Electronics doi: 10.3390/electronics15122633

Authors: Jikyung Hong Sungkye Kim

Irregular Hangeul typefaces present a challenging computer vision problem because complete font generation must generalize from a small number of reference glyphs while preserving both structural consistency and stylistic fidelity. This study investigates few-shot learning for the restoration and expansion of irregular and historical Hangeul typefaces through three experiments spanning relatively regular woodblock print, irregular contemporary type, and highly irregular royal calligraphy. We benchmark a GAN-based model (DM-Font), a VQGAN-based model (VQ-Font), and a diffusion-based model (Diff-Font) under limited supervision and evaluate them using pixel-level similarity, structural indicator, OCR usability, and expert assessment. DM-Font established a feasible baseline for historical restoration (mean SSIM 0.77), whereas VQ-Font obtained the highest structural similarity for irregular contemporary typeface when paired with a structurally designed 10-character pangram reference set (SSIM 0.97; OCR accuracy 99.5% on the evaluated glyph set). For highly irregular royal calligraphy, the two models performed comparably on global similarity (SSIM 0.78 vs. 0.80) and on expert ratings (4.2 vs. 4.3); VQ-Font showed more stable structure-sensitive indicators, whereas Diff-Font better preserved stylistic nuance. The findings suggest that reference-set composition substantially affects generation quality under fixed-budget few-shot conditions, and that model choice should be matched to source regularity and restoration objectives.

Electronics doi: 10.3390/electronics15122632

Authors: Zhe Wei Huitong You Haibo Xu Zhipan Deng

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems.

Electronics doi: 10.3390/electronics15122631

Authors: Nan-I Wu Yung-Chih Lu Min-Shiang Hwang

In wireless sensor networks (WSNs), establishing a robust key agreement is essential for securing communications. Various performance metrics are typically employed to evaluate these schemes, including storage requirements, communication overhead, and computational costs. Group key establishment ensures that sensitive information remains confidential, as only authorized nodes can decrypt broadcast messages. This paper proposes a group rekeying scheme based on symmetric polynomial key pre-distribution. By leveraging multivariable symmetric polynomials, a secure group key is constructed. Furthermore, the scheme incorporates a dynamic rekeying mechanism to update the group key whenever a sensor node is compromised, ensuring continuous forward and backward secrecy. Performance analysis demonstrates that the proposed scheme significantly reduces both communication overhead and computational complexity compared to existing methods.

Electronics doi: 10.3390/electronics15122630

Authors: Shamanth Hariprasad Srinivas Balasubramanian Adnan A. Patel Kyuwon Ken Choi

The increasing computational demands of deep neural network inference drive the need for energy-efficient hardware accelerators that minimize data movement between memory and processing units. Compute-in-memory (CIM) architectures address this bottleneck by embedding computation directly within memory arrays, reducing the overhead of repeated weight transfers in conventional von Neumann systems. Conventional 6T SRAM-based digital CIM bitcells incur significant transistor overhead as arrays scale, motivating exploration of reduced-transistor bitcell alternatives. We propose a compact 4T+2T SRAM-based digital CIM bitcell implemented in 45 nm CMOS, combining a 4T SRAM storage cell with a 2T multiplier for bitwise multiply-and-accumulate (MAC) operations. The proposed design reduces transistor count from 8 to 6 compared to the 6T+2T reference, lowering parasitic capacitance and hardware overhead without compromising memory or computation functionality. Transient simulations confirm correct write, read, and CIM operations. The bitcell achieves a read delay of 26.91 ps, read power of 1.351 nW, and read energy of 0.005403 fJ—reductions of 98.7%, 86.5%, and 73.1% over the 6T+2T reference, respectively. For CIM operation, bitwise multiplication power decreases from 1.772 µW to 0.8014 µW and energy from 10.63 fJ to 4.808 fJ, representing a 54.8% reduction in both metrics, with only a marginal CIM delay increase of 3.13 ps. Monte Carlo analysis across 100 samples confirms robust write behavior under process variation, with write delay ranging from 55.02 to 69.59 ps and write energy from 0.05870 to 0.06557 fJ. Static noise margin analysis yields an SNM of 83.7 mV under nominal conditions, confirming stable data retention. These results demonstrate that the proposed 4T+2T bitcell offers strong transistor efficiency, energy savings, and computational correctness, making it a promising candidate for area-efficient digital CIM architectures targeting edge AI inference.

Electronics doi: 10.3390/electronics15122629

Authors: Ye Tian Taiyu Gu Rui Li Jie Zhao Fugen He Yidong Zhu Kejian Shi

With the increasing integration of distributed energy sources, fault restoration in power distribution systems faces challenges in terms of real-time performance and adaptability. To effectively manage the uncertainty and volatility of distributed generation, this paper proposes a rapid self-healing strategy based on a dynamic operational grid. By enabling real-time topological reconfiguration and utilizing adaptive resource allocation, the proposed method accommodates the inherent fluctuations of distributed energy sources. First, a dynamic grid weighted graph theory model is constructed, and an emergency control strategy combining particle preprocessing and stepwise optimization is designed to achieve rapid fault response. Then, a “primary-secondary” two-tier restoration mechanism is established: the primary layer integrates the Floyd algorithm with optimized adaptive dynamic programming to achieve millisecond-level restoration of critical loads; the secondary layer employs an improved particle swarm algorithm incorporating Lévy flight perturbations and adaptive weighting to maximize the restoration of general loads. Simulations on a 56-node system demonstrate that this method achieves 100% restoration of critical loads under various fault scenarios. Even under extreme conditions, it can restore 90.88% of secondary loads and 44.63% of tertiary loads, forming a self-healing system characterized by “second-level detection and minute-level restoration,” thereby significantly enhancing system resilience.

Electronics doi: 10.3390/electronics15122628

Authors: Tianyu Wang Yuanyuan Wang

Food allergies impose strict constraints on dietary decision-making, necessitating recommender systems that guarantee safety without compromising nutritional quality or user satisfaction. Existing systems often treat safety as a preference, failing to meet rigorous safety-critical standards or account for complex interactions between allergens, nutrition, and visual appeal. To address this issue, we propose a structured, multi-objective recipe recommendation framework. In this framework, rather than being modeled as an additive objective, allergen safety is prioritized through a safety-aware penalty-based ranking mechanism. The framework integrates three core modules: an allergen safety score accounting for cross-reactivity and cooking conditions; a nutritional balance score aligned with Dietary Reference Intakes (DRIs); and a neural-derived visual appeal score. In evaluation, we conducted a controlled user study (N=20) to evaluate the framework against single-factor baselines. Our integrated strategy consistently outperforms all single-factor baselines across evaluated metrics. Sensitivity analysis further confirms that safety-aware ranking ensures stable recommendation behavior across diverse preference profiles. Notably, behavioral analysis revealed a decision–action discrepancy, wherein users exhibited more risk-averse behavior during actual interactions than their explicitly stated preferences suggested. These findings suggest that prioritizing safety through safety-aware ranking mechanisms, together with multi-objective optimization, provides a robust foundation for personalized health-aware dietary support.

Electronics doi: 10.3390/electronics15122627

Authors: Yu Zhong Benkuan Yuan Mingcheng Fu Guilu Wu

Efficient data processing and signal acquisition are becoming increasingly critical. Pipeline networks present unique topological constraints that complicate the balance between signal sampling efficiency and data-transmission reliability. In this paper, we propose a bi-objective optimization model for the urban pipeline network (UPN). The model optimizes autonomous mobile sensor (AMS) path planning using an Euler path scheme and communication node (CN) deployment using a deterministic deployment scheme. The model aims to minimize both monitoring time (MMT) and data delay (MDD). These two indicators are used as quality of service (QoS) metrics for communication and sensing. By representing the UPN as a graph structure, we establish two mathematical models for the MMT and MDD problems. Then, we introduce a topology-guided heuristic virtual-edge strategy to construct an Euler traversal for the MMT problem. An adaptive simulated annealing (ASA) algorithm is designed to solve the MMT problem. On this basis, the MDD problem is solved using an enhanced ant colony optimization (EACO) algorithm. Simulation results show that the proposed scheme achieves shorter monitoring times and lower data delays. Specifically, the Euler path scheme for the AMS reduces MMT by more than 43.26%, and the deterministic CN-deployment scheme reduces MDD by more than 44.10%.

Electronics doi: 10.3390/electronics15122626

Authors: Shenyang Duan Yufeng Deng Yang Song

Vertebral compression fractures (VCFs) are one of the most common spinal disorders encountered clinically. Untimely diagnosis or inaccurate classification often leads to prolonged pain and functional impairment in patients. To enhance diagnostic accuracy and efficiency, this study addressed the high cost and limited applicability of computed tomography (CT) and magnetic resonance imaging (MRI) examinations by leveraging the universality and convenience of X-ray imaging. We proposed a multi-stage deep learning-based method for identifying vertebral compression fractures. The method first employs Discrete Wavelet Transform-YOLOv5 (DWT-YOLOv5) for preliminary vertebral region localization, followed by Polarized Self-Attention-UNet (PSA-UNet) for precise segmentation. Finally, a ResNet50 network incorporating a Convolutional Block Attention Module (CBAM) performs graded classification, categorizing vertebrae into four types: Non-fracture, Mild fracture, Moderate fracture, and Severe fracture. The experimental results demonstrate that the proposed method achieved average accuracy, precision, recall, specificity, and F1-score of 83.7%, 88.1%, 86.2%, 97.7%, and 87.2%, respectively. The proposed method fully leverages the cost-effectiveness and convenience of X-ray imaging, providing clinicians with an efficient and economical auxiliary diagnostic tool. It enables rapid and accurate identification of vertebral compression fractures in emergency and initial screening scenarios.

Electronics doi: 10.3390/electronics15122625

Authors: Hua Dang Lei Liu Weijiang Wang Shiwei Ren

Wideband direction-of-arrival (DOA) estimation is often formulated as a sparse signal recovery problem with multiple dictionaries, where the commonly adopted ℓ2,1-norm minimization framework exploits the joint sparsity shared across different frequency bins. However, the resulting optimization problem involves a large number of variables and becomes computationally expensive as the problem scale increases. In this paper, a compact reformulation of the multi-dictionary ℓ2,1-norm minimization problem is derived, which significantly reduces the number of optimization variables by introducing an equivalent diagonal representation. Under the special case of uniform linear arrays and harmonic sources, the proposed formulation is further extended to a gridless form, and its equivalence to wideband atomic norm minimization is discussed. For the grid-based compact formulation, an efficient block coordinate descent algorithm is developed, where each update admits a closed-form expression. For the gridless formulation, a first-order solver based on the alternating direction method of multipliers is employed to handle large-scale problems. Numerical simulations demonstrate that the proposed methods achieve substantial reductions in computational complexity, thereby enabling efficient wideband DOA estimation in large-scale scenarios.

Electronics doi: 10.3390/electronics15122624

Authors: Shuoqun Li Chunfeng Ding

With the large-scale commercialization of 5G and rapid evolution of 6G wireless systems, planar interdigital bandpass filters (BPFs) have become the core passive components for low-power RF front-ends. However, state-of-the-art filter design methods either rely heavily on empirical trial-and-error with 8–10 simulation iterations, or fail to resolve the inherent trade-off between center frequency tuning and stopband performance degradation, which cannot meet the demands of rapid customized design for 5G/6G multi-band scenarios. In this paper, a symmetric five-resonator three-segment patch-type interdigital BPF is taken as the research object. Through theoretical derivation, full-wave electromagnetic simulation, parametric scanning and orthogonal experiments, the quantitative mapping between structural parameters and filter performance is established. Notably, the directional tuning mechanism of the resonator’s narrow segment width on the first stopband is first revealed, which realizes lossless stopband optimization without disturbing the center frequency. On this basis, a three-stage standardized design procedure is proposed, which reduces design iterations from 8–10 to 3, shortens the design cycle by over 70%, and achieves 100% compliance of core design indexes. This work provides an implementable, low-threshold engineering method for rapid customized design of planar interdigital BPFs for 5G/6G RF front-ends.

Electronics doi: 10.3390/electronics15122623

Authors: Zhuoxin Luo Huimin Gao Ruizhe Hou Huiming Wang Yusen Li Xiaosheng Wang Jiayu Zhou Yibo Wang Montiê Alves Vitorino Michela Longo Cancan Rong

Simultaneously enabling wireless charging for multiple electronic devices is a distinctive advantage of wireless power transfer (WPT). Nevertheless, the development of dual-receiver WPT systems is constrained by several challenges, including undesired cross-coupling effects, suboptimal spatial utilization, complex control strategies, and insufficient system stability. To overcome the limitations, this article develops a multipolarity decoupled four-coil WPT system with constant voltage (CV) and constant current (CC). The proposed system suppresses undesired cross-coupling to negligible levels, thereby reducing the system complexity. In addition, the compensation network can be designed in a straightforward manner, providing improved design flexibility. A detailed mathematical derivation is presented to rigorously demonstrate the load-independent CV and CC output characteristics. Meanwhile, the inverter can achieve zero phase angle (ZPA), thereby improving the power factor of the WPT system. In addition, the multipolarity decoupled mechanism of the four-coil magnetic coupler is analyzed in detail theoretically. Finally, an experimental prototype is built and tested. The experimental results demonstrate a strong agreement with the theoretical analysis, ensuring load-independent CV and CC outputs of 68 V and 3.5 A, respectively. The system achieves a measured peak efficiency of 85.97%.

Electronics doi: 10.3390/electronics15122622

Authors: Yu Wang Yufa Duan Xiaolu Li

Beamspace adaptive matrix spatial filters have been extensively studied for their superior nulling performance and mathematical elegance. However, a major drawback of current spatial filters is the high computational cost—often reaching O(M4.5)—due to their formulation as second-order cone programming (SOCP) problems that rely on iterative interior-point methods. This paper proposes a robust and efficient matrix filtering framework with adaptive nulling capabilities to suppress interference. The proposed method is formulated as a convex optimization problem that admits a non-iterative, closed-form solution, thereby reducing the complexity to O(M3). Consequently, it can be efficiently implemented on resource-constrained embedded platforms. Furthermore, the algorithm incorporates an explicit passband flatness constraint, which significantly improves compatibility with downstream Direction-of-Arrival (DOA) estimation modules. To achieve even greater efficiency, we introduce a novel dual sequential rank-1 update strategy, further lowering the overall computational complexity to O(M2).

