Range Detection of the Extremely Low-Frequency Magnetic Field Produced by Laptop’s AC Adapter

Brodić, Darko; Amelio, Alessia

doi:10.1515/msr-2017-0001

Cited by 3 publications

(1 citation statement)

References 16 publications

(19 reference statements)

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“…Also, we propose a new measurement geometry which takes into account a user-centric measurement approach similar to that given in refs. [6], [7], [8], [9], [10]. Then, measured data are classified by the Self-Organizing-Map (SOM) classifier for detecting the dangerous magnetic field emission levels in the neighborhood of a typical laptop's AC adapter.…”

Section: Introductionmentioning

confidence: 99%

Self-organizing maps classification with application to the low-frequency magnetic field emitted by portable computers

Brodić

Draganov

2017

2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

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This paper presents an application of the Self-Organizing-Map classification method, which is used for classification of the extremely low frequency magnetic field emission in the near neighborhood of the laptop adapters. The experiment is performed on different laptop adapters of the same characteristics. After that, the Self-Organizing-Map classification on the obtained emission data is performed. The classification results establish the typical emission levels of the laptop adapters, which are far above the safety standards' limit. At the end, a discussion is carried out about the importance of using the classification as a possible solution for safely use the laptop adapters in order to reduce the negative effects of the magnetic field emission to the laptop users.

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Section: Introductionmentioning

confidence: 99%

Self-organizing maps classification with application to the low-frequency magnetic field emitted by portable computers

Brodić

Draganov

2017

2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

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Data Mining: Clustering

Amelio

Tagarelli

2019

Encyclopedia of Bioinformatics and Computational Biology

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Resolution improvement of low frequency AC magnetic field detection for modulated MR sensors

Pan

et al. 2017

Review of Scientific Instruments

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Magnetic modulation methods especially Micro-Electro-Mechanical System (MEMS) modulation can improve the sensitivity of magnetoresistive (MR) sensors dramatically, and pT level detection of Direct Current (DC) magnetic field can be realized. While in a Low Frequency Alternate Current (LFAC) magnetic field measurement situation, frequency measurement is limited by a serious spectrum aliasing problem caused by the remanence in sensors and geomagnetic field, leading to target information loss because frequency indicates the magnetic target characteristics. In this paper, a compensation field produced with integrated coils is applied to the MR sensor to remove DC magnetic field distortion, and a LFAC magnetic field frequency estimation algorithm is proposed based on a search of the database, which is derived from the numerical model revealing the relationship of the LFAC frequency and determination factor [defined by the ratio of Discrete Fourier Transform (DFT) coefficients]. In this algorithm, an inverse modulation of sensor signals is performed to detect jumping-off point of LFAC in the time domain; this step is exploited to determine sampling points to be processed. A determination factor is calculated and taken into database to figure out frequency with a binary search algorithm. Experimental results demonstrate that the frequency measurement resolution of the LFAC magnetic field is improved from 12.2 Hz to 0.8 Hz by the presented method, which, within the signal band of a magnetic anomaly (0.04-2 Hz), indicates that the proposed method may expand the applications of magnetoresistive (MR) sensors to human healthcare and magnetic anomaly detection (MAD).

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Range Detection of the Extremely Low-Frequency Magnetic Field Produced by Laptop’s AC Adapter

Cited by 3 publications

References 16 publications

Self-organizing maps classification with application to the low-frequency magnetic field emitted by portable computers

Self-organizing maps classification with application to the low-frequency magnetic field emitted by portable computers

Data Mining: Clustering

Resolution improvement of low frequency AC magnetic field detection for modulated MR sensors

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