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Acoustic Emission from GaN-on-Sapphire Structures
Machine Learning for Cybersecurity: A Survey of Applications, Adversarial Challenges, and Future Research Directions
Resistive Sensing in Soft Robotic Grippers: A Comprehensive Review of Strain, Tactile, and Ionic Sensors
Comparing Explainable AI Models: SHAP, LIME, and Their Role in Electric Field Strength Prediction over Urban Areas

Journal Description

Electronics

Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Electrical and Electronic Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Electronics include: Magnetism, Microwave, Network, Signals, Software, Microelectronics, Semiconductors and Heterogeneous Integration and Analog.
Journal Cluster of Electronic Engineering and Hardware Systems: Chips, Electronics, Hardware, Journal of Low Power Electronics and Applications, Microelectronics and Microwave.

Impact Factor: 2.6 (2024); 5-Year Impact Factor: 2.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

31 pages, 925 KB

Open AccessArticle

RAISE: Robust and Adversarially Informed Safe Explanations for Reinforcement Learning

by SeongIn Kim and Takeshi Shibuya

Electronics 2026, 15(3), 666; https://doi.org/10.3390/electronics15030666 - 3 Feb 2026

Deep Reinforcement Learning (DRL) policies often exhibit fragility in unseen environments, limiting their deployment in safety-critical applications. While Robust Markov Decision Processes (R-MDPs) enhance control performance by optimizing against worst-case disturbances, the resulting conservative behaviors are difficult to interpret using standard Explainable RL [...] Read more.

Deep Reinforcement Learning (DRL) policies often exhibit fragility in unseen environments, limiting their deployment in safety-critical applications. While Robust Markov Decision Processes (R-MDPs) enhance control performance by optimizing against worst-case disturbances, the resulting conservative behaviors are difficult to interpret using standard Explainable RL (XRL) methods, which typically ignore adversarial disturbances. To bridge this gap, this paper proposes RAISE (Robust and Adversarially Informed Safe Explanations), a novel framework designed for the Noisy Action Robust MDP (NR-MDP) setting. We first introduce the Decomposed Reward NR-MDP (DRNR-MDP) and the DRNR-Deep Deterministic Policy Gradient (DRNR-DDPG) algorithm to learn robust policies and a vector-valued value function. RAISE utilizes this vectorized value function to generate contrastive explanations (“Why action

a

instead of

b

?”), explicitly highlighting the reward components such as safety or energy efficiency prioritized under worst-case attacks. Experiments on a continuous Cliffworld benchmark and the MuJoCo Hopper task demonstrate that the proposed method preserves robust performance under dynamics variations and produces meaningful, component-level explanations that align with intuitive safety and performance trade-offs. Ablation results further show that ignoring worst-case disturbances can substantially alter or invalidate explanations, underscoring the importance of adversarial awareness for reliable interpretability in robust RL. Full article

(This article belongs to the Special Issue The Explainable AI Revolution: From Black Boxes to Transparent Insights)

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15 pages, 978 KB

Open AccessArticle

SpectTrans: Joint Spectral–Temporal Modeling for Polyphonic Piano Transcription via Spectral Gating Networks

by Rui Cao, Yan Liang, Lei Feng and Yuanzi Li

Electronics 2026, 15(3), 665; https://doi.org/10.3390/electronics15030665 - 3 Feb 2026

Automatic Music Transcription (AMT) plays a fundamental role in Music Information Retrieval (MIR) by converting raw audio signals into symbolic representations such as MIDI or musical scores. Despite advances in deep learning, accurately transcribing piano performances remains challenging due to dense polyphony, wide [...] Read more.

Automatic Music Transcription (AMT) plays a fundamental role in Music Information Retrieval (MIR) by converting raw audio signals into symbolic representations such as MIDI or musical scores. Despite advances in deep learning, accurately transcribing piano performances remains challenging due to dense polyphony, wide dynamic range, sustain pedal effects, and harmonic interactions between simultaneous notes. Existing approaches using convolutional and recurrent architectures, or autoregressive models, often fail to capture long-range temporal dependencies and global harmonic structures, while conventional Vision Transformers overlook the anisotropic characteristics of audio spectrograms, leading to harmonic neglect. In this work, we propose SpectTrans, a novel piano transcription framework that integrates a Spectral Gating Network with a multi-head self-attention Transformer to jointly model spectral and temporal dependencies. Latent CNN features are projected into the frequency domain via a Real Fast Fourier Transform, enabling adaptive filtering of overlapping harmonics and suppression of non-stationary noise, while deeper layers capture long-term melodic and chordal relationships. Experimental evaluation on polyphonic piano datasets demonstrates that this architecture produces acoustically coherent representations, improving the robustness and precision of transcription under complex performance conditions. These results suggest that combining frequency-domain refinement with global temporal modeling provides an effective strategy for high-fidelity AMT. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Open-World Computer Vision and Pattern Recognition)

