Electronics

22 pages, 4690 KB

Open AccessReview

Comparative Review of Commercialized Advanced Driver Assistance System (ADAS) Technologies

by Yeongmin Kim, Sohyang Kim, Doyeon Kim and Kibeom Lee

Electronics 2026, 15(10), 2015; https://doi.org/10.3390/electronics15102015 (registering DOI) - 9 May 2026

Recent advancements in autonomous driving technology are transforming the automotive industry, with advanced driver assistance systems (ADAS) recognized as a crucial transitional technology toward fully autonomous driving. ADAS enhances driver safety and comfort through features such as emergency braking, lane-keeping, and adaptive cruise [...] Read more.

Recent advancements in autonomous driving technology are transforming the automotive industry, with advanced driver assistance systems (ADAS) recognized as a crucial transitional technology toward fully autonomous driving. ADAS enhances driver safety and comfort through features such as emergency braking, lane-keeping, and adaptive cruise control, ultimately aiding in traffic accident prevention and reduction in driver fatigue. However, commercial ADAS implementations show substantial variability due to differences in sensor configurations, operational design domain (ODD) definitions, and operational criteria across automakers. To address this gap, this study provides a structured comparative review of commercialized ADAS technologies across 11 major Western and Asian automakers. By encompassing both Western and Asian OEMs, this study compares manufacturer-declared sensor configurations, ODD settings, activation conditions, driver-monitoring requirements, takeover and fallback logic, and update-related characteristics. The review identifies implementation-level differences that affect comparability, user understanding, validation requirements, and standardization needs. Rather than ranking OEM systems by safety performance, this study clarifies the trade-offs among redundancy-oriented, camera-centric, HD-map-dependent, geofenced, and OTA-driven ADAS strategies. The findings support future work on standardized ODD communication, user-centered HMI design, independent validation, and update-aware review frameworks for commercial ADAS. Full article

(This article belongs to the Special Issue Automated Driving Systems: Latest Advances and Prospects)

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28 pages, 7823 KB

Open AccessArticle

Multi-Agent DDPG-Based DC-Link Voltage Balancing Control for Cascaded H-Bridge Rectifiers

by Lihui Zhou and Chunjie Li

Electronics 2026, 15(10), 2014; https://doi.org/10.3390/electronics15102014 (registering DOI) - 9 May 2026

Abstract

Cascaded H-bridge rectifiers suffer from severe DC-link voltage imbalance under unbalanced load conditions. Considering the difficulties of parameter tuning and unsatisfactory dynamic response of traditional voltage balancing control schemes under complex nonlinear operating conditions, this study proposes an intelligent voltage balancing strategy based [...] Read more.

Cascaded H-bridge rectifiers suffer from severe DC-link voltage imbalance under unbalanced load conditions. Considering the difficulties of parameter tuning and unsatisfactory dynamic response of traditional voltage balancing control schemes under complex nonlinear operating conditions, this study proposes an intelligent voltage balancing strategy based on the multi-agent deep deterministic policy gradient (DDPG) algorithm. By constructing an interactive environment between multiple agents and the cascaded H-bridge rectifier, the proposed method enables the agents to autonomously optimize control commands and realize DC-link voltage balance. The proposed method adopts a centralized training and decentralized execution framework to enable coordinated control among submodules. The simulation and hardware-in-the-loop (HIL) experimental results demonstrate that the proposed strategy can effectively suppress DC-link voltage imbalance and improve dynamic performance. Specifically, compared with conventional voltage balancing methods, the maximum total output voltage deviation is reduced from approximately 85 V to 45 V in HIL experiments, while the voltage settling time is shortened from about 260 ms to 120 ms. These results indicate that the proposed method can effectively eliminate steady-state errors and achieve fast and stable DC-link voltage balancing even under severely unbalanced load conditions. Full article

(This article belongs to the Special Issue Application of Machine Learning in Power Electronics)

21 pages, 690 KB

Open AccessArticle

Analytical Server-Side Capacity Planning for Operator-Managed OTT/IPTV Systems with Differentiated Subscription Tiers

by Błażej Nowak, Paweł Andruloniw, Piotr Zwierzykowski and Maciej Stasiak

Electronics 2026, 15(10), 2013; https://doi.org/10.3390/electronics15102013 (registering DOI) - 9 May 2026

Abstract

Server-side capacity dimensioning in operator-managed Over-The-Top (OTT) and Internet Protocol Television (IPTV) systems requires analytical methods that can account for heterogeneous traffic classes, differentiated subscription tiers, and strict grade-of-service (GoS) constraints. This paper proposes a capacity-planning framework based on a full-availability group (FAG) [...] Read more.

Server-side capacity dimensioning in operator-managed Over-The-Top (OTT) and Internet Protocol Television (IPTV) systems requires analytical methods that can account for heterogeneous traffic classes, differentiated subscription tiers, and strict grade-of-service (GoS) constraints. This paper proposes a capacity-planning framework based on a full-availability group (FAG) model and the Kaufman–Roberts recursion for evaluating class-specific blocking probabilities in multi-class OTT/IPTV delivery systems. The framework combines recursive occupancy-distribution computation with an incremental capacity search procedure to determine the minimum server-side delivery capacity satisfying differentiated blocking targets for free, standard, and premium subscription tiers. Three provisioning strategies are analysed within a unified model: dedicated server pools, a shared non-prioritised resource pool, and a shared prioritised resource pool. The analytical results are validated by discrete-event simulation and then used to compare the required capacities under the considered strategies. For the analysed six-class scenario, the shared server configuration reduces the required capacity by 3.82% compared with the dedicated architecture, while the prioritised shared configuration reduces it by 12.44%, while preserving stricter GoS protection for higher-priority traffic. The proposed framework provides network operators with a reproducible analytical tool for translating blocking-probability constraints into concrete server-capacity requirements and infrastructure-planning decisions. Full article

(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)

25 pages, 15258 KB

Open AccessArticle

Dynamic Modeling and Error Analysis of MEMS Ring Gyroscope Based on FTR Mode: Principle and Structural Errors

by Chong Dong, Feng Ye and Jia Jia

Electronics 2026, 15(10), 2012; https://doi.org/10.3390/electronics15102012 (registering DOI) - 9 May 2026

Abstract

This paper presents a unified dynamic-modeling and error-analysis framework for an FTR (force-to-rebalanced)-operated MEMS ring gyroscope. Starting from an equivalent mass-point representation of the ring resonator, a dynamic model including stiffness and damping errors is first established. Principle-related inertial-acceleration errors and structural errors [...] Read more.

