Special Issue "New Trends and Future Challenges in Computational Microwave Imaging"

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

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Prof. Dr. Maokun Li
E-Mail Website
Guest Editor
State Key Laboratory on Microwave and Digital Communications Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: inverse scattering; electromagnetic theory; large-scale electromagnetic problems; electromagnetic compatibility analysis; geophysical explorations; biomedical imaging; fast algorithms in computational electromagnetics; machine learning; deep learning
Dr. Marco Salucci
E-Mail Website
Guest Editor
[email protected] (DISI - University of Trento), Via Sommarive 9, 38123 Trento, Italy
Interests: inverse scattering; multi-resolution techniques; real-time imaging; ground penetrating radar imaging; biomedical imaging; non-destructive testing and evaluation; antenna array design, processing, and characterization; synthesis of complex electromagnetic devices through system-by-design techniques; surrogate-assisted optimization; learning-by-examples; deep learning
Special Issues and Collections in MDPI journals
Dr. Alessandro Polo
E-Mail Website
Guest Editor
[email protected] (DISI - University of Trento), Via Sommarive 9, 38123 Trento, Italy
Interests: inverse scattering; real-time imaging; computational electromagnetics; large-scale electromagnetic problems; deep learning; structural health monitoring; passive localization; unconventional array architectures for 5G and 6G; smart buildings; wireless sensor networks; decision support systems.

Special Issue Information

Dear Colleagues,

The development of fast, accurate, and reliable techniques for solving large-scale complex electromagnetic inverse scattering problems is a fundamental challenge because of its importance in several application scenarios, including biomedical imaging, subsurface prospecting, through-the-wall imaging, non-destructive testing and evaluation, and structural health monitoring. The relevance and interest of academic and industrial research in such areas is driven by several concurring factors, including increased availability of cost-effective data acquisition processes and measurement technologies, computing architectures suitable for large-scale processing, numerically efficient and robust computational microwave imaging methodologies exploiting original formulations, innovative information processing strategies, and new regularization techniques.

In this framework, this Special Issue is aimed at highlighting the challenges, current trends, and most recent advances in computational microwave imaging as applied to several non-invasive EM problems.

Prof. Dr. Maokun Li
Dr. Marco Salucci
Dr. Alessandro Polo
Guest Editor

Manuscript Submission Information

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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 1800 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

  • Computational microwave imaging
  • Inverse scattering
  • Biomedical imaging
  • Subsurface imaging
  • Through-the-wall imaging
  • Non-destructive testing/evaluation
  • Structural health monitoring

Published Papers (5 papers)

