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Keywords = reconstruction of unknown dielectric constant

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12 pages, 2532 KB  
Article
Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface
by Chien-Ching Chiu, Yang-Han Lee, Wei Chien, Po-Hsiang Chen and Eng Hock Lim
Electronics 2025, 14(6), 1236; https://doi.org/10.3390/electronics14061236 - 20 Mar 2025
Viewed by 623
Abstract
In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making [...] Read more.
In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making the process very time-consuming. To resolve this issue, we provide measured scattered fields as training data for the DDCNN to reconstruct the periodic length, dielectric constant, and shape. The numerical results demonstrate that DDCNN can accurately reconstruct rough surface images under high noise levels. In addition, we also discuss the impacts of the periodic length and dielectric constant of the rough surface on the shape reconstruction. Notably, our method achieves excellent reconstruction results compared to DCNN even when the period and dielectric coefficient are unknown. Finally, it is worth mentioning that the trained network model completes the reconstruction process in less than one second, realizing efficient real-time imaging. Full article
(This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing)
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11 pages, 873 KB  
Article
Solution of the Vector Three-Dimensional Inverse Problem on an Inhomogeneous Dielectric Hemisphere Using a Two-Step Method
by Eugen Smolkin, Yury Smirnov and Maxim Snegur
Computation 2024, 12(11), 213; https://doi.org/10.3390/computation12110213 - 22 Oct 2024
Cited by 1 | Viewed by 1058
Abstract
This work is devoted to the development and implementation of a two-step method for solving the vector three-dimensional inverse diffraction problem on an inhomogeneous dielectric scatterer having the form of a hemisphere characterized by piecewise constant permittivity. The original boundary value problem for [...] Read more.
This work is devoted to the development and implementation of a two-step method for solving the vector three-dimensional inverse diffraction problem on an inhomogeneous dielectric scatterer having the form of a hemisphere characterized by piecewise constant permittivity. The original boundary value problem for Maxwell’s equations is reduced to a system of integro-differential equations. An integral formulation of the vector inverse diffraction problem is proposed and the uniqueness of the solution of the first-kind integro-differential equation in special function classes is established. A two-step method for solving the vector inverse diffraction problem on the hemisphere is developed. Unlike traditional approaches, the two-step method for solving the inverse problem is non-iterative and does not require knowledge of the exact initial approximation. Consequently, there are no issues related to the convergence of the numerical method. The results of calculations of approximate solutions to the inverse problem are presented. It is shown that the two-step method is an efficient approach to solving vector problems in near-field tomography. Full article
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