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Sensor Technologies for Microwave Imaging
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Sensor Technologies for Microwave Imaging

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 8545

Special Issue Editor

Prof. Dr. Sandra Costanzo

E-Mail Website
Guest Editor
Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87036 Arcavacata, Italy
Interests: microwave and millimeter-waves antennas and circuits; microwave biomedical applications; innovative materials for antennas; electromagnetics in health safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microwave imaging sensors are used to generate incident radiation and collect the field resulting from the electromagnetic interaction with the structure under testing. Microwave imaging (MWI) is crucial in non-destructive testing (NDT) applications, subsurface sensing, ground penetrating radar (GPR) prospection, and biomedical imaging. The aim of this Special Issue is to highlight the most recent research regarding sensing technologies for microwave imaging. Research articles and reviews that provide a comprehensive insight into sensing technologies for microwave imaging on any aspect of sensor development and applications are welcome. Topics of interest include, but are not limited to, the following:

  • Biomedical imaging;
  • Biosensing;
  • Concealed-weapon detection;
  • Non-destructive testing;
  • Automotive radar;
  • Vehicle guidance;
  • Microwaves and other electromagnetic waves;
  • Industrial and medical applications of microwaves;
  • Materials testing;
  • Microwave measurement techniques. 

Dr. Sandra Costanzo
Guest Editor

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 (3 papers)

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Research

13 pages, 3269 KiB  
Article
Evaluation of Unobtrusive Microwave Sensors in Healthcare 4.0—Toward the Creation of Digital-Twin Model
by Sagheer Khan, Imran M. Saied, Tharmalingam Ratnarajah and Tughrul Arslan
Sensors 2022, 22(21), 8519; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218519 - 05 Nov 2022
Cited by 6 | Viewed by 1997
Abstract
The prevalence of chronic diseases and the rapid rise in the aging population are some of the major challenges in our society. The utilization of the latest and unique technologies to provide fast, accurate, and economical ways to collect and process data is [...] Read more.
The prevalence of chronic diseases and the rapid rise in the aging population are some of the major challenges in our society. The utilization of the latest and unique technologies to provide fast, accurate, and economical ways to collect and process data is inevitable. Industry 4.0 (I4.0) is a trend toward automation and data exchange. The utilization of the same concept of I4.0 in healthcare is termed Healthcare 4.0 (H4.0). Digital Twin (DT) technology is an exciting and open research field in healthcare. DT can provide better healthcare in terms of improved patient monitoring, better disease diagnosis, the detection of falls in stroke patients, and the analysis of abnormalities in breathing patterns, and it is suitable for pre- and post-surgery routines to reduce surgery complications and improve recovery. Accurate data collection is not only important in medical diagnoses and procedures but also in the creation of healthcare DT models. Health-related data acquisition by unobtrusive microwave sensing is considered a cornerstone of health informatics. This paper presents the 3D modeling and analysis of unobtrusive microwave sensors in a digital care-home model. The sensor is studied for its performance and data-collection capability with regards to patients in care-home environments. Full article
(This article belongs to the Special Issue Sensor Technologies for Microwave Imaging)
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16 pages, 5459 KiB  
Article
Gradient Index Metasurface Lens for Microwave Imaging
by Srijan Datta, Antonello Tamburrino and Lalita Udpa
Sensors 2022, 22(21), 8319; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218319 - 30 Oct 2022
Cited by 5 | Viewed by 3564
Abstract
This paper presents the design, simulation and experimental validation of a gradient-index (GRIN) metasurface lens operating at 8 GHz for microwave imaging applications. The unit cell of the metasurface consists of an electric-LC (ELC) resonator. The effective refractive index of the metasurface is [...] Read more.
This paper presents the design, simulation and experimental validation of a gradient-index (GRIN) metasurface lens operating at 8 GHz for microwave imaging applications. The unit cell of the metasurface consists of an electric-LC (ELC) resonator. The effective refractive index of the metasurface is controlled by varying the capacitive gap at the center of the unit cell. This allows the design of a gradient index surface. A one-dimensional gradient index lens is designed and tested at first to describe the operational principle of such lenses. The design methodology is extended to a 2D gradient index lens for its potential application as a microwave imaging device. The metasurface lenses are designed and analyzed using full-wave finite element (FEM) solver. The proposed 2D lens has an aperture of size 119 mm (3.17λ) × 119 mm (3.17λ) and thickness of only 0.6 mm (0.016λ). Horn antenna is used as source of plane waves incident on the lens to evaluate the focusing performance. Field distributions of the theoretical designs and fabricated lenses are analyzed and are shown to be in good agreement. A microwave nondestructive evaluation (NDE) experiment is performed with the 2D prototype lens to image a machined groove in a Teflon sample placed at the focal plane of the lens. Full article
(This article belongs to the Special Issue Sensor Technologies for Microwave Imaging)
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12 pages, 2834 KiB  
Article
Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection
by Sandra Costanzo, Alexandra Flores and Giovanni Buonanno
Sensors 2022, 22(11), 4122; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114122 - 29 May 2022
Cited by 16 | Viewed by 2170
Abstract
In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to [...] Read more.
In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations. Full article
(This article belongs to the Special Issue Sensor Technologies for Microwave Imaging)
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