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A Novel Compact Beamforming Network Based on Quasi-Twisted Branch Line Coupler for 5G Applications
Redundancy-Interpolated Three-Segment DAC with On-Chip Digital Calibration for Improved Static Linearity
Automated Generation of Test Scenarios for Autonomous Driving Using LLMs
Asset Discovery in Critical Infrastructures: An LLM-Based Approach
A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring

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 16.8 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first 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 and Semiconductors and Heterogeneous Integration.
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, 4811 KB

Open AccessArticle

Research on Time–Frequency Domain Characteristics Analysis of Fault Arc Under Different Connection Methods

by Siyuan Zeng, Lei Lei, Gang Tian, Yimin Li and Jianhua Wang

Electronics 2025, 14(24), 4840; https://doi.org/10.3390/electronics14244840 - 8 Dec 2025

Arc fault detection is a key technology for preventing electrical fires. However, existing research has primarily focused on series connections, with insufficient attention paid to parallel load conditions, which are prevalent in real-world residential electricity usage. In accordance with the UL 1699 and [...] Read more.

Arc fault detection is a key technology for preventing electrical fires. However, existing research has primarily focused on series connections, with insufficient attention paid to parallel load conditions, which are prevalent in real-world residential electricity usage. In accordance with the UL 1699 and IEC 62606 standards, this study established an experimental platform for arc faults, incorporating seven single loads (categorized into four types) and nine multi-load combinations. A systematic analysis of the differences in time–frequency characteristics under different connection modes was conducted. Time-domain and frequency-domain analyses revealed that under parallel connection the dispersion of arc fault time-domain characteristics decreases by more than 50% and the fundamental frequency component increases significantly. For parallel multi-load scenarios, the fundamental component of resistive combinations can reach 90%, while the frequency variance of inductive combinations can be as high as 400,000. By elucidating the time–frequency domain characteristics of parallel arc faults, this study proposes an optimized feature parameter analysis scheme for electrical fire monitoring systems. Based on this, this paper proposes an arc fault detection method using the Dual-Channel Convolutional Neural Network (DCNN). The method achieves 97.09% recognition accuracy for arc faults with different connection modes. Comparative experiments with other models and ablation studies show that the model attains 98.52% detection accuracy, verifying the effectiveness of the proposed method. This approach can significantly improve the accuracy of arc fault detection in multi-load environments, thereby enabling early warning of electrical circuit faults and potential fire hazards. Full article

19 pages, 18305 KB

Open AccessArticle

Noise-Resilient Low-Light Image Enhancement with CLIP Guidance and Pixel-Reordering Subsampling

by Seongjong Song

Electronics 2025, 14(24), 4839; https://doi.org/10.3390/electronics14244839 (registering DOI) - 8 Dec 2025

Low-light image enhancement (LLIE) is an essential task for improved image quality that ultimately supports crucial downstream tasks such as autonomous driving and mobile photography. Despite notable advances achieved by traditional, Retinex-based methods, existing approaches still struggle to maintain globally consistent illumination and [...] Read more.

Low-light image enhancement (LLIE) is an essential task for improved image quality that ultimately supports crucial downstream tasks such as autonomous driving and mobile photography. Despite notable advances achieved by traditional, Retinex-based methods, existing approaches still struggle to maintain globally consistent illumination and to suppress sensor noise under extremely dark conditions. To overcome these limitations, we propose a noise-resilient LLIE framework that integrates a CLIP-guided loss (CLIP-LLA) and a pixel-reordering subsampling (PRS) scheme into the Retinexformer backbone. The CLIP-LLA loss exploits the semantic prior of a large-scale vision–language model to align enhanced outputs within the manifold of well-illuminated natural images, leading to faithful global tone rendering and perceptual realism. In parallel, the PRS-based multi-scale training strategy effectively regularizes the network by augmenting structural diversity, thereby improving denoising capability without architectural modification or inference cost. Extensive experiments on both sRGB and RAW benchmarks validate the effectiveness of our design. The proposed method achieves consistent improvements over state-of-the-art techniques, including a

+ 2.73 dB

PSNR gain on the SMID dataset and superior perceptual scores, while maintaining computational efficiency. These results demonstrate that fusing foundation-model priors with transformer-based Retinex frameworks offers a practical and scalable pathway toward perceptually faithful low-light image enhancement. Full article

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22 pages, 4200 KB

Open AccessArticle

An Zero-Point Drift Suppression Method for eLoran Signal Based on a Segmented Inaction Algorithm

by Miao Wu, Xianzhou Jin, Xin Qi, Jianchen Di, Tingyi Yu and Fangneng Li

Electronics 2025, 14(24), 4838; https://doi.org/10.3390/electronics14244838 - 8 Dec 2025

Research on interference suppression technology for enhanced long-range navigation (eLoran) signals is crucial for enhancing receiver performance. To address the zero-point drift phenomenon in eLoran signals during adaptive filtering, we propose a segmented inaction algorithm based on normal time–frequency transform (NTFT), which is [...] Read more.

