Magnetic Induction Sensor Research Articles

Dual Differential Magnetic Induction Sensor for Magnetic Communication in Extreme Environments

Dual Differential Magnetic Induction Sensor for Magnetic Communication in Extreme Environments

Dual Differential Magnetic Induction Sensor for Magnetic Communication in Extreme Environments

Dual Differential Magnetic Induction Sensor for Magnetic Communication in Extreme Environments

Magnetic induction sensor based on a dual-frequency atomic magnetometer

Magnetic induction sensor based on a dual-frequency atomic magnetometer

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

Precise 6-DOF Motion Tracking of Fine Motor Skills of Fingers Based on Wearable Magnetic Induction Sensors

Precise 6-DOF Motion Tracking of Fine Motor Skills of Fingers Based on Wearable Magnetic Induction Sensors

MagSign: Harnessing Dynamic Magnetism for User Authentication on IoT Devices

User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implementability</i> thanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices void of text entry interfaces. Biometrics may not scale well as they require both non-trivial sensors and cumbersome user involvement. Proximity-based methods exploiting shared ambient contexts are vulnerable to co-located malicious attacks. Therefore, a low-cost authentication scheme widely implementable on heterogeneous IoT devices is urgently demanded. To this end, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagSign</i> that leverages two fundamental capabilities owned by common IoT devices: the ubiquity of magnetic induction sensors and the power of screens to change magnetic field. Essentially, MagSign controls screen contents of an authorized device (possessed by a user) to generate specific currents in its electronic components that in turn induce a magnetic signature. This signature, sensed by a nearby device, allows the user to be authenticated and hence to unlock that device. In designing MagSign, we explore critical parameters employable to magnetic signature generation by analyzing electronic components' workflow. Moreover, we innovatively encode binary sequences into magnetic intensity transitions, so that a sequence issued from a trusted server can be converted into a magnetic signature. Different from existing proximity-based approaches relying on shared static environment information, magnetic signature is directly derived from a server-issued sequence, allowing for dynamic signature generation that effectively thwarts potential attacks. The comprehensive experiments show MagSign has a false acceptance rate (FAR) of 0.38% and a false rejection rate (FRR) of 3.13%.

Development of a system of magnetic induction sensors for diagnosing wear of the rolling stock wheelset crest

Development of a system of magnetic induction sensors for diagnosing wear of the rolling stock wheelset crest

Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Experimental Assessment of a Magnetic Induction-Based Receiver for Magnetic Communication

This paper presents the topology of a novel approach to magnetic communication using a differential magnetic induction (MI)-based receiver and a differential MI receiving sensor. In this paper, a differential MI sensor based on two ferromagnetic cores is proposed as a receiving sensor, unlike the air coil-type MI sensor in the conventional search coil sensor concept. This differential MI sensor has the advantages of ultra-high sensitivity characteristics of the pT/√Hz level, which can detect weak magnetic fields in magnetic communication; moreover, the sensor is smaller than a conventional air coil MI sensor. The proposed differential MI sensor contributes to improving sensor performance by increasing its signal-to-noise ratio. The design and fabrication of the proposed MI sensor were based on a printed circuit board (PCB). The pickup coil of the PCB-based MI sensor directly wound the pickup coil onto a ferromagnetic core composed of Ni-Zn ferrite material. To analyze the key factors that affected the performance of the receiver, the magnetic field-to-voltage conversion ratio (MVCR) and equivalent magnetic spectral density measurements of the proposed PCB-based MI sensor were performed. Wireless digital communication using quadrature phase shift keying (QPSK), which is less sensitive to noise and has a high data rate, was used to evaluate the proposed MI-based receiver. The transmitted and received waveforms were compared to confirm that the transmitted digital data were accurately received as a result of the final demodulation of the receiver. Additionally, several performance metrics, such as constellation and error vector magnitude, were measured. The results of the comprehensive analysis confirmed the applicability of the proposed differential MI-based receiver to a magnetic field.

