France



Magnetic hysteresis loop measurement [1]-[4] is crucial for the development of advanced magnetic materials. From the magnetization versus magnetizing field (&lt;i&gt;M&lt;/i&gt;-&lt;i&gt;H&lt;/i&gt;) curve, one can extract the useful magnetic properties, which are important for applications in sensors, transformers, actuators, and miscellaneous electrical devices. The &lt;i&gt;M-H&lt;/i&gt; measurements were usually done by using the vibrating sample magnetometer [1] or the SQUID magnetometer [2], for which the magnetizing field are virtually static during the measurement. The induction-coil-based hysteresis loop and susceptibility measurement systems [3], [4] are rapid and low-cost devices capable of determining the alternating-current (AC) magnetic properties, which is important for inferring the frequency-dependent features such as the energy loss due to eddy currents in conducting materials. The convention hysteresis loop measurement device [3] is designed for the magnetic induction versus field (&lt;i&gt;B&lt;/i&gt;-&lt;i&gt;H&lt;/i&gt;) measurement of toroidal samples. The alternating susceptibility measurement system (AC susceptometer) is capable of determining the frequency dependent initial magnetization and susceptibility. But the induction coil can’t detect the environmental direct-current (DC) field. For the soft magnetic materials, the non-zero environmental magnetic field can significantly alter the measured result, which must be compensated by using a magnetic sensor to detect the unwanted magnetic interference.&lt;br/&gt;To achieve higher accuracy in the &lt;i&gt;M-H&lt;/i&gt; measurement, we proposed a &lt;i&gt;M-H&lt;/i&gt; measurement system combining the signal processing method of &lt;i&gt;B&lt;/i&gt;-&lt;i&gt;H&lt;/i&gt; measurement and the coil design of the AC susceptometer. The interference from the environmental magnetic field is eliminated by using a giant magnetoresistance (GMR) sensor as the zero field detector, as shown in Fig. 1. The system consists of solenoidal magnetization and pickup coils. The pickup coil consists of reference and sensing coils symmetrically positioned with respect to the excitation coil. The sensing coil detects the magnetization of the sample, whereas the reference coil is wound in the opposite direction to null out the magnetizing signal. The signal output of the integrator is proportional to the magnetization of the sample.&lt;br/&gt;The current in the magnetization coil is directly proportional to the magnetizing field strength &lt;i&gt;H&lt;/i&gt;. However, the DC level of the magnetizing field at the sample is usually shifted by the environmental field, which can’t be observed by monitoring the current in the coil. To solve the problem, the GMR sensor, which is the GF708 spin-valve sensor [5] from Sensitec GmbH, is used for the zero field detector. The sensor has a very high sensitivity but the dynamic range less than 2 Oe. The output of GMR is used as the trigger for taking the &lt;i&gt;M-H&lt;/i&gt; curve to eliminate the disturbance from the environmental DC field, as shown in Fig. 2. The 1-Hz sinusoidal current signal measured by a resistor shows a peak-to-peak amplitude of 0.96 A, corresponding the magnetizing field amplitude of 6.7 mT. When the environmental DC field was completely eliminated in the pickup coil, the observed output signal of the GMR has a zero-field-crossing point defined by the trigger level, shown as in Fig. 2. The trigger point to activate the &lt;i&gt;M-H&lt;/i&gt; measurement is at the intersection of the trigger level line and the GMR signal curve for “No external field”. When the DC field (of about 0.77 mT) was applied, the trigger point shifted by 40 ms in comparison with the zero-field curve, while the current waveform in the coil remained unchanged. The new trigger point to activate the &lt;i&gt;M-H&lt;/i&gt; measurement is at the intersection between the trigger level line and the GMR signal curve with “External field”. The coercivity and the asymmetry pinning field of the GMR sensor induces a constant time shift in the trigger point, which can be corrected by employing a calibration procedure provided that the sweeping magnetizing field always saturates the output of GMR on both field polarities. With the GMR zero-field detector to set the trigger point, the high-quality M-H curve can be obtained by taking averaging for multiple cycles of the sweeping magnetizing field. In this way, the accuracy of the &lt;i&gt;M-H &lt;/i&gt;curve measurement for the soft magnetic materials is successfully improved by eliminating the interference from the environmental field without using a costly magnetic shield chamber.&lt;br/&gt;This work is supported by the Ministry of Science and Technology of Taiwan under Grant No. MOST108-2221-E992-083MY2.

**References:**

[1] S. Foner, “Vibrating sample magnetometer”, &lt;i&gt;Review of Scientific Instruments&lt;/i&gt;, 27(7), 548-548 (1956).&lt;br/&gt;[2] R. Nagendran, N. Thirumurugan, N. Chinnasamy, M. P. Janawadkar, &amp; C. S. Sundar, “Development of high field SQUID magnetometer for magnetization studies up to 7 T and temperatures in the range from 4.2 to 300 K”, &lt;i&gt;Review of Scientific Instruments&lt;/i&gt;, 82(1), 015109 (2011).&lt;br/&gt;[3] V. Munoz, M. C. Martinez, &amp; V. Such, “Improved systems for the measurement of hysteresis loops: DC and AC characterization”, Journal of Physics E: Scientific Instruments, 20(7), 861 (1987).&lt;br/&gt;[4] R. B. Goldfarb &amp; J. V. Minervini, “Calibration of ac susceptometer for cylindrical specimens”, &lt;i&gt;Review of scientific instruments&lt;/i&gt;, 55(5), 761-764 (1984).&lt;br/&gt;[5] C. Reig, S. Cardoso, &amp; S. C. Mukhopadhyay, “Giant magnetoresistance (GMR) sensors”, In &lt;i&gt;Smart Sensors, Measurement and Instrumentation&lt;/i&gt;, 6(1), pp.1-301 (2013).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/e67c168e41c249ae72cd97327beac3a7.jpg)
Fig. 1. Schematics of the magnetization measurement system with GMR zero-field detector
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![](https://assets.underline.io/uploads/markdown_image/1/image/53502c98b622d9f578af31fde8a199d5.jpg)
Fig. 2. The trigger points of the GMR zero-field detector for the M-H measurement system
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INTERMAG 2021

 Magnetization measurement system with giant magnetoresistance zero-field detector

