Electronics

13 pages, 1775 KB

Open AccessArticle

Cost-Sensitive Threshold Optimization for Network Intrusion Detection: A Per-Class Approach with XGBoost

by Jaehyeok Cha, Jisoo Jang, Dongil Shin and Dongkyoo Shin

Electronics 2026, 15(7), 1542; https://doi.org/10.3390/electronics15071542 - 7 Apr 2026

Viewed by 411

Machine learning-based Network Intrusion Detection Systems (NIDSs) typically optimize uniform metrics such as accuracy and F1-score, overlooking the asymmetric cost structure of real-world security operations, where a missed attack (False Negative (FN)) far outweighs a false alarm (False Positive (FP)). We propose a [...] Read more.

Machine learning-based Network Intrusion Detection Systems (NIDSs) typically optimize uniform metrics such as accuracy and F1-score, overlooking the asymmetric cost structure of real-world security operations, where a missed attack (False Negative (FN)) far outweighs a false alarm (False Positive (FP)). We propose a cost-sensitive threshold optimization framework based on XGBoost, using a 10:1 FN-to-FP cost ratio derived from established cost models. We first demonstrate that the default threshold of 0.5 is suboptimal and that a globally optimized threshold of 0.08 substantially reduces total cost. However, a single global threshold cannot accommodate the heterogeneous detection characteristics of diverse attack types. We therefore introduce Per-Class Thresholding, which assigns independently optimized thresholds to each attack class. Evaluated on CIC-IDS2018 and UNSW-NB15 across five independent random seeds, our method achieves a 28.19% cost reduction over the Random Forest baseline on CIC-IDS2018, demonstrating that attack classes undetectable under the global threshold—including DDoS attack-LOIC-UDP (100%), DoS attacks-SlowHTTPTest (99.79%), and FTP-BruteForce (98.16%)—can achieve near-complete cost elimination through individual per-class threshold search. Cross-dataset validation on UNSW-NB15 further confirms that per-class thresholding consistently improves class-level detection, with cost reductions of 74.10% for Reconnaissance, 69.06% for Backdoor, and 54.42% for Analysis attacks. These results confirm that class-specific threshold calibration is essential for cost-effective intrusion detection. Full article

(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)

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17 pages, 1473 KB

Open AccessArticle

Key Updatable Cross-Domain-Message Anonymous Authentication Scheme Based on Dual-Chain for VANET

by Mei Sun, Dongbing Zhang, Yuyan Guo and Xudong Zhai

Electronics 2026, 15(7), 1541; https://doi.org/10.3390/electronics15071541 - 7 Apr 2026

Viewed by 259

Abstract

Traditional VANET authentication schemes often face challenges such as centralization bottlenecks and the updating of vehicle keys or pseudonyms. This paper proposes a layered approach that divides VANET into regions, utilizing dual-blockchain to enable anonymous message authentication between vehicles and RSUs, as well [...] Read more.

Traditional VANET authentication schemes often face challenges such as centralization bottlenecks and the updating of vehicle keys or pseudonyms. This paper proposes a layered approach that divides VANET into regions, utilizing dual-blockchain to enable anonymous message authentication between vehicles and RSUs, as well as between vehicles within the VANET. Compared to traditional blockchain authentication methods, this paper introduces an approach that enhances authentication efficiency and ensures information security by establishing secure connections between private and consortium chains through a trusted authority (TA). By leveraging third-party public parameter updates, the automatic updating of private and public keys for VANET nodes is achieved without the need for certificate issuance and updates. This approach facilitates long-term anonymous authentication and communication between VANET nodes, reduces the frequency of authentication interactions, simplifies authentication processes, and lowers computational and communication costs. The proposed scheme is well-suited for practical VANET environments that require low authentication latency and robust large-scale privacy protection. Full article

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27 pages, 1073 KB

Open AccessArticle

An MMSE-Optimized Pre-Rake Receiver with a Comparative Analysis of Channel Estimation Methods for Multipath Channels

by Aoba Morimoto, Jaesang Cha, Incheol Jeong and Chang-Jun Ahn

Electronics 2026, 15(7), 1540; https://doi.org/10.3390/electronics15071540 - 7 Apr 2026

Viewed by 219

Abstract

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. [...] Read more.

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

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

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19 pages, 4124 KB

Open AccessArticle

Prediction of Maximum Usable Frequency Based on a New Hybrid Deep Learning Model

by Yuyang Li, Zhigang Zhang and Jian Shen

Electronics 2026, 15(7), 1539; https://doi.org/10.3390/electronics15071539 - 7 Apr 2026

Viewed by 309

Abstract

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling [...] Read more.

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

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24 pages, 988 KB

Open AccessArticle

An Improved Tracklet Generation Approach for Radar Maneuvering Target Tracking

by Songyao Dou, Ying Chen and Yaobing Lu

Electronics 2026, 15(7), 1538; https://doi.org/10.3390/electronics15071538 - 7 Apr 2026

Viewed by 425

Abstract

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model [...] Read more.

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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13 pages, 2283 KB

Open AccessArticle

Study on RF Parameter Extraction Method for Novel Heterogeneous Integrated GaN Schottky Rectifiers Based on Hierarchical Reinforcement Learning

by Yi Wei, Li Huang, Ce Wang, Xiong Yin and Ce Wang

Electronics 2026, 15(7), 1537; https://doi.org/10.3390/electronics15071537 - 7 Apr 2026

Viewed by 357

Abstract

This study presents a heterogeneous integration micro-assembly process and circuit board packaging solution for GaN Schottky Barrier Diode (SBD) rectifiers, and innovatively constructs a hierarchical reinforcement learning strategy for optimizing SBD RF parameters. By establishing an optimization framework with the goal of efficiency [...] Read more.

