tool. When the
tool accidently hits the peripheral equipment, the
vibration will make the distance of the capacitor
change and therefore trigger the machine to stop. The
new design is illustrated and modelled. The
capacitance, sensitivity and frequency response are
analyzed in details, and finally the fabrication process
are presented. The proposed collision detector can
also be applied to other machine tools. Future work
will build the prototype to test the proposed detector
in the real application scenarios.
Acknowledgements
[4].
[5].
[6].
[7].
[8].
The authors would like to gratefully acknowledge
the financial support from the Natural Sciences and
Engineering Research Council of Canada (NSERC)
and Canada Research Chairs program.
References
[9].
[10].
[1]. Chang Liu, Foundation of MEMS, Pearson Prentice
Hall, 2006.
[2]. L. E. B. Ribeiro, F. Fruett, Analysis of the Planar
Electrode Morphology for Capacitive Chemical
Sensors, in Proceedings of the 6th International
Conference on Sensor Device Technologies and
Applications (SENSORDEVICES’15), Venice, Italy,
23-28 August 2015, pp. 179-182.
[3]. Tsai L. W., Joshi S., Kinematic analysis of 3-DOF
position mechanisms for use in hybrid kinematic
[11].
machines, Journal of Mechanical Design, Vol. 124,
No. 2, 2002, pp. 245-253.
D. Zhang, Kinetostatic analysis and optimization of
parallel and hybrid architecture for machine tools,
Ph.D. thesis, Laval University, Canada, 2000.
Dan Zhang, B. Wei, Design, Analysis and Modelling
of a Capacitive-Based Collision Detector for 3-DOF
Hybrid Robotic Manipulator, in Proceedings
International
Conference
on
of
the
6th
Sensor Device Technologies and Applications
(SENSORDEVICES’15), Venice, Italy, 23-28 August
2015, pp. 31-36.
http://www.marposs.com/technology.php/eng/
sensors_grinders_monitoring
Dong-Hoon Kim, Jun-Yeob Song, Suk-Keun Cha,
etc., Real-Time Compensation of Chatter Vibration in
Machine Tools, International Journal of Intelligent
Systems and Applications, Vol. 5, No. 6, 2013,
pp. 34-40.
Chia-Jung Chen, Rong-Shine Lin, Rong-Guey
Chang, Efficient Web-Learning Collision Detection
Tool on Five-Axis Machine, World Academy of
Science, Engineering and Technology, Vol. 7 No. 7,
2013, pp.1053-1057.
Ebrahim Mohammadiasl, Vibration Detection and
Backlash Suppression in Machine Tools, in
Proceedings of the IEEE/RSJ International
Conference on Intelligent Robots and Systems,
St. Louis, USA, October 2009, pp. 972-977.
T. Rudolf, C. Brecher, F. Possel-Dölken, Contactbased Collision Detection – A New Approach to
Avoid Hard Collisions in Machine Tools. A.
Donmez, L. Deshayes (Eds.), in Proceedings of the
International Conference on Smart Machining
Systems, 2007, pp. 1-4.
Qiaokang Liang, Dan Zhang, Quanjun Song, etc.,
Design and fabrication of a six-dimensional wrist
force/torque sensor based on E-type membranes
compared to cross beams, Measurement, Vol. 43,
Issue 10, December 2010, pp. 1702-1719.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
73
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 74-79
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Numerical Signal Analysis of Thermo-Cyclically Operated
MOG Gas Sensor Arrays for Early Identification
of Emissions from Overloaded Electric Cables
1
Rolf Seifert, Hubert B. Keller, 2 Navas Illyaskutty, Jens Knoblauch
and Heinz Kohler
1
Institute of Applied Informatics (IAI), Karlsruhe Institute of Technology,
Hermann-von-Helmholtz-Platz1, Eggenstein-Leopshafen, D-76344, Germany
2
Institute for Sensor and Information Systems (ISIS), Karlsruhe University of Applied Sciences,
Moltkestr. 30, Karlsruhe, D-76133, Germany
1
Tel.: +49-721-6082-4411, fax: +49-721-6082-5702
E-mail: rolf.seifert@kit.edu, heinz.kohler@hs-karlsruhe.de
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: A thermo-cyclically operated multi metal oxide gas sensor (MOG) array is introduced together with a
novel signal analysis approach (SimSens) for identifying the emissions from overheated isolation cable
materials thereby detecting the fires originated in electrical cabinets at early stages. The MOG array can yield
specific conductance signatures appropriate to specifically identify gases. The obtained results bear good
capability for detection and identification of pyrolysis gas emissions at relatively low sample heating
temperatures even before a visible color-change of the polyvinyl chloride (PVC)-isolation material. The
dynamic conductance signals were evaluated using SimSens, a numerical analysis tool designed for
simultaneous evaluation of conductance profiles. The results show promising pyrolysis gas identification and
concentration determination capabilities in relation to the conductance profile shapes of model gases like carbon
monoxide (CO) and propene. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Oxide gas sensor, Sensor array, Early fire detection, Pyrolysis, Data analysis.
1. Introduction
In the current scenario, developing sensor systems
for early detection of fires instigated by overloaded
electrical circuits has gained great attention due to
increased sensibility for security aspects. Metal oxide
gas sensors (MOG) can be used as appropriate
candidates for detection of conventional fires and
smoke [1]. This type of gas sensor could be utilized
for early detection of fires in electrical installations,
as pyrolysis of cable materials leads to emission of
distinct gas mixtures depending on insulation
74
material composition [2-3]. Identification of those
typical gas mixtures by thermo-cyclic operation of
MOG sensor arrays and simultaneous sampling of
conductance over time profiles (CTPs) together with
numerical analysis of these profiles has been shown
in the past to be an elegant and reliable method for
detection of fires [3-8].
2. Outline of the SimSens Program
In many applications, a multitude of different
gases may occur, which have to be identified and
http://www.sensorsportal.com/HTML/DIGEST/P_2738.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 74-79
analyzed simultaneously. One application of great
interest is the abovementioned detection of fires due
to electrical overload of cables at early stages of
development. Depending on the various coating
materials of the cables a variety of gases or gas
mixtures can be emitted. These gases or gas mixtures
according to the related coating materials have to be
simultaneously identified and analyzed for early
detection of such developing risk. The term
“simultaneously” in this context means that the
measurements are performed with only one sensor
system and the instantaneous analysis is performed
with only one analysis procedure covering all the
different gases or gas mixtures, which may occur at a
certain time but not at once.
The calibration and evaluation program SimSens
was designed to meet these demands on simultaneous
gas identification capability. SimSens is an extension
of the well-introduced program ProSens [10-11],
which was designed to analyze only one target gas or
gas mixture under consideration.
Like ProSens, SimSens consists of a calibration
part and an evaluation part. In the calibration part
SimSens provides the mathematical calibration
models for every gas to be analyzed. Each calibration
model consists of functions for determination of the
component concentration und of functions for
calculating the related so-called theoretical CTP for
substance identification. This means, the calibration
part of SimSens provides calibration models for
every gas or gas mixture under consideration,
whereas ProSens calculates only one calibration
model. The functions included in the calibration
part of SimSens are parametric functions and
the parameters are determined by multiple
linear regressions of the CTP sample values
versus concentration.
These parameters are transferred to the evaluation
part of SimSens for the analysis of an unknown gas
sample. Based on these parameters and the CTP of an
unknown gas sample the evaluation part of SimSens
calculates theoretical CTPs for each calibrated gas
and compares these CTPs with the measured CTP.
If the measured CTP and one of the theoretical
CTPs are close together, i.e., a difference value
calculated from the sum of quadratic differences of
every sample point of the measured CTP and the
theoretical CTP is smaller than a pre-determined
decision value, SimSens identifies the unknown gas
sample as the related calibrated target gas.
Otherwise SimSens recognizes that the gas
sample is none of the calibrated target gases. In case
of
identification,
SimSens
calculates
the
concentration of the gas sample based on the related
calibration model.
Using these algorithms of numerical sensor signal
analysis, SimSens has the capability to identify and
analyze a variety of target gases or gas mixtures. This
is due to the fact that in the calibration part of
SimSens more than only one calibration model can
be determined, namely one calibration model for
each gas or gas mixture, which may occur in the
considered application. Furthermore, in the
evaluation part of SimSens, not only one gas or gas
mixture can be identified, but all gases or gas
mixtures, which are calibrated.
3. Experimental Set
The principal sensing element used for pyrolysis
gas identification studies is a four-fold sensor array
on a 4×4 mm2 alumina chip (Fig. 1), which comprises
four micro-dispensed thick-film sensing layers of
different SnO2/additive-composites [3]. In operation,
the sensor-chip is thermally modulated by applying a
steady slope heater-voltage, which results in a
periodic, nearly triangular temperature profile
between 100 °C and 450 °C, at a cycle time of three
minutes (Fig. 2).
Fig. 1. Multi-sensor-array with four different layers
dispensed on thin-film Inter-Digital-Electrodes. The chip
is mounted on TO 8 header.
Fig. 2. Triangular heater voltage profile applied to operate
the sensor array at thermo-cyclic mode (left ordinate)
and corresponding temperature monitored by IR camera
(right ordinate).
Operating MOG sensors thermo-cyclically and
sampling of the conductance simultaneously yields
gas specific CTPs [3-6], which enable identification
75
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 74-79
of the gas atmosphere from the characteristic
CTP shapes.
Pure SnO2, SnO2/2 %ZnO-, SnO2/1 %PtO- and
SnO2/1 %PdO-composites were selected systematically from a variety of material combinations by
investigating their sensitivity performance. The
various material combinations were investigated
towards their specific conductance behavior to
propene (C3H6), carbon monoxide (CO) and methane
(CH4) at different concentrations in synthetic air and
37.5 % (21 °C) relative humidity (r.H.) and were later
exposed to pyrolysis gases as described in the
following sections. These model gases were
particularly selected as they are found to be the main
components evolved during the pyrolysis of standard
PVC-insulation cable materials. The selected sensing
materials show optimal sensitivity, stability and gas
identification capabilities towards model gases like
CO and propene and to pyrolysis gases. The
sensitivity to methane is very low and not
further reported.
A detailed description of the sensor fabrication,
experimental set up and generation of pyrolysis gases
is depicted in refs [4], [6-7]. Pyrolysis experiments
were conducted with 3.5 g of PVC isolated litz
copper wires (LiY – 0.14 mm2, AWG26, 2A current
rating, yellow). The pyrolysis gases were generated
by heating the samples in a quartz tube reactor and
the evolving gas is carried to the sensor by a constant
synthetic air flow. After the reactor, the constant flow
is mixed with another adjustable flow of synthetic air
in order to get varied gas concentrations. The reactor
temperature was increased in a stepwise fashion
starting from room temperature up to 170 °C. This
fixed temperature was selected after several
systematic experiments conducted below and above
170 °C. Evidently, the wire sample showed no visible
change up to temperatures of 150 °C and even at
170 °C only a slight change of the sample by
shrinking of insulation diameter is observed. At
200 °C discoloring takes place, the sample
turns brown.
4. Results and Discussion
4.1. Sensor Response
An overview of the pyrolysis experiment with
sensing pattern of a pure SnO2 layer towards
pyrolysis gas at different concentrations is given in
Fig. 3. The pyrolysis gas was transported by a
constant primary gas flow and diluted by a dilution
flow of synthetic air. It is demonstrated that at
170 °C, an almost constant emission from the
polymer material is observed over more than five
hours. The absolute conductance-values presented
here vary greatly depending on experimental
parameter values. Several CTPs were recorded at a
reactor temperature of 170 °C, while changing the
dilution level to set defined relative concentrations.
The CTPs at each concentration are highly
76
reproducible at the repeated cycling by showing
similar baseline and peak conductance values (Fig. 3,
inset).
Fig. 3. Measurement sequence of a pure SnO2 layer
exposed with pyrolysis gas of a heated PVC-coated wire
with temperature profile and gas flows. The numbers relate
the pyrolysis gas concentrations in percent. At each
concentration, the heater cycles 10 times. The inset and the
oval in green show the reproducibility of the profiles
with stable baseline and maximum conductance at repeated
temperature cycles.
The CTPs measured by different sensitive
materials to model gases (CO and propene) and
pyrolysis gases at different concentrations are
visualized in Fig. 7 for comparison. In the very most
cases they show very specific features with respect to
gas identification capabilities.
The thermo-cyclically driven array of four
different gas sensitive SnO2/additive-composites
exhibits four different profiles to an exposed gas,
which in fact can enhance the gas identification
capability compared to conventional mono-sensors
operated at isothermal mode. For example, in
consideration of propene and CO response, the
different sensing materials show completely different
CTP shapes (Fig. 7, 1st and 2nd column). In case of
pyrolysis gas, although the profile shapes of all
materials look similar with two distinctive
conductance peaks at the temperature rising and
dropping regions, the conductance peak positions are
dependent on temperature. Also, by the CTP-features
of each sensor the pyrolysis gases can be clearly
distinguished from the model gases (Fig. 4, rows).
4.2. Data Analysis
To demonstrate the performance of the sensor
system and the procedure SimSens, it was assumed
that besides pyrolysis gases propene and CO as
interfering atmospheres may occur. The goal is to
distinguish between the different gas atmospheres,
this means to recognize whether an unknown gas
sample is one of the considered gases. In case of
identification, additionally, the concentration of the
gas has to be determined. The analysis model is
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 74-79
based on the CTP-data of all four gas sensitive
layers of the chip (Fig. 4), which are simply
linked together to one “extended CTP” (ECTP) as
visualized in Figs. 5-7.
Fig. 4. Matrix representation of the comprehensive sensor responses: Conductance over time profiles (CTP) of the four
sensing layers (in columns) when exposed to various concentration of propene, CO and pyrolysis gas (in rows). The propene
and CO concentrations are given in ppm and the pyro gas concentration is given in percentages in relation to the dilution
flow used, (see caption of Fig. 3 for more details).
For the calibration model of pyrolysis gas, the
CTPs of pyrolysis gases at 30 %, 40 % and 60 %
relative gas concentrations (Fig. 3 and Fig. 4) were
used and for the calibration model of propene and
CO, the CTPs were taken at the corresponding
concentrations 125 ppm, 250 ppm and 500 ppm.
To test the capability for identifying the gas
component/mixture and to determine the associated
concentrations, three gas samples were measured and
evaluated by SimSens. These are pyrolysis gas at
50 % relative concentration, propene and CO
at 375 ppm.
The following figures show that the pyrolysis gas
sample at 50 % relative concentration could be
clearly recognized as a pyrolysis gas. This is due to
the fact that in Fig. 5 the difference between
measured CTP and calculated CTP on the basis of the
pyrolysis calibration model is very small. In Fig. 6
and in Fig. 7, the differences between measured CTP
and calculated CTP based on the calibration model of
propene, respectively CO, are much higher.
Analogous results were obtained when evaluating the
propene sample and the CO sample.
Of course, the decision of identification is not
based on a visual impression. For substance
identification, a difference value D is calculated as a
measure of the difference between measured CTP
and calculated CTP.
77
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 74-79
D is the relative sum of quadratic differences of
every sample point of the measured CTP and the
theoretical CTP. If the difference value is smaller
than a so-called decision threshold value eps, the
sample under consideration is identified as the
associated gas. In this investigation, the decision
threshold value was set to eps=4.e-01.
In Table 1, only the red bold numbers are smaller
than the decision threshold value. Therefore, the
pyrolysis sample could be identified as a pyrolysis
gas, the propene sample as a propene gas and the CO
sample as a CO gas.
After identifying the gas samples, SimSens
calculates the associated gas concentrations. The
evaluation results are given in Table 2.
Fig. 5. Measured ECTP of pyrolysis sample at 50 %
dilution and associated calculated ECTP based on the
pyrolysis calibration model.
Table 1. Difference values between measured CTPs
and theoretical CTPs.
Pyrolysis
50 %
Propene
375 ppm
CO
375 ppm
Pyrolysis
Model
Propene
Model
CO Model
2.2e-03
6.6e+02
8.7e+06
9.9e+01
1.3e-02
1.5e+05
1.0e+00
4.3e+02
1.7e-01
Table 2. Comparison of dosed and analyzed
concentration values and relative deviation. About the
meaning of concentrations in % see Fig. 3.
Sample
Fig. 6. Measured ECTP of pyrolysis sample at 50 %
dilution and associated calculated CTP based on the
propene calibration model.
Pyrolysis
50 %
Propene
375 ppm
CO
375 ppm
Dosed
Conc.
Calculated
Conc.
Relative
Error
50 %
47,6 %
4,7 %
375 ppm
388,8 ppm
3,7 %
375 ppm
379,6 ppm
1,2 %
The concentration values estimated by the
calibration model deviate from the experimentally
adjusted values by less than 5 % in all cases.
5. Conclusions and Outlook
Fig. 7. Measured ECTP of pyrolysis sample at 50 %
dilution and associated calculated CTP based on the CO
calibration model.
78
The thermo-cyclic operation of MOG array
combined with simultaneous numerical analysis of
the CTPs has been shown to be an elegant way for
identification of gas mixtures. In this study, the
pyrolysis gases emitted by heated PVC-based
insulation materials are recognized by well
reproducible CTPs, even at temperatures where no
color change of the sample material could be
observed. These CTPs are well distinguishable from
those obtained for two model gases, CO and propene.
The results look promising considering the aim of
early fire detection with high sensitivity.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 74-79
The acquired CTPs were numerically analyzed
employing the SimSens algorithm and the results
showed very good identification capabilities and
concentration estimation accuracy, which can lead to
better incident identification and a very sensitive,
more robust detection with a low false alarm rate.
In case of field applications, gas detection
experiments for fire or any other dangerous gases
using MOG sensors may have to be conducted in a
real environment, where indefinite and unspecific but
typical gas mixtures are present. In such cases, the
numerical analysis of data using SimSens of a
thermo-cyclically operated MOG-array will be of
great advantage because the CTPs of four sensitive
layers provide numerous gas specific features. To
enable these advanced field applications, a
microprocessor-based sensor system has to be
devised, featuring the necessary means.
[4].
[5].
[6].
[7].
Acknowledgements
The authors would like to thank German
Federal Ministry of Education and Research
(BMBF) for financial support of this work
(Project Nr. 16N12262).
[8].
[9].
References
[1]. D. Gutmacher, C. Foelmli, W. Vollenweider,
U. Hoefer, J. Wöllenstein, Comparison of gas sensor
technologies for fire gas detection, Procedia
Engineering, Vol. 25, 2011, pp. 1121-1124.
[2]. G. Korotchenkov, V. Brynzari, S. Dmitriev, SnO2
thin film gas sensors for fire-alarm systems, Sensors
and Actuators B: Chemical, Vol. 54, Issue 3, 1999,
pp. 191-196.
[3]. R. Seifert, H. B. Keller, N. Illyaskutty, J. Knoblauch,
H. Kohler, Early Detection of Emissions Preceding
Fires from Overloaded Electric Cables: Approach
[10].
[11].
with Thermo-Cyclically Operated MOG Sensor
Arrays and Numerical Signal Analysis, in
Proceedings of the Sixth International Conference on
Sensor Device Technologies and Applications
(SENSORDEVICES ‘15), pp. 91-96.
N. Illyaskutty, J. Knoblauch, M. Schwotzer,
H. Kohler, Thermally modulated multi sensor arrays
of SnO2/additive/electrode combinations for gas
identification, Sensors and Actuators B: Chemical,
217, 2015, pp. 2-12.
K. Frank, V. Magapu, V. Schindler, H. Kohler,
H. B. Keller, R. Seifert, Chemical analysis with tin
oxide gas sensors: choice of additives, method of
operation and analysis of numerical signal, Sensor
Letters, Vol. 6, No. 6, 2008, pp. 908-911.
J. Knoblauch, N. Illyaskutty, H. Kohler, Early
detection of fires in electrical installations by
thermally modulated SnO2/additive-multi sensor
arrays, Sensors and Actuators B: Chemical, 217,
2015, pp. 36-40.
J. Knoblauch, et al., Sensor system applying
thermally modulated MOG for early detection of fires
in electrical cabinets, in Proceedings der 15.
Internationale
Tagung
über
Automatische
Brandentdeckung (AUBE 2014), Duisburg, Vol. II,
14-16 October 2014, pp. 105-112.
H. Kohler, R. Becker, N. Link, J. Röber, Apparatus
and process for identifying and/or determining the
concentration of at least one gas component,
Patent No. EP 0 829 718 B1, 2004.
H. B. Keller, R. Seifert, H. Kohler, SimSens – a New
Mathematical Procedure for Simultaneous Analysis
of Gases with Resistive Gas Sensors, Sensors &
Actuators B: Chemical, 2015, pp. 203-207.
R. Seifert, H. B. Keller, K. Frank, H. Kohler,
ProSens - an Efficient Mathematical Procedure for
Calibration and Evaluation of Tin Oxide Gas Sensor
Data, Sensor Letters, Vol. 9, No. 1, 2011, pp. 7-10.
R. Seifert, H. B. Keller, Verfahren zur Klassifikation
und zur Bestimmung der Einzelkonzentrationen eines
Stoffgemisches (Procedure for Classification and
Determination of the Concentration of the
Components of a Gas Mixture), Patentschrift DE10
2004 057 350.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
79
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 80-85
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Analysis of the Planar Electrode Morphology Applied
to Zeolite Based Chemical Sensors
1
1
Luiz Eduardo BENTO RIBEIRO, 2 Glaucio Pedro de ALCÂNTARA,
2
Cid Marcos GONÇALVES ANDRADE, 1 Fabiano FRUETT
School of Electrical and Computer Engineering of State University of Campinas, Campinas, Brazil
2
Department of Chemical Engineering of State University of Maringá, Paraná, Brazil
E-mail: luizebr@dsif.fee.unicamp.br, fabiano@dsif.fee.unicamp.br
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: In order to improve the zeolite chemical sensors sensibility, three different electrode structures are
compared in this work: conventional interdigitated electrodes (IDE), serpentine electrodes (SRE) and ringshaped electrodes (RSE). Simulation results and experimental characterization of these electrodes showed that
ring-shaped electrodes have a slight capacitance increase per unit of area. When used as a zeolite chemical
sensor, the ring-shaped electrodes prove to be more suitable since they take advantage of a better usage of the
drop distribution and a better capacitance per area ratio. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Interdigitated electrodes, Ring-shaped electrodes, Serpentine electrodes, Chemical sensors,
Electrode structure, Interdigital electrodes, Zeolite.
1. Introduction
IDE as capacitive microstructures have been
widely used in electronics applications such as
surface acoustic wave devices [1-2], thin-film
acoustic electronic transducers [3], tunable devices
[4], dielectric spectroscopy [5], dielectric studies on
thin films [6], humidity and chemical sensors [7-9],
etc. They possess interesting features, such as signal
strength control by changing its dimensions, multiple
physical effects in the same structure, simplified
modeling in two dimensions when the aspect ratio of
the electrode length to the space wavelength IDE is
large, and can be used in a wide range of frequencies
[10]. Moreover, it can be manufactured using inert
substrates with multiple materials with different
fabrication processes, or even microfluidic
compatible. Capacitive microstructures used as
chemical sensor, typically have one sensitive layer
deposited over the electrodes. Polymers have been
80
used for organic vapor sensing because they exhibit
rapid reversible vapor sorption and are easy to apply
as thin or thick films by a variety of techniques [10].
The polymer layer can be chosen according to its
affinity to a particular molecule or set of molecules
one wishes to detect. If several sensors with different
polymer layers are used to make a sensor array it is
then possible to evaluate complex organic vapor
samples. These sensor arrays can be part of a socalled electronic nose. Another possibility is the use
of flexible substrates such as sensing element.
As an example the use of polyimide [11] and plastic
foil [12].
Interdigitated electrodes analytical characterization have been received many efforts in order to
improve their capacitance by exploring their
geometrical parameters [13]. Igreja et al developed a
theoretical model of capacitance for this structure
[14]. These capacitors have also been simulated using
different tools. They have typically been adopted as a
http://www.sensorsportal.com/HTML/DIGEST/P_2739.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 80-85
sensor because of the low-power consumption of
capacitive transduction mechanism and being
compact with a large contact area and relative ease of
manufacture. Some authors have analyzed other
morphologies such as serpentine, spiral and
concentric rings electrodes, in order to improve
design performance. Moreover, aside from the
different morphologies, several strategies have been
employed to increase and improve its ability as a
sensor, for example the deposition of a more sensitive
layer on the electrodes. The sensor can also have its
selectivity improved by the deposition of compounds
such as zeolite for detection of air humidity or
gases [15].
In this work, we study different geometries of
electrodes instead of the materials used to maximize
capacitance per area. We can thus improve the
sensitivity of capacitive sensors by increasing the
total capacitance of capacitive microstructures. Here,
we compare the conventional IDE, the serpentine
electrodes (SRE) and ring-shaped electrodes (RSE).
These electrodes are compared in detail as generic
capacitive transducers by numerical simulations. In
these simulations, the geometric parameters that most
influence on the total capacitance are shown. We
measure the capacitance of these thin-film electrodes
made of titanium and gold on a glass substrate. After
that, we compare the experimental results with the
theoretical analysis, including simulations. Finally,
we compared the influence of such geometries under
a LTA structured zeolite layer.
In Section 2, the different electrodes designs and
their geometrical parameters are shown. They are the
main data to the numerical simulation, which is
explained in the Section 3. The fabrication process is
described in Section 4, while the zeolite properties
and deposition method are described in Section 5.
Subsequently, the values of electrical and geometrical
characterization are shown in Section 6. The results
are compared and discussed in Section 7. Finally, the
conclusion is presented in Section 8.
2. Methods and Materials
The layouts of some interdigitated electrode pairs
are shown in Fig. 1. Although they were designed
with the same area, each structure has a particular
capacitance. Therefore, they have different
sensitivities as capacitive transducer only because of
their different geometric structure.
Fig. 1. Ring-shape electrode (a); serpentine electrode (b); and interdigitated electrode (c) structures and used parameters.
We simulate the capacitive structures of IDE,
SRE and RSE with the same area, same substrate and
the same top layer, calculating and comparing the
capacitance of each structure using the multiphysics
numerical simulator: COMSOL Multiphysics
(Comsol, Inc., Stockholm, Sweden). This software,
based on partial differential equations with the finite
element method has been used in the literature to
calculate distribution of potential field in similar
structures. Fig. 2 depicts the simulated structure
showing the 3D multislice view of the electrical
potential distribution around RSE with 20 fingers.
Multiple simulations were performed to compare
dimensions that are relevant to increase the difference
between the capacitances. The main geometrical
parameters analyzed are the electrode length, the gap
between the electrodes, the electrode width (always
kept as same value as gap), the thickness of
electrodes and the number of fingers.
Fig. 2. Distribution of potential field
in a 3D RSE structure.
81
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 80-85
The electrical properties of the substrate and the
top layer are also included in the simulator. Because
of the differences between the vertical and horizontal
dimensions (millimeters to micrometers) critical to
reduce the time simulation, we have applied the
extrapolation method, presented in the Rivadeneyra
work [16]. The thickness has been set to 20 % of
electrode width.
3. Numerical Simulation
We have performed different numerical
simulations comparing the calculated DC capacitance
of IDE, SRE and RSE structures always with the
same distance between digits, surface area and same
materials and manufacturing process. There are
multiple geometrical factors that can be varied in the
simulations, but for the sake of clarity, we focus on
some of them, keeping fixed the rest. Since the goal
is maintaining reduced the surface area of electrodes,
we define different widths and distances between
digits of the structures given that the lowest safe
distance to our manufacturing process is 10 µm.
Therefore, we have used the number, length and
width of fingers and the thickness of the deposited
metal film as simulation parameters. Table 1 is a
summary of these geometric parameters used for
simulation. Remember that fabricated structures for
each type of electrode, SRE, IDE and RSE, was made
with three distances between digits (10 µm, 20 µm
and 50 µm) but the number of electrodes and the
thickness of the metal thin film was kept constant.
an increase of capacitance of 30 fF between the RSE
and the IDE. Whereas electrodes are used as
capacitive transducers, the fact of the geometrical
factor RSE be higher than in other structures means
that the sensitivity of the sensor will always be larger
using the same area. Furthermore, the capacitance
and the increase in sensitivity will increase
proportionally. After that, we calculate the numerical
capacitances in order to improve the performance of
RSE.
Influence of the width of the electrodes (10 to
50 μm) with aspect ratio of the electrode/thickness of
the thin film of 5/1 was evaluated for the RSE, SRE
and IDE with 20 digits. The results showed a slope of
161.1 fF for each micrometer added to the width of
the electrodes to the RSE, a slope of 160.7 fF/µm for
the SRE and 159.3 fF/µm for the IDE. Remembering
that the surface area of each structure was kept the
same for each electrode width.
Electrode length contribution (400 to 800 µm)
was been evaluated for IDE and SRE electrode with
20 fingers and 10 µm of finger width. Results showed
the slope of 4.99 fF/µm for the SRE and 4.74 fF for
the IDE.
We also have performed the contribution of finger
numbers (n) in each structure. The results are
presented in Fig. 3, as this contribution is nonlinear.
Table 1. Geometrical Parameters
of Planar Electrodes Pairs.
Parameter
s
Electrode type
RSE
SRE
IDE
w
1050 µm
1050 µm
1050 µm
g
1050 µm
1050 µm
1050 µm
t
2-10 µm
2-10 µm
2-10 µm
600800 µm
600800 µm
600800 µm
600800 µm
l
d
-
r
-
-
0.63.02 mm
n
20-50
20-50
20-50
Description
Finger
width
Gap
between
fingers
Metal film
thickness
Structure
length
Structure
width
Electrode
external
radium
Number of
electrodes
The first important result was that the capacitance
to RSE was the highest in all comparisons. For
example, simulating the capacitor with w = 10 µm,
t = 2 µm and 20 electrodes we obtain 2.47 pF for
RSE while the capacitance for the SRE was 2.45 pF
and the capacitance for IDE was 2.44 pF. This means
82
Fig. 3. Capacitance vs. number of fingers of RSE,
SRE and IDE.
4. Fabrication Process
The electrodes fabrication started with the
production of the electrode masks where the IDE,
SRE and RSE were designed using high-resolution
direct writing photolithography with a laser beam. In
the next step, a photoresist layer was deposited onto a
square optical glass plate (60 mm side Kodak 1A
High Resolution Glass) and patterned by
conventional ultraviolet light (UV) photolithographic
method following the electrodes masks. The UV
exposures were carried out in a MJB-3 UV300
contact mask aligner (Karl-Suss, Garching,
Germany). Titanium-gold (TiAu) thin films,
deposited with a Leybold Univex 300 ebeam
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 80-85
evaporator (Cologne, Germany), were used as
electrode materials. After thin-film depositions, by
the lift-off technic, the devices were immersed in
acetone to remove the photoresist layer and excess of
metal, leaving the patterned electrodes on the glass
surface [7].
5. Characterization
For the experimental characterization the nominal
capacitance target was 2 pF, taking into account the
minimum distance between electrodes of 10 µm.
Structures with electrode width of 50µm have a total
area of 28.26 mm², and their dimensions are
g = 50 µm, l = 5.95 mm, d = 4.75 mm and
r = 3.02 mm. The area is 4.52 mm² for structures with
w = 20 µm, g = 20 µm, l = 2.38 mm, d = 1.90 mm
and r = 1.21 mm. Structures with w = 10 µm,
minimum secure dimension to our fabrication
process, the area is 1.13 mm² (g = 10 µm,
l = 1.19 mm, d = 0. 95 mm and r = 0.60 mm).
The geometric inspection of the fabricated
structures was performed using the Olympus BX60
microscope as shown in Fig. 4.
Fig. 4. Fabricated RSE (a); SRE (b); and IDE (c) structures.
Electrical characterization has been carried out by
measuring their capacitance and parallel resistance
using the four-wire measurement with a HP 4284A
impedance analyzer (Agilent Tech., Santa Clara, CA,
USA). The applied excitation voltage was
VAC = 1 V and VDC = 0 V. The frequency sweep of
analysis was from 100 Hz to 1 MHz. The four-point
method was used to minimize the contribution of
stray capacitances.
a cubic shape and 24 tetrahedra in an octahedron
shape. The framework is formed by the connection of
every cube corner to the octahedrons creating caged
cavities in-between. Zeolite A is a low-silica based
zeolite and therefore has a high cation concentration
to compensate for the reduction in charge [17]. The
Si/Al ratio is around 1 making this type of zeolite a
fairly hydrophilic material. The pore size is around
4 angstroms, the cage diameter is 11.5 angstroms and
the pore volume is around 0.30 cc/g [18].
6. Zeolite Deposition
The zeolite layers were prepared using microdrop deposition. A top view of zeolite over electrodes
can be observed in the Fig. 5. The process first step
involves suspension of the crystals in alcohol. We
placed 0.1 g of the zeolite crystals with 10 ml of
isopropyl alcohol into a vessel. After that, the vessel
was placed into a sonic bath for 10 minutes. The bath
make the suspension homogenous, allowing
agglomerations to sink to the bottom of the vessel
leaving single crystals dispersed.
Using a micro-syringe, a single droplet (10 µl) of
the solution was carefully deposited on to the
electrodes under a low magnification microscope.
After 10 minutes, the isopropyl alcohol evaporated
and the crystals adhered to the surface by
electrostatic forces. The evaporation of this droplet
takes place leaving a zeolite deposit on the
electrodes. The process was repeated until around
100 µl was achieved (usually repeated 10 times) [15].
The used LTA structured zeolite (zeolite A) is
built up of two types of arrangements; 8 tetrahedra in
Fig. 5. Microstructure during the zeolite deposition process.
7. Experimental Results
Measured and the simulated values of the
capacitances are in agreement, as shown in Table 2.
83
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 80-85
The measurements were carried out in room
conditions (50 % RH and 25ºC) at 100 kHz.
Table 2. Numerical vs. Experimental Capacitance.
Finger
width (µm)
10
20
50
10
20
50
Numerical simulation (pF)
RSE
SRE
IDE
2.4667
2.4446
2.4362
4.0906
4.0653
4.0345
8.9122
8.8723
8.8091
Experimental measurement (pF)
RSE
SRE
IDE
2.2332
2.2141
2.1917
4.2949
4.2667
4.2412
10.5385
9.9979
9.7105
Capacitance measured values have a maximum of
12 % discrepancy compared to the simulated results.
This difference is due to the capacitance of contacts
pads that were not included in the simulation and the
variability of the fabrication process.
After the zeolite deposition, we also measured the
percentage of the capacitance increment when the
structures are with and without the zeolite layer. For
the ring-shape electrode the capacitance with zeolite
layer incremented 139 % (2.39 times) of that without
zeolite. In case of the interdigitated electrodes, the
capacitance with zeolite layer incremented 44 %
(1.44 times) in comparison of that without zeolite.
This preliminary study indicates that the
sensibility of a zeolite chemical sensor can be also
improved by changing the capacitive microstructure.
However, when in contact with the liquid or gas
under analysis, the total impedance must be modeled
to comprise effects such as double layer capacitance
and interfacial polarization phenomenon.
8. Conclusion
A comparison between the capacitances under
different geometric parameters of RSE, SRE and the
conventional IDE was shown on this work.
Numerical simulations of the capacitance have been
carried out to calculate the differences between them
due exclusively to their geometrical characteristics at
constant area. In these conditions, we have shown a
slight capacitance increase for the ring-shaped
electrodes against the serpentine electrodes and the
conventional interdigitated electrodes.
We have validated the numerical results by
experimental characterization of ring-shaped,
serpentine and interdigitated structures capacitance.
Experimental results verified the capacitive
differences between the three structures with same
area, for 10 µm, 20 µm and 50 µm of finger width.
Moreover, ring-shape electrode presents a
geometrical morphology that allows the better usage
of its area. It is a promising base electrode mainly
when used as sensor in application that involves
84
dripping of substances under analysis or dripping of
selective/sensitive substances like zeolite.
While the capacitance of the interdigitated
electrodes with zeolite layer is 1.44 times greater, the
ring-shape electrodes is 2.39 times greater. This
preliminary study with zeolite deposition over the
electrodes show us that the total capacitance can be
further enhanced by the electrode geometry.
Acknowledgements
The authors acknowledge the Center for
Semiconductor Components (CCS), the Multi-User
Laboratory of IFGW (LAMULT), the Device
Research Laboratory (LPD) and the Brazilian
Synchrotron Light Laboratory (LNLS). Financial
support for this project was provided by the National
Council
for
Scientific
and
Technological
Development (CNPq) and the São Paulo State
Research Support Foundation (FAPESP) inside the
National Institute for Science and Technology of
Micro and Nanoelectronic Systems (INCT
NAMITEC) project.
References
[1]. M. I. Rocha-Gaso, C. March-Iborra, Á. MontoyaBaides, A. Arnau-Vives, Surface generated acoustic
wave biosensors for the detection of pathogens: A
review, Sensors, Vol. 9, No. 7, 2009, pp. 5740-5769.
[2]. N. A. Ramli, A. N. Nordin, Design and modeling of
MEMS SAW resonator on Lithium Niobate, in
Proceedings of the 4th International Conference on
Mechatronics (ICOM’11), 2011, pp. 1-4.
[3]. M. W. Kim, Y. H. Song, J. B. Yoon, Modeling,
fabrication and demonstration of a rib-type cantilever
switch with an extended gate electrode, Journal of
Micromechanics and Microengineering, Vol. 21,
2011, 115009.
[4]. R. Mahameed, A. M. El-Tanani, G. M. Rebeiz,
A zipper RF MEMS tunable capacitor with
interdigitated RF and actuation electrodes, Journal of
Micromechanics and Microengineering, Vol. 20,
No. 3, 2010, 035014.
[5]. Heileman, Khalil, Jamal Daoud, Maryam Tabrizian,
Dielectric spectroscopy as a viable biosensing tool for
cell and tissue characterization and analysis,
Biosensors and Bioelectronics, Vol. 49, 2013,
pp. 348-359.
[6]. Z. Chen, A. Sepúlveda, M. D. Ediger, R. Richert,
Dielectric spectroscopy of thin films by dual-channel
impedance
measurements
on
differential
interdigitated electrode arrays, The European
Physical Journal B - Condensed Matter and Complex
Systems, Vol. 85, No. 8, 2012, pp. 1-5.
[7]. L. E. B. Ribeiro, M. H. Piazzetta, A. L. Gobbi,
J. S. Costa, J. A. F. da Silva, and F. Fruett,
Fabrication and Characterization of an Impedance
Micro-Bridge for Lab-on-a-Chip, ECS Transactions,
Vol. 31, No. 1, 2010, pp. 155-163.
[8]. Y. Kim, B. Jung, H. Lee, H. Kim, K. Lee, H. Park,
Capacitive humidity sensor design based on anodic
aluminum oxide, Sensors and Actuators B: Chemical,
Vol. 141, No. 2, 2009, pp. 441-446.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 80-85
[9]. A. Rivadeneyra, J. Fernández-Salmerón, J. Banqueri,
J. A. López-Villanueva, L. F. Capitan-Vallvey,
A. J. Palma, A novel electrode structure compared
with interdigitated electrodes as capacitive sensor,
Sensors and Actuators B: Chemical, Vol. 204, 2014,
pp. 552-560.
[10]. J. W. Grate, G. C. Frye, Acoustic wave sensors,
Sensors, Vol. 2, Issue 1, 1996, pp. 37-83.
[11]. J. Virtanen, L. Ukkonen, T. Bjorninen,
L. Sydanheimo, Printed humidity sensor for UHF
RFID systems, in Proceedings of the IEEE Sensors
Applications
Symposium
(SAS’10),
2010,
pp. 269-272.
[12]. D. Briand, A. Oprea, J. Courbat, N. Bârsan, Making
environmental sensors on plastic foil, Materials
Today, Vol. 14, No. 9, 2011, pp. 416-423.
[13]. L. E. B. Ribeiro, F. Fruett, Analysis of the Planar
Electrode Morphology for Capacitive Chemical
Sensors, in Proceedings of the 6th International
Conference on Sensor Device Technologies and
Applications
Analysis
(SENSORDEVICES’15),
Venice, Italy, 23-28 August 2015, pp. 179-182.
[14]. R. Igreja, C. J. Dias, Analytical evaluation of the
interdigital electrodes capacitance for a multi-layered
structure, Sensors and Actuators A: Physical,
Vol. 112, No. 2-3, 2004, pp. 291-301.
[15]. M. Urbriztondo, I. Pellejero, A. Rodriguez, M. P.
Pina, J. Santamaria, Zeolite-coated interdigital
capacitors for humidity sensing, Sensors and
Actuators B: Chemical, Vol. 157, Issue 1, 2011,
pp. 450-459.
[16]. A. Rivadeneyra, J. Fernández-Salmerón, M. Agudo,
J. A. López-Villanueva, L. F. Capitan-Vallvey,
A. J. Palma, Design and characterization of a low
thermal drift capacitive humidity sensor by inkjetprinting, Sensors and Actuators B: Chemical,
Vol. 195, 2014, pp. 123-131.
[17]. Scott M. Auerbach, Kathleen A. Carrado, Prabir K.
Dutta, Handbook of Zeolite Science and Technology,
Marcel Dekker Inc., New York, Basel, 2003.
[18]. D. W. Breack, Zeolite molecular sieves-structure,
chemistry and use, Robert E. Krieger Publishing
Company, Malabar, Florida, 1984.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
85
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
An Empirical Study for Quantification of Carcinogenic
Formaldehyde by Integrating a Probabilistic Framework
with Spike Latency Patterns in an Electronic Nose
1
1
2
Muhammad HASSAN, 1, 2 Amine BERMAK, 3 Amine Ait Si ALI
and 3 Abbes AMIRA
School of Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong
College of Science and Engineering, Hamad bin Khalifa University, Education City, Doha, Qatar
3
College of Engineering, Qatar University, Doha, Qatar
1
Tel.: 00852 23588526
E-mail: mhassan@connect.ust.hk
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Recently, exposure to formaldehyde has appeared as a major concern since it has been listed as a human
carcinogen. Conventional methods for its long-term monitoring are not feasible due to their high operational cost,
long analysis time and the requirement of specialized equipment and staff. In this paper, we develop an electronic
nose, containing an array of commercially available low cost Figaro gas sensors, to support autonomous and longterm monitoring of formaldehyde. Hardware friendly gas quantification without requiring any manual tuning of
parameters is the major challenge with the electronic nose. We handle this challenge by treating it as a
classification problem because data acquisition at continuously varying concentrations may incur large expense
and a great deal of time. Instead, twenty different concentrations of formaldehyde with 0.25 ppm increment step
in the target range between 0.25 to 5 ppm, spanning commonly found formaldehyde levels in indoor and outdoor
environments, are input to obtain its signatures in order to quantify/classify its levels within this target range. A
computationally efficient bio-inspired spike latency coding scheme, in which spike latencies corresponding to
sensitivity patterns of the sensors in the array shift with the change in concentration, is targeted for this purpose.
However, stochastic variability in the spike latency patterns, corresponding to repeated exposure to the same
formaldehyde concentration level, is observed. We target two Bayesian inference methods, namely multivariate
Bayesian and naive Bayes, to express the uncertainty about the spike latency patterns in terms of a probability
encoding framework. These methods do not require any manual tuning of parameters, in contrast to other state of
the art methods. A best performance of 95.75 % is achieved with the naive Bayes method on the experimentally
obtained data set of formaldehyde. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Carcinogenic formaldehyde, Sensor array, Spike latency pattern, Bayesian inference.
1. Introduction
Formaldehyde (CH2O) is one of the most
ubiquitous and reactive aldehydes in the environment.
It is a colorless and strong-smelling chemical, which
86
is widely used in building and furniture construction
materials [1]. Building residents may be exposed to
CH2O gas when it is emitted from materials containing
this chemical upon thermal or chemical
decomposition. Unvented fuel burning appliances and
http://www.sensorsportal.com/HTML/DIGEST/P_2740.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
tobacco smoke may also cause CH2O inhalation. One
scientific study reveals that overall CH2O emission
from different building sources may exceed 2 parts per
million (ppm) [2]. Short-term exposure to CH2O may
cause skin, nasal, throat, and eye irritation. The United
States Occupational Safety and Health Administration
(OSHA) has set its short-term exposure limit
(15-30 minutes) at 2 ppm and permissible exposure
limit (up to 8 hours) to 0.75 ppm [2]. Precautionary
measures are required for healthy living when the
CH2O concentration reaches one half of the
permissible exposure limit.
Formaldehyde received great attention in 1980,
when its carcinogenicity was reported in rats and mice
after its long-term inhalation [3]. Since that time, its
carcinogenicity has also been studied in humans. In
2004, the International Agency for Research on
Cancer (IARC) classified CH2O as a human
carcinogen based on sufficient evidence that its longterm exposure may cause nasopharyngeal cancer and
leukemia in humans [4]. A recent study reported
increasing concentration levels of CH2O in the indoor
environment of urban areas [5]. This alarming
situation highlights the importance of formaldehyde
monitoring with a low cost and robust solution on a
long-term basis for healthy living. Unfortunately,
traditional methods of gas chromatography and
spectro-fluorimetry [5-7] cannot be used for the longterm monitoring of formaldehyde because specialized
equipment and staff are required for the analysis of air
samples collected from the area being monitored.
Moreover, the cost and analysis time associated with
these methods is very high.
Electronic nose systems, containing an array of gas
sensors, emerged as a successful platform for the fast
identification of gases in the last two decades, and they
are targeted at many applications like food quality
checking [8], diseases diagnosis [9], bacteria
identification [10], environmental monitoring [11],
beverages classification [12], paper quality inspection
[13] and identification of health endangering indoor
gases [14]. In this paper, we develop an electronic nose
system, containing an array of six commercially
available Figaro gas sensors, to quantify CH2O
concentration.
Motivated by the recent experimental findings in
the field of neuroscience which report a logarithmic
relationship between odor concentration and the spike
latency of mitral cells [15], a logarithmic time domain
scheme has been previously presented for gas
classification by translating a sensor array response
into a spike latency pattern [16]. Hardware friendly
rank order based classifiers have then been developed
for gas identification by using this technique [16-18].
In these classifiers, the temporal sequence of spikes is
utilized to distinguish gases. We adopt this scheme to
retrieve concentration information by utilizing the
shifts in the spike latencies with the change in
concentration. However, there is no straightforward
relationship between the shift in the spike latency and
the formaldehyde concentration as there is in the rank
order based classifiers, where the change in relative
times between spikes does not change the
classification performance as long as their temporal
order is not changed. Fig. 1 demonstrates the basic
concept of this approach. The figure shows that the
spike latency of each sensor is changed with the
change in concentration but the arbitrary temporal
sequence remains fixed.
Fig. 1. Demonstration of change in spike latency of each
sensor with the change in concentration.
Generally, gas sensors exhibit randomness in their
responses due to inherent issues in this technology
(such as drift, aging, change in operating conditions
etc.); and as a result, stochastic variability is observed
in the latency patterns, which makes gas quantification
more challenging.
In this paper, we formulate this problem as a
classification problem because the use of discrete
values of concentration, spanning regular intervals
within the target range of interest, is the most feasible
option for obtaining an experimental data set in order
to save time and cost. Data corresponding to each
discrete value of concentration is treated as single
class data.
A Bayesian inference approach [19] is targeted to
deal with the randomness in the latency patterns
because this provides an analytical solution and no
manual tuning of parameters is required. This
approach has been successfully used in neuroscience
to build computational theories for perception and
action [20]. There are two major steps in this approach.
The first step is to learn the probability encoding
model or the tuning curve for the spike latency patterns
at each predefined concentration value of CH2O from
the experimental data obtained through the sensor
array.
The second step is to use a Bayesian decoding
model to estimate the formaldehyde concentration for
a newly test latency pattern by using the learned
probability encoding model. Multivariate Bayesian or
quadratic discriminant analysis (QDA) and a naive
Bayes classifier (NBC) are typically used under this
framework when there are no shared parameters
between the classes. Multivariate Bayesian assumes a
relationship between features, while the naive Bayes
classifier considers independence among features. The
performance of both of these methods, along with
those of other state of the art approaches, is evaluated
by acquiring CH2O data at twenty different
concentration values, spanning from 0.25 to 5 ppm.
87
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
The paper is organized as follows. Section 2
explains the probabilistic inference approach for
CH2O concentration estimation. Next, Section 3
describes the experimental setup for data acquisition
and evaluates the performance of the Bayesian
inference methods along with other state of the art
methods. Finally, the conclusion is drafted in
Section 4.
2. Probabilistic Framework
A logarithmic time-domain encoding scheme has
been used for gas identification in rank order based
classifiers [16-18]. In these classifiers, the spike
latency li (expressed in arbitrary units) of the i-th
sensor corresponding to a target gas is represented as
=
,
intensity. With a particular model, parameterized by a
vector ɵ, we can use maximum likelihood (ML) to
obtain the optimal estimate ɵ̂ for which the latency
patterns are most likely:
= argmax ( | , )
In the multivariate Bayesian method [21], we
assume that latency patterns follow multivariate
Gaussian distribution N( , Σ) and hence second order
statistics, that is, mean and covariance, are sufficient
to learn this distribution. We use ML to estimate these
parameters from the sensor array measurements. If m
is the ML estimate of the true mean ( ) and S is the
ML estimate of the true covariance matrix (Σ), then the
conditional density of the latency patterns with a given
CH2O odor intensity class cj is given by
(1)
1
=
where xi denotes the sensitivity of the i-th sensor and
ai is a sensor dependent parameter which is extracted
through linear regression between the average log
sensitivity of the sensors across the array as an
explanatory variable and the sensitivity of the i-th
sensor as an output variable. The resultant spike
latency patterns carry information about the gas
identity and its concentration. In rank order based
classifiers [16-18], the temporal sequence of spikes is
used for gas identification. In this paper, we utilize the
change in spike latency of each sensor to estimate the
concentration level of CH2O.
The potential challenge with this scheme is that gas
sensors usually exhibit randomness in their responses,
which results in stochastic variability in the latency
patterns. We use a probabilistic inference approach to
retrieve concentration information from the random
latency patterns.
The main objective of using probabilistic inference
is to find the most probable concentration class or level
of the newly test latency pattern by learning the
distribution of latency patterns corresponding to each
concentration level from the available measurements
taken with the electronic nose. Let us consider the
following notations for this probabilistic inference
problem: Suppose we have a set of concentrations
c={c1, c1, . . ., cn}, and the experimentally obtained
latency pattern l={l1, l2, . . ., ld}, where d denotes the
total number of sensors and n represents the total
number of concentration classes or levels. For gas
quantification algorithms, li corresponds to one single
feature and l to a d-dimensional feature vector.
Probabilistic inference is a two-step process [20].
In the first step, we learn a model fitting that captures
the mapping from l to c from the available sensor array
measurements. In the second step, we use Bayesian
decoding to estimate the concentration level cj for the
newly observed test latency pattern.
In order to learn the model fitting, we need to know
the distribution or probability encoding model of the
latency patterns conditioned on the CH2O odor
88
(2)
exp −
(2 )
1
2
−
−
(3)
For a new latency pattern, Bayesian decoding is
used to compute the posterior probability p(cj|l) of
each concentration class cj in the set with a given
latency pattern l. It can be described as
=
()
,
(4)
where p(cj) is the prior probability of the j-th class. In
our case, we consider the same prior for each class.
The denominator term p(l) is for normalization, and
the same for all classes, and hence can be ignored. As
a result, the posterior probability (p(cj|l)) only depends
on the likelihood term (p(l| cj)).
The multivariate Bayesian approach considers
correlations between latencies, and hence involves
substantial computations and requires large storage for
estimated parameters, i.e., n ⨯ (d ⨯ d) for the
covariance matrix and (n ⨯ d) for the mean vector for
n classes. Moreover, the sample covariance matrix is
considered as a poor estimate of the true covariance
matrix in the case when the available measurements
and feature vector size are comparable or their ratio is
not extremely large [22]. On the other hand, the naive
Bayes classifier assumes that features are independent
and require comparatively few parameters, i.e., (n ⨯ d)
for the mean vector and (n ⨯ d) for the variance. Due
to the independent assumption, the likelihood term can
be written as:
=
(5)
After estimating the sample mean (denoted as mij)
and sample variance (denoted as vij) through ML for
the i-th sensor and j-th class, the p(li|cj) can be written
as for univariate Gaussian distribution:
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
=
1
2
exp −
−
2
Table 1. Gas sensors used to acquire CH2O signatures
(6)
No.
Finally, the concentration class with the maximum
posterior probability (represented as ĉ) is selected as
an estimated concentration of the newly observed
latency pattern:
̂ = argmax
1.
2.
3.
4.
5.
6.
Figaro Part
Number
TGS 826
TGS 2600
TGS 2602
TGS 2610
TGS 2611
TGS 2620
Target Compounds
Ammonia
Air contaminants
Volatile organic compounds
Liquefied petroleum gas
Methane
Solvent vapors
(7)
3. Experimental Setup and Performance
Evaluation
We use six commercially available Figaro gas
sensors to build an array for CH2O concentration
estimation. The description of these sensors is listed in
Table 1, along with the names of the target compounds
for which they are mainly marketed. Different part
numbers are used in an attempt to achieve a unique
latency pattern with varying sensitivity.
The experimental setup for acquiring the response
of CH2O at different concentrations is shown in Fig. 2.
The sensor array is embedded in a glass container with
an inlet valve for CH2O exposure and outlet valve for
its outflow. The cylinders of CH2O and air are
connected to mass flow controllers (MFCs), which are
used to control the CH2O concentration by mixing it
with air in different proportions. A computer with a
data acquisition board is used for MFCs programming
in order to achieve the desired concentration of CH2O
and digitize the response of the sensor array.
Fig. 2. (a) Block diagram of experimental setup to acquire data of CH2O at different concentrations; (b) Gas cylinders;
(c) MFCs; (d) Sensor array embedded in a glass chamber; (e) Data acquisition system.
We expose the sensor array to twenty different
concentration values of CH2O in the range between
0.25 ppm to 5 ppm, with a 0.25 ppm increment step.
At the start of the experiment, the sensor array is firstly
exposed to air for 750 seconds to obtain the response
without any target gas vapors. This response is
referred to as a baseline response. The sensor array is
then exposed to CH2O at a specified concentration for
500 seconds to obtain its response. Then air is again
injected for 750 seconds to remove the CH2O
molecules trapped on the sensors in the previous gas
injection phase and to recover the baseline response,
i.e., the response without CH2O vapors. All the sensors
in the array respond to the target concentrations of
CH2O with different values of sensitivity. A typical
response of the sensors in the array to CH2O at two
different concentrations is shown in Fig. 3. In the
figure, the sensor array response corresponding to air
89
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
exposure is shown up to 750 seconds. Upon injection
of CH2O, with concentration value of 0.25 ppm, at
751 seconds, the resistance of each sensor starts
decreasing. At 1250 seconds, air is again injected to
allow the sensors to recover their baseline response. At
2000 seconds, CH2O is again injected with increased
concentration value of 0.5 ppm, which results in a
further drop in the resistance of the sensors. We repeat
this process to acquire 200 response patterns for the
whole concentration range.
Equation (1). Fig. 5 shows the latency values at each
concentration level, where the latency is represented
in arbitrary units resulting from Equation (1). There is
no fixed value of spike latency at any concentration
level. Instead, variability is shown at each
concentration level and the mean value of the latency
varies with the change in concentration.
Fig. 5. Spike latency of each sensor at twenty different
concentrations of CH2O.
Fig. 3. Sensor array response to air and at two different
concentrations of CH2O.
From the resistance values, the sensitivity of each
sensor is computed by dividing the value of the sensor
resistance at the end of gas exposure by the sensor
resistance at the end of air exposure. The regression
coefficient ai of each sensor is computed through
linear regression between the log sensitivity of the i-th
sensor and the average of the log sensitivity across the
sensor array, as shown in Fig. 4.
Bayesian inference methods along with other state
of the art methods, including Gaussian mixture models
(GMM), multi-layer perceptron (MLP) and support
vector machines (SVM) with a linear and radial basis
function (RBF) kernel, are used to estimate the CH2O
concentration class with the resultant data set. A 5 × 2
cross validation technique is used to evaluate the
performance of these methods by dividing the
experimental data into training, validation and testing
sets. The performances of all these methods are
summarized in Table 2.
Table 2. Performance comparison of algorithms for CH2O
concentration quantification/classification
Classification
Method
GMM
MLP
SVM (Lin)
SVM (RBF)
QDA
NBC
Fig. 4. Extraction of regression coefficients for each sensor
in the array through linear regression.
The resultant regression parameters are used to
transform the sensitivity pattern of the sensor array
into a spike latency code or pattern by using
90
Classification
Performance
(%)
91.25
89.25
88.5
92.5
92.75
95.75
Maximum
Concentration
Error (ppm)
0.25
0.50
0.25
0.25
0.25
0.25
In terms of maximum concentration error, the
performance of most of the classifiers is comparable,
but a maximum accuracy of 95.75 % is achieved with
naive Bayes classifier (NBC) to correctly classify the
true concentration class. The better performance of the
naive Bayes classifier among other state of the art
methods has been reported in many different domains
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
[23-27], even when the independence assumption does
not hold, which is justified in [26-27] by showing its
optimality under dependence conditions. The simpler
implementation of the naive Bayes classifier as
compared to other methods also facilitates the
integration of a simple hardware solution with the
