CEBE IAB Meeting, Sept 16-18, 2013 in Tallinn
Research on Signal Processing
Cooperation with ELIKO Competence Center
in electronics and ICT
by Mart Min
mart.min@ttu.ee
Thomas Johann Seebeck Department of Electronics
Tallinn University of Technology
1
Signal Processing: what kind of? for what?
Digital and analog processing (synthesis and analysis) for:
1. Synthesis and generation of excitation signals with predetermined
bandwidth, waveform, spectral content and shape, for obtaining the most
effective excitation for systems/substances to be measured, studied, tested.
2. Analysis of the response signals (the results of excitation) in:
(a) - frequency domain; (b) - joint time-frequency domain; (c) - time domain,
to obtain the maximal amount of information for identification of dynamic and
predominantly time varying systems, circuits, materials, structures.
Remarks:
Our main identification method is impedance spectroscopy of both technical
and living systems (also impedance spectro-tomography).
Impedance – electrical (mostly), but also acoustical, optical, and mechanical.
The terms bioimpedance and (electro-)chemical impedance mean also
electrical impedance, but of biological or chemical matter.
2
Identification of dynamic systems is the goal
Generation of Excitation
Synthesis of
Excitation
Signals
M
u
x
ωexc and Texc
waveform and
energy
Reference
Processing of the Response
Control
.
.
.
Impedance
Ż(ω,t)
Re{Z}; Im{Z}
speed of
changes
.
.
.
Control
System under study
D
e
m
u
x
Processing of
Response
Signals
ω and t
domain:
resolution,
signal-to-noise,
amount of info
Ż(ω,t)
Sampling Reference
3
Focus: finding the best excitation waveforms for the fast and wideband
time dependent spectral analysis: intensity (Re & Im or M & φ)
versus frequency ω and time t
4
Requirements to the Impedance Spectroscopy
A. Fast measurement and signal processing in a wide frequency range;
B. Simple architecture and electronic circuitry (simplicity, dependability);
C. Low power (extremely low in some applications) and low voltage operation;
Excitation waveform:
a) easy to generate;
b) easy to tune;
c) covers the needed frequency range;
d) generated energy must be concentrated into the BW of interest;
e) effective energy packaging (low crest factor - less than 1.5);
f ) simple processing of the response signal.
Signal processing for performing deconvolution:
a) simple algorithms,
b) fast processing of the response signals,
c) getting frequency domain but time dependent results – performing
the joint time-frequency analysis.
5
Problems to be solved by using of chirps
Impedance appears to be non-stationary - their spectra are time dependent.
Examples:
(a) cardiovascular system (beating heart, pulsating blood);
(b) pulmonary system (breathing);
(c) running bio-particles in a microfluidic device.
Excitation must be:
1) as short as possible to avoid significant changes during the spectrum analysis;
2) as long as possible to enlarge the excitation energy (max signal-to-noise ratio).
Which waveform is the best one?
A unique property of chirp waveforms – scalability – enables to reach compromise
between contradictory requirements (1) and (2)
The questions to be answered:
a. A chirp wave excitation contains typically hundreds and thousands of cycles. What
could be the lowest number of cycles applicable if the fast changes take place?
b. Are there any simpler rectangular waveforms (binary or ternary) to replace the
sine wave based chirps in practical spectroscopy?
6
Scalable chirp signals: two chirplets 2
B. Scalability in time domain: duration Texc changes, BW = const = 100 kHz
1.0
0.8
12 cycles
0.5
48 cycles
0.2
0.0
-0.2
-0.5
-0.8
-1.0
0
100u
200u
300u
400u
500u
Texc = 250 μs
600u
700u
800u
900u
1m
Texc = 1000 μs
100m
Bandwidth BW = 100 kHz = const
4.48 mV/Hz1/2
2.24 mV/Hz1/2
Energy E250μs = 125 V2∙μs
Energy E1000μs = 500 V2∙μs
Voltage Spectral Density @ 250μs = 2.24 mV/
10m
Hz1/2
1 mV / Hz1/2
1m
Voltage Spectral Density @ 1000μs = 4.48 mV/ Hz1/2 100u
Changes in the pulse duration Texc
reflect in spectral density
10u
BW = 100 kHz
1u
1k
10k
100k
1M
10M
7
Scalable chirp signals: two chirplets 1
A. Scalability in frequency domain: bandwidth BW changes, Texc = const = 250 μs
1.0
0.8
48 cycles
12 cycles
0.5
t
0.2
0.0
-0.2
-0.5
-0.8
-1.0
0
25u
50u
75u
100u
125u
150u
175u
Texc = 250 μs
200u
225u
250u
Texc = 1000 μs
100m
Excitation time Texc = 250 μs = const
Excitation energy Eexc =
0.5V2 ∙250
μs = 125
2.24 mV/Hz1/2
V2∙μs
10m
1 mV / Hz1/2
1.12 mV/Hz1/2
Voltage Spectral Density @ 100 kHz = 2.24 mV/
Hz1/2
BW = 100 kHz
1m
Voltage Spectral Density @ 400 kHz = 1.12 mV/Hz1/2
BW = 400 kHz
100u
Changes in the frequency span BW
reflect in spectral density
10u
1u
1k
10k
100k
1M
10M
8
A very short Chirplet - Half-cycle linear
sin θ
RMS spectral density (relative)
sin θ(t)
10
10
1
-40 dB/dec
1
θi= θ(ti)
θfin = π
θ
1
θ0= 0
cos θ
0
t =0
t
ti
tfin= Tch = 10 μs
1
2.26 mV/
10
100m
-1
f
10m
ffin= 100 kHz
10-2
100kHz
f(ti)
f0= 0
t=0
t
ti
 t  
10-3
1m
tfin= Tch = 10 μs
100u
10-4
Texc = Tch = 10 μs, BW = 100 kHz
Instant frequency
Hz1/2
1k
1k
10k
10k
100k
100k
d (t )
 2  f fin  t / T,ch, rad/s - a linear frequency growth
dt
1M
1M
f, 10M
Hz
Current phase  t    (t )  dt  2  f fin  t 2 / 2Tch , rad;

