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Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
DOI : 10.5121/sipij.2016.7601 1
A FLUXGATE SENSOR APPLICATION: COIN
IDENTIFICATION
Hakan Çıtak1
, Mustafa Çoramık2
, Yavuz Ege2
1
Balıkesir Vocational High School, Electric Program, Balıkesir University,
Balıkesir 10145, Turkey
2
Necatibey Education Faculty and the Department of Physics, Balıkesir University,
Balıkesir 10100, Turkey
ABSTRACT
Today, coins are used to operate many electric devices that are open to the public service. Washing
machines, play stations, computers, auto brooms, foam machines, beverage machines, telephone chargers,
hair dryers and water heaters are some examples of these devices These devices include coin recognition
systems. In these systems, there are coils at two different radius, which become electromagnets when the
current is passed through them. The AC current supplied to the coils creates a variable magnetic field,
which induces the eddy current on the coil during the passing of money. The magnetic field generated by
the Eddy current reduces the current passing through the coil. The amount of change of current in the coil
gives information about the coin; the type of metal (element) and the amount of metal (element). In this
study, a new coin identification system (magnetic measurement system) is designed. In this system, the
magnetic anomaly generated by the coin as a result of applying direct current to the coils is tried to be
detected by fluxgate sensor. In this study, sensor voltages are acquired in computer environment by using
developed electronic unit and LabVIEW based software. In the paper, experimental results have been
discussed in detail.
KEYWORDS
Metal money, Coil Detection, Eddy current, Fluxgate sensor, Magnetic anomaly.
1. INTRODUCTION
Nowadays, remote sensing is used for different purposes with different methods. The features that
are important in all sensing methods are low power consumption, low price, being unaffected by
environmental changes, high precision, high reliability and stability. Some remote sensing
methods used from past to present include electromagnetic induction (EMI)[1-5], ground effect
radar (GPR)[6-11] and magnetic anomaly [12-15, 18]. Magnetic anomaly is the most widely used
remote sensing method due to its low price, low number of equipment and changeable sensitivity.
This method is based on determining the change in magnetic field caused by an object with
magnetic characteristics, which is located in an external magnetic field [20-29].
The effects of magnetic anomalies caused by coins are also determined in coin operated washing
machines, play station, computers, auto brooms, foam machines, beverage machines and
telephone chargers. Within these devices, there are two coils at different diameters. During the
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
2
passing of coin through the magnetic field created by applying AC current to the coils, eddy
currents are being induced in the coil. The magnitude of this current depends on the conductivity
of the coin. As a result of the high Eddy current, it is expected that the magnetic field generated
by the eddy current is high. So, the current changes on the coil can be detected more easily. In
short, the conductivity of coin determines the current change in the coil. The conductivity of coin
depends on what kind of metal it is made of and how much metal it contains. In all coin operated
devices, since the metal contents of the coins are different, the conductivity of the coins and the
current changes in the coil will be different. Thus, the coins can be distinguished (Fig. 1).
Figure 1. Coin determination system based on the change of current in the coil
However, the changes in current passing through the coil also depend on the winding number of
the coil, the wire cross-section and its type, and the magnetic character of the core. A different
detection method has been used in our study to remove the effects of these variables. In this
method, it is aimed to detect the magnetic anomalies generated by the coin with magnetic sensors
instead of determining the current changes in the coil.
In the presence of magnetic material, the decrease in flux density in the region of the sensor
causes electrical change in the sensor and it changes the output voltage. By analysing these
changes, information about the dimensions and the content of the object has been obtained.
Various sensors such as MR (Magneto-resistive), SQUID (Superconducting quantum interference
devices), Hall and Coil are used to detect the magnetic anomaly [15-19]. In this method, the
FLC100 Fluxgate sensor has been chosen due to its low power consumption, low price, being
unaffected by environmental changes, high precision, high sensing speed, reliability and stability.
In the following section of the paper, the developed experimental setup, the application of the
method, and the experimental results are explained in detail.
2. MATERIAL AND METHOD
The block structure of the magnetic measuring system which is developed in this study with the
aim of money determination is given in Figure 2.
The developed system can be classified under two headings as hardware and software. In the first
phase of the study, the mechanics and the hardware part of the system are produced and then the
software part is programmed with LabVIEW.