Research on interference suppression technology for enhanced long-range navigation (eLoran) signals is crucial for enhancing receiver performance. To address the zero-point drift phenomenon in eLoran signals during adaptive filtering, we propose a segmented inaction algorithm based on normal time–frequency transform (NTFT), which is designed for challenging environments, such as low signal-to-noise ratio (SNR) and complex noise conditions. The algorithm splits the 20 kHz frequency band of the eLoran signal into 200 equal sub-bands, then applies the inaction algorithm sequentially to each sub-band, which exhibits strong noise resistance and high robustness. It is regarded as a pre-filter of the adaptive filter, ensuring a cleaner input signal for subsequent processing. Simulation results indicate that, when processing low-SNR eLoran signals affected by multi-frequency narrow-band interference and band-limited Gaussian noise, the combined algorithm significantly improves root mean square error (RMSE) by 33.3% and relative root mean square error (R-RMSE) by 39.1% compared to the single VSS-LMS method. Additionally, it compensates for zero-point drift (the deviation observed in the time series between the positive zero-crossing point of the third period of the reconstructed signal and that of the original signal) by 79.3% and maintains third-week forward over-zero error at a very low level. The effectiveness of the combined algorithm was further validated through actual measurement experiments. Full article

19 pages, 11023 KB

Open AccessArticle

Contact-Aware Diffusion Sampling for RRT-Based Manipulation

by Kyoungho Lee and Kyunghoon Cho

Electronics 2025, 14(24), 4837; https://doi.org/10.3390/electronics14244837 - 8 Dec 2025

Rapidly exploring Random Trees (RRT) provide probabilistic completeness but often explore inefficiently in high-DOF manipulation tasks. We address this by proposing a contact-aware, two-level planner that couples a learned toggle–subgoal predictor with a conditional diffusion sampler in joint space under a completeness-preserving mixture [...] Read more.

Rapidly exploring Random Trees (RRT) provide probabilistic completeness but often explore inefficiently in high-DOF manipulation tasks. We address this by proposing a contact-aware, two-level planner that couples a learned toggle–subgoal predictor with a conditional diffusion sampler in joint space under a completeness-preserving mixture with uniform sampling. An upper ResNet-based network predicts task-relevant milestones from RGB images: grasp/release “toggle” configurations and intermediate joint-space subgoals that serve as phase-wise, receding-horizon targets between consecutive contact events. Conditioned on these predictions and the current state, a lower-level diffusion model samples tree-extension segments—joint-space directions and step lengths—instead of absolute configurations. These proposals act as a drop-in replacement for uniform sampling in standard RRT/RRT-Connect, while a nonzero fraction of uniform samples preserves probabilistic completeness. By biasing growth toward contact-relevant regions, the planner concentrates the search near feasible approach manifolds without altering nearest-neighbor, steering, or collision-checking primitives. In mug pick-and-place simulations, the proposed method achieves higher success rates than diffusion and other sequence-based policies trained by imitation learning, and requires fewer RRT expansions than uniform and goal-biased RRT as well as prior learning-guided samplers based on CVAE and conditional GAN, under identical collision checking and iteration limits. Full article

(This article belongs to the Special Issue Intelligent Perception and Control for Robotics)

24 pages, 5500 KB

Open AccessArticle

A Comprehensive Study of Autonomous Vehicle Platoon Stability and Safety Under Uncertainties and Delays in Mixed Traffic

by Yulu Dai, Xueli Ge, Mingfeng Dai, Yanbin Liu and Aixi Yang

Electronics 2025, 14(24), 4836; https://doi.org/10.3390/electronics14244836 - 8 Dec 2025

Autonomous Vehicle (AV) platooning is a promising solution for enhancing traffic efficiency and safety. However, real-world deployment faces challenges due to uncertainties and delays, which can impact platoon stability and safety. This study analyzes AV platoon stability and safety, considering control parameter uncertainty, [...] Read more.

