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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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33 pages, 4488 KB  
Article
New Fuzzy Aggregators for Ordered Fuzzy Numbers for Trend and Uncertainty Analysis
by Miroslaw Kozielski, Piotr Prokopowicz and Dariusz Mikolajewski
Electronics 2026, 15(2), 309; https://doi.org/10.3390/electronics15020309 - 10 Jan 2026
Viewed by 492
Abstract
Decision-making under uncertainty, especially when dealing with incomplete or linguistically described data, remains a significant challenge in various fields of science and industry. The increasing complexity of real-world problems necessitates the development of mathematical models and data processing techniques that effectively address uncertainty [...] Read more.
Decision-making under uncertainty, especially when dealing with incomplete or linguistically described data, remains a significant challenge in various fields of science and industry. The increasing complexity of real-world problems necessitates the development of mathematical models and data processing techniques that effectively address uncertainty and incompleteness. Aggregators play a key role in solving these problems, particularly in fuzzy systems, where they constitute fundamental tools for decision-making, data analysis, and information fusion. Aggregation functions have been extensively studied and applied in many fields of science and engineering. Recent research has explored their usefulness in fuzzy control systems, highlighting both their advantages and limitations. One promising approach is the use of ordered fuzzy numbers (OFNs), which can represent directional tendencies in data. Previous studies have introduced the property of direction sensitivity and the corresponding determinant parameter, which enables the analysis of correspondence between OFNs and facilitates inference operations. The aim of this paper is to examine existing aggregate functions for fuzzy set numbers and assess their suitability within OFNs. By analyzing the properties, theoretical foundations, and practical applications of these functions, we aim to identify a suitable aggregation operator that complies with the principles of OFN while ensuring consistency and efficiency in decision-making based on fuzzy structures. This paper introduces a novel aggregation approach that preserves the expected mathematical properties while incorporating the directional components inherent to OFN. The proposed method aims to improve the robustness and interpretability of fuzzy reasoning systems under uncertainty. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks, 2nd Edition)
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35 pages, 1875 KB  
Review
FPGA-Accelerated ECG Analysis: Narrative Review of Signal Processing, ML/DL Models, and Design Optimizations
by Laura-Ioana Mihăilă, Claudia-Georgiana Barbura, Paul Faragó, Sorin Hintea, Botond Sandor Kirei and Albert Fazakas
Electronics 2026, 15(2), 301; https://doi.org/10.3390/electronics15020301 - 9 Jan 2026
Cited by 3 | Viewed by 2844
Abstract
Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time [...] Read more.
Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article
(This article belongs to the Special Issue Emerging Biomedical Electronics)
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24 pages, 3204 KB  
Article
AMUSE++: A Mamba-Enhanced Speech Enhancement Framework with Bi-Directional and Advanced Front-End Modeling
by Tsung-Jung Li, Berlin Chen and Jeih-Weih Hung
Electronics 2026, 15(2), 282; https://doi.org/10.3390/electronics15020282 - 8 Jan 2026
Cited by 1 | Viewed by 1749
Abstract
This study presents AMUSE++, an advanced speech enhancement framework that extends the MUSE++ model by redesigning its core Mamba module with two major improvements. First, the originally unidirectional one-dimensional (1D) Mamba is transformed into a bi-directional architecture to capture temporal dependencies more effectively. [...] Read more.
This study presents AMUSE++, an advanced speech enhancement framework that extends the MUSE++ model by redesigning its core Mamba module with two major improvements. First, the originally unidirectional one-dimensional (1D) Mamba is transformed into a bi-directional architecture to capture temporal dependencies more effectively. Second, this module is extended to a two-dimensional (2D) structure that jointly models both time and frequency dimensions, capturing richer speech features essential for enhancement tasks. In addition to these structural changes, we propose a Preliminary Denoising Module (PDM) as an advanced front-end, which is composed of multiple cascaded 2D bi-directional Mamba Blocks designed to preprocess and denoise input speech features before the main enhancement stage. Extensive experiments on the VoiceBank+DEMAND dataset demonstrate that AMUSE++ significantly outperforms both the backbone MUSE++ across a variety of objective speech enhancement metrics, including improvements in perceptual quality and intelligibility. These results confirm that the combination of bi-directionality, two-dimensional modeling, and an enhanced denoising frontend provides a powerful approach for tackling challenging noisy speech scenarios. AMUSE++ thus represents a notable advancement in neural speech enhancement architectures, paving the way for more effective and robust speech enhancement systems in real-world applications. Full article
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31 pages, 13729 KB  
Article
Stage-Wise SOH Prediction Using an Improved Random Forest Regression Algorithm
by Wei Xiao, Jun Jia, Wensheng Gao, Haibo Li, Hong Xu, Weidong Zhong and Ke He
Electronics 2026, 15(2), 287; https://doi.org/10.3390/electronics15020287 - 8 Jan 2026
Cited by 1 | Viewed by 770
Abstract
In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation [...] Read more.
In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability pAA was defined. Based on the correlation statistics between HFs, kHF, the SOH, and pAA, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article
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15 pages, 3153 KB  
Article
Decentralized Q-Learning for Multi-UAV Post-Disaster Communication: A Robotarium-Based Evaluation Across Urban Environments
by Udhaya Mugil Damodarin, Cristian Valenti, Sergio Spanò, Riccardo La Cesa, Luca Di Nunzio and Gian Carlo Cardarilli
Electronics 2026, 15(1), 242; https://doi.org/10.3390/electronics15010242 - 5 Jan 2026
Cited by 8 | Viewed by 975
Abstract
Large-scale disasters such as earthquakes and floods often cause the collapse of terrestrial communication networks, isolating affected communities and disrupting rescue coordination. Unmanned aerial vehicles (UAVs) can serve as rapid-deployment aerial relays to restore connectivity in such emergencies. This work presents a decentralized [...] Read more.
Large-scale disasters such as earthquakes and floods often cause the collapse of terrestrial communication networks, isolating affected communities and disrupting rescue coordination. Unmanned aerial vehicles (UAVs) can serve as rapid-deployment aerial relays to restore connectivity in such emergencies. This work presents a decentralized Q-learning framework in which each UAV operates as an independent agent that learns to maintain reliable two-hop links between mobile ground users. The framework integrates user mobility, UAV–user assignment, multi-UAV coordination, and failure tracking to enhance adaptability under dynamic conditions. The system is implemented and evaluated on the Robotarium platform, with propagation modeled using the Al-Hourani air-to-ground path loss formulation. Experiments conducted across Suburban, Dense Urban, and Highrise Urban environments show throughput gains of up to 20% compared with random placement baselines while maintaining failure rates below 5%. These results demonstrate that decentralized learning offers a scalable and resilient foundation for UAV-assisted emergency communication in environments where conventional infrastructure is unavailable. Full article
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34 pages, 3066 KB  
Article
Underwater Antenna Technologies with Emphasis on Submarine and Autonomous Underwater Vehicles (AUVs)
by Dimitrios G. Arnaoutoglou, Tzichat M. Empliouk, Dimitrios-Naoum Papamoschou, Yiannis Kyriacou, Andreas Papanastasiou, Theodoros N. F. Kaifas and George A. Kyriacou
Electronics 2026, 15(1), 219; https://doi.org/10.3390/electronics15010219 - 2 Jan 2026
Cited by 2 | Viewed by 2641
Abstract
Following the persistent evolution of terrestrial 5G wireless systems, a new field of underwater communication has emerged for various related applications like environmental monitoring, underwater mining, and marine research. However, establishing reliable high-speed underwater networks remains notoriously difficult due to the severe RF [...] Read more.
Following the persistent evolution of terrestrial 5G wireless systems, a new field of underwater communication has emerged for various related applications like environmental monitoring, underwater mining, and marine research. However, establishing reliable high-speed underwater networks remains notoriously difficult due to the severe RF attenuation in conductive seawater, which strictly limits range coverage. In this article, we focus on a comprehensive review of different antenna types for future underwater communication and sensing systems, evaluating their performance and suitability for Autonomous Underwater Vehicles (AUVs). We critically examine and compare distinct antenna technologies, including Magnetic Induction (MI) coils, electrically short dipoles, wideband traveling wave antennas, printed planar antennas, and novel magnetoelectric (ME) resonators. Specifically, these antennas are compared in terms of physical footprint, operating frequency, bandwidth, and realized gain, revealing the trade-offs between miniaturization and radiation efficiency. Our analysis aims to identify the benefits and weaknesses of the different antenna types while emphasizing the necessity of innovative antenna designs to overcome the fundamental propagation limits of the underwater channel. Full article
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12 pages, 1642 KB  
Article
Polarization-Shift Backscatter Identification for SWIPT-Based Battery-Free Sensor Nodes
by Taki E. Djidjekh and Alexandru Takacs
Electronics 2026, 15(1), 186; https://doi.org/10.3390/electronics15010186 - 31 Dec 2025
Viewed by 592
Abstract
Battery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocol-dependent security mechanisms cannot be employed, leaving BFSNs vulnerable to [...] Read more.
Battery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocol-dependent security mechanisms cannot be employed, leaving BFSNs vulnerable to replay, spoofing, and other security threats. This paper explores a protocol-independent security mechanism that enhances BFSN security by exploiting the power wave for controlled backscattering. The method introduces a Manchester-encoded digital private key generated by the BFSN’s low-power microcontroller and backscattered through a polarization-shifting module enabled by a fail-safe RF switch, thereby avoiding the need for a dedicated backscattering rectifier. A LoRaWAN-based BFSN integrating this add-on module was implemented to experimentally validate the approach. Results show successful extraction of the backscattered key with minimal energy overhead (approximately 95 µJ for a 3 ms identification sequence), while the original high-efficiency RF rectifier used for harvesting remains unmodified. The orthogonal polarization between the incoming and backscattered waves additionally reduces clutter and cross-jamming effects. These findings demonstrate that secure identification can be seamlessly incorporated into existing BFSNs without altering their core architecture, offering an easy-to-integrate and energy-efficient solution for improving security in SWIPT-based sensing systems. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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19 pages, 3394 KB  
Article
Wide Beam Analysis of Phased EM Surfaces
by Jiayue He, Fan Yang, Xiaotao Xu and Shenheng Xu
Electronics 2026, 15(1), 191; https://doi.org/10.3390/electronics15010191 - 31 Dec 2025
Viewed by 581
Abstract
Phased electromagnetic (EM) surfaces offer a versatile platform for beamforming, yet their application to wide-beam radiation—essential for broadcasting and target tracking—has been hindered by the absence of a foundational analytical model. This article establishes an effective model, quantitatively linking the maximum achievable beamwidth [...] Read more.