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Research

Open AccessFeature PaperArticle
A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method
Electronics 2021, 10(3), 352; https://doi.org/10.3390/electronics10030352 - 02 Feb 2021
Viewed by 447
Abstract
In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set [...] Read more.
In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set is generated offline. Then, the average descent direction between an initial background model and the training models is calculated. Finally, the reconstruction of the testing thorax model is achieved based on the average descent directions online. The feasibility using One-Step SDM for thorax imaging is studied. Numerical results indicate that the structural information of thorax can be reconstructed. It has potential for real-time imaging in future clinical diagnosis. Full article
(This article belongs to the Special Issue New Trends and Future Challenges in Computational Microwave Imaging)
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Open AccessFeature PaperArticle
Multi-Step Learning-by-Examples Strategy for Real-Time Brain Stroke Microwave Scattering Data Inversion
Electronics 2021, 10(1), 95; https://doi.org/10.3390/electronics10010095 - 05 Jan 2021
Viewed by 517
Abstract
This work deals with the computationally-efficient inversion of microwave scattering data for brain stroke detection and monitoring. The proposed multi-step approach is based on the Learning-by-Examples (LBE) paradigm and naturally matches the stages and time constraints of an effective clinical diagnosis. Stroke detection, [...] Read more.
This work deals with the computationally-efficient inversion of microwave scattering data for brain stroke detection and monitoring. The proposed multi-step approach is based on the Learning-by-Examples (LBE) paradigm and naturally matches the stages and time constraints of an effective clinical diagnosis. Stroke detection, identification, and localization are solved with real-time performance through support vector machines (SVMs) operating both in binary/multi-class classification and in regression modalities. Experimental results dealing with the inversion of laboratory-controlled data are shown to verify the effectiveness of the proposed multi-step LBE methodology and prove its suitability as a viable alternative/support to standard medical diagnostic methods. Full article
(This article belongs to the Special Issue New Trends and Future Challenges in Computational Microwave Imaging)
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Open AccessArticle
Stochastic Optimization Methods for Parametric Level Set Reconstructions in 2D through-the-Wall Radar Imaging
Electronics 2020, 9(12), 2055; https://doi.org/10.3390/electronics9122055 - 03 Dec 2020
Viewed by 404
Abstract
In this paper, a comparison of stochastic optimization algorithms is presented for the reconstruction of electromagnetic profiles in through-the-wall radar imaging. We combine those stochastic optimization approaches with a shape-based representation of unknown targets which is based on a parametrized level set formulation. [...] Read more.
In this paper, a comparison of stochastic optimization algorithms is presented for the reconstruction of electromagnetic profiles in through-the-wall radar imaging. We combine those stochastic optimization approaches with a shape-based representation of unknown targets which is based on a parametrized level set formulation. This way, we obtain a stochastic version of shape evolution with the goal of minimizing a given cost functional. As basis functions, we consider in particular Gaussian and Wendland radial basis functions. For the optimization task, we consider three variants of stochastic approaches, namely stochastic gradient descent, the Adam method as well as a more involved stochastic quasi-Newton scheme. A specific backtracking line search method is also introduced for this specific application of stochastic shape evolution. The physical scenery considered here is set in 2D assuming TM waves for simplicity. The goal is to localize and characterize (and eventually track) targets of interest hidden behind walls by solving the corresponding electromagnetic inverse problem. The results provide a good indication on the expected performance of similar schemes in a more realistic 3D setup. Full article
(This article belongs to the Special Issue New Trends and Future Challenges in Computational Microwave Imaging)
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Open AccessArticle
Wavelet-Based Subspace Regularization for Solving Highly Nonlinear Inverse Scattering Problems with Contraction Integral Equation
Electronics 2020, 9(11), 1760; https://doi.org/10.3390/electronics9111760 - 23 Oct 2020
Viewed by 555
Abstract
A wavelet transform twofold subspace-based optimization method (WT-TSOM) is proposed to solve the highly nonlinear inverse scattering problems with contraction integral equation for inversion (CIE-I). While the CIE-I is able to suppress the multiple scattering effects within inversion (without compromising the accuracy of [...] Read more.
A wavelet transform twofold subspace-based optimization method (WT-TSOM) is proposed to solve the highly nonlinear inverse scattering problems with contraction integral equation for inversion (CIE-I). While the CIE-I is able to suppress the multiple scattering effects within inversion (without compromising the accuracy of the physics), proper regularization is needed. In this paper, we investigate a new type subspace regularization technique based on wavelet expansions for the induced currents. We found that the bior3.5 wavelet is a good choice to stabilize the inversions with the CIE-I model and in the meanwhile it also can rectify the contrast profile. Numerical tests against both synthetic and experimental data show that WT-TSOM is a promising regularization technique for inversion with CIE-I. Full article
(This article belongs to the Special Issue New Trends and Future Challenges in Computational Microwave Imaging)
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Open AccessArticle
Electromagnetic Micro-Structure Non-Destructive Testing: Sparsity-Constrained and Combined Convolutional Recurrent Neural Network Methods
Electronics 2020, 9(11), 1750; https://doi.org/10.3390/electronics9111750 - 22 Oct 2020
Viewed by 395
Abstract
How to locate missing rods within a micro-structure composed of a grid-like, finite set of infinitely long circular cylindrical dielectric rods under the sub-wavelength condition is investigated. Sub-wavelength distances between adjacent rods and sub-wavelength rod diameters require super-resolution, beyond the Rayleigh criterion. Two [...] Read more.
How to locate missing rods within a micro-structure composed of a grid-like, finite set of infinitely long circular cylindrical dielectric rods under the sub-wavelength condition is investigated. Sub-wavelength distances between adjacent rods and sub-wavelength rod diameters require super-resolution, beyond the Rayleigh criterion. Two different methods are proposed to achieve this: One builds upon the multiple scattering expansion method (MSM), and it enforces strong sparsity-prior information. The other is a data-driven method that combines convolutional neural networks (CNN) and recurrent neural networks (RNN), and it can be applied in effect with little knowledge of the wavefield interactions involved, in much contrast with the previous one. Comprehensive numerical simulations are proposed in terms of the missing rod number, shape, the frequency of observation, and the configuration of the tested structures. Both methods are shown to achieve suitable detection, yet under more or less stringent conditions as discussed. Full article
(This article belongs to the Special Issue New Trends and Future Challenges in Computational Microwave Imaging)
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