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Magnetic hysteresis loop measurement [1]-[4] is crucial for the development of advanced magnetic materials. From the magnetization versus magnetizing field (M-H) curve, one can extract the useful magnetic properties, which are important for applications in sensors, transformers, actuators, and miscellaneous electrical devices. The M-H measurements were usually done by using the vibrating sample magnetometer [1] or the SQUID magnetometer [2], for which the magnetizing field are virtually static during the measurement. The induction-coil-based hysteresis loop and susceptibility measurement systems [3], [4] are rapid and low-cost devices capable of determining the alternating-current (AC) magnetic properties, which is important for inferring the frequency-dependent features such as the energy loss due to eddy currents in conducting materials. The convention hysteresis loop measurement device [3] is designed for the magnetic induction versus field (B-H) measurement of toroidal samples. The alternating susceptibility measurement system (AC susceptometer) is capable of determining the frequency dependent initial magnetization and susceptibility. But the induction coil can’t detect the environmental direct-current (DC) field. For the soft magnetic materials, the non-zero environmental magnetic field can significantly alter the measured result, which must be compensated by using a magnetic sensor to detect the unwanted magnetic interference. To achieve higher accuracy in the M-H measurement, we proposed a M-H measurement system combining the signal processing method of B-H measurement and the coil design of the AC susceptometer. The interference from the environmental magnetic field is eliminated by using a giant magnetoresistance (GMR) sensor as the zero field detector, as shown in Fig. 1. The system consists of solenoidal magnetization and pickup coils. The pickup coil consists of reference and sensing coils symmetrically positioned with respect to the excitation coil. The sensing coil detects the magnetization of the sample, whereas the reference coil is wound in the opposite direction to null out the magnetizing signal. The signal output of the integrator is proportional to the magnetization of the sample. The current in the magnetization coil is directly proportional to the magnetizing field strength H. However, the DC level of the magnetizing field at the sample is usually shifted by the environmental field, which can’t be observed by monitoring the current in the coil. To solve the problem, the GMR sensor, which is the GF708 spin-valve sensor [5] from Sensitec GmbH, is used for the zero field detector. The sensor has a very high sensitivity but the dynamic range less than 2 Oe. The output of GMR is used as the trigger for taking the M-H curve to eliminate the disturbance from the environmental DC field, as shown in Fig. 2. The 1-Hz sinusoidal current signal measured by a resistor shows a peak-to-peak amplitude of 0.96 A, corresponding the magnetizing field amplitude of 6.7 mT. When the environmental DC field was completely eliminated in the pickup coil, the observed output signal of the GMR has a zero-field-crossing point defined by the trigger level, shown as in Fig. 2. The trigger point to activate the M-H measurement is at the intersection of the trigger level line and the GMR signal curve for “No external field”. When the DC field (of about 0.77 mT) was applied, the trigger point shifted by 40 ms in comparison with the zero-field curve, while the current waveform in the coil remained unchanged. The new trigger point to activate the M-H measurement is at the intersection between the trigger level line and the GMR signal curve with “External field”. The coercivity and the asymmetry pinning field of the GMR sensor induces a constant time shift in the trigger point, which can be corrected by employing a calibration procedure provided that the sweeping magnetizing field always saturates the output of GMR on both field polarities. With the GMR zero-field detector to set the trigger point, the high-quality M-H curve can be obtained by taking averaging for multiple cycles of the sweeping magnetizing field. In this way, the accuracy of the M-H curve measurement for the soft magnetic materials is successfully improved by eliminating the interference from the environmental field without using a costly magnetic shield chamber. This work is supported by the Ministry of Science and Technology of Taiwan under Grant No. MOST108-2221-E992-083MY2.

**References:**

[1] S. Foner, “Vibrating sample magnetometer”, Review of Scientific Instruments, 27(7), 548-548 (1956). [2] R. Nagendran, N. Thirumurugan, N. Chinnasamy, M. P. Janawadkar, & C. S. Sundar, “Development of high field SQUID magnetometer for magnetization studies up to 7 T and temperatures in the range from 4.2 to 300 K”, Review of Scientific Instruments, 82(1), 015109 (2011). [3] V. Munoz, M. C. Martinez, & V. Such, “Improved systems for the measurement of hysteresis loops: DC and AC characterization”, Journal of Physics E: Scientific Instruments, 20(7), 861 (1987). [4] R. B. Goldfarb & J. V. Minervini, “Calibration of ac susceptometer for cylindrical specimens”, Review of scientific instruments, 55(5), 761-764 (1984). [5] C. Reig, S. Cardoso, & S. C. Mukhopadhyay, “Giant magnetoresistance (GMR) sensors”, In Smart Sensors, Measurement and Instrumentation, 6(1), pp.1-301 (2013).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/e67c168e41c249ae72cd97327beac3a7.jpg)
Fig. 1. Schematics of the magnetization measurement system with GMR zero-field detector
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![](https://assets.underline.io/uploads/markdown_image/1/image/53502c98b622d9f578af31fde8a199d5.jpg)
Fig. 2. The trigger points of the GMR zero-field detector for the M-H measurement system
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poster

As one of the emerging neurostimulation techniques, the Transcranial Magnetic Stimulation (TMS) regulates the synaptic activity of neurons to treat several psychiatric conditions and neurological disorders. During TMS, E-fields are induced within the brain through the application of time-varying magnetic fields, using coils located outside the head [1-4]. Different definitions are proposed in the literature to measure the focality of the E-field in the brain and estimate the stimulated area during TMS. However, most of them differ in measurement methodologies, showing lack of homogeneity and diverse criteria toward a unique conception, without evaluating possible scenarios of under or overstimulation of the biological tissue. Another common fact in the field is the use of definitions of “focality” without reference to a target area and/or focal distance, parameters intrinsically related and required for comparison between coils. Furthermore, to evaluate the degree of stimulation, a criterion indicating how well the stimulated area overlaps the targeted area is required. This work proposes a generalized function and methodology for the focality quantification in TMS coils, considering target area, focal distance, maximum magnitude and stimulation threshold. New definitions such as “specific focality (sf)” and “focality form factor (dn)” are introduced, seeking to describe the suitability of coils for specific stimulation applications. The proposed definitions also offer a general framework to compare coils using homogeneous methodology, parameters and nomenclature, with potential impact in the industry on the standardization of TMS measurement methods. We have performed finite element simulations over an anatomically accurate human head model, including three of the common TMS coils in the literature (single circular coil, figure of eight coil and quadruple butterfly coil [5]). The head model was built based on MRI of an adult healthy subject and defined by layer including the scalp, skull, cerebrospinal fluid, gray matter and white matter. All the layers were assigned typical electromagnetic properties reported in the literature. Magnetic flux densities and electric fields were obtained in the surface of the primary motor cortex (M1) using the head model, over the subregion that controls the first dorsal interosseous (FDI) muscle. Then, the introduced "specific focality" measure was used. This is defined as the normalized dimensionless focality based on new proposed mathematical functions. The functions designed are called Adjusted Even Symmetry Error Function Complement (AES-erfc) and Corrected-Adjusted Even Symmetry Error Function Complement (CAES-erfc). They define the focality as a quantity between zero and one (interchangeable to a percentage notation), as a function of the defined "focality form factor" (dn), a normalized ratio between the stimulated area and the targeted area. The result is a measurement system that identifies different scenarios of focal stimulation (dn=0), overstimulation (dn≥0), understimulation (dn ≤ 0) or no stimulation (dn=-1 in CAES-erf) [see Fig.1]. The calculated results of the specific focality over target areas and volumes were compared with other pre-existing measures in the literature, such as the area of the half-value (AE½) [6] and area of the half-energy (AEE) [67], for the surface specific focality [Fig.2, table 1], and area of the half-value (A½) and tangential fields spread (S½) [6-7], for the volumetric specific focality [Fig.2, table 2]. Our study shows -consistently with other currently used measures- that the QBC is the coil with highest focality for the defined target, followed by the F8C. The proposed specific focality measure assigns a dimensionless value to each coil compared to results in surface units of existing definitions. With this new parameter definition, it becomes easier to evaluate the focality of a TMS coils in the context of a specific target area -or volume- and focal distance, as well as to identify scenarios of stimulation that is focal and with either understimulation or overstimulation with respect to the target size.