This study presents a heterogeneous integration micro-assembly process and circuit board packaging solution for GaN Schottky Barrier Diode (SBD) rectifiers, and innovatively constructs a hierarchical reinforcement learning strategy for optimizing SBD RF parameters. By establishing an optimization framework with the goal of efficiency in the load-input power two-dimensional space, a dual-layer optimization mechanism is employed: the high-level strategy dynamically selects optimization regions and parameter combinations, while the low-level strategy implements specific parameter adjustments. This approach effectively addresses the challenges of device parameter modeling and circuit design. Experimental data shows that the efficiency error for the SBD1 rectifier remains stable within 2%, and the average error for SBD2 is reduced to 1.5%. This method enables efficient and accurate optimization of RF parameters, providing a reliable technical pathway for the engineering application of Wireless Power Transmission systems. Full article

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25 pages, 6093 KB

Open AccessArticle

Reliability-Aware Heterogeneous Graph Attention Networks with Temporal Post-Processing for Electronic Power System State Estimation

by Qing Wang, Jian Yang, Pingxin Wang, Yaru Sheng and Hongxia Zhu

Electronics 2026, 15(7), 1536; https://doi.org/10.3390/electronics15071536 - 7 Apr 2026

Viewed by 378

Abstract

Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity [...] Read more.

Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity of large-scale grids. To address these issues, this paper proposes ST-ResGAT, a spatio-temporal residual graph attention framework for nonlinear state estimation under heterogeneous sensing conditions. The proposed method models the problem on an augmented heterogeneous factor graph, employs a reliability-aware heterogeneous graph attention mechanism with residual propagation to adaptively fuse measurements of different quality, and further refines the graph-based estimates through a lightweight LSTM post-processing module that exploits short-term temporal continuity. All datasets are generated using pandapower on the IEEE 30-bus, IEEE 118-bus, and IEEE 1354-bus benchmark systems to ensure full reproducibility of the experimental pipeline. Experimental results show that the proposed method consistently achieves lower estimation errors than WLS, DNN, GAT, and PINN baselines across all three systems, while also exhibiting more compact node-level error distributions and stronger spatial consistency. Multi-seed ablation studies further indicate that residual propagation, reliability-aware attention, and temporal refinement play complementary roles across different system scales. Robustness experiments additionally show that, under random measurement exclusion as well as bias, Gaussian, and mixed corrupted-measurement settings, ST-ResGAT exhibits smooth and progressive degradation, including on the newly added large-scale IEEE 1354-bus benchmark. These results suggest that the proposed framework is a promising direction for data-driven state estimation under controlled mixed-measurement benchmark conditions. Full article

(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)

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25 pages, 11063 KB

Open AccessArticle

Tac-Mamba: A Pose-Guided Cross-Modal State Space Model with Trust-Aware Gating for mmWave Radar Human Activity Recognition

by Haiyi Wu, Kai Zhao, Wei Yao and Yong Xiong

Electronics 2026, 15(7), 1535; https://doi.org/10.3390/electronics15071535 - 7 Apr 2026

Viewed by 562

Abstract

Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high [...] Read more.

Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high computational costs, unsuitable for edge devices. To address these challenges, we propose Tac-Mamba, a lightweight cross-modal state space model. First, we introduce a topology-guided distillation scheme that uses a Spatial Mamba teacher to extract structural priors from visual skeletons. These priors are then explicitly distilled into a Point Transformer v3 (PTv3) radar student with a modality dropout strategy. We also developed a Trust-Aware Cross-Modal Attention (TACMA) module to prevent negative transfer. It evaluates the reliability of visual features through a SiLU-activated cross-modal bilinear interaction, smoothly degrading to a pure radar-driven fallback projection when visual inputs are corrupted. Finally, a Lightweight Temporal Mamba Block (LTMB) with a Zero-Parameter Cross-Gating (ZPCG) mechanism captures long-range kinematic dependencies with linear complexity. Experiments on the public MM-Fi dataset under strict cross-environment protocols demonstrate that Tac-Mamba achieves competitive accuracies of 95.37% (multimodal) and 87.54% (radar-only) with only 0.86M parameters and 1.89 ms inference latency. These results highlight the model’s exceptional robustness to modality missingness and its feasibility for edge deployment. Full article

(This article belongs to the Section Artificial Intelligence)

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Graphical abstract

18 pages, 2592 KB

Open AccessArticle

Image Aesthetics Assessment Based on GNN-Guided Deformable Attention for Electronic Photography

by Lin Li, Jichun Zhu, Mingxing Jiang and Jingli Fang

Electronics 2026, 15(7), 1534; https://doi.org/10.3390/electronics15071534 - 7 Apr 2026

Viewed by 463

Abstract

With the increasing demand for high-quality imaging in consumer electronics, image aesthetics assessment (IAA) has been widely applied to electronic cameras and display devices. Although the deformable attention mechanism has been introduced into IAA due to its perceptual capabilities, enabling models to refine [...] Read more.

