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Electromagnetic Sensors

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Physical Sensors".

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Editor

Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, 33100 Udine, Italy
Interests: scientific computing; applied and computational electromagnetics; sensors and biosensors; inverse problems and imaging; image processing; topological data analysis
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Topical Collection Information

Dear Colleagues,

Many sensors use electromagnetic fields as a sensing mechanism to capture a large quantity of information from the environment around them. The range of applications of sensors based on electric, magnetic or electromagnetic fields is wide and varied. Amongst them, we can find robotics, industry, security, plant control, medicine, agriculture and environmental monitoring, quality control and inspection, steel production, autonomous driving, powertrain management, automotive and marine sectors.

Electromagnetics is the sensing principle of many proximity and position sensors, pressure sensors, flowmeters, current sensors, magnetic field sensors, anemometers, wind vanes, accelerometers, thermometers, non-destructive testing devices based on eddy currents, magnetic induction tomography, magnetic resonance imaging (MIT) and biosensors based on the surface plasmon resonance (SPR), electrical impedance spectroscopy (EIS) or electrical impedance tomography (EIT).

An emerging and important aspect of the sensor design is its optimization, which aims at reducing as much as possible the sensor error in the measurements. For this aim, it is necessary to develop a virtual prototype of the sensors with the help of electromagnetic modelling and computer simulation. A fast and predictive model is, in fact, an enabling technology for automatic sensor optimization and the real exploitation of the potential advantages of digital twins.

This topical collection aims to attract original research articles in the fields of electromagnetic sensors and their applications, including the research on novel sensing principles, sensor fabrication and characterization, and sensor modelling and optimization. Review articles on the recent progress or survey of previous works are also welcome.

This Topical Collection invites contributions in the following topics (but is not limited to them):

  • Proximity sensors.
  • Rotary position sensors.
  • Linear position sensors, level sensors.
  • Arc position sensors.
  • Absolute or incremental position sensors.
  • Magnetic encoders, digital resolvers, inductive position sensors (IPS), Hall and magnetoresistive position sensors, Linear Variable Differential Transformer (LVDT), IPS with resonant target.
  • Capacitive position sensors.
  • Anemometers, wind vanes.
  • Electromagnetic flowmeters.
  • Electromagnetic pressure sensors.
  • Current sensors, Rogowski Coils.
  • Accelerometers.
  • Magnetic sensors, Hall sensors, giant magnetoresistance (GMR) sensors, anisotropic magnetoresistance (AMR) sensors, magnetoimpedance (MI) sensors, fluxgate sensors, optical magnetometers, atomic magnetometers, and superconducting quantum interference devices (SQUIDs)).
  • Non-destructive evaluation applications using magnetic sensors or eddy currents.
  • Near-field electric and magnetic field probes.
  • Magnetic resonance imaging (MRI).
  • Biomedical sensing using electromagnetic sensors, biosensors.
  • Electrical Impedance Tomography (EIT).
  • Electrical Impedance Spectroscopy (EIS).
  • Electromagnetic sensors for steel production.
  • Electromagnetic sensors for agriculture and marine monitoring.
  • Sensor modelling and optimization, digital twins.

Dr. Ruben Specogna
Collection Editor

Manuscript Submission Information

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Published Papers (5 papers)

2023

Jump to: 2022

19 pages, 1298 KiB  
Article
Low-Pass Filters for a Temperature Drift Correction Method for Electromagnetic Induction Systems
by Martial Tazifor Tchantcho, Egon Zimmermann, Johan Alexander Huisman, Markus Dick, Achim Mester and Stefan van Waasen
Sensors 2023, 23(17), 7322; https://doi.org/10.3390/s23177322 - 22 Aug 2023
Viewed by 966
Abstract
Electromagnetic induction (EMI) systems are used for mapping the soil’s electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data [...] Read more.
Electromagnetic induction (EMI) systems are used for mapping the soil’s electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm−1 for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm−1, which is considerably lower than the RMSE values of up to 4.5 mSm−1 obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects. Full article
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2022