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
3
Figure 2. Block diagram of the magnetic measurement system
In the developed magnetic measurement system, the mechanical control and data acquisition is
done with the NI 6008 DAQ data acquisition card, which has 8 analog inputs (12 bits, 10 kS / s),
2 analog outputs (12 bits, 150kS / s) and 12 digital inputs / outputs The developed system has 8
FLC 100 sensors with 0.5 nT sensitivity and low noise (0.1 - 10 Hz), which can be used for more
accurate measurements than Hall or MR magnetometers. The sensors are mounted on a platform
with 2.5 cm spacing (Fig.3a). Since NI 6008 DAQ, the card (NI 6008 DAQ) has eight analog
inputs, at most eight sensors are used in the system. The total length of the sensor network is 17.5
cm. In addition, the electronic unit in the system contains an electronic card that processes data
from the sensor and a stepper motor driver card.
Figure 3. a) Fluxgate sensor platform, b) Electronic unit
In the study, an iron core coil with a length of 6.8 cm and a diameter of 2.4 cm is used as the
magnetic field source. The coil has a winding number of 5000 and the wire thickness is 0.1 mm.
(Fig.4)
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
4
Figure 4. Magnetic field source
In this study, there is a distance of 10 cm between the coil and the sensor platform and the tested
coins are located in the middle of this distance. The center of the coin is set to the center of the
scan plane and the sensor platform is moved to the start position of the scan. The scanning area of
the sensor platform is a 10cm * 10cm area. The sensor data is collected by moving the platform at
intervals of 0.2 mm along the x-axis and 0.05 mm along the y-axis.
The scanning path of the sensor platform in the “Area scan” process is given in Figure 5.
Figure 5. Scanning path of the sensor platform in the “Area scan” process
The sensor platform first moves along the x-axis at specified step intervals (Movement 1). The
platform returns to its initial state after reaching the total scan length (Movement 2). The system
advances along the y-axis by the user-specified step length (Movement 3) Once the system has
reached the final position, it returns to its initial state of motion and ends the measurement
(Movement 5 and 6).
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
5
The LabVIEW program, developed by National Instruments, is used to program the measurement
and motion control system. LabVIEW has a graphical programming language which is also called
data flow programming and consists of two different interfaces, block diagram and front panel.
Block diagram is the section where programming is performed. The front panel can be defined as
a "user interface" where users can enter variables and observe the results.
In the program, the "event case" structure is used in general. With this structure, the user can
perform a desired operation after entering the preliminary information.
The sensor data received by the program is instantly saved in Excel format with a ".tdms"
extension. Into the register file, the positions of the sensors in the x and y axes and the sensor
output voltages are recorded.
The front panel (control panel) of the LabVIEW-based program developed for the magnetic
measurement system is shown in Figure 6.
Figure 6. Control panel and sections of the developed LabVIEW based program
Signal & Image Processing : An International Journal
3. EXPERIMENTAL RESULTS
The technical information of the coin samples which are tested with the magnetic measuring
system is given in Table 1.
Reverse of the
Coin
Obverse of
the Coin
In this study, 15 volts of voltage and 0.5 amp current are applied to the coil which is used as a
magnetic field source. The coin to be tested is placed 5 cm away
sensor platform, 5 cm away from the coil, is brought to the beginning of the scan. After that, the
platform is scanned a surface area of 10 cm * 10 cm (Fig. 5). The collected sensor data is
processed in Origin which is a 3
shown in Figure 7.
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
ESULTS
The technical information of the coin samples which are tested with the magnetic measuring
Diameter Thickness Mass Elements
17.5 mm
1.65 mm
2.9 g
65% Cu
18%
17%18.5 mm 3.15 g
20.5 mm 4 g
23.85 mm
1.9 mm
6.8 g
Outer Ring:
79 % Cu,4% Ni
Inner Circle:
65% Cu,18%
26.15 mm 8.2 g
Outer Ring:
65% Cu,18%
Inner Circle:
79% Cu,4% Ni
In this study, 15 volts of voltage and 0.5 amp current are applied to the coil which is used as a
The coin to be tested is placed 5 cm away from the coil. Then, the fluxgate
sensor platform, 5 cm away from the coil, is brought to the beginning of the scan. After that, the
platform is scanned a surface area of 10 cm * 10 cm (Fig. 5). The collected sensor data is
processed in Origin which is a 3D drawing program. The graphs obtained for each sample are
(SIPIJ) Vol.7, No.6, December 2016
6
The technical information of the coin samples which are tested with the magnetic measuring
Elements
Cu
Ni
Zn
Outer Ring:
Ni,17% Zn
Inner Circle:
Ni,17% Zn
Outer Ring:
Ni,17% Zn
Inner Circle:
Ni,17% Zn
In this study, 15 volts of voltage and 0.5 amp current are applied to the coil which is used as a
from the coil. Then, the fluxgate
sensor platform, 5 cm away from the coil, is brought to the beginning of the scan. After that, the
platform is scanned a surface area of 10 cm * 10 cm (Fig. 5). The collected sensor data is
D drawing program. The graphs obtained for each sample are
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
7
Figure 7. The graphs of tested coin samples a)5 Kurus, b)10 Kurus, c)25 Kurus, d)50 Kurus,
e)1 Turkish Lira
4. DISCUSSION AND CONCLUSION
When we look at the experimental graphs obtained in the study, it is seen that there is a magnetic
field change in the scan area as much as the cross section of the magnetic field source of the coil.
