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Applied Sciences
Special Issues
Recent Advances in Microwave Devices and Intelligent Systems
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Recent Advances in Microwave Devices and Intelligent Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 842

Special Issue Editors

Prof. Dr. Darius Plonis

E-Mail Website
Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania
Interests: waveguides algorithms; neural networks and artificial intelligence; microwave photonics; optoelectronics; optics and photonics; electromagnetics metamaterials; fibre optics; microwave engineering; wave propagation; antennas and propagation; terahertz electronics; antenna engineering; crystal fibres; computational electromagnetics; microwave technology; microwave filters; electromagnetic engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Andrius Katkevičius

E-Mail Website
Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania
Interests: microwave devices; artificial intelligence; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emerging technologies and application trends encourage the search for new methods for the design and analysis of microwave devices. This poses new challenges regarding how to make the design process fast and accurate. This is especially important when the analysis needs to be repeated many times until the optimal solution is reached. Therefore, this Special Issue intends to present new ideas regarding the implementation of intelligent methods in the design and analysis of microwave devices and other intelligent systems.

Areas relevant to Microwave Devices and Intelligent Systems include, but are not limited to, the design of microwave devices, the analysis of microwave devices, migration from conventional full-wave methods to intelligent methods, intelligent algorithms and systems, and parallel computing.

This Special Issue will publish high-quality original research papers in the overlapping fields of: 

  • Design of microwave devices;
  • Analysis of microwave devices;
  • Intelligent algorithms and systems;
  • Migration from conventional full-wave methods to intelligent methods;
  • Parallel computing.

Dr. Darius Plonis
Dr. Andrius Katkevičius
Guest Editors

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. Applied Sciences 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 2400 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

  • microwave devices
  • artificial intelligence
  • control systems
  • signal processing
  • features extraction and classification

Published Papers (1 paper)

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Research

18 pages, 2913 KiB  
Article
Meander Structure Analysis Techniques Using Artificial Neural Networks
by Diana Belova-Plonienė, Audrius Krukonis, Vytautas Abromavičius, Artūras Serackis, Vytautas Urbanavičius and Andrius Katkevičius
Appl. Sci. 2024, 14(13), 5766; https://doi.org/10.3390/app14135766 - 1 Jul 2024
Viewed by 404
Abstract
Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims [...] Read more.
Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time (td), reflection coefficient (S11), and transmission coefficient (S21) in the 0–10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic td was predicted with a 3.3 ps average MAE. The characteristic S21 was predicted with a 0.64 dB average MAE, and S11 was predicted with a 2.47 dB average MAE. The TDNN allowed the average MAEs to be reduced to 0.9 ps, 0.11 dB, and 1.63 dB, respectively. Using a CNN, the average MAEs were 27.5 ps, 0.44 dB, and 1.36 dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min. Full article
(This article belongs to the Special Issue Recent Advances in Microwave Devices and Intelligent Systems)
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