Jump to: 2023

33 pages, 5078 KiB  
Article
Physics of Composites for Low-Frequency Magnetoelectric Devices
by Mirza Bichurin, Oleg Sokolov, Sergey Ivanov, Viktor Leontiev, Dmitriy Petrov, Gennady Semenov and Vyacheslav Lobekin
Sensors 2022, 22(13), 4818; https://doi.org/10.3390/s22134818 - 25 Jun 2022
Cited by 8 | Viewed by 1475
Abstract
The article discusses the physical foundations of the application of the linear magnetoelectric (ME) effect in composites for devices in the low-frequency range, including the electromechanical resonance (EMR) region. The main theoretical expressions for the ME voltage coefficients in the case of a [...] Read more.
The article discusses the physical foundations of the application of the linear magnetoelectric (ME) effect in composites for devices in the low-frequency range, including the electromechanical resonance (EMR) region. The main theoretical expressions for the ME voltage coefficients in the case of a symmetric and asymmetric composite structure in the quasi-static and resonant modes are given. The area of EMR considered here includes longitudinal, bending, longitudinal shear, and torsional modes. Explanations are given for finding the main resonant frequencies of the modes under study. Comparison of theory and experimental results for some composites is given. Full article
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13 pages, 7774 KiB  
Article
Design Optimization of PCB-Based Rotary-Inductive Position Sensors
by Aldi Hoxha, Mauro Passarotto, Gentjan Qama and Ruben Specogna
Sensors 2022, 22(13), 4683; https://doi.org/10.3390/s22134683 - 21 Jun 2022
Cited by 7 | Viewed by 2556
Abstract
This paper introduces a novel methodology to optimize the design of a ratiometric rotary inductive position sensor (IPS) fabricated in printed circuit board (PCB) technology. The optimization aims at reducing the linearity error of the sensor and amplitude mismatch between the voltages on [...] Read more.
This paper introduces a novel methodology to optimize the design of a ratiometric rotary inductive position sensor (IPS) fabricated in printed circuit board (PCB) technology. The optimization aims at reducing the linearity error of the sensor and amplitude mismatch between the voltages on the two receiving (RX) coils. Distinct from other optimization techniques proposed in the literature, the sensor footprint and the target geometry are considered as a non-modifiable input. This is motivated by the fact that, for sensor replacement purposes, the target has to fit a predefined space. For this reason, the original optimization technique proposed in this paper modifies the shape of the RX coils to reproduce theoretical coil voltages as much as possible. The optimized RX shape was obtained by means of a non-linear least-square solver, whereas the electromagnetic simulation of the sensor is performed with an original surface integral method, which are orders of magnitude faster than commercial software based on finite elements. Comparisons between simulations and measurements performed on different prototypes of an absolute rotary sensor show the effectiveness of the optimization tool. The optimized sensors exhibit a linearity error below 0.1% of the full scale (FS) without any signal calibration or post-processing manipulation. Full article
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16 pages, 3369 KiB  
Article
Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems
by Martial Tazifor, Egon Zimmermann, Johan Alexander Huisman, Markus Dick, Achim Mester and Stefan Van Waasen
Sensors 2022, 22(10), 3882; https://doi.org/10.3390/s22103882 - 20 May 2022
Cited by 1 | Viewed by 1810
Abstract
Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to [...] Read more.
Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to mitigate drift effects in EMI instruments rely on a temperature drift calibration, where the instrument is heated up to specific temperatures in a controlled environment and the observed drift is determined to derive a static thermal apparent electrical conductivity (ECa) drift correction. In this study, a novel correction method is presented that models the dynamic characteristics of drift using a low-pass filter (LPF) and uses it for correction. The method is developed and tested using a customized EMI device with an intercoil spacing of 1.2 m, optimized for low drift and equipped with ten temperature sensors that simultaneously measure the internal ambient temperature across the device. The device is used to perform outdoor calibration measurements over a period of 16 days for a wide range of temperatures. The measured temperature-dependent ECa drift of the system without corrections is approximately 2.27 mSm−1K−1, with a standard deviation (std) of only 30 μSm−1K−1 for a temperature variation of around 30 K. The use of the novel correction method reduces the overall root mean square error (RMSE) for all datasets from 15.7 mSm−1 to a value of only 0.48 mSm−1. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm−1. The results show that modeling the dynamic thermal characteristics of the drift helps to improve the accuracy by a factor of four compared to a purely static characterization. It is concluded that the modeling of the dynamic thermal characteristics of EMI systems is relevant for improved drift correction. Full article
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15 pages, 2600 KiB  
Article
Analysis of Uncertainties in Inductance of Multi-Layered Printed-Circuit Spiral Coils
by Myounggyu Noh, Thien Vuong Bui, Khanh Tan Le and Young-Woo Park
Sensors 2022, 22(10), 3815; https://doi.org/10.3390/s22103815 - 18 May 2022
Viewed by 2063
Abstract
Eddy-current sensors are widely used for precise displacement sensing and non-destructive testing. Application of printed-circuit board (PCB) technology for manufacturing sensor coils may reduce the cost of the sensor and enhance the performance by ensuring consistency. However, these prospects depend on the uniformness [...] Read more.
Eddy-current sensors are widely used for precise displacement sensing and non-destructive testing. Application of printed-circuit board (PCB) technology for manufacturing sensor coils may reduce the cost of the sensor and enhance the performance by ensuring consistency. However, these prospects depend on the uniformness of the sensor coil. Inductance measurements of sample coils reveal rather considerable variations. In this paper, we investigate the sources of these variations. Through image analysis of cut-away cross-sections of sensor coils, four factors that contribute to the inductance variations are identified: the distance between layers, the distance between tracings, cross-sectional areas, and misalignment among layers. By using and extending existing method of calculating inductance of spiral coils, the inductance distributions are obtained when these factors are randomly varied. A sensitivity analysis shows that the inductance uncertainty is most affected by the uniformness of the spacings between coil traces and the distances between layers. Improvements in PCB manufacturing process can help to reduce the uncertainty in inductance. Full article
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