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15 pages, 817 KB

Open AccessArticle

Design of a DetNet Framework in a 3GPP 5G System

by Jaehyun Kim, Kyeongjun Ko, Seung-Chan Lim, Joon-Seok Kim, Jaeho Im and Jungtai Kim

Electronics 2026, 15(3), 664; https://doi.org/10.3390/electronics15030664 - 3 Feb 2026

Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can [...] Read more.

Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can provide low-latency, highly reliable, and deterministic communications over Ethernet, whereas IETF deterministic networking (DetNet) seeks to provide a complementary network layer to support ultra-low latency communications. DetNet, as standardized in the IETF, provides time-sensitive characteristics that assure extremely low packet loss and latency for ultra-reliable low-latency communications. This study develops a novel framework to enable 3GPP support for DetNet functionalities. First, the proposed framework seeks to support IP-based DetNet traffic and urgent data transmission in the network overload conditions of 3GPP 5G systems. Additionally, the proposed design supports DetNet service connectivity between non-DetNet and DetNet service areas. Based on simulation results, the proposed framework can guarantee deterministic latency requirements and critical data transmission for DetNet compared with conventional approaches. The proposed scheme can achieve more effective performance for moving DetNet devices. Full article

(This article belongs to the Special Issue Edge-Intelligent Sustainable Cyber-Physical Systems)

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21 pages, 374 KB

Open AccessArticle

FL-SPDP: Spatially Modulated Differentially Private Federated Learning for Robust Satellite Image Recognition

by Zhijie Yang, Xiaolong Yan, Guoguang Chen and Xiaoli Tian

Electronics 2026, 15(3), 663; https://doi.org/10.3390/electronics15030663 - 3 Feb 2026

Satellite image recognition increasingly relies on data collected by geographically distributed institutions, but centralizing geospatial imagery is often infeasible due to policy and privacy constraints. Federated learning enables collaborative training, yet standard aggregation (e.g., FedAvg) degrades under strong geographic non-IID shifts, and adding [...] Read more.

Satellite image recognition increasingly relies on data collected by geographically distributed institutions, but centralizing geospatial imagery is often infeasible due to policy and privacy constraints. Federated learning enables collaborative training, yet standard aggregation (e.g., FedAvg) degrades under strong geographic non-IID shifts, and adding client-level differential privacy (DP) can further reduce utility—especially for rare land-cover classes—due to gradient clipping and injected noise. We propose FL-SPDP, a spatially modulated DP federated framework that leverages coarse spatial priors to reweight and aggregate client updates among geographically related clients, improving robustness to heterogeneity while preserving formal privacy guarantees. Experiments on SEN12MS and BigEarthNet show that FL-SPDP improves accuracy and macro-F1 at a fixed privacy budget (

ε \leq 3.5

,

δ = 10^{- 5}

) and strengthens rare-class performance, demonstrating an effective privacy–utility trade-off for satellite image analysis. Full article

(This article belongs to the Special Issue Security and Privacy in Distributed Machine Learning)

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23 pages, 2837 KB

Open AccessArticle

Link Prediction Using Temporal Graph Neural Network Model

by Dominika Dudziak-Gajowiak, Krzysztof Juszczyszyn, Dawid Marcin Chudzicki and Dariusz Skorupka

Electronics 2026, 15(3), 662; https://doi.org/10.3390/electronics15030662 - 3 Feb 2026

In this work, we present a Temporal Graph Neural Network (TGNN) architecture specifically designed for link prediction in dynamic graphs. The proposed approach is evaluated on a dynamic social network constructed from internal email communication between employees of Wrocław University of Science and [...] Read more.