Electronics doi: 10.3390/electronics15122621

Authors: Lumeng Luo Qiang Li Hui Fang Hongji Xiang Junpeng Ma

Commutation failure is likely to occur when an AC fault occurs at the receiving end of an LCC-HVDC system. This threatens transient stability. Conventional commutation failure prevention (CFPREV) control mainly responds to commutation-voltage magnitude variation. However, commutation-voltage phase variation is not fully considered. Its fixed start-up threshold also makes it difficult to adapt to different fault severities. To address these problems, this paper establishes a transient nonlinear large-signal model of the inverter. The model incorporates power angle variation and describes the coupled effects of DC current rise, commutation-voltage drop, and power angle deviation on the extinction angle. Phase-portrait analysis is then used to illustrate the transient evolution and critical characteristics of first commutation failure (FCF). The critical commutation voltage is predicted under different fault severities and further converted into a dynamic CFPREV start-up threshold. Simulations based on the CIGRE LCC-HVDC benchmark model verify the prediction accuracy. They also show that the improved CFPREV strategy suppresses FCF mainly by starting up at an appropriate instant rather than increased compensation strength.

Electronics doi: 10.3390/electronics15122619

Authors: Amy Sadeghi Alan Ta Caleb Armstrong Anthony McDonald Farzan Sasangohar

Post-Traumatic Stress Disorder (PTSD) is a prevalent and costly mental health condition characterized by symptoms such as hyperarousal, avoidance, and re-experiencing. While machine learning (ML) approaches have shown promise in detecting PTSD-related physiological patterns, most validation efforts rely on computational metrics rather than real-world user perceptions. This study evaluates the perceived precision of a smartwatch-based ML tool designed to detect PTSD hyperarousal events using heart rate and activity data. The tool, previously developed using XGBoost 1.0.0, was deployed in a 21-day naturalistic study with 12 participants diagnosed with PTSD. Quantitative results showed a median perceived precision of 65.27%, with substantial variability across participants. A Mann–Kendall trend analysis revealed a significant increase in perceived precision over time for most participants, suggesting calibration of trust. Qualitative findings indicated high usability, general trust in the system, and acceptance of false positives, though concerns about notification design and battery life were noted. The results highlight the importance of incorporating user-centered, real-world validation into the evaluation of ML-based mental health monitoring tools. This work provides preliminary evidence supporting the feasibility of wearable-based PTSD monitoring and underscores the role of perceived precision in technology adoption and sustained use.

Electronics doi: 10.3390/electronics15122620

Authors: Kaichen Tang Qi Xu

Affective Image Stylization (AIS) converts an emotional intent into executable artistic visual styles. Existing methods are often limited to discrete label settings and provide limited interpretability of how target emotions are realized. We propose LLaVA-Emo, an interpretable AIS framework built on multimodal Chain-of-Thought (CoT) reasoning. Our method decouples generation into two structured outputs: <reasoning> provides visual–affective causal explanations grounded in the input image evidence, and <style_prompt> expresses actionable, renderer-ready style instructions that directly condition a frozen diffusion renderer. We constructed a training set by screening ArtEmis’ sentiment interpretations and fine-tune LLaVA-1.5-7B with LoRA, where SFT mainly supervises the structured intermediate <reasoning> (and output format), while the true executability of <style_prompt> is enforced by our DPO stage via render-and-reward feedback. The rendering stage remains training-free, and we further apply DPO for preference optimization to align candidate outputs with both emotion fidelity and instruction executability. Experiments on the EmoEdit inference set demonstrate that LLaVA-Emo improves emotion alignment while providing stronger process interpretability.

Electronics doi: 10.3390/electronics15122618

Authors: Chinapat Sakunrasrisuay Pakarat Musikawan Yanika Kongsorot Phet Aimtongkham Chatchai Punriboon Nutthanon Leelathakul Chakchai So-In

Tomato is one of Thailand’s most significant economic crops, generating substantial export value and serving as a primary source of income for local farmers. However, the traditional manual grading process often fails to comply with the Thai Agricultural Standard TACFS 1503–2007, as grading decisions rely heavily on individual experience and subjective perception, resulting in inconsistent quality. Existing automated systems face the challenges of low accuracy, high costs, and complex hardware, while many are incompatible with Thailand’s grading standards. This study presents a multi-view tomato grading system (MVT-Grader), utilizing a dataset acquired from Doi Kham Food Products Co., Ltd. (Third Royal Factory, Tao Ngoi) under controlled lighting conditions. Subsequently, MVT-Grader is built on a custom-designed lightweight CNN architecture with an adjusted spatially aware loss function to enhance the model’s sensitivity in detecting subtle surface defects and color variations. The proposed model was trained using tomato images captured from two and three different viewpoints via a low-cost webcam setup and processed by a GPU-embedded system. Experiments conducted using stratified 5-fold cross-validation on a real-world industrial dataset demonstrate average grading accuracies of 99.43% (two-view) and 99.64% (three-view). Furthermore, the proposed Real-Time Lightweight CNN with Spatially Aware Loss Optimization achieves processing speeds of 87 ms and 114 ms per tomato for two- and three-view cases, respectively. Compared with MVCNN-Siamese, SDF-ConvNets, and Multi-View Spatial Network, the proposed system outperforms the others in both accuracy and speed, improving accuracy by 1.6–6.11% and reducing processing time by 39–49 ms.

Electronics doi: 10.3390/electronics15122617

Authors: Jiahao Liu Yiran Shi Hongxi Zhao Wenchao He Haoran Wang Hewei Sun

Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework.

Electronics doi: 10.3390/electronics15122616

Authors: Hongyu Xu Liye Shi Pengfei Shao Yunkai Zhuang

Multimedia recommenders can use behavioral records together with visual and textual item information, but unreliable interactions and sparse histories still make user preference modeling difficult. Most graph-based methods propagate messages over observed user–item edges as if all interactions were equally informative, so incidental or semantically inconsistent behaviors may distort the learned representations. The standard recommendation loss also provides limited context for modeling dependencies within a user’s historical sequence. We propose MGDSL, a MGDSL applies a multimodal-aware topology denoising module to calculate edge reliability weights for historical interactions from collaborative, textual, and visual evidence, and uses these weights for reliability-aware historical aggregation. In parallel, a masked self-supervised auxiliary task reconstructs masked items from sequence context, adding supervision for latent preference learning. Experiments on three benchmark datasets show that MGDSL consistently improves recommendation accuracy over competitive baselines, with particularly clear gains on the sparsest dataset.

Electronics doi: 10.3390/electronics15122615

Authors: Fatemeh Daraee Saeed Mozaffari Shahpour Alirezaee

Scene understanding is a fundamental task in autonomous driving, requiring effective integration of semantic and geometric information from heterogeneous sensors. Although vision–language models (VLMs) provide powerful semantic representations, their integration with LiDAR-based geometric perception remains challenging. This paper proposes a multimodal late-fusion framework for multi-label scene classification that combines semantic embeddings extracted from camera images using a frozen CLIP (ViT-B/32) encoder with geometric features derived from LiDAR Bird’s-Eye-View (BEV) representations. To improve multimodal compatibility, modality-specific adaptation networks are employed to refine visual and geometric features before fusion. The proposed framework was evaluated on an annotated subset of the nuScenes dataset containing synchronized camera–LiDAR samples and nine scene-level labels. Experimental results show that the proposed late-fusion architecture outperforms both unimodal and early-fusion baselines, achieving a Hamming Accuracy of 0.950, a Micro-F1 score of 0.925, and a mean Average Precision (mAP) of 0.908. Additional experiments using a CLIP-based early-fusion baseline demonstrate that the observed performance gains are primarily attributable to the proposed modality-specific refinement and late-fusion strategy rather than the visual encoder alone. These findings indicate that modality-aware late fusion of pretrained semantic representations and LiDAR geometric information provides an effective and scalable solution for multimodal perception in autonomous driving.

Electronics doi: 10.3390/electronics15122614

Authors: Luca Baruffaldi Nicoletta Matera Michela Longo

The second-hand Battery Electric Vehicle (BEV) market in Italy is affected by substantial information asymmetry, particularly with regard to battery State of Health (SOH), residual value, and expected maintenance costs. This lack of transparency limits consumer confidence and reduces the potential of used BEVs to support a broader and more inclusive electric mobility transition. In this study, a data-driven decision-support framework is developed to improve the evaluation of second-hand BEVs in the Italian market. The proposed approach combines market data collected from major online platforms with historical price reconstruction and an assessment of the information asymmetries that limit user confidence in the second-hand BEV market. It also incorporates a semi-empirical SOH estimation model based on observable vehicle characteristics. The results reveal a consistent depreciation gap between BEVs and comparable internal combustion engine vehicles across different market segments and indicate that battery-related uncertainty appears to be one of the factors associated with consumer hesitation. The framework shows that combining non-invasive battery-health estimation with maintenance-related information can support a more objective assessment of used electric vehicles. Overall, the study demonstrates the potential of integrated digital and engineering-based tools to reduce uncertainty and enhance transparency in the second-hand BEV market.

Electronics doi: 10.3390/electronics15122613

Authors: Jing Shan Jianan Yin Beijing Zhou Minghua Hu

Efficient multi-hop prediction over large-scale spatio-temporal knowledge graphs of aircraft taxiing trajectories remains challenging, as existing methods focus either on static multi-hop relations or on accuracy improvement for spatio-temporal single-hop predictions, leading to computational inefficiency. This paper proposes a vector-index-supported multi-hop prediction method. First, a knowledge graph embedding technique that integrates spatio-temporal features maps the trajectory graph into a low-dimensional complex vector space. Then, a hierarchical query acceleration structure based on IndexIVFFlat is constructed. A clustering strategy guided by the distribution of trajectory data partitions the vector space into subspaces, and approximate nearest neighbor search within those subspaces rapidly prunes the candidate set to accelerate multi-hop retrieval. Experiments on real aircraft taxiing trajectory datasets and general benchmarks show that the proposed method substantially improves prediction efficiency while maintaining competitive accuracy. The results demonstrate that the vector index mechanism effectively balances accuracy and efficiency, and the efficiency has been improved by at least 56.65%. This work provides a key technical foundation for real-time analysis and intelligent prediction of large-scale aircraft taxiing trajectories.

Electronics doi: 10.3390/electronics15122612

Authors: Kamal Abuqaaud Ali Bou Nassif Ismail Shahin

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems.

Electronics doi: 10.3390/electronics15122611

Authors: Jing Si Mengfei Yang Chaowei Tang Zhuo Zeng Qingsong Yuan Liangxuan Wang Jingwen Lu

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions.

Electronics doi: 10.3390/electronics15122609

Authors: Wanlin Du Ling Wang Shijie Huang Hong Lv Hang Zhou Hangya Liu Jiaxin Yuan Yiqi Song Feiran Xiao

Power systems worldwide are evolving toward interconnection and long-distance transmission, resulting in issues of uneven power flow distribution. Single-core independent phase shifting transformers (SCIPSTs) can regulate voltage magnitude and phase angle, serving as an effective solution to this problem. To provide guidance on the parameter design for practical applications, this paper conducted a study on the topological structure, regulation characteristics, and parameter design of SCIPSTs. First, the topological composition and working principle of SCIPSTs are elaborated. Regulation of voltage magnitude and phase shift angle is achieved by controlling the magnitude and phase of the compensation voltage. Second, the voltage regulation characteristics of SCIPSTs are analyzed, and the constraint mechanisms of the source-side winding transformation ratio and load-side winding transformation ratio on the compensation voltage range are revealed. Third, the influence of symmetric and asymmetric parameter designs on the regulation range is discussed, illustrating the key parameter design methods. Finally, a system simulation model is built to verify the voltage regulation and power flow control effects of SCIPSTs. Simulation results show that SCIPSTs can realize independent regulation of active power and reactive power within a certain range, with significantly improved regulation precision and performance.

Electronics doi: 10.3390/electronics15122610

Authors: Haoyang Cheng Xiao Li Qi Tian Wei Han Xiaoliang Zhang Jing Liang Zheng Yang

Radar signal sorting (RSS) and radar emitter recognition (RER) constitute foundational yet challenging operations in electronic reconnaissance, where RSS aims to accurately segregate interleaved radar pulse streams and RER aims to recognize their originating emitters. Existing methods typically address RSS and RER as separate processes within a sequential streaming framework, which neglect the inherent interdependence and collaborative potential between them, thereby resulting in error accumulation and performance bottleneck. In this paper, we redefine the radar signal sorting and recognition (RSSR) problem from an integrated modeling perspective, decomposing it into three sub-problems, i.e., signal pattern detection, signal pattern extraction, and detection result integration. In order to effectively solve these problems, we propose a novel Unified Framework inspired by Object Detection (UFiOD). Firstly, an end-to-end neural network is constructed to simultaneously optimize the regression of signal temporal occurrence regions and the recognition of signal categories. Then, a template matching algorithm is designed to extract corresponding pulses from the regions based on the signal categories. Finally, an integration algorithm based on temporal correlation and direction of arrival (DOA) fuses the detection results to generate object-level sorting and recognition conclusions. We extensively validate the effectiveness of the proposed method on simulation datasets. It demonstrates robust performance under various interleaving scenarios, including the interleaving of homogeneous radar emitters. Notably, it exhibits impressive capability for handling unknown signals, further highlighting its practical utility.

Electronics doi: 10.3390/electronics15122608

Authors: Wei Zhao Luyao Liu Wenzhe Liu Yue Zhang Aming Wu

The extraction of speech emotion features—particularly at the utterance level—constitutes a critical yet challenging aspect of Speech Emotion Recognition (SER). Emotion represents high-level paralinguistic information, and not all segments within a speech signal carry emotionally salient cues, especially in longer utterances. Conventional methods that process entire utterances may thus waste computational resources and introduce irrelevant acoustic interference. To address this, we propose AdaPre-Selection, an adaptive pre-selection mechanism designed to identify and extract emotion-rich segments from speech signals. Acting as a flexible front-end compression module, AdaPre-Selection consists of two complementary components: an Active Emotion Positioning (AEP) module and a Passive Emotion Constraint (PEC) module. Within AEP, Temporal Length Selection (TLS) and Start Time Point Selection (STPS) operate jointly to adaptively locate the optimal emotional segment in each utterance. Evaluated on two benchmark datasets (IEMOCAP and MSP-IMPROV) using four state-of-the-art SER models, AdaPre-Selection consistently outperforms common preprocessing baselines and delivers the most substantial improvement in recognition performance.