It is also clear that the change shown in Figure 7a, b, c are different from that of Figure 7d, e. In
this work, it is necessary to focus on the magnetic character, not the conductivity of metal
particles. To concentrate the magnetic flux produced by the coil in the coin, the coin must have
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
8
high magnetic permeability. As it is known, metals with the highest magnetic permeability are Fe,
Ni, Co. So it is important that the tested coins contain these metals or not and what amount they
contain. Table 1 shows that 5, 10 and 25 Kurus contain 18% nickel, 50 and 1 Lira contain 17%
Nickel. A decrease in the Ni ratio will reduce the magnetic flux density on the platform side of
the coin. That's why the graphics in Figure 7d and Figure 7e (for 50 Kurus and 1 Lira) are
different from the others. Therefore, this measurement system can be able to identify groups of
coins that have different Nickel content. If the diameters of the coins are determined by optic
methods (as seen in Fig 1.), all the coins will be classified.
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10
[27] Nazlibilek, S., Kalender, O., Ege, Y., (2011). “Mine Identification and Classification by Mobile
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Geometric Dimensions”, Signal & Image Processing: An International Journal (SIPIJ), Vol.7, 5, pp
11-22
AUTHORS
Hakan Çıtak received the B.S. and Ph.D. degrees from Department of Electric
Education, Institute of Science, Marmara University, Istanbul, Turkey, in 1995 and
2014, respectively.
He is currently working for the Balikesir Vocational High School, Electric Program,
Balikesir University, Balikesir. His research interests are magnetism and power
electronics.
Mustafa Çoramık received the B.S. degrees from Department of Physics Education,
Institute of Science, Balikesir University, Balikesir, Turkey, in 2012.
He is currently working for the Necatibey Education Faculty and Physics Education
Department, Balikesir University, Balikesir. His research interests are magnetism and
power electronics.
Yavuz Ege received the B.S. and Ph.D. degrees from Department of Physics, Institute
of Science, Balikesir University, Balikesir, Turkey, in 1998 and 2005, respectively.
He is currently working for the Necatibey Education Faculty and Physics Education
Department, Balikesir University, Balikesir. His research interests are solid physics,
magnetism and power electronics.