With the increasing demand for high-quality imaging in consumer electronics, image aesthetics assessment (IAA) has been widely applied to electronic cameras and display devices. Although the deformable attention mechanism has been introduced into IAA due to its perceptual capabilities, enabling models to refine attention regions by learning interest points and their corresponding offsets, existing methods often lack guidance from aesthetic composition features during the offset generation process, which limits their performance in aesthetic evaluation tasks. To address this issue, we propose a graph neural network (GNN)-guided deformable attention module that incorporates composition information into the generation of interest points by modeling image features as graphs and applying the GNN to guide interest point selection. In addition, we design an improved transformer model that employs neighborhood attention to further enhance IAA performance. We evaluate the proposed model on two aesthetic datasets, AVA and TAD66K, and the experimental results demonstrate its effectiveness in improving overall model performance. Full article

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

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26 pages, 2454 KB

Open AccessArticle

Evolving LLMs from Next-Token Prediction to Multi-Token Prediction via Self-Distillation

by Yang Xu and Wanxiang Che

Electronics 2026, 15(7), 1533; https://doi.org/10.3390/electronics15071533 - 6 Apr 2026

Viewed by 1155

Abstract

Mainstream Large Language Models (LLMs) work under the paradigm of Next-Token Prediction (NTP). Multi-Token Prediction (MTP) is motivated by higher decoding efficiency, extending NTP to enable LLMs to draft multiple tokens during each forward pass. However, existing MTP approaches pretrain MTP along with [...] Read more.

Mainstream Large Language Models (LLMs) work under the paradigm of Next-Token Prediction (NTP). Multi-Token Prediction (MTP) is motivated by higher decoding efficiency, extending NTP to enable LLMs to draft multiple tokens during each forward pass. However, existing MTP approaches pretrain MTP along with the target LLM, making it difficult to unlock MTP for LLMs without official support. In this work, we propose a post-hoc approach to training an MTP module for a target LLM, providing an efficient way to evolve the LLM from NTP to MTP. The proposed approach features two main characteristics. (1) No changes to the target LLM, since it is frozen during MTP training. (2) Efficient MTP training via self-distillation from the target LLM’s native NTP capability. Results show that our approach can post-hoc train a performant MTP module via lightweight pretraining. Full article

(This article belongs to the Special Issue Innovative Applications of Large Language Models in Natural Language Processing (NLP))

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26 pages, 2108 KB

Open AccessArticle

Dynamic Relay Assignment Scheme for Efficient V2V Content Precaching in Content-Centric Internet of Vehicles

by Jongpil Youn, Youngju Nam and Euisin Lee

Electronics 2026, 15(7), 1532; https://doi.org/10.3390/electronics15071532 - 6 Apr 2026

Viewed by 321

Abstract

The rapidly growing data demands of autonomous driving and onboard multimedia services pose significant challenges to traditional roadside unit (RSU) based content delivery, particularly under limited cache capacity and coverage gaps. In the content-centric Internet of Vehicles (CIoV), vehicle-to-vehicle (V2V) precaching has emerged [...] Read more.

The rapidly growing data demands of autonomous driving and onboard multimedia services pose significant challenges to traditional roadside unit (RSU) based content delivery, particularly under limited cache capacity and coverage gaps. In the content-centric Internet of Vehicles (CIoV), vehicle-to-vehicle (V2V) precaching has emerged as an effective solution to mitigate these limitations. However, existing schemes rely on static precaching roles, leading to inefficiencies when a precaching vehicle initiates its own content request. To address this issue, we propose a dynamic relay assignment scheme (DRAS) that enables seamless role transitions without discarding cached data. Upon detecting such a role-transition event, the RSU assigns two new precaching vehicles to independently serve the original requester vehicle and the newly transitioned requester vehicle, ensuring continuous service. Furthermore, we extend this to an energy-efficient DRAS (EE-DRAS) that incorporates vehicle-to-infrastructure (V2I) and V2V transmission energy costs into the selection process, achieving a balanced trade-off between energy consumption and delivery efficiency. Extensive NS-3 simulations show that DRAS reduces average delay by up to 53% and improves throughput by 8% over existing baselines. EE-DRAS further reduces energy consumption by up to 66% while maintaining service fairness. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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21 pages, 1719 KB

Open AccessArticle

DA-UNet: A Direction-Aware U-Net for Leaf Vein Segmentation in Tissue-Cultured Plantlets

by Qiuze Wu, Qing Yang, Dong Meng and Xiaofei Yan

Electronics 2026, 15(7), 1531; https://doi.org/10.3390/electronics15071531 - 6 Apr 2026

Viewed by 473

Abstract

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we [...] Read more.

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we propose Direction-Aware U-Net (DA-UNet), an improved U-Net architecture that incorporates a Direction-Aware Context Pooling (DACPool) module and Topology-aware Segmentation loss (TopoSeg loss). The DACPool module explicitly exploits vein orientation to aggregate directional contextual information, while the TopoSeg loss jointly optimizes pixel-level accuracy and topological continuity. DA-UNet achieves efficient leaf vein segmentation with improved continuity and structural integrity, according to evaluations on the self-constructed Tissue-Cultured Plantlet Vein Dataset 2025 (TCPVD2025). Comparative experiment results show that the improved model outperforms PSPNet, DeepLabV3+, U-Net, TransUNet, Swin-UNet, CCNet, and SegNeXt, as evidenced by Recall, Dice, and CONNECT scores of 71.35%, 69.08%, and −2.25, while maintaining competitive Precision of 66.98%. Ablation experiment results provide further evidence for the efficacy of the TopoSeg loss and the DACPool module. The results demonstrate the effectiveness of the proposed vein segmentation framework for generating outputs that are both accurate and structurally consistent, thus enabling reliable automated processes for plant genetic transformation. Full article

(This article belongs to the Special Issue Advances in Image Recognition, Image Segmentation, Image Fusion, and Singal Processing)

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29 pages, 11454 KB

Open AccessArticle

CASGNet: A Lightweight Content-Aware Spatial Gating Network for Cross-Regional Wheat Lodging Mapping from UAV Imagery

by Yueying Zhang, Zhuangzhi Nie, Chaowei Hu, Shouguan Xiao, Yuxi Wang, Shuqing Yang and Fanggang Wang

Electronics 2026, 15(7), 1530; https://doi.org/10.3390/electronics15071530 - 6 Apr 2026

Viewed by 450

Abstract

We investigate wheat lodging segmentation from UAV RGB imagery acquired over real production fields rather than controlled experimental sites. Besides pixel-level accuracy, our evaluation also emphasizes robustness under heterogeneous farmland conditions and deployment-oriented efficiency. We propose CASGNet, an edge-oriented segmentation network with a [...] Read more.