Electronics doi: 10.3390/electronics15122607

Authors: Jaewoon Cho Yujun Shin Seongho Woo

This study presents a new analytical approach to determine the optimal resonant frequency of a shielding (SH) coil, effectively minimizing leakage magnetic fields in inductor-capacitor-capacitor-series (LCC-S) wireless power transfer (WPT) systems. This method mitigates leakage magnetic fields by integrating an SH coil into the transmitter side. By establishing an analytical relationship between the SH coil reactance and the system operating frequency, the proposed method determines the condition where the resultant current phasor produced by the transmitter (TX), receiver (RX), and SH coils becomes minimal, thereby identifying the optimal SH resonant frequency that achieves maximum destructive interference. The effectiveness of the proposed method was evaluated using simulation and measurement results, confirming a maximum leakage magnetic field reduction of 52.64% by applying the optimized SH coil resonant frequency. This study presents an analytical design approach that optimizes the SH coil resonant frequency to effectively cancel leakage magnetic fields.

Electronics doi: 10.3390/electronics15122606

Authors: Tanya Yadav Mohammad Masum

Phishing email remains a persistent and operationally critical cybersecurity threat, yet existing detection approaches, including traditional machine learning and single-pass large language model systems, either lack native interpretability or provide explanations that are difficult to standardize and audit. This paper introduces an explainable multi-agent LLM framework that decomposes phishing evidence across three role-specialized agents focused on linguistic patterns, psychological manipulation, and sender identity consistency. The framework then aggregates specialist outputs through schema-governed synthesis, enabling each intermediate and final decision to be structured, comparable, and auditable. The central contribution is the treatment of role-specialized evidence decomposition and explanation structure as first-class design constraints rather than post hoc additions. The framework is evaluated on a fixed 1000-email subset drawn from a unified TREC/Nazario corpus of 56,212 emails under controlled zero-shot conditions. The full multi-agent Meta-Judge system achieves Macro-F1 = 98.28% and phishing recall = 99.45%, improving Macro-F1 by 6.3 percentage points over a zero-shot single-model GPT-4o-mini baseline. Paired statistical testing confirms that this improvement is significant and is driven primarily by reduced false positives on legitimate emails while preserving high phishing recall. Additional evaluation on an independent LLM-attributed email benchmark shows a consistent Macro-F1 improvement of 0.0773 over the zero-shot baseline under distribution shift. Ablation results show that role-specialized decomposition is the primary performance driver, while deterministic voting provides a competitive raw-classification aggregator and Meta-Judge synthesis provides structured, analyst-facing explanations. These results indicate that role-specialized evidence decomposition combined with schema-governed explanation can improve both detection reliability and auditability in phishing classification workflows.

Electronics doi: 10.3390/electronics15122605

Authors: Peiran Zhang Haizhou Wang

Large language models (LLMs) have significantly enhanced the fluency, consistency, and adaptability of social bots, raising new concerns about their ability to integrate into online communities. However, community integration requires more than text generation alone. Social bots often lack a systematic understanding of community culture, struggle to maintain consistency between persona settings and posting behavior, and have difficulty identifying users with higher interaction potential. To address these challenges, this paper proposes HSEF-CI, a human-like social bot enhancement framework for community integration. The framework constructs community profiles from target communities, reshapes bot identities and long-term memory, adopts a staged text generation workflow, and selects interaction targets via homophily-based matching. Experiments on multiple English-speaking communities show that the framework lowers detectability across several detectors, improves social bots’ ability to integrate into target communities, and increases users’ willingness to interact. These findings highlight the importance of jointly modeling community profiles, identity reshaping, adaptive text generation, and target selection in studying LLM-driven social bots for community integration. The proposed framework also helps reveal how social bots adapt to and integrate into online communities, and provides an empirical baseline for the future development of detectors targeting community integration behaviors.

Electronics doi: 10.3390/electronics15122604

Authors: Andrei García Cuadra Alberto Brunete González Francisco Santos Olalla

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications.

Electronics doi: 10.3390/electronics15122603

Authors: Shulei Wang Yun Ling Daolin Cai Hao Zhang Mingxin Liu Cheng Cheng Qihang Ding Zhu Fu Jiale Zhao Haoyu Zhou Junxin Zhang

A Bayesian convolutional neural network (BCNN) quantifies prediction uncertainty by introducing randomness into weights or activations, which is important for safety-critical applications such as medical diagnosis and autonomous driving. However, BCNN inference typically relies on Monte Carlo sampling requiring multiple forward passes, leading to computation and energy consumption far beyond standard CNN hardware acceleration. FPGA, with its parallel processing, reconfigurability, and high-energy efficiency, are ideal platforms for dedicated BCNN accelerators. This paper designs and implements an FPGA acceleration method for BCNN-using high-level synthesis. First, convolution, pooling, and fully connected modules are individually optimized. Then, a mean/variance dual-path parallel expansion is adopted, combined with mixed-precision quantization and global scaling compensation, local reparameterization sampling, parameter reordering, and ping-pong buffering, achieving low resource usage and high-energy efficiency while enabling uncertainty evaluation. Experimental results on Bayes VGG16 show resource utilization of 24,776 LUT, 23,378 FF, 115 BRAM, and 129 DSP, with total power of 2.049 W. Compared with an unoptimized Bayesian implementation, the proposed design reduces inference latency to about one-third, and its latency is only 17% higher than that of the classical VGG16. Compared with PC-based floating-point models, the accuracy loss on four BCNN models (tested on CIFAR-10) is within 1%. The predictive entropy effectively distinguishes normal, noisy, and out-of-distribution (OOD) samples, validating the uncertainty quantification capability of the BCNN FPGA accelerator.

Electronics doi: 10.3390/electronics15122602

Authors: Igor Sirotić Stjepan Stipetić Marinko Kovačić

The transition from sinusoidal to pulse width-modulated (PWM) voltage excitation introduces high-frequency ripple, generating small remagnetization cycles within the main magnetization cycle and increasing total iron losses. Soft magnetic materials are essential for constructing many electrical devices, and accurate loss data are critical for reliable design and thermal dimensioning. However, magnetic material data are typically available only under sinusoidal excitation, and there is no generally accepted method for calculating PWM-induced losses during the design phase. To address this issue, loss measurements under DC-biased magnetization were performed on laminated ring cores, and the data were collected in the form of three-dimensional (3D) loss maps defined by the variables ΔB, dBdt and Bbias. Based on these maps, a method referred to as 3DLMB is proposed to calculate the contribution of PWM-induced losses to total iron losses by comparing minor-loop variables obtained from AC excitation with those measured under DC bias conditions. The method is experimentally validated on three ring cores with different geometrical parameters, showing agreement between calculated and measured total AC losses within ±5% over a range of switching frequencies. The reported agreement applies to the investigated M400-50A material, ring-core geometries, and operating range, while applying it to other materials or geometries requires constructing the corresponding DC-bias 3D loss map.

Electronics doi: 10.3390/electronics15122601

Authors: Lihao Luo Yuting Li Lin Wei Di Han Ruifeng Cao Bo Chen Yuechen Pan Yunfan Chen

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work.

Electronics doi: 10.3390/electronics15122596

Authors: Qingqing He Xiaocheng Ding Jianxiong Yuan Wenzhe Zhao Chunhao Zhai Song Xiong

In single-phase PWM rectifiers, due to the inherent time-varying characteristics of the source voltage and current as well as the periodic operation of the converter bridge, the instantaneous input power on the AC side inevitably exhibits a twice-fundamental-frequency pulsation. This phenomenon consequently generates a double-line-frequency (100 Hz) voltage ripple on the DC-link capacitor, which causes an inherent contradiction in conventional voltage outer-loop control between steady-state ripple suppression and dynamic response speed. To address this issue, this paper proposes a control strategy based on an Adaptive Time-Delayed Feedforward Neural Network (Adaptive TD-FNN). The proposed method explicitly introduces the delayed voltage error of half a ripple period into the network state input, thereby achieving time-domain decoupling of the 100 Hz low-frequency disturbance. In addition, a physics-driven training framework is constructed by integrating the rectifier’s discrete difference equation, thereby strengthening the network’s capacity to learn the dynamic characteristics of the system. On this basis, a dynamic adaptive smoothness-weight penalty mechanism is designed to adjust the weighting factor of the current command smoothness constraint in the loss function according to the system operating state. Specifically, the penalty weight is increased under steady-state conditions to suppress command oscillations caused by ripple disturbances, while it is rapidly reduced during load or grid-voltage transients to release the network’s transient optimization capability. Simulation and experimental results show that the proposed Adaptive TD-FNN controller can simultaneously achieve smooth steady-state current command output and fast dynamic voltage regulation without introducing additional complex digital notch-filtering algorithms. Compared with conventional dual-loop control, the proposed strategy reduces the total harmonic distortion (THD) of the grid-side input current from 8.45% to 3.42%, satisfying grid-connected power quality requirements. Meanwhile, under large load transients and grid-voltage disturbance conditions, the DC-link voltage recovery time is about 40 ms, verifying the comprehensive advantages of the proposed method in ripple suppression, dynamic response, and operating-condition adaptability.

Electronics doi: 10.3390/electronics15122600

Authors: Guoren Yao Gaoming Yang Xintian Liu

Recently, super-resolution transmission methods have been introduced on limited GPU. However, due to the stereotyped style exchange process, the GPU consumption and diversity of super-resolution style transfer are still challenging. In addition, existing methods can inadvertently lead to content leakage and uneven stroke size distribution, resulting in less attractive results. Hence, we introduce a fast and diverse super-resolution transfer (FDST) model, which can realize more flexible super-resolution multi-style transfer by mapping noise and information from another style in the style encoder. In addition, we propose two loss functions within the existing framework to support the preservation of content structure: Patch Content-Consistent Patch Loss (Patch-CCPL) and Patch contrastive loss. The proposed method effectively and elaborately integrates colors and texture structures. The key idea of FDST is to divide the super-resolution image into small patches, and then perform diverse style conversions on each small patch by injecting subtle noise or sub-style images. We implemented theoretical analyses and extensive results to qualitatively and quantitatively evaluate our method and compare it with the state-of-the-art algorithm. Extensive experiments on 4K content images demonstrate that FDST achieves a user preference score of 0.207, SSIM of 0.492, and LPIPS of 0.526, outperforming existing methods in content preservation while requiring only 2.573 GB model storage. Ablation studies confirm the contribution of each component, and a discussion of security applications including watermarking and forensic analysis is provided.

Electronics doi: 10.3390/electronics15122599

Authors: Yiyi Cai Wenyi Jing Jingneng Ren Haodong Bai Simin Li Yu Sun Min Xie

With the development of the low-altitude economy, low-altitude intelligent agents such as delivery robots, courier drones, and outdoor cleaning robots are gradually moving towards widespread application. One of the core challenges faced by such systems is localization and mapping in complex scenarios characterized by satellite signal denial and unknown environmental prior information. To address this requirement, this paper proposes ESKF-g2o-SLAM, a stereo visual-inertial SLAM system that integrates an ESKF (Error-State Kalman Filter)-based visual-inertial odometry front-end with an ORB-feature-based g2o graph optimization back-end in a cascaded, loosely coupled manner. The proposed method was evaluated on 11 sequences of the EuRoC dataset and compared with state-of-the-art approaches including ORB-SLAM2 (stereo), MSCKF-VIO, OKVIS, and VINS-Fusion (stereo). Ablation studies show marginal improvements on selected sequences and suggest potential robustness advantages under more challenging visual conditions. Experimental results show that our method achieves competitive accuracy in terms of both Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), exhibiting good robustness and stability.

Electronics doi: 10.3390/electronics15122597

Authors: Kai Liu Mengyue Zhang Zengchao Wang Wangsong Wu Hanhua Luo Yanpeng Hao Yuan La Xiaoguo Chen Fuzeng Zhang

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain.

Electronics doi: 10.3390/electronics15122598

Authors: Dustin Mink Anthony Simpson Sikha S. Bagui Subhash C. Bagui

Research examining the security of network intrusion detection systems is vital for protecting modern digital infrastructure from increasingly sophisticated threats. This study investigates how machine learning network security models, trained with tactical frameworks like MITRE ATT&CK, respond to adversarial examples crafted through black-box optimization techniques. Using three attack algorithms, HopSkipJump, Simultaneous Perturbation Stochastic Approximation Attack and the Square Attack algorithms, we demonstrate that the Random Forest model remains vulnerable despite tactical framework integration. For example, the HopSkipJump attack achieved a 92% success rate in causing malicious traffic to appear benign. Our analysis reveals which network traffic features are most susceptible to manipulation, with model performance metrics declining significantly under adversarial conditions. These findings highlight an important gap between theoretical security frameworks and practical implementation that must be addressed to develop more robust defense systems. By identifying specific vulnerabilities, this research contributes valuable insights that can inform improved adversarial robustness in operational network security environments.

Electronics doi: 10.3390/electronics15122595

Authors: Ting-Wei He Pei-Chi Chen Tzung-Her Chen

High dynamic range (HDR) image reconstruction from a single low dynamic range (LDR) input remains an important problem for computational photography, particularly when practical deployment on consumer-grade hardware is considered. With the increasing availability of hardware supporting HDR, public demand for capturing and viewing HDR images has grown significantly. Recent research has explored deep learning-based approaches to reconstruct HDR images from low dynamic range (LDR) inputs by extracting regional pixel features or leveraging the camera response function (CRF) for model training. Many of these approaches employ Convolutional Neural Network (CNN) architectures and utilize skip connections to preserve learned information. Nevertheless, the configuration-level effects of data augmentation in HDR reconstruction remain insufficiently discussed. Existing CNN-based approaches, such as HDRCNN, HDRUNet, and ExpandNet, have demonstrated promising reconstruction ability, but they may involve a heavy backbone architecture, a long training time, or a limited discussion of how preprocessing configurations affect reconstruction performance. This study presents an engineering-oriented HDR reconstruction framework derived from HDRCNN, focusing on practical efficiency, structural fidelity, and training feasibility. The proposed framework introduces three modifications: (1) a configuration-level comparison of composite data augmentation settings, including unsharp masking, denoising, Gaussian blur, and brightness–contrast adjustment; (2) the replacement of the original VGG16 backbone with a ResNet50-based encoder enhanced with attention blocks and squeeze-and-excitation (SE) blocks for improved multi-scale feature extraction and channel-wise recalibration; and (3) the integration of mixed-precision training with cosine annealing learning-rate scheduling to reduce computational cost. Experimental results on the SI-HDR dataset show that the best composite augmentation configuration improves PSNR from 19.05 dB to 22.10 dB and SSIM from 0.6444 to 0.7714 without increasing the training time. Compared with the original VGG16-based HDRCNN setting, the ResNet50-based model reduces training time while improving SSIM from 0.2705 to 0.8512. Under the adopted comparison protocol, the proposed model achieves the shortest training time and slightly higher PSNR than HDRUNet, while HDRUNet retains a higher SSIM. This indicates a trade-off among pixel-wise fidelity, structural similarity, and computational efficiency. The current evaluation is limited by a small test setting, composite rather than operation-level augmentation analysis, and the use of PSNR and SSIM only; therefore, future work should include full benchmark evaluation, additional perceptual/HDR-specific metrics, and controlled component-level ablation studies.

