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Electronics
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Electromagnetic Applications in Industry and Medicine
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Electromagnetic Applications in Industry and Medicine

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 6867

Special Issue Editors

Dr. Roberta Palmeri

E-Mail Website
Guest Editor
Institute for the Electromagnetic Sensing of the Environment, National Council of Research (IREA-CNR), 80124 Naples, Italy
Interests: microwave imaging and diagnostics; antenna synthesis and diagnostics; phase retrieval; inverse scattering problems; inverse design
Dr. Colin Gilmore

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: inverse problems; electromagnetic imaging; microwave tomography; biomedical imaging
Prof. Dr. Tommaso Isernia

E-Mail Website
Guest Editor
Department of Information Engineering, Infrastructures, and Sustainable Energy, Università Mediterranea of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: nonlinear inverse problems; antenna power synthesis; phase retrieval; inverse scattering problems; field synthesis

Special Issue Information

Dear Colleagues,

The number of electromagnetic applications in industry and medicine has grown in recent years thanks to great technological advantages in these fields. Researchers have put a good deal of effort in developing tools and apparatus in line with expected performance for a given application.

The aim of this Special Issue is to collect recent expertise and provide a comprehensive overview of new solutions in applied electromagnetics. Topics of interest include but are not limited to industrial, agricultural and medical tomography, electromagnetic therapy, computational methods, antenna systems and sensors, theoretical and experimental works, and analysis and interpretation of data.

Dr. Roberta Palmeri
Dr. Colin Gilmore
Prof. Dr. Tommaso Isernia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable sensors
  • imaging systems
  • experimental facilities
  • electromagnetic modeling
  • medical imaging
  • inverse scattering
  • tomography
  • inverse design
  • machine learning
  • MRI
  • hyperthermia treatments
  • computational electromagnetism
  • electromagnetic materials
  • bioelectromagnetism

Published Papers (4 papers)