Phased electromagnetic (EM) surfaces offer a versatile platform for beamforming, yet their application to wide-beam radiation—essential for broadcasting and target tracking—has been hindered by the absence of a foundational analytical model. This article establishes an effective model, quantitatively linking the maximum achievable beamwidth to the surface’s core physical parameters. A direct scaling equation is first derived for an idealized continuous aperture, revealing a proportionality among beamwidth, the quadratic phase coefficient, and aperture size, which demonstrates the potential for quasi-omnidirectional coverage. The model is then extended to practical scenarios, showing that the main-lobe taper is directly controlled by the aperture amplitude taper, establishing a decoupling principle for independent control of beam shape and width. Finally, by modeling the array factor of a discrete aperture, the trade-off between element spacing and maximum beamwidth is quantified, providing clear design rules to prevent grating lobe distortion. This work provides an intuitive, physics-based foundation for the systematic design and performance prediction of wide-beam phased EM surfaces. Full article
(This article belongs to the Special Issue Advanced Antennas and Propagation for Next-Gen Wireless)
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19 pages, 1730 KB  
Article
Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters
by Minhaz Uddin Ahmed, Md Ohirul Qays, Stefan Lachowicz and Parvez Mahmud
Electronics 2026, 15(1), 177; https://doi.org/10.3390/electronics15010177 - 30 Dec 2025
Cited by 10 | Viewed by 1465
Abstract
The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address [...] Read more.
The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article
(This article belongs to the Special Issue Data-Related Challenges in Machine Learning: Theory and Application)
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21 pages, 1238 KB  
Review
Wi-Fi RSS Fingerprinting-Based Indoor Localization in Large Multi-Floor Buildings
by Inoj Neupane, Seyed Shahrestani and Chun Ruan
Electronics 2026, 15(1), 183; https://doi.org/10.3390/electronics15010183 - 30 Dec 2025
Cited by 3 | Viewed by 2736
Abstract
Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in [...] Read more.
Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article
(This article belongs to the Special Issue Machine Learning Approach for Prediction: Cross-Domain Applications)
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57 pages, 12554 KB  
Article
Multi-Fidelity Surrogate Models for Accelerated Multi-Objective Analog Circuit Design and Optimization
by Gianluca Cornetta, Abdellah Touhafi, Jorge Contreras and Alberto Zaragoza
Electronics 2026, 15(1), 105; https://doi.org/10.3390/electronics15010105 - 25 Dec 2025
Cited by 2 | Viewed by 2857
Abstract
This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on [...] Read more.
This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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47 pages, 6988 KB  
Article
A Hierarchical Predictive-Adaptive Control Framework for State-of-Charge Balancing in Mini-Grids Using Deep Reinforcement Learning
by Iacovos Ioannou, Saher Javaid, Yasuo Tan and Vasos Vassiliou
Electronics 2026, 15(1), 61; https://doi.org/10.3390/electronics15010061 - 23 Dec 2025
Cited by 3 | Viewed by 1334
Abstract
State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized [...] Read more.
State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article
(This article belongs to the Special Issue Smart Power System Optimization, Operation, and Control)
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28 pages, 1728 KB  
Article
A Lightweight Learning-Based Approach for Online Edge-to-Cloud Service Placement
by Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed and Calin Curescu
Electronics 2026, 15(1), 65; https://doi.org/10.3390/electronics15010065 - 23 Dec 2025
Cited by 2 | Viewed by 871
Abstract
The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity [...] Read more.
The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity for real-time decision-making. Because determining an optimal service placement in such networks is an NP-complete problem, the existing solutions rely on fast but suboptimal heuristics or computationally intensive metaheuristics. Neither approach meets the real-time demands of online scenarios, owing to its inefficiency or high computational overhead. In this study, we propose a lightweight learning-based approach for the online placement of services with multi-version components in edge-to-cloud computing. The proposed approach utilises a Shallow Neural Network (SNN) with both weight and power coefficients optimised using a Genetic Algorithm (GA). The use of an SNN ensures low computational overhead during the training phase and almost instant inference when deployed, making it well suited for real-time and online service placement in edge-to-cloud environments where rapid decision-making is crucial. The proposed method (SNN-GA) is specifically evaluated in AR/VR-based remote repair and maintenance scenarios, developed in collaboration with our industrial partner, and demonstrated robust performance and scalability across a wide range of problem sizes. The experimental results show that SNN-GA reduces the service response time by up to 27% compared to metaheuristics and 55% compared to heuristics at larger scales. It also achieves over 95% platform reliability, outperforming heuristics (which remain below 85%) and metaheuristics (which decrease to 90% at larger scales). Full article
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16 pages, 5350 KB  
Article
A Scalable Ultra-Compact 1.2 kV/100 A SiC 3D Packaged Half-Bridge Building Block
by Junhong Tong, Wei-Jung Hsu, Qingyun Huang and Alex Q. Huang
Electronics 2026, 15(1), 29; https://doi.org/10.3390/electronics15010029 - 22 Dec 2025
Viewed by 1146
Abstract
This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than [...] Read more.
This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than 90% while maintaining full compatibility with standard PCB manufacturing processes. The vertically laminated DC+/DC− conductors and symmetric PCB–die–PCB stack establish a tightly confined commutation loop, resulting in a measured power-loop inductance of 2.2 nH and a 3.8 nH gate-loop inductance—representing up to 94% and 89% reduction relative to discrete device implementations. Because the parasitic parameters are intrinsically well-balanced across replicated units and the mutual inductance between adjacent modules remains extremely small, the structure naturally supports current sharing during parallel operation. Thermal and insulation evaluations further confirm the suitability of copper filling via high-Tg laminated PCB substrates for high-power SiC applications, achieving withstand voltages exceeding twice the rated bus voltage. The proposed module is experimentally validated through finite-element parasitic extraction and 950 V double-pulse testing, demonstrating controlled dv/dt behavior and robust switching performance. This work establishes a manufacturable and parallel-friendly packaging approach for high-density SiC power conversion systems. Full article
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15 pages, 1613 KB  
Article
Exploring the Cognitive Capabilities of Large Language Models in Autonomous and Swarm Navigation Systems
by Dawid Ewald, Filip Rogowski, Marek Suśniak, Patryk Bartkowiak and Patryk Blumensztajn
Electronics 2026, 15(1), 35; https://doi.org/10.3390/electronics15010035 - 22 Dec 2025
Cited by 2 | Viewed by 1765
Abstract
The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research [...] Read more.
The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research investigates whether multimodal LLMs, specifically a customized version of LLaVA 7B (Large Language and Vision Assistant), can serve as a central decision-making unit for autonomous vehicles equipped with cameras and distance sensors. The developed prototype integrates a Raspberry Pi module for data acquisition and motor control with a main computational unit running the LLM via the Ollama platform. Communication between modules combines REST API for sensory data transfer and TCP sockets for real-time command exchange. Without fine-tuning, the system relies on advanced prompt engineering and context management to ensure consistent reasoning and structured JSON-based control outputs. Experimental results demonstrate that the model can interpret real-time visual and distance data to generate reliable driving commands and descriptive situational reasoning. These findings suggest that LLMs possess emerging cognitive abilities applicable to real-world robotic navigation and lay the groundwork for future swarm systems capable of cooperative exploration and decision-making in dynamic environments. These insights are particularly valuable for researchers in swarm robotics and developers of edge-AI systems seeking efficient, multimodal navigation solutions. Full article
(This article belongs to the Special Issue Data-Centric Artificial Intelligence: New Methods for Data Processing)
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33 pages, 894 KB  
Review
Impacts of Connected and Automated Driving: From Personal Acceptance to the Effects in Society: A Multi-Factor Review
by Nuria Herrero García, Nicoletta Matera, Michela Longo and Felipe Jiménez
Electronics 2026, 15(1), 27; https://doi.org/10.3390/electronics15010027 - 21 Dec 2025
Viewed by 1804
Abstract
This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel [...] Read more.
This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel approach, this paper analyses the parameters of user acceptance of technology and how these are reflected in the overall impacts of automated and connected driving. Thus, based on a behavioral intention to use the new technology model, we aim to analyze the state of the art of the overall impacts that may be correlated with individual interests. To this end, a multi-factor approach is applied and potential interactions between factors that may arise are studied in a holistic and quantitative assessment of their combined effects on transportation systems. This impact assessment is a significant challenge, as numerous factors come into play, leading to conflicting effects. Since there is no significant penetration of vehicles with medium or high levels of automation, conclusions are often obtained through simulations or estimates based on hypotheses that must be considered when analyzing the results and can lead to significant dispersion. The results confirm that these technologies can substantially improve road safety, traffic efficiency, and environmental performance. However, their large-scale deployment will critically depend on the establishment of coherent regulatory frameworks, infrastructural readiness, and societal acceptance. Comprehensive stakeholder collaboration, incorporating industry, regulatory authorities, and society, is essential to successfully address existing concerns, facilitate technological integration, and maximize the societal benefits of these transformative mobility systems. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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11 pages, 2336 KB  
Article
A Novel Feeding Technique for a Quadrifilar Helix Antenna
by Alessandro Di-Carlofelice, Emidio Di-Giampaolo and Piero Tognolatti
Electronics 2026, 15(1), 28; https://doi.org/10.3390/electronics15010028 - 21 Dec 2025
Viewed by 1410
Abstract
This paper proposes a novel method for feeding a half-turn quadrifilar helix antenna (QHA) operating in backfire mode. A self-phasing and self-supporting antenna is obtained using a specific method demonstrated numerically. Four straight parallel wires, by which a couple of short-circuited stubs are [...] Read more.