Electronics doi: 10.3390/electronics15122594

Authors: Jonathan Duvall Benjamin Gebrosky Garrett Grindle Stephen Layton Arianna Ciregna Rory A. Cooper

Powered mobility devices have used lead–acid batteries for decades with some recent designs using lithium-ion batteries. However, both lead–acid and lithium-ion batteries have concerns related to safety and environmental impact. Additionally, powered mobility device users have expressed a desire for new and alternative power sources. Nickel–zinc batteries can charge much faster and are safer and more environmentally friendly. However, nickel–zinc batteries must be discharged at high rates to prevent degradation of the batteries. This project developed a prototype power system using nickel–zinc batteries and supercapacitors to power a scooter. The design uses the nickel–zinc batteries to periodically and quickly charge the supercapacitors which then provide the power the scooter. Testing confirmed that the power system maintained appropriate voltage and current during use and that the scooter was able to perform with the same range, speed, and power as a current commercially available scooter.

Electronics doi: 10.3390/electronics15122593

Authors: Jiajun Jiang Yaodan Zhang Ziyang Xue Chuzheng Wang

The steel surface defect detection is crucial for steel quality and usage safety. The high computational cost and low detection accuracy are still the main issues in current steel detection models. To efficiently address the issues above, this paper proposes a new steel surface defect detection model named DIDW-YOLOv11. In the proposed DIDW-YOLOv11, the YOLOv11 C3k2 module is first innovatively improved by C3K2-DIMB, which integrates C3K2 and DIMB by introducing DynamicInceptionDWConv2d (DIDW) to sufficiently strengthen the detailed feature extraction for tiny defects and weak-texture defects, improving the matching degree of multi-scale receptive fields. Then the YOLOv11 SPPF module is enhanced by integrating the IDWFSPPF module for optimizing the fusion of local and global information, which combines average pooling and max pooling to enhance the model’s multi-scale feature fusion capability. An auxiliary detection head (ADH) is finally proposed with an additional coarse loss function to process shallow feature information into the model, which uses extra supervision for shallow features to suppress background noise and reduce false detections. Experimental results on the NEU-DET and GC10-DET datasets show that DIDW-YOLOv11 achieves 4.9% and 3.8% improvements in mAP@0.5 compared to the baseline model YOLOv11s. Our research indicates that DIDW-YOLOv11 exhibits stronger recognition ability and robustness in complex and diverse defect detection, providing an effective solution for steel defect detection in industrial production. In addition, experimental results show that our model offers improved performance over the baseline methods.

Electronics doi: 10.3390/electronics15122592

Authors: Fei Zhang Yinghui Wang Shangqian Zhuo Pengbo Wang

The primary goal of defocus deblurring is to restore details in blurred images, enhancing clarity and usability. Despite recent progress, current methods struggle with uneven blur distribution, particularly in distinguishing multi-scale boundaries, causing inaccurate pixel blur estimation and boundary detail loss. To address this, we propose an end-to-end deep learning approach incorporating a self-attention mechanism. Our method employs a Gaussian Kernel Mixture (GKM) model for compact blur kernel representation, then builds upon the Gaussian Kernel Mixture Network (GKMNet) to design a novel Attention-based GKM Network (GKMANet) via iterative fixed-point expansion. GKMANet introduces a scale-recursive architecture with self-attention to estimate mixing coefficients for effective deblurring. Experiments demonstrate significant superiority over existing techniques, especially in scenarios with indistinct blurred/clear boundaries, highlighting stronger robustness and higher restoration accuracy. This provides a reliable solution for detail recovery in defocus deblurring.

Electronics doi: 10.3390/electronics15122591

Authors: Mustafa Akdağ Mehmet Salih Mamiş Düzgün Akmaz

Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established.

Electronics doi: 10.3390/electronics15122590

Authors: Benjamin Ilo Hongwei Zhang

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management.

Electronics doi: 10.3390/electronics15122589

Authors: GwanTae Kim SangWook Park

This paper proposes a coupling-path reconfigurable mixed-coupling wireless power transfer (CPRMPT) coupler for improving coupler-level transmission stability under rotational misalignment. The proposed coupler forms two coupling modes, namely the adjacent coupling path (ACP) and diagonal coupling path (DCP), by changing the feeding polarity arrangement within the same physical structure. An equivalent-circuit model is used to describe the mode-dependent synthesis of self and mutual LC components, and 3D full-wave analysis is performed under a 100 mm transfer distance and 0–180° rotational conditions. The ACP mode maintains a near-unity maximum transmission coefficient over most rotation angles but shows a transmission null at 90°. In contrast, the DCP mode maintains near-unity transmission at 0°, 90°, and 180°, while null points occur at 45° and 135°. The extracted mutual parameters show that the ACP Lm and Cm decrease from 5.35 μH and 0.052 pF at 0° to nearly zero at 90°, whereas the DCP mutual parameters decrease to nearly zero at 45° and 135°. These results demonstrate that coupling-path reconfiguration can provide complementary transmission paths for rotation-tolerant MPT coupler design.

Electronics doi: 10.3390/electronics15122588

Authors: Mingyue Shi Ming Yan Lu Liu Errui Zhou Peng Xu

The detector array chips can be used to capture the transient space-time signal of the pulse radiation field. It is mainly composed of a photoelectric array detector and a readout circuit. However, the metal leads used to connect the detector and the readout circuit have long spacing. This can easily introduce additional delays, resulting in a decrease in the response performance of the chip, which cannot meet the goal of simultaneous transmission of ultra-fast detection signals. In recent years, the rapid development of three-dimensional interconnect technology has enabled the chip to achieve shorter interconnect spacing, smaller parasitic parameters and smaller delay time, thereby improving system response performance. The integrated detector array chips composed of three-dimensional interconnects has the advantages of fast signal interconnection transmission speed, high bandwidth, process compatibility and functional expansion compared with the traditional planar architecture. At the same time, there are some limitations and challenges. Therefore, this paper mainly reviews the evolution characteristics of the stacked architecture of the detector array chips, the process development and the nanosecond-level transmission integration challenges. This paper effectively incorporates the three into a unified framework. This provides a solution for the realization of integrated nanosecond detector array chips. Furthermore, it promotes the application and expansion of the chip in the pulse radiation field diagnosis technology.

Electronics doi: 10.3390/electronics15122587

Authors: Saidi Gao Kangqiao Ma Qiuyang Hou Lifeng Zhang

To address the issues of low voltage levels and insufficient reliability in dynamic regulation and voltage stabilization in existing high-voltage power supplies for electron-curtain accelerators, this paper presents a 150 kV/30 kW DC high-voltage power supply specifically designed for electron-curtain accelerators. The main circuit employs an LC high-frequency resonant topology and a step-up transformer with eight secondary windings, utilizing a parallel step-up and series output architecture to increase the output voltage level. During the charging phase, a dual-closed-loop frequency conversion scheme combined with duty cycle feedforward is employed to accelerate charging speed, while the voltage stabilization phase utilizes hysteresis burst control to improve accuracy. Simulation results indicate that the system can charge to 155 kV in 102 ms, with a voltage ripple less than 0.1%, a linear regulation of 0.01%, and a load regulation of 0.5%. Tests on a low-voltage prototype confirmed that the power devices can achieve zero-current soft switching, with a resonant current peak of 40 A and overall efficiency reaching 96%. The accompanying filament power supply can stably output 24 V/20 A, and the closed-loop voltage regulation is stable and reliable, providing technical support for the engineering application of high-voltage power supplies in high-power electron beam accelerators.

Electronics doi: 10.3390/electronics15122586

Authors: Shengtao Yang Jing Lian

Constant current/voltage (CC/CV) output of wireless power transfer (WPT) systems deviates due to increased load resistance during charging and mutual inductance variations caused by misalignment. Dynamically regulating the DC input voltage can maintain a stable output at the preset value, and predicting the mutual inductance and load resistance can help monitor charging status. However, joint prediction of characteristics and regulation degree can be nonlinear and complicated. This work proposes a data-driven method for characteristic prediction and output optimization for WPT systems based on the current waveform from only the transmitter side. A Multi-Scale Parallel Convolutional (MSPC) neural network is applied to simultaneously predict the load resistance, mutual inductance, output deviation factor and regulation coefficient. By leveraging its multi-scale feature extraction capabilities, it can accurately estimate the aforementioned parameters based on only the AC current waveform at the transmitter side. To improve the model’s generalizability under practical conditions, transfer learning (TL) is utilized to minimize the discrepancy between simulated and physical data. Finally, a 140 W prototype of the series-series (SS)-compensated WPT system is built to validate the effectiveness of the proposed method.

Electronics doi: 10.3390/electronics15122585

Authors: Ling Ji Heng Chi Mingjun Xue Qing Xu Fei Xu Lei Chen Ling Hao Jingxi Luo

Medium- and long-term dispatching of renewable energy bases is an important method for ensuring large-scale transmission and consumption. However, most existing medium- and long-term dispatching methods ignore the uncertainties of wind and photovoltaic power output, resulting in excessive maintenance-window margins and insufficient regulation reserves. However, relevant studies that consider such uncertainties are mostly limited to short-term scheduling and are therefore inadequate for medium- and long-term dispatching needs. To this end, a two-stage robust optimal dispatch method for renewable energy bases that considers the impacts of wind and photovoltaic output uncertainties and unit maintenance is proposed. Firstly, the first stage decision variables consist of the on/off and maintenance statuses of thermal power units. Next, the output of each power source is taken as the conventional decision variables in the second stage, while the curtailed wind/photovoltaic power and load shedding are taken as the unconventional decision variables when the balance cannot be achieved by adjusting the power source output under the given wind and solar power output scenarios. In the end, a polyhedron set based on an uncertainty budget was adopted to describe the fluctuations in wind and photovoltaic output, and the minimum scheduling cost in the worst scenarios was solved using the column and constraint algorithm. A renewable energy base in Northwest China was selected as a case to validate the proposed model’s effectiveness. The results show that the proposed model significantly reduces the operating cost in actual operation compared to deterministic optimization and pre-maintenance robust optimization.

Electronics doi: 10.3390/electronics15122584

Authors: Kewei Zhang Zeyu Li Xiang’en Zhang Lei Ding Leijing Yang Dejun Liu Hao Li Yongjun Wang

As the modulation rate in high-speed optical communication systems continues to increase and modulation formats become increasingly complex, conventional electrical-domain sampling techniques, limited by the “electronic bottleneck,” are unable to meet the time-domain analysis requirements of optical vector signals with bandwidths exceeding 100 GHz. In this paper, a system based on linear optical sampling (LOS) is implemented for time-domain analysis of high-speed polarization-division-multiplexed (PDM) optical vector signals. An unbalanced input method is proposed to ensure the integrity of the sampling clock when the power of the signal under test is zero; a resampling method combined with soft integration is proposed to replace the conventional peak detection method, improving the accuracy of sampling point position and amplitude information extraction; and an adaptive frequency offset estimation algorithm is proposed to compensate for the continuously varying frequency offset caused by the use of low-repetition-rate sampling pulses. We constructed a signal acquisition system for optical vector signal measurement based on LOS. Using the above methods, the eye diagrams and constellation diagrams of 50 Gbaud PDM-QPSK (quadrature phase-shift keying), PDM-16QAM (quadrature amplitude modulation), and PDM-32QAM signals are successfully measured, and related parameters, including error vector magnitude (EVM) and signal-to-noise ratio (SNR), are calculated. The experimental results show that the proposed system achieves quasi-real-time measurement of 500 Gbps optical vector signals, and the measured performance parameters are on the same order of magnitude as those obtained from a commercial high-speed oscilloscope.

Electronics doi: 10.3390/electronics15122583

Authors: Rui Liu Shujun Han Wenzhao Zhang Yacong Liang Mengying Sun Xiaodong Xu

In this paper, to satisfy the diverse task demands of Internet of Remote Things (IoRT) devices, we propose a multi-satellite collaborative model deployment and satellite–terrestrial inference framework for IoRT devices. Moreover, we formulate a joint model deployment, task scheduling, and resource allocation (MTR) problem for IoRT devices, aiming to minimize the long-term average cost measured by weighted latency and energy consumption under constraints. Considering the different timescales of these subproblems, we decompose the MTR problem into a model deployment subproblem and a task scheduling–resource allocation subproblem. We define the model deployment subproblem as a large-timescale process and the task scheduling–resource allocation subproblem as a small-timescale process. For the model deployment subproblem, we propose a large-timescale surrogate-assisted model deployment (LT-SAMD) algorithm. For the task scheduling–resource allocation subproblem, we model it with a constrained Markov decision process (CMDP), and propose asmall-timescale hybrid proximal policy optimization and convex optimization (ST-HPCO) algorithm to solve it. In addition, we propose a global two-timescale decouple execution (TT-DE) algorithm that integrates ST-HPCO and LT-SAMD algorithms to solve the MTR problem.Simulation results demonstrate that, compared with the PPO-only baseline and the AOS-PPO algorithm, our proposed algorithm achieves cost reductions of up to 60% and 28%, respectively.

Electronics doi: 10.3390/electronics15122582

Authors: Xuekun Zhang Weijian Fan Chi Zhang Guowei Chen Pengzhou Zhang

Fake news detection has become critical for safeguarding social media users and maintaining a reliable news ecosystem. However, existing methods rely mainly on context information and propagation structure and do not consider the news structure from framing theory. As a highly structured genre, news implies writing intention and organizational logic in its discourse frame, which provides vital clues for authenticity verification. In this paper, we propose structure-guided iterative fusion with a transformer network for fake news detection (SIFTNet), which contains four modules: a structural label generator, an information architecture representation module, a structure-enhanced representation module, and a structure-guided iterative fusion module. Guided by framing theory, SIFTNet captures the semantics at both the local sentence level and global structure level. Extensive experiments demonstrate that our model achieves state-of-the-art performance on both Chinese and English datasets, exhibiting superior effectiveness and robustness. These findings validate the efficacy of applying framing theory to improve fake information detection.