sensor array.
A simplified gas quantification system can be
developed with the naive Bayes classifier as shown in
Fig. 6. Parameters for latency formation and univariate
Gaussian distribution are computed from the training
data and stored in memory. A newly test sensitivity
pattern is first transformed to a latency pattern by
using the corresponding regression coefficients and
then the stored distribution parameters of each class
are used to compute the conditional probability.
Finally, the concentration class with maximum
probability is assigned to the new test pattern.
Fig. 6. Block diagram for CH2O quantification with naive Bayes classifier by utilizing spike latency patterns.
4. Conclusion
In this paper, we have proposed a low cost and
compact solution in the form of an electronic nose,
containing an array of commercially available low cost
gas sensors, to estimate the concentration of health
endangering formaldehyde. As compared to this
solution, commercial methods for formaldehyde
monitoring are more accurate but cannot be adopted
for continuous monitoring on a long-term basis due to
the time, cost and complex sequence of procedures
involved in these methods. Instead of focusing on
exact knowledge of concentration, we treat this
problem as a classification problem by dividing the
target concentration range at evenly spaced intervals.
A bio-inspired spike latency coding scheme is used to
extract knowledge of concentration. In order to
estimate concentration in the presence of stochastic
variability in the latency patterns, different
classification methods are explored. The best
performance is achieved with a naive Bayes classifier.
Besides providing an analytical solution and not
requiring any manual tuning of parameters, the naive
Bayes classifier also facilitates hardware friendly gas
quantification/classification which can be realized
with a simple hardware platform. A building resident
can continuously monitor formaldehyde concentration
levels with the electronic nose and can set a threshold
to indicate when to take preventive action, such as
removal of the formaldehyde source or provision of
proper ventilation to reduce its concentration.
Acknowledgements
This paper was made possible by the National
Priorities Research Program (NPRP) grant No. 5 - 080
- 2 - 028 from the Qatar National Research Fund (a
member of the Qatar Foundation). The statements
made herein are solely the responsibility of the
authors.
References
[1]. M. N. Indang, A. S. Abdulamir, A. A. Bakar, A. B.
Salleh, Y. H. Lee, Y. N. Azah, A review: methods of
determination of health-endangering formaldehyde in
diet, Research Journal of Pharmacology, Vol. 3,
No. 2, 2009, pp. 31-47.
[2]. T. Salthammer, S. Mentese, R. Marutzky,
Formaldehyde in the indoor environment, Chemical
Reviews, Vol. 110, No. 4, 2010, pp. 2536-2572.
[3]. W. D. Kerns, K. L. Pavkov, D. J. Donofrio, E. J.
Gralla, J. A. Swenberg, Carcinogenicity of
91
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 86-92
[4].
[5].
[6].
[7].
[8].
[9].
[10].
[11].
[12].
[13].
[14].
formaldehyde in rats and mice after long-term
inhalation exposure, Cancer Research, Vol. 43, No. 9,
1983, pp. 4382-4392.
International Agency for Research on Cancer, IARC
classifies formaldehyde as carcinogenic to humans,
Press release. June15, 2004, URL: http://www.iarc.
fr/en/mediacentre/pr/2004/pr153.html
[accessed:
2015-09-09].
P. Guo, K. Yokoyama, F. Piao, K. Sakai, Sick building
syndrome by indoor air pollution in Dalian,
International Journal of Environmental Research and
Public Health, Vol. 10, No. 4, 2013, pp. 1489-1504.
J. G. Dojahn, W. E. Wentworth, S. D. Stearns,
Characterization
of
formaldehyde
by
gas
chromatography using multiple pulsed-discharge
photoionization detectors and a flame ionization
detector, Journal of Chromatographic Science,
Vol. 39, No. 2, 2001, pp. 54-58.
H. L. Pinheiro, M. V. de Andrade, P. A. de Paula
Pereira, J. B. de Andrade, Spectrofluorimetric
determination of formaldehyde in air after collection
onto silica cartridges coated with Fluoral P,
Microchemical Journal, Vol. 78, No. 1, 2004,
pp. 15-20.
S. Capone, P. Siciliano, F. Quaranta, R. Rella,
M. Epifani, L. Vasanelli, Analysis of vapours and
foods by means of an electronic nose based on a solgel metal oxide sensors array, Sensors and Actuators
B-Chemical, Vol. 69, No. 3, 2000, pp. 230-235.
A. P. F. Turner, N. Magan, Electronic noses and
disease diagnostics, Nature Reviews Microbiology,
Vol. 2, No. 2, 2004, pp. 161-166.
M. Trincavelli, S. Coradeschi, A. Loutfi, B.
Söderquist, P. Thunberg, Direct identification of
bacteria in blood culture samples using an electronic
nose, IEEE Transactions on Biomedical Engineering,
Vol. 57, No. 12, 2010, pp. 2884-2890.
R. E. Baby, M. Cabezas, E. N. Walsöe de Reca,
Electronic nose: a useful tool for monitoring
environmental contamination, Sensors and Actuators
B-Chemical, Vol. 69, No. 3, 2000, pp. 214-218.
M. Penza, G. Cassano, Classification of food,
beverages and perfumes by WO3 thin-film sensors
array and pattern recognition techniques, Sensors and
Actuators B-Chemical, Vol. 73, No. 1, 2001,
pp. 76-87.
M. Holmberg, F. Winquist, I. Lundström, J. W.
Gardner, E. L. Hines, Identification of paper quality
using a hybrid electronic nose, Sensors and Actuators
B-Chemical, Vol. 27, No. 1, 1995, pp. 246-249.
M. Hassan, A. Bermak, Threshold detection of
carcinogenic odor of formaldehyde with wireless
electronic nose, in Proceedings of the IEEE Sensors,
Valencia, November 2014, pp. 1368-1371.
[15]. T. W. Margrie, A. T. Schaefer, Theta oscillation
coupled spike latencies yield computational vigour in
a mammalian sensory system, Journal of Physiology,
Vol. 546, No. 2, 2002, pp. 363-374.
[16]. H. T. Chen, K. T. Ng, A. Bermak, M. K. Law,
D. Martinez, Spike latency coding in biologically
inspired microelectronic nose, IEEE Transactions on
Biomedical Circuits and Systems, Vol. 5, No. 2, 2011,
pp. 160-168.
[17]. M. Hassan, S. Brahim Belhaouari, A. Bermak,
Probabilistic rank score coding: A robust rank-order
based classifier for electronic nose applications, IEEE
Sensors Journal, Vol. 15, No. 7, 2015, pp. 3934-3946.
[18]. J. A. Yamani, F. Boussaid, A. Bermak, D. Martinez,
Glomerular latency coding in artificial olfaction,
Frontiers in Neuroengineering, Vol. 4, art. 18, 2012,
pp. 1-9.
[19]. E. Alpaydin, Introduction to Machine Learning, MIT
Press, 2010.
[20]. D. C. Knill, A. Pouget, The Bayesian brain: the role of
uncertainty in neural coding and computation, Trends
in Neuroscience, Vol. 27, No. 12, 2004, pp. 712-719.
[21]. M. Hassan, A. Bermak, A. A. S. Ali, A. Amira,
Bayesian inference using spike latency codes for
quantification of health endangering formaldehyde, in
Proceedings of the 6th International Conference on
Sensor Device Technologies and Applications
(SENSORDEVICES’ 2015), Venice, Italy, 23-28
August 2015, pp. 79-82.
[22]. A. P. Dempster, Covariance Selection, Biometrics,
Vol. 28, No. 1, 1972, pp. 157-175.
[23]. B. Turhan, A. Bener, Analysis of Naive Bayes’
assumptions on software fault data: An empirical
study, Data & Knowledge Engineering, Vol. 68,
No. 2, 2009, pp. 278-290.
[24]. J. M. A. Luz, M. S. Couceiro, D. Portugal, R. P. Rocha,
Hélder Araújo, Gonçalo Dias, Comparison of
classification methods for golf putting performance
analysis, in Computational Intelligence and Decision
Making, Springer, Netherlands, 2013, pp. 35-45.
[25]. Y. Li, R. Anderson-Sprecher, Facies identification
from well logs: A comparison of discriminant analysis
and naive Bayes classifier, Journal of Petroleum
Science and Engineering, Vol. 53, No. 3, 2006,
pp. 149-157.
[26]. H. Zhang, Exploring conditions for the optimality of
naive Bayes, International Journal of Pattern
Recognition and Artificial Intelligence, Vol. 19, No. 2,
2005, pp. 183-198.
[27]. P. Domingos, M. Pazzani, Beyond independence:
conditions for the optimality of the simple Bayesian
classifier, in Proceedings of the International
Conference on Machine Learning, 1996, pp. 105-112.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
92
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Alternative Processes for Manufacturing of Metal
Oxide-based Potentiometric Chemosensors
1
Winfried VONAU, 1 Manfred DECKER, 1 Jens ZOSEL,
1
Kristina AHLBORN, 1 Frank GERLACH, 2 David HALDAN
and 2 Steffen WEISSMANTEL
1
Kurt-Schwabe-Institut für Mess- und Sensortechnik,
Kurt-Schwabe-Straße 4, 04736 Waldheim, Germany
2
Hochschule Mittweida, Fakultät Ingenieurwissenschaften,
Technikumplatz 17, 09648 Mittweida, Germany
1
Tel.: +49 34327 608 0, fax: +49 34327 608131
1
E-mail: info@ksi-meinsberg.de
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: New possibilities for the preparation of partially selective redox electrodes based on passivated metals
of the subgroups IV to VI of the periodic system are presented by the example of vanadium. The gas phase oxidation at controlled oxygen partial pressures (CPO) and the pulsed laser deposition (PLD) as an high-vacuum
method are utilised as alternative methods beside the well-established chemical and electrochemical passivation
which usually lead to the highest possible oxidation state of the passivated metal. These newly available methods
enable in principle the tailoring of oxidation states in the sensitive layer and therefore the optimisation of the
electrochemical sensitivity and selectivity of sensors equipped with it. The use of vanadium as basic electrode
material is crucial because it shows in several matrices a remarkable corrosion susceptibility. This problem can
be solved by the introduction of stable alloys with high vanadium contents. These materials can be efficiently
produced by pulsed laser deposition (PLD). Copyright © 2015 IFSA Publishing, S. L.
Keywords: Partial selective redox electrode, Chemical passivation, Electrochemical passivation, Gas phase oxidation, Pulsed laser deposition.
1. Introduction
Measurements of the oxidation/reduction potential
(ORP) are suited for online determination of oxidising
agents like halogens or hydrogen peroxide e.g. in process and waste waters, if the electrode materials provide a partial selectivity [1]. It is already known that
systems made of passivated metals of high purity
http://www.sensorsportal.com/HTML/DIGEST/P_2741.htm
(> 99.5 wt.-%) of the subgroups IV to VI of the periodic system are suited for this purpose. The favourable
sensory behaviour is related to the semiconducting and
corrosion properties of the oxide layers with electronic
and ionic defects. Investigations have shown that for
the quality of the sensor functionality an n-type conducting mechanism is favourable, accordingly the passivating sensing oxide should be an n-type semiconductor. The sensor is working like a Schottky-diode
93
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
[2]. Fig. 1 presents in this context exemplarily the electrochemical process for a chlorine determination at a
passivated titanium electrode.
with glass based or other pH electrodes in strong acidic
media should be considered.
To tailor the most important parameters sensitivity
and selectivity of the described partial selective electrochemical sensors it might be advantageous to prepare oxide layers in contact with the electrolyte which
also possess several lower oxidation states with sufficient chemical stability.
Fig. 1. Electron migration at a titanium/ titanium
oxide electrode.
700
600
1000 ppm nitrite
500
400
Potential [mV]
The preparation of the oxide layers was carried out
so far by chemical passivation [M(ethod) 1)] using air
or pure oxygen as oxidising agent or by anodic oxidation [M 2)] in half concentrated acids (e.g. sulfuric and
nitric acid) [3], leading usually to the highest possible
valence state of the metal at the outer electrode surface. The inner oxide layer near the metal bulk can
contain also lower valence states.
In Table 1, appropriate metals for the construction
of potentiometric sensors with partial selectivity versus selected analytes are presented. Fig. 2 shows a possible design for the described electrode according to
the state of the art.
Fig. 2. Redox electrode with passivated metal disk
as partial selective membrane.
300
200
100
0
-100
-200
-300
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
pH value
Table 1. Membrane materials for the fabrication
of partial selective electrodes.
analyte in aqueous phase
Cl2
Br 2
I2
NO2
H2 O2
Fe3+
Ti
x
x
electrode material
Ta
W Nb V
x
x
x
x
x
x
x
x
x
Generally, electrodes made of these materials exhibit a cross sensitivity to the pH value which should
be known and considered. As an example for such a
behaviour Fig. 3 shows results for the determination of
nitrite by means of an anodically passivated tungsten
based indicator electrode (according to Fig. 2) at pH
values between -1.4 … 9. There are two ranges with
negligible influence of the pH value on the nitrite concentration related electrode potential (range 1:
pH= -1.4 … 1; range 2: pH= 4 … 9). Therefore, a simultaneous pH determination is always advantageous,
whereupon the existing problems of pH measurement
94
Fig. 3. Nitrite determination with tungsten oxide electrodes vs. Ag/AgCl, Cl-sat .
In this connection in section 2, the preparation,
characterisation and application of membrane materials for partial selective electrodes based on passivated
pure metals of subgroups IV-VI is described exemplarily by using vanadium as source material. Here, the
final goal was to develop a potentiometric electrode
for the determination of H2O2 in process-relevant applications. In addition to the known use of M 1 and M
2 for this purpose electrode preparations also were carried out by means of gas phase oxidation at controlled
temperature and oxygen partial pressure [CPO (M 3)]
and pulsed laser deposition [PLD (M 4)].
Based on the knowledge gained during the first potentiometric sensory applications of these novel materials in section 3 new perspectives are presented. The
innovative production technologies allow a cost-and
material-saving manufacturing of tailored passivated
electrodes on the base of alloys of the above-mentioned subgroup metals. This has particular relevance
for the fabrication of vanadium oxide electrodes which
show remarkable corrosion in aggressive media resulting in a reduced service life.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
2. Electrode Preparation, Characterisation and Application
2.1. Preparation
M1
Vanadium disks were fabricated from a metal bar
(purity: 99.8 wt.-%, Ø: 1 cm) using a diamond saw,
contacted with a Pt-wire, encased by an insulating polymer (e.g. polypropene) and wet grinded with sandpaper (grain size: 4000). After cleaning of the metal surface by back sputtering passivation was carried out using air or pure oxygen (low content of carbon dioxide
and water vapour) as oxidising agent at temperatures
< 100 °C.
M2
Fig. 4 shows the electrochemical polarisation
curve of a high-purity vanadium disk (prepared as described before) in sulfuric acid obtained within the
range of UP = 0 ... 6 V vs. Ag/AgCl, Cl-(sat.) at the scan
rate 100 mV/s which was recorded in the course of the
production of a related partial selective electrode. The
observed colour changes of the vanadium surface
seem to be in correlation with the formation of VO
(green), VO2 (blue-black) and V2O5 (orange) as the final state. The anodic oxidation process was carried out
using a potentiostat and an electrochemical cell according to Fig. 5.
Fig. 4. Anodic polarisation curve of pure vanadium
in 50 wt.-% H2SO4 measured vs. Ag/AgCl, Cl-(sat.).
Fig. 5. Electrochemical cell for the anodic oxidation and
potentiometric measurements of partial selective electrodes; 1 partial selective electrode, 2 reference electrode,
3 Pt-counter electrode, 4 Haber-Luggin-capillary,
5 gas injection system, 6 gas outlet system.
M3
The gas phase oxidation method (CPO) offers the
possibility to establish tailored oxidation states of vanadium oxides smaller than +5 by adjusting temperature and pO2. The thermodynamic ranges of the different oxidation states of vanadium given in Fig. 6 provide an orientation for the parameter adjustment in the
gas phase.
Fig. 6. Phase diagram of the system vanadium-oxygen
published in [4].
As illustrated in Fig. 7, the oxygen partial pressure
of a mixture of N2/air/H2 is adjusted precisely within
the range p(O2) = 10-30 ... 0.2 bar by a combination of
a solid electrolyte pump cell and a solid electrolyte
measuring cell [5]. After gas passage of the vanadium
sample positioned in a separately heated transparent
flow through cell, the oxygen partial pressure can be
controlled again to measure the oxygen uptake by the
vanadium surface [6]. Polished vanadium discs were
placed inside the transparent cell and treated at different partial pressures and temperatures up to the point,
where an oxide layer was visible by the unaided eye.
Since the melting point of V2O5 amounts to 690 °C in
contrast to that of VO2 at 1970 °C the change between
the two oxidation states could easily be noticed by the
observer in the transparent cell at probe temperatures
above 690 °C. This change from liquid V2O5 to solid
VO2 occurred at T = 1000 K at pO2 ≈ 100 Pa which is
about on order of magnitude higher than indicated in
Fig. 6.
Fig. 7. Schematic drawing of the experimental setup
for gas phase oxidation of vanadium.
95
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
This pO2-shift is caused by a kinetic phenomenon,
the oxygen mass transfer from the gas phase to the oxide layer, enabling significant oxygen diffusion to the
non-oxidized vanadium surface. This diffusion leads
to a continuous increase of the thickness of the oxide
layer and the corresponding oxygen consumption of
the inner vanadium surface establishes a gradient of
oxygen partial pressure within the oxide layer and the
adjacent gas phase according to Fig. 8.
M4
Vanadium oxide films with defined oxidation
states were also prepared by using pulsed laser deposition (PLD) either in an oxygen background gas or
with oxygen ion beam bombardment of growing films
(see Fig. 10) [8].
Fig. 10. High vacuum pulsed laser deposition system used
for the preparation of vanadium oxide films.
Fig. 8. Establishment of an oxygen pressure gradient at a
heated vanadium surface due to oxygen diffusion through
the oxide layers with different oxygen content x, calculation
from thermodynamic data, published in [7].
Cooling the vanadium discs after heating to
T = 1000 K over 15 min at constant p(O2) leads to different oxidation states, as results of XPS analysis of
the prepared oxide layers given in Fig. 9 clearly indicate. Adjusting oxygen partial pressure at pO2 ≈ 1 Pa
the oxide surface is dominated by VO2 while
pO2 ≈ 2·104 Pa leads to a surface consisting of V2O5
exclusively.
CPO with tailored temperature, oxygen partial
pressure and treatment time enables therefore the separate adjustment of surface oxidation state and thickness of the oxide layer without introducing additional
chemicals, essentially important for the electrode behaviour.
The method allows the preparation of metastable
phases of vanadium oxide. For the preparation of the
films, a KrF-excimer laser with 248 nm wavelength,
1 J laser pulse energy and 30 ns laser pulse duration
was used for ablation from a pure vanadium target. For
this, the laser beam was introduced into a high vacuum
chamber and focused onto the target surface. A relatively high laser fluency of 8 J/cm² was used for ablation, the laser pulse repetition rate was 50 Hz. The
films were prepared in an oxygen background gas using partial pressures in the range of p(O2) = 5 × 10-4 ...
5 × 10-2 mbar. The substrate temperature during film
deposition was constantly 400 °C.
The method is characterised by a high degree of
more than 50 % of ionised species having high mean
kinetic energy of several 10 eV in the ablated particle
current. The ablated vanadium particles collide on
their way to the substrate with the oxygen molecules
of the background gas resulting in the dissociation of
the oxygen molecules. Thus, highly reactive oxygen
atoms form and can combine with the vanadium atoms
and ions at the substrate surface. In a proper energy
range the metastable vanadium oxide VO phase is
formed. This was established by measuring the ratio of
oxygen and vanadium in the films by using EDX (see
Table 2), which is nearly 1. The variation of the oxygen partial pressure did not lead to significant changes
in this ratio, so metastable VO is formed at all applied
background gas pressures.
2.2. Characterisation
Fig. 9. XPS spectra of two different vanadium samples
treated at 700 °C and (a): pO2 = 2·104 Pa, (b): pO2 = 4·10-9
Pa compared to a spectrum of (c): polished metallic surface.
96
The preparation of vanadium oxides by method
M 1 showed only irreproducible results. This was in
accordance with [1], where investigations concerning
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
the passivation of Ti, Ta and W yielded in comparable
observations. For that reason the following experiments have been executed with vanadium oxide-based
electrodes prepared with the other oxidising methods.
Table 2. Composition of vanadium oxide films deposited
by PLD at various oxygen partial pressures (Si results from
the substrate and Pt from a thin conduction layer sputtered
on the films. The thickness of the vanadium oxide films increased with increasing partial pressure so that the Si signal
becomes smaller.)
p(O2)
[mbar]
5.0E-04
5.0E-03
5.0E-02
O
(W[%])
22.93
30.92
41.63
Si
(W[%])
47.2
27.11
13.63
V
(W[%])
26.45
38.04
43.03
Pt
(W[%])
3.5
3.93
1.71
2.3. Application
Fig. 12 shows that with all discussed methods sensory active systems of the type V/VxOy with partial
sensitivity to H2O2 are realisable. The investigated
measuring range is fitted to the requirements of the
galvanotechnical industry. The knowledge of H2O2concentration is relevant for the correct adjustment of
the ratio of Cu+/Cu2+ in the electrolyte [9]. The electrode functions vary widely. While with M 2 there are
method-conditioned restrictions in relation to the stoichiometry of the functional oxide, M 3 and M 4 offer
clearly more options concerning the sensor optimisation. The highest sensitivity was obtained by the membrane produced with the PLD technology.
160
M(ethod) 2 --->V2O5
M 3 ---> VO2
140
M 4 ---> VO
120
100
ΔU[mV]
Fig. 11 shows microscopic/ SEM shootings of the
surfaces of the vanadium oxide based membranes generated on metallic structures demonstrating that the
methods result in functional films that cover the whole
surface but differ significantly in their microscopic appearance. It has to be stated that only M 3 and M 4
resulted in metal oxides with vanadium valences less
than +5.
80
60
40
20
0
-2
-1
0
1
log c H2O2[mol/L]
20
20µm
µm
2020µm
µm
(b)
(a)
Fig. 12. Potentiometric determinations of H2O2 with systems of the type V/VxOy realised by different preparation
methods (M 2, M 3, M 4) at ϑ = 25°C in 5% Na2SO4 with
additives of H2O2 at pH ≈2 (all vs. Ag/AgCl, Cl-sat); M 2:
anodic oxidation, M 3: CPO; M 4: PLD.
3. Perspectives
µm
2020µm
(c)
Experiences gained from measurement campaigns
with indicator electrodes for the determination of
H2O2 based on anodic oxidation of vanadium have
demonstrated that a use of the pure transition group
metal is unfavourable since the functional layers corrode quickly during the measurement (s. Fig. 13).
Fig. 11. SEM images of vanadium oxide films fabricated by different preparation methods; (a) V2O5 formed
by M 2 in 50 % H2SO4 with a scan rate UP= 4.3 V vs.
Ag/AgCl, Cl-sat at ϑ = 25 °C; (b) VO2 prepared by M 3;
(c) VO formed by M 4.
Apart from the method-specific created different
chemical compositions of the oxides determined by
XPS measurements (an example was given in Fig. 9)
these changes are mainly caused by variations of layer
thicknesses. Furthermore, the layer presented in
Fig. 11(c) contains a number of droplets, a known
drawback of the PLD method.
Fig. 13. Corroded electrode membrane after two week
dwell time in a nickel electrolyte (ENTHONE) (mag. 500).
97
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
It has been proven to be advantageous to alloy vanadium with titanium before passivation playing an
important role for the use of e.g. orthopaedic implants
because of their corrosion stability and the improvement of the biocompatibility [10]. By adjusting an optimal alloy-ratio of both metals, it is possible to retain
the functionality of the electrode over a very long period. In this context, results of corrosion measurements given in Fig. 14 clearly indicate that the corrosion stability of the electrode membrane can be increased significantly by adding titanium to the alloy.
For the choice of an appropriate alloy composition,
it is necessary to investigate the upper alloying level
of Ti at which the partial selectivity for hydrogen peroxide is maintained in appropriate degree. In the case
of the investigated V-Ti-alloys the optimal titanium
content for the hydrogen peroxide determination with
partially selective electrodes can be amounted to
20 wt.-% according to Fig. 14 and 15.
Investigations in [11] give rise to the assumption
that the application of ternary alloys may lead to even
better results. The method 4 offers in this field the best
requirements. For instance by an introduction of different targets (V, Ti, Nb) in the PLD-chamber and a
supply of an appropriate gas mixture for the oxidation
the fabrication of ternary mixtures should be cheaper
than a melting of the alloys with additional electrochemical oxidation [12].
Fig. 14. Current density for activation ja and passivation jp of V-Ti-alloys.
Because on pure vanadium based electrodes are
not suitable for aggressive matrices the application of
corrosion stable passivated binary or ternary vanadium
alloys can be a reasonable alternative. This will require
an optimisation process concerning the corrosion resistance and the necessary electrode sensitivity and selectivity. For the manufacturing process the PLD technology offers the best preconditions.
References
Fig. 15. Electrode functions of electrodes of V and V-Ti alloys vs. Ag/AgCl, Cl-sat in 5 wt.-% Na2SO4 at ϑ = 25 °C in
a H2O2 concentration range of b = 1 g/L … 20 g/L.
4. Conclusions
Beside anodic passivation, also CPO and PLD deliver suitable vanadium oxide based membranes for
the potentiometric determination of H2O2. The last two
mentioned methods allow for vanadium in principle a
largely variable realisation of oxidation states differing from the value +5. This has an impact on the performance of resulting electrochemical indicator electrodes. In future work, these effects shall be examined
more closely and the new manufacturing methods for
the specific application will be improved, too.
98
[1]. W. Habermann, Partial selective redox electrodes for
the on-line determination of oxidising agents, Ph. D.
Thesis, Martin Luther University, Halle, Germany,
1990.
[2]. W. Habermann, P. John, H. Matschiner, and H. Spähn,
Partially selective semiconductor redox electrodes, J.
Anal. Chem., Vol. 356, 1996, pp. 182-186.
[3]. T. Bachmann, U. Guth, and W. Vonau, Physical and
electrochemical properties of vanadium alloy oxide
layers prepared by anodic passivation, Ionics, Vol. 7,
2001, pp. 172-177.
[4]. W. Brückner, H. Oppermann, W. Reichelt, J. I.
Terukow, F. A. Tschudnowski, and E. Wolf,
Vanadiumoxide.
Darstellung,
Eigenschaften,
Anwendung, Akademie-Verlag, Berlin, 1983.
[5]. M. Schelter, J. Zosel, W. Oelßner, U. Guth, and
M. Mertig, A solid electrolyte sensor for trace gas
analysis, Sens. Actuators B: Chemical, Vol. 187, 2013,
pp. 209–214.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 93-99
[6]. V. Vashook, L. Vasylechko, J. Zosel, W. Gruner, H.
Ullmann, and U. Guth, Crystal structure and electrical
conductivity of lanthanum–calcium chromites–titanates La1−xCaxCr1−yTiyO3−δ (x=0–1, y=0–1), J. Solid
State Chem., Vol. 177, 2004, pp. 3784-3794.
[7]. H. Endo, M. Wakihara, M. Taniguchi, T. Katsura,
Phase Equilibria in the V2O3-VO2 System at High
Temperatures, Bull. Chem. Soc. Japan, Vol. 46, 1973,
pp. 2087-2090.
[8]. G. Reiße, S. Weissmantel, B. Keiper, B. Steiger,
H. Johanson, T. Martini, and R. Scholz, Influence of
ion bombardment on the refractive index of laser pulse
deposited oxide films, Appl Surf. Sci., Vol. 86, 1995,
p. 107.
[9]. T. Bachmann, W. Vonau, and P. John, Hydrogen Peroxide Concentration in Acid Etching Baths, GIT Laboratory Journal, Vol. 7, pp. 2003, 149-150.
[10]. T. Bachmann, Electrochemical investigations of oxide
layers consisting of vanadium and vanadium alloys,
PhD. Thesis, University of Technology, Dresden,
Germany, 2008.
[11]. P. John, Indicator electrode with sensitive surface and
application therefor, German Patent, DE 199 53 218
C2.
[12]. W. Vonau, M. Decker, J. Zosel, K. Ahlborn, F. Gerlach, and S. Weissmantel, New methods for the preparation of partial selective redox electrodes for the determination of H2O2, in Proceedings of the 6th International Conference on Sensor Device Technologies and
Applications (SENSORDEVICES '15), Venice, Italy,
28-30, August 2015, pp. 28-30.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
99
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 100-105
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Improvement in Humidity Sensing of Graphene Oxide
by Amide Functionalization
Sumita RANI, Dinesh KUMAR, Mukesh KUMAR
Electronic Science Department, Kurukshetra University
Kurukshetra, Haryana, India
E-mail: dineshelsd2014@gmail.com
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: In this work, the effect of water adsorption on the electrical properties of graphene oxide (GO) and
Amide functionalized graphene oxide (AGO) was studied using direct current measurements. AGO was
synthesized by chemical method at room temperature. Fourier transform infrared spectroscopy, X-ray diffraction
and scanning electron microscopy measurements were carried out to verify the functionalization of GO. The
films of GO and AGO between aluminum electrodes on SiO2/p-Si (100) substrate were formed by drop casting
method. The variation in the I-V characteristics was recorded at different humidity level. It has been observed
that the interaction between water molecules with AGO was more as compared to GO. It was reported that
electrical properties of GO and AGO are humidity and applied voltage dependent. At low humidity level the
response of GO sensor was poor, however at high humidity the conductivity of GO increases. Compared to GO,
AGO shows good response. The resistance of the AGO film was approximately 9.87 kΩ at 10 % relative
humidity (RH), and decreases to 1.5 kΩ at 90 % RH. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Amide
I-V characteristics.
functionalized
graphene
oxide,
1. Introduction
Sensor technology plays vital role in industry and
agricultural production, public safety, military etc.
Due to low power consumption and ease of resistance
measurements chemiresistive sensor become
important from last few decades [1]. The
measurement in variation in humidity is necessary to
monitor, detect and control the ambient humidity by
precise sensors [2]. Humidity sensors are important
for fabrication of integrated circuits [3], in
semiconductor industry it is essential to monitor
moisture levels constantly. In medical field, humidity
sensors are essential for respiratory equipment,
sterilizers, incubators and biological processing. In
agriculture, humidity sensors are used for green-
100
Graphene
oxide,
Relative
humidity
sensing,
house air-conditioning, plantation protection, soil
moisture monitoring, etc. In general, humidity
sensors are used for moisture detection by various
paper, textile and food processing industries. In
modern humidity sensor there is requirement of high
sensitivity and wide detection range for fast response
and short recovery time to meet industry applications.
Many sensing materials such as silicon
nanostructures,
ceramic
nanomaterials,
semiconductor metal oxide [1], carbon nanotubes
(CNTs), metal oxide etc. are widely used for
moisture sensing application due to high surface to
volume ratio [4-5].
Two dimensional, graphene analog to CNTs, has
emerged out as an important material for sensing due
to its extraordinary electronic, thermal, chemical
http://www.sensorsportal.com/HTML/DIGEST/P_2742.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 100-105
properties [2, 4-5]. The sensors based on graphene
shows very high sensitivity to gases like NH3, NO2
[6-8] etc. The studies reported on humidity sensing
based on graphene shows long response time hinder
its application in humidity sensing [9].
The graphene precursor, graphene oxide (GO)
shows advantage as a material for humidity detection
because of its large surface area [2-5]. GO has
hydroxyl, epoxy and carboxylic acid functional group
bonded in two-dimensional network of sp2 and sp3
hybridized carbon atoms arranged in a honeycomb
structure [3]. The oxygen containing functional
groups of GO enhance hydrophilic properties of it but
these groups makes it an insulator by decreasing its
conductivity [2, 8]. But oxygen functional groups of
GO allow fast passage of water within the GO layers
[10], which makes fast response of the sensor based
on GO. But improving the electrical properties of GO
by surface modification via noncovalent or covalent
functionalization is a promising way for fabrication
of high performance GO-based sensor [11-12].
Already functionalized GO have been successfully
used for detection of acetone, hydrogen sulfide,
nitrogen dioxide (NO2), etc [13-14]. Covalent
functionalization of GO enhance its physicochemical
properties, for instance, isocyanate-treated GO has
been exfoliated and form a stable dispersion in polar
aprotic solvents [15]. Such functionalization
improves the mechanical, electrical, thermal
properties and dispersion of functionalization GO in
to organic solvents [16]. Recently, dodecylamine and
ethylenediamine functionalized GO was used for
hydrogen sulphide gas detection [17].
In view of above observations, heteroaryl/phenyl
amine was grafted onto GO sheets by the amide
formation between amine functionality of
heteroaryl/phenyl amine and oxygen-containing
groups (e.g., carboxyl and lactone groups) of GO to
give amide functionalized GO (AGO). Afterwards,
the fabrication and characterization of AGO
chemiresistors was reported to understand effect of
AGO on the sensor response. It has been observed
that AGO has strong electrical response for water as
compared to GO.
2. Experimental
2.1. Materials
Graphite powder (purity 99.99 %), sodium nitrate
(99.0 %), sulphuric acid, potassium permanganate
(99 %), hydrogen peroxide, hydrochloric acid,
sodium hydroxide (NaOH), hydroxybenzotriazole
(HOBt),
2-aminothiazole,
N,N'-dicyclohexylcarbodiimide (DCC) were used to prepare GO
and AGO.
2.2. Synthesis of GO
To synthesize GO, graphite oxide was prepared
by the oxidation treatment of graphite with KMnO4.
For this, Graphite (2 g) and NaNO3 (1 g) were put in
cooled concentrated sulphuric acid (46 ml) under
stirring in ice bath. KMnO4 (6 g) was gradually added
to the above placed mixture with stirring and cooling
so that the temperature of mixture was maintained
between 10–15°C. The reaction mixture was then
stirred at 40°C for 30 minutes. Afterward, 80 ml of
high purity water was added to the formed paste,
followed by another 90 minutes stirring at 90°C.
Successively, the oxidative reaction was terminated
by addition of 200 ml water. 6 ml of 30 % H2O2 was
added in above mixture sequentially to destroy the
excess KMnO4. The complete removal of KMnO4
was indicated by color changed to yellow. Sometimes
the solution’s color was yellow before addition of
H2O2 which indicated complete reduction of KMnO4.
The solution was then washed with HCl (10 %) to
remove sulphate. Subsequently, it was filtered and
washed several times with DI water. The filtered
paste was dissolved in 100 ml DI water. The solution
was ultrasonicated for 1 hour and centrifuged for
20 minutes at 4000 rpm. GO powder thus obtained
was collected and dried at room temperature.
2.3. Amide Functionalization of GO
The AGO was obtained by condensation of amine
group of heteroaryl/phenyl amine with lactone group
of GO. The oxygenated GO sheet prepared by the
chemical method was treated with NaOH to open the
lactone groups (–CO-O–) on the basal plane and
convert them into hydroxyl and carboxyl groups.
Treatment of resulting reaction mixture with organic
amines in the presence of DCC and HOBt leads to
the amidation of the carboxyl groups to give AGO.
AGO was synthesized by dispersing GO (0.3 g) in
30 ml DMF by ultrasonication for 60 minutes at room
temperature. Then, NaOH (0.3 g; 7.5 mmol) was
added and resulting solution was stirred for
60 minutes at room temperature. Afterward,
2-aminothiazole (3.1 mmol), HOBt (3.1 mmol)
followed by DCC (3.1 mmol) addition to the above
reaction mixture and stirred for 24 hours at room
temperature.
AGO
powder
collected
by
centrifugation was added to pure DMF and the
resulting suspension was again centrifuged to remove
side products. This process was repeated twice
with DMF and then with water to remove DMF to
give pure AGO. The prepared AGO was dried at
60°C overnight.
2.4. Preparation of Sensor
The sensor was obtained by depositing a pair of
aluminum electrodes of thickness of 200 nm was
deposited on SiO2/p-Si substrate by thermal vacuum
coating unit. To prepare a sensing layer GO and
AGO was dispersed into water through
ultrasonication for 1 hour. The sensing layer was
prepared by drop casting method on SiO2/Si substrate
101
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 100-105
between the aluminum electrodes. The prepared
sensors of GO and AGO2 were kept in the chamber
one by one and a particular humidity environment
was maintained inside the glass chamber by
introducing water vapors into chamber by inlet. The
sensor I–V characteristics were measured at different
humidity level using Keithley 2400 Series Source
Measurement Unit via two probe method by stepping
the voltage in the range of 0 V to 5 V.
2.5. Characterization
The crystal phase was characterized by X-ray
diffraction (XRD) XPERT-PRO diffractometer
(45 kV, 40 mA) equipped with a Giono-meter
PW3050/60 working with Cu Kα radiation of
wavelength 1.5406Å in the 2θ range from 5 to 80°).
The functional surface group was studied by Perkin
Elmer Fourier transform infrared (FTIR) model
SPETRUM 65 system. Dried solid samples were
mixed with KBr powder and were pelletized before
performing the scan from wave number 4000 to
400 cm-1. SEM characterization was carried out by
using JSM-6510LV Series Scanning Electron
Microscope (SEM) having pre centered W hairpin
filament (with continuous auto bias) and equipped
with accelerating voltage of 500 V to 30 kV with
high magnification of 300,000.
3. Results and Discussion
The amidation occurs in the GO after treatment
with amine can be verified easily by FTIR, XRD, and
SEM techniques. Structural changes of AGO were
investigated by comparing the FTIR spectra of GO
and AGO (Fig. 1). FTIR spectrum of GO and AGO is
plotted in the range of 2000 cm-1 to 1000 cm-1. The
most characteristic features in the FTIR spectrum of
GO was the adsorption bands corresponding to the
C=O carbonyl stretching at 1721 cm-1, the stretching
bands for C=C bonds at 1591 cm-1, the O–H
deformation vibration at 1392 cm-1 and the C–O
stretching at 1051 cm-1 [18-19]. As depicted in FTIR
spectra of AGO the peak intensity and position of
these peaks are changed after amidation. The FTIR
spectra of AGO, the C–O stretching peak present in
GO at 1051 cm-1 was disappear and a new peak at
about 1634 cm-1 corresponding to the amide carbonyl
(C=O) stretch (amide I) and the peak at about
1585 cm-1 for amide II (C–N in-plane stretching and
CHN deformation) [20], which demonstrate that
amines has been grafted onto GO as amide bond.
XRD patterns for graphite, GO and AGO are
shown in Fig. 2. XRD of graphite represent peak at
26.3˚, which shifted towards lower value of angle for
GO (2θ = 11.42˚), which is the characteristic peak of
the GO, with increase in interspacing from 0.34 nm
(graphite) to 0.77 nm. This confirms the oxidation
of graphite.
102
Fig. 1. FTIR spectra of GO and AGO.
Fig. 2. XRD of graphite, GO and AGO.
After functionalization of GO the peaks shift to
the lower value of angle (2θ) and increase in the
interlayer spacing from 0.77 nm (GO) to 0.84 nm for
AGO, was observed. The larger value of interlayer
spacing suggests the incorporation of additional
functionality on the surface of the basal plane of GO.
The SEM images represent the surface
morphology of the GO and AGO (Fig. 3). These
images show that the resulting sample of AGO was
obviously different from GO. Morphology of GO
was observed to have flaky texture indicating its
layered microstructure. Different surface morphology
of AGO clearly indicates the surface modification of
GO after functionalization.
Fig. 3. SEM image of GO and AGO.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 100-105
The DC electrical properties, i.e., current–voltage
(I-V) characteristics of the GO and AGO films were
measured with a voltage sweeping mode at various
humidity points. To measure the I-V characteristics
one electrode was loaded with the sweeping voltage
bias, and the other electrode is grounded. I-V
characteristics of GO and AGO films was
investigated for 2 to 5 V sweeping voltages at various
humidity levels, respectively. The different
percentage of humidity level was achieved in sensing
chamber by introducing the water vapors.
Fig. 4 (a) shows the measured I-V characteristics
of GO film in the relative humidity (RH) range of
10 % to 90 %. From Fig. 4 (a) it is clear that the
channel current of the GO film increases with
increasing RH, indicating that the water adsorption
results in a decrease in the resistance of GO films
[21]. Water adsorption easily takes place in GO
because of presence of large number of oxygen
containing functional groups in it. The result revealed
that GO is a weak conductor with a continuous water
adsorption onto GO films. It is also clear that the
current increases with increase in humidity level.
At higher humidity (>60 %) GO films gives
good response.
The sensor resistance (Rs), as a function of
humidity is calculated by:
compared to GO. This may be due to increase in
surface defects of GO with functionalization [12].
Rs=δV/δI,
where δV is the incremental voltage and δI is the
incremental current.
The variation of Rs with humidity is shown in
Fig. 5. The Rs of the GO film was 0.76 MΩ at 10 %
RH. High value of resistance at low RH can be
explained on the basis of interaction of water
molecules on the GO film. At low RH, water
molecules are adsorbed on to the available sites of
GO by double hydrogen bond, called first layer
adsorption of water. The first layer water molecules
are unable to move freely due to strong hydrogen
bonding. Thus at low RH, GO film exhibits strong
electrical resistance. At high RH, multilayer
adsorption of water molecules occurs. The second
adsorbed layer of water molecules was attached by
single hydrogen bond on hydroxyl groups of GO. In
higher adsorbed layers water molecules become free
and move like bulk liquid [4]. This adsorbed water
can be ionized and produce large number of
hydrogen ions which involves in the reduction of
GO. Therefore, the resistance of GO films decreases
in case of high applied voltage at high humidity level.
GO + 2H+ + 2ereduced GO + H2O
The resistance in GO film decreases to 0.48 MΩ
at 90 % RH. Further, at high RH, water molecules
penetrate in to interlayer of GO and hydrolyzed the
oxygen containing functional groups. These ions
contribute to ionic conductivity, this explain the
humidity sensing behavior of GO at low and high
humidity level.
However, it can be observed from Fig. 4(b) that
for AGO, the variation in current is more as
Fig. 4. I-V variation in (a) GO and (b) AGO with different
humidity from 2V to 5V sweeping voltage.
Fig. 5. Variation in sensor resistance with humidity.
So, AGO is preferred for moisture sensing as
compared to GO. The resistance of the AGO film
was 9.87 kΩ at 10 % RH, which decreases to 3.52 kΩ
at 30 % RH. The resistance decrease further with
increase in humidity and it becomes 1.5 kΩ at 90 %
RH. The resistance of film at 90 % RH was
approximately seven times smaller than that at 10 %
103
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 100-105
RH. The smaller resistance of AGO at low RH is due
to amidation which decreases the number of oxygen
containing groups as compared to GO, so AGO gives
better sensitivity. When ambient RH is high, the
numbers of adsorbed water molecules are large. As a
result, the ionization process generates more
hydrogen ions, result in reduction of AGO film.
Thereby, AGO film shows high conductivity for
higher applied voltage at high humidity level.
[6].
[7].
[8].
4. Conclusions
An efficient and easy approach has been used to
synthesize the covalent functionalized GO by a
simple amidation reaction using 2-aminothiazole. DC
measurement method was used to investigate the
effect of humidity on the electrical properties of
AGO films. Through electrical characterizations, the
strong interaction of water molecules with AGO
films was observed. The electrical properties of GO
and AGO films were affected by humidity and the
amplitude of applied voltage. At low RH (<60 %),
GO films exhibited small variation in current due to
the presence of sp3-bonded hybridized carbon atoms
and presence of oxygen containing functional groups.
AGO shows much better response at lower and
higher RH (10 % to 90 %) indicates its effectiveness
as compared to GO. The results are useful for the
development of graphene-based sensors.
Acknowledgments
[9].
[10].
[11].
[12].
[13].
One of author Sumita Rani is thankful to
INSPIRE, Department of Science and Technology
(DST), India for funding support.
[14].
References
[1]. N. Hu, Y. Wang, J. Chai, R. Gao, Z. Yang,
E. S. Kong, Y. Zhang, Gas sensor based on pphenylenediamine reduced graphene oxide, Sensors
and Actuators B, Vol. 163, 2012, pp. 107-114.
[2]. S. Borini, R. White, D. Wei, M. Astley, S. Haque,
E. Spigone, N. Harris, J. Kivioja, T. Ryha¨nen,
Ultrafast Graphene Oxide Humidity Sensors, ACS
Nano, Vol. 7, 2013, pp. 11166-11173.
[3]. D. Kumar, S. Rani, Synthesis of Amide
Functionalized Graphene Oxide for Humidity
Sensing Application, in Proceedings of the 6th
International Conference on Sensor Device
Technologies
and
Applications
(SENSORDEVICES’15), Venice, Italy, 23-28 August,
2015, pp. 142-145.
[4]. H. Bi, K. Yin, X. Xie, J. Ji, S. Wan, L. Sun, M.
Terrones, M. S. Dresselhaus, Ultrahigh humidity
sensitivity of graphene oxide, Scientific Reports,
Vol. 3, 2013, 2714.
[5]. U. Mogera, A. A. Sagade, S. J. George,
G. U. Kulkarni, Ultrafast response humidity sensor
using supramolecular nanofibre and its application in
104
[15].
[16].
[17].
[18].
[19].
monitoring breath humidity and flow, Scientific
Reports, Vol. 4, 2014, 4103.
J. T. Robinson, F. K. Perkins, E. S. Snow, Z. Q. Wei,
P. E. Sheehan, Reduced graphene oxide molecular
sensors, Nano Letters, Vol. 8, No. 10, 2008,
pp. 3137-3140.
R. Arsat, M. Breedon, M. Shafiei, P. G. Spizziri,
S. Gilje, R. B. Kaner, K. Kalantar-Zadeh,
W. Wlodarski, Graphene-like nano-sheets for surface
acoustic wave gas sensor applications, Chemical
Physics Letters, Vol. 467, No. 4, 2009, pp. 344-347.
I. Jung, D. Dikin, S. Park, W. Cai, S.L. Mielke,
R. S. Ruoff, Effect of water on electrical properties of
individual reduced graphene oxide sheets, Journal of
Physical Chemistry C, Vol. 112, No. 51, 2008,
pp. 20264-20268.
A. Ghosh, D. J. Late, L. S. Panchakarla,
A. Govindaraj, C. N. R. Rao, NO2 and humidity
sensing characteristics of few-layer graphenes,
Journal of Experimental Nanoscience, Vol. 4, No. 4,
2009, pp. 313-322.
R. R. Nair, H. A.Wu, P. N. Jayaram, I. V. Grigorieva,
A. K. Geim, Unimpeded Permeation of Water
Through
Helium-Leak–Tight
Graphene-Based
Membranes, Science, Vol. 335, No. 6067, 2012,
pp. 442-444.
C. Marichy, P. A. Russo, M. Latino, J. Tessonnier,
M. Wilinger, N. Donato, G. Neri, N. Pinna,
Tin dioxide-carbon heterostructures applied to gas
sensing: structure-dependent properties and general
sensing mechanism, Journal of Physical Chemistry
C, Vol. 117, Issue 38, 2013, pp. 19729-19739.
R. Sumita, K. Mukesh, S. Sumit, K. Dinesh,
Characterization and dispersibility of improved
thermally stable amide functionalized graphene
oxide, Material Research Bulletin, Vol. 60, 2014,
pp. 143-149.
S. Mao, S. Cui, G. Lu, K. Yu, Z. Wei, J. Chen,
Tuning gas-sensing properties of reduced graphene
oxide using tin oxide nanocrystals, Journal of
Material Chemistry, Vol. 22, No. 22, 2012,
pp. 11009-11013.
P. A. Russo, N. Donato, S. G. Leonardi, S. Baek,
D. E. Conte, G. Beri, N. Pinna, Room-temperature
hydrogen sensing with heteronanostructures based
onreduced graphene oxide and tin oxide, Angewandte
Chemie International Edition, Vol. 51, Issue 44,
2012, pp. 11053-11057.
S. Stankovich, R. D. Piner, S. T. Nguyen,
R. S. Ruoff, Synthesis and exfoliation of isocyanatetreated graphene oxide nanoplatelets, Carbon,
Vol. 44, No. 15, 2006, pp. 3342-3347.
E. Fuente, J. A. Menéndez, D. Suárez, M. A. MontesMorán, Basic surface oxides on carbon materials:
A global view, Langmuir, Vol. 19, No. 8, 2003,
pp. 3505-3511.
M. M. Alaie, M. Jahangiri, A. M. Rashidi,
A. Haghighi Asl, N. Izadi, A novel selective H2S
sensor using dodecylamine and ethylenediamine
functionalized graphene oxide, Journal of Industrial
and Engineering Chemistry, Vol. 29, 2015,
pp. 97-103.
G. I. Titelman, V. Gelman, S. Bron, R. L. Khalfin,
Y. Cohen, H. Bianco-Peled, Characteristics and
microstructure of aqueous colloidal dispersions of
graphite oxide, Carbon, Vol. 43, No. 3, 2005,
pp. 641-649.
J. I. Paredes, S. Villar-Rodil, A. Martı´nez-Alonso, J.
M. D. Tascón, Graphene oxide dispersions in organic
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 100-105
solvents, Langmuir, Vol. 24, No. 19, 2008,
pp. 10560-10564.
[20]. H. Gunzler, H. U. Gremlich, IR spectroscopy, WileyVSH, Winheim, 2002, pp. 223-227.
[21]. H. F. Teoh, Y. Tao, E. S. Tok, G. W. Ho, C. H. Sow,
Electrical current mediated interconversion between
graphene oxide to reduced graphene oxide, Applied
Phyical Letters, Vol. 98, 2011, 173105.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
105
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
PbS Infrared Detectors: Experiment and Simulation
1
S. KOUISSA, 1A. DJEMEL, 1 M. S. AIDA, 2 M. A. DJOUADI
1
2
Department of Physics, University of Constantine1, 25000, Algeria
Laboratoire ID2M, Institut des Matériaux Jouhn-Rouxel-CNRS, Université de Nantes, France
1
Tel.: (213) 31 81 11 07, fax: (213) 31 81 11 08
E-mail: skouissa@yahoo.fr, djemelamor@yahoo.fr, aida_2salah@yahoo.fr,
Abdou.djouadi@cnrs-imn.fr
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: The present work deals with the characterization and simulation of lead sulfide (PbS)
photoconductors infrared detectors growth by Chemical Bath Deposition (CBD) method. Three different
solutions bath are used in order to explore the doping effect and oxidant agent on detection capabilities.
Photoelectrical characterization indicates that detectors performances depend strongly on oxidant and doping
agents. A simulation study with surface state model is also presented. The physical parameters are deduced and
are found to be in agreement with those published in the literature. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Chemical Bath Deposition, Photoconductors, Infrared detectors, Surface state model, PbS.
1. Introduction
Thin film lead sulfide detectors have been widely
used over the past years for radiation sensing in 1 to
3 µm spectral region. They are mainly very useful in
academic, commercial and military applications. In
military application, PbS detectors are used for both
tactical and strategic systems, with a very strong
emphasis towards an increasing requirement for large
area multiple element arrays.
Unlike most other semiconductors IR detectors,
lead sulfide materials are used in the form of
polycrystalline films approximately 1 µm thick and
with individual crystallites ranging in size from
approximately 0.1 µm to 1 µm. They are usually
prepared by chemical bath deposition (CBD), which
generally yields better uniformity of response and
more stable results than the evaporate methods [1-6].
As-deposited PbS films exhibit very low
photoconductivity, however, a post deposition
process are used to achieve final sensitization. To
obtain high performance detectors, lead chalcogenide
106
films need to be sensitized by oxidation. This
oxidation may be carried out using additives in the
deposition bath, post-deposition heat treatment in the
presence of oxygen, or chemical oxidation of
the films.
Others impurities added to the chemical –
deposition solution for PbS have a considerable effect
on photosensitivity films characteristics [6-10]. They
may increase the photosensitivity by some order of
magnitude more than films prepared without these
impurities [9-10].
This work deals with characterization and
simulation of PbS photoconductors infrared detectors
prepared by CBD. The effect of oxidant agent and
Bismuth Nitrate additives on the performances of
PbS detectors is also examined. Finally, a simulation
study with surface state model proposed in [11-12] is
also presented. This paper is organized as follows:
Section 1 presents the particularities associated with
PbS infrared photo- detectors development. Section 2
details the experimental procedure for devices
fabrication and characterization. After analysis of
http://www.sensorsportal.com/HTML/DIGEST/P_2743.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
surface morphology and film structure in
Subsection 3.1, we performed optical measurements
(3.2) in order to understand the effect of additives of
oxidation and doping on structural and optical
properties of deposited PbS films. Measurements of
frequency response and time constant of detectors
prepared by solution Bath2 are presented in
Subsection 3.4.1. Based on surface state model
reviewed in Subsection 3.3, a comparison between
photoelectrical measurements of developed PbS
photoconductors and theoretical prediction is
proposed in Subsection 3.4.2. The conclusions and
perspectives are described in Section 4.
2. Experimental Details
The setup deposition of E. Pentia, et all. [10] was
used for growing the PbS films over three bath
solutions, summarized in Table 1. The Bath 1 contain
the basic precursors which are Lead Nitrate
Pb(NO3)2, Sodium Hydroxide (NaOH) and Thiourea
SC(NH2)2, the Bath 2 contain in addition an oxidant
agent, named Hydroxylamine Hydrochloride
(NH2OH-HCl), and the Bath 3 contain in addition to
Bath 2, the Bismuth Nitrate Bi(NO3)3 as
doping agent.
Table 1. Solutions bath.
Bath 1
Bath 2
Bath 3
Pb(NO3)2
NaOH
SC(NH2)2
Bath1 + NH2OH.HCl
Bath2 + Bi(NO3)3
Aqueous solution of 0.069 M lead nitrate, 0.69 M
NaOH and 0.24 M thiourea were used. In order to
prepare PbS films, the following procedure was
adopted: 20 ml of lead nitrate solution was mixed
with 20 ml of NaOH with constant stirring. The
initial color solution was milky, after it became
transparent, 20 ml of thiourea solution was gradually
added followed by addition of oxidant with
20 ml of 0.086 M Hydroxylamine Hydrochloride
(NH2OH.HCL), again with constant stirring, the
global solution was diluted with 20 ml of water.
Finally, a small quantity of Bismuth nitrate with
1.5 ml of 2.0610-4 M was added to some reactions.
The PbS films were deposited on microscope glass
substrate cleaned, for about 48 h, in a mixture of
(HNO3, (K2Cr2O7: H2SO4; 1:10), 1 % EDTA
followed by rinsing in distilled water.
After drying, one facet of this substrate was stuck
with an inert paste on a support having T format
introduced vertically in the reaction bath containing
the chemical mixture. After some time, the
transparent color solution started to change to
become completely black after one (01) hour.
The measured film thicknesses, using DEKTAK
profilometer, were about 150 nm for both films
elaborated with and without oxidant. Thicker films
were obtained by repeated deposition. Gold
electrodes were evaporated on the surface of PbS
films for electric and photoelectric measurements in a
coplanar configuration. The films characterizations
were performed after annealing in air at 80 °C for
approximately 70 h.
Structural properties were evaluated by x-rays
diffraction using a D5000 Siemens diffractometer.
The scans were carried out at room temperature, in
the conventional θ/2θ mode using Cu-Kα radiation
(0.1542 nm). The morphology was observed by
scanning electron microscopy (SEM) using a JEOL
6400F microscope. The optical properties were
studied with a CARY 5000 UV-Vis-NIR double
beam
spectrometer.
The
photoconductivity
measurements were performed with a system
constituted by a standard IR light source, an Oriel
MS257 monochromator operated in the range
1-20 µm, a chopper fixed at 400 Hz and the
acquisition equipment composed with spectrum
analyzer, lock-in amplifier and oscilloscope.
3. Results and Discussions
3.1. Surface Morphology and Film Structure
Fig. 1 and Fig. 2 show SEM micrographs of PbS
films deposited with and without oxidant. It appears
that the average grain size increases with the used
oxidant.
Fig. 1. SEM image of PbS films prepared
without oxidant agent.
Concerning the films structural properties, Fig. 3
and Fig. 4 show the effect of annealing treatment
(T=80 °C, for 72 h) and Bismuth doping on the XRD
(X Rays Diffraction) patterns of deposited PbS films.
As can be seen, films deposited with hydroxylamine
hydrochloride additive are less textured when
compared to films prepared without this additive,
which are (200) preferentially oriented, the grains
become oriented quasi-equally with (200) and (111)
crystallographic direction. The effect of annealing at
107
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
80°C for 72 h and doping with bismuth nitrate,
affects in opposite manner the XRD patterns. As
seen, the annealing increases, while the doping
decreases the films texture.
3.2. Optical Properties
Fig. 5 and Fig. 6 show the transmittances (Tcorr)
and (αhν)2 plots of PbS films prepared with different
baths and annealed at 80°C for 72 h respectively.
1,0
1: without additives_0,7µm:Eg=0.42eV
2: NH2OH.HCl_1µm:Eg=0.50eV
3: NH2OH.HCl + Dopage Bi_450nm:Eg=0.56eV
0,8
3
1
5
-1
α (cm )
Τcorr
10
0,6
2
2
4
0,4
10
1
0,4
3
0,6
0,2
0,8
1,0
1,2
hν (eV)
1,4
1,6
1,8
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
λ(µm)
Fig. 2. SEM image of PbS films prepared
with oxidant agent.
2500
Fig. 5. Transmittances of PbS thin films prepared
with different bath solution.
700
PbS Without additives
600
(200)
2000
8
5x10
PbS with
additives
1: without additive :Eg=0.42eV
2: NH2OH.HCl :Eg=0.50eV
3: NH2OH.HCl + Dopage Bi :Eg=0.56eV
500
annealing)
2:Annealed at 80° C
1000
300
8
200
2
100
for 72 h
1
0
20
500
30
40
50
60
70
(220)
(211)
(222)
2
8
1x10
30
40
50
60
70
80
2θ
500
400
300
200
With Bi(NO2)3
100
Without Bi(NO2)3
0
40
0,45
3
0,50
0,55
0,60
0,65
0,70
The inset shows the absorption coefficient of
these films. As shown, oxidant and doping affect the
optical properties. The optical band gap was
calculated from the spectral absorption near the
fundamental absorption edge. The direct band gap of
all synthesized PbS films was estimated using the
Tauc relation given as follow
PbS with NH2OH.HCl
600
30
1
0,40
Fig. 6. Plot of (αhν)2 for PbS thin films prepared
with different bath solution.
700
20
0
0,35
hν(eV)
Fig. 3. Effect of annealing on the XRD patterns
of PbS films.
Intensity(a,u)
8
2x10
80
1
20
50
60
70
80
2θ
Fig. 4. Effect of doping with Bi(NO2)3 on the XRD patterns
of PbS films.
108
3x10
2θ
(111)
2
0
2
1:As deposited (without
(α.hν)
1500
In t e n s it y (a ,u )
Intensity(a,u)
8
4x10
400
α hν = (hν − E g )2 ,
1
(1)
where α is the parameter which depends on the
transition probability. For direct transition in the
fundamental absorption, (αhν)2 have linear
dependence on the photon energy (hν). The intercept
on energy axis gives the direct band gap energy.
Based on the optical transmission measurements, we
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
have obtained the direct band gap energy for PbS
films grown by different baths, which is 0.42 eV for
films prepared without additive (referred as 1), it
becomes 0.5 eV for films prepared with oxidant
(referred as 2) and 0.56 eV for films prepared with
doping agent (referred as 3).
3.3. Surface State Model
Photoconductivity in PbS films can be explained
by two models depending upon whether the
semiconductor is single-crystalline or polycrystalline,
which are recombination and barrier models
respectively. The recombination model assumes that
change in conductivity on illumination results from
the change in the number of conducting electrons or
holes per unit volume. In the barriers model, it is
assumed that illumination produces little or no
change in the density of charge carriers but an
increase in their effective mobility. A large number
of surface defects are produced along the crystalline
boundaries. These surface defects capture electrons
from the interior of the single-crystalline and produce
space charge barriers. Illumination reduces the
number of electrons in the surface defects and
thereby lowers the barrier height. In real
polycrystalline
material,
the
observed
photoconductivity may be due to a combination of
both recombination and barrier processes. One of
these combination models is the surface state model
proposed by [11-12] (Fig. 7).
Infrared incident
radiation IR
Applied
electric field
Bulk region
surface depletion
region Zd
l
O
Ea
W
Ei
Zd
Vapp
d
IPH
RL
BC
NT
Et
EFI
P-PbS
EFP
≅ 3µm
0.41eV
BV
Eb
Fig. 8. Energetic diagram.
Ohmic
contacts
X
capture the minority carriers and thereby extending
the life time of material. As mentioned above,
without the sensitization step, lead sulfide has very
short life time and a low response.
The free surfaces of semiconductor constitute a
localized discontinuity in comparison to bulk
electronic states. The introduction of surfaces gives
rise to new eigenstates for which the wave function is
localized at the surface, the so-called surface states.
The presence of free surface constitutes an intrinsic
defect, which altered dramatically the lifetime of
device. The extrinsic defect is characterized by the
oxidation, adsorption and contamination. In
semiconducting materials, the defects is generally
charged induced a bend bending in the energetic
diagram, however, a potential barrier is formed under
the free surface, the electric field separate the charge
carriers (é-h) in two opposite direction, and create a
depletion region (Fig. 8). The majority carriers
created by irradiation have two ways: scanned by the
é field and contribute to photocurrent, or trapped with
surface state and contribute to noise.
Y
surface
(internal)
electric field
VPH
Z
Fig. 7. Schematic structure of surface state model [11].
This model treats the free surface with different
manner of precedent model [13-14]. It was
characterized by surface state density (Nt), unique
energy level (Et) associated with defect localized in
the forbidden gap and effective cross section (σ)
rather than the surface recombination velocity. In this
model, the barrier height at the semiconductor free
surface is modified under photonic excitation. It was
suggested that recombination influence directly the
quantum efficiency of detector. All models accept
that, the role of oxidant is assumed to introduce a
trapping state that inhibits recombination; these traps
The concentration of excess carriers density; the
key parameter of this model, allows us to explore all
the theoretical equations of detectors figure of merit.
This parameter is calculated with a self-consistent
way, taking into account the resolution of continuity
equation of majority and minority carriers in the
depletion and neutral (Bulk) region of material, given
by [11-12].
  z − zd
Δn( z ) = Bn exp − 
  Ln

