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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: 15 July 2025 | Viewed by 2877

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Prof. Dr. Darius Plonis

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Guest Editor

Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), Plytinės g. 25, LT-10105 Vilnius, Lithuania
Interests: neural networks; electronics engineering; machine learning; signal processing; tracking; embedded systems; beekeeping
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Dr. Andrius Katkevičius

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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

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Keywords

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

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Published Papers (3 papers)

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Research

14 pages, 1376 KiB

Open AccessArticle

Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation

by Sandis Migla, Kristaps Rubuls, Nikolajs Tihomorskis, Toms Salgals, Oskars Ozolins, Vjaceslavs Bobrovs, Sandis Spolitis and Arturs Aboltins

Appl. Sci. 2025, 15(8), 4222; https://doi.org/10.3390/app15084222 - 11 Apr 2025

Viewed by 260

Abstract

This research presents a novel approach to 28

GHz

This research presents a novel approach to 28

GHz

impulse radio ultra-wideband (IR-UWB) transmission using pulse position modulation (PPM) over an analog radio-over-fiber (ARoF) link, investigating the impact of fiber-based fronthaul on the overall performance of the communication system. In this setup, an arbitrary waveform generator (AWG) is employed for PPM signal generation, while demodulation is performed with a commercial time-to-digital converter (TDC) based on an event timer. To enhance the reliability of transmitted reference PPM (TR-PPM) signals, the transmission system integrates Gray coding and Consultative Committee for Space Data Systems (CCSDS)-standard-compliant Reed-Solomon (RS) error correcting code (ECC). System performance was evaluated by transmitting pseudorandom binary sequences (PRBSs) and measuring the bit error ratio (BER) across a 5-m wireless link between two 20

d B i

gain horn (Ka-band) antennas, with and without a 20

k m

single-mode optical fiber (SMF) link in transmitter side and ECC at the receiver side. The system achieved a BER of less than 8.17 × 10⁻⁷, using a time bin duration of 200

p s

and a pulse duration of 100

p s

, demonstrating robust performance and significant potential for space-to-ground telecommunication applications. Full article

(This article belongs to the Special Issue Recent Advances in Microwave Devices and Intelligent Systems)

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23 pages, 1130 KiB

Open AccessArticle

A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner

by Janis Semenako, Sandis Migla, Tatjana Solovjova, Nikolajs Tihomorskis, Kristaps Rubuls, Darja Cirjulina, Sandis Spolitis and Arturs Aboltins

Appl. Sci. 2024, 14(24), 11641; https://doi.org/10.3390/app142411641 - 13 Dec 2024

Viewed by 791

Abstract

Passive pulse shaping at frequencies above 1 GHz is mainly achieved through frequency-domain processing using filters. Unfortunately, the conventional frequency domain approach does not allow precise control of the impulse response of the filter, therefore, setting limitations to the pulse shaping accuracy. Sub-nanosecond pulse expansion that preserves steep pulse transitions is one of the ultra-wideband (UWB) applications where frequency domain approaches do not provide satisfactory results. This paper proposes a highly innovative approach based on time-domain signal processing using a set of parallel microstrip delay lines connected in a network accompanied by a splitter at the input and a combiner at the output. The proposed design, analogous to finite impulse response (FIR) filters in digital signal processing (DSP), provides fine-grained control over time-domain characteristics and supports the implementation of complex functions, including pulse expansion. This paper presents a detailed analysis of previous work and theoretical considerations regarding the advantages and limitations of UWB pulse time-domain processing. Moreover, detailed HFSS simulations of components, such as a microstrip pulse splitter, delay lines, a combiner, and their combinations, are presented. Finally, the results of the experimental validation of the device, fabricated on an FR-4 substrate, are presented. Technology for effective implementation of a pulse splitter, delay lines, and a pulse combiner, as well as their matching, can be considered as key findings of the given research. Limitations associated with matching and delay estimation for pulsed UWB signals are highlighted. Full article

(This article belongs to the Special Issue Recent Advances in Microwave Devices and Intelligent Systems)

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Figure 1

18 pages, 2913 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1044

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 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 (

t_{d}

), reflection coefficient (

S_{11}

), and transmission coefficient (

S_{21}

) 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

t_{d}

was predicted with a

3.3

ps average MAE. The characteristic

S_{21}

was predicted with a

0.64

dB average MAE, and

S_{11}

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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