**References:**

[1] M. T. Wilson, A. D. Tang, K. Iyer, H. McKee, J. Waas, and J. Rodger, “The challenges of producing effective small coils for transcranial magnetic stimulation of mice,” Biomed. Phys. Eng. Express, vol. 4, no. 3, Apr. 2018, doi: 10.1088/2057-1976/aab525. [2] P. Rastogi, E. G. Lee, R. L. Hadimani, and D. C. Jiles, “Transcranial Magnetic Stimulation-coil design with improved focality,” AIP Adv., vol. 7, no. 5, May 2017, doi: 10.1063/1.4973604. [3] Z.-D. Deng, S. H. Lisanby, and A. V Peterchev, “Electric field depth-focality tradeoff in transcranial magnetic stimulation: simulation comparison of 50 coil designs.,” Brain Stimul., [4] I. P. de Sousa, C. R. H. Barbosa, and E. C. Monteiro, “Safe exposure distances for transcranial magnetic stimulation based on computer simulations,” PeerJ, vol. 2018, no. 6, 2018. [5] P. Rastogi, Y. Tang, B. Zhang, E. G. Lee, R. L. Hadimani, and D. C. Jiles, “Quadruple Butterfly Coil with Passive Magnetic Shielding for Focused Transcranial Magnetic Stimulation,” IEEE Trans. Magn., vol. 53, no. 11, Nov. 2017, doi: 10.1109/TMAG.2017.2711962. [6] Z. De Deng, S. H. Lisanby, and A. V. Peterchev, “Electric field depth-focality tradeoff in transcranial magnetic stimulation: Simulation comparison of 50 coil designs,” Brain Stimul., vol. 6, no. 1, pp. 1–13, Jan. 2013, doi: 10.1016/j.brs.2012.02.005. [7] L. M. Koponen, J. O. Nieminen, and R. J. Ilmoniemi, “Minimum-energy coils for transcranial magnetic stimulation: Application to focal stimulation,” Brain Stimul., vol. 8, no. 1, pp. 124–134, Jan. 2015, doi: 10.1016/j.brs.2014.10.002.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/348e03c65e29cc891fd71995cd8244f3.jpg)
Fig.1. Proposed AES-erfc and CAES-erfc functions for the focality assesment.

![](https://assets.underline.io/uploads/markdown_image/1/image/80626ad77508d360116122aabb3f6dff.jpg)
Fig.2. Tables 1 and 2.

 Estimation of the Focality of Coils and Quality of Stimulation of Biological Tissues During Transcranial Magnetic Stimulation

There were many machine failures caused by the damage of the rolling bearing, for which the main reason was the quality of the steel ball [1]. Many works have been reported on assessing the geometric quality of steel balls, such as shape, size, and surface defects. In contrast, testing the material properties of steel balls is rarely mentioned [2]. As the quality and working life of the bearing depend primarily on the material properties of the steel ball, it is necessary to unambiguously classify the properties of steel balls in making bearings and other applications to ensure the reliability of the mechanical machine system. In this digest, we report on the eddy-current technique for the classification of steel balls based on the amplitude and phase of the eddy-current signal. Three types of 4-mm steel balls, including carbon, chrome, and stainless 304 steel balls, were tested to determine if it had undergone the surface hardening process. Fig. 1 shows the components of the RLC-resonance eddy-current system, which was designed to convert the electromagnetic induction of the steel ball into the phase-sensitive voltage output signals. The pickup coil is mounted statically to detect the eddy-current signal when the steel ball moves back and forth through the pickup coil by the stepping motor translation stage. A microcontroller (Arduino Uno) is used as the main unit to gather and process the data of the stage, such as the position and velocity. The micro-step determined on the driver is set to be 1/32. Fig. 1. The experimental eddy-current system for steel ball sorting. To detect the signal, the reference waveform for the lock-amplifier is synchronized to the excitation signal of the eddy current sensor. The lock-in amplifier receives the output signal from the sensor and generates the analog output voltages in proportion to the in-phase (real) and quadrature-phase (imaginary) components of the signal. The voltages are digitized by the data acquisition (DAQ) device NI USB 6008 from the National Instruments. The translation stage and the DAQ device are both controlled by the computer with the graphic user interface software designed using the Visual Studio C# programming language. The real and imaginary voltage components of the recorded eddy-current signals are subsequently converted into the probability map to show whether the steel balls can be classified by eddy-current sensors. The change in the detected signal should be due to the difference in the electromagnetic properties of the steel balls since the ball diameters are all 4 mm with an error less than 10 μm. Fig. 2. Eddy-current classification of steel balls: (a) determining the resonant frequency, (b) finding out the optimal resistance, and (c) voltage probability map of the steel balls before and after the surface hardening process. The impendence of the pickup coil can be calculated according to Dodd and Deeds method [3]: Zc=[(2πωµn2rm)/(l2-l1)2(r2-r1)2]∫I2[(r2,r1)/α5]{(l2-l1)(1+e-α/α)-1/α}dα (1) where Zc is the impedance of the coil (Ohm), µ is the permeability (Henry/meter), I is the applied current (ampere), α is the separation constant (meter-1), r2 is the outer radius of the coil (meter), r1 is the inner radius of the coil (meter), rm is the average radius of the coil, ω is the angular frequency (rad/second), l2 is the distance from metal testing to top of the coil (meter), l1 is the distance from metal testing to bottom of the coil (meter), n is the number of turns (turns), respectively. Fig. 2(a) shows the voltage amplitude measured at the output terminal when the driving frequency varies from 50 kHz to 200 kHz. The output response is maximized at the resonant frequency of 116 kHz for the RLC circuit. To achieve the higher response, the voltage outputs with various resistors are measured, as shown in Fig.2(b), where the optimal resistance is found to be 20 kΩ. The demodulated eddy-current signals were collected and repeated 100 times for each type of ball, and the result is converted into the voltage probability map in Fig. 2(c). It is found that the steel balls before and after the surface hardening treatment can be classified based on the difference in the eddy-current signal caused by the variation in electrical conductivity and permeability. The result indicates that the proposed method is feasible for application in sorting the material quality of steel balls, which plays a crucial role in mechanical systems. The authors would like to thank Tan Kong Precision Tech Co. Ltd. for providing the steel ball samples. This work is supported by the Ministry of Science and Technology of Taiwan under Grant No. MOST108-2221-E992-083MY2.