Electronics doi: 10.3390/electronics15122581

Authors: Irina Rastvorova Anastasia Kiseleva Vladislav Filatov Fedor Chmilenko Yuriy Perevalov

Against the background of observed climate change, which increases the risk of glacier-system degradation and the formation of hidden crevasses, the development of lightweight, wideband, and highly directional antenna systems has become a key factor in ensuring the safety of logistics operations and enhancing the spatial resolution and interpretability of ground-penetrating radar monitoring of near-surface snow–ice layers. The effectiveness of such systems is largely determined by the characteristics of the antenna unit, including the operating frequency band, gain, radiation pattern, weight, and resilience under extreme climatic conditions. The aim of this review is to provide a systematic analysis of modern Vivaldi antenna designs and Vivaldi-based antenna arrays, as well as to assess their prospects for application in X-band ground-penetrating radar systems for probing Antarctic snow-ice media. The paper considers the main types of ground-penetrating radar (GPR) antennas, their advantages and limitations, substantiates the priority of detecting hazardous near-surface inhomogeneities, and analyzes the capabilities of the X-band for the high-resolution identification of these inhomogeneities. Particular attention is paid to modern modifications of Vivaldi antennas, including antipodal, balanced, director-loaded, metamaterial-based, and array configurations. The analysis shows that Vivaldi antennas represent a promising basis for lightweight, wideband, and directional GPR systems; however, they require further improvement in terms of gain enhancement, sidelobe and back-lobe suppression, radiation-pattern stabilization, and adaptation to Antarctic operating conditions. Future research should focus on the development of adaptive and phased Vivaldi arrays, the use of metamaterials, Electromagnetic Band-Gap/Frequency-Selective Surfaces (EBG/FSS) structures, and director elements, the creation of lightweight, frost-resistant substrate materials, the advancement of multi-polarization multiple-input multiple-output (MIMO) systems, and the integration of antenna arrays with synthetic aperture radar (SAR) processing adapted to a multilayer snow–ice medium.

Electronics doi: 10.3390/electronics15122580

Authors: Zhenghan Yang Huadong Sun Nuohan Lv

Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training convergence. To address these issues, this paper proposes an adaptive multi-scale watermarking algorithm based on collaborative dual-attention mechanisms. The algorithm designs an adaptive multi-scale feature fusion module (MA-FFM) with a dynamic gating network in the encoder, which flexibly combines local multi-scale textures with global contextual information, overcoming the limitation of fixed fusion weights. In the decoder, a multi-level channel attention module is embedded to strengthen the extraction of watermark signals. The two attention modules work synergistically: the encoder focuses on adaptive feature fusion while the decoder leverages channel attention to selectively enhance watermark-related features, forming a dual-attention synergy that balances robustness and imperceptibility. Moreover, the dynamic gating network adaptively adjusts the contribution of local versus global features via learnable weights, whose evolution from approximately 0.51 to about 0.89 improves model interpretability. Experiments are conducted on the COCO 2017 dataset. Compared with HiDDeN, the proposed algorithm reduces the bit error rate (BER) from 0.1696 to 0.1538 under no attack with a relative reduction of 9.3%, increases PSNR by 0.61 dB, and improves SSIM from 0.9058 to 0.9077. Under various attacks—including JPEG compression, Gaussian noise, salt-and-pepper noise, and brightness/contrast adjustments—the BER remains consistently lower than that of HiDDeN. Ablation studies confirm the effectiveness of each module. Overall, the proposed algorithm preserves visual quality, improves the accuracy of watermark embedding and extraction, and exhibits strong generalization robustness against common image distortions.

Electronics doi: 10.3390/electronics15122579

Authors: Gongxun Lin Jincheng Jiang Jiaheng Cai Xingjian Luo Zihao Wang Hao Sun Ziyuan Pu

Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework, where structured pruning and mixed-precision quantization-aware training (QAT) are jointly optimized with TensorRT–DeepStream for efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured pruning strategy that applies asymmetric channel pruning to convolutional and feature-fusion modules while compressing the Swin Transformer prediction heads and bottleneck stacks, thereby reducing parameters and computation with limited impact on multi-scale representation capability. Second, we develop a hardware-aware mixed-precision QAT scheme that maps computation-intensive backbone layers to INT8 while preserving the Transformer-related modules in FP16, improving inference efficiency while mitigating the accuracy loss caused by uniform low-bit quantization. Third, we compile the optimized network with TensorRT and integrate the resulting inference engine into a DeepStream-based asynchronous video pipeline on the edge platform, enabling end-to-end acceleration by reducing decoding, preprocessing, and memory-transfer overheads. Experimental results on the VisDrone2019-DET dataset and the NVIDIA Jetson Orin NX demonstrate that DUST-YOLO achieves 43.7% mAP@0.5 accuracy with an end-to-end latency of 36.3 ms and a throughput of 27.5 FPS. Compared with the state of the art, DUST-YOLO reduces end-to-end latency by 56.9% and improves end-to-end video throughput by 2.31×.

Electronics doi: 10.3390/electronics15122578

Authors: Xiaoyu Zhang Baohua Guo Sen Wang Anthony Sigama David Bassir

To address the issues of insufficient real-time performance and inadequate modeling of temporal features in traffic police gesture recognition, this paper proposes a method based on skeleton keypoints and hybrid temporal modeling. First, YOLOv11m-Pose is employed to detect human skeleton keypoints in video sequences, extracting reliable two-dimensional skeleton features. Second, this study designs a temporal modeling network that integrates a bidirectional long short-term memory (BiLSTM) with a Transformer. The BiLSTM models local temporal continuity and action transition features between adjacent frames, capturing short-term dynamic changes. The Transformer, through its self-attention mechanism, models global temporal dependencies and weights critical time steps to extract long-range discriminative information. Experimental results demonstrate that the proposed method achieved 98.91% for both Accuracy and F1-Score. In terms of Accuracy, it outperformed the BiLSTM and Transformer models by 2.43% and 7.67%, respectively. It outperforms most methods based on recurrent neural networks and feature fusion. Meanwhile, the model achieves an average inference time of just 1.3299 s per gesture sequence. Consequently, this approach strikes a favorable balance between recognition accuracy and real-time performance, demonstrating significant practical value.

Electronics doi: 10.3390/electronics15122577

Authors: Sheng Jin Joel Yi Yang Loh

Nonlinear Model Predictive Control (NMPC) has garnered significant attention in autonomous systems due to its ability to predict future states and manage complex vehicle dynamics. However, the adaptability of existing NMPC methods is constrained by having to manually set the weight coefficients in the NMPC cost function. This study aims to explore a novel approach that integrates NMPC with Reinforcement Learning (RL), specifically employing Proximal Policy Optimization (PPO), to dynamically adjust NMPC weight matrices. The investigation begins by establishing a physics-based model for a two wheeled differential drive vehicle. A PPO model is then trained and deployed in real time to adapt to the NMPC weight matrices, achieving a 71% reduction in tracking error compared with the NMPC baseline. Importantly, the performance gain arises from PPO’s ability to reshape the NMPC cost function in real time, amplifying both orientation and lateral penalties in curves while relaxing them on straights, thereby enabling adaptive trade-offs between accuracy and control effort that static-weight NMPC cannot achieve. To enhance safety, the controller is integrated with a Control Barrier Function (CBF) layer for real-time obstacle avoidance, while PPO’s real-time weight adaptation contributes to improved tracking performance relative to NMPC+CBF. Finally, robustness evaluations under friction uncertainty, sensor noise, and path disturbances demonstrate that the PPO+NMPC+CBF method maintains reliable tracking accuracy and safety margins.

Electronics doi: 10.3390/electronics15122576

Authors: Feng Jiang Bin Hu Yulong Liu Xiaonian Chen Wei Zhang Yong Li

Driver distraction causes accidents in mining trucks, posing significant safety risks in open-pit mining operations. Estimating the driver’s head pose is a key task for detecting distraction. However, accurate head pose estimation typically requires large amounts of high-quality annotated data. Obtaining a high-precision head pose estimation model under conditions of limited labeled data is challenging. To address the scarcity of annotated data in mining scenarios, this paper proposes a semi-supervised framework named the semi-supervised cascade head pose estimator (SemiCHPE) for driver head pose estimation. The framework adopts a two-stage cascade architecture: the first stage involves a semi-supervised head detector (HeaDet) for head detection, while the second stage comprises a semi-supervised head pose estimator (HPE) for pose estimation. Extensive experiments conducted on our proprietary dataset of mining truck drivers demonstrate that, using only 10% of the dataset, the proposed framework achieves an F1-Score of 99.3% for head detection and a mean absolute error (MAE) of 2.8° for head pose estimation. When deployed on an NVIDIA Orin NX platform within operational mining trucks, the framework attains real-time inference at 32 frames per second with an accuracy of 91.6%, validating its effectiveness for real-world deployment in intelligent mining transportation systems.

Electronics doi: 10.3390/electronics15122575

Authors: Junyi Huang Yuansheng Tu Hai Huang Yanghai Gui

By synthesizing BiSbO4 material with a molar ratio of Bi2O3 to Sb2O3 of 0.6:1 and calcining it at 700 °C, a relatively pure compound was obtained. Additionally, the effects of varying BiSbO4 content on the microstructure and electrical properties of ZnO varistor ceramics were investigated. Results indicate that as BiSbO4 content increased from 0% to 3%, the voltage gradient of the varistor rose with increasing BiSbO4 content while leakage current gradually decreased. The nonlinear coefficient continued to rise, while the residual voltage ratio first decreased then increased. At a BiSbO4 content of 2%, outstanding electrical properties were achieved: voltage gradient (E1mA) = 346 V·mm−1, leakage current JL = 0.14 μA·cm−2, nonlinear coefficient α = 32, and residual voltage ratio K = 1.73. Furthermore, after undergoing a 100 kA surge, the U1mA value remained at 93.5% of its initial value, demonstrating outstanding surge stability. This provides a new approach for fabricating high-gradient, high-stability varistors.

Electronics doi: 10.3390/electronics15122574

Authors: Sichao Fang Lu Dai Jiwei Zou Junbo Wang Tao Chen

Satellites become critical to space exploration, global communication, Earth observation, and navigation. There is a growing need for satellite avionics that are highly integrated, reliable, and low-cost, which is essential for mass production and reliable on-orbit operation. This work demonstrates integrated satellite avionics with high reliability and low cost based on a monolithic programmable system-on-chip (SoPC) through highly synergistic hardware–software co-design, with successful on-orbit validation. The system highly integrates satellite management, attitude and orbit control, power management, telecontrol and telecommand (TC&TM), and data storage into a monolithic PolarFire® SoC (System-on-Chip), and leverages an asymmetric multiprocessing (AMP) architecture. It achieves significant reductions in size, weight, power, and cost (SWaP-C) while ensuring comprehensive functionality and operational reliability. The Jilin-1 Gaofen-05A mission verified the proposed SoPC-based satellite avionics for low Earth orbit (LEO) commercial satellites. Long-term telemetry data confirms its stable operation, with a bus voltage ranging from 11.4 to 12.3 V, an average power consumption of 33.4 W, and a solar array output current of 6.2–6.5 A, all of which meet the design expectations. This work offers a feasible technical approach and engineering reference for commercial integrated satellite avionics featuring high reliability and cost efficiency.

Electronics doi: 10.3390/electronics15122573

Authors: Geunhee Cho Yoomin Go Mihui Kim

Recently, there has been an increasing prevalence of software supply chain attacks on software component suppliers. These attacks have targeted suppliers with relatively weak security or they have exploited vulnerabilities in open-source software. The software bill of materials (SBOM) has gained significant attention as a mechanism for improving software supply chain transparency and traceability. In this study, we propose an SBOM distribution architecture based on Hyperledger Fabric, which is a permissioned blockchain platform, to facilitate secure SBOM management. This approach utilizes Hyperledger Fabric private data collections (PDCs) to separate SBOM metadata from sensitive component information, thereby enabling confidential data sharing while reducing the blockchain storage overhead compared to a fully on-chain approach. The proposed PDC-based architecture achieves lower latency and higher throughput than the fully on-chain approach under the evaluated workload conditions, while supporting integrity verification and controlled sharing of sensitive component data.

Electronics doi: 10.3390/electronics15122572

Authors: Jin Zhang Zekang Bian Liang Zhang Feng Wang

In sparse representation learning-based linear k-nearest neighbors methods, the linear representation assumption frequently fails when applied to nonlinear distributed data, leading to degraded generalization and a loss of physical interpretability. To address this, we propose the Kernelized Manifold-Optimized Linear Nearest Neighbor (KMOLNN) method. Methodologically, KMOLNN projects the data into a high-dimensional kernel space to capture the nonlinear relationships, while introducing an adaptive manifold-preserving regularization term—via an adaptive Laplacian matrix—to dynamically preserve the local geometric structures. Theoretically, this study provides a mathematical proof of the nearest neighbor group effect for the kernel framework and reveals that its weight optimization behavior implicitly implements the Bayesian decision rule. Furthermore, we derive a rigorous generalization error bound using Rademacher complexity to validate its theoretical robustness. Empirically, we evaluate KMOLNN on 15 small-to-medium-scale benchmark datasets against eight comparative methods, including recent variants. The results demonstrate significant numeric superiority, with KMOLNN achieving an average accuracy of 90.76% and a Macro F1-score of 88.62% across the evaluated datasets. Finally, we present a comprehensive runtime analysis, explicitly acknowledging that these gains in generalization capability and theoretical interpretability present a practical trade-off, requiring increased computational runtime due to the iterative alternating optimization process.

Electronics doi: 10.3390/electronics15122571

Authors: Yang Yang Zhuozhuo Bai Zhi Chen Xiaoxue Cao Zhitao Chen Guo Chen

To address the complex spatiotemporal dependencies and dynamically evolving spatial relationships in tunnel traffic flow prediction, a macro–micro collaborative two-stage prediction method is proposed. The Grey Wolf Optimizer (GWO) is first employed to optimize the GRU model for predicting incoming traffic flow at the tunnel entrance, providing reliable macro-level input for subsequent modeling. Based on this, a spatiotemporal graph structure is constructed, and an FSE-ST-GCN model integrating an adaptive adjacency matrix with spatial and channel attention mechanisms is developed to capture dynamic spatial dependencies and enhance key feature representation. Experiments are conducted using real-world traffic flow data collected from the Shizuizi Tunnel on the Jilin–Caoshi Expressway. The results show that the proposed method outperforms baseline models in terms of MAE, RMSE, and MAPE, achieving superior prediction accuracy and stability. This work provides effective technical support for refined tunnel traffic management and lighting control.