In this work, we present a Temporal Graph Neural Network (TGNN) architecture specifically designed for link prediction in dynamic graphs. The proposed approach is evaluated on a dynamic social network constructed from internal email communication between employees of Wrocław University of Science and Technology that was collected over a continuous period of 605 days. To capture short-term fluctuations in communication behavior, we introduce the use of very short temporal aggregation windows, down to a single day, for constructing temporal graph snapshots. This fine-grained temporal resolution allows the model to accurately learn evolving interaction patterns and adapt to the dynamic nature of social communication networks. The TGNN model demonstrates consistently high predictive performance, achieving 99.28% ROC-AUC (Receiver Operating Characteristic—Area Under Curve) and 99.17% Average Precision in link prediction tasks. These results confirm that the model is able to distinguish between existing and emerging communication links with high reliability across temporal intervals. The architecture, optimized exclusively for temporal link prediction, effectively utilizes its representational capacity for modeling edge formation processes in time-dependent networks. The findings highlight the potential of focused TGNN architectures and short-time-window modeling in improving predictive accuracy and temporal resolution in link prediction applications involving evolving social or organizational structures. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)

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23 pages, 4259 KB

Open AccessArticle

Assessment of an FPGA Implementation of a Hybrid PUF Based on a Configurable Transient Effect Ring Oscillator and Ring Oscillator (TERORO-PUF)

by Alejandro Casado-Galán, Juan Núñez, Erica Tena-Sánchez, Francisco Eugenio Potestad-Ordóñez and Antonio José Acosta

Electronics 2026, 15(3), 661; https://doi.org/10.3390/electronics15030661 - 3 Feb 2026

In the current situation of the Internet of Things (IoT) with its billions of interconnected devices, security in this low-resource environment is paramount. A Physical Unclonable Function (PUF) is a very useful cryptographic primitive which allows us to extract unique information from a [...] Read more.

In the current situation of the Internet of Things (IoT) with its billions of interconnected devices, security in this low-resource environment is paramount. A Physical Unclonable Function (PUF) is a very useful cryptographic primitive which allows us to extract unique information from a particular device in a non-reproducible way. This allows us to use a PUF in cryptography for authentication or secret-key generation. Ring Oscillators (ROs) and Transient Effect Ring Oscillators (TEROs) are oscillating structures used in both FPGAs and ASICs to build PUFs. In this paper we present an FPGA implementation of a PUF based on what we call the “TERORO” cell (TERO + RO), which is a hybrid structure that allows us to use the different functionalities of both RO and TERO in a single building block. We assess all the possible methods of extracting bits of information from the PUF based on TERORO cells. Finally, we tested the circuit and presented experimental results in terms of its uniqueness, uniformity, and reliability. In RO-counter mode, we obtain

49.74 %

uniqueness,

54.66 %

uniformity, and

97.81 %

reliability across devices, while TERO-based XOR mixing achieves

52.83 %

uniformity,

45.79 %

uniqueness, and

93.15 %

reliability. The FPGA footprint is 142 LUTs, 36 registers, and 82 slices. Full article

(This article belongs to the Special Issue Recent Advances in Hardware Security for Embedded Systems and the Internet of Things)

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18 pages, 1417 KB

Open AccessArticle

A Comparative Investigation of Study ROI: Multimodal Personalized English Learning Environment Versus Traditional English Learning Environment

by Cunqian You, Yang Wang, Ping Li, Xiaoyu Zhao, Huijuan Lu, Xiaojun Wang, Yudong Yao and Wenzhong Chen

Electronics 2026, 15(3), 660; https://doi.org/10.3390/electronics15030660 - 3 Feb 2026

Limited study time constrains university EFL vocabulary learning, so efficiency should be evaluated alongside accuracy. A web-based multimodal environment was developed that uses a large language model for contextualized drills and tutoring, text-to-speech for pronunciation and listening rehearsal, and an interactive 3D mastery [...] Read more.