We investigate wheat lodging segmentation from UAV RGB imagery acquired over real production fields rather than controlled experimental sites. Besides pixel-level accuracy, our evaluation also emphasizes robustness under heterogeneous farmland conditions and deployment-oriented efficiency. We propose CASGNet, an edge-oriented segmentation network with a content-aware spatial gating mechanism that reweights intermediate features according to local structural variation. Instead of uniformly aggregating features, the module suppresses responses in homogeneous regions while preserving activation in structurally complex areas. In practice, this improves the continuity of irregular lodging shapes and reduces spurious responses in relatively homogeneous backgrounds. The dataset spans 46 farms across Jiaozuo, Jiyuan, and Luoyang, covering progressively fragmented farmland. Under a stricter mission-level data-isolation protocol, CASGNet achieves 94.4% mIoU and 90.38% IoU for the lodging class on the combined dataset. Under sequential regional adaptation, performance remains relatively stable in continuous parcels, and degradation is less severe than most compact baselines in highly fragmented landscapes. On Jetson Nano, CASGNet achieves 1.94 FPS embedded inference under the 5 W mode. Smaller networks achieve higher speed but show reduced structural continuity in complex scenes. The results indicate that CASGNet provides a favorable balance between structural fidelity and computational cost, while its robustness remains constrained by scene complexity. Full article

(This article belongs to the Collection Electronics for Agriculture)

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19 pages, 4189 KB

Open AccessArticle

A Precision Operational Amplifier with eTrim-Based Offset Calibration and Two-Point Temperature Drift Trim

by Yongji Wu and Weiqi Liu

Electronics 2026, 15(7), 1529; https://doi.org/10.3390/electronics15071529 - 6 Apr 2026

Viewed by 558

Abstract

This work introduces a trimming technique based on eTrim technology to minimize both the input-referred offset voltage and its temperature drift in the operational amplifiers. The proposed low-voltage op-amp utilizes the body effect to maintain a constant bandwidth across the rail-to-rail input common-mode [...] Read more.

This work introduces a trimming technique based on eTrim technology to minimize both the input-referred offset voltage and its temperature drift in the operational amplifiers. The proposed low-voltage op-amp utilizes the body effect to maintain a constant bandwidth across the rail-to-rail input common-mode range under low supply voltages. During input common-mode transitions, the current in the folded cascode stage remains stable, ensuring a robust output stage. Furthermore, a specialized gain-boosting structure enhances the low-frequency gain while preventing occasional latch-up during low-voltage power-up. A pin-multiplexing scheme is employed for trimming data input, thereby eliminating the need for dedicated trimming pins and mitigating post-package parameter variations. At room temperature, a constant-current injection mechanism reduces the DC offset to microvolt levels. At high temperature, temperature-compensated current injection cancels the first-order drift component. Implemented in a low-voltage operational amplifier, post-layout simulation results demonstrate that with a 100-pF capacitive load, the amplifier achieves a gain–bandwidth product exceeding 10 MHz, a low-frequency gain greater than 140 dB, and an input-referred noise of 2.54 µVp-p for the P-channel input and 3.95 µVp-p for the N-channel input. The trimming process reduces the residual offset to the microvolt range and effectively suppresses offset drift, ensuring accurate offset compensation across the specified temperature range. Full article

(This article belongs to the Section Microelectronics)

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29 pages, 547 KB

Open AccessArticle

MRHL: Multi-Relational Hypergraph Learning for Next POI Recommendation

by Sai Zhao, Caisen Chen and Shuai He

Electronics 2026, 15(7), 1528; https://doi.org/10.3390/electronics15071528 - 6 Apr 2026

Viewed by 338

Abstract

With the rapid advancement of location-based services, next Point-of-Interest (POI) recommendation has emerged as a critical task in personalized mobility modeling and recommendation systems. It aims to predict users’ future locations based on their historical trajectories, thereby enhancing the personalization and intelligence of [...] Read more.

With the rapid advancement of location-based services, next Point-of-Interest (POI) recommendation has emerged as a critical task in personalized mobility modeling and recommendation systems. It aims to predict users’ future locations based on their historical trajectories, thereby enhancing the personalization and intelligence of recommendation systems. Despite the promising progress, two key challenges remain insufficiently addressed. First, many existing methods overlook the dynamic evolution of user trajectories across multiple perspectives, resulting in entangled representations that fail to capture user intent accurately. Second, they often ignore the latent synergy across diverse perspectives, which limits the effective utilization of complementary information for recommendation. To address these issues, we propose a novel framework called MRHL. MRHL constructs multiple hypergraphs to represent distinct views of user behavior, including interaction frequency, time decay, and geographical proximity. An enhanced hypergraph convolutional network is employed to effectively model the high-order relationships within them. We propose a cascaded enhancement fusion mechanism that progressively integrates multi-view hypergraph representations to enrich the semantic information of user representations. In addition, a multi-relational contrastive learning strategy is developed to capture the consistent signals across different views, thereby enhancing the robustness and discriminative capability of user and POI representations. Extensive experiments on three public datasets consistently demonstrate that MRHL outperforms a range of strong baselines. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Graph Neural Networks)

