Electronics

14 pages, 1430 KB

Open AccessArticle

Evaluating Multimodal Physiological Signals for Biometric Human Recognition Using GSR, ECG, and PPG Signals

by Shaimaa Hagras, Hany S. Khalifa and O. G. Elbarbary

Electronics 2026, 15(13), 2976; https://doi.org/10.3390/electronics15132976 - 7 Jul 2026

Viewed by 454

In recent years, physiological signal-based biometrics has gained increasing attention due to its resistance to spoofing attacks, suitability for continuous authentication, and compatibility with wearable devices. This study investigates a multimodal biometric framework based on three physiological signals: galvanic skin response (GSR), electrocardiogram [...] Read more.

In recent years, physiological signal-based biometrics has gained increasing attention due to its resistance to spoofing attacks, suitability for continuous authentication, and compatibility with wearable devices. This study investigates a multimodal biometric framework based on three physiological signals: galvanic skin response (GSR), electrocardiogram (ECG), and photoplethysmography (PPG). Although GSR has demonstrated promising performance in emotion recognition and animal recognition studies, it remains relatively underexplored in biometric human recognition applications compared with ECG and PPG. As a non-invasive signal that can be easily acquired through simple skin contact, GSR offers several advantages, including low-cost sensing, ease of integration into wearable devices, user convenience, and suitability for continuous monitoring. To address this gap, the proposed framework combines features extracted from ECG, PPG, and GSR signals and employs machine learning algorithms for human recognition. The approach was evaluated using two publicly available datasets, CLAS and MAUS. Experimental results demonstrate that multimodal fusion significantly enhances recognition performance, achieving accuracies of 97% and 99% on the CLAS and MAUS datasets, respectively, with the K-Nearest Neighbors (KNN) classifier. These findings highlight the potential of integrating GSR with ECG and PPG signals to develop biometric systems for wearable and continuous authentication applications. Full article

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21 pages, 1590 KB

Open AccessArticle

Effect of Additive Noise on the Performance of Quantum Computing: An Engineering Approach

by Rajnish Kumar, Shlomi Arnon and Torben Larsen

Electronics 2026, 15(13), 2975; https://doi.org/10.3390/electronics15132975 - 7 Jul 2026

Viewed by 378

Abstract

Quantum computing algorithms are usually built by using gates and circuits to manipulate qubits. In this article, our analysis examines the impact of additive noise in a quantum circuit as an engineering simplification model. We evaluate these effects by computing the probability, fidelity, [...] Read more.

Quantum computing algorithms are usually built by using gates and circuits to manipulate qubits. In this article, our analysis examines the impact of additive noise in a quantum circuit as an engineering simplification model. We evaluate these effects by computing the probability, fidelity, and signal-to-noise ratio (SNR). We study quantum noise from an amplitude-domain signal-processing perspective, bridging classical additive noise models with quantum state perturbations. We have analyzed the results of various quantum gates, such as X, Y, Z, and Hadamard gates. This study provides an approximation of the quantitative effect and qualitative analysis of the effect of additive noise on quantum gates, thereby contributing to a deeper understanding of the challenges and potential solutions in quantum computing. However, in some cases, the model does not represent the physical results due to approximation. Full article

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

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29 pages, 5988 KB

Open AccessArticle

MA-SPMA: A Multi-Hop Adaptive MAC Protocol for Flying Ad Hoc Networks Based on Two-Dimensional Queueing and Dual-Round Decision

by Yu Wu, Xianghua Zeng and Byung-Seo Kim

Electronics 2026, 15(13), 2974; https://doi.org/10.3390/electronics15132974 - 7 Jul 2026

Viewed by 383

Abstract

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization [...] Read more.

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization of channel opportunities, this paper proposes a multi-hop adaptive SPMA protocol (MA-SPMA) suitable for dynamic multi-hop scenarios. The protocol adopts the Neighbor-Priority Two-Dimensional Queueing (NPTQ) mechanism to store packets jointly according to the next-hop neighbor and priority. A Priority-Utility Dual-round Decision (PUDD) mechanism is designed: in the first round, candidate queues that meet channel load conditions are selected in parallel; in the second round, a utility function constructed by normalized delay, priority, and the end-to-end transmission success rate is used to select the optimal packet for transmission. Theoretical analysis shows that the time and space complexity of MA-SPMA are linearly related to the number of neighbor nodes, with controllable overhead, which is suitable for resource-constrained Unmanned Aerial Vehicle (UAV) platforms. In the MATLAB simulation environment, the Reference Point Group Mobility (RPGM) model is used to construct a multi-hop topology, and comparisons are conducted with two typical improved protocols for multi-hop networks: DCLS-SPMA and BiLSTM-SPMA. The results show that the proposed protocol can significantly improve the end-to-end transmission success rate and network throughput, with more obvious advantages in scenarios with a high proportion of multi-hop services. This paper provides an effective solution for Medium Access Control (MAC) protocol design in FANETs. Full article