**References:**

[1]. H. K. D. H. Bhadeshia, "Steels for bearings", Progress in materials Science, 57(2) 268-435 (2012). [2]. D. Rifai, A. N. Abdalla and K. Ali, "Giant magnetoresistance sensors: A review on structures and non-destructive eddy current testing applications", Sensors, 16(3) 298 (2016). [3]. C. V. Dodd, and W. E. Deeds, "Analytical solutions to eddy-current probe-coil problems", Journal of applied physics, 39(6) 2829-2838 (1968).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/94dabd982116d237599a76ed67b91482.jpg)

![](https://assets.underline.io/uploads/markdown_image/1/image/26fdf226ab9d948c74b150d794f1b90c.jpg)

 Steel ball sorting based on electromagnetic induction using eddy current technique

Background Magnetocardiograms (MCGs) have become increasingly relevant for clinical research, due to its potential to detect early stages of heart disease. Especially, establishment of technology that can detail specify lesion parts of heart disease is expected. The estimation result by the current spatial filter method contains errors (eLORETA[1], and so on …). It cannot be said to be sufficient estimation accuracy for clinical application. If the current spatial filter method is used for magnetocardiography, the estimated solution of the signal source at the deep position tends to be wider than the estimated solution of the signal source at the shallow position. Factors include the fact that the number of sensors in the signal source estimation is very small compared to the unknown number, and that the magnetic field provided by the signal source is greatly attenuated according to the distance from the sensor. To solve these problems, improvement of the measurement system such as increasing the number of sensors is being considered[2], but the increase in equipment and cost becomes an obstacle. Therefore, in this study, we proposed a method focusing on the relationship between the analysis area of signal source estimation and the sensor plane. Methods Conventionally, the sensor plane is bigger than the analysis area at size in the xy plane, because the analysis area is designed the same size of the heart, on the other hand, the sensor plane is designed the sufficiently larger than the heart size. However, in that case, the information of the sensors located at the ends could not be utilized, and the amount of information that affects the estimation result was different for each sensor, which was a cause of deriving a biased estimated solution. Therefore, in this research, we consider that the amount of information of each sensor can be used equally by setting an analysis area larger than the sensor area, and propose a new environment setting method for signal source estimation. Simulation We carried out a simulation to consider which size of the analysis area is the best. We preformed the source signal estimation by eLORETA using the R-wave peak averaging data that the actual MCGs that the xiphoid process of the subject was adjusted to the 51ch sensor. We compared the following three sizes of the analysis area. Analysis area (a) is (x=120 mm, y=120 mm, z=80 mm), (b) is (x=176 mm, y=176 mm, z=80 mm), (c) is (x=224 mm, y=224 mm, z=80 mm). Other simulation conditions are shown in Fig. 1. Simulation Results Fig. 2 shows the signal source estimation result of each analysis area at R-wave peak. Red frame indicate sensor plane, blue frame indicate each analysis area, green frame indicate analysis area (a). Since the signals of the heart are originally considered to be single block, it is desirable that the estimated solution is a single block. Shown in Fig. 2, It was estimated as a single block except for the analysis area (a), which is the conventional method. In addition, the position and size of the estimated solution shown in Fig. 2 are very similar to the area of the heart obtained from CT images. Furthermore, we evaluated three analysis area using GOF (Goodness of fit) that is evaluation method that indicate the degree of agreement between the magnetocardiogram created by the estimated signal source and the measured magnetocardiogram. As a result, the GOF of each analysis area was about 0.996, which was not a big difference. Summary We examined the relationship of the sensor plane and analysis area using the actual MCG data in order to apply the spatial filter method MCG proposed a new analysis setting. By changing the size of the analysis area, the division of the estimated solution was suppressed and the shape of the heart could be roughly estimated. It found that the estimated result by using analysis area (b) and (c) are very similar to the area of the heart obtained from CT images. From these results, it was shown that it is possible to visualize the movement of the myocardium by estimating the time-series waveform of the magnetocardiogram.

**References:**

References: [1] R.D. Pascual-Marqui. Discrete, “3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization.”, arXiv :0710.3341 [math-ph], 2007. [2] W. Sun and K. Kobayashi. “Simulation of Extended Source Localization using sLORETA Method for Magnetocardiography”. Journal of the Magnetics Society of Japan, vol. 41, pp. 75-80, 2017.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/eda772818e2a071aa00c1e92b6437aa7.jpg)
Fig. 1 Simulation conditions.

![](https://assets.underline.io/uploads/markdown_image/1/image/d4dd3d9c735a0795d9376c361864fc26.jpg)
Fig. 2 the result of each analysis area's signal source estimation at R-wave peak.

 Study on Sensor and Analysis area in The Signal Source Estimation by Spatial Filter for Magnetocardiogram

Steel wire ropes used in roped transport devices like lifts, hoists, cableways, winches, are subjected to bending fatigue. Many parameters affect bending fatigue lifetime, such as tensile load, sheave diameter, and rope composition. According to the application area, steel wire ropes are often exposed to atmospheric influences, and the impact of corrosion is inevitable [1]. Non-destructive damage detection is a critical way to assess damage states to guarantee wire ropes reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made significant progress. Microelectromechanical systems (MEMS) have allowed magnetic field sensors with potential applications such as automotive industry, navigation systems telecommunications, and non-destructive testing [2]. Optically-pumped magnetometers (OPMs) have been developed since the 1960s. OPMs have generated renewed interest over the last 15 years, in part due to their increased sensitivity, when operated in the spin-exchange relaxation free (SERF) regime. Micro-Fabricated Atomic Magnetometer (MFAM) is a magnetometer system which can be used for land, airborne or even marine survey applications [3]. The Geometrics’ MFAM is a laser pumped cesium magnetometer module that measures the total magnetic field strength. It uses cesium atoms contained in a small, evacuated glass vapour cell as the sensing element. The cesium is solid at room temperature. Hence, the cell is heated to increase the vapour pressure and the number density, of the cesium atoms in the cell. The cell is also filled with nitrogen buffer gas. The photon's angular momentum in the pump beam is transferred to the atom in the excitation process. As the atom decays non-radiatively through a quenching pathway provided by the nitrogen buffer gas, it loses random amount of angular momentum. As the spin polarization precesses, the intensity of the probe light transmitted through the cell is modulated at the Larmor frequency. The probe light is detected with a photodiode and the photocurrent amplified to generate the oscillating probe signal. The resultant Larmor spin precession can be detected with near-resonant light and serves as a measure of the ambient magnetic field. A schematic of the sensor is shown in Fig. 1 [4], [5]. The paper presents one of the first applications of MFAM sensors for diagnostics steel wire ropes. The module features two sensors that can be used independently or as an intrinsic gradiometer. The miniature scalar Optical Atomic Magnetometer [OAM] sensor used here, measures only 2.5cm x 2.5 cm x 3 cm. The Bell-Bloom type atomic magnetometer measures the Larmor precession of cesium atoms through non-resonant absorption of a probe beam. The examined cable is the 7x7+7x19W+IWRC steel wire rope type of 6,5 mm diameter, coated, with a metallic central core strand and crossed to the right. Cable construction is robust and widely used in the industrial field, mainly, in lifting applications. The examination aims to check the influence of the Earth's magnetic field on the diagnosis of steel rope using MFAM Technology. The rope permanently attached to the device shown in Fig 2. Under the rope, at a distance of 80mm, an MFAM Magnetometer (2 sensors: M1 and M2) was attached, which with the use of appropriate software could move at a constant speed of 3 mm/s. I recorded the measurement of the magnetic field around the rope over a measuring length of 380 mm. Then I increased the distance between the rope and the sensors to 120mm and 160mm. Each measurement was carried out three times by moving back and forth. I concluded MFAM technology's future on the non-destructive damage detection methods for steel wire ropes based on the research. The MFAM method is future-proof but requires further investigation. It is crucial to choose the appropriate distance from the tested object (the Earth's magnetic field's influence is a significant factor). In practical applications, the biggest challenge in using MFAM sensors is the stability of the vapour cell's operating temperature and need to separate the Earth's magnetic field. A promising avenue for research is to combine various methods to characterize and detect damages from multiple dimensions. For example, the combination of the electromagnetic and optical methods can grasp the size of the effective cross-sectional area of wire ropes. The types of damages are real-time and intuitively master its surface morphology characteristics to provide more valuable steel wire rope health evaluation parameters. Although there are many issues and challenges, as new technologies and algorithms are introduced, it is believed that MFAM technology will realize evident and accurate non-destructive diagnostics of steel wire ropes.