Limited study time constrains university EFL vocabulary learning, so efficiency should be evaluated alongside accuracy. A web-based multimodal environment was developed that uses a large language model for contextualized drills and tutoring, text-to-speech for pronunciation and listening rehearsal, and an interactive 3D mastery view for self-regulated tracking. Vocabulary knowledge is modeled as a discrete mastery state (m = 0–5), updated after each attempt, and an adaptive scheduler allocates practice across mastery strata. Learning ROI is defined as newly mastered words per hour and computed from logged study time and mastery transitions. In a three-month deployment (N = 171), learners achieved a mean ROI of 9.8 words/hour, about 60% higher than conventional estimates (5–6 words/hour); high-adherence users reached 17–21 words/hour. End-of-trial surprise review results indicated retention above 85%. For CET-4, the platform cohort obtained the highest mean score (457.66) and pass rate (74.24%) compared with Baicizhan (442.22; 64.81%) and traditional instruction (428.60; 53.70%). The results provide quantitative support for the hypothesis that multimodal personalization improves time-based vocabulary gains and their durability. Full article

(This article belongs to the Special Issue Emerging Technologies Integration in IoT and Wireless Networks: Advancing Security and Performance in Cross-Domain Applications)

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26 pages, 619 KB

Open AccessArticle

Benchmarking LLM-as-a-Judge Models for 5W1H Extraction Evaluation

by José Cassola-Bacallao, José Morales-Donaire, Paula Hernández-Montoya and Brian Keith-Norambuena

Electronics 2026, 15(3), 659; https://doi.org/10.3390/electronics15030659 - 3 Feb 2026

Evaluating 5W1H (Who, What, When, Where, Why, and How) information extraction systems remains challenging, as traditional information retrieval metrics like ROUGE and BLEU fail to capture semantic accuracy and narrative coherence. The LLM-as-a-Judge paradigm offers a promising alternative, yet systematic comparisons of judge [...] Read more.

Evaluating 5W1H (Who, What, When, Where, Why, and How) information extraction systems remains challenging, as traditional information retrieval metrics like ROUGE and BLEU fail to capture semantic accuracy and narrative coherence. The LLM-as-a-Judge paradigm offers a promising alternative, yet systematic comparisons of judge models for this task are lacking. This study benchmarks multiple large language models, including state-of-the-art models such as GPT, Claude, and Gemini as evaluators of 5W1H extractions from Spanish news articles. We assess judge performance across six quality criteria: Factual Accuracy, Completeness, Relevance and Conciseness, Clarity and Readability, Faithfulness to Source, and Overall Coherence. Our analysis examines inter-judge agreement, score distribution patterns, criterion-level variance, and the relationship between evaluation quality and computational cost. Using two Spanish-language corpora (BASSE and FLARES), we identify which criteria exhibit consistent cross-model agreement and which prove most sensitive to judge selection. The main contribution of this work is providing the first systematic benchmark of LLM-as-a-Judge models for 5W1H extraction evaluation in Spanish, validated against expert journalistic judgment. Results reveal that all evaluated models achieve alignment levels above 90% across all metrics. Specifically, Claude Sonnet 4.5 emerges as the most accurate evaluator with a Global Judgment Acceptance Rate (JAR) of 99.79%. Furthermore, meta-evaluation with human experts demonstrates a substantial inter-annotator agreement of

κ = 0.6739

. Finally, we provide recommendations for judge model selection based on task requirements and resource constraints, contributing practical guidance for researchers implementing LLM-based evaluation pipelines for information extraction tasks. Full article

(This article belongs to the Special Issue Multimodal Learning for Multimedia Content Analysis and Understanding)

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16 pages, 416 KB

Open AccessArticle

An Adaptive IoT-Based ForecastingFramework for Structural and Environmental Risk Detection in Tailings Dams

by Raul Rabadán-Arroyo, Ester Simó, Francesc Aguiló-Gost, Francisco Hernández-Ramírez and Xavier Masip-Bruin

Electronics 2026, 15(3), 658; https://doi.org/10.3390/electronics15030658 - 3 Feb 2026

Tailings dams represent one of the most environmentally sensitive infrastructures in the mining industry. To address the need for continuous and accurate monitoring, this paper presents an adaptive forecasting framework that combines Internet of Things (IoT) technologies with machine learning (ML) models to [...] Read more.