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A FLUXGATE SENSOR APPLICATION: COIN IDENTIFICATION

  • 1. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 DOI : 10.5121/sipij.2016.7601 1 A FLUXGATE SENSOR APPLICATION: COIN IDENTIFICATION Hakan Çıtak1 , Mustafa Çoramık2 , Yavuz Ege2 1 Balıkesir Vocational High School, Electric Program, Balıkesir University, Balıkesir 10145, Turkey 2 Necatibey Education Faculty and the Department of Physics, Balıkesir University, Balıkesir 10100, Turkey ABSTRACT Today, coins are used to operate many electric devices that are open to the public service. Washing machines, play stations, computers, auto brooms, foam machines, beverage machines, telephone chargers, hair dryers and water heaters are some examples of these devices These devices include coin recognition systems. In these systems, there are coils at two different radius, which become electromagnets when the current is passed through them. The AC current supplied to the coils creates a variable magnetic field, which induces the eddy current on the coil during the passing of money. The magnetic field generated by the Eddy current reduces the current passing through the coil. The amount of change of current in the coil gives information about the coin; the type of metal (element) and the amount of metal (element). In this study, a new coin identification system (magnetic measurement system) is designed. In this system, the magnetic anomaly generated by the coin as a result of applying direct current to the coils is tried to be detected by fluxgate sensor. In this study, sensor voltages are acquired in computer environment by using developed electronic unit and LabVIEW based software. In the paper, experimental results have been discussed in detail. KEYWORDS Metal money, Coil Detection, Eddy current, Fluxgate sensor, Magnetic anomaly. 1. INTRODUCTION Nowadays, remote sensing is used for different purposes with different methods. The features that are important in all sensing methods are low power consumption, low price, being unaffected by environmental changes, high precision, high reliability and stability. Some remote sensing methods used from past to present include electromagnetic induction (EMI)[1-5], ground effect radar (GPR)[6-11] and magnetic anomaly [12-15, 18]. Magnetic anomaly is the most widely used remote sensing method due to its low price, low number of equipment and changeable sensitivity. This method is based on determining the change in magnetic field caused by an object with magnetic characteristics, which is located in an external magnetic field [20-29]. The effects of magnetic anomalies caused by coins are also determined in coin operated washing machines, play station, computers, auto brooms, foam machines, beverage machines and telephone chargers. Within these devices, there are two coils at different diameters. During the
  • 2. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 2 passing of coin through the magnetic field created by applying AC current to the coils, eddy currents are being induced in the coil. The magnitude of this current depends on the conductivity of the coin. As a result of the high Eddy current, it is expected that the magnetic field generated by the eddy current is high. So, the current changes on the coil can be detected more easily. In short, the conductivity of coin determines the current change in the coil. The conductivity of coin depends on what kind of metal it is made of and how much metal it contains. In all coin operated devices, since the metal contents of the coins are different, the conductivity of the coins and the current changes in the coil will be different. Thus, the coins can be distinguished (Fig. 1). Figure 1. Coin determination system based on the change of current in the coil However, the changes in current passing through the coil also depend on the winding number of the coil, the wire cross-section and its type, and the magnetic character of the core. A different detection method has been used in our study to remove the effects of these variables. In this method, it is aimed to detect the magnetic anomalies generated by the coin with magnetic sensors instead of determining the current changes in the coil. In the presence of magnetic material, the decrease in flux density in the region of the sensor causes electrical change in the sensor and it changes the output voltage. By analysing these changes, information about the dimensions and the content of the object has been obtained. Various sensors such as MR (Magneto-resistive), SQUID (Superconducting quantum interference devices), Hall and Coil are used to detect the magnetic anomaly [15-19]. In this method, the FLC100 Fluxgate sensor has been chosen due to its low power consumption, low price, being unaffected by environmental changes, high precision, high sensing speed, reliability and stability. In the following section of the paper, the developed experimental setup, the application of the method, and the experimental results are explained in detail. 2. MATERIAL AND METHOD The block structure of the magnetic measuring system which is developed in this study with the aim of money determination is given in Figure 2. The developed system can be classified under two headings as hardware and software. In the first phase of the study, the mechanics and the hardware part of the system are produced and then the software part is programmed with LabVIEW.
  • 3. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 3 Figure 2. Block diagram of the magnetic measurement system In the developed magnetic measurement system, the mechanical control and data acquisition is done with the NI 6008 DAQ data acquisition card, which has 8 analog inputs (12 bits, 10 kS / s), 2 analog outputs (12 bits, 150kS / s) and 12 digital inputs / outputs The developed system has 8 FLC 100 sensors with 0.5 nT sensitivity and low noise (0.1 - 10 Hz), which can be used for more accurate measurements than Hall or MR magnetometers. The sensors are mounted on a platform with 2.5 cm spacing (Fig.3a). Since NI 6008 DAQ, the card (NI 6008 DAQ) has eight analog inputs, at most eight sensors are used in the system. The total length of the sensor network is 17.5 cm. In addition, the electronic unit in the system contains an electronic card that processes data from the sensor and a stepper motor driver card. Figure 3. a) Fluxgate sensor platform, b) Electronic unit In the study, an iron core coil with a length of 6.8 cm and a diameter of 2.4 cm is used as the magnetic field source. The coil has a winding number of 5000 and the wire thickness is 0.1 mm. (Fig.4)
  • 4. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 4 Figure 4. Magnetic field source In this study, there is a distance of 10 cm between the coil and the sensor platform and the tested coins are located in the middle of this distance. The center of the coin is set to the center of the scan plane and the sensor platform is moved to the start position of the scan. The scanning area of the sensor platform is a 10cm * 10cm area. The sensor data is collected by moving the platform at intervals of 0.2 mm along the x-axis and 0.05 mm along the y-axis. The scanning path of the sensor platform in the “Area scan” process is given in Figure 5. Figure 5. Scanning path of the sensor platform in the “Area scan” process The sensor platform first moves along the x-axis at specified step intervals (Movement 1). The platform returns to its initial state after reaching the total scan length (Movement 2). The system advances along the y-axis by the user-specified step length (Movement 3) Once the system has reached the final position, it returns to its initial state of motion and ends the measurement (Movement 5 and 6).