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23 pages, 8681 KB

Open AccessArticle

Deadbeat Predictive Current Control for CMG Ultra-Low Speed PMSM Emulator Based on Cascaded Extended State Observer

by Jianpei Zhao, Ruihua Li, Hanqing Wang, Jie Jiang and Bo Hu

Electronics 2026, 15(7), 1527; https://doi.org/10.3390/electronics15071527 - 6 Apr 2026

Viewed by 319

Abstract

The gimbal servo system in a control moment gyroscope (CMG) is critical for high-precision spacecraft attitude control, where comprehensive performance testing and evaluation are essential for ensuring spacecraft reliability and service life. Traditional motor testbenches exhibit limitations, whereas the electric motor emulator (EME) [...] Read more.

The gimbal servo system in a control moment gyroscope (CMG) is critical for high-precision spacecraft attitude control, where comprehensive performance testing and evaluation are essential for ensuring spacecraft reliability and service life. Traditional motor testbenches exhibit limitations, whereas the electric motor emulator (EME) based on power electronic converters is a promising alternative for testing extreme operating conditions, such as ultra-low speed operation and fault scenarios. However, existing EME control methods suffer from limited system bandwidth and insufficient emulation accuracy, which limits their applicability. To address these issues, this paper proposes an improved current control strategy for the ultra-low speed permanent magnet synchronous motor (PMSM) emulator. First, a mathematical model of the EME based on the topology of the voltage source converter is established. Then, based on the deadbeat control concept, a deadbeat predictive current control (DPCC) strategy is developed to enhance the dynamic performance. Furthermore, to suppress the parameter mismatch disturbance, an optimization scheme based on a cascaded extended state observer (CESO) is introduced. The first-stage ESO is applied to estimate and compensate for total disturbances, while the second-stage ESO is a supplement to suppress the remaining disturbances in the EME system, which improves the robustness of the DPCC controller. Finally, the effectiveness of the improved emulation accuracy of the proposed method is verified through experiments. Full article

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16 pages, 2876 KB

Open AccessArticle

Design and Implementation of a High-Resolution Real-Time Ultrasonic Endoscopy Imaging System Based on FPGA and Coded Excitation

by Haihang Gu, Fujia Sun, Shuhao Hou and Shuangyuan Wang

Electronics 2026, 15(7), 1526; https://doi.org/10.3390/electronics15071526 - 6 Apr 2026

Viewed by 566

Abstract

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based [...] Read more.

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor. Full article

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25 pages, 7467 KB

Open AccessArticle

Double Cost-Volume Stereo Matching with Entropy-Difference-Guided Fusion

by Huanchun Yang, Hongshe Dang, Xuande Zhang and Quanping Chen

Electronics 2026, 15(7), 1525; https://doi.org/10.3390/electronics15071525 - 6 Apr 2026

Viewed by 457

Abstract

To address the reduced accuracy of stereo matching networks near object boundaries and disparity discontinuities, a double cost–volume stereo matching network with entropy-difference-guided fusion is proposed. The proposed network was built based on RAFT-Stereo. It employs a pretrained backbone to extract multi-scale features [...] Read more.

To address the reduced accuracy of stereo matching networks near object boundaries and disparity discontinuities, a double cost–volume stereo matching network with entropy-difference-guided fusion is proposed. The proposed network was built based on RAFT-Stereo. It employs a pretrained backbone to extract multi-scale features and uses deformable attention for cross-scale feature fusion. A shallow image-guided branch was used to generate pixel-wise constraint information to limit the magnitude of sampling offsets and alleviate cross-structure sampling. Based on the extracted features, a group-wise correlation cost–volume and a normalized correlation cost–volume were constructed. Both cost–volumes were regularized by 3D Hourglass networks, and a structure-consistent intra-scale aggregation module was introduced during the regularization of the group-wise correlation cost–volume. The two aggregated results were then fused by the entropy-difference-guided fusion module to obtain the final cost–volume. The experimental results show the effectiveness of the proposed network in the Scene Flow, KITTI, and ETH3D datasets, achieving an endpoint error of 0.45 px and a >3 px error rate of 2.41% on the Scene Flow dataset. Full article

(This article belongs to the Section Artificial Intelligence)

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23 pages, 1708 KB

Open AccessArticle

Content-Based File Classification and Organization System Using LLMs

by Wonbin Son and Hyungjoon Kim

Electronics 2026, 15(7), 1524; https://doi.org/10.3390/electronics15071524 - 6 Apr 2026

Viewed by 620

Abstract

Conventional file management systems primarily rely on structured metadata such as filenames, file extensions, and creation dates to manage and organize files. However, such metadata alone fails to capture the actual content or semantic meaning of a file, often leading to results misaligned [...] Read more.