Generated chirplet sin  t   sin
2

(
t
)

dt

2


f

t
/ 2Tch
fin

9
A very short Chirp - 2x quarter-cycle linear chirplet
Normalised level
RMS spectral density,
normalised
1.0
20
0.5
0.0
0
Time, μs
-0.5
-1.0
0
2
4
6
8
10 12 14 16 18 20
Frequency, MHz (max 100kHz)
0.10
0.08
f =fmax(t /Tch
0.06
)2
0.04
-80 dB/decc
-20
-40
-60
-80
1k
0.02
0.00
0
2
4
6
8
100k
1M
10M
Frequency, Hz
10 12 14 16 18 20
Time, μs
10k
 

C 2t   sin 2 kch t 3 / 3
10
Spectra and power of binary/ternary chirps
0
Binary (0)
18
100kHz
30
Ternary (30)
Pexc – excitation power
within (BW)exc=100kHz
Binary(0): Pexc= 0.85P
Ternary (21.2): Pexc= 0.94P – max. possible!
11
Classical sinc waveform – mathematically the best
Relative time
12
Several sine waves simultaneously – Multisine excitation
Fast simultaneous measurement
at the specific frequencies of interest!
+ Simultaneous/parallel measurement and
analysis (fast);
+ Frequencies can be chosen freely;
+/- Signal-to-noise level is acceptable;
− complicated synthesis restricts the number
of different frequency components.
Signal space is limited between +1 and -1 (ΣAi = 1)
Max crest factor maxCF = ΣAi / (RMS)Σ = 2.83
Min(RMS)Σ = 0.36 (worst case)
0
Max(RMS)Σ = 0.72 (optimised phases)
13
Crest factors CF of optimised multisine excitation
(a sum of n sine wave components, n = 3 to 20)
CF = ΣAi /RMS for optimally synthesized multisine signals
The best known before
For a single sine wave CF=√2=1.414
Jaan Ojarand’s
algorithm
14
Optimised multisine waveform
Relative time
15
Binary multifrequency waveform
Relative time
Less than 10%
of total RMS
16
Synthesized multifrequency binary sequences
(4 components – 1, 3, 5, 7f)
Equal-level components
Growing-level components !
17
Energy and RMS of different excitation waveforms
BMF- binary multifrequency
MS- multisine
A single sine wave has: energy- 50%, RMS - 71% (less than MS!)
bipolar sinc
sinc
1- binary multifrequency (BMF)
2- optimal multisine (MS)
3- modified sinc (bipolar)
5- sinc (classic)
18
Collaboration with industry through ELIKO
Impedance spectroscopy devices using MBS:
laboratory devices prototyped in ELIKO
The Main Unit
The Pick-up Unit
I+
Clock
&
Sync
USB
excitation
Differential
Current
Source
DSP
unit
PGA1
+5V
Power
Supply Unit
response
Voltage
Buffer
Cable 1
I-
Ż(jω)
.
VZ
Cable 2
20
Texas
The project with Electrolux Italy S.p.a
Partners: Food and Fermentation Competence Center and ELIKO
Meat quality assessment
CAROMETEC A/S just bought a license to use the impedance spectroscopy
method (CEBE patent) for meat quality assessment (13.10.2013).
Carometec is a world leader in production of
meat quality equipment for the food industry
22
Real-time in vivo identification of various physiological condition of organs 23
using a range of needles. The foundations are: the different electrical properties of
human tissues (bioimpedance), advanced measurement technology (CEBE patent)
we gave over to Injeq Oy, Finland, and proprietary needle designs (Injeq’s patent)
Summary
The research center CEBE is founded for making
fundamental science.
The scientific results can be and have been
transferred into industry and commercialised
using Technology Competence Centres as
ELIKO – electronics and ICT, and
FFCC – food and fermentation.
Thank you for listening!
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