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Technologies
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Modern Circuits, System Technologies, and Selected Papers from MOCAST
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Modern Circuits, System Technologies, and Selected Papers from MOCAST

A topical collection in Technologies (ISSN 2227-7080).

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Editors

Prof. Dr. Spyridon Nikolaidis

E-Mail Website
Collection Editor
Physics Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: digital design; low power design techniques; applications development on embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Valeri Mladenov

E-Mail Website
Collection Editor
Department Fundamentals of Electrical Engineering, Technical University of Sofia, Kliment Ohridski St. 8, 1000 Sofia, Bulgaria
Interests: electrical engineering; electronics; artificial intelligence; neural networks; power systems; applied mathematics; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ronald Tetzlaff

E-Mail Website
Collection Editor
Institute of Circuits and Systems, TUD, Dresden University of Technology, 01062 Dresden, Germany
Interests: circuit theory; memristors; chaotic circuits; nonlinear dynamics; AI; machine learning; cellular neural networks; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

We are planning to publish a Topical Collection related to the MOCAST conference series and contributions beyond it. The MOCAST conference, whose latest events can be found at https://www.mocast.eu/, has become a reference point for modern technologies in the field of circuits and systems. All participants and their colleagues are encouraged to submit their work to this Topical Collection, which will also address contributions beyond MOCAST related to modern circuits and system technologies.

This Topical Collection covers all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application, including the following topics:

  • Analog/RF and mixed-signal circuits;
  • Digital circuit and system design;
  • Nonlinear circuits and systems;
  • Device and circuit modeling;
  • High-performance embedded systems;
  • Systems and applications;
  • Sensors and systems;
  • Artificial intelligence, machine learning, and their applications;
  • Emerging technologies and devices;
  • Communication systems;
  • Network systems;
  • Power electronics and management;
  • Imagers, MEMS, medical equipment, and displays;
  • Radiation front ends (nuclear and space application);
  • Education in circuits, systems, and communications.

Prof. Dr. Spyridon Nikolaidis
Prof. Dr. Valeri Mladenov
Prof. Dr. Ronald Tetzlaff
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 collection 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. Technologies is an international peer-reviewed open access monthly 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 1600 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

  • electronic circuit technologies
  • electronic system technologies
  • modeling, design and implementation of circuits and systems
  • systems and applications

Published Papers (6 papers)