Conventional file management systems primarily rely on structured metadata such as filenames, file extensions, and creation dates to manage and organize files. However, such metadata alone fails to capture the actual content or semantic meaning of a file, often leading to results misaligned with user intent. To overcome these limitations, we developed the Content-based File Classification and Organization System (CFCOS), which integrates a Large Language Model (LLM) to perform content-aware file analysis. The LLM generates semantic summaries of file contents and classifies files into meaningful categories based on composition-derived criteria, enabling organization strategies that go beyond rigid, rule-based methods. Through a range of evaluations, we analyze how CFCOS addresses key limitations of conventional file management systems and characterize the properties of LLM-based approaches to content-aware file organization. Furthermore, these results suggest that our approach can be generalized beyond file systems, enabling the semantic and personalized transformation of existing services through prompt engineering. Full article

(This article belongs to the Section Artificial Intelligence)

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13 pages, 459 KB

Open AccessArticle

An Adaptive Binary Particle Swarm Optimization with Hybrid Learning for Feature Selection

by Lan Ma, Pei Hu and Jeng-Shyang Pan

Electronics 2026, 15(7), 1523; https://doi.org/10.3390/electronics15071523 - 5 Apr 2026

Viewed by 384

Abstract

Particle swarm optimization (PSO) improves classification performance and reduces computational complexity in feature selection. However, it frequently experiences from premature convergence and insufficient exploration. To address these constraints, this paper suggests an adaptive binary PSO (ABPSO) algorithm specifically designed for feature selection. First, [...] Read more.

Particle swarm optimization (PSO) improves classification performance and reduces computational complexity in feature selection. However, it frequently experiences from premature convergence and insufficient exploration. To address these constraints, this paper suggests an adaptive binary PSO (ABPSO) algorithm specifically designed for feature selection. First, an adaptive transfer function and two adaptive learning coefficients are introduced to achieve a better balance between exploration and exploitation during the search process. Second, a hybrid learning mechanism that integrates personal best, global best, and elite solutions is utilized to enhance population diversity. Finally, a simulated annealing (SA)–based local search strategy is employed to further refine candidate solutions and improve convergence behavior. Experimental results demonstrate that ABPSO outperforms binary PSO (BPSO), harris hawks optimization (HHO), whale optimization algorithm (WOA), and ant colony optimization (ACO) in classification accuracy. In particular, ABPSO achieves the lowest classification error rates on the Dermatology (0.0106), Ionosphere (0.0705), Lung (0.1521), Sonar (0.0996), Spambase (0.0758), Statlog (0.1446), and Wine (0.0280) datasets. Full article

(This article belongs to the Special Issue Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges)

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16 pages, 479 KB

Open AccessArticle

NOMA-Based Interference-Limited Power Allocation for Next-Generation Cellular Networks

by Aysha Ebrahim

Electronics 2026, 15(7), 1522; https://doi.org/10.3390/electronics15071522 - 5 Apr 2026

Viewed by 432

Abstract

Non-orthogonal multiple access (NOMA) has become one of the main enabling technologies for next-generation cellular networks. The ability to allocate multiple users on the same frequency resources simultaneously leads to improved spectral efficiency. This paper examines power allocation and user pairing for NOMA [...] Read more.

Non-orthogonal multiple access (NOMA) has become one of the main enabling technologies for next-generation cellular networks. The ability to allocate multiple users on the same frequency resources simultaneously leads to improved spectral efficiency. This paper examines power allocation and user pairing for NOMA networks with an objective to enhance the sum spectral efficiency (sum capacity, bps/Hz) while guaranteeing the target rate of the far user. Two benchmark methods were used to evaluate the performance of the proposed scheme: (1) fixed power allocation, in which fixed power coefficients are allocated to the near and far users, and (2) random power allocation, where random coefficients are assigned to the users. However, these static methods fail to adapt to instantaneous channel conditions and may lead to reduced performance for the weak user and inefficient power utilization. To manage these limitations, a novel interference-limited power allocation (IL-PA) scheme is proposed. In the IL-PA, the power allocation coefficients are dynamically allocated to users according to an interference threshold. The proposed scheme guarantees that the interference induced by the near user does not exceed a predefined interference threshold; thus, the target rate of the far user is achieved. The proposed interference threshold is derived theoretically to enhance the overall system capacity and optimize the signal-to-interference-plus-noise ratio (SINR). Additionally, a user pairing scheme, which separates users into two groups according to their channel gains, is proposed to reduce complexity while preserving good performance. The simulation results show that the proposed power allocation and user pairing scheme outperforms the benchmark methods in terms of overall capacity. Full article

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15 pages, 7541 KB

Open AccessArticle

Two Compact T-Coil-Based Topologies for Wideband Four-Way Power Division in Ka-Band

by Qianran Zhang, Weiqing Wang, Fangkai Wang, Xudong Wang and Pufeng Chen

Electronics 2026, 15(7), 1521; https://doi.org/10.3390/electronics15071521 - 4 Apr 2026

Viewed by 347

Abstract

This paper presents two broadband four-way power dividers based on a novel T-coil topology, operating in the 22–32 GHz band (covering the K/Ka bands). Type I adopts a cascaded power division structure, while Type II employs a direct-feed integrated architecture. The innovation lies [...] Read more.

This paper presents two broadband four-way power dividers based on a novel T-coil topology, operating in the 22–32 GHz band (covering the K/Ka bands). Type I adopts a cascaded power division structure, while Type II employs a direct-feed integrated architecture. The innovation lies in the introduction of isolating capacitors at the input and output ports, which significantly shortens the critical transmission line lengths in both topologies. This effectively reduces the equivalent inductance and raises the self-resonant frequency, achieving wideband response while maintaining structural simplicity, compact size, and ease of integration. Both circuits were fabricated using a standard 45 nm CMOS process. The measured core chip areas (excluding pads) are only 0.125 mm² for Type I and 0.066 mm² for Type II, demonstrating excellent integration density. Through even-mode and odd-mode theoretical analysis and full-wave electromagnetic simulation verification, both power dividers exhibit good impedance matching and port isolation across the target frequency band. Measurement results further confirm their performance: across the entire 22–32 GHz band, both power dividers achieve a return loss better than 11 dB and isolation exceeding 15 dB; the insertion loss is 1.1–1.4 dB for Type I and 0.8–1.3 dB for Type II; the amplitude imbalance is below ±0.3 dB and ±0.1 dB, respectively; and the phase imbalance is less than ±5° and ±3°, respectively. All measured data show good agreement with simulation results. In summary, Type I offers advantages in layout flexibility and isolation performance, while Type II excels in insertion loss and chip size. Both provide practical circuit solutions for broadband, high-performance, and compact power division systems. Full article

