Year: 2024
Type: article
Abstract: Electromagnetic imaging achieves enhanced resolution by leveraging the advanced sensing and data analysis capabilities of Internet of Things (IoT) systems. This paper introduces a novel learning approach for generative adversarial networks (GANs) to tackle significant challenges in electromagnetic sensing. The proposed method involves deploying additional transmitters and receivers to irradiate TM (transverse magnetic) and TE (transverse electric) polarization waves around uniaxial objects to capture the scattered field in free space. Subsequently, scattered field generative adversarial networks (SF-GANs) are utilized to simulate and learn the characteristics of Maxwell’s equations. Numerical simulations and experimental results demonstrate the superior performance of the SF-GANs compared to backpropagation generative adversarial networks (BP-GANs). Furthermore, it is worth noting that our method is capable of reconstructing high-dielectric-constant objects.
Source: Electronics
Authors: Chien-Ching Chiu, Po-Hsiang Chen, Hao Jiang, Bo-Yu Shi
Institutions: Tamkang University, San Francisco State University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4403396779