Electronics doi: 10.3390/electronics15122570

Authors: Xiaoda Li Xianghui Zhan Jingde Huang Zhichao Gong

In the current Industrial Internet of Things (IIoT) environment, data security for product lifecycle management is greatly challenged, particularly in scenarios involving vertical multi-level Bill of Materials (BOM) deep nesting and lifecycle dynamic evolution. The traditional case-bounding model, in large-scale deployment, easily leads to rule expansion and an increase in database I/O overhead, thus causing authorization lag, authority boundary ambiguity and other problems. To address these limitations, this paper proposes a Decoupled Hybrid Access Resolution (DHAR) framework. The framework separates static organizational roles from dynamic lifecycle constraints, and the complexity of authorization configuration is reconstructed from case-dependent growth into an object-instance-independent bounded structure; combined with the state-based pre-filtering mechanism and memory cache strategy, redundant recursive query is reduced. Experiments on increasing BOM depths show that, under a 20-layer topology, DHAR reduces average access latency from 285.8 ms to 1.3 ms. Under a 20-layer BOM with 1000 concurrent requests, DHAR maintains an average latency of 5.2 ms, while compressing the authorization rule set from millions to hundreds. These results indicate that, within the studied vertical multi-level BOM setting, DHAR improves response performance while preserving data consistency and strengthening protection against unauthorized modification.

Electronics doi: 10.3390/electronics15122569

Authors: Chunyang Wan Yaxin Zhao Wanli Wu Kailong Zhu

Multimedia protocol implementations are susceptible to security vulnerabilities, yet the existing fuzzing methods fail to generate seed corpora covering all supported resource types due to the resource type sensitivity characteristic, resulting in limited code coverage. We propose LLMSGen, a method leveraging Large Language Models (LLMs) to generate diverse seeds for multimedia protocol fuzzing. Through prompt chaining, LLMs analyze the source code of the target program to identify supported resource types and extract URI construction rules. Diverse request seeds are then generated by replacing URI fields in captured traffic, and the corresponding resource files are placed in the server’s working directory to ensure valid responses during fuzzing. Evaluated on three RTSP-based open-source programs (Live555, RTSPServer, Gstreamer), LLMSGen increased resource type coverage by an average of approximately 98.4% and improved code line coverage by approximately 36.8% compared to AFLNet’s original seed corpus.

Electronics doi: 10.3390/electronics15122568

Authors: Genmao Zhou Yinquan Ding Zhennan Du Yiwei Tang Li Chen Guohui Yang Gang Zhang

In industrial applications such as in situ leaching and uranium mining, permanent magnet synchronous motors (PMSMs) for submersible pumps are frequently connected to frequency converters via long cables. During this long-distance transmission, traveling wave reflections induced by high-frequency pulse width modulation (PWM) generate severe transient overvoltages that threaten motor insulation. Because installation space at deep-well motor terminals is severely restricted, overvoltage suppression must be implemented at the inverter output. Here, the parameter design and optimization of a passive LC filter specifically developed for 250 Hz high-frequency PMSMs are presented. The optimal inductance and capacitance parameters were determined by balancing multiple operational constraints, including fundamental voltage drop, high-frequency harmonic attenuation, and the avoidance of low-order harmonic resonance. Furthermore, the anti-saturation performance of the magnetic core material, evaluated thermal characteristics through electromagnetic-thermal co-simulation, and analyzed the risk of self-excited oscillation between the filter capacitors and the motor was analyzed. Finally, hardware experiments conducted on a 20 m cable test bench validate that the designed LC filter effectively mitigates terminal overvoltage. The peak terminal voltage was reduced from 900 V to 505 V, and total harmonic distortion (THD) was limited to below 5%. This design provides a highly reliable, space-efficient solution for overvoltage suppression in high-speed, long-cable motor drive systems.

Electronics doi: 10.3390/electronics15122567

Authors: Qianren Yang Yong Li Xiang Ma

Retrieval-augmented generation improves the factual consistency, knowledge timeliness, and scenario adaptability of large model inference services by incorporating external knowledge. However, it also introduces structural privacy risks, including private-knowledge leakage, prompt injection, and progressive information extraction in multi-turn interactions. To address these issues, this paper proposes Private-RAG, a privacy-preserving retrieval-augmented generation method for large model inference. The method constructs a composite threat model and a quantitative evaluation framework for the RAG pipeline, and further develops a layered collaborative defense mechanism consisting of controlled retrieval, sensitivity-aware context minimization, structured prompt isolation, and multi-criterion output gating. In addition, a risk feedback-driven budget accounting method is introduced to enable dynamic risk control in multi-turn interaction scenarios. Experimental results show that Private-RAG effectively reduces private-knowledge leakage, improves robustness against prompt injection, and suppresses cumulative privacy exposure while maintaining question-answering utility and a controllable deployment latency (e.g., 1165 ms), demonstrating superior privacy protection and inference robustness.

Electronics doi: 10.3390/electronics15122566

Authors: Stephen Flowerday Mauricio Papa Ethan Flowerday

Critical infrastructure cybersecurity increasingly needs frameworks that move beyond recovery toward bounded improvement under disruption, but empirically grounded theories for operational technology remain limited. This paper develops a Theory of Antifragility (AFT) for critical infrastructure (CI) cybersecurity, anchored in a five-state Resilient System Model and a bounded mathematical definition built around Jensen gain and post-disruption gain. A two-layer empirical design pairs a CI-relevant subset of the CISSM Cyber Events Database with the HAI hardware-in-the-loop industrial control dataset and tests three confirmatory hypotheses and one exploratory proposition. OT-adjacent sectors show significantly higher shares of disruptive or mixed events than comparison sectors (65.3% versus 46.8%, p < 0.001) and a heavier concentration of physical-attack and data-attack subtypes. In HAI, attack-labeled observations were 7.43 times more likely than normal observations to exceed the 95th percentile of baseline deviation (p < 0.001). Across successive attack windows, mean process-state deviation declined significantly (Spearman ρ = −0.688, p = 0.007), providing evidence of measurable response variation rather than proof of adaptive gain. Together, the findings establish the following two prerequisites for future antifragility testing: differentiated fragility burden and process-level perturbation observability.

Electronics doi: 10.3390/electronics15122565

Authors: Xinen Liu Qiang Qu Yushan Meng

This paper investigates the problem of adaptive event-triggered control with prescribed performance for a class of strictly feedback-controlled nonlinear systems with unknown initial tracking conditions. To overcome the dependence of traditional prescribed performance control on the system’s initial tracking conditions, this paper introduces a novel algebraic saturation function that first maps tracking error initial values of arbitrary magnitude to a bounded interval and then imposes predefined performance constraints on this bounded interval. This strategy ensures that, even when the system’s initial state is unknown, the tracking error still converges to a small neighborhood near the equilibrium point in accordance with the prescribed performance. Furthermore, the strategy employs a fixed-threshold event-triggered mechanism, which effectively reduces the system’s update frequency and alleviates the communication load. Furthermore, by combining a logarithmic barrier Lyapunov function with neural network-based unknown function approximation techniques, this strategy proposes an adaptive prescribed performance event-triggered controller that is independent of the system’s initial state, in other words, independent of the initial tracking conditions. Simulation results validate the effectiveness and superiority of the proposed controller.

Electronics doi: 10.3390/electronics15122564

Authors: Haleh Jahanbakhsh Basherlou Naser Ojaroudi Parchin Chan Hwang See

This paper presents a compact fractal-based super-wideband multiple-input multiple-output (MIMO) antenna for millimeter-wave (mmWave) 5G new radio (NR) and prospective 6G applications. The MIMO system comprises four Koch fractal monopole elements integrated with a modified shared ground plane. By adopting the second Koch iteration, the antenna achieves enhanced impedance bandwidth and stable radiation behavior compared with lower-order iterations. The elements are arranged in a polarization-diversity configuration within a 30 × 30 mm2 footprint on a 0.8 mm-thick Rogers RO4835 substrate (εr = 3.5, δ = 0.0025). The proposed design provides an impedance bandwidth exceeding 14 GHz over 26.5–41 GHz, covering key bands at 28, 32, 38, and 40 GHz, while maintaining high inter-element isolation (around 30 dB over the operating range). The optimized ground modification enables a fully connected common ground and suppresses mutual coupling without additional decoupling structures. The antenna achieves 4–6 dBi realized gain with radiation efficiency exceeding 95%. MIMO performance metrics, including the envelope correlation coefficient (ECC), mean effective gain (MEG), and diversity gain (DG), confirm excellent diversity characteristics. The antenna is further evaluated under bending, demonstrating stable matching and isolation for conformal and wearable scenarios, and the concept is extendable to a non-planar 12-port configuration within the same footprint. Measured results agree well with simulations, validating the proposed design for wideband mmWave 5G/6G devices.

Electronics doi: 10.3390/electronics15122563

Authors: Rui Huang Jiawei Zhao Junxuan Chen Yidan Xu Xiaojing Li Wuzhen Lin Mingyue Ji Zhengyu Chen Xiaoli Yu

High-nickel NCA/Si–C 21700 cells exhibit strongly condition-dependent degradation, but the coupled influence of temperature and rate on electrochemical, thermal, and structural evolution remains insufficiently resolved. Here, Samsung INR21700-50E cells were aged under a 3 × 3 matrix of ambient temperatures (0, 23, and 40 °C) and C-rates (0.5C, 1C, and 2C). Periodic reference performance tests were used to track capacity, 10 s direct-current internal resistance, electrochemical impedance, pseudo-open-circuit voltage, differential voltage/incremental capacity behavior, heat generation, and post-mortem morphology. Guided by the hypothesis that temperature and rate history change not only the speed but also the dominant pathway of aging, the results show that both ambient temperature and the charge/discharge rate program govern the aging trajectory. Low-temperature cycling accelerates capacity loss and resistance growth through severe polarization and lithium plating, indicating dominant loss of lithium inventory. High-temperature operation promotes interfacial side reactions, impedance rise, and cathode structural degradation, leading to stronger loss of active material at later stages. An increasing C-rate amplifies these effects by raising overpotential and thermal load. Heat generation power increases markedly with aging and depends strongly on temperature–rate history. Scanning electron microscopy confirms cathode cracking, anode surface film thickening, and separator degradation under severe conditions. These experimental indicators are integrated into a mechanism-aware diagnostic framework that maps capacity retention, DCIR/EIS parameters, ICA/DVA indices, and heat generation metrics to dominant aging modes, supporting BMS state-of-health estimation, lifetime prediction, thermal management, and second-life screening of high-nickel NCA cells. The condition-averaged trajectories are further converted into a semi-empirical aging law that links capacity loss, resistance growth, and heat generation increase for BMS-oriented lifetime prediction.

Electronics doi: 10.3390/electronics15122562

Authors: Youchen Chen Ye Liu Xing Wang Qingze Zhang Xingrui Zhang Kerang Cao Hoekyung Jung

Although state space models (SSMs) represented by Mamba have achieved long-range dependency modeling with linear complexity, they still struggle to realize differentiated processing of high-frequency and low-frequency components in image super-resolution (SR) tasks. To address the drawbacks of existing wavelet-Mamba methods, such as texture distortion and unstable training caused by independent frequency band modeling, this paper proposes a frequency-aware bidirectional interactive mamba network (FABIMNet). The network uses discrete wavelet transform to decouple image frequency-domain components. The core lies in the proposed bidirectional high-frequency enhancement (Bi-HFE) module, which constructs an interaction mechanism of low-frequency guiding high-frequency generation and high-frequency feedback correcting low-frequency structure, and cooperates with the lightweight cross-band interaction (LCBI) module to achieve information synchronization and complementarity during deep feature extraction. Extensive experiments demonstrate that the proposed method achieves a competitive trade-off between computational efficiency and reconstruction performance across five benchmark datasets.

Electronics doi: 10.3390/electronics15122561

Authors: Gabriele De Boni Lorenzo Mantione Lucia Frosini

Electric motor control may be challenging due to nonlinearity, cross-coupling and current and voltage constraints. Multivariable action may enhance the effectiveness of control on electric motors. This work presents the real-time implementation of a Model Predictive Control (MPC) strategy for current regulation in a low-power synchronous electric motor, on a low-cost microcontroller platform. The experimental setup employs a back-to-back configuration with a DC motor operating as a generator, enabling comparative analysis of the impact of different cost-function formulations on the closed-loop dynamics. Both transient and steady-state capabilities have been investigated through suitable key performance indexes.

Electronics doi: 10.3390/electronics15122560

Authors: Minji Kim Jiun Oh Younghun Han June-O Song Joon Seop Kwak

p-GaN gate enhancement-mode GaN High Electron Mobility Transistors (HEMTs) are promising normally off power devices, but their high-temperature reliability is strongly affected by the gate-contact scheme. This study compares Pd ohmic and Ni Schottky p-GaN gate HEMTs fabricated on the same GaN-on-Si epitaxial platform by combining temperature-dependent electrical characterization, post-temperature-dependent-test (TDT) room-temperature recovery analysis, and thermoreflectance thermal mapping. Electrical measurements were performed in a temperature range from room temperature to 500 °C using gate leakage, transfer, and output characteristics, while thermal maps were obtained before and after the TDT under identical bias conditions. The Pd ohmic devices exhibited a higher initial current drive but a larger operating gate-current penalty and greater degradation of normalized on-state characteristics at elevated temperature. After the TDT, reduced transconductance and maximum drain current were accompanied by weaker active-channel heating, indicating degradation-type cooling associated with reduced gate–channel modulation efficiency. In contrast, the Ni Schottky devices showed a lower gate-current penalty and better normalized output retention up to approximately 300 °C; however, post-TDT increases in transconductance and drain current occurred together with degraded subthreshold swing and persistent localized heating, indicating apparent on-state activation with weakened gate/depletion control. These results show that p-GaN gate reliability should be assessed through coupled electrical and thermal signatures rather than single electrical or thermal metrics.

Electronics doi: 10.3390/electronics15122558

Authors: Lama Ayash Ashwag Alasmari Hassan Alhuzali

Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suicide risk, and related clinical signals using text mining, neural classification, transformer-based models, and, more recently, large language models. However, most systems treat text primarily as a clinical signal rather than examining the social and cultural contexts in which distress is expressed. Arabic NLP research remains even more limited, largely focusing on detecting clinical conditions while overlooking contextual factors that shape mental health questions. This work introduces ContextMental, a multi-label annotation schema and benchmark dataset for modeling sociocultural context in Arabic mental health questions. The dataset contains 2677 questions, including 552 instances with contextual labels, enabling fine-grained analysis of social, cultural, and religious dimensions. An AraBERT-based classification framework is further developed using imbalance-aware optimization, semi-supervised pseudo-labeling, and adaptive threshold calibration. Experimental results indicate that pseudo-label augmentation improves overall classification performance, suggesting that semi-supervised learning can support context-aware Arabic mental health classification. This study provides a context-aware annotation framework, a benchmark dataset, and an AraBERT-based baseline modeling pipeline for Arabic mental health NLP, thereby supporting future research on socially, culturally, and religiously grounded language technologies.

