Volume 56, Number 4, 2015 143
Faculty of Electrical Engineering and Computers Science, Stefan cel Mare University of Suceava, Romania
Abstract - This paper presents a system and a mathematical model for detection of the electrostatic field. The system has two modules: an external module for detection of atmospheric charge, an internal module for detection of victims of natural disasters with communication to PC interface in Labview, and a database that provides information regarding the positioning of the rescued person.
Keywords – detection, electrostatic field, electrostatic sensor, natural disasters, victims.
1.
INTRODUCTION
2.
SYSTEM SETUP
This system detects the electrostatic field being composed of the two modules: an external module and an internal module. The external module is used for detection of atmospheric charge and the internal module is used for detection of victims of natural disasters. The mathematical models of electrostatic discharge were studied to determine the optimum system capable to visualize the electric field wave forms generated by human body under different conditions, depending on environment humidity, temperature, etc. The effectiveness of our system consists of detecting and saving victims from ruins in case of earthquakes, landslides or avalanches, and in protecting objects, such as home appliances in case of severe storms. There are methods to localize and save victims under the above mentioned circumstances but they are time consuming and have a low rate of success.
Therefore, we built this system which ensures a better rate of success for saving victims and monitoring atmospheric charge in real time [1], [12].
The first part of this paper presents the hardware support and the performances of our system for the detection of electrostatic field. The second part of this paper presents the mathematical model realized in
Matlab & Simulink and Labview, as well as an example of graphical interface. victim’s localization facilitates an efficient, rapid and accurate intervention for saving victims of natural disasters. Our system works differently than conventional ones, since it searches victims from inside the damaged building, not from the outside as it usually happens when using the current procedures and methods. Consequently, our system will provide rescuers the location and the number of survivors in real time. Block diagram for this system is presented in figure 1.
Fig. 1 System components interconnection.
2.1.
The external module
To realize this module, we used a minimum set of electronic components. It offers several advantages: it is user-friendly, its functioning does not require specialized personnel, and it is low cost. The antenna which is connected to this module captures the atmospheric electrostatic field [2], [5], [9], [11] . The electronic circuit of this module is presented in figure 2.
The applications for this system are the detection of atmospheric charge and the detection of living victims of natural disasters .
The external module continuously monitors the atmospheric electric field at ground level, having as main purpose timely warning in case of significant increase. The internal module for
Fig. 2 Monitoring electronic circuit fo atmospheric charge.
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144 ACTA ELECTROTEHNICA
For connection with PC, we used NI-6008 from
National Instruments [14]. This circuit is connected to an analogue input and the resulting signal is measured at an analogue output. The electronic schemes for the analogue input and analogue output are presented in figures 3 and 4.
Fig. 3 Analogue input [14].
MUX - The NI USB-6008 has one analogue-to-digital converter (ADC) [14].
PGA - The programmable-gain amplifier provides input gains of 1, 2, 4, 5, 8, 10, 16, or 20 when configured for differential measurements, and gain of 1, when configured for single-ended measurements. The
PGA gain is automatically calculated based on the voltage range selected in the measurement application
[14].
ADC - The analogue-to-digital converter (ADC) digitizes the AI signal by converting the analogue voltage into digital code [14].
AI FIFO - The NI USB-6008 can perform both single and multiple analogue-to-digital conversions of a fixed or infinite number of samples. A first-in-first-out
(FIFO) buffer holds data during AI acquisitions to ensure that no data is lost [14].
Fig. 6 Scheduling in Labview of NI-6008 [14].
2.2.
The internal module
Localizing the survivors is carried out using a special device embedded in a special shell designed to resist the collapse of a building, and/or to fire.
To detect the people who are caught alive under the ruins of a building system, we used an electrostatic sensor. The electronic circuit of this sensor is presented in figure 7.
Fig. 4 Analogue output [14].
The main block featured in the NI USB-6008 analogue output circuitry is the digital-to-analogue converter (DAC), which converts digital codes to analogue voltages. There is one DAC for each analogue output line [14].
The programing mode for NI-6008 is presented in figures 5 and 6.
Fig. 5 Analogue input – Labview block diagram [14].
Fig. 7 The electronic circuit of the electrostatic sensor.
This sensor is realized using a field effect transistor (JFET), which presents the rest of the source and drain junction, crossing resistance of very low