This paper presents a unified dynamic-modeling and error-analysis framework for an FTR (force-to-rebalanced)-operated MEMS ring gyroscope. Starting from an equivalent mass-point representation of the ring resonator, a dynamic model including stiffness and damping errors is first established. Principle-related inertial-acceleration errors and structural errors are then analyzed within the same framework. The results show that, under practical rate-measurement conditions, inertial-acceleration errors have negligible effects on both the drive and sense modes. In contrast, structural errors, including modal-frequency perturbation, damping-decay-time mismatch, mass-distribution mismatch, and electrode angular misalignment, impair drive-mode amplitude control and frequency tracking, introduce in-phase bias components into the sense-mode output, and produce quadrature signals through frequency coupling. The analysis further indicates that electrostatic mode matching should be implemented in two steps: quadrature-stiffness correction followed by modal-frequency tuning. The proposed model provides a concise and physically transparent basis for resonator design, parameter identification, and control compensation in high-performance MEMS ring gyroscopes. Full article

28 pages, 1681 KB

Open AccessArticle

A Closed-Loop Modular Language Agent with Step Verification and Local Correction for Multi-Step Task Solving

by He Li, Lihang Feng and Dong Wang

Electronics 2026, 15(10), 2011; https://doi.org/10.3390/electronics15102011 (registering DOI) - 9 May 2026

Abstract

Multi-step task solving with large language models in intelligent electronic systems and interactive environments requires stronger process control and execution reliability. To address local error accumulation during multi-step execution, this paper proposes a closed-loop modular language agent framework integrating task planning, action execution, [...] Read more.

Multi-step task solving with large language models in intelligent electronic systems and interactive environments requires stronger process control and execution reliability. To address local error accumulation during multi-step execution, this paper proposes a closed-loop modular language agent framework integrating task planning, action execution, step verification, local regeneration, and replanning. A process-supervision data construction method is further introduced, in which real execution steps are retained as valid samples and invalid samples are automatically synthesized through action substitution, input perturbation, observation replacement, and subgoal mismatch, providing step-level supervision for validity prediction. In the proposed framework, step verification functions as a process-level control signal that supports hierarchical recovery through local regeneration and replanning, rather than as a standalone filtering module. Experiments are conducted on mathematical reasoning and web interaction tasks. On mathematical reasoning tasks, the proposed framework achieves an accuracy of 0.650, compared with 0.317 for Integrated Training, 0.460 for CoT Training, 0.568 for ReFT, and 0.617 for Agent Lumos. On web interaction tasks, the proposed framework achieves a step success rate of 0.424, compared with 0.246 for Integrated Training and 0.310 for Agent Lumos. Among the cases where recovery is triggered, local regrounding succeeds in 85.7% of reground attempts, while replanning succeeds in 53.3% of replanning attempts. These results indicate that the proposed framework improves process stability and recovery capability in multi-step task solving. Full article

(This article belongs to the Section Artificial Intelligence)

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27 pages, 2771 KB

Open AccessArticle

CBW-DETR: A Lightweight Detection Transformer for Small Object Detection in UAV Imagery

by Suning Qin, Ke Cheng and Yuanquan Wang

Electronics 2026, 15(10), 2010; https://doi.org/10.3390/electronics15102010 (registering DOI) - 9 May 2026

Abstract

Small object detection in Unmanned Aerial Vehicle (UAV) imagery faces critical challenges, including extreme scale variations, dense spatial distributions, and stringent computational constraints, for real-time deployment. To address these challenges, this paper proposes a CBW-based Detection Transformer (CBW-DETR), an enhanced transformer-based detection framework [...] Read more.

Small object detection in Unmanned Aerial Vehicle (UAV) imagery faces critical challenges, including extreme scale variations, dense spatial distributions, and stringent computational constraints, for real-time deployment. To address these challenges, this paper proposes a CBW-based Detection Transformer (CBW-DETR), an enhanced transformer-based detection framework that integrates architectural efficiency with scale-aware mechanisms throughout the detection pipeline. The framework comprises three coordinated innovations. First, a Context-Guided Feature Extraction (ContextGFE) module reduces model parameters and theoretical computational cost through adaptive receptive field selection and wavelet-domain enhancement while maintaining representational capacity. Second, a Scale-Aware Feature Pyramid Network (SAFPN) employs spatial-variant compensation factors and cross-scale attention to facilitate balanced gradient flow across pyramid levels, particularly benefiting small object detection. Third, an Adaptive Scale IoU (ASIoU) loss function implements uncertainty-aware gradient modulation and scale-specific optimization to enhance localization accuracy for objects of varying sizes. Extensive experiments on VisDrone2019 and Dataset for Object Detection in Aerial Images (DOTA) datasets demonstrate that CBW-DETR achieves substantial improvements in detection accuracy while reducing model parameters by 28.1% and theoretical computation by 18.0% compared to the Real-Time Detection Transformer-R18 (RT-DETR-R18) baseline. These reductions in model complexity come at a moderate cost in inference throughput (73.6 frames per second (FPS) vs. 94.1 FPS), attributable to memory-access-intensive operations introduced by multi-branch convolutions and wavelet transforms. Among the evaluated detectors, including You Only Look Once (YOLO) series variants and transformer-based methods, CBW-DETR achieves a competitive detection accuracy with a notably compact model footprint. Visualization analysis confirms its robust performance across diverse challenging scenarios including nighttime conditions, dense object distributions, and severe occlusions, validating the framework’s practical applicability for UAV-based detection applications. Full article

3 pages, 139 KB

Open AccessEditorial

Advances in Mobile Networked Systems

by Wei Cui, Yaoming Zhuang and Wei Zhou

Electronics 2026, 15(10), 2009; https://doi.org/10.3390/electronics15102009 (registering DOI) - 9 May 2026

Abstract

Mobile networked systems have fundamentally transformed the landscape of modern communication and information sharing [...] Full article

(This article belongs to the Special Issue Advances in Mobile Networked Systems)

21 pages, 10361 KB

Open AccessArticle

U-Net-Based Model Design for Semantic Segmentation of Class-Imbalanced Semi-Synthetic Roads

by Artur Morys-Magiera, Marek Długosz and Paweł Skruch

Electronics 2026, 15(10), 2008; https://doi.org/10.3390/electronics15102008 (registering DOI) - 9 May 2026

Abstract

Accurate semantic segmentation of roads and overlaid markings is essential for multi-camera multi-robot visual localization systems, yet lane markings occupy a tiny fraction of the image area, making them difficult to segment reliably. This paper presents a U-Net design study for semantic segmentation [...] Read more.