This paper proposes a novel method for feeding a half-turn quadrifilar helix antenna (QHA) operating in backfire mode. A self-phasing and self-supporting antenna is obtained using a specific method demonstrated numerically. Four straight parallel wires, by which a couple of short-circuited stubs are realized and connected in series with helix loops, constitute both the mast of the QHA and the feeding network. A prototype operating at 1 GHz is designed, realized, and measured. The results show a good axial ratio (measured cross-polar gain is about 25 dB below the co-polar one at the boresight) and good impedance matching over an adequately large frequency band. Full article
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14 pages, 4914 KB  
Article
Demonstration of 2D Optoelectronic THz-Wave Beam Steering
by Bo Li, Hussein Ssali, Yuanhao Li, Ming Che, Shenghong Ye, Yuya Mikami and Kazutoshi Kato
Electronics 2025, 14(24), 4980; https://doi.org/10.3390/electronics14244980 - 18 Dec 2025
Cited by 3 | Viewed by 845
Abstract
Advanced two-dimensional (2D) beam steering is essential for unlocking the full potential of terahertz (THz) systems in future 6G communications and high-resolution imaging. However, achieving wide-angle, high-speed, and high-precision 2D beam control within a compact THz platform remains a significant challenge. In this [...] Read more.
Advanced two-dimensional (2D) beam steering is essential for unlocking the full potential of terahertz (THz) systems in future 6G communications and high-resolution imaging. However, achieving wide-angle, high-speed, and high-precision 2D beam control within a compact THz platform remains a significant challenge. In this work, we experimentally demonstrate an optoelectronic 2×2 THz antenna array that enables flexible 2D beam steering, beam hopping, and beam scanning around the 300 GHz band. This work employs a 2×2 microstrip patch antenna (MPA) array directly driven by InGaAs/InP UTC-PDs on a silicon carbide (SiC) substrate. The relative phases of the four radiating elements are precisely programmed using an optical phased array (OPA), which provides fully decoupled and low-latency phase control in the optical domain. Experimentally, we demonstrate 2D beam steering and 2D beam hopping among three representative directions at a polar angle of 25 and azimuth angles of 60, 180, and 300. Furthermore, continuous 2D beam scanning at a fixed polar angle of 25 is achieved, enabling a full 360 azimuth sweep within 0.43 s while maintaining high beam quality. These results confirm that the proposed UTC-PD based 2×2 MPA array provides a practical and robust approach for 2D THz beam manipulation, and offers strong potential for future 6G wireless links and THz imaging applications. Full article
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22 pages, 2100 KB  
Article
A Novel Execution Time Prediction Scheme for Efficient Physical AI Resource Management
by Jin-Woo Kwon and Won-Tae Kim
Electronics 2025, 14(24), 4903; https://doi.org/10.3390/electronics14244903 - 13 Dec 2025
Cited by 2 | Viewed by 1101
Abstract
Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to [...] Read more.
Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to guarantee that tasks meet timing constraints. As a result, resource-aware execution time prediction becomes essential for efficient resource management in physical AI systems. However, existing methods typically assume specific environments or static resource usage and often fail to generalize to new environments. In this paper, we propose CARE-D (Calibration-Assisted Resource-aware Execution time prediction), which trains a deep neural network to model the nonlinear relationships among hardware characteristics, resource levels, and task features across environments. The model predicts the execution time of tasks under diverse hardware and dynamically allocated computing resources, using a few execution records from new environments. CARE-D applies few-history-based calibration using only 1 to k execution records from target environments to adjust predictions without retraining the model. Experiments show that CARE-D improves prediction accuracy by about 7.3% over zero-history predictors within a 10% relative error and outperforms regression and deep learning baselines, using only one to five records per target environment. Full article
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16 pages, 8281 KB  
Article
The Study on Real-Time RRT-Based Path Planning for UAVs Using a STM32 Microcontroller
by Shang-En Tsai, Shih-Ming Yang and Wei-Cheng Sun
Electronics 2025, 14(24), 4901; https://doi.org/10.3390/electronics14244901 - 12 Dec 2025
Cited by 3 | Viewed by 1977
Abstract
Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating [...] Read more.
Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating that real-time autonomous navigation can be achieved under low-power computation constraints. The proposed framework integrates a three-stage process—path pruning, Bézier curve smoothing, and iterative optimization—designed to minimize computational overhead while maintaining flight stability. By leveraging the STM32’s limited 72 MHz ARM Cortex-M3 core and 20 KB SRAM, the system performs all planning stages directly on the microcontroller without external computation. Experimental flight tests verify that the UAV can autonomously generate and follow smooth, collision-free trajectories across static obstacle fields with high tracking accuracy. The results confirm the feasibility of executing a full RRT-based planner on an STM32-class embedded platform, establishing a practical pathway for resource-efficient, onboard UAV autonomy. Full article
(This article belongs to the Section Systems & Control Engineering)
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35 pages, 2974 KB  
Article
Multi-Agent Coordination Strategies vs. Retrieval-Augmented Generation in LLMs: A Comparative Evaluation
by Irina Radeva, Ivan Popchev, Lyubka Doukovska and Miroslava Dimitrova
Electronics 2025, 14(24), 4883; https://doi.org/10.3390/electronics14244883 - 11 Dec 2025
Cited by 2 | Viewed by 4299
Abstract
This paper evaluates multi-agent coordination strategies against single-agent retrieval-augmented generation (RAG) for open-source language models. Four coordination strategies (collaborative, sequential, competitive, hierarchical) were tested across Mistral 7B, Llama 3.1 8B, and Granite 3.2 8B using 100 domain-specific question–answer pairs (3100 total evaluations). Performance [...] Read more.
This paper evaluates multi-agent coordination strategies against single-agent retrieval-augmented generation (RAG) for open-source language models. Four coordination strategies (collaborative, sequential, competitive, hierarchical) were tested across Mistral 7B, Llama 3.1 8B, and Granite 3.2 8B using 100 domain-specific question–answer pairs (3100 total evaluations). Performance was assessed using Composite Performance Score (CPS) and Threshold-aware CPS (T-CPS), aggregating nine metrics spanning lexical, semantic, and linguistic dimensions. Under the tested conditions, all 28 multi-agent configurations showed degradation relative to single-agent baselines, ranging from −4.4% to −35.3%. Coordination overhead was identified as a primary contributing factor. Llama 3.1 8B tolerated Sequential and Hierarchical coordination with minimal degradation (−4.9% to −5.3%). Mistral 7B with shared context retrieval achieved comparable results. Granite 3.2 8B showed degradation of 14–35% across all strategies. Collaborative coordination exhibited the largest degradation across all models. Study limitations include evaluation on a single domain (agriculture), use of 7–8B parameter models, and homogeneous agent architectures. These findings suggest that single-agent RAG may be preferable for factual question-answering tasks in local deployment scenarios with computational constraints. Future research should explore larger models, heterogeneous agent teams, role-specific prompting, and advanced consensus mechanisms. Full article
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18 pages, 2727 KB  
Article
Heterogeneous Graph Neural Network for WiFi RSSI-Based Indoor Floor Classification
by Houjin Lu and Seung-Hoon Hwang
Electronics 2025, 14(24), 4845; https://doi.org/10.3390/electronics14244845 - 9 Dec 2025
Cited by 2 | Viewed by 1092
Abstract
Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural [...] Read more.
Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural network (GNN) framework that models WiFi signals using two types of nodes: reference points and Media Access Control (MAC) address. The edges between reference points and MAC addresses are weighted by normalized RSSI values, allowing the model to capture signal strength interactions and perform relation-aware message passing. Through this graph-based representation, the model can learn spatial and signal dependencies more effectively than conventional vector-based approaches. The proposed model was extensively evaluated under both benchmark and practical settings. On small-scale datasets, it achieved performance comparable to that of a conventional convolutional neural network trained on large-scale datasets, confirming its effectiveness with limited samples. In addition, the proposed model consistently outperformed other models under noisy conditions, achieving 93.88% accuracy on the widely used UJIIndoorLoc dataset and 97.3% accuracy in real-time experiments conducted at a test site. These values are significantly higher than those achieved using conventional machine learning (ML) baselines, highlighting the ability of the proposed model to handle real-world signal variations. These findings highlight that the heterogeneous GNN effectively captures spatial and signal-level dependencies, offering a robust and scalable solution for accurate indoor floor classification. Overall, this work presents a promising pathway for improving the performance and reliability of future wireless positioning systems. Full article
(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)
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16 pages, 1309 KB  
Article
Ant Colony Optimization for CMOS Physical Design: Reducing Layout Area and Improving Aspect Ratio in VLSI Circuits
by Arnab A. Purkayastha, Jay Tharwani and Shobhit Aggarwal
Electronics 2025, 14(24), 4825; https://doi.org/10.3390/electronics14244825 - 8 Dec 2025
Viewed by 1099
Abstract
This paper presents an enhanced Ant Colony Optimization (ACO) algorithm tailored for optimizing CMOS physical design in VLSI circuits. As device complexity escalates, traditional placement techniques struggle with multiobjective goals such as minimizing layout area, wirelength, and achieving effective aspect ratios. The proposed [...] Read more.