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19 pages, 5823 KB

Open AccessArticle

A Human-Centric AI-Enabled Ecosystem for SME Cybersecurity: Cross-Sectoral Practices and Adaptation Framework for Maritime Defence

by Kitty Kioskli, Eleni Seralidou, Wissam Mallouli, Dimitrios Koutras, Pedro Tomás and Dimitrios Kallergis

Electronics 2026, 15(7), 1520; https://doi.org/10.3390/electronics15071520 - 4 Apr 2026

Viewed by 552

Abstract

Artificial intelligence (AI) is increasingly integrated into cybersecurity tools to improve threat detection, anomaly identification, and incident response. However, organisations, particularly small- and medium-sized enterprises (SMEs), often struggle to discover, evaluate, and effectively use AI-enabled cybersecurity solutions due to skills gaps, usability challenges, [...] Read more.

Artificial intelligence (AI) is increasingly integrated into cybersecurity tools to improve threat detection, anomaly identification, and incident response. However, organisations, particularly small- and medium-sized enterprises (SMEs), often struggle to discover, evaluate, and effectively use AI-enabled cybersecurity solutions due to skills gaps, usability challenges, and fragmented tool ecosystems. This paper presents the advaNced cybErsecurity awaReness ecOsystem for SMEs (NERO), a human-centric cybersecurity ecosystem that combines a cybersecurity marketplace with a competency-based training and awareness platform to support the practical adoption of advanced cybersecurity technologies. The NERO Marketplace enables structured discovery, comparison, and assessment of cybersecurity tools based on usability, operational relevance, and competency alignment. Complementing this, the NERO Training Platform delivers modular, multi-modal training aligned with the European Cybersecurity Skills Framework (ECSF) to develop the human competencies required to operate advanced cybersecurity systems. This study contributes a socio-technical framework that addresses the gap between AI tool availability and organisational readiness through ECSF role-based competency mapping and iterative design-based evaluation. The platform targets technical roles like Cybersecurity Implementer to ensure training is aligned with the operational requirements of critical infrastructure protection. Results from cross-sector SME training activities show measurable improvements in cybersecurity awareness, knowledge, and user satisfaction, with knowledge gains exceeding 30% in some modules. Finally, the paper provides a structural mapping of these cross-sectoral results to the maritime defence domain, specifically addressing legacy OT systems and intermittent connectivity constraints. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity: Practices, Challenges, and Innovations)

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52 pages, 14386 KB

Open AccessReview

Trustworthy Intelligence: Split Learning–Embedded Large Language Models for Smart IoT Healthcare Systems

by Mahbuba Ferdowsi, Nour Moustafa, Marwa Keshk and Benjamin Turnbull

Electronics 2026, 15(7), 1519; https://doi.org/10.3390/electronics15071519 - 4 Apr 2026

Viewed by 495

Abstract

The Internet of Things (IoT) plays an increasingly central role in healthcare by enabling continuous patient monitoring, remote diagnosis, and data-driven clinical decision-making through interconnected medical devices and sensing infrastructures. Despite these advances, IoT healthcare systems remain constrained by persistent challenges related to [...] Read more.

The Internet of Things (IoT) plays an increasingly central role in healthcare by enabling continuous patient monitoring, remote diagnosis, and data-driven clinical decision-making through interconnected medical devices and sensing infrastructures. Despite these advances, IoT healthcare systems remain constrained by persistent challenges related to data privacy, computational efficiency, scalability, and regulatory compliance. Federated learning (FL) reduces reliance on centralised data aggregation but remains vulnerable to inference-based privacy risks, while edge-oriented approaches are limited by device heterogeneity and restricted computational and energy resources; the deployment of large language models (LLMs) further exacerbates concerns surrounding privacy exposure, communication overhead, and practical feasibility. This study introduces Trustworthy Intelligence (TI) as a guiding framework for privacy-preserving distributed intelligence in IoT healthcare, explicitly integrating predictive performance, privacy protection, and deployment-oriented system design. Within this framework, split learning (SL) is examined as a core architectural mechanism and extended to support split-aware LLM integration across heterogeneous devices, supported by a structured taxonomy spanning architectural configurations, system adaptation strategies, and evaluation considerations. The study establishes a systematic mapping between SL design choices and representative healthcare scenarios, including wearable monitoring, multi-modal data fusion, clinical text analytics, and cross-institutional collaboration, and analyses key technical challenges such as activation-level privacy leakage, early-round vulnerability, reconstruction risks, and communication–computation trade-offs. An energy- and resource-aware adaptive cut layer selection strategy is outlined to support efficient deployment across devices with varying capabilities. A proof-of-concept experimental evaluation compares the proposed SL–LLM framework with centralised learning (CL), federated learning (FL), and conventional SL in terms of training latency, communication overhead, model accuracy, and privacy exposure under realistic IoT constraints, providing system-level evidence for the applicability of the TI framework in distributed healthcare environments and outlining directions for clinically viable and regulation-aligned IoT healthcare intelligence. Full article

(This article belongs to the Special Issue Engineering Multimodal Medical Digital Twins: Sensor Fusion, Multimodal Learning, and Edge–Cloud AI for Real-Time Personalized Care)

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23 pages, 3544 KB

Open AccessArticle

Multi-Cell Extended Equalization Circuit and Dual Closed-Loop Control Method Based on the Boost–LC Architecture

by Yu Zhang, Yi Xu, Jun Wang and Haiqiang Hong

Electronics 2026, 15(7), 1518; https://doi.org/10.3390/electronics15071518 - 4 Apr 2026

Viewed by 362

Abstract

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism [...] Read more.