Accurate semantic segmentation of roads and overlaid markings is essential for multi-camera multi-robot visual localization systems, yet lane markings occupy a tiny fraction of the image area, making them difficult to segment reliably. This paper presents a U-Net design study for semantic segmentation of imbalanced segmentation of a dominant class and two similar, minority classes, that occur on top of the dominant class. We analyze the problem of designing a multi-head U-Net for segmenting semi-synthetic Duckietown model road map images into roads, stop-line markings, and lane-line markings. The multi-head design decomposes the task into a binary road segmentation head and a ternary marking segmentation head, connected through a road-aware loss that restricts marking supervision to predicted road regions. Our work assesses the nine loss functions to approach the class imbalance problem in the marking head—including cross-entropy, focal loss, Tversky loss, Lovász-softmax, and a subset of combinations thereof. These configurations are systematically evaluated on a dataset of semi-synthetic map images generated using an evolutionary algorithm described in a previous work of the authors, where road marking classes are a minority. The Tversky–Lovász combination achieves the highest per-class IoU across all segmentation targets, being statistically significantly better than other configurations. The results demonstrate that the Tversky loss combined with a direct IoU surrogate, Lovász-softmax, is particularly effective for small-object segmentation under severe class imbalance. Full article

(This article belongs to the Special Issue Deep/Machine Learning in Visual Recognition and Anomaly Detection)

18 pages, 168992 KB

Open AccessArticle

BC-FR: Bijective Contrast and Fusion Reconstruction Networks for Unpaired Image-to-Image Translation

by Shibin Wang, Dehuang Qin, Yubo Xu, Yu Wang, Qi Yu and Dong Liu

Electronics 2026, 15(10), 2007; https://doi.org/10.3390/electronics15102007 (registering DOI) - 8 May 2026

Abstract

Image-to-image translation aims to learn some mapping relationships between two different domains to implement cross-domain conversion. And symmetric dual learning is one of the classic architectures. However, if the generators are expected to achieve the cycle-consistency, this high requirement frequently causes mode collapse, [...] Read more.

Image-to-image translation aims to learn some mapping relationships between two different domains to implement cross-domain conversion. And symmetric dual learning is one of the classic architectures. However, if the generators are expected to achieve the cycle-consistency, this high requirement frequently causes mode collapse, which constrains the performance. In this paper, to meet this challenge, we propose a novel dual learning framework, bijective contrast and fusion reconstruction (BC-FR) network. On the one hand, drawing inspiration from the widely adopted contrastive representation learning, we propose the bijective contrast (BC) network. Specifically, a reverse contrastive learning from output to input patches is designed, which enables two embedding spaces to learn the mapping relationship in a bijective way. This strategy provides a richer pixel-level domain shift for object. On the other hand, to further improve the visual performance, we also propose the fusion reconstruction (FR) network, which provides an unsupervised fusion approach to achieve the cycle-consistency. Specifically, it separates and reassembles different text elements of input and output images to achieve the reconstruction work. Experiments on various pixel-level benchmark datasets show that BC-FR can obtain comprehensive quantitative metrics and yield high-fidelity visual outputs. Furthermore, the sub-scheme FR can be extended to semantic-level datasets. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 1525 KB

Open AccessArticle

Enhanced Sensorless Backstepping Control of Brushless Doubly Fed Reluctance Generators Using an Adaptive High-Gain Observer

by Abdelfattah Salhi, Zoheir Tir, Khaled Laadjal and Mohamed Sahraoui

Electronics 2026, 15(10), 2006; https://doi.org/10.3390/electronics15102006 (registering DOI) - 8 May 2026

Abstract

This study proposes a strategy for managing wind-turbine energy systems through the utilization of a high-gain observer in a sensorless backstepping control method applied to brushless double-fed reluctance generators (BDFRG). The paper initially introduces a vector control technique for brushless doubly fed reluctance [...] Read more.

This study proposes a strategy for managing wind-turbine energy systems through the utilization of a high-gain observer in a sensorless backstepping control method applied to brushless double-fed reluctance generators (BDFRG). The paper initially introduces a vector control technique for brushless doubly fed reluctance generators, followed by the integration of the backstepping control method and the high-gain observer strategy within the overall system. Moreover, the research investigates the “maximum torque per inverter ampere” strategy, which enables the brushless doubly fed reluctance generators to achieve full magnetization by the primary winding, resulting in a reduction in the power factor. The stability of the system is established through the application of the Lyapunov theory. The simulation outcomes validate the efficacy and importance of this approach. Full article

(This article belongs to the Special Issue Power Converters for Modern Power Systems: Analysis, Modeling, and Control)

20 pages, 3175 KB

Open AccessArticle

Multimodal Automatic Music Transcription Using Piano Audio and Hand-Skeleton Information

by Kosuke Yamada, Satoshi Nishimura and Jungpil Shin

Electronics 2026, 15(10), 2005; https://doi.org/10.3390/electronics15102005 (registering DOI) - 8 May 2026

Abstract

Automatic Music Transcription (AMT) for piano is difficult for audio-only systems due to dense polyphony, resonance, and reverberation, which lead to false positives and unstable onset decisions. We present a multimodal AMT framework that fuses Omnizart audio probability maps with visual cues from [...] Read more.

Automatic Music Transcription (AMT) for piano is difficult for audio-only systems due to dense polyphony, resonance, and reverberation, which lead to false positives and unstable onset decisions. We present a multimodal AMT framework that fuses Omnizart audio probability maps with visual cues from hand-skeleton tracking. A graph-based model called HandSkeletonNet estimates per-key onset probabilities from hand trajectories, and the two modalities are merged via a weighting-and-masking scheme or a compact CNN-based merger. Experiments show consistent improvements over the audio-only baseline on our self-compiled dataset, while evaluations with external datasets primarily improve frame-level sensitivity. The frame-level F1 score improved from 75.12% to 75.76% for the PianoYT dataset and from 54.68% to 57.57% for the PianoVAM dataset compared with the audio-only baseline. Our experiments also reveal limited onset-level gains under domain shift. Remaining errors are largely explained by timing/misalignment and note fragmentation in MIDI decoding, suggesting that robustness to missing hand detections and explicit temporal alignment are key directions. Full article

(This article belongs to the Special Issue Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications)

20 pages, 954 KB

Open AccessReview

A Unified Structural Framework for Time–Frequency Analysis and Machine Learning in Condition Monitoring

by Serdar Bilgi and Tahir Cetin Akinci

Electronics 2026, 15(10), 2004; https://doi.org/10.3390/electronics15102004 (registering DOI) - 8 May 2026

Abstract

Condition monitoring in engineering systems requires analytical frameworks that connect physically meaningful signal representations with statistically consistent decision mechanisms. Although spectral analysis, time–frequency methods, and machine learning have each advanced significantly, they are often treated as separate methodological domains. This work presents a [...] Read more.

