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Editorial Board Members' Collection Series: Electronic Sensors, Devices and Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1884

Special Issue Editors


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Guest Editor
Department of Engineering, University Della Campania “Luigi Vanvitelli”, 81031 Aversa, Italy
Interests: numerical computation of electromagnetic fields; inverse problems in low-frequency electromagnetism; thermonuclear fusion; superconducting magnets

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Guest Editor
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, 46022 Valencia, Spain
Interests: printed sensors; printed electronics; thick-film sensors; textile sensors; screen-printed technology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Dipartimento d'Ingegneria, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Interests: radar and radiometric sensors; high data-rate transceivers; microwave electronic circuits; power amplifiers for wireless communications; RFID systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since its launch in 2001, Sensors has provided an advanced forum for the science and technology of sensors and their applications. During this time, the journal broadened its scope to devices and systems representing the object of measurements. The editor’s choice of providing open access to the articles in the journal, the continuous effort of the editorial team, and the rigor of reviewers providing their valuable work have helped the journal to achieve outstanding relevance in the scientific community. To celebrate the latest achievements of the journal, the Section “Electronic Sensors” is now compiling a collection of feature papers invited by the Section Editorial Board Members (EBMs) and submitted by outstanding scholars in this research field.

This Special Issue aims to provide the state of the art in this field to the broadest possible audience and promote the diffusion of recent results and networking among researchers in the areas of sensors, sensing technologies, and data processing.

We invite you to submit the most advanced results in:

  • Sensor devices, technology, and application;
  • Sensing principles and advanced materials for sensing;
  • Wearable sensors, devices, and electronics;
  • Sensor interface, signal processing, data fusion, and deep learning in sensor systems;
  • Remote sensors, sensor networks and arrays;
  • Smart/Intelligent sensors, Internet of Things, and human–computer interaction.

Prof. Dr. Alessandro Formisano
Prof. Dr. Eduardo García Breijo
Dr. Federico Alimenti
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. Sensors 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 2600 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.

Published Papers (1 paper)

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Research

17 pages, 4248 KiB  
Article
Learning-Based Approaches to Current Identification from Magnetic Sensors
by Sami Barmada, Paolo Di Barba, Alessandro Formisano, Maria Evelina Mognaschi and Mauro Tucci
Sensors 2023, 23(8), 3832; https://doi.org/10.3390/s23083832 - 08 Apr 2023
Cited by 2 | Viewed by 1037
Abstract
Direct measurement of electric currents can be prevented by poor accessibility or prohibitive technical conditions. In such cases, magnetic sensors can be used to measure the field in regions adjacent to the sources, and the measured data then can be used to estimate [...] Read more.
Direct measurement of electric currents can be prevented by poor accessibility or prohibitive technical conditions. In such cases, magnetic sensors can be used to measure the field in regions adjacent to the sources, and the measured data then can be used to estimate source currents. Unfortunately, this is classified as an Electromagnetic Inverse Problem (EIP), and data from sensors must be cautiously treated to obtain meaningful current measurements. The usual approach requires using suited regularization schemes. On the other hand, behavioral approaches are recently spreading for this class of problems. The reconstructed model is not obliged to follow the physics equations, and this implies approximations which must be accurately controlled, especially if aiming to reconstruct an inverse model from examples. In this paper, a systematic study of the role of different learning parameters (or rules) on the (re-)construction of an EIP model is proposed, in comparison with more assessed regularization techniques. Attention is particularly devoted to linear EIPs, and in this class, a benchmark problem is used to illustrate in practice the results. It is shown that, by applying classical regularization methods and analogous correcting actions in behavioral models, similar results can be obtained. Both classical methodologies and neural approaches are described and compared in the paper. Full article
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