Tailings dams represent one of the most environmentally sensitive infrastructures in the mining industry. To address the need for continuous and accurate monitoring, this paper presents an adaptive forecasting framework that combines Internet of Things (IoT) technologies with machine learning (ML) models to detect early signs of structural and ecological risks. The proposed system architecture is modular and scalable and enables the automated training, selection, and deployment of predictive models for multivariate sensor data. Each sensor data flow is independently analyzed by using a configurable set of algorithms (including linear, convolutional, recurrent, and residual models). The framework is deployed via containers with a CI/CD pipeline and includes real-time visualization through Grafana dashboards. A use case involving tiltmeters and piezometers in an operational tailing dam shows the system’s high predictive accuracy, with mean relative errors below

4 %

across all variables (in fact, many of them have a mean relative error below

1 %

). These results highlight the potential of the proposed solution to improve structural and environmental safety in mining operations. Full article

(This article belongs to the Special Issue Empowering IoT with AI: AIoT for Smart and Autonomous Systems)

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21 pages, 672 KB

Open AccessArticle

C-T-Mamba: Temporal Convolutional Block for Improving Mamba in Multivariate Time Series Forecasting

by Rongjie Liu, Wei Guo and Siliu Yu

Electronics 2026, 15(3), 657; https://doi.org/10.3390/electronics15030657 - 3 Feb 2026

In recent years, Transformer-based methods have demonstrated proficiency in capturing complex patterns for time series forecasting. However, their quadratic complexity relative to input sequence length poses a significant bottleneck for scalability and real-world deployment. Recently, the Mamba architecture has emerged as a compelling [...] Read more.

In recent years, Transformer-based methods have demonstrated proficiency in capturing complex patterns for time series forecasting. However, their quadratic complexity relative to input sequence length poses a significant bottleneck for scalability and real-world deployment. Recently, the Mamba architecture has emerged as a compelling alternative by mitigating the prohibitive computational overhead and latency inherent in Transformers. Nevertheless, a vanilla Mamba backbone often struggles to adequately characterize intricate temporal dynamics, particularly long-term trend shifts and non-stationary behaviors. To bridge the gap between Mamba’s global scanning and local dependency modeling, we propose C-T-Mamba, a hybrid framework that synergistically integrates a Mamba block, channel attention, and a temporal convolution block. Specifically, the Mamba block is leveraged to capture long-range temporal dependencies with linear scaling, the channel attention mechanism filters redundant information, and the temporal convolution block extracts multi-scale local and global features. Extensive experiments on five public benchmarks demonstrate that C-T-Mamba consistently outperforms state-of-the-art (SOTA) baselines (e.g., PatchTST and iTransformer), achieving average reductions of 4.3–18.5% in MSE and 3.9–16.2% in MAE compared to representative Transformer-based and CNN-based models. Inference scaling analysis reveals that C-T-Mamba effectively breaks the computational bottleneck; at a horizon of 1536, it achieves an

8.8 \times

reduction in GPU memory and over 10× speedup compared to standard Transformers. At 2048 steps, its latency remains as low as 8.9 ms, demonstrating superior linear scaling. These results underscore that C-T-Mamba achieves SOTA accuracy while maintaining a minimal computational footprint, making it highly effective for long-term multivariate time series forecasting. Full article

(This article belongs to the Section Artificial Intelligence)

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16 pages, 841 KB

Open AccessArticle

Optimal Capacity Configuration of Photovoltaic-Storage Power Stations Based on an Improved Sparrow Search Algorithm

by Luting Zhang, Wei Zhao, Jinhui Zeng and Jie Liu

Electronics 2026, 15(3), 656; https://doi.org/10.3390/electronics15030656 - 2 Feb 2026

To address the issues of high electricity costs for industrial loads in enterprise parks, significant peak-valley price differences, and insufficient utilization of renewable energy, a multi-objective capacity optimization method for photovoltaic and energy storage systems has been proposed, incorporating price-based demand response (PDR) [...] Read more.

To address the issues of high electricity costs for industrial loads in enterprise parks, significant peak-valley price differences, and insufficient utilization of renewable energy, a multi-objective capacity optimization method for photovoltaic and energy storage systems has been proposed, incorporating price-based demand response (PDR) and cycle life constraints. Firstly, a multi-objective function was constructed by integrating the aforementioned constraints, aiming to minimize the equivalent annualized comprehensive cost and the energy imbalance rate. Then, to overcome the limitations of the traditional sparrow search algorithm (SSA), such as low convergence speed, limited precision, and the tendency to fall into local optima, an improved SSA was proposed. This improved algorithm was enhanced by the integration of chaotic mapping, adaptive inertia weight, Harris Hawks encircling, and predation strategies. Through these improvements, both the convergence speed and accuracy in solving high-dimensional problems were significantly improved. Finally, a case study was conducted using real load data from an enterprise park in Zhuzhou City. The proposed algorithm achieves a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms while further enhancing power supply reliability. Full article