Autonomous Vehicle (AV) platooning is a promising solution for enhancing traffic efficiency and safety. However, real-world deployment faces challenges due to uncertainties and delays, which can impact platoon stability and safety. This study analyzes AV platoon stability and safety, considering control parameter uncertainty, mechanical delay, and perception delay. The research also extends the analysis to mixed traffic environments, where AVs interact with human-driven vehicles (HVs). A modified Adaptive Cruise Control (ACC) model is used, incorporating delays and uncertainties. Time–domain and frequency–domain stability analyses evaluate the impact of these factors on platoon stability, while Time-to-Collision (TIT) and Collision Probability Index (CPI) metrics assess safety. Results show that delays and uncertainty significantly degrade platoon stability, with the damping ratio falling below critical levels. Mixed traffic environments further increase collision risks. Increasing AV penetration improves safety, but HV behavior remains a challenge. The study emphasizes the need for adaptive control strategies to ensure stable and safe AV platoon operations in real-world conditions. Full article

(This article belongs to the Section Systems & Control Engineering)

29 pages, 1844 KB

Open AccessArticle

Throughput Maximization in EH Symbiotic Radio System Based on LSTM-Attention-Driven DDPG

by Yanjun Zhu, Lin Kang, Jinrong Su and Di Yang

Electronics 2025, 14(24), 4835; https://doi.org/10.3390/electronics14244835 - 8 Dec 2025

Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput [...] Read more.

Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput maximization in an EHSR network with a nonlinear EH model. To solve this non-convex problem, we designed a two-layered optimization algorithm combining convex optimization with a deep reinforcement learning (DRL) framework. The derived optimal power, time allocation factor, and the time-varying environment state are fed into the proposed long short-term memory (LSTM) attention mechanism combined Deep Deterministic Policy Gradient, named the LAMDDPG algorithm to achieve the optimal long-term throughput. Simulation results demonstrate that by equipping the Actor with LSTM to capture temporal state and enhancing the Critic with channel-wise attention mechanism, namely Squeeze-and-Excitation Block, for precise Q-evaluation, the LAMDDPG algorithm achieves a faster convergence rate and optimal long-term throughput compared to the baseline algorithms. Moreover, we find the optimal number of PDs to maintain efficient network performance under NLPM, which is highly significant for guiding practical EHSR applications. Full article

25 pages, 7157 KB

Open AccessArticle

Markerless AR Navigation for Smart Campuses: Lightweight Machine Learning for Infrastructure-Free Wayfinding

by Elohim Ramírez-Galván, Cesar Benavides-Alvarez, Carlos Avilés-Cruz, Arturo Zúñiga-López and José Félix Serrano-Talamantes

Electronics 2025, 14(24), 4834; https://doi.org/10.3390/electronics14244834 - 8 Dec 2025

This paper presents a markerless augmented reality (AR) navigation system for guiding users across a university campus, independent of internet or wireless connectivity, integrating machine learning (ML) and deep learning techniques. The system employs computer vision to detect campus signage “Meeting Point” and [...] Read more.

This paper presents a markerless augmented reality (AR) navigation system for guiding users across a university campus, independent of internet or wireless connectivity, integrating machine learning (ML) and deep learning techniques. The system employs computer vision to detect campus signage “Meeting Point” and “Directory”, and classifies them through a binary classifier (BC) and convolutional neural networks (CNNs). The BC distinguishes between the two types of signs using RGB values with algorithms such as Perceptron, Bayesian classification, and k-Nearest Neighbors (KNN), while the CNN identifies the specific sign ID to link it to a campus location. Navigation routes are generated with the Floyd–Warshall algorithm, which computes the shortest path between nodes on a digital campus map. Directional arrows are then overlaid in AR on the user’s device via ARCore, updated every 200 milliseconds using sensor data and direction vectors. The prototype, developed in Android Studio, achieved over 99.5% accuracy with CNNs and 100% accuracy with the BC, even when signs were worn or partially occluded. A usability study with 27 participants showed that 85.2% successfully reached their destinations, with more than half rating the system as easy or very easy to use. Users also expressed strong interest in extending the application to other environments, such as shopping malls or airports. Overall, the solution is lightweight, scalable, and sustainable, requiring no additional infrastructure beyond existing campus signage. Full article

(This article belongs to the Section Computer Science & Engineering)