 +

z

− z − zd
 − (z + z '+2 zd ) 
Ln
− exp 
G ( z ' )exp
 dz '

2 Dn z d
Ln
Ln



, (2)
where Bn is the concentration of excess carriers
density at free surface (z=0), which is determined by
the appropriate conditions of limits [15], Zd, Ln, Dn
and G(z) are width of depletion region, diffusion
length, diffusion coefficient and generation rate
respectively. Detailed calculation of the surface
analysis, optical generation and photoconductor
performances (Signal, Spectral response or
responsivity and specific detectivity) is presented
respectively in the appendix.
109
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
3.4. Photo Detection Performances
Measurement and Simulation
3.4.1. Measurement of Frequency Response
and Time Constant of Detector
Fig. 9 shows the setup measurement test to
determine the variation of signal voltage as a function
of chopping frequency. Select a low chopping
frequency (50 Hz). Tune the wave analyzer to this
frequency and record the signal voltage. Repeat this
process at successively higher frequencies. All
measurements are normalized to the initial low
frequency measurement and plotted as shown in
Fig. 10. This curve can be used to determine time
constant of the detector, which provides a convenient
number to describe the speed with which a detector
responds to a change in incident flux. The variation
of response with frequency can be described by
Rf =
R0
1 + 4π 2 f 2τ 2
,
(3)
where τ is the time constant.
Blackbody
Temperarure
Controller
Wave
analyzer
Oscilloscope
Chopper
variation
speed
Blackbody
Source (500°K)
Radian power
Detector
Preamp
Bias Supply
Detection
synchrone
Frequence
controller
Fig. 9. Basic test set for frequency response measurement.
Taking f1 as the frequency at which the
responsivity is 0.707 times its low-frequency value,
the time constant is given by
τ=
1
2π f 1
(4)
From the frequency response curve for the
photo-detector developed by solution Bath 2, shown
in Fig. 10.
100
R%
Photoconductor of Bath2
2
S:0.3 Cm
RL=100KΩ
Δf=50Hz
Ouverture:0.2inch
90
10
100
f(Hz)
f1=640Hz
1000
Fig. 10. Frequency response of PbS photoconductor
prepared by Bath 2.
110
The value of f1 is about 640 Hz: the calculated
value of time constant is τ=0.25 ms, the
corresponding carriers diffusion length is about
Lp=617 µm the calculated value of time constant is in
agreement of those published in the literature of PbS
detectors at ambient temperature, which varied
between 0.1 ms and 0.5 ms [16-20].
3.4.2. Signal, Spectral Response and
Detectivity Measurement
and Simulation
In order to investigate the influence of oxidant
and doping on detection and capabilities of PbS
films, three photoconductive detectors prepared with
and without additives (Table 1) have been analyzed.
To avoid the Flicker noise, manifested at low
frequency, the chopping frequency at 400 Hz and the
total polarization voltage at 50 V have been fixed.
The test set used provides radiant flux in a very
narrow spectral band centered about any desired
wavelength (λ). There are three functional controls
on the monochromator:
• The centered λ of the exciting beam;
• The width of the spectral interval centered at λ;
• The amount of flux passing through the
monochromator.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
Bath1
Bath3
7
D*(Cm, Hz1/2W-1)
A constant flux per unit λ interval should be
maintained for any selected λ center in the interval of
[1-4 µm]. According to application data sheet of the
infrared source, the power of infrared radiation is
calculated manually by assuming the IR source as a
blackbody at 1230 °C (the IR source is a Silicon
Carbide SiC emitted in the range of 0.7-28 µm, the
irradiance of this source is nearly the same of
blackbody heated at 1230 °C, particularly in
0.7-3 µm spectral range) [21].
It is interesting to notice, here, that the noise is
measured in the absence of light, with lock-in
amplifier and spectrum analyzer under equivalent
noise band width of Δf=50, the effective value of
noise is done by Vn/ (Δf )1/2 [22]. The values of Vn
(noise voltage in rms) for photoconductors
prepared with three baths are 55.15, 86.3 and
63.6 µV respectively.
This measurement of noise is crucial in the
calculating of spectral specific detectivity, which
depends of the ratio of spectral response and noise.
Fig. 11, Fig. 12 and Fig. 13 show the signal,
spectral response and specific detectivity of these
photoconductive detectors fitted with surface state
model [11-12]. The oxidant enhances the capabilities
of detection comparatively to detectors prepared
without oxidant. The doping increases again these
capabilities and decreases the peak wavelength.
10
Bath2
10
10
6
10
1
λ(µm)
Experimental points
Surface state model
9
10
0,1
λ(µm)
1
10
Fig. 13. Simulation of spectral detectivity with surface
state model.
The best adjustments are obtained with the data
reported in Table 2. It should be noted that, the
confrontation between experimental and calculated
data of signal is excellent, but a small discrepancy in
spectral response and specific detectivity for the first
point of measurement is observed. It is probably due
to high fluctuation of signal at low wavelength. Also,
the parameters issued with this simulation are in
agreement with the published ones in the literature
[16-20, 22, 24].
Table 2. Simulation results.
3,5
5
Bath1
Detector
3,0
V p h (m V )
Bath3
4
2,5
2,0
Eg (eV)
Lp (µm)
Zd (nm)
Na (cm-3)
Et (eV)
σ (cm2)
Nt (cm-2)
1,5
Vph (V)
1,0
3
0,5
0,0
Bath2
2
-0,5
0
1
2
λ(µm)
3
4
5
1
Experimental points
Surface state model
0
0
1
2
3
λ(µm)
4
5
Fig. 11. Simulation of signal with surface state model.
Bath1
100
Bath3
R (V /W )
80
5
R(V/W)
1,2x10
60
40
20
4
8,0x10
0
0
1
2
3
4
5
λ(µm)
Bath2
4
4,0x10
Experimental points
Surface state model
0,0
0
1
2
3
4
λ(µm)
Fig. 12. Simulation of spectral response with surface
state model.
Bath 3
0.48
400
10
51017
0.47
2.4510-17
51011
0.45
0.4
0.25
4. Conclusions
5
1,6x10
d(µm)
Ad (cm2)
RL (MΩ)
Bath 1
Bath 2
Simulated parameters
0.43
0.43
500
500
30
10
51017
51017
0.42
0.42
3.410-16
2.5510-14
1.51012
51011
Introduced parameters
0.7
1
0.4
0.3
0.1
0.1
5
In the present work, detectors based on sensitized
thin films, growth by chemical bath deposition are
studied. The photoelectrical characterization allowed
that, detectors developed without oxidant has
approximately very low performances (Signal,
Responsivity and Specific Detectivity) compared
with those developed with oxidant. The use of doping
agent increases again these performances along with
decreasing the peak position wavelength of detector.
A simulation study of proposed infrared
photoconductors, using surface state model, has been
also presented. The plots show the ability of this
model to adjust their performance behavior. The
extracted physical parameters are in agreement with
those published in literature.
111
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
Appendix
1
A. Surface Analysis
Assuming that Q is the absolute charge at the
surface absorber material, then
Q = eZd Na = eNt (1− f )
1
(A8)
τ s .z d
The conductance of film (Fig. 7) is given by:
Zμ n e
l
Δg =
where Zd is the depletion region width and f the
occupation probability of the donor energy level
given by [11] [15]:
Et − Ei
)
kT
f =
E − Et
)
Δn(0) + n0 + Δp (0) + p0 + 2ni cosh( i
kT
where Δn(0)(Δp(0)),ni, Et and Nt are the electron
(holes) excess carriers concentrations at the surface,
intrinsic carriers concentration, energy level of
surface states and surface states density, respectively.
The electron (hole) concentration at the surface n0
(p0) is given by:
R=
(A4)
= I
ph
ph
,
.R L
(A10)
R0
1 + (2πfτ )
(V/W),
(A11)
2
where R0:
(A3)
E
p 0 = N a exp( − b )
kT
(A9)
0
where V is the bias voltage.
The spectral response is given by:
R0 =
n2
E
n 0 = i exp( b )
kT
Na
 Δ n (z )dz
Iph = Δ g .V
V
(A2)
d
The signal in current and voltage is given by:
Δn(0) + n0 + ni exp(
V ph
(V/W),
(A12)
Pinc
f is the modulation frequency and τ the bulk carrier
lifetime (τ = LP2/DP).
The specific detectivity is given by:
The barriers height is given by:
D
eN a
=
Z d2
2ε
*
=
Ri
Ad Δ f
=
in
(A5)
Ad Δ f
RV
(A13)
Vn
The total noise expression is done by
2
i n2 = i G2 − R + i12/ f + i joh
B. Optical Generation
The optical generation rate is given by:
Gph(z) = Ff α η e
−α z
,
i n2 = 4 G 2 q ( q η E q A d + qg
(A6)
where Ff is the front surface flux modeled by Plank
function, α is the absorption coefficient and the η is
the quantum efficiency calculated by [17]:
]
(1 − r ). 1 − e −αd τ eff
η=
1 − r.e −αd
τ
B
i
1
(A7)
Δf +
f
The
f
th
(A14)
d . Ad ) Δ f +
2
f
B1 /
In which r is the reflection coefficient and τeff is
the effective lifetime given by [17]:
112
+
C. Signal, Responsivity and Detectivity
(A1)
[
1
τ
With τ, and τs are the bulk and surface electron
lifetime respectively.
N
Z d = t (1 − f ) ,
Na
Eb
=
τ eff
1/f
(A15)
4 kT
Δf
Rd
noise
is
given
by
[25]
where
C , C=0.1, Ad is the detector area, Nt is
=
N t Ad
the surface state density and Δf is the electrical
bandwidth of detector.
At room temperature and high modulation
frequency, the dominant compound of noise is the
thermal generated-recombination part given by [26]:
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 106-113
in2 = 4 q g i0 Δf
,
(A16)
where i0 is the dark current, Δf is the noise equivalent
frequency bandwidth given by 1/(2tint) where tint is the
electronic integration time and g is the gain of
photoconduction.
References
[1]. S. Kumar, T. P. Sharma, M. Zulfequar, M. Husain,
Characterization of vacuum evaporated PbS thin
films, Physica B, Vol. 325, 2003, pp. 8-16.
[2]. J. N. Humphrey, Optimum utilization of lead
sulphide detectors under diverse operating conditions,
Appl. Opt., Vol. 4, Issue 6, 1965, pp. 665-675.
[3]. T. H. Johnson, H. T. Cozine, B. N. McLean, Lead
selenide detector for ambient temperature operation,
Appl. Opt., Vol. 4, Issue 6, 1965, pp. 693-696.
[4]. D. E. Bode, Lead Salt Detectors, in Physics of Thin
Films, Vol. 3, (G. Hass and R. E. Thun, Eds.),
Academic Press, New York, 1966, pp. 275-301.
[5]. T. S. Moss, G. J. Burrel, B. Ellis, Semiconductor
Optoelectronics, Butterworths, London, 1973.
[6]. S. Kouissa, A. Djemel, M. S. Aida, M. A. Djouadi,
Characterization
and
Simulation
of
PbS
Photoconductors Prepared by Chemical Bath
Deposition, in Proceedings of the 6th International
Conference on Sensor Device Technologies and
Applications (Sensordevices’15), Venice, Italy,
23-29 August 2015, pp. 1-6.
[7]. E. Pentia, L. Pintilie, C. Tivarus, I. Pintilie, T. Botila,
Influence of Sb3+ ions on photoconductive properties
of chemically deposited PbS films, Materials Science
and Engineering, B80, 2001, pp. 23-26.
[8]. R. Dalven, A review of semiconductors properties of
PbTe, PbSe, PbS and PbO, Infrared Physics, Vol. 9,
No. 4, 1969, pp. 141-184.
[9]. V. M. Simic, Z. B. Marinkomc, Influence of
impurities on photosensitivity in chemically
deposited lead sulphide layers, Infrared Physics,
Vol. 8, 1968, pp. 189-195.
[10]. E. Pentia, L. Pintilie, T. Botila, I. Pintilie, A.
Chaparro, C. Maffiotte, Bi influence on growth and
physical properties of chemical deposited PbS films,
Thin Solid Films, Vol. 434, 2003, pp. 162-170.
[11]. S. Kouissa, M. S. Aida, A. Djemel, Surface state
simulation model for photoconductors infrared
detectors, Journal of Materials Science: Materials in
Electronics, Vol. 20, 2009, pp. 400-406.
[12]. S. Kouissa, A. Djemel, M. S. Aida, Surface state
dependence of PbS and PbSe infrared noise and
detectivity, Journal of Materials Science: Materials
in Electronics, Vol. 23, No. 12, 2012, pp. 2083-2088.
[13]. J. C. Slater, Barrier theory of the photoconductivity
of lead sulfide, Phys. Rev, Vol. 103, No. 6, 1956,
pp. 1631-1644.
[14]. R. L. Petritz, Theory of photoconductivity in
semicon-ductors films, Phys. Rev, Vol. 104, No. 6,
1956, pp. 1508-1516.
[15]. A. Djemel, R. J. Taronto, J. Castnaing, Y. Marfaing,
A. Nouiri, Study of electronic surface properties of
GaAs in cathodoluminescence experiment, Phys.
Stat. Solid (a), 1998, Vol. 168, No. 2, pp. 425-432.
[16]. E. Dereniak, G. D.Boreman, Infrared Detector and
Systems, Wiley Interscience, 1996.
[17]. A. Rogalski, Infrared photon detectors, SPIE Optical
Engineering Press, 1995.
[18]. William L. Wolfe, George J. Zissis (Eds.), The
infrared Handbook, IRIA, 1993.
[19]. J. Piotrowski, A. Rogalski, High-operatingtemperature infrared photodetectors, SPIE Optical
Engineering Press, 2007.
[20]. R. Hudson, Infrared system engineering, John Wiley
& Sons. Inc, 1969.
[21]. Calculation of Radiant Flux for Restricted
Wavelength Regions, SLS. APP 0005, Application
Data Sheet, Infrared Industries, Inc., 1989
(Private Document).
[22]. J. D. Vincent, Fundamentals of Infrared Detector
Operation and Testing, Wiley-Interscience, 1990.
[23]. E. Dereniak, G. D. Boreman, Infrared Detector and
Systems, Wiley Interscience, 1996.
[24]. N. B. Kotaiya, A. J. Kothari, D. Tiwari,
T. K. Chaudhuri, Photoconducting nanocrystalline
lead sulphide thin films obtained by chemical bath
deposition, Appl. Phys A, Vol. 108, Issue 4, 2012, pp.
819-824.
[25]. W. Z. Shen, A. G. U. Perera, Low frequency noise
and interface states in GaAs homojunction farinfrared detectors, IEEE Trans-Electron Devices,
Vol. 46, No. 4, 1999, pp. 811-814.
[26]. E. Rosencher, B. Vinter, Optoélectronique, Edit.
Masson, Paris, 1998.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
113
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Amplitude to Phase Conversion Based on Analog Arcsine
Synthesis for Sine-cosine Position Sensors
*
Mohieddine BENAMMAR, Antonio Jr. GONZALES
College of Engineering, Qatar University, P. O. Box 2713, Doha, Qatar
*
Tel.: +974-4403-4203, fax: +974-4403-4201
*
E-mail: mbenammar@qu.edu.qa
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Sinusoidal encoders, including Hall effect sensors, are position sensors that provide analog sine and
cosine signals of angular position. All schemes used for converting these signals into measure of the angle
require either trigonometric or inverse trigonometric function implementation. The proposed converter is based
on the use of the alternating pseudo-linear segments of the sensor signals together with a simple and effective
linearization technique. The theoretical absolute error of non-linearity of the converter is 0.05 degree over the
full 360 degree range. The converter may be implemented numerically or electronically. The paper describes the
proposed method, full details of its analog implementation, and experimental results obtained using both a
computer-based sensor emulator and a Hall effect sensor. Results demonstrate agreement between theory and
experimental results. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Amplitude-phase conversion, Inverse sine synthesis, Linearization, Position measurement,
Sinusoidal encoder, Hall effect sensor.
1. Introduction
Rotational speed and position measurement and
control is often required in various applications in
industry, military, avionics, communication and other
fields. Sinusoidal encoders whether operating on
optical, inductive, Hall effect or magnetoelectric
principles produce quadrature electrical signals in
which the unknown angle is encoded [1-10]. In
practice, it is common to observe phase and
amplitude imbalances in the sensor signals; therefore
the sensor outputs may be written as:
U S ( θ ) = A × sin (θ )
,

U C ( θ ) = A (1 + α ) × cos (θ + β )
(1)
where A is the maximum amplitude of the sinusoidal
component of US(θ), θ is the shaft angle of the rotor
114
of the sensor, and α and β are the amplitude and
phase imbalances respectively. Note that some
sensors may produce signals with dc offset
components; however, these may easily be removed
before further processing. Amplitude and phase
balancing may be achieved in a number of ways
[11-14]. In this work, we propose balancing by
generating a perfect cosine signal, from US(θ) and
UC(θ), with the same amplitude as the sine signal,
Uˆ S ( θ ) = U S ( θ ) = A × sin (θ )


U C (θ)
+ tan β × U S ( θ ) = A × cos (θ )
Uˆ C ( θ ) =
(1 + α ) cos β

(2)
The sensor requires a suitable converter in order
to determine θ from its signals (2); many open loop
and closed loop conversion schemes have been
described in literature. Some open loop converters
http://www.sensorsportal.com/HTML/DIGEST/P_2744.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
based on the linearization of the pseudo-linear
segments of sinusoidal signals have been described
[10, 15-23]. Ratiometric techniques based on
arctangent method are also used in open loop
converters [24-27]; these require the use of look up
tables or linearization techniques. Other open loop
converters based on the use of reference ac signals
and time measurement techniques have also
been described [8-9, 28-30]. Closed loop
converters employ the phase-locked loop (PLL)
technique [31-38].
In the present work, we present an open-loop
amplitude-to-phase converter that uses a simple
arcsine synthesis technique which can be easily
implemented with few standard electronic
components. The remainder of the paper is organized
as follows. In Section 2, the principle and theory of
operation of the proposed converter, including a
novel linearization technique, are described in details.
A dedicated signal shaping technique used for
implementing the converter is also described. In
Section 3, the practical implementation of the
converter and experimental results are described.
Section 4 concludes the paper.
2. Proposed Converter
The basic principle of the proposed converter
(Fig. 1) is based on making use of the alternating
pseudo-linear segments of the sensors signals (2) in
order to produce a signal U0(θ) which is almost
proportional to the unknown angle θ in each of its
four quadrants (Fig. 2), as has been reported in
previous works [5-19, 23]. The four quadrants are
identifiable using two binary outputs whose states
depend on the signs of the sum and difference of the
transducer signals (i.e., LOW state for negative and
HIGH state for positive values):
(
)
 Bit1(θ) = sign Uˆ (θ) + Uˆ (θ)

S
C

ˆ
 Bit 0(θ) = Bit1(θ) ⊕ sign U S (θ) - Uˆ C (θ)
(
)
(3)
The signal Bit0(θ) is used to control the
multiplexer that selects the pseudo-linear segments of
the sensor signal to produce U0(θ),
) (
(
U 0 (θ) = Bit 0(θ) × Uˆ C (θ) + Bit 0(θ) × Uˆ S (θ)
)
(4)
The sign of ÛS(θ) - ÛC(θ) is used together with a
synchronous rectifier to generate a rectified signal
U0R(θ), a sawtooth-like waveform made up of four
identical and positive-slope sections as shown in
Fig. 1 and Fig. 2. The signal U0R(θ) requires further
linearization in order to obtain a piecewise linear
output U0RL(θ). The angle may be determined from
the linearize signal V0RL(θ) using a simple linear
equation within each quadrant of input angle with
minimal non-linearity error. Within the full 360
degree range, the computed angle noted θC (in
degree) is determined from:
θc = 45° ×
[(
)
2 /A ×U 0 RL (θ) + 2 × Bit0 (θ) + 4 × Bit1 (θ)
]
(5)
Note that in the range 315° to 360°, the angle
determined using (5) is negative (i.e., measured
clockwise). Evidently, the residual error (θC-θ)
depends on the quality of linearization scheme.
Previous works have presented various linearization
methods with different degrees of complexity and
precision [10, 15-22]. These schemes that use
multipliers/dividers add complexity and cost,
particularly, in analog implementation. In this work,
the proposed linearization method is based on signal
shaping techniques.
Signal shaping networks are usually associated
with trigonometric and inverse trigonometric function
synthesis. In this application, the pseudo-linear
segments of U0R(θ) belong to sinusoidal signals.
Hence, linearization requires arcsine function
synthesis, or in other words sine-to-triangle
conversion. It is well known that signal shaping can
be implemented using well established piece-wise
linear approximation techniques involving diodes
and/or transistors [39-41]. Other techniques based on
differential pairs of transistors offer attractive and
simpler solutions that closely approximate sine
function [42-48]. In the present application, a signal
converter based on the non-linear I-V characteristics
of the base-emitter junction of low power bipolar
junction transistors. The basic circuit diagram of the
proposed linearization scheme is shown in Fig. 3.
This is a translinear sine-triangle converter inspired
from the triangle-sine scheme presented in [48].
Fig. 1. Basic diagram of the proposed converter.
115
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
 R  
 I + i 
≈ − 1 + 2  × 2 ( R1 / / R2 ) i + VT ln 

R
 I − i 
1  

 R  
 1 + i / I 
≈ − 1 + 2  × 2 ( R1 / / R2 ) i + VT ln 

R
 1 − i / I 

1 

 1 + U0R / IR 
 R + R   2R R U
≈ −  1 2  ×  2 1 0R + VT ln 

 R1   R1 + R2 R
 1 − U0R / IR 
≈ −2R2
1 + U0R / (IR) 
U0R  R2 
− 1 + VT ln 

R  R1 
1 −U0R / (IR) 

R 
U0 RL = − 1 + 2  × [2(R1 // R2 ) i + VBE1 − VBE2 ]
R1 

 1 + U0R /( IR ) 

2R
R 
≈ − 2 U0R −  1 + 2 VT ln

R
R1 

 1 − U0R /( IR ) 
Fig. 2. Converter signals: input signals ÛS(θ) and ÛC(θ),
non-linearized signals U0(θ) and U0R(θ) and binary outputs
Bit0(θ) and Bit1(θ).
(6)
Note that the emitter currents for Q1 and Q2 are
(I+i) and (I-i) respectively. The Maclaurin series
expansion of the logarithmic part of (6), which is of
the form ln((1 + x) /(1 − x)) with x<1, yields:
U 0RL = −
 R  ∞ (U /(IR)) 2n +1
2R2
U 0R − 21 + 2 VT  0R
R
2n + 1
 R1  n =0
= −2
 R2  
U 0R 
R2 I + 1 + VT 
( RI ) 
 R1  
(7)
3
5

 R  1  U  1  U 
− 21 + 2 VT   0R  +  0R  + ...
R
RI
RI
3
5



1 


 
The objective is to convert the sinusoidal signal
U0R(θ) into a sinusoidal current which is in turn
converted into a triangular output voltage U0RL(θ):
U 0RL = −K1 × arcsin(U 0R /(RI ))
Fig. 3. Basic principle of the proposed linearizer.
The following analysis assumes that the
transistors in the scheme of Fig. 3 are matched.
Because of symmetry, the current source of
magnitude 2I ensures that the individual bias currents
I in the two trans-diodes are equal. The sinusoidal
segments of the rectified voltage U0R(θ) applied to
the linearization circuit result in a current
i= -U0R(θ)/R. Note that this current should always be
lower than I; the maximum value should correspond
to the maximum amplitude A of the sinewaves from
which the segments of U0R(θ) are extracted and
therefore imax=A/R=I. By invoking the Shockley
equation ( I E ≈ I S eV /V where VT is the thermal voltage,
BE
T
approximately 26mV at room temperature, and IS is
the saturation current) for the base-emitter junctions
of the transistors, we can write:
 R 
U 0 RL = − 1 + 2  ×  2 ( R1 / / R2 ) i + VBE1 − VBE 2 
R1 

 I +i 
 I − i 
 R  
≈ − 1 + 2  ×  2 ( R1 / / R2 ) i + VT ln 
 − VT ln 

R
I

1  

 S 
 I S  
116
∞
(2n!)
(U 0R /(RI ))2n+1
2
n
+
4
(
!
)
(
2
1
)
n
n
n =0
= −K1 
(8)
 U  1  U 

3 U 
= −K1 ×  0R  +  0R  +  0R  + ...
RI
RI
RI
6
40







3
5
By comparing (7) and (8), and considering the
first three terms of the expansions, we can deduce the
conditions for a successful conversion:
 R2 
1 + VT + R2 I = K1 / 2
 R1 

U0RLmax = K1π / 4

 R2 
 R2 

K1 / 6 = 21 + R VT / 3 or 3 K1 / 40 = 21 + R VT / 5
1
1 




(9)
Note that the maximum amplitude of U0RL(θ)
occurs at the peak of U0R(θ) which is equal to 2-½A;
therefore U0RLmax=K1×arcsin(2½A/(RI))= K1π/4. Since
K1 cannot satisfy both requirements defined by the
last condition of (9), a compromise should be sought
to keep K1 within the boundaries defined by the last
condition of (9):
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
4(1 + R2 / R1 )VT ≤ K1 ≤ 80(R2 / R1 )VT / 15
(10)
Straightforward analysis shows that when
considering (10), the two conditions in (9) may be rewritten as:
25VT
VT
 I ≤ R1 // R2 ≤ 15I


15U RL0max − 1 ≤ R2 ≤ U RL0max − 1
πVT
R1
 20πVT
(11)
Assuming operation at room temperature
(VT=26 mV), a current I= 0.25 mA, sensor signals
amplitude A=10 V, and choosing a peak output
voltage U0RLmax=10 V, simple analysis yields:
104Ω ≤ R1 // R2 ≤ 173Ω

