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Technologies
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Selected Papers from the MOCAST Conference Series
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Selected Papers from the MOCAST Conference Series

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 Special Issue related to the MOCAST conference series. The latest events can be found at https://www.mocast.eu/. All the participants of the MOCAST conference series and their colleagues are encouraged to submit their work to this Special Issue.

The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue aims to publish extended versions of top-ranking papers of the conference. The topics of MOCAST include the following:

  • Analog/RF and mixed-signal circuits;
  • Digital circuits and systems 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, 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 (4 papers)

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2025

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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
Viewed by 471
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, 3700 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 240
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
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 455
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 1698
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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