(This article belongs to the Special Issue Smart Communication and Networking in the 6G Era, 2nd Edition)

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18 pages, 4138 KB

Open AccessArticle

A Lightweight Hybrid Mobile Groupcasting Protocol for Spatially Heterogeneous Sink Groups in WSNs

by Hyunseok Choi, Jeongcheol Lee and Euisin Lee

Electronics 2026, 15(13), 2973; https://doi.org/10.3390/electronics15132973 - 7 Jul 2026

Viewed by 324

Abstract

Efficient data dissemination to mobile sink groups with heterogeneous spatial distributions that are globally sparse but locally dense remains a critical challenge in wireless sensor networks (WSNs). To address severe energy inefficiencies in conventional single-strategy approaches, we propose an energy-efficient, strictly lightweight hybrid [...] Read more.

Efficient data dissemination to mobile sink groups with heterogeneous spatial distributions that are globally sparse but locally dense remains a critical challenge in wireless sensor networks (WSNs). To address severe energy inefficiencies in conventional single-strategy approaches, we propose an energy-efficient, strictly lightweight hybrid mobile groupcasting protocol that dynamically integrates unicasting and partial flooding. The proposed protocol eliminates in-network computational overhead by shifting the entire subgrouping burden exclusively to the data source. The source formulates data dissemination as an analytical cost minimization problem and executes a highly scalable heuristic subgrouping algorithm that operates in linear time,

O (| M |)

, relative to the number of member sinks. By embedding this optimal configuration directly into the data packet header, resource-constrained intermediate sensor nodes are completely relieved from heavy clustering calculations and only need to execute simple, predefined geographic forwarding or localized flooding rules. The simulation results using the QualNet 4.0 platform validate that our source-delegated architecture significantly reduces redundant transmissions and unnecessary flooding regions. The proposed protocol achieves up to 24% and 44.5% reductions in communication energy consumption compared to conventional unicasting-based and flooding-based protocols, respectively, while maintaining reliable data delivery under realistic network dynamics. Full article

(This article belongs to the Section Networks)

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23 pages, 19729 KB

Open AccessArticle

MFJD-Seg: Morphological Fitting Meets Jeffreys Divergence for Efficient Active Contour Segmentation

by Jian Su, Guirong Weng and Fuzheng Zhang

Electronics 2026, 15(13), 2972; https://doi.org/10.3390/electronics15132972 - 7 Jul 2026

Viewed by 382

Abstract

Image segmentation in complex scenes remains challenging due to intensity inhomogeneity, intricate textures, and noise interference. Traditional active contour models (ACMs) offer topological adaptability while suffering from over-segmentation and boundary leakage under such conditions. In this paper, we propose MFJD-Seg, a novel ACM [...] Read more.

Image segmentation in complex scenes remains challenging due to intensity inhomogeneity, intricate textures, and noise interference. Traditional active contour models (ACMs) offer topological adaptability while suffering from over-segmentation and boundary leakage under such conditions. In this paper, we propose MFJD-Seg, a novel ACM that integrates morphological fitting with an energy formulation derived from Jeffreys divergence for robust and efficient image segmentation. Morphological erosion and dilation are applied to construct foreground and background fitting images, which capture fine-grained structural features while suppressing background interference. Subsequently, a symmetric discrepancy consistent with Jeffreys divergence is leveraged to quantify the statistical difference between the original image and the fitting representations, enabling the compact construction of an unbiased energy function. An arctangent energy constraint and mean filtering are further incorporated to stabilize contour evolution and suppress redundant artifacts. Extensive experiments on BSDS, ADE20K, and COCO datasets show that MFJD-Seg achieves the best mIoU and mDSC in comparisons with five representative ACMs and five mainstream deep learning segmentation models, improving ACM baselines by up to 4.8% in both metrics while maintaining the highest FPS among ACMs and competitive speed against deep learning counterparts. These results verify the superior segmentation capabilities of MFJD-Seg in challenging imaging scenarios. Full article

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16 pages, 7027 KB

Open AccessArticle

A Hierarchical 54 V/12 V Dual-Plane Multi-Phase DC Power Delivery Architecture for High-Computing-Power AI Servers

by Shaohang Xu, Huijie You, Yan Li, Wenfang Li and Rikang Zhao

Electronics 2026, 15(13), 2971; https://doi.org/10.3390/electronics15132971 - 7 Jul 2026

Viewed by 576

Abstract

In recent years, the rapid evolution of large artificial intelligence (AI) models has placed unprecedented demands on the computing power of data center servers, driving an explosive growth in data center computing requirements. The power consumption of core computing components, represented by GPUs, [...] Read more.