Condition monitoring in engineering systems requires analytical frameworks that connect physically meaningful signal representations with statistically consistent decision mechanisms. Although spectral analysis, time–frequency methods, and machine learning have each advanced significantly, they are often treated as separate methodological domains. This work presents a unified structural framework that integrates classical spectral techniques, time–frequency representations, and supervised learning within a coherent monitoring architecture. Rather than providing a systematic survey, the study adopts a conceptual perspective to explicitly describe the analytical linkage between signal transformation, feature construction, and statistical inference. The discussion begins with Fourier-based descriptors and power spectral density formulations, and extends to short-time Fourier transform and continuous wavelet transform frameworks, highlighting their resolution characteristics for non-stationary signals. These representations are then connected to feature-space construction and learning-based decision models through an explicit mapping between physical signal properties and statistical inference mechanisms. An illustrative synthetic analysis is included to demonstrate how representation fidelity influences feature-space structure and downstream classification behaviour under transient conditions. These results are intended to provide conceptual insight rather than generalizable performance claims. Applications across multiple engineering domains are discussed to highlight the generality of the proposed framework. Finally, key research challenges, including dynamic operating regimes, data imbalance, interpretability, and computational constraints, are outlined. The proposed framework emphasises the complementary roles of transform-based representation and learning-based inference, providing a structured foundation for scalable and interpretable condition monitoring systems. Full article

(This article belongs to the Section Circuit and Signal Processing)

25 pages, 3288 KB

Open AccessArticle

Full-State Event-Triggered Control for a Class of Nonlinear Systems with Input Delay

by Weigang Zhang, Ye Liu and Le Cao

Electronics 2026, 15(10), 2003; https://doi.org/10.3390/electronics15102003 (registering DOI) - 8 May 2026

Abstract

This paper addresses the tracking control problem for a class of uncertain strict-feedback nonlinear systems with input delay under communication constraints. The main difficulty is that the input delay degrades tracking performance, while full-state event-triggered transmission provides only intermittent state measurements, which are [...] Read more.

This paper addresses the tracking control problem for a class of uncertain strict-feedback nonlinear systems with input delay under communication constraints. The main difficulty is that the input delay degrades tracking performance, while full-state event-triggered transmission provides only intermittent state measurements, which are not directly compatible with the recursive backstepping design. To overcome this difficulty, an adaptive full-state event-triggered backstepping control scheme is developed. First, a Padé approximation is used to transform the delayed-input system into an augmented delay-free model. Then, an improved continuous-state estimator is introduced to reconstruct smooth surrogate state signals from the event-triggered measurements, thereby preserving the implementability of the recursive backstepping design. Based on the reconstructed states, an adaptive controller and an error-dependent event-triggering mechanism are designed to achieve practical tracking with reduced state transmissions. It is shown that all closed-loop signals remain bounded, the tracking error converges to an adjustable compact neighborhood of the origin, and Zeno behavior is excluded. Comparative simulation results further show that the proposed scheme reduces the triggering frequency and estimator-side computational burden compared with the high-order estimator-based scheme considered in the simulations, while maintaining satisfactory practical tracking performance. Full article

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

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15 pages, 1233 KB

Open AccessArticle

Applicability Analysis of LSK and P2 Fusion in YOLOv11 for Insulator Defect Instance Segmentation

by Jie Guo, Yanhan Zhao, Ying Zhang, Chao Li, Bei Jian, Qian Zhou and Chao Yuan

Electronics 2026, 15(10), 2002; https://doi.org/10.3390/electronics15102002 (registering DOI) - 8 May 2026

Abstract

Insulator defect instance segmentation in unmanned aerial vehicle (UAV)-based power inspection scenarios remains challenging because of large target-scale variation, complex backgrounds, weak defect textures, and limited annotated samples. To examine whether common structural enhancement strategies can improve performance in this small-sample setting, this [...] Read more.

Insulator defect instance segmentation in unmanned aerial vehicle (UAV)-based power inspection scenarios remains challenging because of large target-scale variation, complex backgrounds, weak defect textures, and limited annotated samples. To examine whether common structural enhancement strategies can improve performance in this small-sample setting, this study investigates the applicability of two modifications to YOLOv11-seg: introducing a Large Selective Kernel (LSK) module into deep backbone stages and incorporating a P2 high-resolution feature map into the feature fusion network. Experiments were conducted on an expanded insulator defect instance segmentation dataset containing 836 images, including 138 images with defect instances. To reduce the influence of a single random partition, three independent stratified data splits were constructed, and all results were reported as mean ± standard deviation across the three splits. The results show that, within the YOLOv11-seg framework, none of the LSK-based, P2-based, or LSK+P2 variants provides a clear and consistent improvement over the baseline. Although some variants achieve slightly higher mean values in individual box-level metrics, the differences remain within the range of split-to-split variation and do not support a robust performance advantage. In addition, external comparisons with Mask R-CNN, pretrained YOLOv8s-seg, and pretrained YOLOv11s-seg provide a broader reference for the performance level of different instance segmentation frameworks under the current setting. The results show that YOLOv11s-seg remains competitive among YOLO-family models, while YOLOv8s-seg achieves slightly higher average performance. These findings suggest that increasing structural complexity does not necessarily lead to robust performance gains in small-sample and class-imbalanced insulator defect instance segmentation and that the practical value of structural modifications should be evaluated cautiously under repeated data splits. Full article

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

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38 pages, 6158 KB

Open AccessReview

Machine Learning Strategies for Power Grid Resilience: A Functional and Bibliometric Review

by Cesar A. Vega Penagos, Omar F. Rodriguez-Martinez, Jan L. Diaz, Guiselle A. Feo-Cediel, Adriana C. Luna and Fabio Andrade

Electronics 2026, 15(10), 2001; https://doi.org/10.3390/electronics15102001 (registering DOI) - 8 May 2026

Abstract

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper [...] Read more.

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper presents a structured review of ML strategies for power-grid resilience applications using a four-phase resilience lens (Prevention and Improvement, Control and Mitigation, Restoration, and Cyber Resilience). The literature is organized through a functional taxonomy that includes fault diagnosis, event prediction, control and stability support, restoration, and cyber resilience. In addition to the qualitative synthesis, a quantitative analysis of a dataset of 13,647 peer-reviewed publications (2015–2026) is conducted to characterize research activity across resilience functions and implementation contexts. This analysis is used to illustrate the increasing adoption of machine learning approaches and to distinguish between simulation-based and real-world applications. The results indicate a methodological shift toward Deep Learning and Reinforcement Learning for complex tasks, while federated and edge-based approaches are gaining attention for privacy preserving and real-time applications. These findings provide a structured view of current research directions and support the growing relevance of machine learning in resilience-oriented power system applications, offering a foundation for the development of intelligent and scalable cyber-physical energy systems. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

36 pages, 959 KB

Open AccessArticle

Authentication in Three-Party Password-Authenticated Key Exchange: Definitions, Relations, and Composition for the Digital Identity Model

by Wenting Li and Haibo Cheng

Electronics 2026, 15(10), 2000; https://doi.org/10.3390/electronics15102000 (registering DOI) - 8 May 2026

Abstract

Three-party authentication architectures are central to modern Internet identity systems such as single sign-on, federated login, and cross-domain authentication. In this setting, a three-party password-authenticated key exchange (3-PAKE) protocol must not only authenticate a user to a verifier using a low-entropy password, but [...] Read more.