Corrigendum: Increasing the measurement of soil water content with the characterization of magnetic field induction sensors using model equations for the internet of thing application (2022, vol. 20, no 1, pp. 137-144)

CLARIFICATION Upon the written remark of the corresponding author Mr. Doan Perdana from Telkom University, Faculty of Electrical Engineering, Bandung, Indonesia, the authors of the paper "Increasing the measurement of soil water content with the characterization of magnetic field induction sensors using model equations for the internet of things application" 20(2022)1, 913, 137-144, DOI: 10.5937/jaes0-30730 requested to add acknowledgement section to their paper. The acknowledgement has to be added as follows: "This work was supported by Skema Invitasi, LPDP, Kementrian Keuangan, 2020-2021, with Contract No. PRJ56/LPDP/2020." This addition does not affect the original paper.

Increasing the measurement of soil water content with the characterization of magnetic field induction sensors using model equations for the internet of thing application

This study proposes an increase in the measurement of soil water content with sensor characterization that can be integrated with the internet of things. The main contribution of this work is the improvement in measurement accuracy compared to measurements using a moisture meter. This is achieved through an electromagnetic approach using a pair of transceiver coils as a sensor. Determination of water content in the soil is carried out through the formulation of an equation model that connects the measured voltage on the receiving coil with the mass of water contained. It is known that the use of the equation model in the test data results in better accuracy with an error of 2.03% - 17.43%, compared to measurements using a moisture measuring device with an error of 13.21% - 32%. This equation model that uses the electromagnetic method provides an alternative solution for determining the soil water for wider land use so that can be used for internet of things application.

Analytical modeling and experimental validation of the six pole axial active magnetic bearing

• Design, modelling and prototype of the novel Axial Active Magnetic Bearing. • Validated analytical model is convergent with experiments and numerical simulation. • Schwarz-Christoffel transformation is utilized in study of the actuator end effect. • Three-dimensional complex permeance function represents the stator pole pieces. • Computational time of analytical model is significantly shorter then numerical one. The article presents a novel design of the axial active magnetic bearing with six poles. An analytical magnetic bearing model was developed to provide the axial magnetic induction distribution in 3D. The model utilizes magnetic vector potential formulation and Schwarz-Christoffel mapping. The paper covers in-depth deliberations on the end effect influence and the conjugate complex permeance function. Numerical model and simulations were provided in 3D mode with support of COMSOL Multiphysics software. The six pole axial active magnetic bearing was manufactured and investigated experimentally. Identification of the magnetic field distribution was carried out with a single axis magnetic induction sensor using custom automatic field scanner. High convergence of the modeling results and experimental research was demonstrated. The main advantage of the proposed analytical method is significantly shorter computation time compared to the numerical one that is useful from the modeling and controller study point of view. The configuration, modeling, simulation and experimental investigation results are well illustrated for the better overview.

Wearable magnetic induction-based approach toward 3D motion tracking

Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.

The measurement of a weak alternating magnetic field in unshielded space

In connection with attempts to use various types of sensors for measuring weak magnetic fields in geophysics, magnetobiology, and medicine in an unshielded space, the problem of comparing the results of these measurements arose. The issues of measuring a weak alternating magnetic field by various magnetic induction sensors in an unshielded space in the absence of obvious geomagnetic variations are considered. It is shown that the amplitude of natural geomagnetic noise in a quiet geomagnetic field in the absence of geomagnetic variations has a random character; therefore, gradient methods for measuring a weak alternating magnetic field are limited from below by the level of natural geomagnetic noise. The influence of the size of sensors of a weak alternating magnetic field on the results of measurements of broadband random geomagnetic noise is noted.

Application of Smart Sensor in Underwater Weak Object Detection and Positioning

This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation‐based weak object detection algorithm and underwater positioning technology based on the three‐dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U‐shaped network‐ (U‐Net‐) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic induction intensity. The results show that the image‐enhanced object detection method improves the accuracy of Yellow Croaker, Goldfish, and Mandarin Fish by 3.2%, 1.5%, and 1.6%, respectively. In terms of sensor positioning technology, under the positioning Signal‐to‐Noise Ratio (SNR) of 15 dB and 20 dB, the curve trends of actual distance and positioning distance are consistent, while SNR = 10 dB, the two curves deviate greatly. The research conclusions read as follows: an underwater scene segmentation‐based weak object detection method is proposed for invalid underwater object samples from poor labeling, which can effectively segment the background from underwater objects, remove the negative impact of invalid samples, and improve the precision of weak object detection. The positioning model based on a 3D coil magnetic induction sensor can obtain more accurate positioning coordinates. The effectiveness of 3D omnidirectional magnetic induction coil underwater positioning technology is verified by simulation experiments.