Volume 56, Number 4, 2015 145 value of 200 Ω.
The variation of drain current of the
JFET transistor is presented in figure 8.
4 x 10
-3 Curentul de drena
3.5
3
2.5
2
1.5
1
0.5
0
-10 -9 -8 -7 -6 -5
ID[A]
-4 -3 -2 -1 0
Fig. 8 Variation of drain current of the JFET transistor [16]
The information collected from electrostatic sensor is transmitted via radio transmitter TX-FM-
TWS-DS. The signal detection device will be detected by rescuers equipped with a radio receiver composed of a receiver RX-FM-RWS-371 and a development kit
Arduino UNO. Consequently, they will know exactly where to look for survivors. Our device will count and memorize the number of the saved people. The information will be stored in a database and displayed on the internet when the device is connected to a PC.
The system will permanently take into account the number of human beings who are inside a building using a device consisting of PIR sensor connected to the same acquisition board built using ATmega328 microcontroller [3], [4].
2.2.1.
The simulation of electrostatic sensor
The simulation is realized in Matlab & Simulink using the scheme in figure 11, [14], [15], [17].
Fig. 11 Scheme for simulation in Matlab & Simulink [16]
The simulation result is shown in figure 12.
Fig. 12 Signal input, signal for LED and signal output
2.2.2.
The performances of electrostatic sensor in time domain
In order to verify the sensor performance, we performed simulations in time domain with step response, respectively impulse response [16] in figures
13 and 14.
Fig. 9 The connectors for Arduino UNO [15].
For the signal acquisition from electrostatic sensor, we used a toolkit Labview for Arduino UNO. An example is presented in figure 9.
Fig. 10 Bloc diagram for connection Arduino UNO to Labview [14].
Fig. 13 Step response.
The step response for this sensor is very short where: the settling time value is 0.021 s, the rise time value is 0.0118 s and the final value is 0.0849 s.

146 ACTA ELECTROTEHNICA
Fig. 14 Impulse response.
For the impulse response of this sensor the settling time value is 4.15e-11 s and at time value is 2.67e-13 s.
2.1.3
The performances of electrostatic sensor in frequency domain
The sensor stability was verified according to frequency domain with Bode diagram and Nyquist diagram. These diagrams are presented in figures 15 and 16.
Fig. 15 Bode diagrams for the electrostatic sensor.
These representations show that the sensor is very stable [16]
3.
MATHEMATICAL MODEL FOR THE
ELECTROSTATIC DISCHARGE
The current pulse produced by electrostatic discharge can be described using several mathematical relationships. The best known is the relation (1), the analytical model for atmospheric discharge. Relation
(2) approximates wave form of the discharge current.
This current impulse form is realized in Matlab and is presented in figure 17. i
1
= i
0

 e
− t t
1
− e
− t t
2 i
2
= i
1 e
− t t
1
− i
2 e
− t t
2


(1)
(2) where: i
0
, i
1
, i
2
– current constants
t, t
1
, t
2
– time constants
Current impulse
8 relation (1) relation (2)
7
6
5
4
3
2
1
0
0 20 40 60 80 100 t[ns]
120 140 160 180 200
Fig. 17 Current impulse of electrostatic discharge.
Relation (2) is simulated in Labview and presented in figure 17, and respectively in figure 18,
[14].
Fig. 16 Nyquist diagram of the electrostatic sensor. Fig. 18 Bloc diagram of Labview.

Volume 56, Number 4, 2015 147
Fig. 19 Front panel of Labview.
4.
THE GRAPHICAL INTERFACE OF THE
SYSTEM
A model for graphical interface for this monitoring of atmospheric charge is presented in figures 19 and 20 [14].
Fig. 20 Front panel of external module.
This graphical interface offers the possibility to save the monitoring history, and set the upper limit to trigger a circuit for the interruption of power supply
[14].
Fig. 21 Block diagram of external module.
5.
CONCLUSIONS
The system for detection of electrostatic field is a revolutionary device that acting differently from other systems, related in literature and/or implemented in rescue missions. The difference between our approach and the similar systems consists in the detection of electrostatic field in real time and the saving of historical monitoring. This system is simple, it involves a minimum set of components, and therefore it is low cost and efficient.
The development of the system will focus on improving the placement of the electrostatic sensors.
We recommend adapting the sensor placement to the dimensions and geometry of the room in order to the sensitive radius. Furthermore, for large buildings, we recommend to install a network of sensors. We will also increase the sensor’s autonomy from 36 hours to
48 hours or even more. It is necessary the improvement of graphical interface and the connection to a database with web interface.
ACKNOWLEDGMENT
This paper has been financially supported within the project entitled „SOCERT. Knowledge society, dynamism through research”, contract number
POSDRU/159/1.5/S/132406. This project is cofinanced by European Social Fund through Sectoral
Operational Programme for Human Resources
Development 2007-2013. Investing in people!
”
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***www.ni.com
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***www.arduino.cc
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Corneliu Buzduga
Faculty of Electrical Engineering and Science Computers,
Stefan cel Mare University of Suceava, 13, University Street,
Suceava, Romania cbuzduga@eed.usv.ro