This paper presents an enhanced Ant Colony Optimization (ACO) algorithm tailored for optimizing CMOS physical design in VLSI circuits. As device complexity escalates, traditional placement techniques struggle with multiobjective goals such as minimizing layout area, wirelength, and achieving effective aspect ratios. The proposed ACO framework simulates artificial ant colonies exploring layout configurations and reinforcing promising solutions through a pheromone-guided heuristic. Evaluated on a benchmark containing ten typical logic blocks—Adder, Multiplier, Shifter, MUX, Register, ALU, Decoder, Control, Cache, and Buffer—the ACO method achieves a maximum layout area reduction of 27.27% (from 1760 to 1280 units2) and improves the aspect ratio from 3.64 to 5.0 compared to traditional layouts. The mean area reduction observed across different parameter settings is approximately 20%. The system also includes a fully configurable and modular automation tool designed for flexible parameter tuning and the rapid benchmarking of the ACO algorithm. This tool enables users to easily adjust key parameters such as number of ants, iteration count, pheromone evaporation rate, and heuristic influences, allowing for a comprehensive exploration of the optimization space. Experimental results demonstrate ACO’s scalability, adaptability, and effectiveness, establishing it as a viable approach for automation in complex physical designs. Future work will focus on hybrid algorithms and multi-objective optimization extensions. Full article
(This article belongs to the Special Issue Recent Advances in AI Hardware Design)
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20 pages, 8243 KB  
Article
Evolution of Programmable Boards as a Response to Market Changes and Technological Paradigm Developments
by Emilian Świtalski and Krzysztof Górecki
Electronics 2025, 14(24), 4810; https://doi.org/10.3390/electronics14244810 - 6 Dec 2025
Cited by 1 | Viewed by 1176
Abstract
This article examines the evolution of programmable development boards in the context of dynamic market and technological changes, as well as new approaches to embedded system design. It presents the history of evaluation board development and its impact on engineering, emphasizing the shift [...] Read more.
This article examines the evolution of programmable development boards in the context of dynamic market and technological changes, as well as new approaches to embedded system design. It presents the history of evaluation board development and its impact on engineering, emphasizing the shift from closed ecosystems to open-source software. Particular attention is paid to the role of open source and modular architectures in shaping modern solutions. An important component of this paper is the description of the original OpenCPLC evaluation system, dedicated to microcomputer control systems. The OpenCPLC project illustrates how a layered design approach can increase the flexibility and range of PLC applications in both industry and education. The evolution of development boards indicates a clear trend in which openness and modularity are becoming key factors in determining the future of embedded systems. Full article
(This article belongs to the Section Industrial Electronics)
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34 pages, 3902 KB  
Article
Comparing Explainable AI Models: SHAP, LIME, and Their Role in Electric Field Strength Prediction over Urban Areas
by Ioannis Givisis, Dimitris Kalatzis, Christos Christakis and Yiannis Kiouvrekis
Electronics 2025, 14(23), 4766; https://doi.org/10.3390/electronics14234766 - 4 Dec 2025
Cited by 19 | Viewed by 9156
Abstract
This study presents a comparative evaluation of state-of-the-art Machine Learning (ML) and Explainable Artificial Intelligence (XAI) methods, specifically SHAP and LIME, for predicting electromagnetic field (EMF) strength in urban environments. Using more than 19,000 in situ EMF measurements across Catalonia, Spain, combined with [...] Read more.
This study presents a comparative evaluation of state-of-the-art Machine Learning (ML) and Explainable Artificial Intelligence (XAI) methods, specifically SHAP and LIME, for predicting electromagnetic field (EMF) strength in urban environments. Using more than 19,000 in situ EMF measurements across Catalonia, Spain, combined with high-resolution geospatial features such as building height, built-up volume, and population density, six ML algorithms were trained and assessed over 50 randomized train–test splits. The k-Nearest Neighbors (kNN) model achieved the highest predictive accuracy (RMSE = 0.623), followed by XGBoost (RMSE = 0.711) and LightGBM (RMSE = 0.717). Explainability analysis showed that SHAP consistently identified built-up volume, building height, degree of urbanization, and population density as the dominant global predictors of EMF strength, whereas LIME revealed that degree of urbanization, population density, and building height were the most influential at the local (micro-scale) level. The results demonstrate that integrating interpretable ML frameworks with enriched geospatial datasets improves both predictive performance and transparency in EMF exposure modeling, supporting data-driven urban planning and public health assessment. Full article
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26 pages, 736 KB  
Article
Communication-Efficient Federated Optimization with Gradient Clipping and Attention Aggregation for Data Analytics and Prediction
by Shengyuan Tang, Linwan Zhang, Shengzhe Xu, Xinyue Zeng, Peng Hu, Xinyi Gong and Manzhou Li
Electronics 2025, 14(23), 4778; https://doi.org/10.3390/electronics14234778 - 4 Dec 2025
Viewed by 1347
Abstract
To address the challenge of collaborative strategy optimization caused by non-independent and identically distributed data in cross-institutional scenarios, a federated quantitative learning framework integrating Path Attention Aggregation Module (PAAM), Gradient Clipping and Compression (GCC), and a Heterogeneity-Aware Adaptive Optimizer (HAAO) is proposed to [...] Read more.
To address the challenge of collaborative strategy optimization caused by non-independent and identically distributed data in cross-institutional scenarios, a federated quantitative learning framework integrating Path Attention Aggregation Module (PAAM), Gradient Clipping and Compression (GCC), and a Heterogeneity-Aware Adaptive Optimizer (HAAO) is proposed to achieve efficient return optimization and robust risk control. The framework is validated across multi-market and multi-institutional environments, with experiments covering three key dimensions: return performance, risk management, and communication efficiency. The results demonstrate that the proposed model achieves an annualized return (AR) of 16.57%, representing an approximate 19.7% improvement over the traditional FedAvg model; the Sharpe ratio (SR) increases to 1.25, while the maximum drawdown (MDD) decreases to 15.92% and volatility remains controlled at 8.83%, indicating superior balance between return and risk. In the communication efficiency evaluation, when the number of communication rounds is reduced to 50 and 25, the model maintains accuracy at 94.2% and 92.8%, recall at 93.5% and 91.7%, and precision at 94.8% and 92.3%, respectively. Overall, the proposed framework achieves a dynamic balance between global convergence and risk constraints through path weighting, gradient sparsification, and frequency-domain learning rate adjustment. This research provides a novel and scalable paradigm for distributed financial prediction that ensures both privacy preservation and communication efficiency, demonstrating substantial engineering feasibility and practical applicability in intelligent financial modeling. Full article
(This article belongs to the Special Issue Machine Learning in Data Analytics and Prediction)
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9 pages, 1376 KB  
Article
Comparative Study on Device Type Configurations of 2T0C DRAM for Compute-in-Memory Applications
by Seonghwan Kong and Wonbo Shim
Electronics 2025, 14(23), 4742; https://doi.org/10.3390/electronics14234742 - 2 Dec 2025
Cited by 4 | Viewed by 1904
Abstract
In this article, we systematically analyze the electrical characteristics of 2T0C DRAM unit cells for compute-in-memory (CIM) applications, focusing on the on/off current ratio, coupling effects, and retention time, with respect to the NN (NMOS-NMOS), NP (NMOS-PMOS), PN (PMOS-NMOS), and PP (PMOS-PMOS) device [...] Read more.
In this article, we systematically analyze the electrical characteristics of 2T0C DRAM unit cells for compute-in-memory (CIM) applications, focusing on the on/off current ratio, coupling effects, and retention time, with respect to the NN (NMOS-NMOS), NP (NMOS-PMOS), PN (PMOS-NMOS), and PP (PMOS-PMOS) device types. We designed 65 nm CMOS-based 2T0C DRAM unit cells using Sentaurus 3D TCAD simulation, while keeping the physical parameters and doping concentrations of the NMOS and PMOS transistors identical to impartially compare their electrical characteristics. As a result, the NP- and PN-type 2T0C DRAM cells exhibited much shorter retention times for data ‘0’ (<0.55 ns) and smaller on/off current ratios (<4.36) than the NN- and PP-type cells, clearly indicating that the NP- and PN-type cells are not favorable for CIM applications. Furthermore, we investigated the cause of the storage node voltage fluctuation immediately after a read pulse using the equivalent RC models of the NN- and PP-type cells. The origin of this fluctuation is attributed to three factors: the coupling effects between the storage node and the read transistor, the drain current in the read transistor, and the subthreshold leakage current in the write transistor. Full article
(This article belongs to the Section Semiconductor Devices)
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19 pages, 2731 KB  
Article
Adaptive Channel-Aware Garbage Collection Control for Multi-Channel SSDs
by Hyunho Mun and Youpyo Hong
Electronics 2025, 14(23), 4741; https://doi.org/10.3390/electronics14234741 - 2 Dec 2025
Viewed by 1034
Abstract
Solid-State Drives (SSDs) have become the dominant storage medium in performance-sensitive systems due to their high throughput, reliability, and energy efficiency. However, inherent constraints in NAND flash memory—such as out-of-place writes, block-level erase operations, and data fragmentation—necessitate frequent garbage collection (GC), which can [...] Read more.
Solid-State Drives (SSDs) have become the dominant storage medium in performance-sensitive systems due to their high throughput, reliability, and energy efficiency. However, inherent constraints in NAND flash memory—such as out-of-place writes, block-level erase operations, and data fragmentation—necessitate frequent garbage collection (GC), which can significantly degrade user I/O performance when not properly managed. This paper presents a channel-aware GC control mechanism for multi-channel SSD architectures that limits GC concurrency based on real-time storage utilization. Unlike conventional controllers that allow GC to proceed simultaneously across all channels—often leading to complete I/O stalls—our approach adaptively throttles the number of GC-active channels to preserve user responsiveness. The control logic uses a dynamic thresholding function that increases GC aggressiveness only as the SSD approaches full capacity, allowing the system to balance space reclamation with quality-of-service guarantees. We implement the proposed mechanism in an SSD simulator and evaluate its performance under a range of real-world workloads. Experimental results show that the proposed adaptive GC control significantly improves SSD responsiveness across various workloads. Across all workloads, the proposed adaptive GC control achieved an average latency improvement factor of 4.86×, demonstrating its effectiveness in mitigating GC-induced interference. Even when excluding extreme outlier cases, the method maintained an average improvement of 1.55×, with a standard deviation of 1.17, confirming its consistency and robustness across diverse workload patterns. Full article
(This article belongs to the Section Computer Science & Engineering)
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35 pages, 3408 KB  
Article
Unleashing GHOST: An LLM-Powered Framework for Automated Hardware Trojan Design
by Md Omar Faruque, Peter Jamieson, Ahmad Patooghy and Abdel-Hameed A. Badawy
Electronics 2025, 14(23), 4745; https://doi.org/10.3390/electronics14234745 - 2 Dec 2025
Cited by 7 | Viewed by 2670
Abstract
Traditionally, inserting realistic Hardware Trojans (HTs) in complex hardware systems has been a time-consuming manual process, requiring comprehensive knowledge of the design and navigating intricate Hardware Description Language (HDL) codebases. Machine Learning (ML)-based approaches have attempted to automate this process but often struggle [...] Read more.