27 pages, 5481 KB

Open AccessArticle

Coarse-to-Fine Open-Set Semantic Adaptation for EEG Emotion Recognition in 6G-Oriented Semantic Communication Systems

by Changliang Zheng, Honglin Fang, Lina Chen and Yang Yang

Electronics 2025, 14(24), 4833; https://doi.org/10.3390/electronics14244833 - 8 Dec 2025

Electroencephalogram (EEG)-based emotion recognition has emerged as a key enabler for semantic communication systems in next-generation networks (5G-Advanced/6G), where the goal is to transmit task-relevant semantic information rather than raw signals. However, domain adaptation approaches for EEG emotion recognition typically assume closed-set label [...] Read more.

Electroencephalogram (EEG)-based emotion recognition has emerged as a key enabler for semantic communication systems in next-generation networks (5G-Advanced/6G), where the goal is to transmit task-relevant semantic information rather than raw signals. However, domain adaptation approaches for EEG emotion recognition typically assume closed-set label spaces and fail when unseen emotional classes arise, leading to negative transfer and degraded semantic fidelity. To address this challenge, we propose a Coarse-to-Fine Open-set Domain Adaptation (C2FDA) framework, which aligns with the semantic communication paradigm by extracting and transmitting only the emotion-related semantics necessary for task performance. C2FDA integrates a cognition-inspired spatio-temporal graph encoder with a coarse-to-fine sample separation pipeline and instance-weighted adversarial alignment. The framework distinguishes between known and unknown emotional states in the target domain, ensuring that only semantically relevant information is communicated, while novel states are flagged as unknown. Experiments on SEED, SEED-IV, and SEED-V datasets demonstrate that C2FDA achieves superior open-set adaptation performance, with average accuracies of 41.5% (SEED → SEED-IV), 42.6% (SEED → SEED-V), and 48.9% (SEED-IV → SEED-V), significantly outperforming state-of-the-art baselines. These results confirm that C2FDA provides a semantic communication-driven solution for robust EEG-based emotion recognition in 6G-oriented human–machine interaction scenarios. Full article

(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)

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20 pages, 2676 KB

Open AccessArticle

Memory-Efficient Iterative Signal Detection for 6G Massive MIMO via Hybrid Quasi-Newton and Deep Q-Networks

by Adeb Salh, Mohammed A. Alhartomi, Ghasan Ali Hussain, Fares S. Almehmadi, Saeed Alzahrani, Ruwaybih Alsulami and Abdulrahman Amer

Electronics 2025, 14(24), 4832; https://doi.org/10.3390/electronics14244832 - 8 Dec 2025

The advent of Sixth Generation (6G) wireless communication systems demands unprecedented data rates, ultra-low latency, and massive connectivity to support emerging applications such as extended reality, digital twins, and ubiquitous intelligent services. These stringent requirements call for the use of massive Multiple-Input Multiple-Output [...] Read more.

The advent of Sixth Generation (6G) wireless communication systems demands unprecedented data rates, ultra-low latency, and massive connectivity to support emerging applications such as extended reality, digital twins, and ubiquitous intelligent services. These stringent requirements call for the use of massive Multiple-Input Multiple-Output (m-MIMO) systems with hundreds or even thousands of antennas, which introduce substantial challenges for signal detection algorithms. Conventional linear detectors, especially the linear Minimum Mean Square Error (MMSE) detectors, face prohibitive computational complexity due to high-dimensional matrix inversions, and their performance remains inherently restricted by the limitations of linear processing. The current research suggested an Iterative Signal Detection (ISD) algorithm with significant limitations being occupied with the combination of Deep Q-Network (DQN) and Quasi-Newton algorithms. The method incorporates the Broyden-Net, which could be faster with less memory training than the model in the case of spatially correlated channels, a Quasi-Newton method, and DQN to improve the m-MIMO detection. The proposed techniques support the computational efficiency of realistic 6G systems and outperform linear detectors. The simulation findings proved that the DQN-improved Quasi-Newton algorithm is more appropriate than traditional algorithms, since it combines the reward design, limited memory updates, and adaptive interference mitigation to shorten convergence time by 60% and increase the confrontation to correlated fading. Full article

(This article belongs to the Special Issue Advances in MIMO Communication)

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17 pages, 1021 KB

Open AccessArticle

A Lightweight CNN-Based Method for Micro-Doppler Feature-Based UAV Detection and Classification

by Luyan Zhang, Gangyi Tu, Yike Xu and Xujia Zhou

Electronics 2025, 14(24), 4831; https://doi.org/10.3390/electronics14244831 - 8 Dec 2025

To address the high computational cost and significant resource consumption of radar Doppler-based target recognition, which limits its application in real-time embedded systems, this paper proposes a lightweight CNN (Convolutional Neural Network) approach for radar target identification. The proposed approach builds a deep [...] Read more.