90 ≤ R2 / R1 ≤ 126
(12)
Since
A=RI,
the
required
value
for
R=10 V/0.25 mA=40 kΩ. Simulation of U0RL(θ)
according to (6) suggest that with R=40.04 kΩ,
R1=130 Ω and R2=14130 Ω, the residual error in the
determination of θ using (5) is within ±0.048° in the
full 360° range as shown in Fig. 4.
Fig. 4. Simulation of the performance of the proposed
linearization scheme. U0RL(θ) is computed according
to approximation in (6) with A=10 V, I=0.25 mA,
R=40.04 kΩ, R1=130 Ω, R2=14130 Ω, and VT=26 mV.
Note that:
1) The inversion of U0RL(θ) with respect to U0R(θ)
is due to the inverting configuration of the
operational amplifier;
2) The amplitude A of the sensor signals is
assumed to be 20 Vpp and therefore U0R(θ) is
20/2½ Vpp.
It is important to note that the operating
temperature usually affects signal shaping schemes;
this can be seen in (6) through the thermal voltage
term. However, some degree of temperature
compensation may be achieved by selecting a
suitable current source element with appropriate
positive temperature coefficient.
It is important to note that since the proposed
scheme is open loop, the bandwidth is only limited
by the dynamics of the components used, mainly the
operational amplifiers and analog switches. In all
cases, the bandwidth should be much higher than
what is required in practical positioning applications:
e.g., for a maximum rotational speed of 24000 rpm
(i.e., considered to be very high for real applications),
the corresponding signals’ frequency would be
24000/60=400 Hz only which does not pose any
problem even for standard components.
3. Experiment
The practical converter is a straightforward
implementation of the scheme of Fig. 1 using few
standard electronic components. Fig. 5 depicts the
detailed circuit implementation of the proposed
converter. Matched dual NPN transistors (MAT01)
and ±10 V reference source (REF01 IC with an opamp inverter with a gain of -1) have been used in the
implementation of the linearizer. In order to simplify
implementation of the linearizer without affecting
performance, the current sources shown in Fig. 3
have been omitted, and the required currents
(I and 2I) have been derived from a reference voltage
as shown in Fig. 5. The current I is set by RI (i.e.,
I=10 V/RI). Because the 10 V reference voltage is
much greater than the voltage VB at the bases of
transistors Q1 and Q2, the current through R2I is
nearly constant. Extensive computer simulation using
Multisim software showed that the best performance
of the linearizer (i.e., lowest absolute error of the
converter) is achieved with RI =39.5 kΩ and
R2I = 18.7 kΩ, resulting in I≈252 μA. The remaining
components for the converter are straightforward
implementation of the diagram of Fig. 1. The offset
cancellation, amplification, amplitude equalization
and phase correction of the encoder output signals are
not shown. The amplitudes of the converter’s input
signals ÛC(θ) and ÛS(θ) are 20 Vpp, and the linearized
output is 20 Vpp.
The proposed converter has been characterized
under controlled environment using a sensor
emulator built around LabVIEW and a high
performance USB-6259 DAQ board from National
Instruments, characterized by four 16-bit, 2.8 MS/s
analog output channels (Fig. 6).
117
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
Fig. 5. Detailed implementation of the proposed open
loop converter.
Fig. 6. Experimental set-up incorporating a sensor emulator
for the characterization of the converter.
The sensor emulator was created under LabVIEW
environment and provided the two input signals
ÛC(θ) and ÛS(θ) of the converter and an additional
analog sawtooth signal UREF used as reference for
assessing the linearity of the converter output signal
U0RL(θ). The sensor emulator enables characterization
of the proposed converter under various controlled
conditions, including transient condition which is not
practically possible to perform with a real sensor.
The various signals have been recorded using a
Tektronix MDO3024 oscilloscope. Further tests of
the converter have been conducted with a real sensor
as shown below.
Fig. 7 shows the converter input signals ÛS(θ) and
ÛC(θ), the signal U0(θ) obtained from the multiplexer,
the rectified signal U0R(θ) and the linearized analog
output U0RL(θ) and binary outputs Bit0(θ) and
Bit1(θ); the sensor was simulated to rotate at a fixed
forward rotational speed of 600 rpm (i.e., equivalent
to sensor signals frequency of 10 Hz).
Fig. 8 depicts the effectiveness of linearization
achieved by the proposed linearizer whose inputs and
outputs are -U0R(θ) and U0RL(θ), respectively. Notice
that the scales used for the two traces are different.
Fig. 9 depicts the input and output signals of the
converter when the sensor rotates at a reverse speed
of 1200 rpm. By comparing traces of Fig. 7 and
Fig. 9, it is clear that the converter provides
unambiguous and absolute measurement of the angle.
118
Fig. 7. Converter signals at forward speed of 600 rpm.
In order to assess the linearity of the segments of
the 20 Vpp output U0RL(θ) of the converter, the setup
of Fig. 6 has been used to generate a reference
sawtooth signal UREF synchronized with ÛC(θ) and
and
therefore
with
U0RL(θ).
An
ÛS(θ),
instrumentation amplifier was used to determine the
deviation of U0RL(θ) from UREF as per the diagram of
the setup in Fig. 6. The results are depicted in Fig. 10
where U0RL(θ) and UREF are shown using the same
scale but with slightly different ground levels for
clarity (i.e., in order to distinguish both signals). The
deviation of U0RL(θ) from UREF is also shown; this
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
residual error of linearity is within 100 mV peak-topeak without fine adjustment of the various
components of the converter, especially in relation to
the linearizer.
Fig. 8. Comparison between the linearized signal U0RL(θ)
and the non-linear segment U0R(θ) showing the effect
of linearization.
Fig. 9. Converter signals at reverse speed of 1200 rpm.
segments of U0RL(θ), see Fig. 4. The results suggest
an almost immediate response of the output, as
expected for an open loop converter.
Fig. 11. Transient response of the input and output signals
of the converter by stepping the angle from 0° to 150°.
The transient response performance depicted at a
time scale of 10μs/division in Fig. 12 indicate that the
input signals ÛC(θ) and ÛS(θ), which are generated
by the sensor emulator, presented some jitter and did
not change in a step fashion due to slow response
LabVIEW and associated DAQ card. Hence the
apparent first-order-like response of the converter
(with a time constant of merely 10μs) may not be
attributed to the converter itself.
After its characterization with the sensor
emulator, the converter was tested with a Hall effect
sensor (model HSCB22) as shown in Fig. 13. This
sensor produces two quadrature sine and cosine
signals with a nominal peak-to-peak amplitude of 4 V
and an offset of 2.5 V. Appropriate analog signal
conditioning (not shown in Fig. 5) is applied to the
sensor signals in order to
1) Remove the offset;
2) Correct the phase between its signals using (2);
3) Adjust their amplitudes to 20 Vpp.
The sensor was mounted on a miniature rotary
table (model A5990TS with an integral 90:1 gear
ratio and 100 arc-second accuracy) that enabled
precise manual control of the angle θ.
Fig. 10. Characterization of the resolver using the setup
of Fig. 6.
Fig. 11 depicts the excellent transient response of
the converter by stepping the angle from 0° to 150°
using the sensor emulator of Fig. 6. Clearly this
would not be possible using a real sensor. Note that
the step change from 0° to 150° has been chosen to
demonstrate the performance of the converter even
when the resulting output of the converter angle
changes from one quadrant to the another; in this
case from the first (θ=0°) to the third (θ=150°)
Fig. 12. Transient response of the input and output signals
of the converter at a time scale of 10 μs/div.
119
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
and was experimentally tested. The results have been
excellent and showed that the proposed scheme can
be used to measure angles in the full 360° range with
an overall error lower than 0.09°.
References
Fig. 13. Experimental setup for testing the converter
with commercial Hall Effect encoder HSCB22.
Fig. 14 show the experimental results obtained
with the setup of Fig. 13 in the angle range 0° to
180°. The results indicate that the residual error of
non-linearity of the converter and associated encoder
is within 0.15° peak-to-peak.
10V
Output, U0RL(θ)
5V
0V
-5V
-10V
0.10°
Error, θC-θ
0.05°
0.00°
-0.05°
-0.10°
0°
45°
90°
Angle, θ
135°
180°
Fig. 14. Characterization of the converter
using the experimental setup of Fig. 13.
The overall absolute error in the determination of
the angle θC from the converter output U0RL(θ) was
lower than 0.09° in the range 0° to 180° of input
angle; this compares well with the theoretical error
estimated above in Fig. 4. Despite the simplicity and
low cost of the proposed scheme, its error
compares well with those of other more complex
closed loop and open loop schemes reported in
literature, e.g., 0.01° in [15], 0.10° in [17], 0.04° in
[20], 0.18° in [38].
4. Conclusions
In this paper, low-cost and simple-to-implement
open-loop method for amplitude-to-phase conversion
was proposed for use with sinusoidal Hall effect
sensors. The conversion was based on a simple and
effective linearization technique, full theory of which
was given. The theoretical error of non-linearity of
the converter is below 0.05°. The scheme was
implemented using standard electronic components
120
[1]. N. H. Duc, B. D. Tu, N. T. Ngoc, V. D. Lap,
D. T. H. Giang, Metglas/PZT-Magnetoelectric 2-D
Geomagnetic Device for Computing Precise Angular
Position, IEEE Transactions on Magnetics, Vol. 49,
Issue 8, 2013, pp. 4839-4842.
[2]. D. T. Huong Giang, P. A. Duc, N. T. Ngo,
N. T. Hien, N. H. Duc, Spatial angular positioning
device with three-dimensional magnetoelectric
sensors, Review of Scientific Instruments, Vol. 83,
Issue 9, 2012, pp. 095006-1 - 095006-6.
[3]. Y. Netzer, Linear electric encoder with facing
transmitter
and
receiver,
US
Patent
US2004/02525032A1, Dec. 16, 2004.
[4]. P. Kejik, S. Reymond, R. S. Popovic, Purely CMOS
angular position sensor based on a new hall
microchip, in Proceedings of the IEEE 34th Annual
Conference on Industrial Electronics, Orlando, FL,
Nov. 10-13, 2008, pp. 1777-1781.
[5]. P. Kejik, S. Reymond, R. S. Popovic, Circular hall
transducer for angular position sensing, in
Proceedings of the Conference on Transducers
Eurosensors XXI, Lyon, France, Jun. 10-14, 2007,
pp. 2593-2596.
[6]. K. Bienczyk, Angle measurement using a miniature
hall effect position sensor, in Proceedings of the
Conference
on
International
Students
Electrodynamics and Mechatronics, Opole, Poland,
19-21 May 2009, pp. 21-22.
[7]. D. Wang, J. Brown, T. Hazelton, J. Daughton, 360◦
angle sensor using spin valve materials with SAF
structure, IEEE Transactions on Magnetics, Vol. 41,
No. 10, Oct. 2005, pp. 3702-3705.
[8]. C. S. Anoop, B. George, Electronic Scheme for
Computing Inverse-Cosine and its Application to a
GMR Based Angle Sensor, IEEE Transactions on
Instrumentation and Measurement, Vol. 61, No. 7,
July 2012, pp. 1991-1999.
[9]. C. S. Anoop, B. George, Jagadeesh K. V., Tunneling
Magneto-Resistor based Angle Transducer, in
Proceedings of the 5th International Conference on
Sensing Technology (ICST’11), Palmerston North,
New Zealand, 28 Nov. – 1 Dec. 2011, pp. 431-435.
[10]. G. Ye, et. al, Design of a precise and robust
linearized converter for optical encoders using a
ratiometric technique, Measurement Science and
Technology, Vol. 25, 2014, pp. 125003-125011.
[11]. D. C. Hanselman, Techniques for Improving
Resolver-to-Digital Conversion Accuracy, IEEE
Transactions on Industrial Electronics, Vol. 38,
No. 6, Dec. 1991, pp. 501-504.
[12]. A. Bünte, S. Beineke, High-performance speed
measurement by suppression of systematic resolver
and encoder errors, IEEE Transactions on Industrial
Electronics, Vol. 51, No. 1, 2004, pp. 49-53.
[13]. J. Zhang, Z. Wu, Automatic calibration of resolver
signals via state observers, Measurement Science and
Technology, Vol. 25, No. 9, 2014.
[14]. S. Balemi, Automatic Calibration of Sinusoidal
Encoder Signals, in Proceedings of the World
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
[15].
[16].
[17].
[18].
[19].
[20].
[21].
[22].
[23].
[24].
[25].
[26].
[27].
[28].
Congress of the International Federation of
Automatic Control (IFAC), Prague, Jul. 2005.
M. Benammar, L. Ben-Brahim, M. A. Alhamadi,
A high precision resolver-to-DC converter, IEEE
Transactions on Instrumentation and Measurement,
Vol. 54, No. 6, Dec. 2005, pp. 2289-2296.
M. Benammar, L. Ben-Brahim, Mohd A. Alhamadi,
A Novel Resolver-to-360 Degree Linearized
Converter, IEEE Sensors Journal, Vol. 4, No. 1,
2004, pp. 96-101.
M. Benammar, A novel amplitude-to-phase converter
for sine/cosine position transducers, International
Journal of Electronics, Vol. 94, No. 4, 2007,
pp. 353-365.
M. Benammar, M. Bagher, M. Al Kaisi, Digitallytuned resolver converter, in Proceedings of the
Eurosensors XXIII Conference, Procedia Chemistry,
Vol. 1, Issue 1, 2009, pp. 449-452.
D. Živanović, J. Lukić, D. Denić, A Novel
Linearization Method of Sin/Cos Sensor Signals
Used for Angular Position Determination, Journal of
Electronics in Engineering Technology, Vol. 9,
No. 4, 2014, pp. 1437-1445.
M. Benammar, L. Ben-Brahim, M. A. Alhamadi,
M. Al-Naemi, A novel method for estimating the
angle from analog co-sinusoidal quadrature signals,
Sensors and Actuators-A, Vol. 142, No. 1, March
2008, pp. 225-231.
V. Riewruja, A. Kaewpoonsuk, OTA-based sine-totriangular wave converter, in Proceedings of the
Conference on Circuits Systems Signal Processing,
Vol. 25, 2006, pp.753-765.
A. Kaewpoonsuk, W. Petchmaneelumka, A.
Rerkratn, S. Tammaruckwattana, V. Riewruja, A
novel resolver-to-DC converter based on OTA-based
inverse-sine function circuit, in Proceedings of the
Conference on Society of Instrument and Control
Engineers (SICE) of Japan, Japan, 20-22 Aug. 2008,
pp. 609-614.
M. Benammar, A. Gonzales, Simple and Precise
Analog Arcsine Synthesis Applied to Amplitude to
Phase Conversion for Hall Effect Position Sensors, in
Proceedings of the 6th International Conference on
Sensor Device Technologies and Applications,
(SENSORDEVUCES’15), Venice, Italy, 23-28
August 2015, pp. 13-18.
M. Benammar, M. Bagher, M. Al Kaisi, Novel
Linearizer for Tangent/Cotangent Converter, in
Proceedings of the 16th IEEE International
Conference on Electronics, Circuits, and Systems
(ICECS’09), Hammamet, Tunisia, 13-16 Dec. 2009,
pp. 575-578.
S. C. M. Reddy, K. N. Raju, Inverse Tangent Based
Resolver to Digital Converter - A Software
Approach, International Journal of Advanced
Engineering and Technology, Vol. 4, Issue 2, Sept.
2012, pp. 228-235.
A. Attaianese, G. Tomasso, Position measurement in
industrial drives by means of low-cost resolver-to
digital
converter,
IEEE
Transactions
on
Instrumentation and Measurement, Vol. 56, No. 6,
Dec. 2007, pp. 2155-2159.
S. Sarma, V. K. Agrawal, S. Udupa, Software-based
resolver-to-digital conversion using a DSP, IEEE
Transactions on Industrial Electronics, Vol. 55,
No. 1, Jan. 2008, pp. 371-379.
L. Ben-Brahim, M. Benammar, M. A. Alhamadi, A
resolver angle estimator based on its excitation
[29].
[30].
[31].
[32].
[33].
[34].
[35].
[36].
[37].
[38].
[39].
[40].
[41].
[42].
[43].
[44].
signal, IEEE Transactions on Industrial Electronics,
Vol. 56, No. 2, Feb. 2009, pp. 574-580.
L. Ben-Brahim, M. Benammar, M. Alhamadi, N.
Alhamadi, M. Alhitmi, A new low cost linear
resolver converter, IEEE Sensors Journal, Vol. 8,
No. 10, Oct. 2008, pp. 1620-1627.
M. Benammar, L. Ben-Brahim, K. Besbes, An
apparatus for the determination of the angle from its
sine/cosine values, UK Patent Application 2471458
(A), 5 Jan. 2011.
J. Bergas-Jané, C. Ferrater-Simón, G. Gross,
R. Ramírez-Pisco, S. Galceran-Arellano, J. RullDuran, High-Accuracy All-Digital Resolver-toDigital Conversion, IEEE Transactions on Industrial
Electronics, Vol. 59, No. 1, 2012, pp. 326-333.
G. A. Woolvet, Digital transducers, Journal of
Physics E: Scientific Instruments, Vol. 15, No. 12,
Dec. 1982, pp. 1271-1280.
C. H. Yim, I. J. Ha, M. S. Ko, A resolver-to-digital
conversion method for fast tracking, IEEE
Transactions on Industrial Electronics, Vol. 39,
No. 5, Oct. 1992, pp. 369-378.
D. A. Khaburi, Software-Based Resolver-to-Digital
Converter for DSP-Based Drives Using an Improved
Angle-Tracking Observer, IEEE Transactions on
Instrumentation and Measurement, Vol. 61, No. 4,
April 2012, pp. 922-929.
N. Noori, D. A. Khaburi, A new software-based
method for rotor angle calculation, in Proceedings of
the 5th Power Electronics, Drive Systems and
Technologies Conference (PEDSTC’14), Teheran,
Iran, 5-6 Feb. 2014, pp. 305-310.
L. Idkhajine, E. Monmasson, M. W. Naouar, A.
Prata, K. Bouallaga, Fully Integrated FPGA-Based
Controller for Synchronous Motor Drive, IEEE
Transactions on Industrial Electronics, Vol. 56,
No. 10, Oct. 2009, pp. 4006-4017.
L. Ben-Brahim, M. Benammar, A New PLL Method
for Resolvers, in Proceedings of the International
Power Electronics Conference (IPEC’10), Sapporo,
Japan, 21-24 June 2010, pp. 299-305.
N. Al-Emadi, L. Ben-Brahim, M. Benammar, A New
Tracking Technique for Mechanical Angle
Measurement, Measurement, Vol. 54, 2014,
pp. 58-64.
C. J. Paull, W. A. Evans, Waveform shaping
techniques for the design of signal sources, Radio and
Electronic Engineer, Vol. 44, No. 10, 1974,
pp. 523-532.
V. Schiffer, W. A. Evans, Approximations in
sinewave generation and synthesis, Radio and
Electronic Engineer, Vol. 48, No. 3, 1978,
pp. 113-121.
D. Kubanek, Diode piecewise-linear function
approximation circuit with current input and output,
in Proceedings of the 34th International Conference
on Telecommunications and Signal Processing
(TSP’11), 2011, pp. 284-287.
B. Gilbert, Circuits for the Precise Synthesis of the
Sine Function, Electronics Letters, Vol. 13, August
1977, pp. 506-508.
O. Ishizuka, Z. Tang, H. Matsumoto, MOS Sine
Function
Generator
Using
Exponential-Law
Technique, Electronics Letters, Vol. 27, Issue 21,
October 1991, pp. 1937-1939.
W. A. Evans, V. Schiffer, A low distortion tri-wave
to sine converter, Radio and Electronic Engineer,
Vol. 47, No. 5, 1977, pp. 217-224.
121
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 114-122
[45]. C. A. dos Reis Filho, M. P. Pessatti, J. P. C. Cajueiro,
Analog triangular-to-sine converter using lateral-pnp
transistors in analogue pCMOS process, in
Proceedings of the 9th International Conference on
Electronics, Circuits and Systems, 2002, Vol. 1,
2002, pp. 253-256.
[46]. H. Hassan, FET differential amplifier as a tri-wave to
sine converter, in Proceedings of the 36th
Southeastern Symposium on System Theory, 2004,
pp. 427-430.
[47]. S. Galecki, Multiphase sine-shaper circuit, IEEE
Journal of Solid-State Circuits, Vol. 32, No. 1, 1997,
pp. 126–129.
[48]. R. G. Meyer, W. M. C. Sansen, S. Lui, S. Peeters,
The differential pair as a triangle-sine converter,
IEEE Journal of Solid-state Circuits, Vol. SC-11,
No. 3, 1976, pp. 418-420.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
122
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
New Design-methodology of High-performance TDC
on a Low Cost FPGA Targets
Foudil Dadouche, Timothé Turko, Wilfried Uhring, Imane Malass,
Norbert Dumas, Jean-Pierre Le Normand
ICube, UMR 7357, University of Strasbourg and CNRS
23, rue du Loess BP 20, F-67037 Strasbourg Cedex 2, France
Tel.: +33 (0)3 88 10 68 27, fax: +33 (0)3 88 10 65 48
E-mail: wilfried.uhring@unistra.fr
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: This work aims to introduce a design methodology of Time-to-Digital Converters (TDCs) on low cost
Field-Programmable Gate Array (FPGA) targets. First, the paper illustrates how to take advantage of the
presence of carry chains in elementary logic elements of the FPGA in order to enhance the TDC resolution.
Then, it describes how to use the Chip Planner tool to place the partitions composing the system in user
specified physical regions. This allows the placement of TDC partitions so that the routing paths are constrained.
As a result, the user controls the propagation delay effectively through the connection network. The paper ends
by applying the presented methodology to a case study showing the design and implementation of high
resolution TDC dedicated to time correlated single photon counting system. The resolution of 42 ps as well as
the INL, DNL and mean Jitter values (22 ps rms, 13 ps rms and 26 ps rms, respectively) obtained using a low
cost FPGA target Cyclone family are very promising and suitable for a large amount of fast applications.
Copyright © 2015 IFSA Publishing, S. L.
Keywords: Time-to-digital converter, FPGA, Chip planner, Carry chain logic, Time correlated single photon
counting.
1. Introduction
Nowadays, numerous applications require a
precise measurement of time duration separating two
or several physical events. 3D scanners or 3D console
games represent typical application requiring precise
time quantification of the interval time to reconstitute
a three-dimensional scene. Such systems are
generally based on Time of Flight (TOF)
measurement of the light emitted by a laser diode or a
light-emitting diode (LED) and detected by a suitable
light sensors after reflection by an object. The TOF
of the light is proportional to the distance traveled by
http://www.sensorsportal.com/HTML/DIGEST/P_2745.htm
the latter. The measurement is made independently
by several pixels allowing the reconstitution of the
3D scene [1-2].
To measure this duration, we use devices capable
of converting extremely low time durations (some
tens of picoseconds) into digital values
understandable for downstream processing and
conditioning chain. These devices are commonly
known as Time-to-Digital Converters (TDCs) [3].
The latter is largely used for several years in
numerous smart sensor systems, particle and highenergy physics applications as well as measurement
and instrumentation applications such as digital
scopes and logic analyzers [3-4].
123
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
In order to design such systems there are several
techniques which are proposed in literature. Some of
the techniques that can be readily identified are
[3, 5-6]: Tapped Delay Lines (TDL), Delay Locked
Loop (DLL), Vernier Delay Line (VDL), Multilevel
TDC, etc. All of these Time-to-Digital conversion
techniques are usually designed as ApplicationSpecific Integrated Circuits (ASICs) [7]. The latter
have the advantage to have high performances but
suffer from a higher cost, slow time to market and
limited reconfiguration possibilities. It is also worth
noting that the ASIC solutions are not suitable for
integration into reconfigurable digital designs mostly
described in Hardware Description Languages
(HDL). As a result, numerous solutions for
implementing TDCs on FPGA circuits have emerged
[8-13]. However, the most significant limitation of
these architectures is the difficulty to predict the
placement and routing delays as well as the time
delay of the logic gates itself. The consequence of
this inevitable hardware restriction is a non-stable
resolution of the designed TDC [10].
In this work, we aim to extend our contribution
presented at SENSORCOMM 2015, consisting of
introducing a design methodology for high resolution
TDC on low cost FPGA targets [14] by including
some improvements leading to new interesting
results. This methodology enables the mastering of
the network routing delays as well as the
delays of the gates themselves. Therefore, it leads
to an optimized TDC design with stable and
accurate resolutions.
In order to give some background, the functional
principle as well as the structure of the studied TDC
in given in the second section. We also point out
some associated difficulties encountered while using
classical inverters as delay cells. The third section is
dedicated to present our approach of implementing a
TDC on an FPGA. Firstly, we show how to take
advantage of the Carry Chain Logic to enhance and
optimize TDC resolution. Secondly, we illustrate
how to use the Chip Planner to define the exact
physical layout location in the Chip. Therefore, we
point out the importance of this operation. Section 4
provides a detailed case study consisting of
implementing a 42 ps resolution TDC on Altera
Cyclone IV low cost FPGA. The implemented TDC
is associated to an FTDI (Future Technology Devices
International) USB interface circuit operating in
parallel mode with transfer rates reaching up to
40 Mbytes per second. Finally, we end our work by
providing some final observations.
information to binary sequence understandable for a
downstream processing chain.
For an accurate time duration measurement,
generally, a TDC is composed of three blocks: two
fine measurement blocks and a coarse one. The
coarse one counts the number (N) of clock periods
between enabling to disabling the measured interval,
and the fine blocks evaluate the uncertainties in both
sides that cannot be counted since their duration is
shorter than the clock period.
To understand the role of each one of the three
blocks, we illustrate by the timing diagram of Fig. 1
the functioning principle of a generic TDC.
Fig. 1. Functioning principle of a generic TDC.
As we can see from this timing diagram, the time
interval to be measured (Tm) is a combination of
three individual durations:
1) TCoarse, which represents the number of clock
periods from enabling to disabling coarse
measurement;
2) TFine1, representing the time between the
measured signal active edge and the first following
rising clock edge;
3) TFine2 which is the time between the falling
edge of the measured interval and the following
rising clock edge. Accordingly to this timing
diagram, the measured time will be expressed
as follows:
Tm = TFine1 + TCoarse − TFine 2
(1)
However, given the fact that the:
TCoarse = N .Tclk
(2)
we obtain the following expression:
2. Setting in the Context
2.1. Functional Principle of the Studied TDC
A TDC is an electronic system that measures the
interval time between two occurring events of a given
signal. Its main purpose is to convert temporal
124
Tm = TFine1 + N .Tclk − TFine 2
(3)
In practice, TDCs are mostly used in fast imaging
systems needing to know the delay separating a
photon emission by a laser diode and the detection of
that photon by a Single-Photon Avalanche Diode
(SPAD). Therefore, the events can be represented by
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
two signals: (1) a START signal, which can be
synchronized with the coarse counter clock, and (2) a
STOP signal that means that the SPAD has detected a
photon. In that case, the whole TDC can be
reduced to a coarse counter associated to a
fine TDC measuring TFine2. Consequently, the
measured interval time will be given by the
expression hereafter:
Tm = N .Tclk − TFine 2
(4)
Since the coarse block is a simple counter
incremented by the system clock, we will focus in
the following section on the implementation of the
fine TDC.
2.2. Structure of the Studied TDC
Start_out
Start_in
D
HIT
Q
Q
Clk
Fig. 3. Simple TDC elementary cell.
As mentioned previously, there are different
techniques of designing TDCs. In this work, we focus
on the commonly used Tapped Delay Lines (TDL)
architecture depicted in Fig. 2.
td
td
circuit entirely different from the desired function.
Indeed, if the input signal Start_in and the output
signal Start_out have the same logic equation, the
used HDL software (Quartus II) will simplify the
logical equation giving the output versus the input so
that it saves place and time.
To illustrate this phenomenon, we represent in
Fig. 4 the RTL view resulting from the
implementation of a simple TDC chain composed of
four elementary cells.
td
Start
D
Q
Clk
D
Q
D
Clk
Q
Clk
HIT
Q0
Q1
QN
Fig. 2. Tapped delay line TDC.
A TDL TDC consists of N cascaded delay
elements whose inputs are stored in D Flip Flops
(DFFs). We would then have as many DFFs as there
are delay elements. Therefore, each delay element
can be regrouped with its associated DFF to form an
elementary cell of the TDC.
The number (N) of these elementary cells
depends of the common DFF clock frequency, as
well as the propagation time of the delay element (td).
This is given by the ratio of clock period to
propagation time td. Since the value of the
previous parameter is not provided, it is
determined experimentally.
2.3. Design and Validation of the
Elementary Cell
In order to design the elementary cell of the
studied TDC, we first used a simple invertor as a
delay element associated with a DFF as illustrated
by Fig. 3.
However, implementing a TDC chain on an
FPGA by duplication of this cell leads to a simplified
Fig. 4. RTL view of the implementation of a simple
4-cells TDC.
It can readily be seen that, in spite of the presence
of inverters, the software has simplified the logical
equations. Consequently, all the inverted signals are
grouped independently of the non-inverted ones. It is
thus evident that this method is not suitable for
designing a TDC. Nevertheless, it is worth to notice
125
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
that, if it is not possible to prevent Quartus II
software to optimize data path, it is quite possible to
create this path manually by operating directly on the
logical resources of the FPGA. Indeed, the Quartus II
Chip Planner tool allows physical access to logical
resources available on the chip.
Using this tool, we can perform a customized
configuration of the logic elements and impose the
data path. However, the manual configuration of
logic elements is tedious and time consuming in
particular for systems with a certain complexity such
as TDCs.
Even if we can use this technique to implement a
TDC on an FPGA, given the large number of logic
elements to be configured individually, it is still
difficult to set up. Moreover, the TDC chain size can
vary from an application to another; it will be
therefore preferable to automate the configuration so
that the solution will be generic and adaptable.
Hence, we propose an appropriate design
methodology in the following section.
3. Design Methodology
In order to provide solutions to the above raised
issues, in this section we suggest an alternative
approach that can carry out a TDC structure fulfilling
the following needs:
• Avoid the software data path simplification;
• Increase TDC resolution by reducing the
propagation time through delay elements;
• Automate the elementary cells set-up process
to optimize the design time and make possible
the development of generic and adaptable
structures;
• Use a low cost FPGA target to implement the
TDC.
This method is focused on two main areas:
• Using adders as delay elements and utilization
of the Carry Chain Logic of the FPGA;
• Using the Chip Planner tool.
3.1. Using Adders and Carry Chain Logic
The implementation of digital circuits on FPGA
targets depends on the architecture of the logical
resources of the target. In this work, we are aiming to
use a low cost FPGA from Altera Cyclone family.
The selected target is the Cyclone IV
(EP4CE55F23C8) based on the logic element shown
by Fig. 5 [15].
Fig. 5. Cyclone IV logic element structure.
The Cyclone IV logical element, provides a
dedicated path for fast carry propagation. The role of
this carry chain is to use specific fast paths for carry
propagation instead of general-purpose routing
network. By doing so, it makes it possible to
drastically optimize the propagation time. This is
ideal for the enhancement of the TDC resolution.
Moreover, it allows harmonizing the delays of the
TDC elementary cells.
126
The problem is that customized handling of carry
chains is reserved to high performance FPGAs as
such as the Stratix family from Altera whose cost is
outstandingly high. However, it is possible to
configure the Quartus II synthesis tool to optimize
speed. In this case, the synthesis tool uses the carry
chain logic automatically when synthetizing an HDL
model involving adders.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
It is therefore possible to use the carry chain logic
to minimize and harmonize propagation delays for
components involving adders. It is precisely the idea
that is exploited here to design TDC elementary cells
based on simple adders. This was done by developing
a simple behavioral VHDL model for an adder with a
customizable number of elementary cells. The
number of the cells depends on the data width
modeled by a generic parameter called
DATA_WIDTH. The whole model is given by Fig. 6.
same TDC’s elementary cell in different logic
elements, as shown in Fig. 8. The direct consequence
of component misplacing is that the delay time is no
longer identical for all cells. This inevitably generates
unpredictable artifacts. To ensure a reliable
operation, it is necessary to overcome this problem
by constraining the placement tool to bring together
the components of the same cell in the same logic
element. This is the purpose of the next section.
Fig. 7. Implementation of a TDC elementary cell
by a logic element.
Fig. 6. Adder VHDL model.
The fine TDC using adders can be performed by:
1) Applying the TDC input signal STOP to the
carry input signal (cin) of the adder;
2) Choosing values for adder operand inputs
(a and b) so that an output carry is generated
(cout=‘1’) if input carry is equal to ‘1’.
The output carry is then an exact replication of
the input carry delayed by a transmission time
through the cell. To do so, all it takes is to set all the
bits of the first operand to ‘1’ and the bits of the
second operand to ‘0’. For each bit (i) the arithmetic
sum a(i)+b(i) gives ‘1’. When the input carry is
activated (cin=‘1’) by the TDC input signal (STOP),
the arithmetic sum a+b+cin gives ‘0’ and the carry
output moves to ‘1’.
The Fig. 7 illustrates the implementation of one
elementary cell of a TDC by a logic element of the
Cyclone IV target. The adder cell is obtained by the
look up table (LUT) and the DFF by the sequential
configurable output register.
Theoretically, to obtain a TDC chain similar to
the TDL structure shown by Fig. 2, it is sufficient to
duplicate the structure of Fig. 7 as often as necessary
to reach the number of desired cells. However, when
implementing such a chain on the FPGA, some DFFs
of the TDC elementary cells are dissociated of their
corresponding 1-bit adder cells even if the data path
is perfectly respected.
This phenomenon occurs randomly and leads to
the placing of the DFF and the delay element of the
Fig. 8. Random placing of DFFs on the chip.
3.2. Using Chip Planner
Using a TDC in fast imaging systems requires the
measurement of very short time durations. It is
therefore necessary to master all of the signal
propagation delays through the cells as well as the
routing network.
As we have seen in the previous section,
unconstrained automatic implementation of a TDC
on an FPGA usually leads to an inhomogeneous and
irreproducible
structure.
Consequently,
the
measurement results are tainted by these
uncertainties. Therefore, it is necessary to control the
exact physical location of TDC cells on the chip.
This could be achieved by using the Chip Planner
tool provided by Altera. The latter, according to the
user's needs, allows the defining of specified
implementation regions on the chip for blocks
constituting the whole system. In addition, it supports
incremental compilation to preserve the wellimplemented parts and reduce the compilation time.
This operation takes place in three distinct steps:
• Creating Design Partitions: the first step
consists of dividing the design in individual partitions
according to system complexity as well as user needs.
127
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
• Defining logic regions: after partitioning the
design, it is necessary to define logical zones that will
be associated to the partitions. This allows individual
compiling and optimizing of each region. The tool
used to perform this operation is LogicLock Region
(LLR) within Chip Planner.
• Physical assignment of logic regions: in order
to physically preserve the logic regions defined in the
previous step, by means of the LLR tool, physical
regions of the chip are assigned to implemented
partitions.
The physical delimitation of regions permits to
constrain the placing and root tool to put partitions in
their specified regions defined by the user. Doing so,
it allows not only avoiding the random placement of
certain DFFs away from their associated delay
elements, but also implementing the concerned
partitions as close as possible to input signal pins
(HIT and STOP). The purpose of the latter operation
is to reduce the propagation delays of input signal
before reaching the blocks to which they are intended
to be applied. For illustrative purposes, we represent
on Fig. 9 the assignments of physical allocations of
the partitions defined using LLR and a close-up view
of the layout of a 16-cells fine TDC implemented
using the method presented above on Fig. 10.
Fig. 9. Layout of implemented partitions of a TDC.
Fig. 10. Physical implementation of 16-cells TDC.
128
The TDC fits perfectly within the reserved region
that would be assigned to it. Consequently, the DFF
and the delay element of each TDC elementary cell
are now implemented by the same logic element. The
transmission delays are then identical for all cells.
4. Case Study: Implementation of A 42 ps
TDC on A cyclone IV FPGA
In this section we aim to apply the presented
methodology to a realistic case:
1) We begin by introducing the experimental
measurement conditions;
2) Then we discuss the effect of constraining cell
placement by using the Chip Plannar tool;
3) And finally we present improved results
obtained by overcoming a hardware limitation of the
Dallas Logic FPGA design kit.
4.1. Experimental Measurement Conditions
The proposed TDC design has been implemented
within the Cyclone IV (EP4CE55F23C8) FPGA
target. The coarse counter clock is 200 MHz, i.e., the
clock period is 5 ns. The delay line for the fine TDC,
based on carry chain adder architecture, comprises
128 cells in order to cover a dynamic of more than
5 ns. The signal that needs to be measured propagates
through the delay chain, until the FPGA clock
disables the DFFs to block their outputs and then
memorizes their states. The value of these DFFs
describes the time spent between the signal STOP
and Clock.
The data is then transmitted to a USB port via an
FTDI FT232H operating in parallel mode with
transfer rates reaching up to 40 Mbyte per second. To
acquire data measurements, we developed a specific
application using LabVIEW software.
In order to reduce the size of data transmitted to
the USB port, we developed a VHDL model of a
specific encoder converting the 128 bits to a one byte
data. Moreover it filters potential errors. The
functioning of the latter is described in the section
4.1.1 here after.
Finally, to avoid some failure and misleading
measurements, it was necessary to complete the
principal test bench described below by
synchronizing the fine and coarse counters as
introduced in section 4.1.2.
All this blocks are summarized in Fig. 11
showing the synoptic view of the whole system.
The TDC has been characterized on its whole
dynamic, i.e., from 0 to 640 ns with a step of 5 ps. A
Stanford research DG 645 digital delay generator has
been used to generate the START and STOP signals.
At this range of delay, the jitter of the delay generator
is lower than 25 ps rms.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
CLK
200 Mhz
START
STOP
Enable
Coarse
Counter
Coarse
Counter
8
0
1
Fine
Counter
128
Encoder
8
8
FTDI
FIFO
Write
Request
Fifo Full
Read
Request
USB
Port
TXE
CLK 60 Mhz
Enable
Fine
Counter
Fifo Empty
Fig. 11. Synoptic view of the implemented TDC system.
‘1’ to ‘0’ transition
4.1.1. Encoding Fine Counter Output Binary
Stream
As described previously the output binary data
stream of the fine counter, representing the measured
time, is applied to an encoder. The latter prepares the
data before saving it into a FIFO (First In First Out)
memory. Indeed, at the output of the delay chain,
data are presented as a string made of zeros (‘0’) on
the left and ones (‘1’) on the right. The encoder’s role
is to count the number of consecutives bits switched
to ‘1’ and generate the corresponding 8-bits binary
code. So the first idea is to use a simple priority
encoder to detect the position of the most significant
bit moved to ‘1’ and gives the corresponding binary
code. To illustrate this we show on Fig. 12 a 14-cells
TDC output when a half of the cells were crossed by
the measured signal. As we can see the input of the
encoder is set to “00000001111111” and its
associated output is set to “111”. However, the
problem with such an approach is that, because of
manufacturing variations, such as Setup time
mismatch, flip-flops located further in the delay chain
can sometimes react before other flip-flops or vice
versa. Consequently, the TDC’s output can be
erroneous as illustrated by Fig. 13. Indeed, the input
of the encoder can be set to “00000101111111” for
the same delay of the previous case because a flip
flop present a shorter setup time and thus detect the
data prematurely. In this case, the output of the
encoder will be the wrong code “1001”, instead of
the good code “0111”.
To overcome similar situations it is necessary to
build a robust encoder. The latter, in addition to
encoding the 128 bits in one byte data, is designed
such that it detects failed measures due to the flipflops Setup and Hold Times.
TDC output: 00000001111111
TDC Fine
Counter
14-bits
Encoder
Encoder output:
8-bits
111
Fig. 12. The theoretical string FOR 14-cells TDC.
Wrong ‘1’ to ‘0’ transition
Expected ‘1’ to ‘0’ transition
TDC output: 00000101111111
TDC Fine
Counter
14-bits
Encoder
Encoder output:
8-bits
1001
Fig. 13. Illustration of potential errors in the data string.
The method adopted here is to add a
supplementary detection condition. The last one
consists of detecting a sequence of ‘011’ instead of
‘01’. In this way all the sequences including ‘1’
between two or more zeros (‘0’) are identified as
wrong behavior of corresponding DFF. Such events
are ignored and only the events including at least two
consecutive following ones (‘1’) are considered.
Thanks to this method, we can guarantee the data
coherence, even if a false code appears as it shown on
Fig. 14. For this encoder, a code “00000101111111”,
will generate a good output equal to “0111”.
129
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
Ignored ‘1’ to ‘0’ transition
Validated ‘11’ to ‘0’ transition
Encoder output:
TDC output: 00000101111111
TDC Fine
Counter
14-bits
Encoder
8-bits
111
To prevent this problem, a second clock is
instantiated. Its operating frequency is the same as
the main clock in the circuit, except its phase will be
slightly shifted.
One of the two clock rate the coarse counter and
the other one rate the fine counter. By changing the
second clock phase, it is possible to perfectly
synchronize the fine and coarse counter.
Fig. 14. Correction of potential errors by the encoder.
4.2. Effect of Constraining Cell Placement
by the Chip Planer Tool
4.1.2. Synchronization of Fine and Coarse
Counters
The last step in the process is the synchronization
of the Fine and the Coarse counter. Without this step,
measure noise could promptly be equal to the least
significant bit of the coarse counter during its
state changes.
To show the effect of constraining cell placement
by the Chip Planer tool we report on Fig. 15 the
detail of the unconstrained and constrained fine TDC
measurements between two reference clock edges,
i.e., on a range of 5 ns.
120
y = 0.0241*x - 34.1
Fine TDC RAW data (lsb)
100
80
60
40
Unconstrainted TDC raw data
Constrainted TDC raw data
linear fit
20
0
1500
2000
2500
3000
3500 4000
Time (ps)
4500
5000
5500
6000
Fig. 15. Responses of Fine unconstrained and constrained TDC.
The unconstrained fine TDC response (blue)
shows a large discrepancy of the LSB value
indicating that some DFFs have been randomly
placed. The resulting large steps make the
unconstrained fine TDC unusable for sub nanosecond
timing. Consequently, the use of the Chip Planner
tool as described in section III is mandatory to obtain
the behavior of the constrained fine TDC represented
by the green curve. A linear fit is then used to assess
the LSB value of the fine TDC which is given by the
inverse of the linear fit slope, i.e., 41.5 ps in this
study case.
4.3. Jitter, INL and DNL Evaluation
The noise visible on the fine TDC response,
depicted on Fig. 15 bellow, is due to the jitter. The
latter adds uncertainty on each measurement and it
can be evaluated by computing the standard deviation
130
of a set of measurements at a given fixed delay
between the START and STOP signals.
The jitter depicted in Fig. 16 has been
characterized for different delays corresponding to a
given signal propagation along the fine TDC line. As
each fine TDC elementary cell adds its own jitter
[16], the global jitter will then increase as a square
root of the number N cells as given by the
following expression:
α2 + β2 ⋅N ,
(5)
where α is the initial jitter present at the input of the
first cell and β the single cell jitter.
A curve, following this law is fitted on the jitter
profile to underline the jitter’s variation relationship
in the delay line. The extraction of this parameters
leads to an initial jitter α of 62 ps rms and a single
cell jitter β of 5.8 ps rms.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
The accumulated jitter across the fine TDC delay
line leads to a mean jitter of 90 ps rms. Thus, the line
length has to be kept as low as possible in
order to obtain the best accuracy. This can be done by
using the fastest achievable frequency for the
coarse counter.
The integral non linearity error (INL) and the
differential non linearity (DNL) have been measured
over the entire range of the TDC. For illustrative
purposes, the results from a delay of 0 to 160 ns are
represented by Fig. 17 hereafter. It can be seen that,
the implemented system shows an INL of 132 ps rms
and a DNL of 50 ps rms.
The measured Jitter is quite high compared to
those extracted from literature (Table 1). Moreover
the distribution of jitter does not show a Gaussian
shape, indicating that this noise is probably correlated
to a parasitic signal.
Different
verification
tests
have
been
investigating on the effect of the FPGA input/output
buffer delays, the FPGA oscillator frequency and the
USB communication interface in order to determine
the origin of the discrepancy. Finally, most of the
jitter arise from a noise present on the 1.2 core
voltage FPGA power supply. Indeed, to generate this
voltage the Dallas Logic FPGA design kit uses a
switching DC/DC converter. The last one presents a
periodic noise of about 10 millivolts at a frequency
close to 42 kHz. To overcome this limitation the
provided power supply has been unsoldered and
replaced by an external linear regulated power supply
generator. The new measurement of jitter, INL and
DNL are reported on Fig. 18 and Fig. 19. It can
readily be seen that the mean jitter is improved and
reaches 26 ps rms, the INL and the DNL are reduced
to 22 ps rms and 13 ps rms, respectively. The
periodic behavior of the jitter is due to the transition
between two stages of the tapped delay line. The
spikes visible in the INL at 3100 and 8100 ns
correspond to the toggle of a coarse counter bit.
Fig. 16. Jitter measurement according to the elementary level, the jitter increases as the signal propagates
along the fine TDC cells.
INL (ps)
200
0
-200
0
2
4
6
8
Time (ps)
10
12
14
16
4
x 10
DNL (ps)
200
0
-200
0
2
4
6
8
Time (ps)
10
12
14
16
4
x 10
Fig. 17. INL and DNL errors of the implemented TDC over a range of 160 ns.
131
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
Table 1. Comparison of recent implementations of TDC on different FPGA targets.
Reference Date
[18]
[19]
Architecture
Multi Channel
2011
Tapped Delay Line
Dual Phase Tapped
2015
Delay Line
[20]
2010 Vernier Delay Line
[13]
2008
[21]
2006 Vernier Delay Line
[21]
2006 Vernier Delay Line
[22]
2012 Tapped Delay Line
[23]
2010 Tapped Delay Line
[9]
2013
[11]
2015 Hybrid Delay Line
[12]
2009 Tapped Delay Line
[8]
2010
Pulse Shrinking
This work 2015
Tapped Delay
Line
Ring Oscillator
Vernier Delay Line
Manual Rooting
FPGA Resolution
Jitter
DNL
INL
Xilinx
10 ps
[7.38;14.24] ps
[-1;1.5] LSB
[-2.25;1.61] LSB
Virtex-6
Xilinx
10 ps
12.83 ps-rms
[-1;1.8] LSB
[-2.20;2.60] LSB
Virtex-6
Xilinx
321.5 ps
/
[-0.28;0.3] LSB
[-0.3;0.65] LSB
Virtex 4
Altera
40 ps
/
<1 LSB
<1 LSB
Stratix II
Altera
91.5 ps
/
[-0.416;0.783] LSB [-0.567;0.687] LSB
ACEX 1 K
Xilinx
68.5 ps
/
[-0.953;1.051] LSB [-2.003;1.855] LSB
Virtex-II
Xilinx
30 ps
56.5 ps rms
[-1;3] LSB
[-4;4] LSB
Virtex-5
Virtex-II
17 ps
24 ps rms
[-1;2] LSB
[-1.5;3.5] LSB
Pro
Xilinx
9 ps
<1 LSB
< 0.11 LSB
/
Virtex-5
Xilinx
30 ps
± 154 ps
/
/
Spartan 3E
Xilinx
55 ps
/
[-2.5;1] LSB
[-4.5;3] LSB
Virtex-5
Xilinx
42 ps
24 ps
[-0.98;0.417] LSB [-4.21;3.36] LSB
Spartan 3E
Altera
0.62 LSB
[-0.4; 0.5] LSB
[-1;2] LSB
42 ps
26 ps rms
22 ps rms
13 ps rms
Cyclone IV
50
Jitter (ps rms)
40
30
20
10
0
0
20
40
60
Elementary TDC cell
80
100
120
Fig. 18. New Jitter measurement after replacing the Dallas Logic power supply core by a DC regulated power supply.
100
INL (ps)
50
0
-50
-100
0
1000
2000
3000
4000
5000
Time (ps)
6000
7000
8000
0
1000
2000
3000
4000
5000
Time (ps)
6000
7000
8000
20
DNL (ps)
10
0
-10
-20
Fig. 19. New INL and DNL errors of the implemented TDC over a range of 160 ns after disabling
the Dallas Logic supply core.
132
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
5. Conclusions
This paper has proposed a global methodology to
design and implement Time-to-Digital Converters on
low cost FPGA targets. It presents how to use
different tools to enhance the TDC resolution by
reducing propagation delays through the connection
network as well as the logic gates themselves. First,
the use of adders as delay elements, to benefit from a
dedicated carry chain logic path, is presented. Then
we detailed how to take advantage of the chip
planner, to constrain the placing and root tool to put
the partitions of the system in user specified physical
regions. Doing so, it allowed the mastering of
propagation delays and consequently improved the
resolution and the stability of the TDC.
The work is ended by a case study that applied
this methodology to design a TDC with a resolution
of about 42 ps on a Cyclone IV FPGA. The
implemented TDC presents a jitter of only 26 ps rms,
and the DNL and the INL has been measured
respectively to 22 and 13 ps rms. As we can see from
the state of the art summarized in Table 1,
comparable values of our results are obtained but by
using high performances FPGA targets as such as
Virtex 6 of Xilinx which are highly expensive.
The highlighted results in this paper are very
promising, not only because they are suitable for
domains requiring high performances, but also
because they are achieved by using a low cost FPGA
family which opens the door to a broader use in a
great amount of fast application fields.
As a perspective in the near future, we plan to
integrate the presented TDC in different applications
such as image photon counting devices and
microfluidic experimentations [17].
[8].
[9].
[10].
[11].
[12].
[13].
[14].
[15].
[16].
References
[1]. L. Li, Time-of-flight camera – an introduction, Texas
Instruments, SLOA190B – Technical White Paper,
January 2014, revised May 2014.
[2]. E.
Charbon,
M.
Fishburn,
R.
Walker,
R. K. Henderson, C. Niclass, SPAD-based sensors
TOF Range-Imaging Cameras, F. Remondino and D.
Stoppa (Eds.), Springer-Verlag, Berlin Heidelberg,
2013, pp. 11-38.
[3]. S. Henzler, Time-to-Digital Converters, Springer
Science+Business Media B. V., 2010.
[4]. S. Y. Yurish, Smart Optoelectronic Sensors and
Intelligent Sensor Systems, Sensors & Transducers,
Vol.
14-1,
Special
Issue,
March
2012,
pp. 18-31.
[5]. J. Kalisz, Review of methods for time interval
measurements
with
picoseconds
resolution,
Metrologia, Vol. 41, No. 1, 2004, pp. 17-32.
[6]. C. S. Hwang, P. Chen, H. W. Tsao, A high-precision
Time-to-Digital converter using a two-level
conversion scheme, IEEE Transactions on Nuclear
Science, Vol. 51, No. 4, 2004, pp. 1349-1352.
[7]. I. Malasse, W. Uhring, J. Le Normand, N. Dumas,
F. Dadouche, 10-ps Resolution Hybrid Time to
Digital Converter in a 0.18 µm CMOS Technology,
[17].
[18].
[19].
[20].
[21].
in Proceedings of the IEEE 12th International New
Circuits and Systems Conference (NEWCAS), June
2014, pp. 105-108.
J. Kalisz, R. Szplet, J. Pasierbinski, A. Poniecki,
Field-programmable-gate-array-based time-to-digital
converter with 200-ps resolution, IEEE Transactions
on Instrumentation and Measurement, Vol. 46,
Issue 1, 1997, pp. 51-55.
I. Vornicu, R. Carmona-Galán, Á. RodríguezVázquez, Wide range 8-ps incremental resolution
time interval generator based on FPGA technology,
in Proceedings of the IEEE 21st International
Conference on Electronics, Circuits and Systems
(ICECS’14), 2014, pp. 395-398.
M. Lin, G. Tsai, C. Liu, S. Chu, FPGA-Based high
area efficient Time-to-Digital IP design, in
Proceedings of the IEEE Region 10 Conference
(TENCON’06), 2006, pp. l-4.
R. Narasimman, A. Prabhakar, N. Chandrachoodan,
Implementation of a 30 ps resolution Time-to-Digital
Converter in FPGA, in Proceedings of the IEEE
International Conference on EDCAV, 2015,
pp. 12-17.
A. Aloisio, P. Branchini, R. Giordano, V. Izzo,
S.
Loffredo,
High-precision
Time-to-Digital
converter in a FPGA device, in Proceedings of the
IEEE Nuclear Science Symposium Conference
Record (NSS/MIC), Vol. 13, 2009, pp. 290-294.
S. S. Junnarkar, P. O’Connor, R. Fontaine, FPGA