Electronics doi: 10.3390/electronics15122559

Authors: Orieb Abualghanam Malik Al-Essa Wesam Almobaideen Mohammad Qatawneh Ahmad Sami Al-Shamayleh

The rapid growth of cyberattacks necessitates the development of more sophisticated detection techniques. DoS and DDoS are well-known harmful attacks that affect organizations. This paper proposes a proactive, behavior-based DoS and DDoS detection framework that integrates threat intelligence and machine learning to analyze attack behavior and enhance early detection. XGBoost is used to train the proposed model and evaluate feature importance. The evaluation of the proposed model and the generated rules is conducted using three different datasets: CICIoT2023, BoT-IoT, and Edge-IIoT. Experimental results demonstrate high detection performance, achieving up to 99.98% accuracy and 99.89% F1-score, while maintaining low false positive rates across diverse datasets. Integrating threat intelligence into SIEM has been evaluated using two datasets, DDoS-AT-2022 and CIC-DDoS2019. The rule-based detection technique enhances detection rates and mitigates false positives. Moreover, the proposed framework enhances detection accuracy.

Electronics doi: 10.3390/electronics15122557

Authors: Zhirong Tang Xin Wei Zhaobin Wei Fei Tan Cong Tian Ying Tang Xuedou Xiong

Accurate estimation of the utility harmonic impedance at the Point of Common Coupling (PCC) is critical for harmonic pollution management in industrial power grids. Existing non-invasive methods rely heavily on restrictive assumptions that are rarely satisfied in practice, and conventional filtering-based approaches suffer from accuracy degradation in dynamic scenarios due to fixed-rule updates of the noise covariance. This paper proposes a deep reinforcement learning (RL)-optimized adaptive extended Kalman filter (AEKF) method for robust harmonic impedance estimation. A state-space model is established without restrictive assumptions, and a deep Q-network (DQN) framework is designed to optimize noise covariance updates adaptively. Simulation results show that the method achieves reliable estimation under normal conditions. Although errors rise under strong noise, it remains stable and exhibits better noise robustness than conventional methods. Field measurements in actual power grid environments further verified the feasibility and application potential of the proposed method in field engineering.

Electronics doi: 10.3390/electronics15122556

Authors: Jiayin Zhou Liqiang Zhong Dongkang Wang Wenqiang Zhao Wenhua Chen

The contact performance of automotive airbag electrical connectors directly affects the stable conduction of the initiator circuit, yet sufficient failure data are difficult to obtain for such long-life safety-critical components. This study develops a degradation model for connectors with stainless-steel pins, beryllium-bronze sockets, and Ni/Au composite coatings, using the contact resistance increment as the degradation measure. Considering the accumulation of oxidation corrosion products under thermal stress, as well as the local film rupture and re-oxidation induced by fretting wear under combined temperature-vibration stress, a nonlinear time scale tα is introduced to describe the nonlinear growth of contact resistance. A random-drift nonlinear Wiener process is then constructed: the diffusion term represents local fluctuations within each sample trajectory, while the random drift rate captures growth-rate differences among samples. Parameter estimation was performed using degradation data obtained from 160 °C high-temperature and 160 °C temperature-vibration accelerated degradation tests. The estimation results show that the stress-class-specific time-scale model better reflects the different degradation mechanisms than a common time-scale model, and that the temperature-vibration group exhibits higher resistance growth and stronger trajectory fluctuations. Model diagnostics support the description of the main increment distribution and sample-to-sample differences, while EDS and XPS results provide supplementary evidence for oxidation-related surface composition changes and coating-state evolution.

Electronics doi: 10.3390/electronics15122555

Authors: Pavol Belany Roman Budjac Stanislav Kriz

The growing penetration of residential photovoltaic systems increases the need for effective demand-side management strategies that improve on-site electricity utilization without battery storage. This study investigates the impact of different electric water heater control strategies on the energy and economic performance of a residential PV system. A simulation-based analysis was performed in the PV*SOL Premium environment using a 5.4 kWp household PV installation and an electric water heater as a flexible thermal load. Five operating modes with different levels of control granularity, ranging from uncontrolled operation to continuous power modulation, were evaluated under climatic conditions representative of Dunajská Streda, Slovakia. The analyzed indicators included the self-consumption ratio, self-sufficiency ratio, electricity import and export, and total variable electricity costs. Compared to the reference mode, continuous control increased the self-consumption ratio from 38.73% to 66.43% and reduced electricity export from 3340 kWh/year to 1830 kWh/year. Total variable electricity costs decreased by 31.86%, from €725.53 to €494.44 per year. The results confirm a saturation effect, where increasing control complexity provides only marginal additional benefits. Moderately complex multi-level control, therefore, represents an effective and economically attractive solution for residential PV systems without battery storage.

Electronics doi: 10.3390/electronics15122554

Authors: GwangHyun Ahn Dongkyoo Shin

Existing Cyber Resilience Assessment Guidelines, including those of the Bank of Korea (BoK), focus on governance-oriented compliance and lack quantitative criteria for measuring the operational effectiveness of security technologies—a Policy–Technology Gap also common in general enterprise settings. To address this gap, this study proposes D3-CREF, a technology-centric cyber resilience evaluation framework that maps the MITRE D3FEND taxonomy to financial security domains and introduces a Normalized Resilience Index (NRI) aggregating four dimensions—Coverage, Maturity, Automation, and Timeliness—via a closed-form weighted geometric mean with AHP-elicited weights (consistency ratio CR = 0.04). All NRI indicators are anchored to MITRE ATT&CK techniques and exemplar CVE entries, enabling threat-informed measurement. The framework was validated through a three-round Delphi study with 50 experts (Kendall’s W = 0.78, p < 0.001; Cronbach’s α = 0.89; CVR 0.68–0.92) and a Cyber Range-based simulation. For three institutions with identical BoK scores (92/100), NRI yielded discriminative values of 0.83, 0.44, and 0.09 (CV = 0.68 vs. 0.00 for the baseline), confirming a shift from compliance-based to performance-driven assessment.

Electronics doi: 10.3390/electronics15122553

Authors: Changqing Sun Jiawei Zhang Xinghua Li

As Flying Ad Hoc Networks (FANETs) are highly vulnerable to security threats such as identity spoofing, session replay and man-in-the-middle attacks in open-air channels, it is crucial to design an authentication key agreement (AKA) scheme to ensure the security of unmanned aerial vehicle (UAV) swarm networking within FANETs. However, existing AKA schemes for FANETs often struggle to balance authentication efficiency and high dynamism within UAV swarms whilst meeting necessary security requirements. To address the issue, this paper proposes CUBAT-AKA (Collaborative UAV Batch Authentication and Tree-based Key Agreement), a lightweight UAV swarm authentication and key agreement scheme based on batch verification and a binary tree structure. The scheme constructs a secure and lightweight three-party authentication mechanism based on aggregated verification and the Chinese Remainder Theorem (CRT). By offloading computational tasks to the authentication center and aggregating authentication responses in batches, it significantly improves the efficiency of UAV access authentication in large-scale FANET scenarios. To address the dynamic nature of UAVs frequently joining and leaving clusters in FANETs, an improved binary tree-based key agreement method has been designed, reducing key update overhead to a logarithmic level and enabling lightweight session key distribution and updates for UAV clusters. Security analysis demonstrates that, under the random oracle model, CUBAT-AKA is resistant to eavesdropping, replay, man-in-the-middle, impersonation and collusion attacks, whilst ensuring forward and backward security during member changes. Performance analysis indicates that this scheme offers significant advantages over comparable solutions in terms of both UAV cluster access authentication efficiency and dynamic key agreement overhead.

Electronics doi: 10.3390/electronics15122552

Authors: Tamás Orosz

This paper revisits Problem A of the TEAM 35 benchmark from the viewpoint of robustness estimation under manufacturing uncertainty. Rather than treating the original extremal-position-based sensitivity metric as the formulation to be improved, it is used only as a baseline for comparison with other metrics. In this work, robustness is evaluated as the largest degradation of the nominal magnetic-field homogeneity objective observed over prescribed sets of admissible manufacturing perturbations. In addition to turn-position uncertainties, the present study also includes uncertainty in the excitation current density. While turn-position errors affect each turn individually, current-density uncertainty affects the error contributions of all turns simultaneously through a common term. This common-mode excitation uncertainty represents an extension of the original benchmark formulation and is one of the paper’s main focal points. Several Design of Experiments (DoE) methodologies, as well as search-based robustness estimation strategies, are compared in terms of error in estimated robustness and computational demand. The results show that the original extremal-position-based approximation can substantially underestimate the sampled robustness of the nominal field-homogeneity objective. Including current-density uncertainty further increases the discrepancy between the original metric and the sampled robustness estimates.

Electronics doi: 10.3390/electronics15122551

Authors: Eva Góngora-Rodríguez Irene Rivas-Blanco Álvaro Galán-Cuenca Carmen López-Casado Isabel García-Morales Víctor F. Muñoz

Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of a supervised autonomous robotic aspirator for detecting and removing bleeding in an in vitro experimental model. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level action execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate high blood removal rates (above 80% in all cases) and high suction efficiency. The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased task autonomy and reduced need for continuous manual suction control during minimally invasive procedures.

Electronics doi: 10.3390/electronics15122549

Authors: Yaser Ibrahim Rashed Alshdaifat Krishnamachar Prasad Jeff Kilby

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma.

Electronics doi: 10.3390/electronics15122550

Authors: Seung-Hyeon Lee Yong-Seok Seo Jee-Taeck Seo Tae-Hyun Kim Jeong-Hun Lee Ryun-Yeong Kim Kwang-Hyun Baek

This paper presents a 7-bit 1.6 GS/s hybrid capacitive-to-charge-injection DAC (C-CIDAC)-based flash-assisted time-interleaved (FATI) successive-approximation-register (SAR) analog-to-digital converter (ADC) that improves the limited input range and temperature-induced gain variation in conventional CIDAC-based SAR ADCs. In the proposed architecture, a DAC voltage common-mode (VCM) shift up to 48 LSBs is internally generated during the coarse conversion, enabling a rail-to-rail ADC input range while improving VCM independence. In addition, a fully on-chip background gain-calibration scheme is introduced to compensate for the gain error between the CDAC and CIDAC caused by temperature variation. By taking advantage of the pulse-activation-based CIDAC operation scheme, the proposed calibration achieves robust gain tracking without any external bias control. The proposed four-channel FATI-SAR ADC was designed using a 65 nm CMOS process and occupies 13,628 μm2, including the background calibration circuitry. The peak differential nonlinearity (DNL) and integral nonlinearity (INL) are +0.60/−0.60 LSB and +0.72/−0.76 LSB at −40 °C and 105 °C, respectively. At Nyquist input, the simulated SNDR and SFDR are 41.52 dB and 53.36 dB, respectively. The ADC consumes 8.551 mW and achieves an FoMW of 54.6 fJ/conversion step. Comprehensive post-layout simulation results show that the proposed FATI-SAR ADC operates at 1.6 GS/s and maintains an ENOB above 6.3 across a temperature range from −40 °C to 105 °C at Nyquist input.

Electronics doi: 10.3390/electronics15122548

Authors: Muhammad Tariq Mahmood

In this paper, we propose a novel Depth from Focus (DFF) framework that formulates depth estimation as an energy minimization problem and unrolls the corresponding iterative optimization into a trainable neural architecture. Given a focal stack, a deep feature extractor constructs a learned focus volume that encodes defocus and structural cues. Based on this representation, multiple candidate depth maps are generated using a plane-based probabilistic formulation, while an attention mechanism adaptively assigns pixel-wise confidence weights to each candidate. The depth estimation is performed through an iterative refinement process, where each stage corresponds to a learned proximal update implemented via lightweight conditional networks. These updates incorporate focus consistency, adaptive step sizes, and learned regularization priors, enabling effective integration of physical imaging constraints with data-driven modeling. A final refinement module further enhances prediction accuracy by fusing the refined depth, focus volume features, and candidate hypotheses to estimate residual corrections. The entire framework is trained end-to-end, ensuring coherent optimization across all components. Experimental results demonstrate that the proposed method achieves improved robustness and accuracy, particularly in low-texture and noisy regions, while preserving interpretability through its unfolding-based design.

Electronics doi: 10.3390/electronics15122544

Authors: Furkan Karlitepe

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing.

Electronics doi: 10.3390/electronics15122547

Authors: Fernando Hidalgo-Castelo Antonio Guerrero-González Francisco García-Córdova Francisco Lloret-Abrisqueta Antonio Piñera-Marín

Access to operational information in industrial plants forces operators to interrupt their tasks, walk to the human–machine interface (HMI) terminals, and navigate heterogeneous platforms—namely programmable logic controllers (PLC), supervisory control and data acquisition (SCADA) systems, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems—consuming 15–30 min per query. Previous work integrated local large language models (LLMs) into a five-layer cognitive architecture deployed in a precast concrete plant, reducing that time to 14–23 s through voice-based conversational queries; however, model inference accounted for 55.3% of total latency and the system remained reactive. This work incorporates the event-driven paradigm as a non-intrusive augmentation layer that keeps the operational context permanently updated, continuously monitoring the process and refreshing knowledge only when significant changes occur. The architecture is fully local, cloud-independent, graphics processing unit (GPU)-free, and containerized via Docker Compose. Experimental results demonstrate a 26–31% reduction in response times (means of 9.84 s, 11.23 s, and 16.47 s for simple, moderate, and complex queries), an 8.4 °C reduction in peak hardware temperature (from 79.6 °C to 71.2 °C), a 41.6% decrease in thermal variability, and an expansion of the safety margin before central processing unit (CPU) throttling from 5.4 °C to 13.8 °C. The system achieved 100% success rate and availability over 30 min of autonomous operation, validated in a real industrial environment.