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

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27 pages, 8355 KB

Open AccessArticle

Calibration of Roughness of Standard Samples Using Point Cloud Based on Line Chromatic Confocal Method

by Haotian Guo, Ting Chen, Xinke Xu, Yuexin Qiu, Jian Wu, Lei Wang, Huaichu Ye, Xuwen Chen and Ning Chen

Electronics 2026, 15(7), 1517; https://doi.org/10.3390/electronics15071517 - 4 Apr 2026

Viewed by 448

Abstract

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back [...] Read more.

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with sample block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration Full article

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33 pages, 6049 KB

Open AccessArticle

Blockchain-Based Mixed-Node Auction Mechanism

by Xu Liu and Junwu Zhu

Electronics 2026, 15(7), 1516; https://doi.org/10.3390/electronics15071516 - 4 Apr 2026

Viewed by 365

Abstract

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data [...] Read more.

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency. Full article

(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)

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30 pages, 6766 KB

Open AccessArticle

Broadband Two-Port Rectangular Patch Radiating Element Based on Self-Complementary Structure

by Yordanis Alonso-Roque, Francisco Marante, Pablo Otero and Alfonso Ariza

Electronics 2026, 15(7), 1515; https://doi.org/10.3390/electronics15071515 - 3 Apr 2026

Viewed by 463

Abstract

In this article, a new approach to the applicability of the self-complementarity concept in a classical two-port microstrip patch antenna element is presented. This was accomplished through an illustrative design and an electromagnetic analysis of a broadband two-port rectangular printed radiating element in [...] Read more.

In this article, a new approach to the applicability of the self-complementarity concept in a classical two-port microstrip patch antenna element is presented. This was accomplished through an illustrative design and an electromagnetic analysis of a broadband two-port rectangular printed radiating element in transmission configuration. A calculated ultra-wide matching bandwidth up to approximately 11 GHz was achieved (BW_{sim-RL≥10 dB} ≈ 11 GHz, f_o = 5.5 GHz, i.e., BW_{sim-relative-matching} ≈ 200%). One of the advantages of this topology is that only two degrees of freedom are needed to acquire a very wide impe-dance bandwidth: the length and the width of the slot. Full-wave analysis shows that sui-table combinations of the patch and slot dimensions allow to obtain the broadband mat-ching behavior. It has broadside radiation toward both hemispheres, which is conserved and considerably stable over a wide frequency range. Its linear polarization, radiation patterns, gain values, and radiation efficiency are adequate from 1 to 8 GHz (BW_{sim-radiation} ≈ 7 GHz, f_{o [sim-rad]} = 4.5 GHz, i.e., 63.6% of its BW_sim-matching, and 156% of its f_{o [sim-rad]}). Moreover, the gain and radiation efficiency exhibit very good flatness across wide frequency ranges. Measurements of S-parameters and radiation patterns validate the calculated results. The proposed antenna element is simple, compact, and light-weight. It has a very wide ope-ration bandwidth (7 GHz), its design is easy and flexible, and it is simple to manufacture. It could be used as a radiating element in different linear polarized antenna arrays. Full article

(This article belongs to the Special Issue Antenna Design and Performance Enhancement Techniques and Applications in Wireless Systems)

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25 pages, 887 KB

Open AccessArticle

Transformation of Real-World Contracts to Smart Contracts for Blockchain Applications

by Cecilia E. Chen, Xuanyu Liu, Limin Jia, Bo Liang, Yan Zhu and Tong Wu

Electronics 2026, 15(7), 1514; https://doi.org/10.3390/electronics15071514 - 3 Apr 2026

Viewed by 493

Abstract

The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text [...] Read more.

The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text Markup Language (CTML), an extensible markup language specifically designed to facilitate the automatic generation of smart contracts from multilingual contracts. CTML overcomes traditional method limitations by employing a two-stage transformation process: (1) Contract Abstraction and Markup: CTML redefines grammar rules and incorporates encoding extensions to transform multilingual contracts into structured, marked-up contracts. This process effectively abstracts the essential details of the original contract, enabling language-agnostic interpretation. (2) Domain-Specific Language (DSL) Translation and Smart Contract Code Generation: The marked-up contract is then seamlessly translated into a DSL program, capturing the legal concepts in a machine-readable format. Finally, the DSL program is automatically compiled into executable smart contract code, ready for deployment on the blockchain. The effectiveness of the proposed approach is demonstrated using a legal contract in both English and Chinese. Therefore, the CTML-based approach can automatically generate smart contracts from multilingual contracts, enabling a more inclusive and accessible smart contract ecosystem. Full article

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

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25 pages, 3190 KB

Open AccessArticle

Forecast-Guided KAN-Adaptive FS-MPC for Resilient Power Conversion in Grid-Forming BESS Inverters

by Shang-En Tsai and Wei-Cheng Sun

Electronics 2026, 15(7), 1513; https://doi.org/10.3390/electronics15071513 - 3 Apr 2026

Viewed by 427

Abstract

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, [...] Read more.

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Electrical Power Conversion and Advanced Control)

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