In recent years, the rapid evolution of large artificial intelligence (AI) models has placed unprecedented demands on the computing power of data center servers, driving an explosive growth in data center computing requirements. The power consumption of core computing components, represented by GPUs, has surged dramatically. When facing extremely high power densities, the traditional 12 V single-voltage power delivery architecture exposes severe limitations, including increased transmission link losses, thermal management difficulties, and low system efficiency. To address these challenges, this paper proposes and designs a hierarchical 54 V/12 V dual-plane multi-phase DC power delivery architecture for high-computing-power AI servers. By conducting refined hierarchical identification of system loads, this architecture introduces a 54 V high-voltage DC power plane for high-power loads while retaining the 12 V power plane for conventional loads. Within each power plane, multi-phase interleaved parallel Buck converters integrated with Turbo-COT control strategies and high-density DrMOS are deployed. Experimental results demonstrate that this power architecture exhibits excellent electrical characteristics: under steady-state conditions, the peak-to-peak (PK-PK) ripple voltage fluctuation amplitude of the 54 V power plane under different loads is compressed to between ±0.22% and ±0.26%, while the PK-PK ripple voltage fluctuation amplitude of the 12V power plane under different loads reaches ±0.66% to ±0.68%; in dynamic load step (0–50% and 50–100%) tests, the PK-PK voltage fluctuations of the 54 V plane are ±1.42% and ±1.33%, whereas the PK-PK voltage fluctuations of the 12 V power plane are ±2.36% and ±1.83%. Furthermore, the peak conversion efficiency of the 54 V power plane approaches 97%, and the maximum efficiency of the 12 V power plane reaches 94%, showing a measurable efficiency improvement under the tested conditions. The hierarchical multi-phase power delivery technology comprehensively reduces power supply link losses and enhances power stability, providing an important theoretical basis and engineering reference for the design of next-generation high-density AI servers and the optimization of green, energy-saving networks in data centers. Full article

(This article belongs to the Special Issue Advanced Power Electronics and Sustainable Energy Systems: Recent Developments, Challenges and Future Perspectives)

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44 pages, 6943 KB

Open AccessArticle

HFW-NPO: A Dual a Paradigm Hybrid Filter–Wrapper Nomadic People Optimizer Framework for High-Dimensional Alzheimer’s Gene Expression Classification

by Almuntadher Mahmood Alwhelat and Rahib H. Abiyev

Electronics 2026, 15(13), 2970; https://doi.org/10.3390/electronics15132970 - 7 Jul 2026

Viewed by 664

Abstract

Alzheimer’s Disease (AD) necessitates high-resolution transcriptomic biomarkers for early detection, yet current computational methods are hampered by high-dimensional search space and publication bias regarding imbalanced datasets. We propose the Hybrid Filter–Wrapper Nomadic People Optimizer, a three-stage pipeline integrating a tri-criterion filter, an enhanced [...] Read more.

Alzheimer’s Disease (AD) necessitates high-resolution transcriptomic biomarkers for early detection, yet current computational methods are hampered by high-dimensional search space and publication bias regarding imbalanced datasets. We propose the Hybrid Filter–Wrapper Nomadic People Optimizer, a three-stage pipeline integrating a tri-criterion filter, an enhanced NPO wrapper with adaptive Lévy-scale anti-stagnation mechanism, and a five-member soft-voting ensemble. The system was evaluated using a dual-paradigm protocol; Scenario A (balance brain tissue; GEO dataset GSE 33000, GSE 132903, GSE122063) and Scenario B (imbalanced peripheral blood: GSE 63060 + GSE 636061). In scenario A, HFW-NPO outperformed 13 published methods, achieving balanced accuracy of 85.28%, 87.16%, and 96.67% while identifying compact panels of 29–32 probes per fold (observed range: 24–38). Scenario B, evaluated on a merged 478-samples peripheral blood cohort (GSE63060 + GSE 636061 imbalanced 1.48:1) with z-score batch harmonization and RSKF (5 × 10) cross-validation, achieved a balanced accuracy of 59.53% and MCI Recall of 63.50 ± 14.02%, providing the first reproducible baseline for this clinically challenging task, while acknowledging that 59.53% balanced accuracy does not yet reach clinically actionable levels. By providing transparent reporting across both balanced and severely imbalanced datasets, this study establishes a state-of-the-art, reproducible framework for AD biomarker discovery and provides a critical baseline for the challenging task of transcriptomic-based classification in peripheral blood samples. Result is currently scoped to Illumina HumanHT-12 microarray data, and cross-platform validation on RNA-seq cohorts is identified as a priority future extension. Full article