Electronics doi: 10.3390/electronics15122546

Authors: Simas Krušniauskas Šarūnas Grigaliūnas Rasa Brūzgienė Mert Cayir

Enterprise infrastructure operators face a critical challenge in prioritizing post-quantum migration, as quantum-related risk is not uniformly distributed across data in transit, in use, and at rest. Existing assessments rely on system-level evaluations or protocol-specific analyses, which do not capture the heterogeneity of exposure across infrastructure layers. This paper extends the Quantum-Adjusted Risk Scoring (QARS) model introduced in into an evidence-based, layer-specific framework that evaluates in-transit, in-use, and at-rest data separately. QARS applies a unified five-factor scoring framework separately to each data state and introduces a quantum-vulnerability attenuation mechanism grounded in Grover-bounded residual security that prevents overstating urgency for non-Shor-vulnerable symmetric protection. Observable host-level evidence determines the binary and ratio descriptors used by the model, while the fixed affine mapping coefficients are treated as transparent semi-quantitative calibration parameters. These coefficients are documented separately and subjected to coefficient-level sensitivity analysis to evaluate whether the reported layer ordering depends on their nominal values. The model is demonstrated through an illustrative controlled experiment using real infrastructure observations. Strengthening storage protection reduces the aggregate system risk from 0.707 (high) to 0.414 (moderate), a 41.5% reduction. However, the maximum-layer score remains high (0.657), indicating that the transport layer continues to dominate migration urgency. Sensitivity analysis confirms that the dominance of the transport layer is stable under wide perturbations of the calibration parameters. These findings demonstrate that risk reduction in one layer does not eliminate overall exposure but shifts the dominant vulnerability. By distinguishing between overall system posture and the most critical remediation priority, QARS supports infrastructure operators in identifying high-risk components and planning structured, evidence-based post-quantum migration.

Electronics doi: 10.3390/electronics15122545

Authors: Ying Yang Jinyi Huang Hao Zhu Zibin Cai Weijia Zheng

Aiming at the problem of weak fault features and difficult localization of adjacent nodes in distribution networks, we constructed a two-stage cascaded architecture to decouple the diagnosis task into fault classification and section location. The feature layer fuses MS-CNN, SimAM, and Transformer to form a spatio-temporal dual attention mechanism that synchronously captures spatial saliency and global temporal logic. A prototype network is introduced at the fault location decision layer, and metric learning is used to solve the problem of feature aliasing of adjacent nodes. The experimental results show that the accuracy of fault classification and localization are 98.61% and 94.22%, respectively, and it exhibits graceful degradation under extremely low-SNR conditions, which verifies the effectiveness of the proposed strategy in the refined fault diagnosis of distribution networks.

Electronics doi: 10.3390/electronics15122543

Authors: Fei-Lung Wu Jung-Sheng Liu Chia-Mei Peng Li-Wei Kao Pei-Hsuan Ko I-Fong Chen

This paper presents a compact modified monopole antenna tailored for 5G NR on-glass automotive applications operating in the n78 band. The design overcomes 3D radiation pattern limitations inherent in conventional monopole and inverted-F antennas (IFAs). Unlike traditional structures where auxiliary branches serve impedance matching or grounding, this design integrates open- and short-circuited stubs with a coplanar waveguide (CPW) feed to eliminate discrete components. By utilizing a resonant mechanism distinct from IFAs, it enables precise control over the current distribution and phase on the radiator to achieve passive 3D beam shaping without active switches or arrays. This suppresses the inherent elevation null, enhancing upper-hemisphere radiation. A prototype operating from 3.3 to 3.6 GHz was fabricated on a flexible printed circuit (FPC) and verified on a glass substrate. This study focuses strictly on radiation characteristics at the antenna element level; to ensure a focused investigation on dielectric-antenna interactions, large-scale vehicle body scattering and full-scale vehicle integration are excluded from this scope. The results, including S-parameters, gain, total efficiency, and 3D patterns, demonstrate superior elevation coverage and comparable impedance performance under on-glass boundary conditions. The proposed methodology offers a high-feasibility, low-complexity, and cost-effective solution for passive 3D radiation control in on-glass 5G wireless links.

Electronics doi: 10.3390/electronics15122542

Authors: Wenliang Yin Yudun Li Kuan Li Maozeng Lu

With the increasing penetration of distributed renewable energy, distribution networks face severe operational challenges during grid faults, where rapid power restoration and system stability are crucial. Traditional fault restoration strategies often rely on static dynamic zoning or simple power balancing, neglecting the critical electrical interactions among nodes. To address these limitations, this paper innovatively proposes a hierarchical coordinated control framework for distribution network fault recovery, combining dynamic zoning and coordinated load shedding. The core novelty of this research lies in integrating the node electrical correlation degree into the load grading process to assist in coordinating dynamic network dynamic zoning. By comprehensively evaluating real-time power flow, the regulation capabilities of distributed resources, and intra-region electrical correlations, the proposed framework adaptively optimizes both the zoning structure and the load shedding sequence. Simulation results demonstrate that, compared with conventional static or uncoordinated methods, the proposed approach significantly minimizes load loss while improving grid recovery efficiency and voltage stability. Ultimately, this coordinated control strategy effectively enhances the resilience and operational safety of high-penetration renewable distribution networks, providing robust support for distribution network operations under a high proportion of renewable energy integration.

Electronics doi: 10.3390/electronics15122541

Authors: Cemalettin Albayrak Serkan Kurt Mehmet Cemil Kazanbaş

This paper presents a modular FPGA-based Smart Multi-Functional Display (SMFD) architecture designed for low-power and real-time avionics applications. Conventional SMFD systems are typically based on tightly coupled monolithic architectures, which limit scalability, maintainability, and subsystem flexibility while increasing system complexity and power consumption. To address these limitations, the proposed architecture separates processing, display, and communication functions into independent hardware modules, enabling flexible system integration and subsystem-level optimization. It consists of four primary modules: an FPGA-based Programmable Logic Device (PLD) module for deterministic video processing and display timing control, an NXP i.MX8X CPU module for application-level management, a high-resolution LCD module, and a dedicated I/O module supporting avionics communication interfaces, including AFDX and RS422. The architecture combines FPGA-assisted real-time processing with power-aware task partitioning strategies to improve both timing predictability and energy efficiency. Experimental evaluation performed on the implemented hardware prototype demonstrates that the proposed architecture achieves approximately 40% reduction in power consumption compared to a conventional baseline configuration while maintaining real-time operational capability with an average processing latency of 12.7 ms. In addition, the FPGA-based implementation enables dynamic display reconfiguration with a measured switching time of approximately 235 ms. The results indicate that the proposed modular architecture provides an effective balance between power efficiency, real-time performance, scalability, and system flexibility for next-generation avionics display applications.

Electronics doi: 10.3390/electronics15122540

Authors: Zahra Ahmadimonfared Stefan Eichner

In the original publication [...]

Electronics doi: 10.3390/electronics15122539

Authors: Xianghong Chen Ziji Liu Wengang Huang Xiaozong Huang Yuan Yuan Chengshi Li

This paper proposes a cooled infrared focal plane circuit and detector imaging test system. The system features online acquisition, temperature monitoring, bias voltage programming, real-time imaging, and online testing capabilities, improving the testing and verification efficiency of infrared focal plane detector imaging systems. Temperature monitoring accuracy reaches ±0.02 K; bias voltage programming keeps bias noise below 100 nV/Hz1/2; and a 2 × 2 pixel image array is used for data transmission, enabling algorithmic computation on pixel data for convenient image processing. Furthermore, this paper proposes various image algorithms and overall algorithm structures based on a single-chip pixel-level ADC, the dynamic range is 141.8 dB, and the maximum input charge capacity is 4.46 Ge−. The overall structure of the multifunctional image algorithm for infrared focal plane detectors is summarized, including background subtraction, blind pixel compensation, non-uniformity correction, windowing, pixel merging, spatial filtering, histogram equalization, and time delay integration (TDI). Non-uniformity is improved by 98.3%, blind pixel rate is reduced by 92.3%, background subtraction performance is improved by 93%, and spatial filtering and TDI improve relative spatial noise by 63% and 89%, respectively. Pixel merging can increase the gray mean by 4.09 times, enabling arbitrary windowing and uniform histogram distribution. These highly practical research efforts will drive the further development of future larger-array cooled infrared focal plane array (IRFPA) detector imaging test technology and more intelligent infrared image algorithms.

Electronics doi: 10.3390/electronics15122538

Authors: Yang Yang Jinglong Wang Yang Li Shuliang Wang

In hybrid-series-converter-based LCC-HVDC systems, controllable capacitor modules can provide additional voltage–time area during commutation, thereby improving inverter-side fault tolerance under AC faults. However, their switching behavior makes the commutation path impedance state-dependent, while most existing commutation-failure prediction methods still rely on fixed-reactance assumptions. To address this problem, this paper proposes a reactance-corrected predictive control and coordinated switching method. First, a capacitor switching coefficient is introduced to describe the insertion state of the controllable capacitor modules, and an equivalent commutation reactance of the HSC valve arm is derived. Then, the corrected reactance is incorporated into an extinction-angle margin index and an energy-margin index to quantify the influence of reactance variation on commutation capability. A segmented firing-angle controller with smooth compensation is further designed, and energy-margin feedback is coordinated with capacitor insertion control. PSCAD/EMTDC simulations verify that the proposed method reduces prediction error, provides a prediction lead time of 0.7–4.5 ms, and improves fault ride-through capability.

Electronics doi: 10.3390/electronics15122536

Authors: Jiaoyang Xia Jin Zhang Jianbo Zheng

Hypergraph Neural Networks (HGNNs) have demonstrated exceptional capability in modeling high-order correlations; however, their vulnerability to adversarial attacks remains inadequately addressed due to the limited scope of existing security investigations. The prevailing white-box structural attack, HyperAttack, relies exclusively on gradient-derived information and overlooks the semantic affinities between nodes and hyperedges. This oversight limits attack efficacy because gradient signals can be noisy or ambiguous under certain conditions (e.g., saturated regions or local optima), whereas semantic similarities provide complementary cues that help identify hyperedges whose perturbation more reliably alters the target node’s representation. To mitigate this limitation, this paper introduces a semantic enhanced adversarial attack framework for hypergraph neural networks, termed SE-HyperAttack. Specifically, hyperedge features are first aggregated, and semantic similarity scores are computed based on the feature similarity between target nodes and their incident hyperedges to capture latent semantic correlations. These semantic similarity scores are subsequently integrated with integrated gradient scores via a weighted summation scheme, refining the precision of hyperedge selection. Extensive experiments on two datasets demonstrate that the proposed SE-HyperAttack achieves an optimal average attack success rate (ASR) of 79.4%, showing an improvement of 2.6% over HyperAttack. Ablation studies further ascertain that a semantic weight of 30% yields peak performance, beyond which degradation is observed. Notably, the proposed approach preserves computational efficiency commensurate with HyperAttack, incurring negligible additional overhead. These findings substantiate that the integration of semantic information effectively enhances adversarial attack effectiveness on hypergraph neural networks without compromising efficiency.

Electronics doi: 10.3390/electronics15122537

Authors: Nonchanutt Chudpooti Kitiphon Sukpreecha Kamol Boonlom Prayoot Akkaraekthalin

This paper presents a practical implementation of a 3D-printed spherical Luneburg lens for gain enhancement of a WR-28 open-ended waveguide antenna operating in the Ka-band. The lens is designed based on Luneburg theory and realized using a six-zone discretized gradient-index structure, providing a balance between theoretical performance and fabrication feasibility. The proposed design enables the realization of the required permittivity distribution using a single dielectric material, where the effective permittivity of each zone is controlled through infill variation in a fused deposition modeling (FDM) process. To facilitate fabrication, the lens is divided into two hemispherical parts, enabling reliable manufacturing and assembly while maintaining the intended dielectric profile. The antenna performance is experimentally evaluated through reflection coefficient (S11) measurements and radiation pattern characterization in both the XZ and YZ planes over the frequency range of 26.5–40 GHz, including co-polarized and cross-polarized responses. The proposed antenna achieves a simulated realized gain ranging from 17.6 dBi to 19.83 dBi, while the measured realized gain ranges from 16.42 dBi to 18.43 dBi, with a maximum deviation of 1.47 dB. In comparison, the standalone WR-28 open-ended waveguide exhibits a measured realized gain of 7.22–8.01 dBi. The integration of the six-zone Luneburg lens results in a realized gain enhancement of 9.20–10.97 dB across the operating band. These results confirm that the proposed approach provides a simple, low-cost, and experimentally validated solution for high-gain millimeter-wave antenna applications, while maintaining good agreement between simulation and measurement.

Electronics doi: 10.3390/electronics15122535

Authors: Yeoleum Gang Hyewon Park Yohan Park

The Internet of Drones (IoD) has become an important platform for applications such as smart agriculture, industrial monitoring, and large-scale aerial sensing. However, securing IoD communications remains challenging because drones often operate in open environments and have limited computation, storage, and energy resources. Existing authentication and key agreement protocols still face practical limitations, including high computational overhead, exposure to physical capture attacks, and reliance on centralized servers for session-key generation. In this paper, we first analyze a recent IoD authentication scheme and show that it is vulnerable to session-key disclosure, offline identity/password guessing, and mobile device/drone impersonation attacks. To address these issues, we propose a lightweight Physically Unclonable Function (PUF)-based end-to-end authentication protocol for IoD environments. The proposed scheme avoids storing long-term secret keys in drone memory and enables the mobile device and drone to establish a session key directly, without involving the Ground Station Server in key derivation. The security of the proposed protocol is evaluated through informal analysis, BAN logic, the Real-or-Random model, and AVISPA simulation. The results show that the scheme resists common attacks, including replay, impersonation, stolen verifier, physical capture, and offline password guessing attacks. Performance evaluation further indicates that the protocol maintains low computational cost while providing stronger security guarantees, making it suitable for resource-constrained IoD deployments.

Electronics doi: 10.3390/electronics15122533

Authors: Yu-Feng Tan Xiao Liu Dong-Sheng La

In this paper, a high-gain wideband filtering antenna with metasurface structures is presented for Sub-6 GHz 5G applications. The proposed antenna consists of a 3 × 3 metasurface array, a driven patch, a short-circuited stepped impedance resonator (SIR) feedline, and two parasitic patches. The metasurface is used to manipulate the modal behavior of the radiator and to introduce an additional resonant mode for bandwidth enhancement. Meanwhile, two radiation nulls are generated by different mechanisms to realize filtering performance. The low-frequency radiation null at 2.81 GHz is introduced by the short-circuited SIR feedline, whereas the high-frequency radiation null at 5.76 GHz is produced by radiation cancelation among the driven patch, parasitic patches, and metasurface. The measured results show a 10 dB impedance bandwidth of 35.5% from 3.62 to 5.18 GHz and an average realized gain of 8.61 dBi. In addition, the proposed antenna achieves lower- and upper-band selectivity of 42.57 dB/GHz and 33.43 dB/GHz, respectively. The proposed antenna also achieves a compact radiation aperture of 0.60 × 0.60 λ02 and effective out-of-band radiation suppression, making it a promising candidate for integrated 5G RF front-ends.