Traditionally, inserting realistic Hardware Trojans (HTs) in complex hardware systems has been a time-consuming manual process, requiring comprehensive knowledge of the design and navigating intricate Hardware Description Language (HDL) codebases. Machine Learning (ML)-based approaches have attempted to automate this process but often struggle with the need for extensive training data, learning time, and limited generalizability across diverse hardware design landscapes. This paper introduces GHOST, an automated tool that leverages Large Language Models (LLMs) for rapid generation and insertion of HT. The research encompasses both the development of the GHOST framework and a comprehensive evaluation of its effectiveness across three state-of-the-art LLMs-GPT-4, Gemini-1.5-Pro, and Llama-3-70B. According to our evaluations, GPT-4 demonstrates the best performance by successfully generating and inserting HTs in 88.9% of its attempts. This study also highlights the security risks posed by LLM-generated HTs, as 100% of successful GHOST-generated HTs that completed inference within the time limit evaded detection by a state-of-the-art ML-based HT detection tool. These results underscore the need for advanced detection and prevention mechanisms in hardware security to address the emerging threat of LLM-generated HTs. Full article
(This article belongs to the Section Artificial Intelligence)
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17 pages, 25231 KB  
Article
Low-Cost and Fully Metallic Reconfigurable Leaky-Wave Antenna Based on 3D-Printing Technology for Multi-Beam Operation
by Miguel Díaz-Martín, Carlos Molero, Ginés Martínez-García and Marcos Baena-Molina
Electronics 2025, 14(23), 4723; https://doi.org/10.3390/electronics14234723 - 30 Nov 2025
Cited by 1 | Viewed by 1195
Abstract
Global data consumption is experiencing exponential growth, driving the demand for wireless links with higher transmission speeds, lower latency, and support for emerging applications such as 6G. A promising approach to address these requirements is the use of higher-frequency bands, which in turn [...] Read more.
Global data consumption is experiencing exponential growth, driving the demand for wireless links with higher transmission speeds, lower latency, and support for emerging applications such as 6G. A promising approach to address these requirements is the use of higher-frequency bands, which in turn necessitates the development of advanced antenna systems. This work presents the design and experimental validation of a reconfigurable, low-cost leaky-wave antenna capable of controlling the propagation direction of single-, dual-, and triple-beam configurations in the FR3 frequency band. The antenna employs slotted periodic patterns to enable directional electromagnetic field leakage, and it is based on a cost-effective and simple 3D-printing fabrication process. Laboratory testing confirms the theoretical and simulated predictions, demonstrating the feasibility of the proposed antenna solution. Full article
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28 pages, 3446 KB  
Article
Reaction Wheel Pendulum Stabilization Using Various State-Space Representations
by Jacek Michalski, Mikołaj Mrotek, Tymoteusz Tomczak, Jakub Wojciechowski and Dariusz Pazderski
Electronics 2025, 14(23), 4719; https://doi.org/10.3390/electronics14234719 - 29 Nov 2025
Cited by 2 | Viewed by 1565
Abstract
This paper addresses the problem of stabilizing an inverted pendulum actuated by a reaction wheel, a system relevant for robotic balancing platforms and aerospace applications. The study compares several state-space representations of the system and examines their implications for controller synthesis and parameter [...] Read more.
This paper addresses the problem of stabilizing an inverted pendulum actuated by a reaction wheel, a system relevant for robotic balancing platforms and aerospace applications. The study compares several state-space representations of the system and examines their implications for controller synthesis and parameter identification. A unified nonlinear model formulation is introduced, enabling a structural Lyapunov-based robustness analysis that reveals how variations in the gravitational gain affect closed-loop stability. Control strategies based on pole placement and Linear Quadratic Regulator (LQR) design are implemented and compared across the different representations. The analysis highlights a robustness–fidelity trade-off between model complexity and sensitivity to parameter uncertainty, providing insight that extends beyond the specific laboratory setup. Theoretical results are validated on a real laboratory platform. The controllers are evaluated in both upright and downward equilibrium configurations, and the influence of parameter shifts is assessed experimentally using global identification and performance indices. The work offers general modeling and robustness guidelines for reaction-wheel-based stabilization systems and related underactuated nonlinear mechanisms. Full article
(This article belongs to the Section Systems & Control Engineering)
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17 pages, 4978 KB  
Article
Optimizing Periodic Intervals in Multi-Stage Waveguide Stub Bandstop Filters for Microwave Leakage Suppression
by Yusuke Kusama, Hao-Hui Chen, Yao-Wen Hsu, Kyohei Murayama and Robert Weston Johnston
Electronics 2025, 14(23), 4660; https://doi.org/10.3390/electronics14234660 - 27 Nov 2025
Cited by 1 | Viewed by 922
Abstract
Waveguide bandstop filters (BSFs) play a key role in preventing electromagnetic wave leakage from gaps or sample entrances and exits, which can compromise safety, work efficiency, and electromagnetic compatibility. This study designs a waveguide BSF using a finite periodic structure of cascaded short-circuited [...] Read more.
Waveguide bandstop filters (BSFs) play a key role in preventing electromagnetic wave leakage from gaps or sample entrances and exits, which can compromise safety, work efficiency, and electromagnetic compatibility. This study designs a waveguide BSF using a finite periodic structure of cascaded short-circuited E-plane stubs (chokes) to achieve a stopband with the transmission coefficient |S21| ≤ −30 dB and a 4% relative bandwidth. We investigate the impact of stub width on bandwidth broadening and stub spacing in cascade connections on spurious passband suppression. Electromagnetic and circuit simulations, validated experimentally, reveal that stub spacing at odd multiples of a quarter guided wavelength (λg/4) minimizes spurious passbands, with wider stubs and larger spacings enhancing stopband characteristics. This indicates that there is a great advantage in reducing the number of resonators and manufacturing costs. These findings provide practical and new design guidelines for designing efficient BSFs for preventing microwave leakage and may have applications in other filters or array antennas using periodicity. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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36 pages, 2307 KB  
Article
From Energy Efficiency to Energy Intelligence: Power Electronics as the Cognitive Layer of the Energy Transition
by Nikolay Hinov
Electronics 2025, 14(23), 4673; https://doi.org/10.3390/electronics14234673 - 27 Nov 2025
Cited by 5 | Viewed by 1550
Abstract
The exponential growth of artificial intelligence (AI), electrified transport, and renewable generation is accelerating a structural shift in how societies produce, deliver, and consume electricity. We argue that the next frontier is not incremental efficiency but Energy Intelligence (EI): the embedding of predictive [...] Read more.
The exponential growth of artificial intelligence (AI), electrified transport, and renewable generation is accelerating a structural shift in how societies produce, deliver, and consume electricity. We argue that the next frontier is not incremental efficiency but Energy Intelligence (EI): the embedding of predictive analytics, adaptive control, and material-aware design directly into power-conversion hardware. In this view, power electronics functions as the cognitive layer that links digital intelligence to the physical flow of energy. Wide-bandgap (WBG) semiconductors—gallium nitride (GaN) and silicon carbide (SiC)—provide the material foundation for higher switching frequencies, superior power density, and real-time controllability, enabling compact and efficient converters for data-centers, EV charging, and grid-interactive resources. We formalize an EI reference architecture (predictive, adaptive, material-efficient, data-driven), review the convergence of AI methods with converter design and operation, and outline a GaN/SiC-enabled data-center power path as an illustrative case. Finally, we examine sustainability and sovereignty, highlighting exposure to critical materials (Ga, Si, In, rare earths) and proposing a roadmap that integrates technology, policy, and education. By reframing power electronics as an intelligent, learning infrastructure, this work sets an agenda for systems that are not only efficient but also self-optimizing, explainable, and resilient. Full article
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19 pages, 3438 KB  
Article
Geometry-Aware Cross-Modal Translation with Temporal Consistency for Robust Multi-Sensor Fusion in Autonomous Driving
by Zhengyi Lu, Jinxiang Pang and Zhehai Zhou
Electronics 2025, 14(23), 4663; https://doi.org/10.3390/electronics14234663 - 27 Nov 2025
Cited by 1 | Viewed by 1583
Abstract
Intelligent Transportation Systems (ITSs), particularly autonomous driving, face critical challenges when sensor modalities fail due to adverse conditions or hardware malfunctions, causing severe perception degradation that threatens system-wide reliability. We present a unified geometry-aware cross-modal translation framework that synthesizes missing sensor data while [...] Read more.