To address the high computational cost and significant resource consumption of radar Doppler-based target recognition, which limits its application in real-time embedded systems, this paper proposes a lightweight CNN (Convolutional Neural Network) approach for radar target identification. The proposed approach builds a deep convolutional neural network using range-Doppler maps, and leverages data collected by frequency-modulated continuous wave (FMCW) radar from targets such as drones, vehicles, and pedestrians. This method enables efficient object detection and classification across a wide range of scenarios. To improve the performance of the proposed model, this study incorporates a coordinate attention mechanism within the convolutional neural network. This mechanism fine-tunes the network’s focus by dynamically adjusting the weights of different feature channels and spatial regions, allowing it to concentrate on the most informative areas. Experimental results show that the foundational architecture of the proposed deep learning model, RangDopplerNet Type-1, effectively captures micro-Doppler features from range-Doppler maps across diverse targets. This capability enables precise detection and classification, with the model achieving an impressive average recognition accuracy of 96.71%. The enhanced network architecture, RangeDopplerNet Type-2, reached an average accuracy of 98.08%, while retaining a compact footprint of only 403 KB. Compared with standard lightweight models such as MobileNetV2, the proposed architecture reduces model size by 97.04%. This demonstrates that, while improving accuracy, the proposed architecture also significantly reduces both computational and storage overhead.The deep learning model introduced in this study is specifically tailored for deployment on resource-constrained platforms, including mobile and embedded systems. It provides an efficient and practical approach for development of miniaturized low-power devices. Full article

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13 pages, 3553 KB

Open AccessArticle

Design of the Active-Control Coil Power Supply for Keda Torus eXperiment

by Qinghua Ren, Yingqiao Wang, Xiaolong Liu, Weibin Li, Hong Li, Tao Lan and Zhen Tao

Electronics 2025, 14(24), 4830; https://doi.org/10.3390/electronics14244830 - 8 Dec 2025

Active-control coils on Keda Torus eXperiment (KTX) are used to suppress error fields and mitigate MHD instabilities, thereby extending discharge duration and improving plasma confinement quality. Achieving effective active MHD control imposes stringent requirements on the coil power supplies: wide-bandwidth and high-precision current [...] Read more.

Active-control coils on Keda Torus eXperiment (KTX) are used to suppress error fields and mitigate MHD instabilities, thereby extending discharge duration and improving plasma confinement quality. Achieving effective active MHD control imposes stringent requirements on the coil power supplies: wide-bandwidth and high-precision current regulation, deterministic low-latency response, and tightly synchronized operation across 136 independently driven coils. Specifically, the supplies must deliver up to ±200 A with fast slew rates and bandwidths up to several kilohertz, while ensuring sub-100 μs control latency, programmable waveforms, and inter-channel synchronization for real-time feedback. These demands make the power supply architecture a key enabling technology and motivate this work. This paper presents the design and simulation of the KTX active-control coil power supply. The system adopts a modular AC–DC–AC topology with energy storage: grid-fed rectifiers charge DC-link capacitor banks, each H-bridge IGBT converter (20 kHz) independently drives one coil, and an EMC filter shapes the output current. Matlab/Simulink R2025b simulations under DC, sinusoidal, and arbitrary current references demonstrate rapid tracking up to the target bandwidth with ±0.5 A ripple at 200 A and limited DC-link voltage droop (≤10%) from an 800 V, 50 mF storage bank. The results verify the feasibility of the proposed scheme and provide a solid basis for real-time multi-coil active MHD control on KTX while reducing instantaneous grid loading through energy storage. Full article

(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control, 2nd Edition)

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20 pages, 4513 KB

Open AccessArticle

Novel Hybrid Processing Techniques for Wideband HF Signals Impaired by Ionospheric Propagation

by Ilia Peshkov

Electronics 2025, 14(24), 4829; https://doi.org/10.3390/electronics14244829 - 8 Dec 2025

In this paper, hybrid space–time–polarization schemes for processing high-frequency (HF) radio signals transmitted through the ionospheric layers are proposed. Ionospheric radio wave propagation is characterized by several impairments, including attenuation, scintillation, dispersion, and Faraday rotation. The use of hybrid schemes combining spatial digital [...] Read more.