(This article belongs to the Special Issue Machine Learning in Electronic and Biomedical Engineering, 4th Edition)

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31 pages, 1792 KB

Open AccessArticle

Robust Hybrid Beamforming and Dynamic Subarray Design for Near-Field mmWave ISAC Systems Under Unknown Interference

by Dahai Ni, Chaolin Zeng, Hongbo Yin, Kun Chen, Xiangning Fan and Peng Chen

Electronics 2026, 15(13), 2969; https://doi.org/10.3390/electronics15132969 - 7 Jul 2026

Viewed by 497

Abstract

This paper investigates a near-field millimeter-wave (mmWave) integrated sensing and communication (ISAC) system under unknown interference. A base station equipped with a partially connected dynamic subarray hybrid architecture serves a legitimate user while performing target-oriented transmit beampattern shaping. Unlike existing works that assume [...] Read more.

This paper investigates a near-field millimeter-wave (mmWave) integrated sensing and communication (ISAC) system under unknown interference. A base station equipped with a partially connected dynamic subarray hybrid architecture serves a legitimate user while performing target-oriented transmit beampattern shaping. Unlike existing works that assume perfect interference knowledge, we characterize the unknown interference channels via a robust spatial covariance uncertainty model. To exploit spatial degrees of freedom for interference suppression, the user employs a fully connected hybrid receiver. We formulate a robust transmit power minimization problem subject to worst-case communication signal-to-interference-plus-noise ratio (SINR) and sensing beampattern constraints, alongside constant-modulus and dynamic subarray hardware constraints. To solve this highly non-convex mixed discrete–continuous problem, we propose a two-layer alternating optimization framework. The inner layer optimizes the continuous and phase-quantized beamformers using successive convex approximation, while the outer layer refines the binary subarray connections via a penalty-augmented local discrete search. Extensive simulations demonstrate that explicitly modeling worst-case uncertainties ensures reliable ISAC performance in adversarial environments, and the dynamic subarray architecture systematically outperforms conventional fixed topologies in power efficiency. Additional robustness and sensitivity analyses show that these gains are most pronounced when sufficient spatial degrees of freedom remain, whereas excessive antenna failures, unmodeled strong multipath, or covariance drift outside the uncertainty envelope can erode the communication and sensing margins. Full article

(This article belongs to the Special Issue RF and Millimeter-Wave Technologies for Next-Generation Wireless Communications)

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22 pages, 1472 KB

Open AccessArticle

Robust Secrecy-Aware Power Allocation for UAV-Assisted IoT Sensing Networks Under Worst-Case Eavesdropping

by Mohammad Ahmed Alnakhli

Electronics 2026, 15(13), 2968; https://doi.org/10.3390/electronics15132968 - 7 Jul 2026

Viewed by 335

Abstract

We investigate secure data transmission in a unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) sensing network, focusing on maximizing multi-sensor uplink secrecy capacity under practical power constraints and severe co-channel interference. Due to the coupled signal-to-interference-plus-noise ratio (SINR) expressions and the non-smooth [...] Read more.

We investigate secure data transmission in a unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) sensing network, focusing on maximizing multi-sensor uplink secrecy capacity under practical power constraints and severe co-channel interference. Due to the coupled signal-to-interference-plus-noise ratio (SINR) expressions and the non-smooth secrecy-rate function, the formulated power allocation problem is highly nonconvex and mathematically challenging. To efficiently solve this problem, we exploit a novel mathematical reformulation by introducing a smooth approximation of the secrecy metric and developing a computationally efficient optimization framework based on sequential quadratic programming (SQP) with analytically derived gradients. The main strength of this framework lies in its low-complexity, deterministic nature, which eliminates the need for computationally exhaustive search heuristics while guaranteeing fast, stable convergence to a Karush–Kuhn–Tucker (KKT) point. Furthermore, we incorporate a robust worst-case eavesdropper modeling approach to guarantee secure communication under severe adversarial conditions. Numerical results demonstrate that the proposed method significantly improves sum secrecy performance compared to conventional equal-power and baseline allocation schemes, proving highly scalable for real-time data collection in environmental monitoring, smart cities, and surveillance applications. Full article

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

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24 pages, 3034 KB

Open AccessArticle

An Explainable CS-Mitigation Triangular (ECSMT) Framework to Secure Graph Neural Networks

by Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen and Wen-Chao Yang

Electronics 2026, 15(13), 2967; https://doi.org/10.3390/electronics15132967 - 7 Jul 2026

Viewed by 344

Abstract

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, [...] Read more.