Intelligent Transportation Systems (ITSs), particularly autonomous driving, face critical challenges when sensor modalities fail due to adverse conditions or hardware malfunctions, causing severe perception degradation that threatens system-wide reliability. We present a unified geometry-aware cross-modal translation framework that synthesizes missing sensor data while maintaining temporal consistency and quantifying uncertainty. Our pipeline enforces 92.7% frame-to-frame stability via an optical-flow-guided spatio-temporal module with smoothness regularization, preserves fine-grained 3D geometry through pyramid-level multi-scale alignment constrained by the Chamfer distance, surface normals, and edge consistency, and ultimately delivers dropout-tolerant perception by adaptively fusing multi-modal cues according to pixel-wise uncertainty estimates. Extensive evaluation on KITTI-360, nuScenes, and a newly collected Real-World Sensor Failure dataset demonstrates state-of-the-art performance: 35% reduction in Chamfer distance, 5% improvement in BEV (bird’s eye view) segmentation mIoU (mean Intersection over Union) (79.3%), and robust operation maintaining mIoU under complete sensor loss for 45+ s. The framework achieves real-time performance at 17 fps with 57% fewer parameters than competing methods, enabling deployment-ready sensor-agnostic perception for safety-critical autonomous driving applications. Full article
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24 pages, 9332 KB  
Article
Controlled Operation of Table ASSIST-EW Motion Assisting Device
by Earnest Ugonna Ofonaike and Marco Ceccarelli
Electronics 2025, 14(23), 4674; https://doi.org/10.3390/electronics14234674 - 27 Nov 2025
Viewed by 618
Abstract
Table ASSIST-EW is a lightweight, portable, and ergonomic exoskeletal device that is designed to support upper limb rehabilitation and to facilitate regular exercise in elderly users. Targeting the elbow and wrist joints, the device delivers smooth controlled assistance through a cable-driven actuation system [...] Read more.
Table ASSIST-EW is a lightweight, portable, and ergonomic exoskeletal device that is designed to support upper limb rehabilitation and to facilitate regular exercise in elderly users. Targeting the elbow and wrist joints, the device delivers smooth controlled assistance through a cable-driven actuation system that mimics natural muscle–tendon action. The system works with a scalable modular control architecture that enables the regulation of joint motion across a range of user needs and therapeutic contexts. The control design integrates force and motion feedback to implement assist-as-needed strategies, ensuring both safety and adaptability. Built on a bioinspired mechanical framework with revolute joint alignment and a soft inner interface for enhanced comfort, the device accommodates varied arm geometries and motion patterns. Simulation of key parameters—torque, stress, and energy demands—informed component selection and controller tuning. Experimental validation results confirm consistent performance across passive, active–assistive, and resistive control modes. Full article
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41 pages, 3435 KB  
Systematic Review
Exploring the Educational Applications of Large Language Models: A Systematic Review and Topic Analysis
by Bianca-Raluca Cibu, Liliana Crăciun, Anca Gabriela Molănescu and Liviu-Adrian Cotfas
Electronics 2025, 14(23), 4683; https://doi.org/10.3390/electronics14234683 - 27 Nov 2025
Cited by 8 | Viewed by 4255
Abstract
In light of the accelerated growth of artificial intelligence (AI), large language models (LLMs) have become a central topic of interest in scientific research and practical applications across various fields. The present paper aims to perform a comprehensive systematic review of the scientific [...] Read more.
In light of the accelerated growth of artificial intelligence (AI), large language models (LLMs) have become a central topic of interest in scientific research and practical applications across various fields. The present paper aims to perform a comprehensive systematic review of the scientific literature on LLMs in education published between 2023 and 2024, based on a dataset from the Web of Science, which includes 507 documents from 322 sources. The accelerated dynamics of research in this field are confirmed by the high annual growth rate of 369.66%. The study identifies the themes presented in the scientific literature by using thematic maps and analyzing the evolution of said thematic maps. In addition, Latent Dirichlet Allocation (LDA) and BERTopic are used to outline the research field more clearly. Due to LDA’s ability to discover high-level research topics using probabilistic discovery and BERTopic’s ability to capture deeper semantic patterns and the emergence of various topics by searching, this paper first identifies the main research topics in the extracted dataset, which are then discussed in the paper through a review of applications. As a result, a range of applications are discovered in areas related to teaching and learning, academic assessment, integrity, academic feedback, medical education, ethics, bias, regulation, and social challenges. The conclusions provide a roadmap for researchers, practitioners and stakeholders in highlighting the current situation of LLMs in educational practice, while opening the door for future explorations in this domain. Full article
(This article belongs to the Section Artificial Intelligence)
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19 pages, 2140 KB  
Article
AI-Driven Adaptive Segmentation of Timed Up and Go Test Phases Using a Smartphone
by Muntazir Rashid, Arshad Sher, Federico Villagra Povina and Otar Akanyeti
Electronics 2025, 14(23), 4650; https://doi.org/10.3390/electronics14234650 - 26 Nov 2025
Viewed by 1431
Abstract
The Timed Up and Go (TUG) test is a widely used clinical tool for assessing mobility and fall risk in older adults and individuals with neurological or musculoskeletal conditions. While it provides a quick measure of functional independence, traditional stopwatch-based timing offers only [...] Read more.
The Timed Up and Go (TUG) test is a widely used clinical tool for assessing mobility and fall risk in older adults and individuals with neurological or musculoskeletal conditions. While it provides a quick measure of functional independence, traditional stopwatch-based timing offers only a single completion time and fails to reveal which movement phases contribute to impairment. This study presents a smartphone-based system that automatically segments the TUG test into distinct phases, delivering objective and low-cost biomarkers of lower-limb performance. This approach enables clinicians to identify phase-specific impairments in populations such as individuals with Parkinson’s disease, and older adults, supporting precise diagnosis, personalized rehabilitation, and continuous monitoring of mobility decline and neuroplastic recovery. Our method combines adaptive preprocessing of accelerometer and gyroscope signals with supervised learning models (Random Forest, Support Vector Machine (SVM), and XGBoost) using statistical features to achieve continuous phase detection and maintain robustness against slow or irregular gait, accommodating individual variability. A threshold-based turn detection strategy captures both sharp and gradual rotations. Validation against video ground truth using group K-fold cross-validation demonstrated strong and consistent performance: start and end points were detected in 100% of trials. The mean absolute error for total time was 0.42 s (95% CI: 0.36–0.48 s). The average error across phases (stand, walk, turn) was less than 0.35 s, and macro F1 scores exceeded 0.85 for all models, with the SVM achieving the highest score of 0.882. Combining accelerometer and gyroscope features improved macro F1 by up to 12%. Statistical tests (McNemar, Bowker) confirmed significant differences between models, and calibration metrics indicated reliable probabilistic outputs (ROC-AUC > 0.96, Brier score < 0.08). These findings show that a single smartphone can deliver accurate, interpretable, and phase-aware TUG analysis without complex multi-sensor setups, enabling practical and scalable mobility assessment for clinical use. Full article
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11 pages, 428 KB  
Article
RMF-A: An Availability Assurance Framework for Quantitative Evaluation of Operational Resilience
by Cheon-Ho Min and Jin Kwak
Electronics 2025, 14(23), 4644; https://doi.org/10.3390/electronics14234644 - 26 Nov 2025
Viewed by 1013
Abstract
Recent data center incidents have revealed that certification under ISO 22301 and ISO/IEC 27001 does not guarantee real operational resilience. This study presents the Availability Assurance Framework (RMF-A), an extension of the NIST Risk Management Framework that introduces an Availability Assurance Phase. RMF-A [...] Read more.
Recent data center incidents have revealed that certification under ISO 22301 and ISO/IEC 27001 does not guarantee real operational resilience. This study presents the Availability Assurance Framework (RMF-A), an extension of the NIST Risk Management Framework that introduces an Availability Assurance Phase. RMF-A combines ISO-based management controls with NIST’s evidence-driven assessment using the Availability Evidence Model (AEM) and the Availability Assurance Index (AAI). AEM defines measurable indicators—recovery rate (RR), recovery time (RTO), and Detection Effectiveness (DET)—and AAI aggregates them into a quantitative assurance score. Validation using three open datasets—Google Cluster Trace, Azure Cloud Trace, and LANL HPC Logs—showed consistent assurance results: Google (AAI = 0.758, ATO-Conditional), Azure (AAI = 0.720, ATO-Conditional), and LANL HPC (AAI = 0.744, ATO-Conditional). The results confirm that RMF-A provides a reproducible, evidence-based approach to quantify operational resilience and ensure continuous availability. Full article
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21 pages, 4092 KB  
Article
Enabling Scalable and Manufacturable Large-Scale Antenna Arrays Through Hexagonal Subarray Implementation over Goldberg Polyhedra
by Santiago Loza-Morcillo and José Luis Blanco-Murillo
Electronics 2025, 14(23), 4618; https://doi.org/10.3390/electronics14234618 - 25 Nov 2025
Cited by 1 | Viewed by 1441
Abstract
We introduce a scalable and manufacturable approach to conformal large-scale antenna arrays, leveraging Goldberg Polyhedra configurations with hexagonal subarrays to enable cost-effective, high-performance beam steering. Planar array designs face challenges in phase control and beam deformation when steering away from the broadside, leading [...] Read more.
We introduce a scalable and manufacturable approach to conformal large-scale antenna arrays, leveraging Goldberg Polyhedra configurations with hexagonal subarrays to enable cost-effective, high-performance beam steering. Planar array designs face challenges in phase control and beam deformation when steering away from the broadside, leading to increased beamwidth and degraded angular resolution. Our near-spherical Goldberg structures offer a fabrication-friendly, periodic architecture that supports industrial scalability while enabling efficient 360° digital beamforming with minimal distortion. Simulation results confirm significant reductions in sidelobe levels and improved energy concentration, providing enhanced multibeam capabilities and simplified digital beamforming (DBF) control. This approach paves the way for next-generation radar and satellite systems requiring precise directional control, minimal interference, and robust, flexible beam steering performance. Full article
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23 pages, 8756 KB  
Article
Application and Development of a Double Asymmetric Voltage Modulation on a Resonant Dual Active Bridge
by Mattia Vogni, Juan L. Bellido, Fausto Stella, Leonardo Stefanini, Claudio Bianchini and Vicente Esteve
Electronics 2025, 14(23), 4625; https://doi.org/10.3390/electronics14234625 - 25 Nov 2025
Cited by 5 | Viewed by 987
Abstract
The growing market penetration of Electric Vehicles (EVs) requires very efficient bidirectional on-board chargers. These converters must allow the power transfer from the grid to the battery of the vehicle and vice versa, since Vehicle to Grid (V2G) applications enable a mitigation of [...] Read more.