In this paper, hybrid space–time–polarization schemes for processing high-frequency (HF) radio signals transmitted through the ionospheric layers are proposed. Ionospheric radio wave propagation is characterized by several impairments, including attenuation, scintillation, dispersion, and Faraday rotation. The use of hybrid schemes combining spatial digital processing and a single-input multiple-output (SIMO) scheme based on the spatial and polarization principles is proposed. The simulation is based on a preliminary estimate of signal attenuation and spatial coordinates based on ray tracing at a distance of 1000 km between the transmitter and the receiving digital antenna array. Additionally, the bit error rates and data capacity are obtained for various configurations of hybrid spatial and polarizing types of the proposed architectures. In addition, an algorithm for modeling a broadband HF signal in the ionosphere based on the inverse discrete Fourier transform (IDFT) and the Watterson narrowband model is proposed. Schemes for processing the wideband orthogonal frequency division multiplexing (OFDM) signals after passing through the ionosphere layers are represented as well. Results indicate that the optimal configuration employs hybrid processing utilizing ordinary (O) and extraordinary (X) wave polarization, combined with spatial digital processing in a SIMO architecture. Full article

(This article belongs to the Section Networks)

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19 pages, 48003 KB

Open AccessArticle

Risk-Aware Distributional Reinforcement Learning for Safe Path Planning of Surface Sensing Agents

by Jihua Dou, Zhongqi Li, Yuanhao Wang, Kunpeng Ouyang, Weihao Xia, Jianxin Lin and Huachuan Wang

Electronics 2025, 14(24), 4828; https://doi.org/10.3390/electronics14244828 - 8 Dec 2025

In spatially constrained water domains, surface sensing agents(SSAs) must achieve safe path planning, uncertain currents, and sensor noise. We present a decentralized motion planning and collision-avoidance framework based on distributional reinforcement learning (DRL) that models the full return distribution to enable risk-aware decision [...] Read more.

In spatially constrained water domains, surface sensing agents(SSAs) must achieve safe path planning, uncertain currents, and sensor noise. We present a decentralized motion planning and collision-avoidance framework based on distributional reinforcement learning (DRL) that models the full return distribution to enable risk-aware decision making. Each surface sensing agent autonomously proceeds to its designated coordinates without rigid spatial constraints, coordinating implicitly through learned policies and a lightweight safety shield that enforces separation and kinematic limits. The method integrates (i) distributional value estimation for controllable risk sensitivity near hazards, (ii) domain randomization of sea states and disturbances for robustness, and (iii) a shielded action layer compatible with standard reactive rules (e.g., velocity obstacle-style constraints) to guarantee feasible maneuvers. In simulations across cluttered maps and stochastic current fields, the proposed approach improves success rates and reduces near-miss events compared to non-distributional RL and classical planners, while maintaining competitive path length and computation time. The results indicate that DRL-based surface sensing agent navigation is a practical path toward safe, efficient environmental monitoring and surveying. Full article

(This article belongs to the Special Issue The Collaborative Perception, Localization, and Decision Control of Intelligent Vehicles)

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19 pages, 1695 KB

Open AccessArticle

Fault Process Modeling and Transient Stability Analysis of Grid-Following Photovoltaic Converter Grid-Connected System

by Ze Wei, Tao Xu, Yan Wang, Jianan Mu, Lin Cheng, Ning Chen, Luming Ge and Xiong Du

Electronics 2025, 14(24), 4827; https://doi.org/10.3390/electronics14244827 - 8 Dec 2025

With the growing integration of renewable energy into power systems, transient stability throughout the whole fault process has become a critical issue. This process comprises three distinct stages: pre-fault, fault-on, and post-fault recovery. However, existing studies have largely overlooked the influence of active [...] Read more.