Three-party authentication architectures are central to modern Internet identity systems such as single sign-on, federated login, and cross-domain authentication. In this setting, a three-party password-authenticated key exchange (3-PAKE) protocol must not only authenticate a user to a verifier using a low-entropy password, but also securely support coordinated authentication and session-key establishment between the verifier and a relying party. Existing schemes cover many application scenarios, yet they often rely on PKI, provide weak password protection, or lack a security treatment strong enough to justify safe reuse inside larger identity systems. Since 3-PAKE typically serves as a security-critical component together with assertion delivery, session management, and service authorization, it should remain secure under composition. We therefore study 3-PAKE for the digital identity model in the Universally Composable (UC) framework. We define an ideal functionality

F_{3 - PAKE}

that captures three-party authentication, session-key establishment, and attainable password-guessing resistance under different compromise assumptions. We then present a generic construction from authenticated key exchange (AKE) and strong asymmetric password-authenticated key exchange (SaPAKE), and prove that it UC-realizes

F_{3 - PAKE}

. Instantiating the construction with OPAQUE and HMQV yields a practical PKI-free four-round protocol, 3-GenSaPAKE, together with a two-factor extension. AVISPA analysis and concrete performance evaluation show that the proposed scheme achieves strong composable security while remaining efficient and deployable. Full article

(This article belongs to the Special Issue Authentication in Key-Exchange: Definitions, Relations, Composition and Post-Quantum Security)

18 pages, 5091 KB

Open AccessArticle

A Fast-Locking PLL Using Low-Power Cycle Slippage Compensation and Accumulated Phase Error Correction

by Phuoc B. T. Huynh, Gyeong-Seok Lee and Tae-Yeoul Yun

Electronics 2026, 15(10), 1999; https://doi.org/10.3390/electronics15101999 (registering DOI) - 8 May 2026

Abstract

This article presents a fast-locking phase-locked loop (PLL) that incorporates a low-power extended phase frequency detector (LPEPFD) and a discriminator-aided phase detector (DAPD) to simultaneously address cycle slippage and frequency overshoot issues during frequency and phase acquisition, respectively. Specifically, the proposed LPEPFD introduces [...] Read more.

This article presents a fast-locking phase-locked loop (PLL) that incorporates a low-power extended phase frequency detector (LPEPFD) and a discriminator-aided phase detector (DAPD) to simultaneously address cycle slippage and frequency overshoot issues during frequency and phase acquisition, respectively. Specifically, the proposed LPEPFD introduces a novel finite state machine architecture that extends the linear range of a conventional PFD without requiring a power-hungry counter, thereby eliminating cycle slippage and reducing the time required for frequency acquisition while maintaining switching activity and power consumption comparable to those of the conventional design. Moreover, after frequency convergence, the DAPD quantizes the accumulated phase error, which is corrected by adaptively tuning the programmable delay lines without causing significant frequency overshoot seen in conventional PLLs, resulting in improved settling time. Fabricated using a 28 nm complementary metal oxide semiconductor (CMOS) process, the proposed fast-locking PLL occupies an area of 0.36 mm² and operates over a frequency range of 2.6 to 3.2 GHz. Experimental results demonstrate a 0.84-μs settling time for a frequency hop from 2.6 to 3.1 GHz. The designed PLL consumes 5.6 mW of power from a supply of 1 V with an integral root-mean-square jitter of 1.27 ps from 1 kHz to 100 MHz. Full article

(This article belongs to the Special Issue Design of Low-Voltage and Low-Power Integrated Circuits, Volume 2)

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20 pages, 788 KB

Open AccessReview

Urban Air Mobility and Unmanned Aerial Vehicle Path Planning in Dynamic Urban Environments: A Review

by Yang Xu, Xiang Lu, Junru Yang, Chuan Sun, Shucai Xu and Zhixiong Li

Electronics 2026, 15(10), 1998; https://doi.org/10.3390/electronics15101998 (registering DOI) - 8 May 2026

Abstract

The complexity of three-dimensional (3D) dynamic urban environments poses new challenges to emerging unmanned aerial vehicle (UAV) path planning, especially in dense buildings, dynamic obstacles, and multi-UAV collaboration. This paper reviews mainstream 3D path planning algorithms (including RRT, PRM, the ant colony algorithm, [...] Read more.

The complexity of three-dimensional (3D) dynamic urban environments poses new challenges to emerging unmanned aerial vehicle (UAV) path planning, especially in dense buildings, dynamic obstacles, and multi-UAV collaboration. This paper reviews mainstream 3D path planning algorithms (including RRT, PRM, the ant colony algorithm, the artificial potential field method, and A*) and analyzes their core principles, applicable scenarios, advantages, and disadvantages. The study finds that each algorithm has its disadvantages: RRT lacks optimality, PRM has high computational cost, the ant colony algorithm is poor in real-time performance, APF is prone to local optima, and A* performs well in static environments. Future research should explore hybrid strategies combining multiple algorithms to improve adaptability in dynamic complex environments, providing efficient solutions for urban low-altitude UAV operations. Full article

(This article belongs to the Topic Data-Driven Optimization for Smart Urban Mobility)

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31 pages, 1776 KB

Open AccessArticle

A Wide-Range Soft-Switching AHB-Flyback Converter for Flat-Top Pulsed Magnetic Field Power Supplies

by Dandi Zhang, Hongfa Ding, Yingzhe Liu, Shuning Mao, Chengyue Zhao and Wenhao Chen

Electronics 2026, 15(10), 1997; https://doi.org/10.3390/electronics15101997 (registering DOI) - 8 May 2026

Abstract

The central adjustment coil of a gasdynamic Electron Cyclotron Resonance (ECR) ion source requires wide-range bipolar current regulation over ±100 A with flat-top stability within 0.1% (1000 ppm) and a current rise time below 4 ms. Conventional fully controlled H-bridge converters operating under [...] Read more.