**References:**

[1] M. Al-Shaibah et al., ‘Characterization of a Resonant Lorentz Force MEMS Magnetometer’, in 2020 Symposium on Design, Test, Integration Packaging of MEMS and MOEMS (DTIP), Jun. 2020, pp. 1–2 [2] Z. Guo, X. Xu, and T. Zhang, ‘Research on the calibrated method for MEMS magnetometer arrays’, Feb. 2020 [3] G. Oelsner et al., ‘Integrated optically pumped magnetometer for measurements within Earth’s magnetic field’, arXiv:2008.01570 [physics], Aug. 2020, Accessed: Dec. 09, 2020. [Online] [4] R. Zhang et al., ‘Active Magnetic-Field Stabilization with Atomic Magnetometer’, Sensors, vol. 20, no. 15, Art. no. 15, Jan. 2020 [5] R. Zhang, K. Smith, and R. Mhaskar, ‘Highly sensitive miniature scalar optical gradiometer’, 2016 IEEE SENSORS, 2016

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/28bc2739048ffacbbceee619a6af7ab6.jpg)
Fig.1. Schematics showing the internal structure of the sensor

![](https://assets.underline.io/uploads/markdown_image/1/image/9edfb799edfe8a44a5dac68fd2babc82.jpg)
Fig.2. Measuring device

 Influence of the Earth’s magnetic field on the diagnosis of steel rope with the use of MFAM Technology

Introduction: Analyzing and processing the non-linear and non-stationary vibration signals is a critical issue in the motor bearing fault diagnosis. In order to solve this problem, Empirical Mode Decomposition (EMD) was proposed, which decomposes the signal in Intrinsic Mode Functions (IMFs) [1]. However, the EMD has the drawback of the so called “mode mixing” phenomenon, which means that the signal components with different amplitudes in one IMF, or the signal components with a similar amplitude in different IMFs. To address this issue, an effective solution was proposed, which suppresses the mode mixing by adding the Gaussian white noise in the original signal, named as “Ensemble Empirical Mode Decomposition (EEMD)” [2-3]. But it brings in some new harmonics in IMFs. To overcome these problems, the Complete Ensemble Empirical Mode Decomposition (CEEMD) was put forward, which adds the opposite Gaussian white noise in the original signal for the EMD respectively [4]. However, the amplitude of the Gaussian white noise is usually estimated by the practical experience rather than calculating by equations. Besides, the inappropriate Gaussian white noise could not mitigate the mode mixing effectively. The essence of mode mixing is the coupling between IMFs, and IMFs are not orthogonal to each other. This paper focuses on the mode mixing, and optimizes the amplitude of the white noise by the genetic algorithm (GA) to minimize the orthogonality among IMFs. Optimization Method: Mutual information is an indictor which symbolizes the correlation between the data. The mutual information between IMFs obtained by the CEEMD will be affected and changed, when the amplitude of the white noise varies. Therefore, the white noise amplitude (All of these values are multiples of the standard deviation of the signal) is used as the input function, and the minimum mean of the mutual information between IMFs is used as the objective function in the GA. As a consequence, it can suppress the mode mixing phenomenon theoretically, when the amplitude of white noise is the minimum mean of the mutual information. Results: The bearing vibration fault signal used in this paper comes from the Case Western Reserve University [5]. In Fig.1, the values in the bar denote the mean of mutual information of IMFs corresponding to different white noise amplitudes, where 0.1103 is the white noise amplitude selected by GA and 0.1 to 0.4 are the values selected by the experience. The mean of mutual information is 0.5728 when the amplitude of the white noise is 0.1103, which is smaller than the amplitudes of 0.1, 0.2, 0.3 and 0.4 that the mean of mutual information is 0.705, 0.6230, 0.6998 and 0.7093, respectively. Fig.2 shows the amplitude of IMFs in figure Fig.2 (a) and the spectrum diagram of IMFs in figure Fig.2 (b) decomposed by the CEEMD with the optimized white noise. It can be seen from the figure Fig.2 (b) that the main components of each IMF are distributed at different frequencies. Meanwhile, it shows in the spectrum of IMF3 that the five times frequency (539.1 Hz) component of the bearing outer race fault. It proves the effectiveness of the proposed method in motor bearing fault diagnosis. Detailed results and discussion will be given in the full paper.

**References:**

[1] N. E. Huang, Z. Shen, S. R. Long, et al., Proc. R. Soc. Lond. A Math. Phys. Sci., vol. 454, p. 903-995, (1998). [2] Z. WU and N. E. Huang, Adv. Adapt. Data Anal., vol. 01, p. 1-41, (2008). [3] P. Flandrin, G. Rilling and P. Gonçalvès, IEEE Signal Process. Lett., vol. 11, p. 112-114, Feb. (2004). [4] F. Liu, J. Gao and H. Liu, IEEE Access, vol. 8, p. 19810-19819, (2020). [5] C. W. R. University,''https://csegroups.case.edu/bearingdatacenter/pages/12k-drive-end-bearing-fault-data," (2014).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/c71a8b8dc1e20c7f4b86ef846db3f898.jpg)
Fig. 1 The mean mutual information of different amplitude of the white noise.

![](https://assets.underline.io/uploads/markdown_image/1/image/348217bb9979a412c80e3a633833c3b6.jpg)
Fig. 2 IMFs decomposed by the CEEMD with the optimized white noise. (a) Amplitude of IMFs. (b) Spectrum diagram of IMFs.

