Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms
We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/964 |
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| author | Miki Vizel Roger Alimi Daniel Lahav Moty Schultz Asaf Grosz Lior Klein |
| author_facet | Miki Vizel Roger Alimi Daniel Lahav Moty Schultz Asaf Grosz Lior Klein |
| author_sort | Miki Vizel |
| collection | DOAJ |
| description | We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices of a symmetrical and evenly spaced 3 × 3 grid. The main electronic card orchestrates their measurement by supplying the required driving current and amplifying and sampling their output in a synchronized manner. A two-dimensional interpolation of the data collected from the nine sensors fails to yield a satisfactory mapping. To address this, we employed the Levenberg–Marquardt Algorithm (LMA) as a deterministic optimization method to estimate the magnetic source’s position and parameters, as well as machine earning (ML) algorithms, which consist of a Fully Connected Neural Network (FCNN). While LMA provided reasonable results, its reliance on a sparse sensor network and initial guesses for variables limited its accuracy. We show that the mapping is significantly improved if the data are processed with an FCNN that undergoes training and testing. Using simulations, we demonstrate that achieving similar improvement without ML would require increasing the number of sensors to more than 50. |
| format | Article |
| id | doaj-art-18a9b04e4c3243e597db659744436676 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-18a9b04e4c3243e597db6597444366762025-01-24T13:21:30ZengMDPI AGApplied Sciences2076-34172025-01-0115296410.3390/app15020964Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning AlgorithmsMiki Vizel0Roger Alimi1Daniel Lahav2Moty Schultz3Asaf Grosz4Lior Klein5Department of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, IsraelTechnology Division, Soreq NRC, Yavne 8180000, IsraelDepartment of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, IsraelDepartment of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, IsraelDepartment of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 8410501, IsraelDepartment of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, IsraelWe use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices of a symmetrical and evenly spaced 3 × 3 grid. The main electronic card orchestrates their measurement by supplying the required driving current and amplifying and sampling their output in a synchronized manner. A two-dimensional interpolation of the data collected from the nine sensors fails to yield a satisfactory mapping. To address this, we employed the Levenberg–Marquardt Algorithm (LMA) as a deterministic optimization method to estimate the magnetic source’s position and parameters, as well as machine earning (ML) algorithms, which consist of a Fully Connected Neural Network (FCNN). While LMA provided reasonable results, its reliance on a sparse sensor network and initial guesses for variables limited its accuracy. We show that the mapping is significantly improved if the data are processed with an FCNN that undergoes training and testing. Using simulations, we demonstrate that achieving similar improvement without ML would require increasing the number of sensors to more than 50.https://www.mdpi.com/2076-3417/15/2/964Planar Hall Effectmagnetic imagingartificial neural networksensor arraytwo-dimensional interpolationlocalization |
| spellingShingle | Miki Vizel Roger Alimi Daniel Lahav Moty Schultz Asaf Grosz Lior Klein Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms Applied Sciences Planar Hall Effect magnetic imaging artificial neural network sensor array two-dimensional interpolation localization |
| title | Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms |
| title_full | Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms |
| title_fullStr | Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms |
| title_full_unstemmed | Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms |
| title_short | Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms |
| title_sort | magnetic source detection using an array of planar hall effect sensors and machine learning algorithms |
| topic | Planar Hall Effect magnetic imaging artificial neural network sensor array two-dimensional interpolation localization |
| url | https://www.mdpi.com/2076-3417/15/2/964 |
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