With the growing integration of renewable energy into power systems, transient stability throughout the whole fault process has become a critical issue. This process comprises three distinct stages: pre-fault, fault-on, and post-fault recovery. However, existing studies have largely overlooked the influence of active power recovery on transient stability, which leads to conservative estimates of critical fault clearing time (CCT) and potential misjudgment of stability analysis. Accordingly, this paper addresses this gap by examining a grid-following (GFL) photovoltaic (PV) converter grid-connected system. Therefore, this paper investigates the transient stability of a GFL PV converter grid-connected system during the whole fault process. Firstly, a transient stability analysis model is developed using the piecewise linearization method to represent the system behavior across the whole fault process. Secondly, based on the proposed model, the impact mechanism of the control strategy in the fault recovery stage on the transient stability of the system is revealed by using the equal area criterion (EAC). Finally, the accuracy of the theoretical analysis proposed in this paper is verified by the PSCAD/EMTDC simulation platform. The results show that a slower active power recovery rate enhances the system’s transient stability, as it creates a larger equivalent deceleration area. The critical fault clearing time calculated by the proposed model is less conservative. Full article

(This article belongs to the Special Issue Wind and Renewable Energy Generation and Integration)

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18 pages, 4671 KB

Open AccessArticle

A 2.4 GHz CMOS Pulse-Mode Transmitter for RF Body-Contouring Device Applications

by Geonwoo Jeong, Hwayoung Jung, Sijin Jang, Jaeeun Jang and Hyunchol Shin

Electronics 2025, 14(24), 4826; https://doi.org/10.3390/electronics14244826 - 8 Dec 2025

Body-contouring devices deliver controlled thermal energy to treat cellulite, reduce localized fat, and improve skin elasticity. Since the thermal effect is closely related to the delivered RF output power, precise control of the output power is critical for both efficacy and safety. In [...] Read more.

Body-contouring devices deliver controlled thermal energy to treat cellulite, reduce localized fat, and improve skin elasticity. Since the thermal effect is closely related to the delivered RF output power, precise control of the output power is critical for both efficacy and safety. In this study, we propose a 2.4 GHz CMOS pulse-mode transmitter for body-contouring device applications, featuring precise control of the average power delivered to the body. The transmitter comprises a fully integrated phase-locked loop (PLL) synthesizer, pulse modulator (PM), and 10 mW power amplifier (PA). It is fabricated in a 65 nm CMOS with a compact die area of 3.75 mm². The PA provides four-level continuous-mode output control from −0.3 dBm to 11.1 dBm, and the PM performs programmable PA switching for pulse-mode operation of the PA with a wide range of pulse rates and duty ratios. By combining the continuous-mode output power control and pulse-mode on–off time regulation, the average output power delivered to the skin is finely controlled, managing the delivered power within a safe skin temperature below 65 °C. The PLL loop filter is fully integrated with a wide programmability, improving the form factor and bill of materials for the target devices. Measurement results confirm that the designed transmitter can accurately control both the average output power and pulse profile across the 2.4 GHz ISM band, demonstrating its suitability for compact home-use RF body-contouring devices. Full article

(This article belongs to the Special Issue Modern Circuits and Systems Technologies (MOCAST 2024))

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16 pages, 1309 KB

Open AccessArticle

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

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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23 pages, 7258 KB

Open AccessArticle

Optimal Grid-Forming Strategy for a Remote Hydrogen Production System Supplied by Wind and Solar Power Through MMC-HVDC Link

by Wujie Chao, Junwei Huang, Zhibo Zhang, Changgeng Tian, Liyu Dai, Jinke Wang and Xinyi Lin

Electronics 2025, 14(24), 4824; https://doi.org/10.3390/electronics14244824 - 8 Dec 2025

Large-scale renewable power supply system design for remote hydrogen production is a challenging task due to the 100% power electronics sending-end subsystem. The proper grid-forming strategy for a sending-end system to achieve large-scale remote hydrogen production still remains a research gap. This study [...] Read more.

Large-scale renewable power supply system design for remote hydrogen production is a challenging task due to the 100% power electronics sending-end subsystem. The proper grid-forming strategy for a sending-end system to achieve large-scale remote hydrogen production still remains a research gap. This study first designs two grid-forming strategies for the concerned renewable power supply system, with one being based on virtual synchronous generator (VSG) and another one being based on V/f control. Then, the impedance analysis is carried out for ensuring the small-signal stable operation of the sending-end system including wind power plant and PV plant. Numerical simulation results implemented on PSCAD verify that the VSG-based grid-forming strategy configured on the sending-end modular multilevel converter (MMC) station of the MMC-based high-voltage direct-current (HVDC) link has a larger transient stability margin. Hence, the MMC-HVDC-based grid-forming strategy is a better choice for the power supply of large-scale remote hydrogen production. The enhanced stability margin ensures more robust operation under disturbances, which is critical for maintaining continuous power supply to large-scale electrolyzers. Full article