The central adjustment coil of a gasdynamic Electron Cyclotron Resonance (ECR) ion source requires wide-range bipolar current regulation over ±100 A with flat-top stability within 0.1% (1000 ppm) and a current rise time below 4 ms. Conventional fully controlled H-bridge converters operating under hard-switching conditions are unable to satisfy these requirements simultaneously, as the switching loss penalty restricts the control bandwidth and degrades flat-top stability. This paper presents an Asymmetrical Half-Bridge Flyback (AHB-Flyback) converter specifically designed for this application. By incorporating a dedicated resonant branch

L_{r}

–

C_{r}

on the primary side, the converter achieves primary-side Zero-Voltage Switching (ZVS) and secondary-side Zero-Current Switching (ZCS) over the full operating range, enabling 100 kHz operation without incurring the switching losses that would otherwise limit control bandwidth. A decoupled energy management architecture is adopted in which the primary circuit pre-charges an energy storage capacitor during idle intervals, and the coil current is subsequently established through an autonomous capacitor-to-coil discharge, effectively decoupling the peak power demand from the upstream supply network. The operating modes of the flat-top maintenance stage are analyzed through time-domain state equations, yielding an explicit closed-form expression for the Mode 3 duty cycle

D_{T 3}

. This expression demonstrates that

D_{T 3}

is determined solely by the switching frequency and circuit parameters, independent of the load current setpoint, which is the fundamental mechanism enabling stable wide-range current regulation without parameter re-tuning. Parameter selection guidelines are derived from this result. Simulation results across the 20–100 A operating range and experimental validation on a scaled prototype confirm flat-top current stability within 1000 ppm and a current rise time of 4 ms, demonstrating the suitability of the proposed converter for precision ECR ion source power supply applications. Full article

(This article belongs to the Special Issue Advances in Power Electronics Converters for Modern Power Systems)

25 pages, 1333 KB

Open AccessArticle

Prior-Guided Multi-Scale Temporal Modeling for Behavior-Driven Residential Load Forecasting

by Zijie Hong, Xiaoluo Zhou, Yuqian He and Zhenyu Liu

Electronics 2026, 15(10), 1996; https://doi.org/10.3390/electronics15101996 (registering DOI) - 8 May 2026

Abstract

Accurate residential load forecasting is crucial for enhancing the efficiency and reliability of energy systems in smart grid and demand response applications. However, residential load data are characterized by strong stochasticity, high volatility, and pronounced multi-scale temporal dynamics while being highly susceptible to [...] Read more.

Accurate residential load forecasting is crucial for enhancing the efficiency and reliability of energy systems in smart grid and demand response applications. However, residential load data are characterized by strong stochasticity, high volatility, and pronounced multi-scale temporal dynamics while being highly susceptible to noise and outliers. These challenges hinder existing methods from effectively capturing complex temporal patterns and learning reliable inter-variable dependencies, thereby limiting forecasting accuracy and stability. To address these issues, this paper proposes a Prior-Guided Multi-Scale Neural Network (PG-MSNN) for multi-step residential load forecasting. The proposed framework integrates prior-guided dependency modeling with multi-scale temporal representation learning in an end-to-end trainable architecture. Specifically, a learnable periodic prior space is constructed, within which a Prior-Guided Module (PGM) is designed to learn cross-variable dependencies and provide structured global periodic guidance. In parallel, a Multi-Scale Patch-LSTM Encoder (MS-PLE) is developed to model temporal dynamics across multiple scales through patch-based sequence representation and adaptive cross-scale fusion. Extensive experiments on three real-world datasets, including IHEPC, REC, and CN-OBEE, demonstrate that, under within-household temporal forecasting settings, the proposed method achieves consistent and competitive performance across various forecasting horizons. Full article

51 pages, 1161 KB

Open AccessArticle

A Secure Cross-Domain Control Mechanism for Stateful Digital Twin Migration in Edge Computing

by Mikail Mohammed Salim, Farheen Naaz and Kwonhue Choi

Electronics 2026, 15(10), 1995; https://doi.org/10.3390/electronics15101995 (registering DOI) - 8 May 2026

Abstract

Mobility-aware digital twin (DT) migration is increasingly used in edge computing to sustain low-latency service as physical entities and service demand move across domains. However, stateful DT migration across administrative domains requires more than placement adaptation; it also requires target-side legitimacy verification, protected-state [...] Read more.

Mobility-aware digital twin (DT) migration is increasingly used in edge computing to sustain low-latency service as physical entities and service demand move across domains. However, stateful DT migration across administrative domains requires more than placement adaptation; it also requires target-side legitimacy verification, protected-state transfer, continuity-preserving traffic transition, and invalidation of stale source-side instances. This paper presents a secure cross-domain authentication and service continuity mechanism for mobility-aware DT migration in edge computing. The proposed design formulates migration as a six-phase ordered control procedure comprising migration triggering, target-side authorization, protected-state transfer, continuity-aware traffic transition, post-migration activation, and revocation-aware completion. Security analysis examines authorization soundness, migration-state confidentiality and integrity, transition safety, and post-migration uniqueness. Performance evaluation shows that the full mechanism introduces only a bounded increase in migration-related cost while reducing service interruption at 500 MB from approximately 1.79 s without continuity-aware transition control to 285 ms in the full mechanism. The results indicate that the proposed mechanism preserves the operational benefit of mobility-aware DT migration while strengthening migration authorization, state transfer protection, and service continuity under cross-domain relocation. Full article

(This article belongs to the Special Issue Security and Privacy Challenges in Integrated IoT and Edge Systems)

20 pages, 48835 KB

Open AccessArticle

Lightweight Hardware Implementation of a State of Charge Estimation Algorithm Using a Piecewise OCV–SOC Model

by Gahyeon Jang, Seungbum Kang and Seongsoo Lee

Electronics 2026, 15(10), 1994; https://doi.org/10.3390/electronics15101994 (registering DOI) - 8 May 2026

Abstract

State of charge (SOC) estimation is a key function in battery management systems (BMSs) because it directly affects safe operation and available energy prediction. In embedded BMS platforms, information from multiple cells must be processed within tight computation and memory budgets. The estimator [...] Read more.

State of charge (SOC) estimation is a key function in battery management systems (BMSs) because it directly affects safe operation and available energy prediction. In embedded BMS platforms, information from multiple cells must be processed within tight computation and memory budgets. The estimator therefore needs to balance accuracy and implementation cost. This paper presents a lightweight SOC estimation method based on the relationship between open circuit voltage and state of charge (OCV–SOC) in lithium-ion batteries, together with a standalone gauge IP based on finite-state machine (FSM) control. The reference OCV–SOC curve of a commercial 3.7 V lithium-ion cell is approximated by a two-region quadratic model. The IP estimates OCV from the measured terminal voltage with equivalent series resistance (ESR) correction and updates SOC iteratively. To obtain predictable runtime behavior and to suppress oscillatory behavior near convergence, the hardware combines a 1-LSB termination rule with a guard based on a maximum iteration count of

N_{max} = 10

. Real-time validation on an FPGA-based battery measurement testbed achieves an overall normalized mean absolute error (NMAE) of 1.6% over charge and discharge data. When synthesized for an Artix-7 XC7A100T, the proposed gauge IP used only 504 LUTs (0.79%) and 580 FFs (0.46%). A TSMC 28 nm MPW implementation further demonstrates feasibility for integration at chip level. Full article

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27 pages, 4914 KB

Open AccessArticle

A Viewpoint on Event-Driven Perception and Digital Twin Integration for Autonomous Mining Robotics

by Vasiliki Balaska and Antonios Gasteratos

Electronics 2026, 15(10), 1993; https://doi.org/10.3390/electronics15101993 (registering DOI) - 8 May 2026

Abstract

Robotic systems are increasingly being deployed in mining operations to support tasks such as inspection, navigation, environmental monitoring, and safety supervision. However, mining environments present significant challenges for robotic perception due to dynamic terrain conditions, poor illumination, airborne dust, and frequent disturbances caused [...] Read more.