Twenty-four-hour real-time continuous monitoring of acute focal cerebral ischemia in rabbits based on magnetic inductive phase shift

BackgroundAs a serious clinical disease, ischemic stroke is usually detected through magnetic resonance imaging and computed tomography. In this study, a noninvasive, non-contact, real-time continuous monitoring system was constructed on the basis of magnetic induction phase shift (MIPS) technology. The “thrombin induction method”, which conformed to the clinical pathological development process of ischemic stroke, was used to construct an acute focal cerebral ischemia model of rabbits. In the MIPS measurement, a “symmetric cancellation-type” magnetic induction sensor was used to improve the sensitivity and antijamming capability of phase detection.MethodsA 24-h MIPS monitoring experiment was carried out on 15 rabbits (10 in the experimental group and five in the control group). Brain tissues were taken from seven rabbits for the 2% triphenyl tetrazolium chloride staining and verification of the animal model.ResultsThe nonparametric independent-sample Wilcoxon rank sum test showed significant differences (p < 0.05) between the experimental group and the control group in MIPS. Results showed that the rabbit MIPS presented a declining trend at first and then an increasing trend in the experimental group, which may reflect the pathological development process of cerebral ischemic stroke. Moreover, TTC staining results showed that the focal cerebral infarction area increased with the development of timeConclusionsOur experimental study indicated that the MIPS technology has a potential ability of differentiating the development process of cytotoxic edema from that of vasogenic edema, both of which are caused by cerebral ischemia.

Investigation of Hybrid Locomotive Robot for Anti-Personnel Mine Detection

Most of landmine detection robots proposed so far have been strongly restricted from locomotion inside the mine field because they cannot cross over the mine. So we have proposed a mine detection robot with hybrid locomotion, which can enter inside the minefield with low ground surface contact, which can cross over the mine instead of changing its path and scan landmines directly using Electro Magnetic Induction sensor. The hybrid locomotion proposed in the robot uses the frame walking technique and the conventional wheeled locomotion. The robot switches over the locomotion mechanism from wheeled to leg when mine is detected and vice versa with a lead screw mechanism. The leg locomotion is achieved by frame walking technique where the two frames translate with the help of lead screw mechanism. A purpose of adopting this combination is to evade anti-personnel landmines which are relatively smaller in comparison to their anti-tank landmine counterparts. The robot initially starts in wheeled mode and upon detection of metal, pulls in the frame walking algorithm. The robot also deploys an obstacle avoidance algorithm when working in wheel mode.

Whole System Design of a Wearable Magnetic Induction Sensor for Physical Rehabilitation

Whole System Design of a Wearable Magnetic Induction Sensor for Physical Rehabilitation

Whole System Design of a Wearable Magnetic Induction Sensor for Physical Rehabilitation

Wearable sensor technologies attract increasing attention for continuous monitoring of human health. Much effort is devoted to exploit well‐designed materials to realize superior abilities, such as high sensitivity, stability, and responsiveness. However, it hardly meets huge demands for practically wearable applications simply focusing on the development of sensing materials in isolation. Comprehensive consideration is given from upstream materials to endure market, including materials design, sensor assembly, signal analysis, theoretical foundation, and final system performance, such as sensitivity, stability, responsiveness, cost, comfortability, and durability. Herein, a systematic design is presented that combines a conductive fiber fabrication based on surface nanotechnology, device assembly process optimization, signal acquisition and analysis, and theoretical simulation, through a new multidisciplinary strategy integrating material science, textile technology, electromagnetics, and electronic engineering. The as‐constructed magnetic inductance sensing system shows approximately six‐times inductance change with regard to joint bending motions during rehabilitation exercises. This integrated design strategy offers a new concept, namely, a whole sensing system design, for wearable technologies in real‐time health monitoring applications.

Thin-film magnetoresistive sensors and measuring transducers of magnetic induction

The article presents some features of thin-film magnetoresistive magnetic induction sensors ММКК247, ММКК247-01, МЧЭ033 and the digital measurement gauge МРД009 that was developed in JSC NPO IT for aviation and cosmic apparatus navigation systems.