 Adaptive Suppression of Mode Mixing in CEEMD Based on Genetic Algorithm for Motor Bearing Fault Diagnosis

In a standard measurement of diamagnetic susceptibility χDIA (per unit mass), it is necessary to prepare a bulk sample that weigh more than ~0.5 mg. In the present report, we show that χDIA of a small particle is determined from its parabolic movement that is induced by a field gradient generated by a Niobium magnetic circuit, without the necessity of knowing mass m of particle. This principle of measuring χDIA is based on a recent finding that acceleration of a translating particle induced in an area of magnetic field gradient is independent to m; it is uniquely determined by intrinsic χDIA value of material [1][2]. This characteristic derives from a well-known fact that a magnetic body force induced in a particle is proportional to m. The observed results overturn a common recognition that ordinary diamagnetic particles are magnetically inert unless a strong field in applied in the experiment [3]; the result indicate that most solid particles composed of diamagnetic or weak paramagnetic materials may potentially show translation at a low field produced by a permanent magnet, which is usable in practical applications. It was proposed that material identification of a particle is possible[4], by collating the obtained χDIA value with a compiled list of published data [5]; unlike many conventional methods of chemical analysis, the identification is conducted without consuming the small sample. In the present experiment, field-induced parabolic translations were observed in three diamagnetic materials, namely graphite, bismuth and diamond. The particles were released at an initial position as shown in Fig.1 with negligibly small initial velocity, and the particles translated in an area with magnetic field gradient in normal gravity. Fig 2 shows the example of the trajectory observed for a graphite particle and a bismuth particle which proceeded in the xz-plane of Fig.1; they were observed by a high-speed camera (CASIO EX-F1, Japan). The χDIA value of each particles were determined from a relationship between velocity of particle v (x,z) and field intensity B (x, z); the theoretical equation used in the calculation was deduced from an assumption that the variance of potential energy (i.e., magnetic & gravitational) of a particle between two arbitrary positions in the trajectory was completely converted to kinetic energy. The χDIA values obtained in this manner was consistent with the published values [5]. The accuracy of the χDIA values obtained by the present apparatus was significantly improved compared to that obtained in the previous research [3]; the improvement was achieved by optimizing the field distribution of the magnetic circuit, thus decreasing the fluctuation of v(x) values. The accuracy was also increased by precisely identifying the initial position of sample improving the observation using the hi-vision camera. Accordingly, accuracy of χDIA obtained for a submillimetre-particle was improved to a level of ~ 4×10-7emu/g, and variance of χDIA between different materials can be distinguished by the proposed method; in previous studies, the accuracy remained at a level of ~10×10-6emu/g [1][2][4]. The experimental techniques achieved in this study will be a milestone in reducing the measurable size of the sample to micrometre scale. According to recent measurements, the velocities of ferro- (ferri-)magnetic particles [6] and paramagnetic particles [2] during their field-induced translation were also independent to m. Hence, similar motions as reported in the present study is generally expected to occur in all categories of magnetic material by a relatively low magnitude of field-gradient. It is expected from the results shown in Fig.2 that an ensemble of heterogeneous particles can be separated into fractions of different materials [2][4] by releasing the ensemble from the initial position in Fig.1. By improving the compact system, a non-destructive chemical analysis can be conducted shortly after collecting the heterogeneous particles at the site of material explorations in various research fields; such method of pre-treatment will improve the efficiency of reaching a target sample in the research. &lt;div id="UMS_TOOLTIP" style="position: absolute; cursor: pointer; z-index: 2147483647; background-color: transparent; top: -100000px; left: -100000px; background-position: initial initial; background-repeat: initial initial;"&gt; &lt;/div&gt;

**References:**

[1] C. Uyeda, K. Hisayoshi and S. Kano, J. Phys. Soc. Jpn. Vol.79, p.064709 (2010). [2] K. Hisayoshi, C. Uyeda and K. Terada, Sci. Rep. vol.6, p.38431 (2016) [3] G. Maret and K. Dransfield, Adv. Appl. Phys. Vol.46, p.143 (1985) [4] C. Uyeda, K. Hisayoshi and K. Terada, Sci. Rep. Vol.9, p.3971 (2019). [5] R. Gupta, Landolt Bornstein. New Series II, Vol.46 “Diamagnetism”, p.445 (19830 [6] C. Uyeda, K. Kuwada and K. Hisayoshi, J. Jpn. Soc. Powder Metall. Vol.61, p.S78 (2014) &lt;div id="UMS_TOOLTIP" style="position: absolute; cursor: pointer; z-index: 2147483647; background-color: transparent; top: -100000px; left: -100000px; background-position: initial initial; background-repeat: initial initial;"&gt; &lt;/div&gt;

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/e2339049a76984bb17bb885247799039.jpg)
Fig.1 A top view (above) and a side view (below) of a NdFeB magnetic circuit developed in the present study to observe parabolic translation of a diamagnetic particle in terrestrial gravity.

![](https://assets.underline.io/uploads/markdown_image/1/image/22d65a0cb18c05ff3ed752e42a3fb932.jpg)
Fig.2 Trajectories of a graphite particle and a bismuth particle that translated from the initial position described inFig.1. The particles translated in the xz plane in the figure. The χ values obtained from the trajectories was 49×10-7emu/g for graphite and was 11×10-7emu/g for bismuth; the values were both consistent with the published values [4].

 Diamagnetic Susceptibility of a Single Particle Detected from its Parabolic Movement Caused by Magnetic Field gradient and Terrestrial Gravity

With the increasing demand for new high-tech and energy-efficient devices, multi-stimuli functional materials with strong interplay between their structural, magnetic, and electronic subsystem appear at the forefront of research in material science [1,2]. They form a new family of promising materials for applications in different technological areas, like heat-assisted magnetic recording (HAMR), thermal energy storage, novel spintronic devices, and magnetic refrigeration. In a first-order phase transition (FOPT) material [3], the changes caused by external stimuli not only impact one subsystem of the solid (e.g., change of magnetization induced by a magnetic field), but also give rise to significant transformations occurring in other subsystems (e.g., expansion of the crystal lattice, change in electrical resistivity, heating of the sample, etc.). However, the contributions of the different subsystems are usually characterized independently and in different samples, environment, or even laboratories. This makes difficult to assemble scattered pieces of a puzzle into one coherent picture, and this is probably a reason why the mechanisms of magneto-structural coupling remain till now poorly understood. Therefore, it is important to change the existing paradigm and to develop a novel experimental approach allowing the characterization of the magnetic materials exhibiting a FOPT by combining various experimental techniques, including: ● field/temperature dependencies of magnetization both macroscopically (magnetometry) and in the element selective manner (XMCD) ● changes occurring in the crystal lattice (X-ray diffraction) and in the sample as whole (dilatometry) ● thermal response of the material (calorimetry). The key point is that all these measurements should be performed simultaneously on the same sample and under strictly identical experimental conditions. Such an experimental setup is to be implemented at the beamline ID12 at the European Synchrotron Radiation Facility (ESRF) in Grenoble (France) in the framework of the project ULMAG – Ultimate MAGnetic characterization. It will serve as a versatile tool for combined macroscopic and microscopic characterization of magnetic materials open to all users. In our presentation, we describe the ULMAG setup, which is inspired from a previously purpose-built device “MaRS” at TU Darmstadt [4]. The MaRS is based on a commercial VSM option of QD-PPMS [4]. However, in ULMAG, the sample must remain static for the experiments with x-rays at the beamline, impeding the VSM possibility. Thus, the magnetic measurements are performed by means of a GaAs Hall probe situated as close as possible to the sample. This allows to estimate the stray-field produced by the surface of the sample, mirroring the surface magnetization of the sample. The dilatometry and calorimetric properties are measured by means of strain gauges and thermometer, respectively, as described in our previous study [4]. A first prototype was built based on the commercial resistivity option of the QD-PPMS. The sensors are connected to external instruments controlled by the MultiVu software implemented in the QD-PPMS. This prototype serves as a proof of concept and the general scheme of the sample with the different sensors are presented in Fig. 1. At the ESRF, we are building a similar prototype which will have in addition the X-Ray beam (see Fig. 1). With the current ULMAG setup, we can probe under the same conditions and simultaneously the (i) magnetic, (ii) magneto-volumetric and (iii) caloric changes as function of the magnetic field (up to 8 T) and temperature (4-350 K). Preliminary experiments are performed on a quasi-equiatomic FeRh sample with the antiferromagnetic (AFM) to ferromagnetic (FM) transition occurring around 300 K (see Fig. 2). Results show that contrary to what is usually assumed, magnetic and magneto-volumetric changes (parallel and perpendicular) do not evolve hand in hand but rather occur at different fields (or temperatures for temperature induced transition, not shown here). Such peculiarities were also observed in other samples (La-Fe-Si and FeRh) measured with the MaRS [4-6]. The combination of XMCD with the macroscopic properties reported above is planned to be performed in early 2021 at the beamline ID12 of the ESRF. This will be a first achievement to complete a new way of characterizing magnetocaloric materials. In the future, the use of this set-up will be extended to study other types of magnetic compounds like magnetostrictive materials or permanents magnets and beyond. Aknowledgements: This work was funded by the Federal Ministry of Education and Research (BMBF) under the grant number BMBF-Projekt05K19RD2.