(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)

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17 pages, 2511 KB

Open AccessArticle

Adversarial and Hierarchical Distribution Alignment Network for Nonintrusive Load Monitoring

by Haozhe Xiong, Daojun Tan, Yuxuan Hu, Xuan Cai and Pan Hu

Electronics 2026, 15(3), 655; https://doi.org/10.3390/electronics15030655 - 2 Feb 2026

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. [...] Read more.

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. The network aims to reduce the distribution divergence between the source and target domains in both the feature and label spaces, enabling effective adaptation to transfer learning scenarios in which the source domain has limited labeled data and the target domain has abundant unlabeled data. The proposed method integrates adversarial training with a hierarchical distribution alignment strategy that uses Correlation Alignment (CORAL) to align global marginal distributions. It employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to constrain the conditional distributions of individual appliances, thereby enhancing cross-domain generalization. Extensive experiments on three public datasets demonstrate that, in both in-domain and cross-domain settings, the proposed method consistently reduces Mean Absolute Error (MAE) and Signal Aggregation Error (SAE), outperforming baseline approaches in cross-domain generalization. Full article

28 pages, 3542 KB

Open AccessArticle

Zero-Voltage-Switching Buck Converter Using Digital Hybrid Control with Variable Slope Compensation

by Ching-Lung Chu, Ming-Tsung Tsai, Wen-Chuan Fang and Yu-Jui Chen

Electronics 2026, 15(3), 654; https://doi.org/10.3390/electronics15030654 - 2 Feb 2026

This paper proposes a zero-voltage-switching (ZVS) synchronous buck converter employing a digital hybrid control scheme with variable slope compensation. By using an auxiliary-switch network and a digital controller to sense voltage and current, the system can automatically adjust the slope-compensation parameters according to [...] Read more.

This paper proposes a zero-voltage-switching (ZVS) synchronous buck converter employing a digital hybrid control scheme with variable slope compensation. By using an auxiliary-switch network and a digital controller to sense voltage and current, the system can automatically adjust the slope-compensation parameters according to the input and output conditions, thereby enhancing stability over a wide operating range. The proposed method effectively suppresses the reverse-recovery current spike of the synchronous rectifier during continuous conduction mode (CCM) operation and enables the main switch to achieve ZVS, reducing switching loss and improving efficiency. A laboratory prototype with an input voltage range of 30–160 V and output voltage levels of 24 V, 48 V, and 96 V at 5 A is developed to verify the feasibility of the proposed architecture. Full article

27 pages, 1934 KB

Open AccessArticle

An Enhanced Artificial Gorilla Troops Optimizer-Based MPPT for Photovoltaic Systems

by Bernardo Silva and Rui Chibante

Electronics 2026, 15(3), 653; https://doi.org/10.3390/electronics15030653 - 2 Feb 2026

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, [...] Read more.

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs). Full article

(This article belongs to the Special Issue Automation and Monitoring Systems for Mining and Energy Sector Processes)

27 pages, 516 KB

Open AccessArticle

How the Representation of Retrieved Context Affects In-Context Prompting for Commit Message Generation

by Dokyeong An and Geunseok Yang

Electronics 2026, 15(3), 652; https://doi.org/10.3390/electronics15030652 - 2 Feb 2026

High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not [...] Read more.

High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not whether retrieval helps, but how the same retrieved example, when represented differently in the prompt, quantitatively changes generation outcomes. We implement a retrieve-then-generate framework where the target commit’s diff is used as a query for BM25 (Best Matching 25)-based sparse retrieval over a commit-level database, and the top-1 similar commit is optionally injected as an example context. We compare a no-context condition (K = 0) against a minimal-context condition (K = 1) under three context representations: Diff-only, Message-only, and Diff+Message pair. Using Qwen-7B on 8000 evaluation samples with a fixed prompt skeleton, deterministic decoding, and identical post-processing across conditions, we observe negligible differences at K = 0 (BLEU-4 1.14, ROUGE-L 7.47–7.48, METEOR 4.88–4.91), establishing a stable baseline. At K = 1, the same top-1 retrieved case yields systematically different metric responses depending on how it is represented (Diff-only, Message-only, or Diff+Message), even under an identical prompt skeleton, deterministic decoding, and identical post-processing. This indicates that “context representation” is not a cosmetic formatting choice but a first-class prompt-design variable in retrieval-augmented in-context learning for commit message generation. Accordingly, practitioners should select the representation based on the intended objective (e.g., lexical/style alignment vs. change-intent grounding), rather than assuming a universally optimal format. Full article

