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Advanced Technologies and Applications of Microwave and Millimeter Wave

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 573

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Global Innovation Center, Kyoushu University, Kasuga, Japan
Interests: calibration; marine radar; oceanographic techniques; tides; anechoic chambers (electromagnetic); electrocardiography; height measurement; image resolution; lenses; level measurement; light diffraction
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Special Issue Information

Dear Colleagues,

In recent years, significant progress has been made in microwave and millimeter wave technology, with applications ranging from telecommunications, including 5G/6G mobile technologies, to diagnostic systems contributing to sustaining safety and security. Advanced technologies in this field include the development of low-noise amplifiers, detectors/mixers, advanced antennas, and system-on-chip technologies, including those devices. These technologies are essential for applications such as satellite communications, radar systems, and high-speed wireless networks. They are also particularly exploited for their ability to provide high-resolution imaging and sensing, which is beneficial for automotive radar, security scanning, and medical imaging applications.

This Special Issue focuses on the latest advances in microwave and millimeter-wave technologies, exploring their multiple applications in various fields. It aims to showcase innovative research, focusing on developing high-frequency systems, including novel components, devices, and methods that improve performance and efficiency. Topics of interest include but are not limited to, high-speed communication systems, radar technology, imaging and sensing applications, and integration with emerging technologies such as artificial intelligence and the Internet of Things (IoT). The journal aims to foster collaboration between researchers and practitioners, encouraging the exchange of ideas and discoveries that can drive future innovations in microwave and millimeter-wave applications. Contributions may include theoretical studies, designing/fabrication of devices and systems, and their practical implementations, all aimed at addressing the challenges and opportunities presented by these advanced technologies.

Prof. Dr. Atsushi Mase
Guest Editor

Manuscript Submission Information

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Keywords

  • microwave technology millimeter wave
  • technology telecommunications
  • defense systems
  • high-speed wireless networks
  • high-resolution imaging automotive radar
  • artificial intelligence
  • Internet of Things (IoT)

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Published Papers (1 paper)

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Research

18 pages, 3054 KiB  
Article
Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
by Chien-Ching Chiu, Po-Hsiang Chen, Yi-Hsun Chen and Hao Jiang
Appl. Sci. 2025, 15(12), 6723; https://doi.org/10.3390/app15126723 - 16 Jun 2025
Viewed by 202
Abstract
This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the [...] Read more.
This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the scattered field data. To simplify the training process, a Back Propagation Scheme (BPS) is employed initially to calculate the preliminary permittivity distribution, which is then fed into the GAN with SA for image reconstruction. The proposed GAN with SA offers superior performance and higher resolution compared with GAN, along with enhanced generalization capability. The methodology consists of two main steps. First, TM waves are used to estimate the initial permittivity distribution along the z-direction using BPS. Second, TE waves estimate the x- and y-direction permittivity distribution. The estimated permittivity values are used as inputs to train the GAN with SA. In our study, we add 5% and 20% noise to compare the performance of the GAN with and without SA. Numerical results indicate that the GAN with SA demonstrates higher efficiency and resolution, as well as better generalization capability. Our innovation lies in the successful reconstruction of various uniaxial objects using a generator integrated with a self-attention mechanism, achieving reduced computational time and real-time imaging. Full article
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