  • 5. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 5 The LabVIEW program, developed by National Instruments, is used to program the measurement and motion control system. LabVIEW has a graphical programming language which is also called data flow programming and consists of two different interfaces, block diagram and front panel. Block diagram is the section where programming is performed. The front panel can be defined as a "user interface" where users can enter variables and observe the results. In the program, the "event case" structure is used in general. With this structure, the user can perform a desired operation after entering the preliminary information. The sensor data received by the program is instantly saved in Excel format with a ".tdms" extension. Into the register file, the positions of the sensors in the x and y axes and the sensor output voltages are recorded. The front panel (control panel) of the LabVIEW-based program developed for the magnetic measurement system is shown in Figure 6. Figure 6. Control panel and sections of the developed LabVIEW based program
  • 6. Signal & Image Processing : An International Journal 3. EXPERIMENTAL RESULTS The technical information of the coin samples which are tested with the magnetic measuring system is given in Table 1. Reverse of the Coin Obverse of the Coin In this study, 15 volts of voltage and 0.5 amp current are applied to the coil which is used as a magnetic field source. The coin to be tested is placed 5 cm away sensor platform, 5 cm away from the coil, is brought to the beginning of the scan. After that, the platform is scanned a surface area of 10 cm * 10 cm (Fig. 5). The collected sensor data is processed in Origin which is a 3 shown in Figure 7. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 ESULTS The technical information of the coin samples which are tested with the magnetic measuring Diameter Thickness Mass Elements 17.5 mm 1.65 mm 2.9 g 65% Cu 18% 17%18.5 mm 3.15 g 20.5 mm 4 g 23.85 mm 1.9 mm 6.8 g Outer Ring: 79 % Cu,4% Ni Inner Circle: 65% Cu,18% 26.15 mm 8.2 g Outer Ring: 65% Cu,18% Inner Circle: 79% Cu,4% Ni In this study, 15 volts of voltage and 0.5 amp current are applied to the coil which is used as a The coin to be tested is placed 5 cm away from the coil. Then, the fluxgate sensor platform, 5 cm away from the coil, is brought to the beginning of the scan. After that, the platform is scanned a surface area of 10 cm * 10 cm (Fig. 5). The collected sensor data is processed in Origin which is a 3D drawing program. The graphs obtained for each sample are (SIPIJ) Vol.7, No.6, December 2016 6 The technical information of the coin samples which are tested with the magnetic measuring Elements Cu Ni Zn Outer Ring: Ni,17% Zn Inner Circle: Ni,17% Zn Outer Ring: Ni,17% Zn Inner Circle: Ni,17% Zn In this study, 15 volts of voltage and 0.5 amp current are applied to the coil which is used as a from the coil. Then, the fluxgate sensor platform, 5 cm away from the coil, is brought to the beginning of the scan. After that, the platform is scanned a surface area of 10 cm * 10 cm (Fig. 5). The collected sensor data is D drawing program. The graphs obtained for each sample are