(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)

20 pages, 1995 KB

Open AccessArticle

Optimized PAB-RRT Algorithm for Autonomous Vehicle Path Planning in Complex Scenarios

by Jinbo Wang, Weihai Zhang, Jinming Zhang, Wei Liao and Tingwei Du

Electronics 2026, 15(3), 651; https://doi.org/10.3390/electronics15030651 - 2 Feb 2026

Path planning is a pivotal technology for autonomous vehicles, directly influencing driving safety and comfort. Developing algorithms adaptable to diverse scenarios is critical for ensuring the safe operation of autonomous driving systems and advancing their engineering applications. The existing Rapidly exploring Random Tree [...] Read more.

Path planning is a pivotal technology for autonomous vehicles, directly influencing driving safety and comfort. Developing algorithms adaptable to diverse scenarios is critical for ensuring the safe operation of autonomous driving systems and advancing their engineering applications. The existing Rapidly exploring Random Tree (RRT) algorithm has limitations such as low efficiency and tortuous, lengthy paths. To address these issues, this study proposes the PAB-RRT algorithm, which integrates probabilistic goal bias, adaptive step size, and bidirectional exploration into RRT. Comparative simulations were conducted to evaluate PAB-RRT against traditional RRT, RRT*, and single-strategy improved variants (A-RRT, P-RRT, B-RRT). Results show that in static multi-obstacle scenarios, PAB-RRT completes planning with 30 iterations (6.99% of traditional RRT), 0.1255 s computation time (21.9% of traditional RRT), and a 130.83 m path length (7.2% shorter than traditional RRT). In dynamic obstacle scenarios, it requires 19 iterations (0.0434 s) at the initial stage and 37 iterations (0.0861 s) after obstacle movement, with path length stably around 130 m. Overall, PAB-RRT outperforms traditional algorithms in exploration efficiency, path performance, and robustness in complex settings, better meeting the efficiency and reliability requirements of autonomous vehicle path planning under complex scenarios and providing a feasible reference for related technology. Full article

(This article belongs to the Special Issue Advances in Electric Vehicles and Energy Storage Systems)

21 pages, 1606 KB

Open AccessArticle

Forward Reference-Sample Equalization for High-Speed Shallow-Water Acoustic Communication

by Cheng He, Fei Sun, Enhui Ji, Pingyang Min and Tanghao You

Electronics 2026, 15(3), 650; https://doi.org/10.3390/electronics15030650 - 2 Feb 2026

In shallow-water high-speed mobile acoustic channels, severe non-uniform Doppler effects pose significant challenges to traditional equalization methods based on linear and time-invariant channel assumptions. Existing approaches typically rely on inverse compensation strategies, which are inadequate for handling path-dependent nonlinear Doppler distortions and fail [...] Read more.

In shallow-water high-speed mobile acoustic channels, severe non-uniform Doppler effects pose significant challenges to traditional equalization methods based on linear and time-invariant channel assumptions. Existing approaches typically rely on inverse compensation strategies, which are inadequate for handling path-dependent nonlinear Doppler distortions and fail to accurately reflect the underlying physical propagation process. To address these limitations, this paper proposes a forward reference-sample equalization (FRSE) method. Based on estimated channel parameters, forward channel modeling is performed for all possible transmitted symbols to generate a reference-sample matrix that is consistent with channel-induced distortions. At the receiver, a least-squares decision criterion is employed to match the received signal with the closest reference sample, thereby enabling reliable demodulation. Simulation results demonstrate that, at a high relative speed of 30 kn and a signal-to-noise ratio (SNR) of 8 dB, the proposed method achieves a bit error rate (BER) of