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Research

19 pages, 5010 KiB  
Article
Capacitance Estimation for Electrical Capacitance Tomography Sensors Using Digital Processing of Time-Domain Voltage Response to Single-Pulse Excitation
by Praveen Kalarickel Ramakrishnan, Timothy Westwood, Tomé Magalhães Gouveia, Mahdi Taani, Kylie de Jager, Kenny Murdoch, Andrey A. Orlov, Mikhail S. Ozhgibesov, Tatiana V. Propodalina and Natalia Wojtowicz
Electronics 2023, 12(15), 3242; https://doi.org/10.3390/electronics12153242 - 27 Jul 2023
Cited by 1 | Viewed by 1119
Abstract
In this paper, a new approach for capacitance measurement for electrical capacitance tomography (ECT) sensors is proposed. The method is based on the digital processing of the time-domain voltage measurements at the sensor electrodes. Furthermore, a robust capacitance estimation algorithm is developed to [...] Read more.
In this paper, a new approach for capacitance measurement for electrical capacitance tomography (ECT) sensors is proposed. The method is based on the digital processing of the time-domain voltage measurements at the sensor electrodes. Furthermore, a robust capacitance estimation algorithm is developed to convert the measured voltage time-series to inter-electrode capacitances. The proposed measurement technique simplifies the electronic design of the ECT sensor and is suitable for use in applications requiring a compact device with a fast scan time. The accuracy and sensitivity of the method are investigated numerically and experimentally using a prototype sensor. In particular, the sensitivity of the estimated capacitance to measurement noise levels is analyzed in detail. Additionally, an analysis of the parameters that affect the accuracy of estimated capacitances is carried out from which we are able to demonstrate that the method is immune to effects such as stray capacitances between the electrodes and the ground. A prototype sensor with an open curved geometry on a millimeter scale is used to test the method empirically. Experimental results obtained for measurements with mineral oil and water are shown and compared against capacitances obtained using a physics-based forward model of the sensor. The inter-electrode capacitances in the range of tens of femtofarads to a few picofarads are estimated and a close match with the forward model results is obtained. Full article
(This article belongs to the Special Issue Electromagnetic Applications in Industry and Medicine)
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14 pages, 5743 KiB  
Article
A Novel Hybrid Approach for Computing Electromagnetic Scattering from Objects with Honeycomb Structures
by Xiaowei Yuan, Zeng Yang, Weijia He, Minglin Yang and Xinqiing Sheng
Electronics 2023, 12(8), 1851; https://doi.org/10.3390/electronics12081851 - 13 Apr 2023
Viewed by 1071
Abstract
We propose in this paper a novel hybrid numerical modeling method for computing electromagnetic scattering from inhomogeneous targets containing honeycomb structures. In the proposed approach, the whole honeycomb structure is divided into the inner and outer two subregions. Each thin wall of a [...] Read more.
We propose in this paper a novel hybrid numerical modeling method for computing electromagnetic scattering from inhomogeneous targets containing honeycomb structures. In the proposed approach, the whole honeycomb structure is divided into the inner and outer two subregions. Each thin wall of a unit cell in the outer subregion is replaced by a zero-thickness surface, with the aid of a resistive sheet boundary condition (RSBC) to describe the electric and magnetic field discontinuities across the surface. Each unit cell in the inner subregion is homogenized by using the Hashin–Shtrikman and the Mori–Tanaka formulae. The two subregions are further divided into smaller subdomains by introducing the Robin-type transmission condition to couple subregion interfaces, as well as subdomain interfaces. The whole solution region is then discretized and solved using the nonconformal domain decomposition-based hybrid finite element–boundary integral–multilevel fast multipole algorithm (FE-BI-MLFMA). The numerical results demonstrate that the proposed approach exhibits a high accuracy, efficiency, and flexibility. Solutions of scattering by a wing-like object and a practical unmanned aerial vehicle (UAV) model with honeycomb radar-absorbing structures are presented, showing the superior performance of the proposed algorithm. Full article
(This article belongs to the Special Issue Electromagnetic Applications in Industry and Medicine)
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21 pages, 11428 KiB  
Article
Improved Metallic Enclosure Electromagnetic Imaging Using Ferrite Loaded Antennas
by Cena T. Mohamadi, Mohammad Asefi, Sandeep Thakur, Jitendra Paliwal and Colin Gilmore
Electronics 2022, 11(22), 3804; https://doi.org/10.3390/electronics11223804 - 18 Nov 2022
Cited by 1 | Viewed by 1077
Abstract
Three-dimensional electromagnetic imaging can be used to monitor grain within metallic grain bins. Data acquisition requires multiple antennas surrounding the imaging space, which are used to transmit and receive the electromagnetic energy inside the bin. Due to their presence inside a metallic enclosure [...] Read more.
Three-dimensional electromagnetic imaging can be used to monitor grain within metallic grain bins. Data acquisition requires multiple antennas surrounding the imaging space, which are used to transmit and receive the electromagnetic energy inside the bin. Due to their presence inside a metallic enclosure and due to very large mechanical forces these antennas are required to be low profile. In addition, since they are part of the imaging domain, they should be simple to model in the imaging software (i.e., using a point source). Existing half-loop magnetic field antennas meet these design criteria, but can be improved, particularly with better radiation efficiency. Herein, we present an enhanced antenna design: a ferrite-loaded shielded half-loop antenna designed to measure only the tangential component of the magnetic field against the metal enclosure wall, while rejecting the normal component of the electric field. Experimental results in two bins show that the enhanced design improves the signal level over existing probes by 6–18 dB inside a small-scale enclosure and around 20 dB inside a larger 28 m3 (800 bushel) bin. Full 3D imaging results of a high-moisture target within a low-moisture grain background inside the test enclosure show that the enhanced antennas improve the quality of the reconstructed results in the smaller bin, particularly where the antenna performance improvements are prominent. Full article
(This article belongs to the Special Issue Electromagnetic Applications in Industry and Medicine)
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17 pages, 4246 KiB  
Article
Physical Contamination Detection in Food Industry Using Microwave and Machine Learning
by Ali Darwish, Marco Ricci, Flora Zidane, Jorge A. Tobon Vasquez, Mario R. Casu, Jerome Lanteri, Claire Migliaccio and Francesca Vipiana
Electronics 2022, 11(19), 3115; https://doi.org/10.3390/electronics11193115 - 29 Sep 2022
Cited by 13 | Viewed by 2276
Abstract
The detection of contaminants in food products after packaging by a non-invasive technique is a serious need for companies operating in the food industry. In recent years, many technologies have been investigated and developed to overcome the intrinsic drawbacks of the currently employed [...] Read more.
The detection of contaminants in food products after packaging by a non-invasive technique is a serious need for companies operating in the food industry. In recent years, many technologies have been investigated and developed to overcome the intrinsic drawbacks of the currently employed techniques, such as X-rays and metal detector, and to offer more appropriate solutions with respect to techniques developed in the academic domain in terms of acquisition speed, cost, and the penetration depth (infrared, hyperspectral imaging). A new method based on MW sensing is proposed to increase the degree of production quality. In this paper, we are going to present a novel approach from measurements setup to a binary classification of food products as contaminated or uncontaminated. The work focuses on combining MW sensing technology and ML tools such as MLP and SVM in a complete workflow that can operate in real time in a food production line. A very good performance accuracy that reached 99.8% is achieved using the non-linear SVM algorithm, while the accuracy of the performance of the MLP classifier reached 99.3%. Full article
(This article belongs to the Special Issue Electromagnetic Applications in Industry and Medicine)
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