  • 7. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 7 Figure 7. The graphs of tested coin samples a)5 Kurus, b)10 Kurus, c)25 Kurus, d)50 Kurus, e)1 Turkish Lira 4. DISCUSSION AND CONCLUSION When we look at the experimental graphs obtained in the study, it is seen that there is a magnetic field change in the scan area as much as the cross section of the magnetic field source of the coil. It is also clear that the change shown in Figure 7a, b, c are different from that of Figure 7d, e. In this work, it is necessary to focus on the magnetic character, not the conductivity of metal particles. To concentrate the magnetic flux produced by the coil in the coin, the coin must have
  • 8. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 8 high magnetic permeability. As it is known, metals with the highest magnetic permeability are Fe, Ni, Co. So it is important that the tested coins contain these metals or not and what amount they contain. Table 1 shows that 5, 10 and 25 Kurus contain 18% nickel, 50 and 1 Lira contain 17% Nickel. A decrease in the Ni ratio will reduce the magnetic flux density on the platform side of the coin. That's why the graphics in Figure 7d and Figure 7e (for 50 Kurus and 1 Lira) are different from the others. Therefore, this measurement system can be able to identify groups of coins that have different Nickel content. If the diameters of the coins are determined by optic methods (as seen in Fig 1.), all the coins will be classified. REFERENCES [1] Kim, H.; Shoji, T., (2004). “The Detection of Defects in Paramagnetic Materials Using Locally Focused Electromagnetic Field Technique”, Advances In Nondestructive Evaluation, 270-273, pp 625- 629. [2] Plotnikov, Y.A., Bantz, W.J., (2005). “Subsurface Defect detection in Metals with Pulsed Eddy Current”, Review of Progress in Quantitative Non-destructive Evaluation, 760, pp 447-454. [3] Soleimani, M.,(2010). “Improving the Temporal Resolution of Magnetic Induction Tomography for Molten Metal Flow Visualization”, IEEE Transactıons on Instrumentatıon and Measurement, 59, pp 553-557. [4] Gao, P., Collins, L., Garber, P. M., Geng, N., Carin, L., (2000). “Classification of Landmine-Like Metal Targets Using Wideband Electromagnetic Induction”, IEEE Transactions On Geoscience And Remote Sensing, 38, pp 1352-1361. [5] Keiswetter, D., Won, IJ., Barrow, B., Bell, T., (1999). “Object Identification Using Multifrequency EMI Data”, Proceedings of the Symposium on the Applicatıon of Geophysics to Engineering and Environmental Problems, 1, pp 743-751. [6] Daniels, David J., Curtis, P., Lockwood, O., (2008). “Classification of Landmines Using GPR”, 2008 IEEE Radar Conference,1, pp 1-6. [7] Belli, K., Rappaport, C., Wadia-Fascetti, S., (2009). “Forward Time Domain Ground Penetrating Radar Modeling of Scattering from Anomalies in the Presence of Steel Reinforcements”, Research in Nondestructive Evaluation, 20, pp 193-214. [8] Chang, C. W., Lin, C. H., Lien, H.S., (2009). “Measurement Radius of Reinforcing Steel Bar in Concrete Using Digital Image GPR”, Construction and Building Materials, 23, pp 1057-1063. [9] He, X.Q., Zhu, Z.Q., Liu, Q.Y., Lu, G.Y., (2009). “Review of GPR rebar detection”, Progess in Electromagnetics Research Symposium, Beijing, China, March, 1, pp 804-813. [10] Rappaport, C., El-Shenawee, M., (2000). “Modelling GPR Signal Degradation from Random Rough Ground Surface”, International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, 1, pp 3108-3110. [11] Cho, S.J., Tanaka, R., Sato, M., (2005). “Bistatic GPR by Using an Optical electric Field Sensor”, Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 1, pp 348-351. [12] Woloszyn, M., (2008). “Detection of Ferromagnetic Objects in Local Anomaly of the Baltic Sea”, Polish Marıtıme Research, 15, pp 77-82.