based self calibrating 40 picosecond resolution, wide
range Time-to-Digital converter, in Proceedings of
the IEEE Nuclear Science Symposium Conference
Record (NSS '08), 2008, pp. 3434-3439.
F. Dadouche, T. Turko, W. Uhring, I. Malass, J.
Bartringer, J.-P. Le Normand, Design Methodology
of TDC on Low Cost FPGA Targets, in Proceedings
of the 9th International Conference on Sensor
Technologies and Applications (SENSORCOMM’
2015), Venice, Italy, 23-27 August 2015, pp. 29-34.
Cyclone IV Device Handbook, Vol. 1, Chapter 2,
Altera Corporation, April 2014.
M. Zlatanski, W. Uhring, J.-P. Le Normand,
D. Mathiot, A Fully characterizable asynchronous
multiphase delay generator, IEEE Transactions on
Nuclear Science, Vol. 58, No. 2, 2011, pp. 418-425.
J. Léonard, N. Dumas, J. Causse, S. Maillot,
N. Giannakopoulou, S. Barre, W. Uhring, Highthroughput time-correlated single photon counting,
Lab on a Chip - Royal Society of Chemistry, Vol. 14,
No. 22, November 2014, pp. 4338-4343.
H. Menninga, C. Favi, M. W. Fishburn, E. Charbon,
A multi-channel, 10ps resolution, FPGA-based TDC
with 300MS/s throughput for open-source PET
applications, in Proceedings of the IEEE Nuclear
Science Symposium and Medical Imaging Conference
(NSS/MIC), 23-29 Oct. 2011, pp. 1515-1522.
J. Y. Won, S. I. Kwon, H. S. Yoon, G. B. Ko,
J.-W. Son, J. S. Lee, Dual-Phase Tapped-Delay-Line
Time-to-Digital
Converter
with
On-the-Fly
Calibration Implemented in 40 nm FPGA, IEEE
Transactions on Biomedical Circuits and Systems,
Vol. PP, No. 99, p. 1.
L. Arpin, M. Bergeron, M-A.Tetrault, R. Lecomte,
R. Fontaine, A Sub-Nanosecond Time Interval
Detection System Using FPGA Embedded I/O
Resources, IEEE Transactions on Nuclear Science,
Vol. 57, No. 2, April 2010, pp. 519-524.
J. Song, Q. An, S. Liu, A high-resolution time-todigital
converter
implemented
in
field-
133
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 123-134
programmable-gate-arrays, IEEE Transactions on
Nuclear Science, Vol. 53, No. 1, February 2006,
pp. 236-241.
[22]. L. Zhao, X. Hu, S. Liu, J. Wang, Q. An,
A 16-channel 15 ps TDC implemented in a 65 nm
FPGA, in Proceedings of the 18th IEEE-NPSS Real
Time Conference (RT), 9-15 June 2012, pp. 1-5.
[23]. M. Daigneault, J.-P. David, A novel 10 ps resolution
TDC architecture implemented in a 130 nm process
FPGA, in Proceedings of the 8th IEEE International
NEWCAS Conference (NEWCAS), 20-23 June 2010,
pp. 281-284.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
134
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Experiences in Automation and Control in Engineering
Education with Real-world Based Educational Kits
1
1
2
Filomena SOARES, 1 Celina Pinto LEÃO, 2 José MACHADO
and 1, 3 Vítor CARVALHO
Algoritmi R&D-University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
MEtRICs R&D-University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
3
EST-IPCA, Campus of IPCA, 4750-810 Barcelos, Portugal
1
Tel.: +351253510180, fax: +351253510189
E-mail: vcarvalho@ipca.pt
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: The well-known paradigm learning by doing is particularly important in engineering courses. Still, in
some situations, there is a lack of real-world didactic workbenches due to the absence of financial support,
human resources or maintenances restrictions. The authors of this paper have been overcome this difficulty by
designing and implementing virtual and remote laboratories in Process Monitoring, Control and Automation
teaching applied to Mechanical, Electronics and Biomedical Engineering. The goal of this paper is to present the
work developed regarding the real-world workbenches to be used in automation and control practical classes as
an integrated virtual and remote laboratory. Some important points include the modelling and control of Discrete
Event Systems, Continuous Systems and Real-Time Systems as well as Industrial Control Networks. The
physical parts were developed and connected, in a closed-loop configuration, with the respective controllers.
The developed kits and systems were geared towards the engineering students’ needs. This integrated approach
is very useful for providing students with a global set of skills in this domain. Quantitative and qualitative
studies are continuously applied not only for obtaining students feedback but also to gather information to
devise strategies for future virtual and remote laboratory applications development suitable for the target public.
The positive results achieved so far are very encouraging attesting its efficiency not only in terms of students’
learning but also as a first contact to face real-world problems. The less positive identified point is concerned
with technical aspects. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Automation, Control design, Control engineering, Control equipment, Engineering education.
1. Introduction
The
design
of
new
teaching/learning
methodologies implies the definition of tools and
environments that promote students and teachers’
engagement in the knowledge acquisition process.
These tools will serve as a complement to the
traditional face-to-face lectures.
http://www.sensorsportal.com/HTML/DIGEST/P_2746.htm
In engineering courses the laboratory work is very
important since it is when students have the
opportunity to apply the theoretical concepts learned
earlier. So, to install and maintain several working
positions in the laboratory is sometimes a challenge:
the equipment is expensive, laboratorial space and
qualified personnel are not enough. And this problem
is replicated at each school.
135
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
The virtual and remote (V&R) laboratories may
overcome this limitation [1-7]. The students may
access the V&R laboratory at any time and place
testing the real-world case-study through a graphical
animation or a remote webcam.
Nevertheless, a good understanding of the
pedagogical aspects is crucial for an efficient remote
labs use [8]. Remote laboratories can also be used as
a link between the University and the Industry, by
providing remote monitoring for some industrial
applications [9].
With this in mind and regarding Automation and
Control education, a multidisciplinary group of
teachers with different backgrounds, from the
University of Minho (UM) and from the Polytechnic
Institute of Cávado and Ave (IPCA), at North of
Portugal, have been centering their attention in
designing and developing workbenches in order to
enable a practical learning environment. The goal
was to provide to the engineering students different
pedagogical tools/examples in order to have realworld interactive situations, for instance virtual
processes and interactive animations, as well as local
and remote experiences.
Considering the main domains, concerning
Control and Automation, considered at UM and
IPCA - namely: Discrete Event Systems modeling
and control, Continuous Systems modeling and
control, Real-Time Systems modeling and control
and control industrial networks - several
workbenches have been developed. For teaching
these domains several software tools and controllers
can be used. It was, in this spirit, that the
workbenches were developed. Also, the physical part
of the workbenches were developed and connected,
in a closed-loop configuration, with the respective
controllers [1].
Some (five) of the developed solutions include:
the velocity control of a Direct Current (DC) motor
(modeled as a Continuous System, controlled by a
microcontroller in which was implemented a discrete
Proportional, Integral, Derivative (PID) algorithm);
two examples of systems controlled by
Programmable Logic Controller (PLC): an
automation workbench for testing and simulating
PLC programs (modeled as a Discrete Event
System), and the implementation of a small
intelligent house (modeled as a Real-Time System);
and, finally, two workbenches controlled by
Industrial Computers modeled, both, as Real-Time
Systems: one, of them - the level control of a twotank system - using LabVIEW, and the other one the acquisition of physiological signals – using, first,
LabVIEW and, finally, C# and Java. This paper
presents the developed solutions as an integrated
V&R laboratory. Among others, this approach allows
students to support and consolidate the traditional
classes’ model allowing an efficient learning
methodology in the specific areas of Control,
Automation, Domotics, and Biomedical Data
Acquisition fully integrated in one laboratory [1].
136
In this way, in order to describe the work
developed and exchanged experience on best practice
in the use of V&R laboratory for educational purpose
over several years this paper is organized as follows:
Section 2 provides a brief review of Virtual and
Remote Labs, Section 3 presents the five developed
real-world didactic kits used in the work. On
Section 4 the workbench developed to the test and to
validate the PLC programs is described. Section 5
depicts the students’ qualitative and quantitative
feedback obtained during several experiences and
finally, Section 6 enunciates the final remarks.
2. Virtual and Remote Labs
In the past years there has been a special concern
from the scientific community regarding the teaching
and learning process. And here, virtual labs gained
particular important tools as they allow for a virtual
perception of the physical phenomenon and reducing
the material and equipment costs and maintenance.
In [10] Brinson presents a review synthesizing
post-2005 empirical studies that focus on directly
comparing learning outcome achievement using
traditional lab (TL) and non-traditional lab (NTL,
virtual and remote). The study comprised the
scientific
works
(1291
articles)
in
Elsevier/ScienceDirect, EBSCO Suite (Academic
Search Premier, Applied Science and Technology
Source, Education Research, Science Reference
Center), JSTOR, EdITLib, and ERIC databases.
Findings suggest that most studies reviewed (n ¼ 50,
89 %) demonstrate student learning outcome
achievement is equal or higher in NTL versus TL
across all learning outcome categories (knowledge
and understanding, inquiry skills, practical skills,
perception, analytical skills, and social and
scientific communication).
Flowers [11] presents the students’ perceptions of
biology virtual laboratories. The sample size
consisted of 19 undergraduate non-science majors
(n=19) enrolled in an introductory biology course at a
postsecondary 4-year university. In order to allow the
comparison the students participated in a standard
face-to-face laboratories followed by participation in
similar technology-based laboratories. Research
findings indicate that students prefer to participate in
virtual labs compared to face-to-face labs. Data also
indicated that students perceived higher learning
gains as a result of participating in virtual labs
compared to traditional hands-on labs.
Santos-Martin, et al. [12] present the experience
of a problem-based learning approach on teaching
wind energy conversion systems for electricity
generation at an Electrical and Electronic Master's
degree level - problem of finding the response of a
wind turbine to a grid fault. Groups of three students
worked on a cooperative project for 15 weeks,
supervised by the teacher. A virtual wind turbine
simulator and a real wind turbine setup were
available to students. The results show that the
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
students
valued
the
learning
experience,
considering the simulator and the experimental set-up
important tools.
Goodwin, et al. [13] propose the use of
emulation-based virtual laboratories in control
engineering education allowing students (who are
geographically dispersed) to acknowledge the reality
of industrial control design. The laboratories are costeffective solution to respond to students’ preferences
and learning needs.
There are available in the Internet several virtual
laboratories where the User has access to a vast
number of didactic experiments in different
thematic areas. We present some examples
among a vast list a virtual labs: Virtuallaboratory
(http://virtuallaboratory.colorado.edu/) helps students
to recognize, confront, correct, and expand
their understanding of scientific ideas and
techniques
in
chemistry
and
biology;
Biomodel (http://biomodel.uah.es/en/lab/inicio.htm)
has available several virtual labs as: Cybertory a
molecular biology lab, the simulation of cellulose
acetate electrophoresis of proteins, and the simulation
of column chromatography of proteins; Random
(http://www.math.uah.edu/stat/) is a website devoted
to probability, mathematical statistics, and stochastic
processes, and is intended for teachers and
students
of
these
subjects;
BioInteractive
(http://www.hhmi.org/biointeractive/aboutbiointeractive) has multimedia resources, including
apps, animations, videos, interactives, and virtual
labs in Biology, in particular to identify bacteria
based on its DNA, construct a transgenic fruit fly,
and measure traits in fossil fish to record
evolutionary
change;
LabSim
(http://deis1.dei.uminho.pt/labsim/SimLab/Home.html) is a
virtual laboratory in control and numerical methods,
written in Portuguese; NeCTAR Virtual Laboratories
(https://www.nectar.org.au/virtual-laboratories-1) are
web environments that draw together research data,
models, analysis tools and workflows to support
collaborative research in geophysics, climate, marine
among other areas.
A step forward in the development of educational
tools was to enable students to remotely visualize the
real experiments, having the perception of the
physical experiment. The equipment was placed in a
laboratory and shared with different institutions all
over the world - Remote Laboratories.
The government of India as explored this resource
developing a platform (http://vlab.co.in/index.php)
which aggregates a several virtual laboratories in
various disciplines of Sciences and Engineering.
Eguíluz [14] explores the use of virtual reality in
the area of remote and virtual laboratories,
enunciating the main challenges and opportunities in
the teaching of electronic and electrical engineering.
The project “Circuit Warz” was presented.
Nedev [15] shows the development of a
computer-based system for data acquisition and
LabVIEW virtual instruments for humidity and
temperature as Internet-based virtual laboratories.
This setup enables to undertake remotely real
experiments allowing virtual instruments in training
of engineering students where it is of utmost
importance to take practice hours.
Santana, et al. [16] describes the experiences
using remote laboratories for education and research
in the area of Control Engineering. The aim of this
work is to apply remote laboratories in research
applications to share equipment with researchers. The
implementation of remote experimentation to control
a 3-DOF robot by the Distance Laboratory System
(SLD) is presented.
3. Real-World Didactic Kits
The real-world engineering problems are from
automation and control areas. The goal was to
provide students with practical real-world examples
previously learned at class. These examples were
used in engineering courses of different areas
Mechanical, Electronics and Biomedical, at two
institutions, Minho University (Braga and Guimarães,
Portugal) and IPCA (Barcelos, Portugal) with 3rd and
4th year’s students. In the next subsections there are
presented five developed kits: the velocity control of
a DC motor, controlled by a Microcontroller; two
examples of systems controlled by PLC: an
automation workbench for testing and simulating
PLC programs and the implementation of a small
intelligent house; and, finally, two workbenches
controlled by Industrial Computers: the level control
of a two-tank system and the acquisition and
treatment of physiological signals. The booking of
students to access the developed kits is done by a
queue defined by a set of conditions for proper
priority (i.e., role of the participant – administrator or
general user, user with/without previous scheduled
access, etc).
3.1. Velocity Control of a DC Motor
A remote controlled DC motor velocity was
developed for undergraduate control studies [17],
allowing discrete PID algorithm testing and
simulation [18]. A DC Motor “Maxon RE36” is
inserted with an encoder “HEDS-5540 A11” for
velocity measurement. The control methodology was
implemented in a microcontroller. Four different
digital versions of the PID algorithm are available.
The microcontroller PIC16F876 from Microchip is
used to directly control the motor; the control board
is linked to the local personal computer by a serial
RS-232 communication link for monitoring.
The user interface was developed in a LabVIEW
(Fig. 1). Twelve motor velocity values can be
selected (from 7 m/min to 335 m/min). The user
must select the PID algorithm to be tested as
well as the proportional, integral and derivative
controller parameters.
137
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
The connection between workbench and PC is
made by RS-232. The RS-232 will be connected to
MDW-45; and with RS-422 from MDW-45 to both
PLCs. There is a second type of connection but this is
only to see one single PLC. This is made by a USB
2.0 A-to-B cable. Both PLC can use this connection.
Fig. 1. User interface.
3.2. Automation Workbench
The main focus of the workbench is to allow
faster testing and validation of PLC programs,
modeled as discrete event systems. The global goal
of the developed system is to simulate and test the
control programs in order to be sure that, when tested
on the real system, those programs will not damage
the physical part of the platform. It must be
highlighted that this platform can be accessed from
outside by different users – using Internet access from different origins.
Thus, it was decided to use a PLC as the
controller device and another PLC that will interact
with the first one, using a developed network for that,
as the plant simulator. For this purpose, the PLC that
will run the program that simulates the plant behavior
generates all the input signals for the PLC that runs
the program of the controller.
The best configuration to meet the needs of the
proposed workbench is presented in Fig. 2. OMRON
equipment was used due to its availability in the
laboratory. Both the controller and the plant models
were implemented in the controllers OMRON CP1L
M30 with CX-Programmer. The physical connection
between components (the convertor MDW-45 and
Personal Computer, PC and the converter and the
PLC) was done using a serial port RS-422; in the
connection PC-Converter an adaptor USB to serial
port RS-232 was included.
The illustration of the scheme for connection
between the real controller (PLC_1) and the
simulator of the plant (PLC_2) is presented in Fig. 3.
In Fig. 3 the configuration of the represented
lines means:
• Black line: 24 V Conductor for PLC’s;
• Orange line: 230 V Conductor for power
supply and MDW-45;
• Green line: connection between the PLC and
buttons on the bench;
• Red line: connection between the PLC
Controller inputs and PLC Plant outputs;
• Blue line: connection between the PLC
Controller outputs and PLC Plant inputs;
• Red arrow: RS-422 connection between the
MDW-45, PLC Controller and PLC Plant.
138
Fig. 2. Hardware in the Loop Workbench circuit.
Fig. 3. The connection scheme of all components
from the workbench.
The control software used in this prototype is
from OMRON. There were used the CX-One
software and CX-Supervisor. CX-One allows using
CX-Programmer to edit and simulate programs and
CX-Designer that allows creation of simulation
scenarios (drawings, schemes, among others) and
using these scenarios associated to the program
developed in CX-Programmer the user can
understand the simulation of the system. Together
with these tools, CX-Supervisor is devoted to the
design and operation of PC visualization and machine
control. It is not only simple to use, for small
supervisory and control tasks, but it also offers a
wealth of power for the design of the most
sophisticated applications. CX-Supervisor boasts
powerful functions for a wide range of PC based
HMI (Human-Machine Interface) requirements.
Simple applications can be created rapidly with the
aid of a large number of predefined functions
and libraries.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
CX-Supervisor allows interaction between the
user and the PLC program that is running on the
controller and also interaction with the program that
is in the PLC that simulates the plant. Fig. 4 shows
how it is simulated. First the button isn’t pressed, the
program from PLC is not running, the led is Red;
after that, when the button is pressed, the program
from PLC is running and the led is Green.
Fig. 4. CX-Supervisor – view of the simulation of PLC.
At this point it is presented a simple case study,
which main purpose is to illustrate how it must be
done the plant modeling, in order to make useful the
workbench developed specifically for the test and
validation of PLC programs.
For the physical system to be simulated it was
chosen a simple electropneumatic system composed
by three simple-acting cylinders, three respective
monostable directional valves associated and, also,
two respective position sensors (Fig. 5).
The physical system to be modeled and simulated
is composed by:
• Compressor (1 unit);
• Multiple distributor with 3 out way (1 unit);
• Directional valve 3/2 way (3 units);
• Cylinder of a simple effect (3 units);
• Limit switches (6 units).
Fig. 5. Illustration of the physical system to be simulated
by PLC_2.
The input/output circuit designed
workbench is presented in Fig. 6.
for
the
Fig. 6. Inputs and outputs of the considered system.
There were used the CX-One software and CXSupervisor from OMRON. CX-One allows using
CX-Programmer to edit and simulate programs and
CX-Designer that allows creation of simulation
scenarios (drawings, schemes, among others) and
using these scenarios associated to the program
developed in CX-Programmer the user can
understand the simulation of the system. Together
with these tools, CX-Supervisor is devoted to the
design and operation of PC visualization and machine
control. It allows interaction between the user and the
PLC program that is running on the controller and
also the interaction with the program that is in the
PLC that simulates the plant.
3.3. Small Intelligent House
A test kit for a “small intelligent house” [2] with
the following functionalities was developed: alarm
control, temperature control, entrance door
open/close, and illumination control (Fig. 7). The
sensors were positioned in order to allow control of
alarm intrusion, main and internal illumination, main
door opening/closing and attic temperature, using the
PLC CQM1H-CPU61 from Omron. The simulation
of the “small intelligent” house was developed in
LabVIEW (Fig. 8). The LabVIEW interface enables
both simulation and “small intelligent” house
monitoring and actuation, but direct control is
performed by the PLC. The interface allows also
monitoring and testing different proportional, integral
and derivative parameters of the attic temperature
control algorithm, as well as real time monitoring
using a Webcam.
139
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
Fig. 7. Small Intelligent House kit.
tank levels, an electrovalve to stop the flow between
the upper and the lower level tank and a manual
valve for security purposes. The upper tank is being
controlled and the lower tank serves as a buffer.
An 8-bit Atmega16 microcontroller from Atmel is
used for data acquisition, control and pump actuation.
A custom made LabVIEW tool, the Watch Tank, was
developed for monitoring purposes. The control
algorithms, on-off and PID are implemented in the
microcontroller that is connected to a PC by a
RS232 protocol.
The user interface, Watch Tank, developed in
LabVIEW allows the monitoring of the system
(Fig. 10). In the Watch Tank program, the user must
select the PID type algorithm to be tested, as
well as the controller parameters, Kp, Ki and
Kd, the proportional, integral and derivatives
constants, respectively.
Fig. 10. User interface.
Fig. 8. User interface.
3.4. Level Control of a Two-Tank System
This work considers the objective of the design
and implementation of a remote experiment for
controlling the water level in a two-tank
system (Fig. 9) [8].
The user can test the digital algorithms and
parameters, change level reference values and
register the output data. The system can be set
available with either local or remote control
configurations for teaching/learning purposes. A
Webcam is used for real-time monitoring.
3.5. Remote Physiological Signals
Fig. 9. Two tanks system layout.
The system includes two tanks made in acrylic, a
pump to circulate the water from the lower to the
upper tank, two ultrasonic sensors for measuring both
140
The study of human physiology allows
understanding the human body’s structure and the
processes that are carried out within it, which
involves mechanical, electrical and chemical forces.
So, and in order to support the biomedical
engineering undergraduate students’ learning process
on physiological data acquisition studies, an
innovative remote laboratory has been developed:
RePhyS (Remote Physiological Systems) laboratory.
RePhyS is capable of engaging biomedical
engineering students, providing the real-time remote
acquisition of physiological signals from the human
body through a web platform. The educational goals
should be achieved, namely the knowledge of
acquisition methods, the recognition of the signals
and the identification of important components of
each one [19].
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
RePhyS was developed using the commercial
BioStarter®
kit,
with
sensors’
modules:
Electrocardiogram
(ECG),
Electromyography
(EMG), Galvanic Skin Response (GSR), Strain
Gauge and an accelerometer. The system includes
different remote experiments for the acquisition and
study of different physiological signals [20]. The data
processing, storage and transmission are made by a
computer Bluetooth connection. The general
architecture of the RePhyS lab is presented in
Fig. 11. The ShimmerTM device captures the data
through the electrodes placed on the human body.
The Bluetooth (IEEE 802.15.1) connection
established between the computer, in the real
laboratory, and the experiment allows the control and
the configuration of the device, and the transfer of
measured physiological data. The students, located in
a distance place, access the experience through an
interface, on an Internet browser, facilitating the
interaction with the device through various control
buttons. A webcam allows monitoring of
the experience.
Fig. 11. General architecture of RePhyS.
Fig. 12 shows the user interface for the
acquisition of ECG remote data acquisition. The
experiments developed were initially implemented
with LabVIEW being others user-side solutions
(C and Java) under development [20].
Fig. 12. RePhyS user interface during an ECG
remote acquisition.
Thus, the student/user can acquire, visualize and
analyze, in real-time, the results of practical
experiences being able to interact and to control the
measurement parameters. The stored data is always
available for further processing.
Currently, the architecture defined for the first
version of the remote laboratory is being improved.
Thus, RePhyS will allow simultaneous access up to
three users, each having a control experiment, and
will also allow the simultaneous use of several
modules that is, in the same experiment, able to study
various physiological conditions of the system.
4. Students’ Feedback: Qualitative
and Quantitative Study
The authors of this paper have been V&R
laboratories as a tool in using the teaching/learning
process of Automation and Control subjects in
engineering courses since 2006, where simulations
tools were developed showing their importance in the
students’ learning process on these subjects [21-23].
As stated by [24] virtual laboratories can be
used as a student’s first contact with the studied
problem and remote laboratories complement to real
hands-on experiments.
So, if V&R laboratories are to be used as an
educational tool, they must fulfill the pedagogical
purpose that they were developed.
For all educational kits and systems described
herein, and in order to obtain the students’ feedback,
questionnaires were always presented at the end of
each semester course and results analyzed by the
teachers involved in the process. Recently, the
questionnaire also allows the identification of
students’ perception in relation to their own learning
style employed during the experiment performance,
in accordance with the students’ perspective. The
analysis of this factor becomes an important indicator
on how these kits or the V&R laboratories procedure
is appropriate for the students’ learning process and if
they are suitable for the engineering learners (the end
users) [17, 25].
The use of these questionnaires enabled the
improvement of the educational kits in order to
accomplish their objective. The questionnaires had
open questions in order to obtain students feedback
towards specific points as well as quantitative
questions defined in the Likert scale (1, strongly
disagree, to 5, completely agree). The questionnaires
were divided in sections: after the student
characterization, close, open and multiple choices
responses, and the evaluation of several statements
for the experiment evaluation, the simulator
assessment, and to analyze students’ perspectives,
fillings and knowledge before and after carried out
the experiment were used. The analysis was
performed using SPSS software.
The students that participated in this study were
from Mechanical, Electronics and Biomedical
engineering courses (n=200, total number of
students), having an average of 21 years old, of
which 20 % were female (Biomedical presents the
141
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
highest percentage of female students with 40 %).
Concerning the experiment evaluation, on average,
students evaluate it with a positive score (values
higher than 3 for Q1, Q2, Q4, Q5, Q6 and lower than
3 for Q3), as described in Table 1.
important as it suggests that the development efforts
of these didactics kits meet engineering students’
learning styles.
Table 1. Kits average students’ evaluation by item.
Item
Q1: In general, I was motivated for the use
of these kits in the course context.
Q2: In general, I can say that the
performance of the kits…
1: … helped me assimilate the concepts
presented during the course.
2: … made my learning more objective.
3: … increased my chances of getting a
high final evaluation.
4: … motivated me towards the course.
5: … raised my expectations relative to
the assessment.
Q3: Running the kits had nothing to do
with my motivation and my interest in this
course.
Q4: The kits are suitable for my
Control/Automation learning process.
Q5: I recommend the implementation of
these kits and activities, in the next school
year, as a teaching/learning tool.
Q6: The implementation of the remote
experiment …
1: … has increased understanding of the
operation of an on-off controller.
2: … has increased understanding of the
operation of the PID controller.
3: … allow the visualization of the
effect of the off-set reduced when going
from a controller P to PI.
4: motivated to learn the subjects under
study.
Average
4.4
3.6
3.7
3.5
3.8
3.5
2.2
4.2
4.3
3.5
3.1
3.0
3.5
Based on a protocol, Biomedical engineering
students performed several remote experiments in
physiological signals acquisition (RePhyS: ECG,
body acceleration and body temperature acquisition).
All students understood the ECG signal acquisition
and they were able to identify all the waves’
constituents during the data real-time visualization
(as demonstrated in Fig. 12). For the other two
experiments, 89 % identified to have understood and
visualized the experiments.
Regarding students’ perception in relation to their
own learning style and the learning style employed
during the experiment performance, it is possible to
observe that, and based on the Biomedical
engineering students, 67 % prefer to process
information more actively (2nd and 3th quadrants,
Fig. 9), and 67 % also prefer to understand the
concepts through concrete experiences (1st and 2nd
quadrant, Fig. 13). This behavior (expected in some
way) is coherent to an engineering behavior:
motivated to investigate how situations are processed
and to put into practice ideas. This information is
142
Fig. 13. Learning Styles diagram obtained:
1st quadrant – diverging style,
2nd quadrant – accomodating style,
3rd quadrant – covengent style,
4th quadrant – assimilating style.
The qualitative analysis of the data helped to
recognize the necessity of devising and implementing
new learning strategies to fulfill students’ needs. The
students replied to the questionnaire on a voluntary
basis and according to the experiment conducted. A
majority, if not all students who performed the
experience, has accepted this challenge.
The strategy designed that V&R laboratory
development is appropriated for the target public’s
learning style (converging learning style) where
students do the things in an active way and they have
a more abstract perception of the situations.
In general, the students’ feedback about the use of
the kits and system in Automation and Control
Engineering studies is very positive stating that these
kits and activities should be used in the coming years,
and being suitable for the Automation and Control
learning process. The majority of students felt
encouraged in using these kits and sentences like: “an
innovative and useful tool”; “easy to understand”,
and “rewarding experience” were found.
The less positive issues pointed out by students
were concerned to more technical aspects, like
specific problems with the Internet connection, realtime monitoring of an experiment. For a continuous
improvement, new alternatives are under studied to
overcome the identified problems. The feedback from
students to these real-world engineering problems
was very positive allowing the researchers to
continue and improve this methodology.
7. Final Remarks
This work describes the work undertaken at two
Higher Education Institutions, from north of
Portugal, on the development of V&R laboratories as
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
facilitators in the students’ study. The students can be
from different engineering branches, aiming towards
a final objective of optimizing their learning about
different important subjects that must be highlighted
in the context of teaching of Control and Automation
for different future Engineers in many areas,
including mechanical engineering, biomedical
engineering, and electronics engineering that will
need Control and Automation knowledge during their
professional life.
Although some of the didactics kits developed
and presented in this paper could be considered
comparable to some described in the literature (while
it has been authors’ choice to include as they are part
of the automation and control laboratory described),
the acquisition and treatment of physiological signals
correspond to an innovative remote laboratory. So,
this paper describes the development of the virtual
and remote laboratory since its creation until today,
highlighting the strengths of the didactics
kits selected.
A real added value of this project is the use of
dedicated workbenches – covering, in a
complementary way, the domains related with
teaching of control and automation fields - for
dedicated needs of real learning, as commercial
tools available are very specific mismatching
important concepts, together, in the Control and
Automation domains.
Based on the different views expressed by
students, the final users of the kits and system in
Automation and Control Engineering studies,
alongside positive points, there are also a few
negative points to be identified. The positive points
state that these kits and activities are suitable for the
Automation and Control learning process and should
be used in the coming years. The less positive point
identified is concerned to technical aspects, namely
specific problems with the Internet connection and
real-time monitoring of an experiment.
Finally, it is important to highlight that strong
physical and human resources have been optimized
because students from both institutions can use this
remote access laboratory and experiences have been
developed taking into account their specific
Curricular Unit’s needs.
Acknowledgements
[2].
[3].
[4].
[5].
[6].
[7].
[8].
[9].
[10].
[11].
[12].
This work has been supported by FCT –
“Fundação para a Ciência e Tecnologia” in the scope
of the project: PEst-UID/CEC/00319/2013. The
authors are grateful to the students that participated in
this project as designers and as end users.
[13].
References
[14].
[1]. F. Soares, C. P. Leão, J. Machado, V. Carvalho,
Automation
and
Control
in
Engineering,
A Global Approach with Educational Kits, in
Proceedings of the 6th International Conference on
Sensor Device Technologies and Applications
(SENSORDEVICES’15), Venice, Italy, 23-28 August
2015, pp. 7-12.
L. Gomes, S. Bogosyan, Current Trends in Remote
Laboratories, IEEE Transactions on Industrial
Electronics, Vol. 56, Issue 12, December 2009,
pp. 4744-4756.
P. M. Silva, P. C. Oliveira, N. C. Carvalho,
C. P. Leão, F. Soares, Virtual and Remote Control: an
experience by students for students, in Proceedings of
the International Conference on Remote Engineering
and Virtual Instrumentation (REV’07), Porto,
Portugal, June 2007, p. 3.
S. Santos, B. Alves, F. Soares, Remote Laboratory
for Control Studies – A Case-study Developed by
Students for Students, in Proceedings of the 9th
Portuguese Conference on Automatic Control
(Controlo’10), Coimbra, Portugal, September 2010.
T. Karakasidis, Virtual and remote labs in higher
education distance learning of physical and
engineering sciences, in Proceedings of the IEEE
Global
Engineering
Education
Conference
(EDUCON’13), Berlin, Germany, 13-15 March 2013,
pp. 798-807.
T. Machet, D. Lowe, Issues Integrating Remote
Laboratories into Virtual Worlds, in Proceedings of
the 30th Australian Society for Computers in Learning
in Tertiary Education (ASCILITE’13), Sydney,
Australia, 1-4 December, 2013, p. 521-525.
X. Chen, G. Song, Y. Zhang, Y., Virtual and Remote
Laboratory Development: A Review, Earth and
Space, 2010, pp. 3843-3852.
N. Brito, et al., A Remote System for Water Tank
Level Monitoring and Control - a Collaborative Casestudy, in Proceedings of the 3rd IEEE International
Conference on e-Learning in Industrial Electronics
(ICELIE’09), Porto, Portugal, November 2009,
pp. 19-23.
A. Mokhtar, G. R. Mikhail, C. Seong-Joo, A Survey
on Remote Laboratories for E-learning and Remote
Experimentation,
Contemporary
Engineering
Sciences, Vol. 7, Issue 29, 2014, pp. 1617-1624.
J. R. Brinson, Learning outcome achievement in nontraditional (virtual and remote) versus traditional
(hands-on) laboratories: A review of the empirical
research, Computers & Education, Vol. 87,
September 2015, pp. 218-237.
L. O. Flowers, Investigating the Effectiveness of
Virtual Laboratories in an Undergraduate Biology
Course, Journal of Human Resources & Adult
Learning, Vol. 7, Issue 2, December 2011,
pp. 110-116.
D. Santos-Martin, J. Alonso-Martínez, J. Eloy-Garcia
Carrasco, S. Arnaltes, Problem-Based Learning in
Wind Energy Using Virtual and Real Setups, IEEE
Transactions on Education, Vol. 55, Issue 1,
February 2012, pp. 126-134.
G. C. Goodwin, A. M. Medioli, W. Sher,
L. B. Vlacic, J. S. Welsh, Emulation-Based Virtual
Laboratories: A Low-Cost Alternative to Physical
Experiments in Control Engineering Education, IEEE
Transactions on Education, Vol. 54, No. 1,
February 2011, pp. 48-55.
M. J. Callaghan, A. G. Eguíluz, G. McLaughlin,
N. McShane, Opportunities and Challenges in Virtual
Reality for Remote and Virtual Laboratories, in
Proceedings of the 12th International Conference on
Remote Engineering and Virtual Instrumentation
143
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 135-144
[15].
[16].
[17].
[18].
[19].
[20].
(REV’15), Bangkok, Thailand, 25-27 February 2015,
pp. 235-237.
N. Nedev, Z. Nenova, S. Ivanov, Virtual Instruments
for Humidity and Temperature Measurements,
Information Technology Based Higher Education and
Training (ITHET’14), 11-13 September 2014, York,
England, pp. 1-6.
I. Santana, M. Ferre, E. Izaguirre, R. Aracil,
L. Hernández, Remote Laboratories for Education
and Research Purposes in Automatic Control
Systems, IEEE Transactions on Industrial
Informatics, Vol. 9, No. 1, Feb. 2013, pp. 547-556.
F. Soares, C. P. Leão, V. Carvalho, R. M.
Vasconcelos, S. Costa, Automation & Control
Remote
Laboratory:
A
Pedagogical
Tool,
International Journal of Electrical Engineering
Education, Vol. 51 Issue 1, January 2014, pp. 54-67.
K. Ogata, Modern Control Engineering, 4th ed.,
Prentice-Hall, New York, 2001.
C. Barros, C. P. Leão, F. Soares, G. Minas,
J. Machado, RePhyS: A Multidisciplinary Experience
in Remote Physiological Systems Laboratory,
International Journal of Online Engineering, Vol. 9,
No. S5, June 2013, pp. 21-24.
C. Barros, C. P. Leão, F. Soares, G. Minas,
J. Machado, Issues in remote laboratory
developments for biomedical engineering education,
in Proceedings of the International Conference on
Interactive Collaborative Learning (ICL’13), Kazan,
Russia, Sep. 2013, pp. 290-295.
[21]. C. P. Leão, F. O. Soares, Application of new
methodologies in an industrial electronics
engineering course: case study, iNEER Special
Volume: INNOVATIONS 2006 - World Innovations in
Engineering Education and Research, Chapter 28,
A. Win, et al., Eds., 2006, pp. 323-332.
[22]. C. P. Leão, F. O. Soares, V. Carvalho, R. M.
Vasconcelos, Automation and Control Engineering
Laboratory: Students Perspectives, Lectures Notes in
Engineering and Computer Science, Vol. 2185,
Issue 1, 2010, pp. 2017-2021.
[23]. C. P. Leão, F. Soares, S. Costa, Insights on Using
WALC Platform as a Learning Tool, International
Journal of Engineering Pedagogy (iJEP), Vol. 2,
Issue 1, 2012, pp. 12-16.
[24]. C. A. Jara, F. A. Candelas, S. T. Puente, F. Torres,
Hands-on Experiences of Undergraduate Students in
Automatics and Robotics Using a Virtual and Remote
Laboratory, Computers & Education, Vol. 57,
Issue 4, 2011, pp. 2451-2461.
[25]. C. Barros, C. P. Leão, F. Pereira, F. Soares,
J. Machado, D. Matos, V. Carvalho, Assessing
Remote
Physiological
Signals
Acquisition
experiments, in Proceedings of the International
Mechanical Engineering Congress & Exposition
(IMECE’14), Montreal, Canada, Nov. 2014,
pp. V005T05A049-7.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
144
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Improving Systems Dynamics by Means of Advanced
Signal Processing – Mathematical, Laboratorial
and Clinical Evaluation of Propofol Monitoring
in Breathing Gas
1, 2
Dammon ZIAIAN, 2, 3 Philipp ROSTALSKI,
4
Astrid Ellen BERGGREEN, 4 Sebastian BRANDT,
4, 5
Martin GROSSHERR, 4 Hartmut GEHRING,
2, 6
Andreas HENGSTENBERG and 1 Stefan ZIMMERMANN
1
Institute of Electrical Engineering and Measurement Technology,
Appelstraße 9a, 30167 Hannover, Germany
Tel.: +49 511 762 4671,
2
Research Unit, Draegerwerk AG & Co.KGaA, Moislinger Allee 53-55, 23558 Luebeck, Germany
3
Now at Universitaet zu Luebeck, Institut fuer Medizinische Elektrotechnik,
Ratzeburger Allee 160, 23562 Luebeck, Germany
Tel.: +49 451 3101 6200,
4
University of Luebeck, Dept. of Anesthesiology and Intensive Care Medicine,
Ratzeburger Allee 160, 23538 Luebeck, Germany
Tel.: +49 451 500 4057,
5
Segeberger Kliniken, Abteilung für Kardioanästhesie,Am Kurpark 1, 23795 Bad Segeberg, Germany
Tel.: +49 451 593 608
6
Now at SICK AG, Merkurring 20, 22143 Hamburg, Germany
E-mail: dammon.ziaian@web.de, zimmermann@geml.uni-hannover.de,
rostalski@ime.uni-luebeck.de, E-mail: berggreen@anaesthesie.uni-luebeck.de,
sebastian.brandt@uksh.de, hartmut.gehring@uni-luebeck.de, martin_grossherr@hotmail.com,
andreas.hengstenberg@online.de
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Electrochemical sensors are used in various gas measurement applications and are available for
different gases. Depending on the application, the sensor might need to be installed far away from the actual
measurement site, requiring the use of long sampling lines. Examples are portable gas measurement devices in
which remote locations like tanks and chemical reactors need to be monitored. But also medical applications,
where the sensors cannot be positioned in close vicinity to the patient, are common like, e.g., the side-stream
measurement of breathing gas. Due to the characteristics of electrochemical sensors and to the adsorption and
desorption behavior of sampling lines for different gases, the electrical sensor signal may indicate long response
times. In this paper, we propose an on-line signal processing algorithm which is capable to significantly improve
the performance. After characterizing the dynamic behavior of the sensor system, a properly designed
deconvolution filter is used to reduce response time and signal noise. Within this article, we also provide an
http://www.sensorsportal.com/HTML/DIGEST/P_2747.htm
145
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
example of this algorithm for a novel electrochemical sensor for the measurement of the anesthetic agent propofol
in exhaled air. For this application, the acceleration is prerequisite for the measurement chain to be of practical
use in a clinical setting. Our goals, to establish measurement dynamics to record the physiologic parameter and to
reduce non-physiological disturbances, were achieved with additional reserves. This article is based on [1] and is
extended by original clinical data. As an example, we present propofol monitoring in breath of one patient in order
to demonstrate the performance of the introduced algorithm in a real clinical application. We proved that the
electrochemical sensor, associated with the provided algorithm, is capable for real-time monitoring in a clinical
setting. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Deconvolution, Electrochemical sensor, Propofol, Response time, Noise reduction.
1. Introduction
Electrochemical sensors [2] are widely used for
measuring gases in various industries, most notably
process industry, oil and gas, but also in medical
applications. Various research and development
activities have gone into optimizing the design,
material and electrochemical properties of this type of
gas sensor in order to improve response time,
selectivity, accuracy, precision, minimizing the drift
and other adverse effects. However, a certain delay in
the response due to the diffusion, chemical reaction
but also adsorption/desorption is inherent in any
sensor.
We will discuss a specific medical application for
an electrochemical gas sensor, but similar techniques
may be used in other areas. After describing the
specific application and measurement chain for the
physiological signal considered for the remainder of
this article, we will discuss the design of a
deconvolution filter to accelerate the system response
while filtering non-physiological disturbances. The
performance of the filter is derived mathematically
and analyzed, both based on laboratory measurements
and in a clinical setting. The intention of this work is
to assess how far the usage of this electrochemical
sensor for patients is feasible under real clinical
conditions. For that reason breathing gas was sampled
of a number of patients which received propofol for
anesthesia and analyzed online. In Section 2, the
specific application and the experimental setup are
described. Furthermore, mathematical explanations on
modeling and the design of the algorithm are given and
the clinical setup for data collection at the bed side is
introduced. Results of the signal acceleration and
noise reduction are presented in Section 3 together
with breath measurements of one patient during the
awakening phase of anesthesia. At the end, the
application of advanced signal processing is discussed
and concluded in Section 4.
concentration of 2,6-Diisopropylphenol, also known
as propofol, is considered [3]. Propofol is applied as
an anesthetic agent for humans and animals. It is
intravenously administered in the formulation of a
lipid emulsion. Its volatile characteristics allow
detecting propofol in the breathing gas after injection
[4]. During anesthesia the exhaled concentration
appears in the order of around 20 ppb (parts per
billion) [5, 6]. As a result, the required sensitivity and
accuracy of the sensor need to be suitable to ensure a
reliable measurement.
When handling substances in such low
concentrations, effects of adsorption and desorption
along the measurement chain have a particularly
strong impact. In the case discussed here, the carrier
gas is drawn through a 2.5 m long sampling line from
the propofol source which happened to be a gas
cylinder (carrier gas: N2, propofol concentration:
40 ppb ±30 % rel. standard deviation). Since the true
concentration of one cylinder lies between 28 ppb and
52 ppb according to the vendor, the sensitivity of the
sensor cannot be stated based on the actual data. The
schematic of the setup is presented in Fig. 1. A
sampling flow of 180 ml/min is generated with the
help of a pump. Hence, the carrier gas is led through
the sampling line to interact with the sensor.
2. Material and Methods
2.1 Specific Application
As a practically relevant example, an electrochemical gas sensor which is used to quantify the
146
Fig. 1. Schematic of the experimental setup. The side
stream is driven through the sampling line to pass the
electrochemical sensor for detection. A T-piece connector
is used for switching.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
The saturation of propofol on the inner surface of
the line leads to a major dynamic delay in time before
the electrochemical sensor is able to detect the
absolute concentration. Besides the delay caused by
the sampling system, the response characteristics of
the electrochemical sensor itself adds further major
delay. It is assumed that these two effects are
responsible for the main delay. Another cause refers to
the volume of the line which the gas has to pass before
reaching the sensor. This volume leads to a constant,
non-dynamic delay. Considering an inner tubing
diameter of 3.2 mm, given flow rate and given length
of the sampling line, a dead time of 5.4 sec is created.
However, 5.4 sec are negligible in contrast to the
major dynamic delay reasons.
Recorded signals are usually corrupted by noise.
Owing to the pump, to thermal noise within the
electronic components and other effects, all measured
values possess a specific variance. Besides the
minimization of the response time, another objective
of the signal processing algorithm proposed in this
article is to increase signal quality in real-time. We
have been aware of the fact that signal acceleration
might lead to over-proportional amplification of nonphysiological disturbances. In the light of the
aforesaid, it is mandatory to seek for this goal.
Fig. 2. Result of one measurement cycle. Excitation
sequence is shown in green and sensor signal in blue. 90 %
of the ultimate value is reached within 401 sec after
connecting to the main stream.
To quantify the precision of each measurement the
signal to noise ratio (SNR), here defined as
SNR =
2.2. System Identification
Any signal processing procedure needs to be
tailored to the specific application. This can be either
done heuristically by following tuning rules or by
using a model-based procedure. In this publication we
follow the latter route. System identification is the
necessary first step to identify the underlying system
model.
2.2.1. Recording the Step Response
A step change of propofol gas was applied in order
to excite the measurement system. Data was recorded
using the setup illustrated in Fig. 1. At defined times
the propofol-free sampling line was connected and
disconnected to the main stream which contained
propofol saturated gas of approximately 40 ppb. One
standardized cycle consists of three minutes of
recording the baseline with propofol-free room air,
followed by 30 minutes of connection to the main
stream. And finally, the system was purged for
30 minutes with room air by disconnection from the
T-piece connector. In Fig. 2, the input excitation is
shown in green and the resulting step response of the
sensor system in blue. Dotted lines indicate the 90 %
value and the steady state of the response. In this
particular measurement it lasts
t 90 = 401 sec
before 90 % of the steady state value is reached.
(1)
amplitude
standard deviation
(2)
is calculated. The amplitude derives from the mean
value of a short time period towards the end of
exposure to propofol gas and is thus equal to the steady
state value. The SNR may be understood as an intrameasurement precision, whereas the evaluation of a
set of multiple measurements leads to the overall
precision of the sensor system. A higher SNR reflects
a better precision. For the measurement shown in Fig.
2 the resulting ratio appears as 367, indicating a rather
low noise situation. Again, the actual noise situation is
not the reason for seeking a better SNR, but the signal
acceleration, explained in what follows, makes it
mandatory.
2.2.2. Modeling and Parameter Identification
System identification can be done using different
methods, ranging from white box modeling based on
first-principles with parameters derived using
physically measures to black box modeling, where no
prior knowledge about the model is available. We will
follow the latter approach with two assumptions on the
model structure. The step response from Fig. 2
suggests a first or second order response, without
overshoot and no oscillatory components. A
reasonable model structure (second order) in the time
domain is thus given by
t
t