Robotic systems are increasingly being deployed in mining operations to support tasks such as inspection, navigation, environmental monitoring, and safety supervision. However, mining environments present significant challenges for robotic perception due to dynamic terrain conditions, poor illumination, airborne dust, and frequent disturbances caused by excavation and heavy machinery. Conventional frame-based vision systems often struggle under these conditions due to motion blur, latency, and limited dynamic range. This study proposes a system-level conceptual framework for integrating event-based sensing into robotic mining systems in order to support perception in highly dynamic and safety-critical environments, with the aim of improving responsiveness and robustness under such conditions. Event-based cameras, inspired by biological vision, asynchronously detect brightness changes at the pixel level and provide microsecond temporal resolution with high dynamic range and low latency. The proposed framework combines event cameras with complementary sensing modalities including LiDAR, inertial measurement units, and RGB cameras to form a multi-sensor perception architecture. The framework is structured into multiple functional layers encompassing environmental sensing, event-driven perception, sensor fusion and AI processing, digital twin integration, and autonomous decision-making. Potential application scenarios including robotic tunnel inspection, autonomous navigation of mining robots, hazard detection, multi-agent cooperation in mining sites, and real-time digital twin updating are also discussed. The proposed framework provides a unified system-level reference architecture intended to guide future implementation and validation. Full article

(This article belongs to the Special Issue Next-Generation Robotic Intelligence: Active Perception, Adaptive Collaboration and Cross-Domain Digital Twins)

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27 pages, 6115 KB

Open AccessArticle

A 90.4% Efficiency Hybrid Step-Up Converter with Clock-Free Controller and Shunt-Current-Reusing Techniques for Power Burst Applications

by Pengda Qu, Zhiming Xiao and Yue Zhao

Electronics 2026, 15(10), 1992; https://doi.org/10.3390/electronics15101992 (registering DOI) - 8 May 2026

Abstract

This article presents a low ripple, high voltage-conversion-ratio (VCR = 6), two-stage step-up converter intended for power-burst applications. The first boost stage raises the battery voltage to a maximum of 35 V, while the subsequent low dropout regulator (LDO) stage suppresses the [...] Read more.

This article presents a low ripple, high voltage-conversion-ratio (VCR = 6), two-stage step-up converter intended for power-burst applications. The first boost stage raises the battery voltage to a maximum of 35 V, while the subsequent low dropout regulator (LDO) stage suppresses the ripple of the final output. Unlike conventional structures in which control circuits operate above a ground-referenced rail, the proposed shunt-current-reusing technique places most of the control circuits within a narrow floating dropout region (V_DROP) between the boost output (V_BST) and the LDO output (V_OUT), thereby achieving nearly 100% current efficiency through current recycling. Adaptive adjustment of V_DROP (0.5 V at light load and 0.65 V at heavy load) balances output ripple against the loss of the LDO stage. Consequently, the proposed converter achieves both high efficiency (>85%) and low ripple (<2 mV) over a load range from 200 μA to 100 mA, with a peak efficiency of 90.4% at a 20 mA load. Hysteretic control of the boost stage combined with the high bandwidth (BW = 1.2 MHz) of the LDO stage yields a fast transient response (<20 μs). The proposed techniques address the requirements of applications that demand high intermittent power bursts (>1 W) at high supply voltage (>20 V) while maintaining low quiescent current consumption under most load conditions (<10 mA), as exemplified by light detection and ranging (LiDAR), haptic sensors, and micro electromechanical system (MEMS) drivers. Full article

(This article belongs to the Section Microelectronics)

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19 pages, 1648 KB

Open AccessArticle

Adaptive Pilot-Assisted Channel Estimation for OFDM-Based High-Speed Railway Communications

by Khoi Van Nguyen, Toan Thanh Dao and Do Viet Ha

Electronics 2026, 15(10), 1991; https://doi.org/10.3390/electronics15101991 (registering DOI) - 8 May 2026

Abstract

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage [...] Read more.

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems. Full article

(This article belongs to the Section Microwave and Wireless Communications)

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16 pages, 1478 KB

Open AccessArticle

Meta-LSTM-Affine: A Memory-Based Meta-Adaptive Affine Modeling Framework for Non-Stationary Systems

by Yang-Ta Kao, Ching-Ting Tu, Hwei Jen Lin and Yoshimasa Tokuyama

Electronics 2026, 15(10), 1990; https://doi.org/10.3390/electronics15101990 (registering DOI) - 8 May 2026

Abstract

Modeling non-stationary systems with dynamically evolving data distributions remains a fundamental challenge in modern learning and optimization problems. In this work, we adopt a generalized notion of non-stationarity, where distribution shifts across tasks and domains are treated as forms of non-stationary processes. This [...] Read more.

Modeling non-stationary systems with dynamically evolving data distributions remains a fundamental challenge in modern learning and optimization problems. In this work, we adopt a generalized notion of non-stationarity, where distribution shifts across tasks and domains are treated as forms of non-stationary processes. This perspective allows us to study non-stationary behavior in controlled settings such as Few-Shot Learning (FSL) and Source-Free Domain Adaptation (SFDA), where data distributions vary across episodes or domains. Conventional normalization and feature modulation strategies often rely on batch-level statistics, leading to unstable behavior under small-batch, streaming, and distribution-shifted conditions. To address these limitations, we propose Meta-LSTM-Affine, a memory-based meta-adaptive affine modeling (normalization) framework that unifies recurrent temporal memory and meta-learning for robust feature modulation. Unlike batch-statistics-driven normalization, our method employs an LSTM-based affine parameter generator (APG) to dynamically produce channel-wise scale and shift parameters based on both current inputs and historical context. To further enhance task-level adaptability, we introduce three lightweight meta-learning mechanisms—Meta-Initialization, Meta-Conditioning, and Meta-Update—that enable rapid cross-task adaptation without modifying the backbone. A bi-level training strategy with temporal smoothness regularization ensures stable affine parameter dynamics under distributional shifts. We validate Meta-LSTM-Affine on FSL and SFDA benchmarks, including Omniglot, MiniImageNet, TieredImageNet, Office-31, MNIST, SVHN, and USPS. Experimental results show that our method consistently outperforms existing approaches such as BN, MetaBN, MetaAFN, and LSTM-Affine, achieving improved stability and adaptation performance with minimal additional computational overhead. Overall, Meta-LSTM-Affine provides a stable and efficient affine modeling mechanism for learning under generalized non-stationary conditions without relying on batch-level statistics. This generalized formulation of non-stationarity allows us to study distributional changes in controlled and widely used benchmark settings, while maintaining relevance to real-world scenarios such as streaming data, continual learning, and time-evolving environments. Full article