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2025

Jump to: 2024

17 pages, 3364 KiB  
Article
Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
by Georgios Korompilis, Achilles D. Boursianis, Panagiotis Sarigiannidis, Zaharias D. Zaharis, Katherine Siakavara, Maria S. Papadopoulou, Mohammad Abdul Matin and Sotirios K. Goudos
Technologies 2025, 13(4), 133; https://doi.org/10.3390/technologies13040133 - 1 Apr 2025
Viewed by 48
Abstract
As the energy crisis is leading to energy shortages and constant increases in prices, green energy and renewable energy sources are trending as a viable solution to this problem. One of the most rapidly expanding green energy methods is RF (RadioFrequency) energy harvesting, [...] Read more.
As the energy crisis is leading to energy shortages and constant increases in prices, green energy and renewable energy sources are trending as a viable solution to this problem. One of the most rapidly expanding green energy methods is RF (RadioFrequency) energy harvesting, as RF energy and its corresponding technologies are constantly progressing, due to the introduction of 5G and high-speed telecommunications. The usual system for RF energy harvesting is called a rectenna, and one of its main components is an antenna, responsible for collecting ambient RF energy. In this paper, the optimization process of an ultra-wideband antenna for RF energy harvesting applications was studied, with the main goal of broadening the antenna’s operational bandwidth to include 5G New Radio. For this purpose, the Bezier Search Differential Evolution Algorithm (BeSD) was used along with a novel CST-Matlab API, to manipulate the degrees of freedom of the antenna, while searching for the optimal result, which would satisfy all the necessary dependencies to make it capable of harvesting RF energy in the target frequency band. The BeSD algorithm was first tested with benchmark functions and compared to other widely used algorithms, which it successfully outperformed, and hence, it was selected as the optimizer for this research. All in all, the optimization process was successful by producing an ultra-wideband optimal antenna operating from 1.4 GHz to 3.9 GHz, which includes all vastly used telecommunication technologies, like GSM (1.8 GHz), UMTS (2.1 GHz), Wi-Fi (2.4 GHz), LTE (2.6 GHz), and 5G NR (3.5 GHz). Its ultra-wideband properties and the rest of the characteristics that make this design suitable for RF energy harvesting are proven by its S11 response graph, its impedance response graph, its efficiency on the targeted technologies, and its omnidirectionality across its band of operation. Full article
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18 pages, 9690 KiB  
Article
Reducing Energy Consumption in Embedded Systems Applications
by Ioannis Sofianidis, Vasileios Konstantakos and Spyridon Nikolaidis
Technologies 2025, 13(2), 82; https://doi.org/10.3390/technologies13020082 - 16 Feb 2025
Viewed by 662
Abstract
One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically [...] Read more.
One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts. Full article
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21 pages, 7254 KiB  
Article
Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
by Arbër Perçuku, Daniela Minkovska and Nikolay Hinov
Technologies 2025, 13(2), 59; https://doi.org/10.3390/technologies13020059 - 1 Feb 2025
Cited by 2 | Viewed by 1839
Abstract
The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation [...] Read more.
The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting. Full article
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36 pages, 20062 KiB  
Article
Analysis and Optimization of DC-DC Converters Through Sensitivity to Parametric Variations
by Nikolay Hinov, Plamen Stanchev and Gergana Vacheva
Technologies 2025, 13(2), 56; https://doi.org/10.3390/technologies13020056 - 1 Feb 2025
Viewed by 1024
Abstract
The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves [...] Read more.
The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves developing a simulation model that describes the behavior of converters under different conditions and analyzing the effects of parameter variations through simulation tools. Sensitivity analysis of DC-DC converters involves understanding the sources of harmonics, modeling the converter, analyzing the harmonic content, and implementing mitigation techniques. By combining theoretical analysis with practical design modifications, engineers can optimize DC-DC converters for improved performance, efficiency, and compliance with electromagnetic compatibility standards. Examples of harmonic analysis of the main types of DC-DC converters—Buck, Boost, and Buck-Boost—are discussed in the manuscript. Based on a study of the influence of harmonics in the operating modes, ratios have been derived to be applied during design. In this respect, the research presented is useful for designers and for use in power electronics education. Full article
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21 pages, 629 KiB  
Article
Deep Learning Framework Using Spatial Attention Mechanisms for Adaptable Angle Estimation Across Diverse Array Configurations
by Constantinos M. Mylonakis, Pantelis Velanas, Pavlos I. Lazaridis, Panagiotis Sarigiannidis, Sotirios K. Goudos and Zaharias D. Zaharis
Technologies 2025, 13(2), 46; https://doi.org/10.3390/technologies13020046 - 24 Jan 2025
Viewed by 1195
Abstract
Rapid advancement of wireless communication systems and the increasing need for accurate, real-time signal processing have driven innovations in direction-of-arrival (DoA) estimation techniques. This paper introduces a novel convolutional neural network (CNN) architecture that combines spatial attention mechanisms with a transfer learning framework [...] Read more.
Rapid advancement of wireless communication systems and the increasing need for accurate, real-time signal processing have driven innovations in direction-of-arrival (DoA) estimation techniques. This paper introduces a novel convolutional neural network (CNN) architecture that combines spatial attention mechanisms with a transfer learning framework to enhance both accuracy and versatility in DoA estimation. The model integrates spatial attention layers to dynamically prioritize signal regions with the highest information value, allowing it to isolate relevant signals and suppress interference in noisy or crowded signal environments. In addition, we utilize a transfer learning framework that enables the model to generalize across various antenna array configurations (i.e., planar, linear, and circular arrays) with minimal additional training. Extensive simulation results benchmark the proposed model against existing state-of-the-art methods for DoA estimation, achieving improved absolute error across diverse conditions. This hybrid approach not only enhances DoA estimation precision, but also significantly reduces retraining requirements when adapting to new array configurations, positioning it as a robust, scalable tool for next-generation wireless communication systems. Full article
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2024

Jump to: 2025

25 pages, 3232 KiB  
Article
A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study
by Kostas Siozios
Technologies 2024, 12(11), 229; https://doi.org/10.3390/technologies12110229 - 13 Nov 2024
Viewed by 1791
Abstract
The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine [...] Read more.
The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers. Full article
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