The growing market penetration of Electric Vehicles (EVs) requires very efficient bidirectional on-board chargers. These converters must allow the power transfer from the grid to the battery of the vehicle and vice versa, since Vehicle to Grid (V2G) applications enable a mitigation of the peak demand and help regulate both the voltage and the frequency of the grid. In this paper, an innovative double asymmetric modulation was studied and applied to a resonant Dual Active Bridge (DAB), CLLC resonant filter configuration. The results of the study showed a significant efficiency boost and an easier controllability of the converter with respect to more traditional modulations or variable frequency techniques, maintaining Zero-Voltage Switching (ZVS) conditions for all the switches in a wide operating range, from 28 to 100% of the maximum power (4–14 kW). A map of optimum points, where converter losses are minimized, is calculated offline through an algorithm in MATLAB R2024a and a proper interpolation between these points allows any output power for each possible voltage level of the battery to be achieved: from 250 V up to 400 V. The modulations are compared and evaluated through simulations carried out in PLECS, both offline and using hardware-in-the-loop (HIL), as well as through experimental tests. Full article
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20 pages, 4144 KB  
Article
Model Predictive Control Using the Improved Hovorka Model for the Regulation of Blood Glucose Levels in Type 1 Diabetes
by Iqra Shafeeq Mughal, Stefan Koch, Luca Patanè, Martin Steinberger, Riccardo Caponetto and Nebojša Koledin
Electronics 2025, 14(23), 4585; https://doi.org/10.3390/electronics14234585 - 23 Nov 2025
Cited by 1 | Viewed by 1775
Abstract
Type 1 diabetes is an autoimmune disease that occurs when the immune system unintentionally attacks and damages β cells in the pancreas, reducing the organ’s ability to produce insulin. An artificial pancreas is a technology that uses a pump to inject the appropriate [...] Read more.
Type 1 diabetes is an autoimmune disease that occurs when the immune system unintentionally attacks and damages β cells in the pancreas, reducing the organ’s ability to produce insulin. An artificial pancreas is a technology that uses a pump to inject the appropriate amount of insulin subcutaneously after analysing information collected by sensors, including continuous blood glucose monitoring. Over the past thirty years, several methods for controlling an artificial pancreas have been investigated in clinical and simulation environments. The improved Hovorka model, a comprehensive nonlinear model that explains the effects of insulin on transport, disposal and endogenous synthesis in both accessible and inaccessible compartments for blood glucose control by insulin administration, is used for this research. The presented model has the characteristics of a switching nonlinear system. The work proposes to analyse different nonlinear control strategies for blood glucose regulation and shows the effectiveness of the linear model predictive control strategy compared to other nonlinear controllers used in the literature. Full article
(This article belongs to the Section Systems & Control Engineering)
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38 pages, 25113 KB  
Article
A Two-Stage End-to-End Framework for Robust Scene Text Spotting with Self-Calibrated Detection and Contextual Recognition
by Yuning Cheng, Jinhong Huang, Io San Tai, Subrota Kumar Mondal, Tianqi Wang and Hussain Mohammed Dipu Kabir
Electronics 2025, 14(23), 4594; https://doi.org/10.3390/electronics14234594 - 23 Nov 2025
Cited by 3 | Viewed by 2113
Abstract
End-to-end scene text detection and recognition, which involves detecting and recognizing text in natural images, still faces significant challenges, particularly in handling text of arbitrary shapes, complex backgrounds, and computational efficiency requirements. This paper proposes a novel and viable end-to-end OCR framework that [...] Read more.
End-to-end scene text detection and recognition, which involves detecting and recognizing text in natural images, still faces significant challenges, particularly in handling text of arbitrary shapes, complex backgrounds, and computational efficiency requirements. This paper proposes a novel and viable end-to-end OCR framework that synergistically combines a powerful detection network with advanced recognition models. For text detection, we develop a method called Text Contrast Self-Calibrated Network (TextCSCN), which employs pixel-wise supervised contrastive learning to extract more discriminative features. TextCSCN addresses long-range dependency modeling and limited receptive field issues through self-calibrated convolutions and Global Convolutional Networks (GCNs). We further introduce an efficient Mamba-based bidirectional module for boundary refinement, enhancing both accuracy and speed. For text recognition, our framework employs a Swin Transformer backbone with Bidirectional Feature Pyramid Networks (BiFPNs) for optimized multi-scale feature extraction. We propose a Pre-Gated Contextual Attention Gate (PCAG) mechanism to effectively fuse visual and linguistic information while minimizing noise and uncertainty in multi-modal integration. Experiments on challenging benchmarks including TotalText and CTW1500 demonstrate the effectiveness of our approach. Our detection module achieves state-of-the-art performance with an F-score of 88.21% on TotalText, and the complete end-to-end system shows comparable improvements in recognition accuracy, establishing new benchmarks for scene text spotting. Full article
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13 pages, 835 KB  
Article
Layer-Pipelined CNN Accelerator Design on 2.5D FPGAs
by Mengxuan Wang and Chang Wu
Electronics 2025, 14(23), 4587; https://doi.org/10.3390/electronics14234587 - 23 Nov 2025
Cited by 1 | Viewed by 1317
Abstract
With the rapid advancement of 2.5D FPGA technology, the integration of multiple FPGA dies enables larger design capacity and higher computing power. This progress provides a high-speed hardware platform well-suited for neural network acceleration. In this paper, we present a high-performance accelerator design [...] Read more.
With the rapid advancement of 2.5D FPGA technology, the integration of multiple FPGA dies enables larger design capacity and higher computing power. This progress provides a high-speed hardware platform well-suited for neural network acceleration. In this paper, we present a high-performance accelerator design for large-scale neural networks on 2.5D FPGAs. First, we propose a layer pipeline architecture that utilizes multiple accelerator cores, each equipped with individual high-bandwidth DDR memory. To address inter-die data dependencies, we introduce a block convolution mechanism that enables independent and efficient computation across dies. Furthermore, we propose a design space exploration scheme to optimize computational efficiency under resource constraints. Experimental results demonstrate that our proposed accelerator achieves 4860.87 GOPS when running VGG-16 on the Alveo U250 board, significantly outperforming existing layer pipeline designs on the same platform. Full article
(This article belongs to the Special Issue Advances in High-Performance and Parallel Computing)
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31 pages, 1411 KB  
Article
A Source-to-Source Compiler to Enable Hybrid Scheduling for High-Level Synthesis
by Yuhan She, Yanlong Huang, Jierui Liu, Ray C. C. Cheung and Hong Yan
Electronics 2025, 14(23), 4578; https://doi.org/10.3390/electronics14234578 - 22 Nov 2025
Viewed by 1109
Abstract
High-Level Synthesis (HLS) has gained considerable attention for its ability to quickly generate hardware descriptions from untimed specifications. Most state-of-the-art commercial HLS tools employ static scheduling, which excels in compute-intensive applications but struggles with control-dominant designs. While some open-source tools propose dynamic and [...] Read more.
High-Level Synthesis (HLS) has gained considerable attention for its ability to quickly generate hardware descriptions from untimed specifications. Most state-of-the-art commercial HLS tools employ static scheduling, which excels in compute-intensive applications but struggles with control-dominant designs. While some open-source tools propose dynamic and hybrid scheduling techniques to synthesize dataflow-like architectures to improve speed, they lack well-established optimizations from static scheduling like datapath optimization and resource sharing, leading to frequency degradation and area overhead. Moreover, existing hybrid scheduling relies on extra dynamic synthesis support, either by dynamic or static HLS tools, and thereby loses generality. In this work, we propose another solution to achieve hybrid scheduling: a source-to-source compiler that exposes dynamism at the source code level, which reduces both frequency and area overhead while remaining fully compatible with modern static HLS tools without needing extra dynamic synthesis support. Experiments show significant improvements (1.26× speedup) on wall clock time (WCT) compared to VitisHLS and a better area–frequency–latency trade-off compared to dynamic (1.83× WCT speedup and 0.46× area) and hybrid (2.14× WCT speedup and 0.72× area) scheduling-based tools. Full article
(This article belongs to the Special Issue Emerging Applications of FPGAs and Reconfigurable Computing System)
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33 pages, 1391 KB  
Review
Hyperspectral Imaging System Applications in Healthcare
by Krzysztof Wołk and Agnieszka Wołk
Electronics 2025, 14(23), 4575; https://doi.org/10.3390/electronics14234575 - 22 Nov 2025
Cited by 11 | Viewed by 4103
Abstract
Hyperspectral imaging (HSI) is a swiftly developing intraoperative and diagnostic technique in several clinical specialties. By monitoring oxygenation and biochemical markers, it helps with tissue viability, burn depth measurement, wound healing, and tumor detection. HSI facilitates real-time, harmless diagnosis throughout surgeries or outpatient [...] Read more.
Hyperspectral imaging (HSI) is a swiftly developing intraoperative and diagnostic technique in several clinical specialties. By monitoring oxygenation and biochemical markers, it helps with tissue viability, burn depth measurement, wound healing, and tumor detection. HSI facilitates real-time, harmless diagnosis throughout surgeries or outpatient settings, and allows for the detection of tumor boundaries with over 90% accuracy, according to clinical studies. Originally developed for remote sensing and aerospace applications, HSI has rapidly evolved and found increasing relevance across diverse sectors, including agriculture, environmental monitoring, food safety, pharmaceuticals, defense, and especially medical diagnostics. This review explores the origins, development, and expanding applications of HSI, with a particular emphasis on its role in healthcare. It discusses the operational principles and unique features of hyperspectral systems, such as their ability to produce spectral data cubes, perform non-destructive analysis, and integrate with emerging technologies like artificial intelligence and drone-based platforms. By comparing hyperspectral imaging to traditional and multispectral techniques, the review highlights its superior spectral resolution and versatility. Key challenges, including data volume, sensor calibration, and real-time processing, are also addressed. Finally, emerging trends such as miniaturization, integration with the Internet of Things, and sustainable system designs are examined, offering insights into the future directions and interdisciplinary potentials of HSI in both scientific research and practical applications. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Technologies and Applications)
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36 pages, 3549 KB  
Article
Feasibility of Large-Scale Electric Vehicle Deployment in Islanded Grids: The Canary Islands Case
by Alejandro García García, Víctor Rubio Matilla, Juan Diego López Arquillo and Cristiana Oliveira
Electronics 2025, 14(23), 4579; https://doi.org/10.3390/electronics14234579 - 22 Nov 2025
Cited by 3 | Viewed by 1721
Abstract
The present integration of electric vehicles into everyday life has the potential to redefine current standards of urban mobility. However, the territorial impact of this deployment demands a multiscale effort to ensure both efficient and sustainable performance; this is even more necessary in [...] Read more.