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

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18 pages, 25872 KB

Open AccessArticle

PCT-Net: A Multi-Scenario Noise-Adaptive Fusion Network for Bolt Loosening Detection

by Tianxin Wang, Pumeng He, Kai Xie, Rongmei Lei, Yuehao Xiong, Chang Wen, Wei Zhang and Jian-Biao He

Electronics 2026, 15(10), 1989; https://doi.org/10.3390/electronics15101989 (registering DOI) - 8 May 2026

Abstract

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models [...] Read more.

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring. Full article

(This article belongs to the Special Issue Intelligent Sensing Empowered by Artificial Intelligence)

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21 pages, 4557 KB

Open AccessArticle

From Time-Series Prediction to System Modeling: A Dual-Attention Framework for Multi-Source Interaction in Soybean Futures Markets

by Hongjiu Liu, Qingyang Liu and Yanrong Hu

Electronics 2026, 15(10), 1988; https://doi.org/10.3390/electronics15101988 (registering DOI) - 8 May 2026

Abstract

Futures price forecasting is often treated as a time-series prediction task. However, agricultural futures markets function as complex information systems in which prices emerge from the interaction of heterogeneous sources, including trading behavior and news-driven sentiment. Ignoring such cross-domain interactions limits the ability [...] Read more.

Futures price forecasting is often treated as a time-series prediction task. However, agricultural futures markets function as complex information systems in which prices emerge from the interaction of heterogeneous sources, including trading behavior and news-driven sentiment. Ignoring such cross-domain interactions limits the ability of traditional models to capture systemic price dynamics. This study reconceptualizes soybean futures forecasting as a multi-source information interaction problem and proposes a dual-attention LSTM framework to model cross-system coupling effects. A RoBERTa-based sentiment classifier is first developed to quantify market sentiment from news headlines. The extracted sentiment features are then integrated with historical trading variables and fed into an LSTM network equipped with temporal and feature-level attention mechanisms to capture dynamic evolution patterns and heterogeneous factor interactions. Empirical results show that the proposed system consistently outperforms conventional models. With a sliding window of 30 and a forecasting horizon of 7 days, the

R^{2}

improves from 0.922 to 0.9797, demonstrating enhanced capability in modeling medium-term price dynamics. The findings highlight that futures forecasting should be approached as a system-level information integration task rather than a purely statistical extrapolation problem. Full article

(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science, 2nd Edition)

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14 pages, 4537 KB

Open AccessArticle

Design of a 7–16 GHz GaAs Power Amplifier with Adaptive Biasing Technique

by Jeongheon Kim, Jaehun Lee, Dong-Ho Lee and Gwanghyeon Jeong

Electronics 2026, 15(10), 1987; https://doi.org/10.3390/electronics15101987 (registering DOI) - 8 May 2026

Abstract

In this paper, an adaptive biasing technique for an upper-mid band GaAs power amplifier is proposed. The proposed technique applies an adaptive bias circuit (ABC) to the driver stage (DS). In multistage power amplifier architectures, only the minimal current required to drive the [...] Read more.

In this paper, an adaptive biasing technique for an upper-mid band GaAs power amplifier is proposed. The proposed technique applies an adaptive bias circuit (ABC) to the driver stage (DS). In multistage power amplifier architectures, only the minimal current required to drive the power stage (PS) is typically consumed by the DS. Consequently, the overall current consumption of the amplifier is primarily governed by the substantially larger current consumed by the PS. Therefore, for an equivalent improvement in amplitude-to-amplitude (AM-AM) distortion, a higher power-added efficiency (PAE) is achieved when the ABC is applied to the DS than when it is applied to the PS. The proposed power amplifier is operated over the 7 to 16 GHz frequency range, achieving a small-signal gain of 14 to 16 dB, a PAE of 18 to 28% at the 1 dB compression point, and an output power of 21.5 to 24 dBm. Full article

(This article belongs to the Special Issue RF/Microwave Integrated Circuits Design and Application)

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21 pages, 26985 KB

Open AccessArticle

DTKD: Diffusion-to-Transformer Heterogeneous Knowledge Distillation for Efficient and Perceptually Enhanced Super-Resolution

by Jeong Hyeok Park and Byung Cheol Song

Electronics 2026, 15(10), 1986; https://doi.org/10.3390/electronics15101986 (registering DOI) - 7 May 2026

Abstract

Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs and remains fundamentally ill-posed due to the inherent ambiguity of missing high-frequency details. While diffusion-based SR models achieve superior perceptual quality through iterative denoising, their multi-step sampling process results in [...] Read more.

Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs and remains fundamentally ill-posed due to the inherent ambiguity of missing high-frequency details. While diffusion-based SR models achieve superior perceptual quality through iterative denoising, their multi-step sampling process results in substantial computational cost and latency. In contrast, transformer-based SR models offer efficient single-forward inference but are typically optimized for distortion-oriented objectives, limiting perceptual realism. In this paper, we propose DTKD, a diffusion-to-transformer heterogeneous knowledge distillation framework that transfers the perceptual prior of a diffusion teacher into an efficient transformer student. To effectively bridge the representational gap between generative diffusion outputs and deterministic transformer reconstructions, we introduce a frequency-group-aware distillation loss based on two-level discrete wavelet transform (DWT). The loss decomposes images into structured frequency sub-bands and assigns non-uniform weights to emphasize discrepancy-sensitive mid-frequency components. Furthermore, we adopt a progressive scheduling strategy that gradually increases the distillation weight during training to stabilize optimization and balance structural fidelity with perceptual enhancement. Extensive experiments on real-world SR benchmarks demonstrate that the proposed framework consistently improves perceptual quality over a standalone transformer student while maintaining transformer-level inference efficiency. Ablation studies further validate the importance of moderate frequency decomposition, discrepancy-aware weighting, and progressive distillation scheduling. These results suggest that heterogeneous distillation provides an effective and practical approach for transferring diffusion-based generative priors into efficient super-resolution models. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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