1.75 \times 10^{- 4}

, significantly outperforming conventional equalization methods. Furthermore, sea trial experiments validate the robustness of the proposed approach in real shallow-water environments. By avoiding signal inversion, FRSE achieves improved detection reliability and strong robustness against non-uniform Doppler effects, highlighting its potential for practical underwater acoustic communication applications. Full article

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26 pages, 2565 KB

Open AccessArticle

A Novel Framework for Power Extraction Enhancement in PV Systems Based on Hybrid ACO-ANN Optimization

by Mohammed Algarbalje and Ayhan Gün

Electronics 2026, 15(3), 649; https://doi.org/10.3390/electronics15030649 - 2 Feb 2026

The transition to renewable energy, mainly via the use of PV (photovoltaic) systems, is essential for addressing global concerns related to climate change, energy security, and sustainability. Conventional Maximum Power Point Tracking (MPPT) techniques, particularly Perturb and Observe (P&O) and Incremental Conductance methods, [...] Read more.

The transition to renewable energy, mainly via the use of PV (photovoltaic) systems, is essential for addressing global concerns related to climate change, energy security, and sustainability. Conventional Maximum Power Point Tracking (MPPT) techniques, particularly Perturb and Observe (P&O) and Incremental Conductance methods, rely on fixed-step gradient-based control, which leads to steady-state oscillations around the maximum power point, slow convergence during rapid irradiance and temperature variations, and inaccurate tracking under partial shading conditions. These technical limitations often cause the controller to deviate from the global maximum power point, resulting in reduced dynamic efficiency, increased power losses, and degraded power quality in practical PV applications. To overcome these limitations, this research proposes a hybrid optimization model that incorporates ACO (Ant Colony Optimization) and an ANN (Artificial Neural Network) to enrich the effectiveness of MPPT in PV systems. The proposed model is designed to dynamically adapt to variations in solar irradiance and temperature, effectively addressing the inadequacies present in the conventional techniques and also improving the MPPT efficiency in PV systems. By leveraging the unique strengths of both ACO and ANN, the model significantly improves energy extraction and also ensures robustness against environmental fluctuations. Simulation results demonstrate that the proposed ACO–ANN MPPT framework achieves a total harmonic distortion (THD) of 1.39%, representing a reduction of approximately 34–70% compared to conventional and recent AI-based MPPT techniques, while simultaneously delivering higher voltage stability, faster convergence, and increased maximum power extraction. This contribution is vital in paving the way for future advancements in renewable energy systems and provides a more reliable approach to solar power optimization, which can greatly aid in achieving sustainable energy goals. Full article

(This article belongs to the Special Issue Advances in High-Penetration Renewable Energy Power Systems Research)

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17 pages, 3310 KB

Open AccessArticle

Research on an Adaptive Selection Method for GNSS Signals in Passive Radar

by Hongwei Fu, Hao Cha, Yu Luo, Tingting Fu, Bin Tian and Huatao Tang

Electronics 2026, 15(3), 648; https://doi.org/10.3390/electronics15030648 - 2 Feb 2026

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes [...] Read more.

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems. Full article

(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)

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16 pages, 2016 KB

Open AccessArticle

A Deep Learning Phase Noise Compensation Network for Photonic Terahertz OFDM System

by Shenao Cai, Long Zhou, Tong Li and Jianguo Yu

Electronics 2026, 15(3), 647; https://doi.org/10.3390/electronics15030647 - 2 Feb 2026

To address the phase noise issue in terahertz OFDM system, this paper proposes a dual-branch deep learning phase noise compensation network named AdaPhaseNet. The Transformer branch of this network leverages the powerful modeling capability of Transformers for long-range dependencies to achieve long-range phase [...] Read more.

To address the phase noise issue in terahertz OFDM system, this paper proposes a dual-branch deep learning phase noise compensation network named AdaPhaseNet. The Transformer branch of this network leverages the powerful modeling capability of Transformers for long-range dependencies to achieve long-range phase noise estimation and compensation, while the CNN branch is employed for local signal enhancement. Finally, an optimized signal is output through a confidence-driven adaptive fusion module. For experimental validation of the algorithm, we constructed a photonic terahertz communication system comprising 10 km of fiber and 5 m of wireless transmission. Experimental results show that, compared with multiple baseline models, AdaPhaseNet achieves relative BER reductions ranging from 37.0% to 57.9% and EVM gains ranging from 1.4 dB to 3.2 dB. Full article

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