The present integration of electric vehicles into everyday life has the potential to redefine current standards of urban mobility. However, the territorial impact of this deployment demands a multiscale effort to ensure both efficient and sustainable performance; this is even more necessary in a disconnected system like an island. This article addresses the possibility of transforming the existing fossil-fuel-based infrastructure within Europe’s outermost regions into an electric vehicle charging network, with particular emphasis on the Canary Islands’ strategic plans. Using official datasets from Red Eléctrica de España (REE), IDAE, and the Canary Islands’ Energy Transition Plan (PTECan), we develop three scenarios (2025 baseline, 2030, and 2040) to quantify the additional electricity demand, peak load requirements, charging infrastructure needs, and associated greenhouse gas emissions. The methodology combines EV fleet projections, the driving patterns of residents and tourists, and vehicle efficiency data to estimate yearly electricity demand and hourly charging loads. The carbon intensity profiles of each island’s grid are used to calculate well-to-wheel emissions of EVs, benchmarked against internal combustion engine vehicles. The results indicate that achieving 250,000 EVs by 2030 would increase electricity demand by 1.1–1.4 TWh/year (+8–12% of current consumption), requiring approximately 25,000–30,000 public charging points. EV emissions range from 90 to 150 gCO2/km depending on charging time, compared to 160–190 gCO2/km for ICE vehicles. Smart charging and vehicle-to-grid integration could mitigate 15–25% of peak load increases, reducing the curtailment of renewables and deferring grid investments. A comparative analysis with Zealand highlights policy synergies and differences in insular versus continental grids. The findings confirm that large-scale EV adoption in the Canary Islands is technically feasible, but quite difficult, as it requires the deep, coordinated planning of renewable expansion, storage, and a charging infrastructure. BEV WTW advantages become unequivocal once the average grid carbon intensity falls below ≈0.8–0.9 tCO2/MWh, underscoring the primacy of accelerated renewable build-out and demand-side flexibility. Despite uncertainties in adoption and technology trajectories, the approach is transparent and reproducible with official datasets, providing a transferable planning tool for other islanded systems and mainland Europe. The proposed method demonstrates its usefulness in direct linking electrification scenarios with the real capacity of the electricity system, allowing the identification of very critical integration thresholds and guiding evidence-based planning decisions. Full article
(This article belongs to the Special Issue Advances in Electric Vehicle Technology)
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27 pages, 659 KB  
Review
From Vulnerability to Robustness: A Survey of Patch Attacks and Defenses in Computer Vision
by Xinyun Liu and Ronghua Xu
Electronics 2025, 14(23), 4553; https://doi.org/10.3390/electronics14234553 - 21 Nov 2025
Cited by 1 | Viewed by 2447
Abstract
Adversarial patch attacks have emerged as a powerful and practical threat to machine learning models in vision-based tasks. Unlike traditional perturbation-based adversarial attacks, which often require imperceptible changes to the entire input, patch attacks introduce localized and visible modifications that can consistently mislead [...] Read more.
Adversarial patch attacks have emerged as a powerful and practical threat to machine learning models in vision-based tasks. Unlike traditional perturbation-based adversarial attacks, which often require imperceptible changes to the entire input, patch attacks introduce localized and visible modifications that can consistently mislead deep neural networks across varying conditions. Their physical realizability makes them particularly concerning for real-world security-critical applications. In response, a growing body of research has proposed diverse defense strategies, including input preprocessing, robust model training, detection-based approaches, and certified defense mechanisms. In this paper, we provide a comprehensive review of patch-based adversarial attacks and corresponding defense techniques. First, we introduce a new task-oriented taxonomy that systematically categorizes patch attack methods according to their downstream vision applications (e.g., classification, detection, segmentation), and then we summarize defense mechanisms based on three major strategies: Patch Localization and Removal-based Defenses, Input Transformation and Reconstruction-based Defenses, Model Modification and Training-based Defenses. This unified framework provides an integrated perspective that bridges attack and defense research. Furthermore, we highlight open challenges, such as balancing robustness and model utility, addressing adaptive attackers, and ensuring physical-world resilience. Finally, we outline promising research directions to inspire future work toward building trustworthy and robust vision systems against patch-based adversarial threats. Full article
(This article belongs to the Special Issue Artificial Intelligence Safety and Security)
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22 pages, 5988 KB  
Article
Research on Battery Aging and User Revenue of Electric Vehicles in Vehicle-to-Grid (V2G) Scenarios
by Zhiyu Zhao, Shuaihao Kong, Bo Bo, Xuemei Li, Ling Hao, Fei Xu and Lei Chen
Electronics 2025, 14(23), 4567; https://doi.org/10.3390/electronics14234567 - 21 Nov 2025
Cited by 4 | Viewed by 2462
Abstract
With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this [...] Read more.
With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this study, aging models for power batteries in electric passenger vehicles and electric trucks are established. A time-of-use electricity price model and an economic model considering battery aging costs are constructed. Two scenarios were established for daily use and V2G operation. The impacts of different scenarios and charging/discharging patterns on battery life and user profit are analyzed. The results indicate that the additional V2G discharging process increases the cyclic aging rate of EV batteries. Within the studied parameter ranges, the cyclic aging rate increased by 5.89% for electric passenger vehicles and 3.72% for electric trucks, respectively. Additionally, the initial V2G revenue may struggle to cover early-stage battery aging costs, but the subsequent slowdown in degradation may eventually offset these costs. With appropriate charging and discharging strategies, the maximum revenue per year reaches 18,200 CNY for electric trucks and 5600 CNY for electric passenger vehicles. This study may provide theoretical support for optimizing EV charging/discharging strategies and formulating policies in V2G scenarios. Full article
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26 pages, 952 KB  
Article
From Forecasting to Foresight: Building an Autonomous O&M Brain for the New Power System Based on a Cognitive Digital Twin
by Xufeng Wu, Zuowei Chen, Hefang Jiang, Shoukang Luo, Yi Zhao, Dongwei Zhao, Peiyao Dang, Jiajun Gao, Lin Lin and Hao Wang
Electronics 2025, 14(22), 4537; https://doi.org/10.3390/electronics14224537 - 20 Nov 2025
Cited by 8 | Viewed by 1757
Abstract
Despite notable advances in load forecasting and fault detection, current power system operation and maintenance (O&M) technologies remain fragmented into independent and primarily reactive modules. Load forecasting estimates future demand, whereas fault detection identifies whether abnormal conditions exist in the present state. This [...] Read more.
Despite notable advances in load forecasting and fault detection, current power system operation and maintenance (O&M) technologies remain fragmented into independent and primarily reactive modules. Load forecasting estimates future demand, whereas fault detection identifies whether abnormal conditions exist in the present state. This paper proposes a unified and proactive Cognitive Digital Twin (CDT) system. Unlike traditional data-driven approaches, the CDT integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) as cognitive cores to enable deeper reasoning and context-aware decision-making. The CDT system not only mirrors the physical grid but also acts as an intelligent O&M engine capable of understanding, reasoning, predicting, and self-diagnosing. The core innovation lies in prediction-based anomaly detection. The system first estimates the expected healthy state of the grid at future time steps and then compares real-time monitoring data against these predictions to identify incipient anomalies. This enables genuine foresight rather than simple reactive detection. By orchestrating advanced analytical modules, including CNN–LSTM hybrid models and optimization algorithms, the CDT supports autonomous O&M operations with transparent and explainable decision-making. These capabilities enhance grid resilience and improve the system’s capacity for self-healing. Full article
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22 pages, 83077 KB  
Article
Comparative Analysis of SiC-Based Isolated Bidirectional DC/DC Converters for a Modularized Off-Board EV Charging System with a Bipolar DC Link
by Kaushik Naresh Kumar, Rafał Miśkiewicz, Przemysław Trochimiuk, Jacek Rąbkowski and Dimosthenis Peftitsis
Electronics 2025, 14(22), 4522; https://doi.org/10.3390/electronics14224522 - 19 Nov 2025
Cited by 4 | Viewed by 2032
Abstract
The choice of a suitable isolated and bidirectional DC/DC converter (IBDC) topology is an important step in the design of a bidirectional electric vehicle (EV) charging system. In this context, six 10 kW rated silicon carbide (SiC) metal–oxide–semiconductor field-effect transistor (MOSFET)-based dual-active bridge [...] Read more.
The choice of a suitable isolated and bidirectional DC/DC converter (IBDC) topology is an important step in the design of a bidirectional electric vehicle (EV) charging system. In this context, six 10 kW rated silicon carbide (SiC) metal–oxide–semiconductor field-effect transistor (MOSFET)-based dual-active bridge (DAB) converter topologies, supplied by a +750/0/−750 V bipolar DC link, are analyzed and compared in this article. The evaluation criteria include the required volt-ampere semiconductor ratings, loss distribution, efficiency, and thermal considerations of the considered converter configurations. The IBDC topologies are compared based on the observations and results obtained from theoretical analysis, electro-thermal simulations, and experiments, considering the same voltage and power conditions. The advantages and disadvantages of the topologies, in terms of the considered evaluation criteria, are discussed. It is shown that the series-resonant (SR) input-series output-parallel (ISOP) full-bridge (FB) DAB converter configuration is the most suitable design choice for the considered EV charging application based on the chosen operating conditions and evaluation criteria. Full article
(This article belongs to the Special Issue DC–DC Power Converter Technologies for Energy Storage Integration)
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