**References:**

[1] O. Gutfleisch, et al, Magnetic materials and devices for the 21st century: stronger, lighter, and more energy efficient. Adv Mater, vol. 23, p. 821 (2011). [2] O. Gutfleisch, et al., Mastering hysteresis in magnetocaloric materials, Phil. Trans. R. Soc. A, vol. 374, p. 20150308 (2016). [3] J. Y. Law, et al., A quantitative criterion for determining the order of magnetic phase transitions using the magnetocaloric effect. Nat Commun, vol. 9, p. 2680 (2018). [4] D. Yu. Karpenkov et al. Pressure Dependence of Magnetic Properties in La(Fe,Si)13: Multistimulus Responsiveness of Caloric Effects by Modeling and Experiment Phys. Rev. Applied, vol. 13, p. 034014 (2020) [5] K. Skokov et al, to be published [6] K. Skokov et al, to be published

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/5f2a294dbf80c7e20867ad734e257ab5.png)
Fig.1: Schematic of the ULMAG setup for simultaneous measurements of magnetic stray field, magnetovolume change, caloric change and XMCD/XRD, expected to be implemented at ID12 at ESRF (Grenoble, France).

![](https://assets.underline.io/uploads/markdown_image/1/image/daf9ffa9613d66fd4cf451c0943050ce.jpg)
Fig. 2: Simultaneous measurements of the stray field (hall probe), the magnetostriction (strain gage // and ⊥), and the calorimetry (thermometer); showing a magnetic field induced AFM—FM transition at 245 K for a quasi-equiatomic FeRh.

 “ULMAG – Ultimate Magnetic Characterization,” A Novel Experimental Setup for the Beamline ID12 at the European Synchrotron Radiation Facility (ESRF)

An apparatus to obtain magnetic anisotropy Δχ (per unit mass) of a diamagnetic or a paramagnetic crystal is newly desinged and opperated in microgravity (μg) condition. In this experiment, rotational harmonic oscillation of a milimeter size crystal was observed, which was caused by a magnetic ansiotropy energy -½ｍΔχB2 of the crystal with mass m; here oscilation was induced by a homogeneous field B produced in area of μg [1][2]. The period of this oscillation τ is described as τ= 2π (I/m)½Δχ-½B-1, in condition that angle of amplitude is small. Note that τ is independent to m in the above equation, and if oscillation is observable, Δχ can be obtained by measuring τ, I/m and B no matter how small crystal particle may be [1]-[3]. Using a standard comertial apparatus, detection of Δχ is prevented in a small sample due to the background signal from the sample holder, and also due to the difficulty in measuring m. Hence in using the coventional methods, it is nccesary to prepare a single crystal with a milimeter-size to obtain an accurate Δχ value. It is noted that the obtainable size of a single crystal is below sub-milimeter level in many materials, and it is presently difficult to obtain reliable Δχ values in these materials. As described in Fig.1, a pair of ferrite magnetic plate (10cm x 5cm x 1cm) was introduced in the present study to generate B (B = 0.051T). By introducing these plates, area of homogeneous field was enhanced to a spherical space of φ~3 cm , and the mm-size sample released in μg area could remain inside the homogeneous area of during the τ measurement. Note that a solid particle released in μg space tends to freely trasnlate, and in previous researches [3], the samplefrequently moved away from the small homogeneous space of φ~1.0cm during the τ measurement. Due to this distervance, success rate of of Δχ measurement was less than 20%. In the present experiment, Δχ values were obtained in milimetre size crystals of paramagnetic chrorite and a diamagnetic graphite following the aforementioend propcedure. The milimetre-size sample used in the experiment was separated from the bulk single crystal using a wire saw and a titanum knife to avoid ferromagnetic contaminations. The sample was placed on a sample stage that was located at the the field center as shown in Fig,1. A short microgravity condition (duration &lt; 0.5s) was supplied by conducting a free fall of the apparatus which was installed in a wodden rack [3]. Shortly after the biginning of the free fall, the sample stage was slightly lifted from its initial position, which was effective in inducing a smooth release of the small sample in a diffuse area. The rotational-oscillation of the samples were observed by a high-speed camera (ZWO ASI290MC), which could observe and presere the sample motions with a spatial resolution of 0.004 cm and a time resolution of 0.033 fps. Fig.2 shows a time dependent image of a graphite crystal showing that field-induced rotational oscillation occurs at a low field intensity produced by the ferrite plates; it was generally considerd that dinamic motion of diamagneitc and paramagnetic material were relaized by a existance of ultra high magnetic field [4]. The experimental values of τ and I are obtained from the images, and are used in the calculation of Δχ according to the aforementioned procedure. The values obtained in this manner are +(7.7±0.8)x10-6emu/g and -(2.2±0.6) x10-5emu/g for chrorite and graphite, respectively; these values are consistent with the publised data measured by the conventional torque methods [5]. According to the aforementioned principle of the proposed Δχ measurement, further improvement of sensitivity is possible, specifically by increasing the magnitude of B and/or τ; improvement is also possible by decreasing the value of I/m. The experimental techniques achieved in the present study will be a milestone in reducing the measurable size of the sample down to micrometre scale. Diamagnetic anisotropy originate from the 3-dimensional distribution of localized electrons that compoase a material. In case of organic materials, origin of Δχ were explained in terms of a molecular-orbital method; consistency of this method was examined by comparing theoretical and experimental values of Δχ [5]. Similar investigation was not performed in other types of solid materials because the number of experimental Δχ values were limited due to the difficultty of detecting the small Δχ values. By improving the method proposed in the present report, Δχ having a magnitude below 1x10-9emu/g canbe obtained for oxides with high crystal symmetry. The improvement of Δχ sensitivity will increase the number of reliable data to clatify the relationship between diamagnetic anisotropy and spatial electron distribution assinged to individual diamagnetic crystals [2][5].