−
−
T −T
T − T2 

. (3)
f mod el (t ) = k 1 + e T1 z 1 + e T2 z
T1 − T2
T2 − T1 



147
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
Therein k represents the static gain. T1 , T2 and Tz
characterize the dynamics of the system and t is set to
be the time variable. By means of the Laplace
transformation [7] the same relationship may be stated
in the frequency domain as
f mod el (t ) ⇔ Fmodel ( s )
Fmodel ( s ) = k
(4)
s + T1
Tz
z
T1T2 ( s + T1 ) ( s + T1 )
1
Tz s + 1
=k
,
(T1s + 1) (T2 s + 1)
(5)
signal, drawn in orange, can be calculated. The visual
matching indicates that the fitted curve agrees well
with the measurement. The remaining mismatch is
likely to be a result of the inherent non-linearity of
different dynamics for rising and falling
concentrations. With a hybrid model including two
switching dynamics for rising and falling signal
response respectively the fitting curve would show a
better match, albeit at the price of mathematical
complexity and a non-linear behavior.
2
2.3. Design of Algorithm
(6)
where s is defined as the complex angular frequency.
The parameters of this model are computed using
an optimized fitting procedure. With the help of a least
squares method the set of parameters k , Tz , T1 and T2
are determined which yield the smallest sum of
squares error between the modeled and the actual
response. The best values found for this particular
setup are as follows:
k =1
(7)
Tz = 413.03 sec
(8)
T1 = 536.95 sec
(9)
T2 = 52.49 sec
(10)
In Fig. 3 the result of the modeling and parameter
identification is illustrated.
In [8], the physiological lung dynamics regarding
the propofol exchange from blood plasma to breathing
gas are described by a first order differential equation.
Its time constant T, which is defined as the time to
reach 63 % of the final propofol concentration in the
lung due to a sudden change in the blood plasma
propofol level, is estimated using clinical patient data
to be T = 414 sec in mean, approximately
corresponding to a respond time of t90,breath = 952 sec.
We expect that a proper metering for anesthesia
monitoring is performed when the detection happens
three times faster than the parameter might change.
Considering this, the sensing system should not
exceed a maximal response time of t90,max = 317 sec
for a clinically relevant measurement of propofol
in breath.
As mentioned before, the main issue of the
electrochemical measurement system for propofol is
its long response time. Major causes for this are
adsorption/desorption effects in the sampling system
as well as the inherent measurement dynamics of the
electrochemical sensor itself. All of these elements
lead to a delayed response between the propofol
concentration in breath and the signal provided by the
sensor with a typical response time of t90 = 401sec .
Fortunately in a clinical environment most of the
factors determining the delay are almost constant over
time and depend only on the measurement chain. The
delay can thus be compensated by using linear signal
processing. The design of such an algorithm is the
content of this section.
2.3.1. Deconvolution
Fig. 3. Result of the modeling and parameter identification.
The measured time course is displayed in blue
and the modeled signal in orange.
The blue line represents the measurement as
displayed in Fig. 2. After finding an appropriate model
structure and reasonable parameters the modeled
148
The measured signals φ (t ) are the result of the
time course of the propofol concentration in the
breathing gas cbreath (t ) and f system (t ), which is meant
to be the unknown response characteristic of the
measuring system,
φ (t ) = f system (t ) ∗ cbreath (t ),
where ∗ denotes the convolution operator.
(11)
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
During a measurement, f model (t ) (identified in
Section 2.2.2) and all past values of φ (t ) are known.
The aim is to compute the original source signal
cbreath (t ) with these known information. It is possible
to estimate the delayed signal by inverting the slow
dynamic components of the measurement chain.
Mathematically, this means that we need to invert the
effect of the convolution through deconvolution with
all transfer elements between source and electrical
signal of the sensor. Deconvolution is best understood
in frequency domain. Each frequency component is
delayed and damped individually by the measurement
chain
Φ( s ) = Fsystem ( s) Cbreath ( s).
(12)
The idea of deconvolution is simply to shift and
amplify each frequency component accordingly to
reverse this effect. Based on the model identified in
Section 2.2.2, we can approximate Cbreath (s ) through
deconvolution as
2.3.2. Causality and Noise Treatment
One potential solution to overcome the issues
mentioned above is to augment the deconvolution
filter in (17) by a low-pass filter
F filter ( s)
Cˆ breath ( s) = Fsystem ( s ) Cbreath ( s)