(This article belongs to the Special Issue Leveraging AI for Failure Diagnosis: Insights into Clean Energy Storage & Power Systems)

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2 pages, 149 KB

Open AccessCorrection

Correction: Morais et al. IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives. Electronics 2023, 12, 1169

by Davisson F. T. Morais, Gilberto Fernandes, Jr., Gildário D. Lima and Joel J. P. C. Rodrigues

Electronics 2025, 14(24), 4823; https://doi.org/10.3390/electronics14244823 - 8 Dec 2025

In the original publication [...] Full article

22 pages, 4020 KB

Open AccessArticle

From Simulation to Reality: Comparative Performance Analysis of SLAM Toolbox and Cartographer in ROS 2

by İbrahim İnce, Derya Yiltas-Kaplan and Fatih Keleş

Electronics 2025, 14(24), 4822; https://doi.org/10.3390/electronics14244822 - 8 Dec 2025

This paper presents a comparative analysis of SLAM Toolbox and Cartographer mapping performance in both simulated and real-world environments using ROS 2. The aim of the study is to evaluate the effectiveness, accuracy, and resource utilization of each Simultaneous Localization and Mapping (SLAM) [...] Read more.

This paper presents a comparative analysis of SLAM Toolbox and Cartographer mapping performance in both simulated and real-world environments using ROS 2. The aim of the study is to evaluate the effectiveness, accuracy, and resource utilization of each Simultaneous Localization and Mapping (SLAM) tool under identical conditions. The experiments were conducted using the Humble Hawksbill distribution of ROS 2, with mapping tasks performed in indoor environments via Gazebo simulation and physical robot tests. Results show that SLAM Toolbox demonstrated slightly more consistent map generation in environments that included human movement and small object relocations. It achieved an Absolute Trajectory Error (ATE) of 0.13 m, compared to 0.21 m for Cartographer under identical test conditions. However, Toolbox required approximately 70% CPU usage, 293 MB RAM, and a startup time of 5.2 s, reflecting higher computational demand and configuration complexity. In contrast, Cartographer exhibited slower map generation and slightly higher RAM usage (299 MB) in simulation, while requiring higher CPU load (80%) and showing greater sensitivity to parameter tuning, which contributed to less accurate localization in noise-free simulations. This study highlights the advantages and limitations of both SLAM technologies and provides practical guidance for selecting appropriate SLAM solutions in robotic mapping and autonomous navigation tasks, particularly for systems deployed on resource-constrained platforms. Full article

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20 pages, 2793 KB

Open AccessArticle

Spectral-Attention Cooperative Encoding with Dynamic Activation for Remote Sensing Change Detection

by Chuanzhen Rong, Yongxing Jia, Shenghui Zhou and Huali Wang

Electronics 2025, 14(24), 4821; https://doi.org/10.3390/electronics14244821 - 7 Dec 2025

Change detection (CD) in high-resolution remote sensing imagery is vital for resource monitoring and disaster assessment but faces challenges such as spatiotemporal heterogeneity, spectral variability, and computational inefficiency. This paper proposes an efficient CD method that hybridizes Convolutional Neural Networks (CNNs) and Transformers. [...] Read more.

Change detection (CD) in high-resolution remote sensing imagery is vital for resource monitoring and disaster assessment but faces challenges such as spatiotemporal heterogeneity, spectral variability, and computational inefficiency. This paper proposes an efficient CD method that hybridizes Convolutional Neural Networks (CNNs) and Transformers. A CNN backbone first extracts multi-level features from bi-temporal images. A Semantic Token Generator then compresses these features into compact, low-dimensional semantic tokens, reducing computational load. The core of our model is a novel cooperative encoder integrating a Spectral layer and an Attention layer. The Spectral layer enhances sensitivity to high-frequency components like edges and textures in the Fourier domain, while the Attention layer captures long-range semantic dependencies via self-attention. Furthermore, we introduce a Dynamic Tanh (DyT) module to replace conventional normalization layers, using learnable parameters to adaptively adjust activation thresholds, thereby improving training stability and computational efficiency. Comprehensive evaluations on the LEVIR-CD, WHU-CD, and DSIFN-CD benchmarks demonstrate that our method maintains high accuracy while reducing complexity, offering a practical solution for real-time CD in resource-limited environments. Full article

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