**References:**

[1] C.Uyeda, Phys. Chem. Mineral 20, 77–81(1993) [2] C.Uyeda, K.Hisatoshi, T. Tanaka, Appl. Phys. Lett. 28, 094103 (2005). [3] C. Uyeda et al., J. Phys. Soc. Jpn. 79, 064709 (2010). [3] (2011) [4] G.Maret & K. Dransfield, Adv. Appl. Phys. 46, 143 (1985). [5] R. Gupta, Landolt Bornstein. New Series II, 46 “Diamagnetism”, p 445 (1983).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/6362df752c85ff6b2c33c946a8e00ee1.jpg)
An apparatus developed to detect magnetic anisotropy Δχ in a small crystal by observing its field-induced trasnslation in a short μg condition, which is supplied by a comopact drop shaft shown in the right portion of figure.

![](https://assets.underline.io/uploads/markdown_image/1/image/aa8f5055640f26be8ec99f1782214b75.jpg)
Time dependent image of a graphite crystal showing field-induced rotational oscillation by a field produced by a ferrite magnetic circuit. The c-axis of graphite is normal to the sample plane showing oscillation with respect to B. Period of oscillation τ is obtained from these images, and is used to calculate Δχ of the crystal.

 Magnetic anisotropy detected in a small crystal by observing its rotational oscillation caused by a ferrite magnetic circuit

Transcranial magnetic stimulation (TMS) is a non-invasive and painless treatment for brain disorders. Rodent models of TMS provide a possibility to reveal the mechanism of TMS at cellular and molecular level. However, their application has been hampered by lack of stimulating coils with appropriate focus and intensity. In this study, we analyzed various types of coils and proposed conductive shield and iron core materials to improve the coil performance. The results showed that combination of Halo coil and figure of eight coil with shield and iron core could generate better stimulus focus (29.2 mm2) and intensity (87.6 V/m). Keywords: Transcranial magnetic stimulation; Coils; Focus; Mouse model I. Introduction At present, most commercial animal coils generally cannot meet the requirements of stimulus depth, stimulus intensity and stimulus focus simultaneously. Therefore, it is necessary to further develop a special TMS coil for small animals [1, 2]. II. Method In this study, a mouse model was constructed with three-compartment body-skull-brain model of conductivities 0.3344, 0.0203 and 0.106 S/m. The mouse and coil models were shown in Fig.1. The shield conductor (Cu) with a thickness of 1 mm had a window in the middle. The iron core was made of sheets of silicon steel. All the coils were kept at a distance of 5 mm from the highest point of the mouse head. The stimulus intensity is defined as the max of the electric field (Emax), and the focus was defined as the half power region (HPR). III. Results The results showed that Halo coil, shield and iron core could complement each other well. The combination of Halo coil and figure of eight coil with shield, icon core could get a better stimulus focus (29.2 mm2) and a higher stimulus intensity (87.6 V/m), which had increased about 79% and 54% respectively compared to the general commercial coil, as shown in Fig.2. IV. Conclusion Our results suggested that the combination of Halo coil and figure of eight coil with shield and icon core could improve TMS coil performance. We provided an alternative method to deliver TMS to small animals by constructing a coils with better stimulus intensity and stimulus focus.

**References:**

[1] Meng Q, Jing L, Badjo J P, et al. A novel transcranial magnetic stimulator for focal stimulation of rodent brain [J]. Brain Stimulation, 2018, 11(3): 663-665. [2] Alekseichuk I, Mantell K, Shirinpour S, et al. Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human [J]. Neuroimage, 2019, 194: 136-148.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/c980cf69f9b7ab8e29272aa563f9908a.jpg)
Fig.1 Mouse and coil models of this study

![](https://assets.underline.io/uploads/markdown_image/1/image/ff8235f037f228bd4581f42851cc12cf.jpg)
Fig.2 Electric field induced in the brain of the mouse

 Design of Transcranial Magnetic Stimulation Coils for Mouse with Improved Stimulus Focus and Intensity

Many scholars have found that magnetic anomaly signals can reflect the characteristics of magnetic target, such as distance, speed, magnetic moment and other related information [1-3]. This provides a new idea for target recognition by matching magnetic anomaly signals. However, the magnetic anomaly signals in the detection environment are weak and easily disturbed by the strong background noise of the geomagnetic filed, resulting in a low signal-to-noise ratio (SNR) of the signals output by the magnetic sensor [4], which brings great challenges to the traditional signal matching method. In this paper, a method combining OBF (Orthogonal Basis Function) decomposition and EDR (Edit Distance on Real sequence) is proposed to match magnetic anomaly signals under low SNR. The calculation process of OBF-EDR method is divided into three steps (Fig. 1). Step 1: The OBF method is used to decompose the magnetic anomaly signals. According to the characteristics that the background noise is not correlated with the basis function, three sets of discrete basis function coefficient sequences with higher SNR can be obtained [5]. Step 2: The sequences of discrete basis function coefficient are taken as the calculation object, and the EDR method with strong anti-noise ability is used to measure and get their similarity results. Step 3: The matching result of the magnetic anomaly signals is calculated through the similarity results of the three sets of discrete basis function coefficient sequences by the weight method. The simulation shows that the curves of matching accuracy of OBF-EDR and EDR method have the same changing trend with the change of signals’ SNR, but the curve of matching accuracy of OBF-EDR method moves to the direction of lower SNR compared with that of EDR (Fig. 2). It indicates that the OBF-EDR method can match the magnetic anomaly signals correctly at a lower SNR than the EDR method. What’s more, when the matching accuracy of OBF-EDR reaches 0.9, the required signal’s SNR is -2dB, which is 7dB lower than that of EDR method. The above conclusions prove the advantage of the OBF-EDR method to matching magnetic signals under low SNR.

**References:**

 [1] Y. Shen, J. Wang, and J. Shi, J. Magn. Magn. Mater., 484, 164-171, (2019). [2] Y. Shen, D. Hasanyan, and J. Gao, Smart Mater. Struct., 22, 095007, (2013). [3] J. Wang, Y. Shen, and R. Zhao, J. Magn. Magn. Mater., 505, 166761, (2020). [4] O. Faggioni, M. Soldani, and A. Gabellone, J. Appl. Geophys. 72, 46-56, (2010). [5] B. Ginzburg, L. Frumkis, and B. Kaplan, Sens. Actuator A-phys., 102, 67-75, (2002).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/342a1c3ef4df12f64377b274bb525671.jpg)
Fig.1. OBF-EDR method calculation process.

![](https://assets.underline.io/uploads/markdown_image/1/image/5e76e72a07c2b094b80ba3d24bfe86fb.jpg)
Fig.2. Matching accuracy of OBF-EDR and EDR method to match the magnetic anomaly signals under different SNR.

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