Fmodel (s)
(13)
1
Fmodel (s)
,
estimated propofol concentration.
This procedure, however, is not realizable for a
number of reasons in practical setting requiring realtime computation in a medical device. First and
foremost, the deconvolution equation (14) as given
above cannot be realized, at least not without
modification. Any causal system satisfies the property
that its numerator order is equal or lower than its
denominator order. This translates into the fact that at
each point in time we only measure the next signal
value but not its time derivatives.
(T s + 1)(T2 s + 1)
inv (Fmodel (s) ) =
= 1
Fmodel (s)
k (Tz s + 1)
Fmodel (s)
(
=
(18)
s)
) FF filter ((s)
(19)
model
Φ
(s)

F filter ( s )
Fmodel (s)
measurement 


algorithm
.
(20)
If the filter order is chosen high enough, the
causality of the overall system is satisfied. Since
inv(Fmodel (s) ), see equation (15), has a numerator
order of two and a denominator order of one, a lowpass filter F filter (s) with an order of at least one would
therefore fulfill the need for causality.
2.3.3. Low-pass Filter
(15)
Not keeping causality would incorrectly imply that
an effect may appear before its cause.
Another potential issue of a simple inversion is
noise. In reality, (12) can be rewritten as
Φ( s) = Fsystem ( s) Cbreath ( s) + R( s),
F filter ( s )
= Fsystem ( s) Cbreath (s) + R(s)
breath
1
+ R( s)
(14)
where Φ (s ) denotes the frequency transform of the
measured signal, Fmodel (s) is the model identified in
the previous section and Cˆ
( s ) provides the
(17)
Most real systems, including the measurement
chain in question, have a low-pass behavior which
dampens high frequency noise. Inverting the transfer
function of such a system would result in a high-pass
behavior which highly amplifies non-physiological
signal components such as noise and distorts the actual
propofol signal. Our aim, to increase the SNR,
addresses this over-proportional amplification of noise
during deconvolution.
estimation
Cˆ breath ( s ) = Φ ( s) ⋅ inv(Fmodel (s) )
= Fsystem ( s ) Cbreath ( s )
Fsystem ( s) Cbreath ( s )
R( s )
Cˆ breath ( s) =
+
.
Fmodel (s)
Fmodel (s)
(16)
where R(s ) denotes the Laplace transform of the
noise. The estimation of Cˆ breath ( s ) considering the
noise term results in
Dedicated to the electrochemical measurement
system and to the application requirements the
following low-pass filter –a second order Butterworth
filter [9] – has shown sufficient performance. Its
flatness and linearity in the pass band and the
uncomplicated design are beneficial in our case. Other
types of filtering may be more favorable depending on
the application.
In order to determine a suitable cut-off frequency
we considered the appearing dynamics. The dynamics
of propofol exhalation in breathing gas can be derived
from [8, 10]. In a subsequent adjustment procedure our
filter parameters have been fine-tuned during
application to obtain a satisfying compromise between
149
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
noise rejection and response time. With the resulting
cut-off angular frequency of
rad
sec
ω = 2π f = 4 ⋅ 10− 2
(21)
the transfer function of the Butterworth filter is given
as
rad 2
sec 2
.
F filter ( s ) =
rad
rad 2
s 2 + 0.05657 s
+ 0.0016
sec
sec 2
a0n and b0n denote the coefficients characterizing
the algorithm.
Applied on Falgorithm (s) of (24), we find a compact
description to be implemented in any software as a
real-time capable signal processing algorithm:
y (k ) =
1
[b0 x(k ) + b1 x(k − 1) + b2 x(k − 2)
a0
+ b3 x( k − 3) − a1 y ( k − 1)
0.0016
After transformation for a sampling rate of 1 Hz
the coefficients can be found in Table 1. The given
precisions of decimal places are required for stability,
when the algorithm is programmed to perform.
As implied in (20), the algorithm for the signal
processing is composed as
F filter ( s)
Fmodel (s)
.
(23)
For our particular case, the algorithm results in
Falgorithm ( s ) =
(T1s + 1)(T2 s + 1)
k (Tz s + 1)
2
rad
sec 2
⋅
rad
rad 2
s 2 + 0.05657 s
+ 0.0016
sec
sec 2
0.0016
(24)
due to the model characterized in Section 2.2.2 and
due to the filter described in Section 2.3.3. All poles of
Falgorithm (s) are negative, thus stability is ensured.
The continuous frequency domain is helpful for design
matters. But for the implementation as a real-time
capable algorithm few more steps are required. Since
the sensors output is available digitally it exists of
discrete values appearing in a specific rate. Therefore,
it is necessary to transform into the discrete time
domain. With the use of the bilinear transform [11]
and the properties of the z-transform [12] the
algorithms output at a certain point in time can be
stated as a sum. (25) depicts a general description.
Therein, y(k ) expresses the algorithms output and
x(k ) the measured value, while k denotes the discrete
time variable.
y (k ) =
1
[b0 x(k ) + b1x(k − 1) +  + bn x(k − n )
a0
− a1 y (k − 1) +  + an y (k − n )]
(25)
Hence, y(k ) is calculated as a linear combination of
previous calculations and measurements, whereas
150
− a2 y (k − 2) + a3 y (k − 3)]
(22)
2.3.4. The Resulting Algorithm and its
Software Implementation
Falgorithm ( s ) =
(26)
Table 1. Discrete Time Coefficients for a 1 Hz-Clocked
Deconvolution Filter.
Coefficient
Value
a0
a1
a2
a3
b0
b1
b2
b3
1
-2.9551616730526264
2.9113070363222864
-0.95614323255658584
0
0.060676477761667597
-0.12009490846751848
0.059420561418924996
2.4. Clinical Data
As clinical patient data are part of our evaluations,
we will give an overview of how this data was
collected.
With written informed consent and approval of the
local ethnic committee patients undergoing surgeries
demanding anesthesia were monitored. Anesthesia
was performed using propofol and remifentanil. Their
application was performed by means of target
controlled infusion pumps. During a standardized
course of anesthesia, breathing gas was sampled in
order to quantify the propofol concentration in realtime. Standardly, the blood plasma target
concentration was maintained at 2 µg/ml for 15 min
before the intake of propofol and remifentanil were
stopped. This phase took place after the actual surgical
procedure. The connection to the ventilation circuit for
the propofol measurement happened postoperatively.
In Fig. 4, the clinical setup is referred to. While
similarity to the laboratorial setup is given, the main
difference regards the T-piece connector which is
placed between the patient and the ventilator machine.
The quantification of the propofol concentration
occurred in real-time using the technologies explained
in the previous sections.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
results are highly reproducible and that the algorithm
performs similarly in real-time as long as main
parameters of the system do not significantly change
and linearity is given for the tolerated concentration
range. This result is valid for the discussed clinical
practice and also for several other applications of
electrochemical and other sensors.
Fig. 4. Clinical setup for the measurement of propofol
in breathing gas of a ventilated patient.
3. Results
Due to the concise description of the algorithm
presented in the previous section it is possible to
calculate an accelerated signal in real-time as well as
retrospectively. Further on, results are presented for a
step change of the propofol concentration as displayed
in Fig. 2 of Section 2.2.2 and for repeated
measurements.
In Fig. 5, the post-processed signal is illustrated in
red. This example visualizes the improvement
possible through signal processing. In this particular
case, the response time t90 is notably reduced from
401 seconds to 104 seconds. In Section 2.3, the
maximal tolerated response time is mentioned to be
t90, max = 317sec. Therefore, this requirement is
fulfilled with an additional reserve. The secondary
objective stated is an increase of precision. By noise
treatment consideration during the filter design the
SNR is enhanced from 367 to remarkably 1482. As the
measuring system is afflicted with non-linearity the
algorithm shows a different result for rising and falling
signals. Both overshooting and undershooting lead to
higher (102.3 %) and lower (-1.1 %) values. However,
the error stays below ±5 %, which is an acceptable
result compared to the enhancements in response time
and noise reduction.
To express the performance and the repeatability
of the algorithm, repeated measurements were
evaluated. The same setup was used at different times
whereas the algorithm was executed online in realtime. As an example, the results for three repetitions
are displayed in Fig. 6. Pairwise-colored curves denote
sensor signals and their related real-time processed
estimation of the input excitation. It might be that a
drift of the propofol source concentration or of the
sensor sensitivity have occurred during the repetitions.
Even though, the algorithm has performed stable and
with expected results. Summarized, we observe that
Fig. 5. The result of the signal post-processing is displayed
in red, the sensor signal used for the calculation in blue.
Fig. 6. Three reproduced measurements 1- 3 are illustrated
to express the repeatability. The algorithm was applied in
real-time during each measurement. Pairs of the same color
represent related sensor and algorithm signals.
In addition to mathematical and laboratorial
considerations, we present the performance of the
algorithm in a field application while measuring the
exhaled propofol concentration of a narcotized patient.
In Fig. 7, the time series of the raw sensor signal
together with the output of the algorithm are
illustrated. The sampling line of the sensor module
was connected to the main circulatory ventilation right
after the plasma target concentration was set to
2 µg/ml. About 15min later, the target was set to
0 µg/ml leading to an instant stop of infusion. As soon
as the intubated patient woke up, the sampling line was
disconnected
from
the
respiratory
circuit.
151
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
Consequently, room air was sampled hereafter. During
that phase, it is possible to visually recognize that the
use of the algorithm leads to an accelerated signal
when connecting and disconnecting the sampling line.
It performed stable and the noise level was reduced as
expected.
Fig. 7. Schematic of the experimental setup. The side
stream is driven through the sampling line to pass the
electrochemical sensor for detection. A T-piece connector
is used for switching.
4. Discussion and Conclusion
In various practical applications of electrochemical
sensors a sampling line is required to transport the gas
from the sampling site to the sensor. This together with
the dynamics of the sensor itself might lead to
significant delays and adverse measurement
dynamics, rendering the electrochemical measurement
signal useless for the application. To overcome this
obstacle, a solution in form of an accelerating
algorithm is presented.
In this article, we have demonstrated that the
application of advanced signal processing can help to
optimize the performance of electrochemical
measurement systems with long sampling lines. For
the example of an electrochemical propofol sensor, the
response time could significantly be reduced by a
factor of 9.2 while the SNR could be increased at the
same time by a factor of 4. Furthermore, the proposed
procedure involves only straight forward model-based
design steps and should thus easily be transferable to
other applications. Starting with a modeling and
system identification step, the characteristics of the
sensor system are identified. Here, a second order
equation is used to model the sensor response. Noise
considerations lead to the specification of a second
order low-pass Butterworth filter and to the design of
a deconvolution algorithm.
Our primary objective has been to realize a
detecting system able to observe the propofol
concentration in patients’ exhaled breath at least three
times faster than this physiological parameter might
change. With the help of the presented algorithm the
accelerated response time of t90 = 104 sec is 9.2 times
152
faster than the patients average breath propofol
concentration change with t90,breath = 952 sec.
The patient with the fastest exhalation dynamic
observed in [8] does have a time constant of
T = 227 sec . This implies a maximal permitted
response time of t90, max = 174 sec for the measuring
device. Thus, using the presented acceleration
algorithm the propofol sensors system is even suitable
to monitor such, probably exceptional, fast exhalation
dynamics.
Another issue discussed in the paper relates to
noise. On the one hand more accurate signals are
advantageous in general and on the other hand we are
aware of the fact that deconvolution might lead to
exceedingly higher noise levels. With a proper choice
of a low-pass filter, this effect could be coped with,
resulting in a significantly improved SNR, albeit the
SNR has been acceptable before processing.
With the decision to run measurements in a real
clinical environment, we have achieved insights for
propofol monitoring in clinical practice. First of all,
we showed that an electrochemical sensor is capable
to quantify the propofol concentration in breathing gas
in real-time and with an acceptable response time. So
far, clinical circumstances have been a significant
obstacle leading to large response times in all practical
settings. Additionally the interaction with the
ventilator machine, disposable articles and clinical air,
for example, were questionable. As a result of our
study, we observed a stable, accelerating and noise
reducing performance of the algorithm following the
predictions of our laboratory measurements.
One difference to a clinical setup is regarded to the
relative humidity conditions of the gas, which appear
much higher when patients’ breathing gas is sampled.
Influences of humidity were not observable during
other investigation. Thus, the impact is not part of this
work and might be a topic to address in future.
It is worth mentioning that the application of the
presented signal processing is not limited to the
clinical setting. Especially, portable gas detection
devices are often used in conjunction with long
samplings reaching up to 30 m and thus leading to
remarkable delays beside the dead time delays due to
volume.
Technologically, other more advanced signal
processing algorithms come to mind such as Wiener
Filter [13], (linear/nonlinear) Kalman Filter [14] or
moving horizon estimation, however at a price of a
higher complexity. It will be part of ongoing research
activities to evaluate these techniques in the context of
electrochemical sensors with long sampling lines and
to compare the results against the surprisingly simple
and effective solution provided here.
References
[1]. D. Ziaian, P. Rostalski, A. Hengstenberg and
S. Zimmermann, Reducing System Response Time
and Noise of Electrochemical Gas Sensors - Discussed
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153
[2].
[3].
[4].
[5].
[6].
[7].
for Propofol Monitoring in Breathing Gas, in
Proceedings of the 6th International Conference on
Sensor Device Technologies and Applications
(SENSORDEVICES’15), Venice, Italy, August
23 - 28, 2015, pp. 85–90.
P. J. Gellings and H. J. Bouwmeester, The CRC
Handbook of Solid State Electrochemistry, Chapter
10, CRC Press, 1997.
Package leaflet: Information for the user Diprivan
10 mg/ml (1 %) Emulsion for Injection or Infusion,
2015, URL: https://www.medicines.org.uk/emc/
medicine/8375 [accessed: 2015-06-19].
M. Grossherr, A. Hengstenberg, T. Meier, L. Dibbelt,
K. Gerlach, and H. Gehring. Discontinuous
Monitoring of Propofol Concentrations in Expired
Alveolar Gas and in Arterial and Venous Plasma
during Artificial Ventilation, Anesthesiology,
Vol. 104, 2006, pp. 786–790.
M. Grossherr et al. Propofol concentration in exhaled
air and arterial plasma in mechanically ventilated
patients undergoing cardiac surgery, British Journal of
Anesthesia, Vol. 102, No. 5, 2009, pp. 608–613.
A. Taketa, K. Masui, and T. Kalama, On-line
Monitoring of End-tidal Propofol Concentration in
Anesthetized Patients, Anesthesiology, Vol. 106,
pp. 659–664, 2007.
H. Dwyer, The Laplace Transform: Motivating the
Definition, CODEE Journal, Vol. 125, pp. 1231–
1238, 2011, URL: http://www.codee.org/ref/CJ110116 [accessed: 2015-06-19].
[8]. D. Ziaian et al., Pharmacokinetic modeling of the
transition of propofol from blood plasma to breathing
gas, in Proceedings of the IEEE International
Symposium on Medical Measurements and
Applications (MeMeA’14), Lisboan, Portugal, June
11-12, 2014, pp. 80–84.
[9]. D. Gordon, E. Robertson, and J. J. Dowling, Design
and responses of Butterworth and critically damped
digital filters, Journal of Electromyography and
Kinesiology, Vol. 13, 2003, pp. 569–573.
[10]. C. Horns, D. Wieck, S. Prawn, M. Dolce, C. Patel, and
G. Schelling, Time course of expiratory propofol after
bolus injection as measured by ion molecule reaction
mass spectrometry, Analytical and Bioanalytical
Chemistry, Vol. 403, No. 2, April 2012, pp. 555–561.
[11]. G. Zhang, X. Chen, and T. Chen, Digital redesign via
the generalized bilinear transformation, International
Journal of Control, Vol. 82, No. 4, April 2009,
pp. 741–754.
[12]. J. R. Magazine and L. A. Zadie, The analysis of
sampled-data systems, Transactions of the American
Institute of Electrical Engineers, Part II: Applications
and Industry, Vol. 71, No. 5, Nov 1952, pp. 225–234.
[13]. N. Wiener, Extrapolation, Interpolation, and
Smoothing of Stationary Time Series, Wiley, New
York, 1949.
[14]. R. E. Kalman, A New Approach to Linear Filtering
and Prediction Problems, Journal of Fluids
Engineering, Vol. 82, No. 1, March 01 1960,
pp. 35–45.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
153
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
The Use of Gas-Sensor Arrays in the Detection of Bole
and Root Decays in Living Trees: Development of a New
Non-invasive Method of Sampling and Analysis
1
1
Manuela BAIETTO, 1 Sofia AQUARO, 2 A. Dan WILSON,
3
Letizia POZZI, 1 Daniele BASSI
Dipartimento di Scienze Agrarie e Ambientali (DISAA), Università degli Studi di Milano,
via Giovanni Celoria 2, 20133 Milano, Italy
2
Forest Insect and Disease Research, USDA Forest Service, Southern Hardwoods Laboratory, 432
Stoneville Road, Stoneville, MS 38776, USA
3
Demetra Società Cooperativa Sociale ONLUS, Via Visconta 75, 20842 Besana in Brianza, Italy
1
Tel.: +39025031656, fax: +390250316553
E-mail: manuela.baietto@unimi.it
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Wood rot is a serious fungal disease of trees. Wood decay fungi penetrate and gain entry into trees
through pruning cuts or open wounds using extracellular digestive enzymes to attack all components of the cell
wall, leading to the destruction of sapwood which compromises wood strength and stability. On living trees, it is
often difficult to diagnose wood rot disease, particularly during extreme weather conditions when trees can fail,
causing tree parts to fall onto people and property. Today, tree stability evaluation and inner decay detection are
performed visually and by the use of commercial instruments and methods that are often invasive, timeconsuming and sometimes inadequate for use within the urban environment. Moreover, most conventional
instruments do not provide an adequate evaluation of decay that occurs in the root system. A long-term research
project, initiated in 2004, was aimed at developing a novel approach for diagnosing inner tree decays by
detecting differences in volatile organic compounds (VOCs) released by wood decay fungi and wood from
healthy and decayed trees. Different commercial electronic noses (ENs) were tested under laboratory conditions
and directly in the field, on healthy and artificially-inoculated stem wood chips, and root fragments. The first
stage of the research was focused on testing different commercially available electronic noses (e-noses) for the
capabilities of discriminating between different strains and species of wood decay fungi as well as sapwood
belonging to different tree species. In the second stage, sapwood of different tree species was artificially
inoculated with decay fungi to test the diagnostic ability of the e-noses to detect differences in aroma bouquets
emitted by healthy and inoculated woods. Root fragments were then inoculated with specific root decaying fungi
and incubated under different types of soils to assess whether soil odors could influence the ability of the e-nose
to discriminate between non-inoculated and diseased root fragments. For the final stage, soil air was evaluated
for the presence of VOCs released by root-decaying fungi on diseased standing trees cultivated in the urban
environment. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Electronic nose, Decay detection, Urban forestry, VOCs, Tree.
154
http://www.sensorsportal.com/HTML/DIGEST/P_2748.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
1. Introduction
Trees in urban environments are cultivated under
conditions that are extremely hostile (causing many
stresses and negative effects) and consequently
inadequate to sustain healthy plant life. Frequently,
trees must face high levels of air, soil and water
pollution [1]. Root development is often limited by
permanent water stress and very small soil volume
for root expansion, providing inadequate support of
above-ground plant parts [2-3]. Moreover, road
works, hasty or poor pruning methods, and vandalism
increase tree stress in the urban environment. These
adverse environmental factors dramatically increase
physiological stresses that decrease tree fitness and
increase susceptibility to attack by pathogenic
agents [4].
Wood decay fungi are some of the worst
microbial pathogens because they can take advantage
of tree physiologic stresses by attacking and
destroying all woody components, reducing tree
structural stability, leading to failure (breaks)
especially during severe weather events [5-6]. Root
rots (decays) are even more dangerous and severe,
due to difficult detection and the possibility of
causing wind throw to the ground (complete tree
loss). Trunk and root rot diagnoses in standing trees
currently are performed primarily by electrical
conductivity meters, constant feed drills, single pulse
sonic and ultrasonic techniques, core samples,
computerized tomography, and molecular methods
for identification of decay fungi [7]. These tools and
methods are pensive, invasive, require very skilled
personnel, and do not provide systemic information.
For these reasons, a multi-year study aimed at
developing a novel approach for diagnosing inner
tree decays using several gas sensor arrays or ENs
was tested.
2. VOCs Emitted by Wood Decaying
Fungi
Live standing trees containing decayed wood
release a particular mixture of volatile organic
compounds (VOCs) consisting of fungal metabolites,
tree
metabolites,
and
fungus-induced
tree
antimicrobial defense compounds (e.g. phenolic
metabolites,
terpenoids,
isoprenoids,
and
phytoalexins). The composition of metabolites
released by individual fungi is controlled largely by
the types and combinations of metabolic pathways
specific to microbial species, which are regulated by
genetic, substrate and environmental factors [8].
Korpi, et al. [9] found microbes that released
pinenes, acrolein, ketones and acetylenes that were
irritants to mice.
Other investigations have focused on the
identification of VOCs released by food spoilage
fungi [10-11]. The compound 1-octen-3-ol was
detected in damp houses containing various mold
fungi [12]. Numerous other chemical species have
been reported as fungal metabolites, including
complex acids, sesquiterpenes, methyl ketones and
alcohols [13].
Relatively few recent studies have reported on the
release of VOCs by healthy and decayed trees. An
analysis of healthy Populus spp. and Pinus spp.
indicated the presence of mainly monoterpenes,
acetone and small amounts of isoprene [14]. Other
studies have indicated increases in toluene and αpinene emissions associated with P. sylvestris under
pathogen attack [15], and a decrease in isoprene
emissions from diseased Quercus fusiformis L. and
Q.virginiana L. [16].
The bacteriostatic role of plant VOCs was studied
by Gao, et al. [17] who found emissions of
terpenoids, alcohols, aldehydes, organic acids, and
esters released by five healthy coniferous species in
which α-pinene, β-pinene, 2,(10)-pinene, myrcene
and d-limonene represented more than 95 % of total
VOC emissions. Increased levels of α-pinene,
limonene, nonaldehyde and benzaldehyde also were
found in artificially-inoculated wood shaves in the
same study
3. Electronic Noses
The EN is an instrument that mimics the human
olfactory apparatus to detect VOCs through a series
of sensors (sensor array) that provide digital
signatures (sensor patterns) of all volatile chemicals
present in the aroma bouquet of the sample analyte. It
typically consists of a multisensor array, an
information-processing unit such as an artificial
neural network (ANN), software with digital patternrecognition algorithms, and reference-library
databases. The cross-reactive sensor array is
composed of incrementally-different sensors chosen
to respond to a wide range of chemical classes and
discriminate diverse mixtures of possible analytes.
The output from individual sensors are collectively
assembled and integrated to produce a distinct digital
response pattern. Identification and classification of
an analyte mixture is accomplished through
recognition of this unique aroma signature of
collective sensor responses. A reference library of
digital aroma signature patterns for known samples is
constructed prior to analysis of unknowns. The ANN
is configured through a learning process (neural net
training) using pattern recognition algorithms that
look for differences between the patterns of all
analyte types included in the reference library. This
process continues until a previously selected level of
discrimination is met. The results are validated and
assembled into the reference library to which
unknown samples can be compared. Identification of
unknowns is based on the distribution of aroma
attributes or elements that the analyte pattern has in
common with patterns present in databases of the
reference library.
In this experiment, we employed three different
commercially available ENs.
155
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
3.1. AromaScan A32S Electronic Nose
3.3. PEN3 Electronic Nose
The AromaScan 32S (Osmetech Inc., Wobum,
MA, USA) is a conducting polymer (CP) EN that
contains an organic matrix-coated polymer-type
32-sensor array designed for general use applications
with 15 V across sensor paths. The sensor array
response to different VOCs was tested previously [8].
Sensors responses are measured as a percentage
of electrical resistance changes to current flow in the
sensors relative to baseline resistance (%ΔR/Rbase).
The sorption of headspace volatiles, composed of
specific VOC mixtures, to the CP sensor surfaces
induces a change in the electrical resistance to current
flow, which is detected and measured to produce the
sensor array output. Sensor responses varied with the
type of plastic polymer used in the sensor matrix
coating, produced by electropolymerization of either
polypyrrole,
polyaniline
or
polythiophene
derivatives, which have been modified with ring
substitutions of different functional groups and with
the addition of different types of metal ions to the
polymer matrix in order to improve and modulate
sensor response.
All measurements were statistically compared
using normalized sensor outputs from the sensor
array. Conducting polymer analysis methods used
with this instrument employ application-specific
reference libraries for aroma pattern recognition and
neural-net training algorithms.
The PEN3 EN (Airsense Analytics GmbH,
Schwerin, Germany) is a very compact instrument
(255 × 190 × 92 mm), light-weight (2.1 kg) and
portable olfactory system. It consists of an array of
10 different doped metal-oxide semi-conductive
(MOS) gas sensors positioned into a very small
chamber with a volume of only 1.8 ml.
The instrument operates with filtered, ambient air
as a carrier-gas at a flow rate of 10-400 ml min-1,
sample-chamber temperature of 0-45°C, and sensor
array operating temperature of 200-500°C. The
sensing reaction is based on an oxygen exchange
between the volatile gas molecules and the metal
coating material. Electrons are attracted to the loaded
oxygen and result in decreases in sensor conductivity.
Instrument sensitivity to various gas analytes ranges
from 0.1-5.0 ppm.
This research project, a pioneer in the field of
plant pathology and urban forestry, is based on
following steps starting from basic research to be
applied to finalized research solution. In every phase
we have formulated a hypothesis derived from a
question, with the aim to verify if the instrument
could give positive answers.
3.2. Lybranose 2.1 Electronic Nose
4.1. Can the EN Discriminate Between
Healthy and Inoculated Wood Samples?
Operation of this EN is based on the quartz
crystal microbalance (QCM) technology, which can
be described as an ultrasensitive sensor capable of
measuring small changes in mass on a quartz crystal
recorded in real-time. The heart of the QCM is the
piezoelectric AT-cut quartz crystal sandwiched
between a pair of electrodes. The electrodes are
attached to an oscillator. When an AC voltage is
applied over the electrodes, the quartz crystal starts to
oscillate at its resonance frequency due to the
piezoelectric effect. If sample volatiles are evenly
deposited onto one or both of the electrodes, the
resonant frequency will decrease proportionally to
the mass of the adsorbed layer according to the
Sauerbrey equation [18].
The LibraNose 2.1 (Technobiochip, Pozzuoli,
NA, Italy) sensor array consists of eight 20 MHz
AT-cut QCM sensors with a gold surface (Gambetti
Kenologia, Binasco, PV, Italy) coated with either
metalloporphyrines, deposited by solvent casting, or
by polypyrrole polymer) films (Technobiochip
patent. No. 04425560.2-2102) deposited by means of
Langmuir-Blodgett technology using a KSV 5000
film-deposition device (KSV Instruments, Helsinki,
Finland). This process utilizes 0.3 mg/mL polymers
dissolved in chloroform and ultrapure, distilled water
as a subphase.
The first step of the research was aimed at
determining if wood decay fungi emit certain
combinations of VOCs that can be detected and
recognizable by ENs [19]. 11 wood decay fungi
(WDF) strains were selected, cultivated and
inoculated on wood chips samples (sapwood) taken
from 19 tree species: Fraxinus pennsylvanica Marsh.,
Liquidambar styraciflua L., Pinus taeda L., Platanus
occidentalis L., Populus deltoids Bartr. ex Marshall,
Quercus nuttallii Palm., Quercus lyrata Walt., Thuia
occidentalis L., Taxodium distichum L. Acer negundo
L., A. saccharinum L., Aesculus hippocastanum L.,
Castanea sativa Mill., Cedrus deodara (D. Don) G.
Don fil., Celtis australis L., Platanus x acerifolia
Brot., Quercus rubra L., Robinia pseduoacacia L.,
and Tilia spp.
Species were selected from among the hardwood
and conifer species most common in the lower
Mississippi Delta and Northern Italy urban and forest
environment, where the experiments were conducted.
After 6, 12 and 24 months of incubation under
standard conditions we evaluated the discrimination
ability of all three selected ENs. Fig. 1 – Fig. 3 show
some results of this step.
Fig. 1(a) reports about the ability of Lybranose
2.1 in discrimining healthy and inoculated wood
samples of all tree species with all fungal species.
156
4. Main Goals
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
of water and minerals. In trees, structural roots give
mechanical support to the heavy woody structure. The
root system is by nature the least known of all tree
organs as it is not assessable other than by destructing
methods. As far as root decay diagnoses are
concerned, there are not commercially available tools
capable of assessing and diagnosing decays in the
root system.
(a)
Fig. 2. Discrimination of volatiles from artificiallyinoculated decayed wood samples of Tilia spp. by PCA.
Different color labels indicate different wood decay fungi
responsible for decay. Undecayed (control) wood block are
labeled in yellow.
12 months
controls
(b)
Fig. 1. Discrimination of volatiles from healthy
and decayed wood block using PCA. Labels are as follows:
yellow and green labels indicate volatiles from healthy
controls and red and blue labels indicate volatiles
from decayed samples.
Although some zones of the graph show some
overlaps between the two types, it is possible to
assert that WDF emit volatiles which are clearly
discriminable for the instrument. PEN3, differently,
but clearly discriminated healthy and inoculated
wood samples (Fig. 1(b)).
Running Principal Component Analysis (PCA) on
samples belonging to one single tree species
inoculated with different fungal strains, it is clear as
the EN (Fig. 2) can easily discriminate the different
WDF species.
4.2. Can the EN Discriminate Between
Healthy and Inoculated Root Samples
Under Soil Conditions?
The root system is the most important organ for
initiating plant growth as it is dedicated to the uptake
6 months
Fig. 3. Discrimination of VOCs from healthy controls
(green labels) and artificially-inoculated root chips
after 6 months (red labels) and 12 months (blue labels)
from inoculation using PCA.
This phase of the research, aimed at determining if
the presence of VOCs emitted by wood decaying
fungi or decayed living wood can be detected even
under-soil conditions, utilized root tissue form four
species of shade trees [7]. Parts of 1 cm healthy roots
were sampled from each tree in which roots were
prepared and inoculated with four different WDF
strains (two strains of Armillaria mellea, one of
Ganoderma lucidum and one of Heterobasidion
annosum). Inoculated root chips were then incubated
under two different kinds of soils (very poor urban
soil and professional soil for horticulture) for
12 months at standard laboratory conditions.
157
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
Our results show that:
1) PEN 3 EN could easily discriminate between
inoculated and non-inoculated root chips after
12 months of incubation;
2) PEN 3 EN could not discriminate the inoculated
samples from the healthy ones after only 6 months
from the inoculation. This means that under soil
conditions, wood decay fungi take a little more
time to develop enough VOCs to be detected from the
EN (Fig. 3);
3) Soil type does have an influence on the
discrimination capability of the instrument. This is
probably due to the fact that professional soil type,
which is still rich in microorganisms, emits a strong
aroma bouquet.
4.3. Can the EN Detect the Presence of a
Decay in the Root System of Standing
Trees Directly in the Field ?
All previous steps of this long research were
aimed at evaluating the diagnostic feasibility of EN
under the stable and standardized conditions of the
laboratory environment. In this stage, the EN was
employed directly in the field to detect the presence
of VOCs emitted by wood decay fungi attacking the
root system.
A very important postulate of this research is that the
diagnostic system (tool as well as sampling method)
should be totally non-invasive for the plant. Wounds
caused by sampling, diagnosis or analysis method
could eventually be preferred entry points for further
pathogenic attack. According to this, a revolutionary
sampling method was tested based on detecting
decay fungi that emit VOCs which diffuse in soil air
macropores. To sample and analyze soil air, a pump
system was designed and built as seen in Fig. 4, in
which soil air is sucked in by the pump and directly
carried to Nalophan bags for e-nose analysis.
belonging to five different species [Acer negundo L.,
A. negundo ‘Variegatum’, A. pseudoplatanus L.,
Aesculus hippocastanum L., and Platanus x
acerifolia (Aiton) Willd were sampled]. All of these
trees were previously assessed via conventional
methods for the presence of stem and root decays.
Soil air was sampled six times over a period of
two years.
Our preliminary results, shown in Fig. 5 – Fig. 7,
demonstrate that WDF VOCs can be found in soil
macropores, and that their concentration in the zone
of the root system is high enough to be detected by
EN sensors. Fig. 5 show a linear discriminant
analysis (LDA) performed on soil air samples taken
about 30 cm from the bole of healthy and decayed
trees. The etiologic agent causing decays in sample
trees, previously recognized via traditional methods,
was also recognized by the EN. Fig. 6 shows the
diagnostic feasibility of the PEN 3 EN in
discriminating between different WDF species.
Fig. 5. Linear Discriminant Analysis performed
on volatiles in soil air samples taken 30 cm from the bole
of healthy (red labels) and decayed (red labels)
standing trees.
Fig. 4. The automatic pump employed in the field
to put directly soil air in Nalophan bags.
Fig. 6. PCA performed on volatiles in soil air samples
taken 30 cm from the bole of healthy (green labels)
and decayed standing trees. Different colors correspond
to main etiologic decay agent: Armillaria spp. (red labels),
Meripilus giganteus (orange), Ganoderma spp. (pink)
and Perenniporia ssp. (blue labels).
For this final stage of the research, more than
60 trees planted in the city of Milano, Italy,
Soil air or healthy control trees also were used to
check if healthy root systems release the same VOCs
158
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
as those released by trunk sapwood; and if ENs can
discriminate between species on the basis of those
VOCs. Fig. 7 shows the discrimination feasibility of
the PEN 3 EN between different healthy tree species
on the basis of VOC analysis in the aroma bouquet
released by root systems sampled at about 30 cm
from the bole by the use of an air pump.
basis of the analysis of the aroma bouquet present in
the soil air (macropores) sampled near the tree bole.
The EN system is not fully ready to be employed
daily in the field yet, as it is necessary to build
application-specific aroma signature databases of
healthy tree species at different stages of growth,
development and phonologic phase, as well as
diseased tree species, decayed by different wood
decay fungi species, in order to train ENs to yield
immediate answers directly in the field.
Acknowledgements
Fig. 7. PCA performed on volatiles in soil air samples
taken 30 cm from the bole of healthy standing trees.
Different colors correspond to different species as
following: Metasequoia glyptostroboides (pink labels),
Fagus sylvatica (blue), F. sylvatica ‘Pendula’ (red),
Aesculus hippocastanum (green)
and Quercus rubra (orange).
7. Conclusions
Tree cultivation in the urban environment
requires some agronomic works, which are
particularly important and expensive for Public
Administrators. Among these, pruning and
maintaining tree stability most influence the annual
budget of ordinary management. Tree stability
assessment, a fundamental duty to prevent sudden
tree failures so ensure citizen’s safety, is performed
by very skilled personnel who employ commercial
instruments and tools which are, in most of cases,
invasive and very expensive. Decay assessment of
the root system is not currently performed, as there
are no commercially available instruments besides
ENs capable of these assessments.
Our experimental research started about 10 years
ago, was aimed at developing a sampling and
analysis methodology to determine the presence of
active wood decay and root rots on standing trees, in
a rapid and non-invasive way, applicable in all
situations and usable by non-skilled operators.
Through multiple stages of research, we have
demonstrated that three different commercial ENs
can discriminate: between different tree species and
WDF species by analyzing the VOCs in the aroma
bouquet released by healthy (non-inoculated) and
inoculated trunk wood chips; between healthy (noninoculated) and inoculated root chips incubated under
two different kinds of soils; between healthy and
decayed living and standing trees, between different
species of healthy standing trees, and between
etiologic agents of diseased standing trees on the
This multi-year research was funded by Comune
di Milano – Sett.re Tecnico Arredo Urbano e Verde,
and from Demetra Società Cooperativa Sociale Onlus
(Besana in Brianza, MB, Italy). The Authors
would like to thank Dr. Daniele Guarino, Charisse
Oberle, Luca Maccabelli, Raffaello Brambilla and
Lorenzo Presicce for their help in sampling and
analyzing data.
References
[1]. B. Skrbic, M. Snezana, M. Matavulj, Multielement
profiles of soil, road dust, tree bark and wood-rotten
fungi collected at various distances from high
frequency road in urban area, Ecological Indicators,
Vol. 13, 2012, pp. 168-177.
[2]. T. B Randrup, Urban soils as a growing medium for
urban trees, in Urban Forestry in the Nordic
countries, in Proceedings of a Nordic Workshop on
Urban Forestry, T. B. Randrup, K. Nilsson,
P. Nilsson, Eds., Danish Forest and Landscape
Research Institute, Hoersholm, 1996, p. 59.
[3]. S. D. Day, J. R. Seiler, N. Persaud, A comparison of
root growth dynamics of silver maple and flowering
dogwood in compacted soil at differing soil water
content, Tree Physiology, Vol. 20, Issue 4, 2000,
pp. 257-263.
[4]. C. J. Luley, Wood Decay Fungi Common to Urban
Living Trees in the Northeast and Central United
States, Urban Forestry LLC, Naples, NY, 2005.
[5]. D. Lonsdale, Principles of tree hazard assessment and
management, Forest Commission, Farnham, 1999.
[6]. N. P. Mantheny, J. R. Clark, A Photographic Guide to
the Evaluation of Hazard Trees in Urban Areas, 2nd
ed., International Society of Arboriculture, Savoy,
1994.
[7]. M. Baietto, L. Pozzi, A. D. Wilson, D. Bassi,
Evaluation of a protable MOS electronic nose to
detect root rots in shade tree species, Computer and
Electronics in Agriculture, Vol. 93, 2013,
pp. 117-125.
[8]. A. D. Wilson, D. G. Lester, C. S. Oberle,
Development of conductive polymer analysis for the
rapid detection and identification of phytopathogenic
microbes, Phytopathology, Vol. 94, 2004,
pp. 419-431.
[9]. A. Korpi, J. P. Kasanen, Y. Alarie, V. M. Kosma,
A. L. Pasanen, Sensory irritating potency of some
microbial volatile organic compounds (MVOCs) and
a mixture of five MVOCs, Archives of Environmental
Health, Vol. 54, Issue 5, 1999, pp. 347-352.
159
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 154-160
[10]. N. Magan, P. Evans, Volatiles as an indicator of
fungal activity and differentiation between species,
and the potential use of electronic nose technology
for early detection of grain spoilage, Journal of
Stored Products Research, Vol. 36, Issue 4, 2000,
pp. 319-340.
[11]. B. J. P. de Lacy Costello, R. J. Ewen, H. E. Gunson,
N. M. Ratcliffe, P. T. N. Spencer-Phillips, Sensors
for early warning of postharvest spoilage in potato
tubers, British Crop Protection Council, Farnham,
UK, 2002, pp. 425-432.
[12]. G. Strom, J. West, B. Wessen, U. Palmgren,
Quantitative analysis of microbial volatiles in damp
Swedish houses, in Health Implications of Fungi in
Indoor Environments, R. A. Samson, B. Flannigan,
M. E. Flannigan, A. P. Verhoefl, O. C. G. Adan,
E. S. Hoekstra, Eds., Elsevier, Amsterdam, The
Netherlands, 1994, pp. 291-305.
[13]. R. J. Ewen, P. R. H. Jones, N. M. Ratcliffe,
P. T. N. Spencer-Phillips, Identification by
gaschromatography-mass spectometry of the volatile
organic compounds emitted from the woodrotting
fungi Serpula lacrimans and Coniophora puteana, and
from Pinus sylvestris timber, Mycological Research,
Vol. 108, 2004, Issue 7, pp. 806-814.
[14]. I. Villanueva-Fierro, C. J. Popp, R. S. Marin,
Biogenic emissions and ambient concentration of
hydrocarbons, carbonyl compounds and organic
[15].
[16].
[17].
[18].
[19].
acides from ponderosa pine and cottonwood trees at
rural and forested sites in Central New Mexico,
Atmospheric Environment, Vol. 38, 2004,
pp. 249-260.
J. Faldt, H. Solheim, B. Langstrom, A. K. BorgKarlson, Influence of fungal infection and wounding
on contents and enatiomeric compositions of
monoterpens in phloem of Pinus sylvestris, Journal
of Chemical Ecology, Vol. 32, Issue 8, 2006,
pp. 1779-1795.
L. J. Anderson, P. C. Harley, R. K. Monson,
Reduction of isoprene emissions from live oak with
oak wilt, Tree Physiology, Vol. 20, No. 17, 2000,
pp. 1199-1203.
Y. Gao, Y. Jin, H. Li, H. Chen, Volatile organic
compounds and their roles in bacteriostatis in five
conifer species, Journal of Integrative Plant Biology,
Vol. 47, No. 4, 2005, pp. 499-507.
G. Sauerbrey, Verwendung von Schwingquarzen zur
Wägung dünner Schichten und zur Mikrowägung
(The use of quartz oscillators for weighing thin layers
and for microweighing), Zeitschrift für Physik,
Vol. 155, Issue 2, 1959, pp. 206-222.
M. Baietto, A. D. Wilson, D. Bassi, F. Ferrini,
Evaluation of three electronic noses for detecting
incipient wood decay, Sensors, Vol. 10, Issue 2,
2010, pp. 1062-1092.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
160
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Motor Bourn Magnetic Noise Filtering for Magnetometers
in Micro-Rotary Aerial Vehicles
Nathan J. UNWIN, Adam J. POSTULA
School of Information Technology and Electrical Engineering
University of Queensland, Brisbane, Australia
E-mail: n.unwin@uq.edu.au, a.postula@uq.edu.au
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Avionics systems of micro aerial vehicles (MAV) pose unique problems in system design, sensor
signal handling and control. This is evident in micro-rotary aircraft as their whole body rotates with the sensors
of the flight control. The precise calculation of attitude and heading from magnetometer readings is complex due
to the body rotation. It is made even more difficult by noise induced in the geomagnetic signal by fluctuating
magnetic field of the closely positioned motors. Filtering that noise is challenging since the rotation speed of
motors and the vehicle can be very close. This paper presents analysis of motor induced noise, based on
experimental data of brushless micro motors. A novel time domain filter is proposed, designed, implemented in
FPGA hardware, tested and compared to other filters. This filter provides good performance even when the
rotational rate of the motor and vehicle are close and traditional frequency domain filters would perform poorly.
Copyright © 2015 IFSA Publishing, S. L.
Keywords: Magnetic noise, Magnetometer, Rotary body UAV.
1. Introduction
Rotary body aircraft is unique since it is both a
rotary wing and fixed wing aircraft, which produces
lift by spinning like a maple seed. The Papin-Rouilly
Gyroptère [1] built in 1915 as a manned airplane is
the first example of a “monocoptor”, a type of rotary
body aircraft. While the Gyroptère did not fly it is the
basis for contemporary designs of rotary body
unmanned micro-aircrafts. Fig. 1 shows some of the
latest developments of such micro-aircrafts in
industry and academia [2-5].
The interesting property of the rotary body
aircraft is that the core set of sensors of the flight
control system, the inertial measurement unit
containing magnetometer, is always rotating as it is
affixed to the body of the aircraft.
http://www.sensorsportal.com/HTML/DIGEST/P_2749.htm
While this is not a problem for the sensors, it is an
issue for calculation of the attitude and heading of the
vehicle since this rotation must be filtered out of the
geomagnetic signal. This is compounded by the
relatively high rotation rate of these vehicles of up to
10 Hz [6-7].
As the scale of a monocoptor decreases, the speed
at which it rotates needs to increase if efficiency of
flight is to be maintained [7-8]. In a fast spinning and
small aircraft the on-board magnetometer placed
close to the motors is exposed to high level of
magnetic noise generated by the motors which rotate
with speed close to the spin. Filtering that noise with
traditional frequency domain filters is difficult since
frequency separation between noise and signal is
small, necessitating a complex high order filter, and
raising a question if a standard frequency based filter
could be effective at all.
161
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
motor. The brushless DC motor is a permanent
magnet synchronous motor designed to be used with
a square wave input generated by a DC powered
speed controller [9]. The motor is comprised of a
permanently magnetised “rotor” that rotates and the
electro-magnetic “stator” that remains stationary.
This paper focuses on the most popular out-runner
motor type where the rotor is positioned around the
outside of the stator as shown in Fig. 2.
(a)
(b)
(c)
(d)
Fig. 1. (a) Lockheed-Martin Samarai prototype [2];
(b) Lockeed –Martin patent drawing [3];
(c) University of Maryland aircraft [4];
(d) University of Queensland aircraft [5].
This paper presents an alternative: using a
recoded or constructed signal to null the signal
generated by the motor.
The most widely used motor for micro aerial
vehicles is the BrushLess Direct Current (BLDC)
162
Fig. 2. 1- BLDC simplified configuration;
2 - Illustration of localised demagnetisation (not to scale).
The out-runner motor has a number of magnets
arranged with the poles alternating on the faces of a
ring outside the stator, the ring is then connected to a
centre shaft that runs inside the stator (stator
sandwiched between the rotor and shaft) [10-11].
As the motor rotates, the magnets are presented to
different parts of the stator. By energising the
windings to pull the magnet towards the winding or
inverting the power to winding to push the magnet
away, torque is applied to the rotor [12].
The same movement of the magnets generates an
alternating field outside the rotor. This field is used
by some speed controllers to sense the position of the
rotor to determine the optimum way to energise the
stator at that instant. This field also is measured by
magnetometers as noise superimposed on the
geomagnetic measurements.
The strength of the field is dependent on the
construction of the magnet, size of the magnet,
construction around the magnet and distance to
the magnet.
Permanent magnets exhibit a tendency to
demagnetise over time. Demagnetisation exhibits
relationships with temperature, time and subjected
magnetic fields [13-14]. However it has been noted
that there is behaviour where regions of the magnet
will demagnetise in preference to surrounding
regions resulting in poles with non-uniform strength
within the pole region.
Localised demagnetisation is of interest for
sensing applications as it adds higher frequency
components to the signal generated by the rotation of
the motor. Frequencies of these components are
approximately odd multiples of the number of poles.
As each magnet may not deteriorate identically, this
frequency may not be an integer value (but as it is a
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
periodic signal, will be a waveform with an even
number of poles).
This paper is structured as follows: in Section 2
analysis of the noise generated by motors is provided,
in Section 3 we analyse options for filtering and
outline the design of time domain inverse filter, in
Section 4 filter performance is discussed, in Section 5
design options and limitations are examined,
Section 6 details several position sensing methods, in
Section 7 ways to make the filter update on a live
system are investigated and finally in Section 8 we
conclude and outline possible extensions to
this work.
2. External Magnetic Field of Permanent
Magnet Synchronous Motors
Before a method of correction could be
attempted, the properties of the interference due to
the motors needed to be determined. To do this a
motor was operated with a moderate load and the
resulting magnetic interference recorded by a
magnetometer in close proximity. First the effects of
the motor were measured at various speeds. During
this test it was noted that the noise was fairly constant
across the different speeds. The results of
measurements are shown in Fig. 3.
Fig. 3. Raw measured magnetic field of rotating motor.
The external magnetic field with the motor
running (for the tested motor) is approximately
0.05 gauss. The field generated purely due to
permanent magnetic field is also approximately
0.05 gauss for this motor. The above observation
indicates that the field measurements can be
performed with the motor rotated by an external drive
e.g. a stepper motor. Such an arrangement allows for
much better control and more precise measurements.
The result of the magnetic field measurement is a
periodic waveform presented in Fig. 4. It can be
observed that the motor appears to have a higher and
lower frequency components.
Closer analysis using the Fast Fourier Transform
identifies three dominate frequency components, as
shown in Fig. 5.
Fig. 4. Example magnetic field measurement
with a rotating motor.
The lowest frequency component f1 corresponds
to the speed of the motor. From this it can be
established that the motor forms a pair of strong
poles, possibly due to imbalances in the magnets.
This pair of poles forms the strongest field present in
the motor when considering only the permanent
magnetic field.
The medium frequency component f7 corresponds
to the magnets embedded inside the rotor of the
motor: The motor under test was a 14 pole motor,
with each poll corresponding to either a north or
south orientation. As the motor is rotated the polls
will result in a waveform with a number of peaks
equal to the number of poles and a frequency equal to
half the number of poles.
Fig. 5. FFT analysis of magnetic field of rotating BLDC motor.
163
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
The high frequency component f35 is due to
localised demagnetisation. Owing to the fact that the
demagnetisation is not the same for all magnets, the
spectrum had a wider distribution. Different motors
exhibited different centre frequencies and
distributions, but were all around the 35 times
rotation rate.
3. Selection and Design of Filters
3.1. Frequency Domain Filters
Frequency domain filters are the current choice
for filtering noise for sensors on UAVs.
The low pass filter is almost universally used on
all sensors (typically 2nd or 5th order) to remove high
frequency noise from the desired signal. Low pass
filters perform poorly when the desired signal is close
to the noise and unfortunately, that is the case when
the vehicle rotational speed is close to the
motor speed.
A more sophisticated method to allow the speed
of the vehicle to approach the speed of the motor is to
use a combination of tracking notch filter and a low
pass filter. Assuming that the motor and vehicle
rotation speed don’t overlap for long periods,
tracking the motor speed, and centring a notch filter
on the motor speed may provide a filter of superior
performance. A low pass filter would remove high
frequency noise outside the maximum vehicle
dynamics.
We developed and tested a novel method based
on combination of the notch filter tracking the motor
and the band pass filter tracking gyroscope
measurements of the vehicle. This is based on the
observation that if the gyroscope signal is
approximately correct then the gyroscope data can be
used to estimate the frequency of changes in the
geomagnetic field directly related to the motion of
the vehicle. We used a notch filter tracking
motor speed (4th order Butterworth) and a band
pass filter tracking gyroscope measurements
(4th order Butterworth).
up table. Each element corresponds to a small arc of
the motors motion; as the motor rotates, successive
elements in the table are used to provide a correction.
As this method requires accurate position of the rotor,
an optical encoder is added to the motor. The encoder
used for experiments generates a signal twice every
1/800th of a rotation (two edges, spaced apart by
1/1600th of a rotation). This signal is used to estimate
the position of the rotor and to step to the next
element in the look up table.
The design shown in Fig. 6 is the core of the
filter. The index is the current lookup location, Din is
the sensor input and Dout is the corrected output. The
filter was implemented on an FPGA, and is optimised
to make use of the available resources. The BRAM
(Block RAM) acts as the look up table and is loaded
with the correction values.
Fig. 6. Inverse filter structure.
Half wave and Quarter wave symmetry were
applied to reduce the BRAM usage. Symmetry
requires that the index value needs to be folded into a
subset of ranges and an offset is needed to shift the
waveform. This makes the control part of the design
more complex and taking into account the looming
accuracy issue in case of asymmetry of the
waveform, as shown in Fig. 7, makes the approach
less attractive.
3.2. Inverse Filter in Time Domain
The principle of inverse filter is that for periodic
noise signals, such as generated by motors, a period
of noise signal known not to contain the desired
signal is recorded and applied as an inverse signal
super-positioned with the measured signal in time
domain. The expected result should be the desired
signal with only residues of the noise components.
This approach has been used in magnetic tape
playback [15].
In our application, this method has the potential
of providing optimal results assuming that the noise
is only dependent on the angular position of the
motor’s rotor. The noise signal can be divided into
position dependent elements that are stored in a look
164
Fig. 7. Waveform of half wave symmetric table.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
Table 1 shows that the logic size doubles when
symmetry is used, however as the filter is a very
lightdesign, the utilisation of FPGA resources is
very small.
Table 1. FPGA implementation resources.
Logic Slices
(no symmetry)
Logic Slices
(symmetry)
Block Ram
(1024x16bit)
Used
Spartan 3e500 total
available
33
4656
61
4656
1
20
The throughput of the filter is very high, requiring
only 5 clock cycles to perform a correction. The
correction time is as follows:
• 1 cycle to load in the sensor data and
index address.
• 1 cycle to convert the index address to
LUT address.
• 1 cycle to fetch the correction from the LUT.
• 1 cycle to add the correction and sensor data.
• 1 cycle to output the result.
This filter is comparable to the simplest frequency
domain filter (2 point window filter) in both speed
and implementation size. Compared to the band
stop/pass filters the implementation size is
significantly smaller as it requires at most 3 addition
operations rather than iterative addition and
multiplication operations. Given a moderate 50 MHz
clock speed the latency is 100 nanoseconds, which is
more than adequate for this application, where
filtering in the range of kilohertz is required.
is explained by the fact that the notch filter handles
motor rotations which have some variance while the
band pass filters vehicle rotations which are much
more stable due to larger inertia. Their main
disadvantage is much larger cost of implementation
than the inverse filter.
Fig. 8. Results of filters at different noise to signal
frequency factors.
The look up table based inverse filter has showed
good performance and the exceptional performance
was achieved with the addition of a low pass filter for
filtering the f35 component.
Another advantage of the look up table based
method is constant latency and linear phase delay,
which would be achievable with standard FIR filter
but at much higher implementation cost.
5. Inverse Filter Considerations
4. Filter Performance
For all filter designs except the look up table
approach, the frequency separation between signal
and noise is important. The performance was
compared for several conditions:
1. “Traditional” separation that could be
expected for most vehicles; vehicle rotation is
significantly slower than the motor speed (Typically
Fn/Fs > 10).
2. Narrow separation where the vehicle
rotation is still slower than the motor, but they are
with in an order of magnitude. (10 > Fn/Fs > 1).
3. Reversed separation where the motor speed
is slower than the vehicle rotational velocity. This
would only likely be seen if the motor power was
reduced to slow the vehicle (Fn/Fs < 1).
The results in Fig. 8 show that the low pass filters
perform poorly when vehicle and motor rotation
speeds are close.
Both the band pass and notch filters worked well.
Their difference in performance, as shown in Fig. 8,
As Fig. 5 shows, the spectrum of magnetic noise
from the motor contains high frequency component
f35. In a straightforward approach the look up table of
inverse filter would need to have sufficient number of
samples/elements to cover that spectrum. A more
efficient method is to augment inverse filter with a
simple 2nd order low pass filter (LPF in Fig. 8) to
filter out that part of the spectrum.
Results presented in Fig. 8 were obtained with the
highest resolution implemented for the inverse filter,
however depending on the platform it may be
desirable to decrease the number of elements in the
look up table. Tables with 800, 400, 200, 100 and
50 element were tested to determine the impact.
The results in Fig. 9 show that for 800 to 200 look
up elements the deterioration of performance is
limited, while 100 and 50 element table causes
significant decrease in effectiveness. The x axis is the
rotor position error measured in the number of
elements miscounted by the rotor position encoder.
The larger the number of samples, the lesser is the
165
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
impact of the position error since the angle which
each sample represents is smaller. FPGA BRAMs
make implementation of look up tables very efficient,
even if large number of elements is required.
is activated in the start when the vehicle is
stationary [19].
6. Motor Rotation Positioning
The 1600 count optical encoder, used in the rig
for testing the motors, provided test instrument
accuracy and performance, but unfortunately it lacks
compactness, robustness and low cost factor required
in the micro rotational vehicle. Several alternative
sources of motor rotation position were investigated
to identify a low cost alternative:
• Position extrapolation from a low resolution
encoder.
• Brushless motor speed controller’s estimated
position.
•
Magnetic or Capacitive encoder.
Fig. 9. Heading error due to rotor encoder miscounts.
6.1. Position Sensing Using Extrapolation
from a Low Resolution Encoder
Our experiments with a number of motors show
significant variations in the rotating magnetic field
patterns, even for the same type of motor. This is
caused
by
manufacturing
imperfections,
demagnetisation and/or wear and tear. The magnets
vary in strength (or over time loose strength at
differing rates) resulting in the waveform being
compressed for a portion of cycle and expanded for
another portion, and the individual peaks having
varying magnitude. The localised demagnetisation
effect adds a high frequency component that does not
have an instantaneously constant frequency.
The above phenomena mean that the look up
table must be prepared for a specific motor and
updated with the aging of the motor. This can be
done automatically with help of an additional
calibration module in the aircraft control system that
With an assumption that the speed of the motor
does not significantly change or oscillate from one
rotation to the next, it should be possible to estimate
the rotational position by the time passed since the
last encoder signal.
As shown in Fig. 10, in practice there is some
variance in speed and oscillations after a speed
change, but it will be dependent on the speed
controller and motor power compared to the rotating
inertia so would need to be assessed case by case. For
Fig. 10 one combination shows very good damping
and position extrapolation would work almost
immediately after a change, while the other
combination has some oscillations that would need to
decay before accurate position estimates could be
made (in practice this will be on the order
of a second).
Fig. 10. Speed of the same motor controlled by two different ESC [16].
6.2. Position Sensing Using the Speed
Controller’s Internal Estimate
Brushless motor Electronic Speed Controller
(ESC) must maintain an estimate of the motors
166
current rotational angle to correctly energise the
winding of the motor [17]. The ESC identifies three
key points in one rotation cycle based on Back
ElectroMotive Force (BEMF), where the unenergised winding’s voltage crosses zero. Using this
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
estimate for addressing the filter look up table is
similar to using the position derived from the lowresolution encoder. The main advantage is that no
additional sensors are required since the information
is already available from ESC and estimation of
angles between the key points is straightforward,
assuming stable speed during one turn of the motor.
Several problems were identified that would
require solving before this method could be used:
The position is based on back EMF and might not
be a stable point for all speeds and throttles.
The position estimate quality would be dependent
on the ESC’s internal update rate.
The location is not unique; it is only the position
in the current cycle (for the common 14 magnet poll
motor, there are 7 ESC cycles to one rotation).
Following Pt 3, if the motor is unpowered or let
freewheel, the location would be lost and untrackable as there will be no measurable BEMF.
While points 1 & 2 may be resolved with careful
implementation, points 3 and 4 are likely to not be
easy to resolve without some form of external sensor
or some form of online filter updating where the loss
of position would only be a short term issue while the
filter updates.
6.3. Position Sensing Using a Magnetic
or Capacitive Encoder
7.1. Offline Correction
As detailed in Section III, offline correction is
performed by collecting magnetometer readings for
each encoder location and removing DC bias from
the AC waveform formed by the alternating magnets
of the motor (the DC bias contains the geomagnetic
field signal). This method assumes that the
measurement platform is stationary and separated
from other time varying magnetic fields so that only
the motor based magnetic field varies.
This method is not suitable for real time
correction as the method used to remove dc bias
relies on the geomagnetic field (and hard/soft iron
error sources) to remain stationary: as the method
will take any time varying magnetic field as an error
to correct, movement will result in the geomagnetic
field being interpreted as an error source.
Online methods will need to account for the
possibility that the vehicle might be moving.
7.2. Power-up Correction
To allow for correction without user intervention
a power-on measure and correct routine was
developed. The start-up routine automates the offline
method so that when the system is powered on and
relatively stationary, the motor’s magnetic field can
be measured and an up to date correction generated.
These encoders are essentially a drop in
replacement for an optical encoder but without a
large and fragile disc; magnetic or capacitive encoder
offers the possibility of a smaller and more robust
package. Some encoders such as the CUI inc
ATM103 [18] provide 2048 encoder counts per
rotation at a maximum velocity of 7500 RPM or
15000 RPM with a reduced count.
It is expected that results would be identical with
any encoder given the same counts per rotation and
maximum velocity limit so choice would be down to
weight, size and robustness. As the capacitive sensors
are sealed and can be mounted behind the motor, they
might have been a better choice than the optical
sensor used.
7. Online Filter Tuning
Adjusting the filter to match the motor while in
operation offers the possibility of minimising the
motor magnetic noise over longer periods of
operation without the need for manual intervention.
Several methods for automatic adjustment of the
filter were investigated and can be further developed:
•
Baseline offline correction
•
Updating correction on power up of vehicle
•
Attempt to minimise difference of
magnitude from local geomagnetic field.
•
Fitting measured signal to a sinusoid.
Fig. 11. Modified filter to allow online updating.
This method relies on the platform to be
stationary (strictly speaking, only rotation needs to be
as low as possible) and the only time variant signal
being due to the rotation of the motors. The process
for generating the correction is as follows (Fig. 12).
The first step is to have the motor under test
(remember there might be multiple motors)
accelerate to a stable speed. It is important that this
speed be sufficient for the ESC to maintain a constant
speed but not too fast that the vehicle is likely
to react.
167
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
Fig. 12. Software flow for power-up correction.
Once the motor has reached the stable speed,
measurements are made for each LUT index. This
may be difficult on slower processors, so the LUT
can be used a sample and hold buffer if the write
enable to port one is enabled. Ideally several readings
for each index should be made and an average taken.
Removal of the DC bias can be performed a
number of ways, but the simplest method is to sum
all the LUT index values and subtract the result from
all elements.
Updating the LUT was done through the second
port of the Block RAM in the FPGA. This simplified
the updating process and reduced the logic required.
A single port RAM could have been used at the
expense of some logic being needed to swap between
rotor index and software index.
The performance of the Power-up correction was
identical to the offline method (800 elements) as they
are identical in operation. While only the full LUT
method without symmetry was attempted, it is
expected that there would be no issue with using the
methods with symmetry.
7.3. Correction Based on Geomagnetic Field
Correction based on the magnitude of the
measured magnetic field compared to the mapped
local geomagnetic field (geomagnetic field either
measured at a site or taken from a geomagnetic field
map) was attempted. The idea was to take the
difference between the measured field and the
mapped field for each motor encoder index and apply
this difference as a correction.
Rather than taking the full error as the new
correction, a proportional change where the error was
scaled and added to the existing correction value was
used to attempt to prevent movement (and thus
changing magnetic fields) from significantly
changing the correction. This should results in the
filter being driven over time towards an error
minimum. In practice, this method only worked away
from any external sources of magnetic field and any
deviation away from the geomagnetic field would
cause the correction to deviate.
7.4. Sinusoid Fitting Correction
A promising method for online correction is curve
fitting with sinusoids. The correction is produced as a
superposition of sinusoids with frequencies
corresponding to the motor induced signal. An
168
approach is to perform an FFT, remove all frequency
components away from the motor based frequencies
and take the IFFT as the new correction.
As the motors tend to change slowly over time, a
solution would not need to be calculated under a
strict time constraint and could be completed over a
period of time. In a vehicle with some form of
multitasking, this may prove to be a good approach
as the calculation can be done in otherwise idle time.
8. Conclusions and Future Research
We analysed and experimented with the motor
induced noise in magnetometer measurements on
board micro body rotate aircraft. Methods of filtering
that noise were investigated and properties of various
filters assessed for this application. An inverse filter
in time domain, based on look up table principle, has
been designed on FPGA. The filter was tested and
proven to have superior performance in filtering
signals and noise of very close frequencies. This filter
demonstrates a small implementation cost while
offering speed either matching or exceeding the
performance of optimised frequency domain
based filters.
The research presented in this paper focused on
steady state operation of the motor. This was justified
as the motor speed of a rotary body vehicle does not
vary significantly during operation. However for
some applications where the motor speed need to
change rapidly, further research is needed to assess
applicability of our filtering method.
A possible application of our research is also for
assessing the health of a motor by monitoring the
spectrum of motor induced noise in magnetometer
readings. It has been demonstrated that as a motor
ages or is subjected to high loads, the localised
demagnetisation increases, changing its noise
spectrum. This relationship could be possibly used to
estimate the health of the motor.
References
[1]. W. Pearce, Papin-RouillyGyropter (Gyropter),
[Online]. Available from: https://oldmachinepress.
wordpress.com/2012/09/06/papin-rouilly-gyropteregyropter/ [Retrieved 03/06/2015]
[2]. K. Fregene, C. L. Bolden, Dynamics and Control of a
Biomimetic Single-Wing Nano Air Vehicle, in
Proceedings of the American Control Conference
(ACC), Baltimore, MD, USA, 30 June -2 July 2010,
pp. 51-56.
[3]. S. M. Jameson, B. P. Boesch, E. H. Allen, Active
maple seed flyer, United States of America Patent
US7766274 B1, Lockheed-Martin, 3 August 2010.
[4]. E. R. Ulrich, D. J. Pines, J. S. Humbert, From Falling
to Flying: The Path to Powered Flight of a Robotic
Samara Nano Air Vehicle, Bioinspiration and
Biomimetics, Vol. 5, No. 4, 2010, pp. 3-16.
[5]. Pounds P., Singh S., Samara: Biologically inspired
self-deploying sensor networks, IEEE Potentials,
Vol. 34, Issue 2, 2015, pp. 10-14.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 161-169
[6]. C. Hockley, B. Butka, The SamarEye: A Biologically
Inspired Autonomous Vehicle, in Proceedings of the
29th IEEE Digital Avionics Systems Conference
(DASC’10), Salt Lake City, October 2010,
pp. 5.C.1-1 - 5.C.1-9.
[7]. H. Youngren, S. Jameson, B. Satterfield, Design of
the SAMARAI Monowing Rotorcraft Nano Air
Vehicle,
[Online].
Available
from:
http://www.atl.lmco.com/papers/1628.pdf [Retrieved
14/04/2015].
[8]. A. R. S. Bramwell, G. Done, D. Balmford,
Bramwell's Helicopter Dynamics, ButterworthHeinemann, 2001.
[9]. Microsemi User Guide, Field Oriented Control of
Permanent Magnet Synchronous Motors, [Online].
Available from: www.microsemi.com [Retrieved
03/06/2015].
[10]. G. H. Jang, J. H. Chang, D. P. Hong, K. S. Kim,
Finite-Element Analysis of an Electromechanical
Field of a BLDC Motor Considering Speed Control
and Mechanical Flexibility, IEEE Transactions on
Magnetics, Vol. 38, No. 2, 2002, pp. 945-948.
[11]. N. Bianchi, S. Bolofa, F. Luise, Analysis and design
of a brushless motor for high speed operation, in
Proceedings of the Electric Machines and Drives
Conference, Madison, Wisconsin, 2003, pp. 44-51.
[12]. J. Rais, M. P. Donsión, Permanent Magnet
Synchronous Motors (PMSM). Parameters influence
on the synchronization process of a PMSM, in
Proceedings of the International Conference
Renewable
Energies
and
Power
Quality
(ICREPQ'08), Santander, March 2008, pp. 409-413.
[13]. M. Ooshima, S. Miyazawa, A. Chiba, F. Nakamura,
T. Fukao, A Rotor Design of a Permanent MagnetType
Bearingless
Motor
Considering
Demagnetization, in Proceedings of the Power
Conversion Conference, Naqaoka, 1997, pp. 655-660.
[14]. S. Touati, R. Ibtiouen, O. Touhami, A. Djerdir,
Experimental investigation and optimisation of
permanent magnet motor based on coupling boundry
element method with permeances network, Progress
in Electromagnetics Research, Vol. 111, 2011,
pp. 71-90.
[15]. R. W. Kruppa, Method, and apparatus and article of
manufacture for filtering periodic noise from a
magnetic read head, United States of America
Patent 5,887,075, 23 March 1999.
[16]. B. Tefay, FPGA-Based Electronic Speed Control for
Brushless DC Motors, Bachelor of Engineering
Thesis, School of Information Technology and
Electrical Engineering, University of Queensland,
2011.
[17]. D. Torres, AN1160: Sensorless BLDC Control with
Back-EMF Filtering, 2008. [Online]. Available:
http://ww1.microchip.com/downloads/en/AppNotes/0
1160A.pdf [Accessed 12 September 2015].
[18]. CUI Inc, 14 September 2015. [Online]. Available:
http://www.cui.com/product/resource/amt10-v.pdf
[19]. N. Unwin, A. Postula, Filtering of Magnetic Noise
Induced in Magnetometers by Motors of MicroRotary Aerial Vehicle, in Proceedings of the 8th
International Conference on Advances in Circuits,
Electronics and Micro-electronics (CENICS’15),
Venice, Italy, 23-28 August, 2015, pp. 17-22.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
169
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Reflection from Disordered Silver Nanoparticles
on Multilayer Substrate
Victor OVCHINNIKOV
Department of Aalto Nanofab, School of Electrical Engineering, Aalto University, Espoo, Finland
Tel.: +358503523319, fax: +35894123014
E-mail: Victor.Ovchinnikov@aalto.fi
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: Reflection from disordered silver nanoparticles on dielectric and multilayer substrates is studied.
Optical characterization of nanoparticles on transparent substrates is performed and it is shown that positions of
localized surface plasmon resonances in reflection spectra cannot be ascribed to spectrum peaks like it is done in
case of extinction spectra. To clarify positions of plasmon resonances in total reflection spectra optical properties
of samples are analyzed in relation to their design and morphology. The disordered silver nanoparticles arranged
in one- or two layers on substrates from quartz, silicon and oxidized silicon are studied. Extinction and reflection
from as prepared, plasma etched and SiO2 covered samples are compared. It is concluded that coupling between
nanoparticles, phase shift of scattered light and reflection from film interfaces lead to additional features in
reflection spectra in comparison with extinction ones. Recommendations for identification of plasmon resonances
in reflection spectra of disordered nanoparticles on multilayer substrates are proposed. Copyright © 2015
IFSA Publishing, S. L.
Keywords: Ag nanoparticle, Plasmon resonance, Reflectance, Optical cavity, Irregular array.
1. Introduction
Plasmonic nanostructures are widely used in
sensors, metamaterials, solar cells, photonics and
spectroscopy [1-5]. Effective application of these
structures is based on localized surface plasmon
resonance (LSPR) demonstrated in ultraviolet, visible
and infrared. The wavelength of LSPR depends on
material and geometry of nanostructures, their
interaction with each other and electromagnetic
properties of environment, including substrate and
capping layers. Despite on near field nature, LSPR can
be observed in far field optical measurements due to
variation in optical properties of the studied structures.
Extinction is the most popular method of LSPR
registration due to its simple implementation and
straightforward interpretation, i.e., maximum position
170
and width of extinction peak correspond LSPR
wavelength and damping, respectively. However,
extinction can be measured only for non-opaque
structures, e.g., for nanostructures on transparent
substrates or for plasmonic colloidal particles.
Furthermore, extinction spectra are not effective for
overlapped peaks, when spectral deconvolution is not
obvious.
LSPRs on opaque substrates can be visualized by
different kinds of reflection and scattering
measurements. However, peaks and troughs of
specular reflectance do not correspond to LSPRs and
spectrum analysis becomes problematic. Scattering
measurements require special arrangement of light
illumination (dark field) to separate low scattering
signal from strong reflection background. It limits
range of measured samples by plasmonic
http://www.sensorsportal.com/HTML/DIGEST/P_2750.htm
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
nanostructures on substrate surface and, for example,
plasmonic nanoparticles inside of dielectric matrix
cannot be analyzed. In case of correlated scattering
centers, i.e., when array of coupled nanostructures is
analyzed, correspondence between scattered peaks
and LSPRs is broken and LSPRs should be observed
in specular reflectance. Additionally, scattering results
are obtained in arbitrary units and cannot be used for
comparison of different experiments. This happens,
due to the problems with measurement of scattered
reference spectrum for calibration procedure. In
contrast, reflection reference spectrum can be easily
obtained for any material. Combination of total
reflectance and diffuse reflectance is especially useful
for analyzing plasmon structures, because the last one
provides measurement of scattering in absolute values.
In this paper, we continue our study of reflection
from irregular arrays of nanoparticles [6] and propose
to use total reflectance for identification of LSPR on
opaque and transparent substrates. It is demonstrated
that wavelength position of LSPR correlates with peak
and trough of reflectance in a clear way. Furthermore,
overlapped peaks manifest themselves separately in
reflectance spectra and can be easily distinguished.
This paper is organized in a following way. In the
subsequent Section 2, the details of sample preparation
and the measurement procedures are presented. In
Section 3, the results of the work are demonstrated for
quartz and silicon substrates by scanning electron
microscope (SEM) images as well as reflection and
extinction spectra of the fabricated samples. The effect
of additional dielectric layers on reflectance of silver
nanoparticles is discussed in Section 4. Specific
features appearing in UV part of reflection spectra are
considered in Section 5. In Section 6, the conclusions
are drawn.
2. Method
Quartz or crystalline Si wafers (4” in diameter,
0.5-mm-thick) were used as substrates. An Al2O3 layer
was grown on the substrate by atomic layer deposition
(ALD) and a SiO2 layer was created by thermal
oxidation of the Si wafer. Silver layers with a
thickness of 15 nm were deposited by electron-beam
evaporation with deposition rate 0.5 nm/s.
Nanoparticle arrays were fabricated by ion beam