  • 9. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 9 [13] Chauhan, V., Singh, O.P., Kushwah, V., Singh, V., Singh, B., (2009). “Ultra-Low-Frequency (ULF) and Total Electron Content (TEC) Anomalies Observed at Agra and Their Association with Regional Earthquakes”, Journal of Geodynamics, 48, pp 68-74. [14] Kushwah, V., Singh, V., Singh, B., (2009). “Ultra Low Frequency (ULF) Amplitude Anomalies Observed at Agra (India) and Their Association with Regional Earthquakes”, Physics and Chemistry of the Earth, 32, pp 367-372. [15] Clem, T.R., (2002). “Sensor Technologies for Hunting Buried Sea Mines”, Oceans '02 MTS/IEEE, 1, pp 452-460. [16] Sheinker, A., Salomonski, N., Ginzburg, B., Frumkis, L., Kaplan, B. Z., (2005). “Aeromagnetic Search Using Genetic Algorithm”, Progress In Electromagnetics Research Symposium, 1, pp 492- 495. [17] Vyhnanek, J., Janosek, M., Ripka, P., (2011). “AMR Gradiometer for Mine Detection and Sensing”, Procedia Engineering, 25, pp 362-366. [18] Ege, Y., Çoramık, M., Kabadayı, M., Çıtak, H., Kalender, O., Yürüklü, E., Kurt, U., Nazlıbilek, S., (2016). “Anomaly detection with low magnetic flux: A fluxgate sensor network application”, Measurement, Vol: 81, pp 43–56 [19] Vyhnanek, J., Janosek, M., Ripka, P., (2012). “AMR Gradiometer for Mine Detection”, Sensors and Actuators A: Physical, 186, pp 100-104. [20] Nagano, T., Ohno, Y., Uesugi, N., Ikeda, H., Ishiyama, A., Kasai, N., (1998). “Multi-Source Localization by Genetic Algorithm Using MEG”, IEEE Transactions on Magnetics, 34, pp 2976- 2979. [21] Galanzha, E.I., Shashkov, E.V., Kelly, T., Woo-Kim, J., Yang, L., Zharov, V. P., (2009). “In Vivo Magnetic Enrichment and Multiplex Photoacoustic Detection of Circulating Tumour Cells”, Nature Nanotechnology, 4, pp 855-860. [22] Baldoni, J.A., Yellen, B.B., (2007). “Magnetic Tracking System: Monitoring Heart Valve Prostheses”, Transactions on Magnetics, 43, pp 2430-2432. [23] Kang, M.H., Choi, B.W., Koh, K.C., Lee, J.H., Park, G.T., (2005). “Experimental Study of a Vehicle Detector with an AMR Sensor”, Sensors and Actuators A-Physical, 118, pp 278-284. [24] Klein, L.A., Kelley, M.R., Mills, M.K., (1997). “Evaluation of Overhead and In-Ground Vehicle Detector Technologies for Traffic Flow Measurement”, Journal of Testing and Evaluation, 25, pp 205-214. [25] Dimitropoulos, K., Grammalidis, N., Gragopoulos, I., Gao, H.; Heuer, Th., Weinmann, M., Voit, S., Huhnold, M., Stockhammer, C., Hartmann, U., Pavlidou,N., (2008). “Detection, Tracking and Classsification of Vehicles and Aircraft Based on Magnetic Sensing Technology”, Word Academy of Science, Engineering and Technology, 19, pp 815-820. [26] Sadeghi, S.H.H., Toosi, B., Moini, R., (2001). “On the suitability of induction coils for crack detection and sizing in metals by the surface magnetic field measurement technique”, NDT & E International, Vol.34, 7, pp 493-504.
  • 10. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016 10 [27] Nazlibilek, S., Kalender, O., Ege, Y., (2011). “Mine Identification and Classification by Mobile Sensor Network Using Magnetic Anomaly”, IEEE Transactions on Instrumentation And Measurement, Vol.60, pp 1028-1036. [28] Ege, Y., Kabadayı, M., Kalender, O., Çoramık, M., Çıtak, H., Yürüklü, E., Dalcalı, A., (2016). “A New Electromagnetic Helical Coilgun Launcher Design Based on LabVIEW”, IEEE Transactions on Plasma Science, Vol. 44, 7, pp 1208-1218 [29] Ege, Y., Kakilli, A., Çıtak, H., Çoramık, M., (2016). “Determination of Buried Magnetic Material’s Geometric Dimensions”, Signal & Image Processing: An International Journal (SIPIJ), Vol.7, 5, pp 11-22 AUTHORS Hakan Çıtak received the B.S. and Ph.D. degrees from Department of Electric Education, Institute of Science, Marmara University, Istanbul, Turkey, in 1995 and 2014, respectively. He is currently working for the Balikesir Vocational High School, Electric Program, Balikesir University, Balikesir. His research interests are magnetism and power electronics. Mustafa Çoramık received the B.S. degrees from Department of Physics Education, Institute of Science, Balikesir University, Balikesir, Turkey, in 2012. He is currently working for the Necatibey Education Faculty and Physics Education Department, Balikesir University, Balikesir. His research interests are magnetism and power electronics. Yavuz Ege received the B.S. and Ph.D. degrees from Department of Physics, Institute of Science, Balikesir University, Balikesir, Turkey, in 1998 and 2005, respectively. He is currently working for the Necatibey Education Faculty and Physics Education Department, Balikesir University, Balikesir. His research interests are solid physics, magnetism and power electronics.