mixing (IBM) or annealing of silver films. In case of
IBM, Ag films were irradiated by 400 keV Ar ions at
normal incidence and at low (1×1016 Ar/cm-2) or high
(2×1016 Ar/cm-2) ion fluences to produce the
nanoparticles as reported elsewhere [7]. One sample
was processed by IBM with Xe ions at dose
6×1015 Xe/cm-2. In the case of annealing, silver films
were heated at 350 ºC during 10 minutes. Annealing
was done in a diffusion furnace in nitrogen ambient.
Further details about samples and processing can be
found elsewhere [8, 9]. To cover the nanoparticles
with a SiO2 layer we used a plasma enhanced chemical
vapor deposition (PECVD) technique. Metal
deposition and ion irradiation were performed at room
temperature. To avoid silver oxidation, ALD and
PECVD processes were run at reduced temperatures
200 ºC and 170 ºC, respectively. To examine the
nanoparticle formation in the structures created, the
images of the samples were taken with a Zeiss Supra
40 field emission scanning electron microscope. Three
such images, depicting effect of IBM dose and mixing
ions are shown in Fig. 1(a, b, c). One more image of
the sample prepared by annealing of silver film is
presented in Fig. 1 (d). The details of nanoparticle size
distribution and sample surface morphology can be
found elsewhere [7, 10].
a)
c)
b)
d)
Fig. 1. Plan SEM images of (a) low and (b) high dose Ar
IBM samples, (c) Xe IBM sample and (d) annealed sample.
Scale bar is 200 nm.
Optical extinction and reflection spectra were
measured with a Perkin Elmer Lambda 950 UV-VIS
spectrometer in the range from 250 to 850 nm.
Reflection spectra at the angle of light incidence 8º
were obtained by using an integrating-sphere detector
incorporated in the spectrometer. Either total
reflectance or diffuse reflectance only can be
measured by placing spectralon plate at the specular
reflectance angle or removing it, respectively.
3. Ag Nanoparticles on a Dielectric Substrate
In this section, we study spectra of silver
nanostructures on quartz and silicon substrates without
additional sublayers. In subsections 3.1 and 3.2, Ag
nanoparticles on quartz substrates are discussed, while
the subsection 3.3 is devoted to Ag nanoparticles on
silicon.
3.1. Spectra of Ag Nanoparticles on Quartz
Substrate
Silver nanostructures on a transparent substrate
without any additional layers between nanostructures
and the substrate are studied in this subsection. It
simplifies spectrum analysis due to excluding from
consideration interference effects. In Fig. 2 (a, b)
extinction, reflection and scattering of silver
nanoislands on quartz substrate are demonstrated for
171
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
condition is fulfilled for all wavelengths in our
experiments and explains specular reflection of the
arrays despite of scattering of any individual
nanoparticle.
0,6
Total
Diffuse, x10
Extinction
1
0,8
Reflectance
Optical density
0,4
0,6
0,4
0,2
0,2
0
0
200
300
400
500
600
700
800
(a)
Total
Diffuse, x10
Extinction
Absorption
1
0,8
Optical density
Reflectance
0,6
0,4
0,6
0,4
0,2
0,2
0
0
200
300
400
500
600
700
800
(b)
0,6
Total
Diffuse, x2
Extinction
Absorption
,
(1)
where λ is the wavelength, θ is the incident angle and
n is the refractive index of ambient. For θ = 8º and n =
1 the inequality (1) is simplified to Λ < λ. This
172
0,8
Reflectance
0,6
0,4
0,2
0,2
0
0
200
300
400
500
600
700
800
(c)
0,6
0,4
1
0,8
Optical density
Total
Diffuse, x10
Extinction
Absorption
0,6
0,4
0,2
0,2
0
0
<
1
Optical density
0,4
Reflectance
high and low dose of IBM, respectively. The
corresponding SEM images of the samples are given
in Fig. 1 (a, b). In the spectra, there are distinctly
visible two areas: right one (wavelength more than
400 nm) with broad and intense peak in visible (VIS)
range and left one (wavelength is less than 400 nm)
with weak peak in ultraviolet (UV) range. Further, we
call these parts as VIS and UV, respectively. The high
amplitude peak is usually attributed to dipolar LSPR,
whereas the low amplitude one is connected with
quadrupolar LSPR [3, 10, 11]. Theoretically, LSPR
exhibits itself at the same wavelength in extinction and
scattering [2]. However, it is valid only for isolated
nanoparticles without size variation. In Fig. 2 (a, b) we
observe difference in peak positions for extinction and
diffuse reflection, while coinciding for extinction and
total reflection peaks. Standard explanation of the
observed difference is the size variation of plasmon
nanoparticles. Scattering cross-section is higher for
larger nanoparticles possessing lower frequency
LSPR, while extinction cross-section is higher for
smaller nanoparticles having LSPR at higher
frequency. As a result, extinction and scattering peaks
are separated. The same argument is used for
explaining an increased full width at a half maximum
(FWHM) of peaks in comparison with calculated ones
[2]. Peak asymmetry is usually explained by shape
deviation of nanoparticles from sphere to ellipsoid. It
results in splitting of one LSPR in two separate
resonances (redshifted and blueshifted), which can
lead to observable shape of dipolar peak. In Fig. 2
(a, b) the low dose sample demonstrates redshift of
scattering in comparison with the high dose sample,
because with increasing of IBM dose particle shape
variation diminishes and particle size distribution
converges to smaller size.
UV resonance manifestation is usually attributed
to valley near 360 nm in total reflection, as well as to
peak at 350 nm in extinction [7, 10, 12] and is ascribed
to quadrupolar resonance. There is also a peak at
330 nm clearly visible in total reflection of low dose
sample (Fig. 2 (b)). As a whole, UV features are more
intense in low dose sample than in high dose one, but
dipolar peak intensity is practically the same in both
samples.
According to Fig. 2, intensity of diffuse reflection
is 20 times less than intensity of total reflection.
Therefore, we can consider total reflection as specular
one and assume that samples scatter most of radiation
in direction of specular reflection. It is only possible,
if all radiating points, i.e., silver nanoparticles work in
phase and have similar radiation patterns. If we
consider our samples as diffracting gratings, then
specular reflection is possible at the zero-order grating
condition on the period Λ, which is expressed as [13]
200
300
400
500
600
Wavelength (nm)
(d)
700
800
Fig. 2. Spectra of (a) high and (b) low dose Ar mixed
samples, (c) RIE treated sample and (d) SiO2
capped sample.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
where T and R are the transmittance and reflectance,
respectively. The dipolar extinction, absorption and
scattering peaks were blueshifted on 30 - 50 nm, due
to replacing SiO2 (ε=2.1 at 633 nm) between
nanoparticles by air (ε=1). However, the peak of total
reflection was left at the same position. Intensity of
extinction and total reflection decreased 1.5 and 3
times, respectively, but intensity of absorption and
scattering increased 2 and 2.5 times, respectively. The
UV peak of absorptance (peak and valley in
reflectance) was replaced by shoulder in absorptance
and broad depression in reflectance.
One more variation of dielectric environment was
done by covering (capping) of Ag nanoparticles by
oxide layer. It was realized by IBM of silver layer
covered by 12 nm of SiO2, which resulted in Ag
nanoparticles embedded inside of SiO2 matrix. Fig. 2
(d) shows spectra of Ag nanoparticles capped by SiO2.
Intensities of VIS extinction and total reflection
became higher than in uncapped sample. Additionally,
VIS peaks are much wider, due to increased extinction
and reflection at long wavelengths. As in case of RIE
treatment, the sharp UV valley in absorptance
disappeared. The quadrupolar peak of absorptance and
dipolar peak of total reflectance were redshifted on 30
nm and 20 nm, respectively, due to increasing of
ambient ε (SiO2 from top and bottom of particles).
Comparison of spectra in Fig. 2, leads to
conclusion that peaks of extinction and diffuse are
connected with average positions of LSPRs. However,
these positions are ascribed to LSPRs of different
nanoparticles (small and large, respectively) and the
difference exceeds 50 nm. At the same time, behavior
of total reflection is still unclear and several moments
should be considered in details. First of all, it concerns
invariability of total reflection peak in Fig. 2 (a, b, c),
despite of changing of dielectric environment.
According to LSPR theory [2] the peak must be
blueshifted in RIE treated sample due to decreasing of
effective dielectric constant ε. Then, it is unclear why
dipolar and quadrupoalr LSPRs are observed in
different way in total reflection, i.e., as peak and
valley, respectively. And at last, it is required to
consider the difference in peak positions of
absorptance and total reflection of the same sample,
which varies from zero (Fig. 2 (a, b)) to 30 nm
(Fig. 2 (c)).
0,6
Particles
Film
0,4
0,2
0
200
300
400
500
600
700
800
(a)
1
140
-Δφ
R
90
40
Reflectance
(2)
In disordered array of nanoparticles LSPR happens
only in part of nanoparticles at any wavelength. Other
particles reflect in nonresonant mode like a continuous
film consisting from mixture of silver and air. This
reflection is strong, creates interference and should be
distinguished from LSPRs in reflection spectra. In
case of absorption measurements, background from
nonresonant particles is weak and does not affect on
resonance peak position. The identification of
resonance features in total reflection, can be done by
comparison of obtained spectrum with nonresonant
one, e.g., spectrum of silver film. In Fig. 3 (a)
reflectance of low dose IBM sample is given together
with reflectance of 8 nm thick silver film on glass. The
dipolar resonance is responsible for increasing of
reflectance in the range of 380 - 480 nm and
decreasing of reflectance for wavelengths longer than
480 nm. In turn, quadrupolar resonance is connected
with formation of UV valley at 360 nm. However,
LSPR scattering from nanoparticles in isotropic
medium cannot explain decreasing of reflectance,
because total reflection is sum of contributions from
all nanoparticles and cannot decrease. Therefore,
possible reflection from quartz substrate leading to
interference and intensity reduction must be taken into
account.
Reflectance
A =1−T − R ,
3.2. Analysis of Obtained Results
Phase (deg)
The low dose sample was additionally treated by
reactive ion etching (RIE) as reported elsewhere
[5, 14, 15]. As a result, oxide between Ag nanoislands
was removed and SiO2 pillars with a height of 50 nm
were fabricated. Ag nanoparticles were left at the top
of pillars. The purpose of experiment was to change
the dielectric environment and to reduce possible
coupling between nanoparticles. The obtained spectra
of pillar sample are shown in Fig. 2 (c). Additionally,
in Fig. 2 (b, c) is also shown absorptance
0,5
0
200
400
600
Wavelength (nm)
800
(b)
Fig. 3. (a) Comparison of reflectance from nanoparticles
and silver film. (b) Optical properties of Ag/SiO2 interface.
173
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
Interference is inseparably linked with phase
change in the system. In case of plasmon particle on
surface, phase variation happens due to LSPR and
reflection from surface. Nanoparticle radiates light
with phase shift Δφ appearing between incident and
emitted radiation [1]
∆ =
,
(3)
where β is the damping constant, ω0 is the plasmon
resonance frequency and ω is the frequency of
incident wave. According to (3), Δφ is changed from
0º at low frequency to 180º at high frequency and is
equal 90º at the resonance frequency.
Radiation emitted by nanoparticle is reflected by
substrate with variable intensity and phase, which
depend on nanoparticle and substrate properties. Exact
reflection spectra can be obtained with the help of
simulation, but rough estimation can be done by
replacing of Ag nanoparticle by Ag film. For
illustration in Fig. 3 (b) is shown wavelength variation
of reflectance intensity and phase for Ag/SiO2
interface (incident side is Ag). The results are obtained
using expressions from [16] and optical parameters
from [17]. High reflectance and broad phase variation
facilitate appearing of interference. Reflected light
propagates back to nanoparticle and interact with its
electromagnetic field. As a result, substrate and a
nanoparticle create an optical cavity in which the
nanoparticle is one of the mirrors. Length of the cavity
is equal to nanoparticle height (around 45 nm for IBM
samples [7]).
The particle emits light during plasmon excitation
with phase shift Δφpl according to (3). After that total
phase shift of optical cavity is Δφpl + Δφrefl, where Δφrefl
is the phase shift upon reflection at the substrate. For
example, at the wavelength 360 nm (quadrupolar
valley in Fig. 2 (b)) reflectance phase is close to 90º
(Fig. 3(b)), what provides conditions for destructive
interference if Δφpl = 90º, i.e., at resonance frequency
of LSPR. As a result, exciting electromagnetic field
near plasmon nanoparticle is reduced, which leads to
decreasing of nanoparticle radiation at quadrupolar
LSPR and appearing of quadrupolar valley.
Importance of reflecting plane for formation of
total reflection is illustrated by Fig. 2 (c, d). In both
cases sharp quadrupolar valley at 360 nm is not
observed, because back reflection plane disappears.
After RIE treatment common supporting plane of Ag
nanoparticles is replaced by individual pillars, what
leads to diminishing of specular reflection and
increasing of nonresonant scattering. In the sample
with capping SiO2 layer the reflection plane disappears
at all, because substrate and capping layer have the
same refractive index. In both cases the quadrupolar
resonance is observed only in absorptance as 350 nm
shoulder (Fig. 2 (c)) and 380 nm peak (Fig. 2(d)), due
to eliminating of optical cavity. Redshift of
quadrupolar peak at 380 nm after capping by SiO2
layer is explained by increasing of ε.
174
The optical cavity modifies also dipolar peak of
total reflection (Fig. 3a). For wavelengths longer than
500 nm reflectance decreases due to destructive
interference, for shorter wavelengths it increases due
to constructive interference in the optical cavity.
However, dipolar decreasing of reflectance is less
significant than quadrupolar one, because destructive
interference happens not at resonance wavelength
(Δφpl < 90º). In the sample with capping layer dipolar
destructive interference is not possible, what leads to
increasing of reflectance in the red part of spectrum
(Fig. 2 (d)).
For wavelengths shorter than 430 nm steep
decreasing of Δφ is observed for Ag/SiO2 interface
(Fig. 3b). Together with drop of silver extinction
coefficient, it leads to increasing of destructive
interference and decreasing of reflectance in
nonresonant mode. Therefore, the left slope of total
reflection peak is defined by optical properties of
air/Ag and Ag/ SiO2 interfaces. That is why this part
of spectra looks similar for all samples in Fig. 2. And
that is why the peak position of total reflection is not
sensitive to RIE treatment. However, the peak
intensity decreases, because most of LSPR happens at
wavelengths shorter than 430 nm.
The analysis of samples on quartz substrate
demonstrates that total reflection must be carefully
used for LSPR identification in disordered arrays of
nanoparticles. Dipolar resonance corresponds
Δφpl = 90º and can be found between destructive and
constructive interference areas, i.e., on the right slope
of total reflection peak. In case of known scattering,
the position of dipolar LSPR can be considered
between peaks of total and diffuse reflection.
Quadrupolar LSPR position does not coincide exactly
with 360 nm valley of reflection, but corresponds total
phase shift 180º in the particle optical cavity. Peak
position of total reflection at 430 nm is strongly
connected with material properties of Ag and SiO2 and
cannot be significantly blushifted by LSPR. Therefore,
difference in peak positions of absorptance (sensitive
to LSPR) and total reflection (sensitive to optical
properties) can be significant.
3.3. Reflection of Ag Nanoparticles on Si
Substrate
Fig. 4 demonstrates reflection and scattering of
silver nanoparticles on bare silicon substrate. This
sample was prepared by annealing of silver film
deposited on Si substrate with native oxide. The UV
part of total reflection looks similar to reflection of
IBM samples on quartz (Fig. 2 (a, b)), but with
increased peak intensity and redshifted to 380 nm
minimum of reflectance. The VIS part of total
reflection is dissimilar to spectra of Fig. 2 and contents
only one very broad peak at 720 nm.
Silicon has much higher dielectric constant
(ε=15 at 633 nm) than quartz. Due to this and higher
surface density of nanoparticles (Fig. 1 (d)), dipolar
coupling is very strong in this sample. It leads to
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
Total
Diffuse, x10
Al2O3/Ag
c-Si/Al2O3
0,6
Reflectance
spliting of original LSPR peak in blue and red ones. In
this case, atypical nanoparticles are not coupled ones
and diffusion reflection shows position of original
LSPR at 830 nm. Therefore, VIS spectrum is manly
formed by blue component of split LSPR. Phase shift
of radiated light differs in coupled and isolated
nanoparticles. Coupled particles oscillate with Δφpl >
90º and Δφpl < 90º at the frequencies of blue and red
peaks, respectively. It means, that constructive
interference happens for whole VIS part of total
reflection in the optical cavity with nanoparticle and
results in observed spectrum. Position of dipolar LSPR
cannot be identified in reflection spectrum in case of
Si substrate.
0,4
0,2
0
200
300
2∆
Reflectance
0,2
Total
Diffuse, x2
0
200
300
400
500
600
700
Wavelength (nm)
800
900
Fig. 4. Total and diffuse reflection of silver
nanoparticles on silicon substrate.
4. Ag Nanoparticles on a Multilayer
Substrates
500
600
700
800
900
Fig. 5. Total and diffuse reflection of silver nanoparticles
on 100 nm thick Al2O3 layer deposited on silicon substrate.
0,6
0,4
400
+∆
+∆
=
,
(4)
where Δφprop is the phase shift due to propagation of
the wave through cavity, Δφrefl is the phase shift upon
reflection at the cavity mirror and N is integer. From
diffuse reflection spectrum in Fig. 5 follows, that
LSPR of Ag nanoparticles on Al2O3 is situated below
510 nm. Wavelength of local minimum in Fig. 5 is
460
nm,
what
corresponds
2Δφprop=1.47π
(nAl2O3=1.69). Additional phase shift Δφpl=0.53π
provides condition for destructive interference
(Δφrefl=π) in the Al2O3 optical cavity at 460 nm
according to (4). The dipolar resonance itself is
redshifted on 0.03π from the local minimum, because
Δφpl=0.53π and Δφ=0.5π at the resonance.
0,6
High dose, total
Low dose, total
High dose, diffuse, x10
Reflectance
0,4
0,2
0
200
300
400
500
600
700
800
900
(a)
0,6
Total
Diffuse, x2
c-Si/SiO2, calculated
0,4
Reflectance
In the previous section, we demonstrated that
nanoparticle and substrate form an optical cavity. It, in
turn, leads to intensity variation of electromagnetic
field near the nanoparticle. Multilayer substrate
provides additional reflection interfaces, what result in
multiple optical cavities including the same
nanoparticle. Fig. 5 shows spectra of Xe mixed Ag
nanoparticles on 100 nm thick Al2O3 layer above c-Si
substrate. Here is also given reflectance spectrum of
15 nm thick Ag film above Al2O3 before IBM. This
spectrum demonstrates transparency of 15 nm thick
silver film and high quality interference in Al2O3
optical cavity with Si and Ag mirrors. The minimum
at 290 nm and maximum at 380 nm are close to
theoretical interference extrema, calculated with bulk
silver optical constants.
After formation of nanoislands, the minimum at
530 nm is replaced by maximum at 520 nm and
minimum at 470 nm. In this sample, the new optical
cavity is created between reflecting surface of Si
substrate and Ag nanoparticle on Al2O3 layer.
Therefore, presence of the additional optical cavity
creates two local extrema visible in total reflection.
When in the optical cavity one of the mirrors is
replaced with plasmonic structure, the phase shift
balance for extrema is [1]
0,2
0
200
300
400
500
600
700
Wavelength (nm)
(b)
800
900
Fig. 6. Total and diffuse reflection of silver
nanoparticles created by (a) IBM and (b) annealing
on oxidized Si substrate.
175
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
Fig. 6 (a) demonstrates total and diffuse reflection
for Ar mixed Ag nanoparticles prepared on Si
substrate covered by 20 nm of SiO2. The fabrication
procedure is the same as for samples on quartz
substrate in Fig. 2 (a, b). The obtained spectra look
similar to spectra in Fig. 2 (b) in UV part. They include
the same peak at 330 nm and valley at 360 nm.
Nevertheless, VIS parts of the spectra in Fig. 2 (b) and
Fig. 6 (a) are different. There are two reasons for this:
light propagation delay in SiO2 sublayer and reflection
from SiO2/Si interface. The wavelength dependent
Δφprop,
nonresonant
reflection
from
silver
nanoparticles and Δφrefl=π at the SiO2/c-Si interface
form increasing part of reflectance from 510 nm to
900 nm. At wavelengths shorter than 510 nm LSPRs
of Ag nanoparticles in optical cavities increase
reflectance similarly to quartz substrate. The reflection
spectrum of high dose sample (Fig. 6 (a)) demonstrates
one more quadrupolar resonance redshifted to 390 nm.
It is attributed to Ag nanoparticles submerged in SiO2
during IBM [10].
1
1+SiO2/Ag
2
2+SiO2
Reflectance
0,4
similar features in Fig. 6 (a). Larger size of silver
nanoparticles (Fig. 1 (d)) redshifts position of dipolar
LSPR to 550 nm, how it is visible from scattering in
Fig. 6 (b). Additionally, high surface density of
nanoparticles (Fig. 1 (d)), results in splitting of
original LSPR in the same way as for Si substrate. Part
of optical cavities with Ag nanoparticles acquires Δφpl
> 90º at blue peak wavelength and creates constructive
interference in the range 450-600 nm, what leads to
increased FWHM of the VIS peak.
The next two samples for demonstration of cavity
modified LSPRs include two layers of Ag
nanoparticles with SiO2 spacer between them on
quartz substrate. Both nanoparticle layers were
prepared by high dose Ar IBM of silver films. The
samples differ only by thickness of SiO2 layers (73 nm
and 144 nm). Reflection and extinction of samples
with SiO2 thicknesses 73 nm and 144 nm are shown in
Fig. 6 and Fig. 7, respectively. The samples were
characterized at different stages of fabrication: the first
layer of Ag nanoparticles (1), this layer capped by
SiO2 and Ag (1+SiO2/Ag), two layers of Ag
nanoparticles (2), the whole structure capped by SiO2
(2+ SiO2).
1
1+SiO2/Ag
2
2+SiO2
0,4
Reflectance
0,2
0,2
0
200
300
400
500
600
700
800
900
Wavelength (nm)
(a)
0
200
500
600
700
800
900
1
1+SiO2
2
2+SiO2
1,5
1
Optical density
Optical density
400
Wavelength (nm)
(a)
1
1+SiO2
2
2+SiO2
1,5
0,5
1
0,5
0
200
300
400
500
600
700
800
900
Wavelength (nm)
(b)
0
200
Fig. 7. (a) Total reflection and (b) extinction of the
sample with two Ag nanoparticle layers and 73 nm thick
SiO2 spacer.
Spectra of Ag nanoparticles prepared by annealing
on oxidized Si substrate (36 nm thick SiO2) are given
in Fig. 6 (b). They are similar to spectra in Fig. 6 (a) in
UV part. The VIS part of Fig. 6 (b) has a broader
dipolar peak and redshifted valley in comparison with
176
300
300
400
500
600
700
800
900
Wavelength (nm)
(b)
Fig. 8. (a) Total reflection and (b) extinction of the
sample with two Ag nanoparticle layers and 144 nm thick
SiO2 spacer.
The first layer of Ag nanoparticles on quartz
substrate demonstrates identical for both samples
reflectance and extinction spectra (blue curves in
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
substrate ε. The peak can be attributed to variation of
reflection phase at Ag/air and Ag/substrate interfaces
according with wavelength and coincides with
maximum of silver refractive index nAg. Removing of
reflective surface by pillars or capping changes
reflection conditions and the mentioned UV feature
disappears.
Position of UV peak in absorption (360 nm) strictly
coincides with the peak of diffuse reflection
(Fig. 2 (b), Fig. 4 and Fig. 6). It means that this feature
is a quadrupole LSPR [11]. However, its position
weakly depends on dielectric environment and for Si
substrate with high ε the valley is shifted only to
375 nm (Fig. 4). At the same time, quadrupolar LSPR
of submerged in oxide Ag particles is shifted to
390 nm (Fig. 6 (a) and Fig. 7 (b)). Additionally,
extinction coefficient of silver has minimum and
substrate reflection has maximum at the same
wavelength 360 nm (see calculated spectra at Fig. 6
(b)), which in turn, facilitates destructive interference.
We believe that all these factors contribute in stable
position of 360 nm valley.
Fig. 6, Fig. 7), controlled by nanoparticle in an optical
cavity near substrate (quartz cavity). However,
capping of Ag nanoparticles by SiO2 and silver leads
to difference in spectra for 73 and 144 nm thick SiO2.
For thin oxide layer interference picture and reduced
intensity of total reflection in UV are observed (Fig. 7
(a)). For thick oxide layer UV valley disappears and
VIS peak is redshifted (Fig. 8 (a)). The difference is
connected with appearance of additional optical cavity
for every nanoparticle. The new cavity is formed by
SiO2 layer, Ag mirror and Si mirror (SiO2 cavity).
Difference in thickness of oxide layer results in
different propagation phases. For example, at
wavelength 418 nm 2Δφprop=π and 2Δφprop=2π for thin
and thick oxide layers, respectively. Accordingly to
destructive and constructive interference in the SiO2
layer, new features appear in the reflection spectra (red
curves) at this wavelength: the valley in Fig. 7 (a) and
steep increase of reflectance in Fig. 8 (a). Therefore,
radiation from optical cavities (quartz cavity and SiO2
cavity) can be added (Fig. 8) or subtracted (Fig. 7) and
switching of the mode can be done by Δφprop. In case
of addition, contributing components of radiation
cannot be easily distinguished (Fig. 8), because all of
them work in the same phase. In case of subtraction,
quadrupolar LSPR of Ag nanoparticle in SiO2 is
designated by peak at 385 nm (quartz cavity) and
valley at 365 nm (SiO2 cavity). After IBM of upper Ag
layer (black curves), in the sample with thick oxide the
additive dipolar peak at 495 nm and shoulder at 418
nm are visible. In the sample with thin oxide four
dipolar extrema can be distinguished. Due to splitting
in SiO2, the original dipolar LSPR is observed as blue
and red components. Additionally, two optical cavities
support different resonance modes and select two
more wavelengths from both blue and red
components, respectively. As a result, two dipolar
LSPRs are ascribed to the quartz cavity (480 nm and
650 nm) and two dipolar LSPR are connected with the
SiO2 cavity (440 nm and 530 nm). After covering by
capping SiO2 layer, only one optical cavity created by
two nanoparticle layers is left in both samples. The
sample with thick oxide demonstrate one dipolar peak
at 460 nm and shoulder at 380 nm corresponding to
quadrupolar LSPR. The sample with thin oxide shows
dipolar LSPR range between peak at 465 nm and
valley at 620 nm (green curves). In this range variation
of Δφpl provides switching between constructive and
destructive interference in the optical cavity.
It has been demonstrated that peaks and valleys in
reflection spectrum of disordered nanoparticles on
multilayer substrate do not correspond directly to
plasmon resonances. The sample reflectance is
modulated by constructive and destructive
interference in the optical cavity, containing the
nanoparticle. Nevertheless, it is possible to identify
position of LSPR with accuracy of FWHM of
resonance band. This position is situated between two
local extrema of reflectance spectrum, corresponding
phase shift variation during LSPR. Additionally,
LSPR positions can be unmasked by proper design of
studied samples, e.g., by eliminating reflecting
surfaces or by variation of propagation phase shift in
the optical cavity. The spectrum features in UV range
may be attributed either to quadrupolar resonance or
to variation of material properties. In the first case, the
valley position depends on dielectric environment and
geometry of plasmonic structures. In the second case,
the peak is fixed at 330 nm and is observed only in
nanostructures having back reflecting surface. The
obtained results can be used in analysis and design of
plasmonic nanostructures on opaque substrates.
5. UV Features of the Spectra
Acknowledgements
All studied samples, exclude the 100 nm Al2O3/Ag
and capped ones, demonstrate 330 nm peak in the UV
part of reflection spectra. Moreover, in the RIE
processed sample, this feature was observed in as
prepared nanoparticle array and disappeared after RIE
(Fig. 2 (c)). Therefore, existing of reflective surface
with phase shift close to π below Ag nanoparticles is
essential for obtained results. Wavelength of UV peak
(330 nm) does not depend on nanostructure shape and
This research was undertaken at the Micronova
Nanofabrication Centre, supported by Aalto
University.
6. Conclusions
References
[1]. R. Ameling, L. Langguth, M. Hentschel, M. Mesch,
P. V. Braun and H. Giessen, Cavity-enhanced
localized plasmon resonance sensing, Applied Physics
177
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 170-178
[2].
[3].
[4].
[5].
[6].
[7].
[8].
[9].
Letters, Vol. 97, Issue 25, 2010, pp. 253116-1253116-3.
E. C. Le Ru and P. G. Etchegoin, Principles of surfaceenhanced Raman spectroscopy and related plasmonic
effects, Elsevier, 2009.
S. Pillai, K. R. Catchpole, T. Trupke, and M. A. Green,
Surface plasmon enhanced silicon solar cells, Journal
of Applied Physics, Vol. 101, Issue 9, 2007,
pp. 093105-1-093105-8.
P. Biagioni, J.-S. Huang, and B. Hecht, Nanoantennas
for visible and infrared radiation, Reports on Progress
in Physics, Vol. 75, Issue 2, 2012, pp. 024402-1024402-40.
A. Shevchenko, V. Ovchinnikov, and A. Shevchenko
Large-area nanostructured substrates for surface
enhanced Raman spectroscopy, Applied Physics
Letters, Vol. 100, Issue 17, 2012, pp. 171913-1171913-4.
V. Ovchinnikov, Reflection from irregular array of
silver nanoparticles on multilayer substrate, in
Proceedings of the 9th International Conference on
Quantum, Nano/Bio, and Micro Technologies
(ICQNM 2015), Venice, Italy, 23-28 August 2015,
pp. 16-21.
V. Ovchinnikov and A. Priimagi, Anisotropic plasmon
resonance of surface metallic nanostructures prepared
by ion beam mixing, in Proceedings of the 1st
International Conference on Quantum, Nano and
Micro Technologies (ICQNM'07), Guadeloupe,
French Caribbean, 2-6 January 2007, pp. 3-8.
V. Ovchinnikov, Analysis of furnace operational
parameters for controllable annealing of thin films, in
Proceedings of the 8th International Conference on
Quantum, Nano/Bio, and Micro Technologies
(ICQNM 2014), Lisbon, Portugal, 16-20 November
2014, pp. 32-37.
V. Ovchinnikov, Effect of thermal radiation during
annealing on self-organization of thin silver films”, in
[10].
[11].
[12].
[13].
[14].
[15].
[16].
[17].
Proceedings of the 7th International Conference on
Quantum,
Nano
and
Micro
Technologies
(ICQNM 2013), Barcelona, Spain, 25-31 August 2013,
pp. 1-6.
V. Ovchinnikov, Formation and characterization of
surface metal nanostructures with tunable optical
properties, Microelectronics Journal, Vol. 39,
Issue 3-4, 2008, pp. 664-668.
E. Thouti, N. Chander, V. Dutta, and V. K. Komarala,
Optical properties of Ag nanoparticle layers deposited
on silicon substrates, Journal of Optics, Vol. 15,
Issue 3, 2013, pp. 035005-1-035005-7.
V. Ovchinnikov and A. Shevchenko, Surface plasmon
resonances in diffusive reflection spectra of
multilayered silver nanocomposite films, in
Proceedings of the 2nd International Conference on
Quantum,
Nano
and
Micro
Technologies
(ICQNM 2008), Sainte Luce, Martinique, 10-15
February 2008, pp. 40-44.
T. Sang, L. Wang, S. Ji, Y. Ji, H. Chen, and Z. Wang,
Systematic study of the mirror effect in a poly-Si
subwavelength periodic membrane, Journal of the
Optical Society of America A, Vol. 26 Issue 3, 2009,
pp. 559-565.
V Ovchinnikov, A Malinin, S Novikov, and C
Tuovinen, Silicon nanopillars formed by reactive ion
etching using a self-organized gold mask, Physica
Scripta, Vol. T79, 1999, pp. 263-265.
A. Shevchenko and V. Ovchinnikov, Magnetic
excitations in silver nanocrescents at visible and
ultraviolet frequencies, Plasmonics, Vol. 4, Issue 2,
2009, pp. 121-126.
H.W. Edwards, Interference in thin metallic films,
Physical Review, Vol. 38, 1931, pp. 166-173.
RefractiveIndex.INFO (http://refractiveindex.info).
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
178
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
Performance Analysis of Commercial Accelerometers:
A Parameter Review
Stephan Elies
Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, Am Klingelberg 1,
Efringen-Kirchen, 79588, Germany
Tel.: + 49 7628 9050-691
E-mail: stephan.elies@emi.fraunhofer.de
Received: 31 August 2015 /Accepted: 5 October 2015 /Published: 30 October 2015
Abstract: This paper points out the performance and limits of state of the art commercial capacitive, resistive,
piezoresistive, piezoelectric, thermal and optical accelerometers and can be used as a reference work. Therefore,
datasheets of 118 accelerometers from 27 manufacturers of eight countries have been analyzed. Focus of the
analysis were the parameters overload shock limit, measurement range, frequency response, resonance
frequency, volume, weight, power consumption, operating temperature, storage temperature and cost of both
uniaxial and triaxial accelerometers. A strict overload shock limit of 10,000 g for accelerometers with proof
mass and a measurement range of less than 2,000 g was found. Also, that the performance of uniaxial and
triaxial accelerometers differs. Especially uniaxial piezoelectric accelerometers show a better performance with
regard to overload shock, measurement range, resonance frequency, frequency response and operating
temperature in contrast to triaxial ones. Piezoelectric accelerometers show the highest overload shock limits,
measurement range and operating temperature, capacitive accelerometers the lowest power consumption and
volume, piezoresistive accelerometers the widest frequency response. Copyright © 2015 IFSA Publishing, S. L.
Keywords: Commercial accelerometers, Review, Market overview, Shock limit, Performance.
1. Introduction
Today, off-the-shelf accelerometers are widely
used for numerous applications in industries such as
consumer electronics, automotive, biomedical,
robotics and military [1, 2]. Within the conducted
review, seven technologies of today’s commercial
accelerometers could be identified. The capacitive,
piezoresistive and piezoelectric technology with
charge output and also with voltage output are most
commonly used [1-4]. Further technologies are the
resistive, optical and thermal ones. All these
technologies differ in their performance due to their
internal structure according to their different
http://www.sensorsportal.com/HTML/DIGEST/P_2751.htm
transduction principles. The term technology is used
as a generic term for a clear distinction between
piezoelectric accelerometers with charge output and
voltage output due to the fact they base on the same
(piezoelectric) transduction principle. Technology is
the more top level term.
So why a review of well-known and widely used
technologies? A literature review revealed that there
is a general lack of reviews of accelerometers also
stated in [1]. Still, reviews of commercial state of the
art accelerometers [1, 2] are mostly focused on
commonly used technologies, the first three
mentioned ones. Still there are less up to date reviews
and papers [1, 2, 5] and they are only focused on one
179
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
or two commonly used technologies. In addition
there are reviews out-of-date or focused solely on
special applications [1, 2, 6, 7].
Very comprehensive reviews in regard to their
mathematically description of transduction principles,
algorithms, electronic control circuits, fabrication
techniques and other characteristics are [1, 2] but
altogether these reviews analyzed only the following
parameters of accelerometers; weight, resonance
frequency, measurement range, sensitivity, frequency
response and noise floor. For real world applications
parameters such as volume, operating temperature,
power consumption, and cost are important to fit
physical and financial boundary conditions.
A less academic view on accelerometers mostly
including such parameters is addressed in reference
guides by the manufacturers, e.g., [3, 8, 9]. These
guides are made for customers to find they right type
of accelerometer if they are not familiar with the
topic. But they are only focused on technologies of
their own products, mostly two or three technologies.
Hence there is no (known) review or guide of
accelerometers focusing on more than two or three
commercial technologies including parameters such
as volume, power consumption, overload shock limit,
operating and storage temperature and cost. Hence in
this paper we give an overview of state of the art
commercial accelerometers of common used
technologies.
Because we analyzed entire seven technologies
we do not go into detail with regard to transduction
principles, algorithms or electronic control circuits.
Instead we will identify upper and lower limits of
parameters of accelerometers to get a general
overview on different common used commercial
technologies by analyzing:
 A number of 118 accelerometers from
27 manufacturers of eight countries;
 Piezoresistive, resistive, piezoelectric with charge
and with voltage output, capacitive, optical and
thermal accelerometers;
 The
parameters
overload
shock
limit,
measurement
range,
frequency
response,
resonance frequency, volume, weight, power
consumption, operating, storage temperature and
cost for both uniaxial and triaxial accelerometers.
Our review covers very different commercial
accelerometers used in numerous fields. We will
address this fact in detail in Section 4 but we do not
consider accelerometers effected by international
traffic in arms regulations and export restrictions.
In Section 2, the transduction principles of the
analyzed accelerometers are briefly explained and
advantages and drawbacks of each technology are
mentioned. The analyzed database and the process of
data acquisition is addressed in Section 3. In
Section 4, the analyzed parameters measurement
range, overload shock limit, power consumption,
volume, weight, resonance frequency, frequency
response, operating and storage temperature and cost
of accelerometers are discussed. Finally, the results
are summarized in Section 5.
180
2. Transduction Principles of
Accelerometers
The vast majority of accelerometers consist of a
proof mass, acting as a mechanical sensing element,
attached to a reference frame by a mechanical
suspension system [10]. An acceleration applied to
the reference frame leads to a deflection of the proof
mass caused by the inertial force according to
Newton’s second law. There are many different
variations of suspension systems [1, 2, 10] but we
identified two general types for the analyzed
accelerometers. For piezoelectric accelerometers the
proof mass is attached by a spring to a reference
frame and for resistive, piezoresistive, capacitive and
optical accelerometers the proof mass is attached by a
cantilever beam to a reference frame. However they
can be described as a spring-mass-damper
system [10].
There are a number of transduction principles to
detect the displacement of a proof mass or the
bending of a cantilever beam [2, 10]. Common
commercial accelerometers detect the change of
resistance, capacitance, charge, temperature or
optical characteristics. Further used transduction
principles are electromagnetic, resonance and
tunneling principles [2, 6, 10]. But we found no
manufacturer selling accelerometers based on this
principles off-the-shelf.
Each transduction principle needs electronics to
convert the signal, such as the change in resistance,
of a transducing element into an analog or digital
output, and for signal amplifying, filtering and
processing. A transducing element is the part of an
accelerometer that accomplishes the conversion of
motion into a signal [11]. For example, a strain gauge
is the transducing element and the electric resistance
is the signal to be converted into a voltage. Mostly
electronics are combined in one housing but there are
also good reasons for decoupling electronics such as
harsh or hazardous environments with high voltage
levels, temperatures, electromagnetic interference or
environments containing explosive substances. For
optical and piezoelectric accelerometers with charge
output external electronics have to be connected by
cables and can be positioned far from the system to
be measured.
General, accelerometers are classified into two
types with regard to their frequency response also
called bandwidth [3]. An AC-response accelerometer
is capable to measure dynamic acceleration (meaning
change
in
acceleration).
A
DC-response
accelerometer can measure static (constant)
acceleration, such as gravity acceleration, as well as
dynamic acceleration.
There are essential interdependence of parameters
of accelerometers due to their internal structure, the
spring-mass-damper system. Increasing the proof
mass of an accelerometer leads to a more strong
deflection of the proof mass under the same loading.
In other words sensitivity increases. At the same time
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
the resonance frequency and consequently the
bandwidth decreases. In contrast, decreasing the
proof mass leads to a diametric behavior. So it’s not
possible to maximize sensitivity and bandwidth
respective resonance frequency. Subsequent we will
briefly explain the seven technologies and highlight
the most important fact off every technology.
2.1. Piezoelectric Accelerometers with
Charge Output
This type of accelerometer comprise piezoelectric
materials as polarized ceramics or quartz crystals
bonded with a proof mass [12]. An acceleration leads
to a deflection of the proof mass and causes stress in
the material subsequently. Under stress, the
piezoelectric effect causes a charge transfer in the
crystal. The amplitude of charge can be measured on
the surface of the crystal. For this type electronics is
not integrated so these accelerometers provide a
charge output signal which has to be transferred by
special low noise cable to external electronics [12].
They work only for AC-response. One benefit of
decoupling transducing element and electronics is the
wide temperature range of common piezoelectric
materials up to 400 °C and more [11][12]. Due to the
unmodified charge output signal these accelerometers
are interchangeable [9].
2.2. Piezoelectric Accelerometers with
Voltage Output
They work exactly the same way as piezoelectric
accelerometers with charge output, but the electronics
to amplify the charge signal and convert it into a
voltage signal are integrated [12]. For power supply a
constant current source with current levels from
0.5 to 20 mA is mostly needed [9]. They provide a
voltage output signal and can also only be used for
AC-response.
In
contrast
to
piezoelectric
accelerometers with charge output the temperature
range is limited to that which the electronic will
withstand. Furthermore reliability is lower if the
accelerometer is subjected to harsh environments but
the noise level is lower because of shorter cable
length between transducing element and electronics
[11]. Still they may not be interchangeable if the
power requirement is not the same [9].
2.3. Resistive Accelerometers
This type of accelerometers detect the change of
resistance of a metal foil strain gauge or wire bonded
to a cantilever beam [11]. An acceleration leads to a
bending of the cantilever beam and thus to a change
in geometry of the strain gauge. According to the
change in geometry the resistance of the strain gauge
alters, described by the piezoresistive effect [10]. Up
to four strain gauges are configured in a Wheatstone
bridge circuit [11]. They provide a voltage signal and
work for DC-response. In the past wire or foil strain
gauges have been used exclusively, today silicon
strain gauges have a wider distribution because of
their higher sensitivity [11].
2.4. Piezoresistive Accelerometers
They work exactly the same way as resistive
accelerometers, but the strain gauge is fabricated
from semiconductor materials [10]. Single crystal
silicon is up to 100 times more sensitive to strain than
a metal foil strain gauge [10]. For semiconductors the
change of the specific resistance according to stress is
the dominating effect caused by the piezoresistive
effect. The change in resistance due to change in
geometry can be neglected [10]. Configured in a
Wheatstone bridge circuit, they provide a voltage
signal and function also for DC-response. They are
more sensitive to temperature, show lower linearity
and breaking strain than metal foil strain gauges [13].
2.5. Capacitive Accelerometers
These accelerometers detect the change of a
capacitor configuration. The electrodes of the
capacitor are composed of semiconductor material,
such as silicon. Typically the differential change in
capacitance, of a moveable electrode in-between two
stationary electrodes, is detected [10]. The moveable
electrode is attached to a proof mass and deflects due
to acceleration which leads to a differential change in
capacitance. Configured in a Wheatstone bridge
circuit, they provide a voltage signal and work for
DC-response [2]. This is the most common
technology used for accelerometers today [3]. Some
benefits are low cost and low power consumption but
in contrast they can be vulnerable to electromagnetic
interference [6].
2.6. Optical Accelerometers
A variety of transduction principles to convert a
mass displacement into a change in optical
characteristics exists [11]. In particular one
technology have become widespread use, the optical
fiber technology [14]. Still there is a variety of
technics for optical fiber measurement [15] but the
Fiber Bragg Grating (FBG) principle has become
widely known [14] and FBG accelerometers seem to
be the popular optical fiber technology today.
Bragg Gratings are interference filters written into
optical fibers. The gratings reflect only a narrow
spectral component of the induced light. [16] This
characteristic is used for FBG accelerometers. An
acceleration leads to a deformation of an optical fiber
attached to a suspension beam. The deformation of
the optical fiber change the reflection characteristic
of the Bragg gratings. This change can be detected by
comparing the spectral component of the reflected
with the induced light. They work for DC-response
181
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
and one benefit of FBG accelerometers is the
immunity of light of the transducing element to
electromagnetic interference. So usually, electronics
are not integrated and they provide a pure optical
signal. Electronics can be connected by optical fibers.
As well as for piezoelectric technology with charge
output, electronics can be positioned far away from
the measurement position.
Drawback is their greater size in contrast to the
other analyzed accelerometers. There potential for
miniaturization is limited and MEMS-technology
favors other fiber techniques [17]. In the present
work we only analyzed optical FBG accelerometers
because no other commercial optical accelerometers
could be found.
development, industrial, railway, oil, gas, aerospace,
military and automotive industries.
So this paper includes accelerometers for very
different applications, with measurement ranges from
-110,000 g up to 110,000 g, with overload shock
limits up to 200,000 g, with rugged metal housings or
as simple service mounted devices (SMD). The unit g
stands for the gravity acceleration defined as
9.81 meters per square seconds.
All analyzed parameters were stored in a database
to identify limits and correlation between parameters
by data mining.
2.7. Thermal Accelerometers
Generally the scope of the work was to capture
the state of the art of accelerometers from
manufacturers worldwide with a certain focus to
German manufacturers. Hence the procedure of data
acquisition was as follows:
First, we started analyzing accelerometers from
German manufactures and took into account one or
two samples of different technologies of each
manufacturer, for uniaxial and triaxial measurement.
Overall we considered accelerometers of ten German
manufacturers. Off course there are much more
manufacturers and companies in Germany selling
accelerometers but many are only distributers of
accelerometers of manufacturers worldwide. From
the results we deduced benchmarks of state of the art
accelerometers of German manufacturers for further
analysis.
Second, we analyzed accelerometers worldwide
and for the most part we considered only samples of
accelerometers
with
equal
or
outstanding
performance relative to our deduced benchmarks.
During the analysis the benchmarks were updated
with every new sample and so it was harder to find
accelerometers exceeding these benchmark. Thereby
not so many manufacturers of countries worldwide
have been taken into account in contrast to the
number of German manufacturers.
That means, the number of analyzed countries and
manufacturers of a country (Germany as well as
worldwide) is not exhaustive, but we found no
commercial accelerometers with higher overload
shock limit, higher measurement range and so on. So
the database is suitable to point out lower and upper
limits of parameters. In contrast the database is not
suitable for statistics calculations such as calculating
the average value of a parameter.
Thermal accelerometers based on mass
displacement have been studied in [18]. We found no
manufacturer selling these accelerometers off-theshelf. Thermal accelerometers without mass
displacement have been reported among others in
[19]. These types of accelerometers consist of a
heater and thermocouples located around the heater
in a hermetic chamber. Without acceleration, the
heater creates a symmetric temperature profile in the
chamber. When acceleration is applied, the hot air in
the chamber moves and the temperature profile gets
asymmetric. The asymmetry can be detected by the
thermocouples around the heater. There is only one
manufacturer that patented and commercialized this
technology [5]. Electronics is integrated and these
accelerometers provide a voltage signal and work for
DC-response. In the present work, we only analyzed
thermal accelerometers without moving parts. The
greatest benefits is their resilience to very high
overload shocks due to the absence of a proof mass.
3. Database and Procedure of Data
Acquisition
For this paper, we analyzed accelerometers of
manufacturers worldwide. We did not focused to one
special type of accelerometer, e.g., micro-g or high-g
accelerometers. We directed our review on several
different characteristics of accelerometers; high
measurement range and overload shock limit, low
power consumption, low volume, wide operating
temperature range and so on.
Thereby
we
analyzed
very
different
accelerometers, e.g., for crash-, shock- and impact
testing, inertial navigation, machine vibration and
bridge monitoring, modal analysis; for applications in
digital cameras, mp3 players, mobile phones, medical
instrumentations, wind turbines, airbags, sport
diagnostics, marine, motor sports, robotics,
embedded applications, bio dynamics, pedestrian
crash testing; in domains of consumer, research and
182
3.1. Procedure of Data Acquisition and
Significance of Data
3.2. Database
In Table 1 the number of accelerometers
classified by their country of origin into European
Union and worldwide are listed. According to our
explanations before, there is a great focus on
accelerometers of German manufacturers, and the
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
number of manufacturers within a country as well as
the number of accelerometers of a manufacturer is
different.
We chose the headquarters of the manufacturers
as country of origin. Due to globalization, several
production steps such as engineering, manufacturing,
assembly, testing, etc., could be distributed across
several locations worldwide. We think headquarters
is most suitable to compare the performance by
countries. Because of 27 manufacturers, 15 produce
their accelerometers at the headquarters, 7 produce at
the headquarters and additionally at one or more
locations worldwide. Only for 5 manufacturers, the
location of production is not explicitly specified.
Table 1. Number of analyzed accelerometers classified by country of origin into European Union and Worldwide.
Number of
manufactures
10
2
1
7
3
1
1
1
Country of origin
Germany
UK
Denmark
USA
Switzerland
China
Canada
Hong Kong
In Table 2 the number of analyzed accelerometers
of German manufacturers and for the remaining
manufacturers are listed. In addition the technologies
are classified by frequency response.
Most common are the capacitive, piezoelectric
and piezoresistive technology. Many manufacturers
are specialized to one or more of these technologies.
Resistive, optical and thermal accelerometers are not
very common in contrast to the other technologies.
The thermal technology is patented by only one
manufacturer and we took into account two samples
of their products. Accelerometers using resistive
technology are really hard to find because
piezoresistive strain gauges are mostly used. We took
into account four samples of optical accelerometers
because there are only few manufacturers specialized
on this technology, they are made for very special
niches and because in matters of some analyzed
Number of
accelerometers
35
11
11
37
13
9
1
1
Classification by
country of origin
European Union (EU)
Worldwide (Non EU)
parameters their performance is lower in contrast to
the other technologies. We will discuss this issues in
the next section in detail.
Because of small samples of the thermal, resistive
and optical technology we will highlight only special
characteristics of these three principles in the next
section. But note, for these three technologies we
found no accelerometer with higher performances.
Due to the fact that the analyzed accelerometers are
designed for very different applications we will
explain some important facts and differences. The
two thermal, nearly half of the capacitive and three
piezoresistive accelerometers are manufactured as
simple service mounted devices. The remaining
accelerometers are integrated in more or less rugged
housings of aluminum, titanium or plastics. Thereby
volume and weight can be very different within a
technology.
Table 2. Number of analyzed accelerometers of German manufacturers and remaining manufacturers. The technologies are
classified by frequency response.
Technology
Number of accelerometers
of German manufactures
Number of accelerometers of
remaining manufacturers
Capacitive
Piezoresistive
Resistive
Optical
Thermal
Piezoelectric voltage output
Piezoelectric charge output
19
6
1
1
5
3
21
9
6
3
2
25
17
4. Analysis of Parameters
In this section, the parameters measurement
range, overload shock limit, power consumption,
volume, weight, frequency response, resonance
Classification by
frequency
response
DC-response
AC-response
frequency, operating and storage temperature of
accelerometers specified on their datasheets are
analyzed. Additionally to our previous review [20] in
this paper the cost have been analyzed in detail.
183
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
4.1. General Explanation
Before we start with the evaluation of parameters
we will explain the figures in this section in detail.
The figures were designed to correlate the analyzed
parameters among the measurement range. This is the
parameter of choice because accelerometers are made
for measuring acceleration.
First, we classified all analyzed accelerometers by
technology and the technology by the number of
measurement axis, hence there are 13 classes, see
Table 3. Second, we sorted all accelerometers within
a class by ascending measurement range. Third, we
numbered all accelerometers by an identifier (ID)
starting with the class “piezoresistive 1-Axis” and
ended with the class “thermal 3-Axis”, see Table 3.
That means within a class the measurement range
increase with ascending identifier. A number of
118 different accelerometers have been analyzed, so
there are 118 IDs, where each ID is a unique
identifier of an accelerometers. That means an
accelerometer, e.g., with the ID 50 in Fig. 1 has the
same ID in Fig. 2 to Fig. 8.
the measurement range is specified for a symmetric
range. For 24 accelerometers, the lower limit is not
specified and only for one accelerometer, the lower
limit is restricted.
In Fig. 1 the measurement range is plotted against
the accelerometer-ID. Note that this is a three
parameter plot. The visualized parameters are
technology, number of measurement axis and
measurement range. For the sake of completeness the
three
capacitive
accelerometers
with
two
measurement axis are marked by the dotted circle.
Table 3. Classification of accelerometers by technology
and number of measurement axis and numbering of the
accelerometers within a class by identifiers.
Range of ID
1 - 10
11 - 15
16 - 30
31 - 55
56 - 68
69 - 75
76 - 93
94 - 105
106 - 109
110 - 112
113 - 114
115 - 116
117 - 118
Classes
Piezoresistive 1-Axis
Piezoresistive 3-Axis
Capacitive 1-Axis
Capacitive 3-Axis
Piezoelectric charge output 1-Axis
Piezoelectric charge output 3-Axis
Piezoelectric voltage output 1-Axis
Piezoelectric voltage output 3-Axis
Resistive 1-Axis
Resistive 3-Axis
Optical 1-Axis
Optical 3-Axis
Thermal 3-Axis
Fig. 1. Measurement range of 118 accelerometers classified
by technology, number of measurement axis and sorted by
measurement range in ascending order.
Among the 118 accelerometers, there are only
three capacitive ones with two measurement axes.
Because of that small number they were classified
into the class of “Capacitive 3-Axis” accelerometers
and no differentiation was made.
Thermal accelerometers are limited to a measurement
range of 5 g, optical to a range of 200 g, capacitive to
a range of 500 g and resistive to a range of 10,000 g.
Only piezoelectric and piezoresistive accelerometers
are capable of measuring more than 10,000 g with the
restriction that piezoresistive accelerometers are the
only ones for DC-response above 10,000 g up to
60,000 g.
For measuring accelerations of 100,000 g and
more, only piezoelectric accelerometers are capable
with the restriction that triaxial piezoelectric
accelerometers with voltage output are strictly
limited to 10,000 g.
4.2. Measurement Range of Accelerometers
4.3. Overload Shock Limit
In datasheets, the measurement range is mostly
specified for a symmetric range of acceleration such
as from -500 g to 500 g. Rarely the lower limit is
restricted, e.g., from -200 g to 500 g. In this review,
the parameter measurement range stands for the
upper limit of positive range of acceleration. For the
last given example the measurement range is 500 g.
In this review there is no need to analyze the negative
measurement range, as for 95 of 118 accelerometers
Again, before we continue with the analysis, we will
go deeper in detail with the figures in this section
since it is important to understand the concept. In
Fig. 2 the overload shock limit is plotted against the
ID. The arrow illustrate the decrease of overload
shock limit by increasing measurement range. Note
that this is a four parameter plot. The parameters are
technology, number of measurement axis, overload
shock limit and measurement range. The parameter
184
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
measurement range is not explicit presentable. Since
always the same numbering of accelerometers from
Table 3 is used, measurement range increases within
a class with ascending ID. So it’s possible to
correlate the overload shock limit (and the parameters
in the following figures) among the parameter
measurement range.
Besides piezoelectric accelerometers most
accelerometers are not made for overload shocks
beyond the 10,000 g limit. Especially capacitive
accelerometers are strictly limited to the 10,000 g
threshold. General the capacitive technology is
utilized to withstand shock levels up to 20,000 g [11].
We identified only one manufacturer producing and
selling capacitive accelerometers with such overload
shock limits [21, 22]. But this product is under export
control, so we did not take it into account.
Fig. 2. Overload shock limit of 118 accelerometers
classified by transduction principle, number of
measurement axis and sorted by measurement range in
ascending order.
Within this review we want to point to the fact
that a general comparison of overload shock limits of
different accelerometers is only possible to a limited
degree, due to the fact that this parameter is only
valid for a specified profile of acceleration and a
defined period of time. Usually in datasheets the
overload shock limit is specified as the peak of a
semi sinusoidal profile of acceleration. But a uniform
specification of the period of time on datasheets is
missing. In the datasheets we analyzed, the period of
time is specified not uniformly but instead in terms of
the maximum or the minimum time or the rise time
of a semi sinusoidal profile. So the significance is
completely different, which makes a comparison
meaningless. In addition, for altogether 85
accelerometers the period of time is not even
specified on datasheets. Still, often the specification
for overload shocks is further restricted, e.g., to
positive and negative shocks, to the different
measurement axis and to a powered or unpowered
device. For this review we used a conservative
approach and considered the lowest shock limit of an
accelerometer specified on the datasheet.
4.4. Power Consumption
In Fig. 3 the power consumption is plotted against
the ID. Note that piezoelectric accelerometers with
charge output and optical accelerometers are
excluded because electronics is not integrated and
thereby no power is used. Of course, both principles
also need electronics, but in this review, we did not
focused on external electronics since this is beyond
the scope of this review and these do not affect the
analyzed parameters. We will explain this fact in
detail in the following subsections. Furthermore, for
13 accelerometers no power consumption was
specified on datasheets.
Fig. 3. Power consumption of 81 accelerometers classified
by technology, number of measurement axis and sorted by
measurement range in ascending order.
Generally the upper limit of power consumption
for the analyzed accelerometers is about 1 W. The
majority of capacitive accelerometers show the
minimal power consumption in total beside some
(ultra low power) piezoelectric accelerometers with
voltage output. Our analyses show that there is no
significant correlation between power consumption
and measurement range. Particularly the power
consumption
for
uniaxial
piezoelectric
accelerometers with voltage output is nearly constant.
4.5. Volume
In Fig. 4 the volume is plotted against the ID. The
arrows show the decrease of volume by increasing
measurement range. Again, note that the volume for
piezoelectric accelerometers with charge output and
185
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
optical accelerometers does not comprise integrated
electronics as explained in the section before.
The volume of the capacitive technology covers a
range of five decades from 1E-3 cm3 up to 100 cm3.
Because 21 capacitive accelerometers are fabricated
as simple service mounted devices they show a very
low volume. The remaining capacitive ones are
integrated in rugged housings made of plastic or of
aluminum and titanium for applications in harsh
environments. However this technology has the
minimal volume in total. The thermal accelerometers
are also fabricated as service mounted devices but
their volume is one magnitude higher than the lower
limit of capacitive accelerometers.
There is a correlation of decreasing volume with
increasing measurement range for piezoresistive and
piezoelectric accelerometers with voltage output.
Fig. 4. Volume of 118 accelerometers classified by
technology, number of measurement axis and sorted by
measurement range in ascending order.
Piezoresistive and optical accelerometers show a
slightly higher volume in contrast to the other
technologies. Between piezoelectric accelerometers
with charge output and piezoelectric accelerometers
with voltage output (with integrated electronics) there
is no significance difference in volume.
4.6. Weight
In Fig. 5 the weight is plotted against the ID. The
arrows show the decrease of weight by increasing
measurement range. Note again that electronics for
piezoelectric accelerometers with charge output and
optical accelerometers are excluded. The weight is an
important parameter because the weight of an
accelerometer should be less than 10 % the weight of
the structure to by measured [12]. Otherwise the
accelerometer affects the system to be measured.
186
For 20 accelerometers, 18 capacitive and the two
thermal ones, the weight is not specified on
datasheets. They are all fabricated as service mounted
devices, so it seems the specification is missing due
to their probably low weight.
The remaining 98 accelerometers are integrated in
housings and thus are more comparable among each
other. The weight of these technologies span a range
of three decades from 0.1 grams up to 100 grams.
According to the volume, first, there is a tendency
of decreasing weight by increasing measurement
range for piezoresistive accelerometers and
piezoelectric accelerometers with voltage output.
Second, between piezoelectric accelerometers with
charge output and piezoelectric accelerometers with
voltage output (with integrated electronics) there is
no significance difference in weight. Piezoresistive
and optical accelerometers show no nameable higher
weight in contrast to the other technologies
Fig. 5. Weight of 98 accelerometers classified by
technology, number of measurement axis and sorted by
measurement range in ascending order.
For optical and piezoelectric accelerometers with
charge output electronics is separated from the
system to be measured. That means additional
volume and weight for electronics do not affect the
system to be measured.
4.7. Resonance Frequency
In Fig. 6 the resonance frequency is plotted
against the ID. The arrow illustrate the decrease of
resonance frequency by increasing measurement
range. For altogether 40 accelerometers, thereof
entire 21 capacitive, 10 piezoresistive, two optical
and the two thermal ones, this parameter was not
specified on their datasheets. Due to sparse data, we
exclude these four technologies from discussion.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
The upper limit of resonance frequency for
uniaxial piezoelectric accelerometers is 200 kHz. For
uniaxial piezoelectric accelerometers with voltage
output, the resonance frequency correlates with
increasing measurement range. The triaxial versions
show lower limitations of about 90 kHz.
axis. Again, we considered the lowest frequency
response of an accelerometer.
In Fig. 7 the range of the frequency response is
plotted against the ID. Notice that the axis of ordinate
combines a non-continuous logarithmic and linear
scale. Beside few exceptions piezoresistive and
resistive accelerometers are restricted to an upper
limit of 5 kHz and capacitive accelerometers to an
upper limit of 1 kHz. Piezoelectric accelerometers are
capable for a frequency range of 1 Hz up to 20 kHz
and a few can be used for a frequency response down
to 0.2 Hz.
Fig. 6. Resonance frequency of 78 accelerometers
classified by technology, number of measurement axis and
sorted by measurement range in ascending order.
In principle for triaxial accelerometers the
resonance frequency is specified individual for each
axis. Mostly one or two axis show a lower
specification. Due to our conservative approach we
considered the lowest resonance frequency of an
accelerometers.
Fig. 7. Range of frequency response of 110 accelerometers
classified by technology, number of measurement axis and
sorted by measurement range in ascending order.
4.8. Frequency Response
By trend uniaxial piezoelectric accelerometers
show a wider frequency response than the triaxial
ones. The thermal accelerometers are limited to
17 Hz and by frequency extension circuits, this limit
can be pushed up to 100 Hz [5].
First a comparison of the frequency responses of
accelerometers is only possible to a limited degree
because a uniform specification of the tolerance of
the output signal on datasheets is missing. For only
54 of 118 accelerometers, the frequency response is
specified for a tolerance of the output signal within a
range of ±3 dB. For 56 accelerometers, the tolerance
of the output signal is specified non-uniformly for
one or more of the following tolerances, e.g., -18 %,
+15 %, ±10 %, ±7 %, ±5 %, ±10 dB, -3 dB, ±2 dB, or
completely not specified. For eight accelerometers
the specification of the frequency response is
completely missing.
For the analysis we considered the frequency
response within a range of ±3 dB if specified,
because this is the most used uniform tolerance. If
not specified within a range of ±3 dB we took into
account the frequency response within the lowest
tolerance specified on the datasheet.
Furthermore, for triaxial accelerometers the
frequency response is specified individual for each
4.9. Operating Temperature
Fig. 8 presents the operating temperature plotted
against the ID. Piezoelectric accelerometers with
charge output can be used for very high and low
temperature ranges. Their limits of operating
temperature is in the range of -74 °C up to 200 °C
and more. Some are capable to work down to -195 °C
and up to 250 °C as the electronics as the limiting
factor is excluded.
Beside resistive accelerometers all technologies
can be manufactured for operating temperatures in
the range of -54 °C up to 120 °C. For capacitive
accelerometers, the standard temperature is in the
range of -40 °C up to 85 °C. For piezoresistive
accelerometers the standard temperature is in the
range of -20 °C up to 85 °C.
187
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
4.11. Cost per Unit
Fig. 8. Range of operating temperature of 118
accelerometers classified by technology, number of
measurement axis and sorted by measurement range in
ascending order.
We asked all manufacturers for the cost in content
of a technology study. Some manufacturers did not
answer the request, but overall we got the costs per
unit for 107 accelerometers. Excluding the optical
technology, the cost per unit reaches from 1 to
3200 €. All costs include the German value-added tax
of 19 %. The cost per unit are valid for the minimum
purchase, mostly one piece.
Usually additional accessories such as mounting
equipment and cables are excluded. But depending
on the technology and type of housing, cables are
fixed with the accelerometers and thereby included in
the cost.
In Fig. 10 the cost per unit are plotted against the
ID. The capacitive and thermal technology show the
lowest costs. They are available from 1 € but
capacitive accelerometers can cost up to 2700 €. Only
the service mounted device accelerometers are
low cost.
By tendency, the positive operating temperature
for uniaxial capacitive and uniaxial piezoelectric
accelerometers is higher than for the triaxial ones.
4.10. Storage Temperature
In Fig. 9 the storage temperature is plotted against
the ID. In contrast to the operating temperature there
is no important performance gain. Only the
piezoresistive and capacitive technology show
extended storage temperature.
For the remaining technologies the storage
temperature is equal the operating temperature. There
are only a few samples with storage temperatures
down to - 60 °C.
Fig. 10. Costs per unit of 107 accelerometers classified by
technology, number of measurement axis and sorted by
measurement range in ascending order.
Fig. 9. Range of storage temperature of 118 accelerometers
classified by technology, number of measurement axis and
sorted by measurement range in ascending order.
188
The cost for the optical accelerometers have to be
analyzed separated because for two samples the costs
of about 10,000 € incorporates external electronics.
The first one incorporates a single channel sealed,
miniaturized and rugged electronics. The second one
incorporates a two channel electronics for rack
mounting. These two kind of electronics are very
different. Without analyzing the performance of
external electronics it’s difficult to compare costs but
it’s a reference point in contrast with the other
analyzed technologies. Furthermore it is not effective
to compare totally different electronics. This is one
reason why we did not focus on external electronics.
The costs of 500 € and 800 € for the remaining two
optical accelerometers exclude external electronics.
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
Further analyzes of Fig. 10 are not meaningful
because cost are a function of several analyzed
parameters (not only measurement range and number
of measurement axis) and also parameters we have
not considered in our review.
that the thermal principle works without a proof mass
and so it is less fragile to overload shocks. Therefore,
beside accelerometers capable of measuring more
than 10,000 g, it is very difficult to find such ones
with overload shock limits beyond the 10,000 g
limitation.
4.12. Performance by Country of Origin
4.13. Concluding Considerations
For the last figure of this paper, we chose another
design to illustrate two interesting facts. Therefor a
distinction between numbers of measurement axis
has not been made. Fig. 11 presents the overload
shock limit plotted against the measurement range.
Accelerometers from German manufacturers are
highlighted in green, the one from the European
Union are highlighted in orange and the non-EU ones
are highlighted in black.
Before we come to the conclusion we want to
mention some additional facts. We analyzed the
performance of accelerometers as they are specified
on their datasheets. By request some manufacturers
offer a better performance as extended temperature
range for extra money. Also in part it is possible to
customize accelerometers in a limited range.
Products are usually designed and balanced to
special market demands such for automotive
applications. Therefore this review can only point out
the performance of technologies of state of the art
accelerometers. Physical and technical limits of the
analyzed technologies could be higher.
Furthermore a comparison of parameters of
accelerometers is not always easy due to a lack of
uniform or missing specification in datasheets. In this
context, important parameters, such as negative
measurement range, period of time for overload
shock limit, frequency response and resonance
frequency have to be mentioned.
5. Conclusions
Fig. 11. Overload shock limit versus measurement range of
118 accelerometers. The technologies are highlighted by
the shape of the symbols.
First, accelerometers from German manufacturers
are limited to a measurement range of 6,000 g and to
overload shock limits of 10,000 g. Capacitive
accelerometers of German manufacturers are up to
date in comparison with accelerometers worldwide.
But especially piezoelectric and piezoresistive
accelerometers show lower performances of about
one order of magnitude in regard to measurement
range and overload shock limit. But Fraunhofer
Ernst-Mach-Institut, EMI, developed a piezoelectric
accelerometer with state of the art performance [23].
Accelerometers from manufacturers of the European
Union are limited to overload shocks of 100,000 g
and to a measurement range of 80,000 g. This is close
to the performance of state of the art accelerometers
worldwide.
Second, a strict threshold for accelerometers
based on a proof mass is found. Within a
measurement range of up to 2,000 g, they are strictly
limited to overload shocks of 10,000 g. Remember
A variety of very different 118 commercial
piezoelectric, piezoresistive, resistive, capacitive,
thermal and optical accelerometers have been
reviewed. The parameters measurement range,
overload shock limit, power consumption, weight,
volume, frequency range, resonance frequency,
operating and storage temperature and also cost were
analyzed.
The paper shows that accelerometers from
manufacturers of the European Union are nearly state
of the art with regard to measurement range and
overload shock.
A strict overload shock limit of 10,000 g was
identified for accelerometers with proof mass within
a measurement range of up to 2,000 g. We ascertain
that the performance of uniaxial and triaxial
accelerometers is slightly different. Especially
uniaxial piezoelectric accelerometers show a better
performance with regard to measurement range,
overload shock limit, resonance frequency,
frequency response and operating temperature than
the triaxial ones.
In summary, piezoelectric accelerometers show
the highest measurement range up to 100,000 g,
shock limits up to 200,000 g and operating
temperatures up to 200 °C (piezoelectric with charge
output). Capacitive accelerometers show the lowest
189
Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 179-190
power consumption down to 1E-3 W and volume
down to 1E-2 cm3. Piezoresistive accelerometers
show the widest frequency response from 0 Hz up to
10 kHz. Thermal accelerometers show outstanding
shock limits of 50,000 g by low measurement ranges
of 5 g. Optical accelerometers show immunity to
electromagnetic interferences and are capable for
measurement ranges up to 200 g and shock limits up
to 2,000 g. The cost per unit are in the range of 1 € to
3200 € and for optical about 10,000 €. This review
was meant to be a reference work for choosing the
right technology.
[10].
[11].
[12].
[13].
[14].
Acknowledgements
[15].
The author would like to thank Marcel Weber for
the support in extracting accelerometer parameters
from datasheets and proofreading. The author would
like to thank Alexander Stolz and Stefan Ebenhöch
for useful hints preparing the manuscript and for
reviewing the manuscript.
[16].
[17].
References
[1]. V. Narasimhan, H. Li, and M. Jianmin,
Mircomachined high-g accelerometers: a review,
Journal of Micromechanics and Microengineering,
Vol. 25, 3, Feb. 2015, pp. 1-18.
[2]. G. Krishnan, C. U. Kshirsagar, G. K. Ananthasuresh,
and N. Bhat, Micromachined high-resolution
accelerometers, Journal of the Indian Institute of
Science, Vol. 87, 3, Jul.-Sept. 2007, pp. 333-361.
[3]. A. Chu, Choosing the right type of accelerometers,
Application Notes, Measurement Specialties Inc.,
May 2012.
[4]. O. B. Degani, D. J. Seter, E. Socher, S. Kaldor, E.
Scher, and Y. Nemirovsky, Comparative study of
novel micromachined accelerometers employing
MIDOS, Sensors and Actuators A: Physical, Vol. 80,
2, Mar. 2000, pp. 91-99.
[5]. J. Fennelly, S. Ding, J. Newton, and Y. Zhao,
Thermal mems accelerometers fit many applications,
Sensor Magazin, Mar. 2012, pp. 18-20.
[6]. N. Yazdi, F. Ayazi, and K. Najafi, Micromachined
Inertial Sensors, Special Issue of IEEE Proceedings,
Vol. 86, 8, Aug. 1998, pp. 1640-1659.
[7]. G. A. Macdonald, A Review of Low Cost
Accelerometers for Vehicle Dynamics, Sensors and
Actuators A: Physical, Vol. 21, 1-3, Feb. 1990,
pp. 303-307.
[8]. Step to select the right accelerometer, Endevco, 2012.
[9]. J. Lally, Accelerometers Selection Considerations
Charge and ICP® Integrated Circuit Piezoelectric,
[18].
[19].
[20].
[21].
[22].
[23].
Technical Note VIB-TN-17-0805, PCB Piezotronics
Inc., 2005.
S. Beeby, G. Ensell, M. Kraft, and N. White, MEMS
mechanical sensors, Artech House, Inc., Boston
London, 2004.
C. M. Harris, and A. G. Piersol, Harris’ Shock and
Vibration Handbook, Fifth Edition, McGraw-Hill,
New York, 2002.
M. Serridge and T. R. Licht, Piezoelektrische
Beschleunigungsaufnehmer und Vorverstärker –
Theorie und Anwendung, Brüel & Kjær, 1990.
J. Vaughan, Application of B & K Equipment to
Strain Measurements, Brüel & Kjær, 1975.
A. Méndez, Fiber Bragg grating sensors: a market
overview, in Proceedings of the Conference on Third
European Workshop on Optical Fibre Sensors, SPIE,
Vol. 6619, July 2007, pp. 661905-1-661905-6.
K. T. V. Grattan and T. Sun, Fiber optic sensor
technology: an overview, Sensors and Actuators A:
Physical, Vol. 82, 1-3, May 2000, pp. 40-61.
K. O. Hill and G. Meltz, Fiber Bragg Grating
Technology Fundamentals and Overview, Journal of
Lightwave Technology, Vol. 15, 8, Aug. 1997,
pp. 1263-1276.
E. Davies, D. S. George, M. C. Gower, and A. S.
Holmes, MEMS Fabry–Pérot optical accelerometer
employing mechanical amplification via a V-beam
structure, Sensors and Actuators A: Physical,
Vol. 215, Aug. 2014, pp. 22-29.
U. A. Dauderstädt, P. H. S. de Vries, R. Hiratsuka,
and P. M. Sarro, Silicon accelerometer based on
thermopiles, Sensors and Actuators A: Physical,
Vol. 46, 1-3, Jan.-Feb. 1995, pp. 201-204.
A. M. Leung, J. Jones, E. Czyzewska, J. Chen, and
B. Woods, Micromachined accelerometer based on
convection heat transfer, in Proceedings of the IEEE
11th Annual International Workshop on Micro
Electro Mechanical Systems (MEMS’98), Jan. 1998,
pp. 627-630.
S. Elies, S. Ebenhöch, Performance Analysis of
Commercial
Accelerometers
of
Different
Technologies, in Proceedings of the 6th International
Conference on Sensor Device Technologies and
Applications
(SENSORDEVICES
2015),
Venice/Mestre, Italy, 23.-28 August 2015, pp. 54-59.
http://www.colibrys.com/product/hs8000-high-shockinertial-sensor/
S. Habibi, S. J. Cooper, J. M. Stauffer, and B. Dutoit,
Gun Hard Inertial Measurement Unit Based on
MEMS Capacitive Accelerometer and Rate Sensor,
in Proceedings of the Position Location and
Navigation Symposium IEEE/ION (PLANS 2008),
May 2008, pp. 232-237.
R. Kuells, S. Nau, M. Salk, and K. Thoma, Novel
piezoresistive high-g accelerometer geometry with
very high sensitivity-bandwidth product, Sensors and
Actuators A: Physical, Vol. 182, Aug. 2012,
pp. 41-48.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
190