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary AIDS benchmark. Cross-domain testing reveals that defensive efficacy is strongly constrained by dataset characteristics: small-scale datasets such as MUTAG suffer from persistent trigger concentration, while complex graph manifolds such as PROTEINS exhibit high levels of topological noise. Furthermore, mapping these technical outcomes into an enterprise asset framework yields a 61% expenditure compression at critical technological feeder locations and a 98.93% reduction in total systemic loss. This study indicates that the proposed triangular mitigation strategy offers a valuable, scalable blueprint for enhancing the technical resilience and prognostic economic modeling of critical infrastructure networks. Full article

(This article belongs to the Special Issue Secure and Privacy-Enhanced Data Sharing)

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19 pages, 18540 KB

Open AccessArticle

Embedded Control of an Adaptive Luminaire with Active Reflectors and Variable Light Distribution

by Antoni Różowicz, Marcin Leśko and Paweł Szcześniak

Electronics 2026, 15(13), 2966; https://doi.org/10.3390/electronics15132966 - 7 Jul 2026

Viewed by 433

Abstract

This article presents the design and implementation of a control system for an adaptive light luminaire with variable light distribution. The developed solution enables dynamic shaping of the light distribution characteristics by simultaneously controlling the geometry of the optical system and the spatial [...] Read more.

This article presents the design and implementation of a control system for an adaptive light luminaire with variable light distribution. The developed solution enables dynamic shaping of the light distribution characteristics by simultaneously controlling the geometry of the optical system and the spatial distribution of the emitted light flux. The system utilizes two cooperating control mechanisms. The first is implemented by four independently controlled reflectors with adjustable angles of inclination. The second is based on the independent control of eight sections of LED light sources. The coordination of both systems enables the implementation of various operating scenarios, including symmetric, asymmetric, and adaptive configurations, with variants of narrow and wide beam distribution. The central unit of the system is an ESP32 microcontroller that performs control functions, generates PWM signals, and coordinates the operation of the actuators. The system was implemented as a dedicated embedded system. The main contribution of this work is the implementation and experimental validation of an embedded control platform integrating mechanical beam shaping and segmented LED control within a single adaptive lighting system. As part of the work, predefined control scenarios for lighting system configuration were developed and experimentally tested. The developed solution increases the functionality of adaptive lighting systems and may contribute to reducing energy consumption by directing light only where required. However, the quantitative evaluation of the energy savings was beyond the scope of the present study. Full article

(This article belongs to the Special Issue New Trends in Energy Saving, Smart Buildings and Renewable Energy)

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24 pages, 2292 KB

Open AccessArticle

Effective Spectral Efficiency Maximization for Directional Pinching-Antenna-Assisted Multi-User MIMO Systems

by Xiaoye Deng, Fengming Xin and Menghang Liu

Electronics 2026, 15(13), 2965; https://doi.org/10.3390/electronics15132965 - 7 Jul 2026

Viewed by 438

Abstract

Pinching-antenna systems (PASSs) have emerged as a promising waveguide-based architecture for high-frequency wireless communications. Recent directional PASS studies have shown that practical pinching antennas (PAs) exhibit directional, pencil-like radiation rather than idealized omnidirectional radiation. However, most existing designs focus on PA placement or [...] Read more.

Pinching-antenna systems (PASSs) have emerged as a promising waveguide-based architecture for high-frequency wireless communications. Recent directional PASS studies have shown that practical pinching antennas (PAs) exhibit directional, pencil-like radiation rather than idealized omnidirectional radiation. However, most existing designs focus on PA placement or instantaneous sum-rate maximization and neglect the reconfiguration time required for PA movement and rotation. This paper investigates reconfiguration-aware joint optimization for a multi-user downlink directional PASS with a finite frame duration. To evaluate transmission performance, an effective spectral efficiency metric is defined to account for both reconfiguration-induced rate gain and effective transmission-time reduction. Based on this metric, a joint optimization problem is formulated for user scheduling, PA positions, PA orientations, and digital beamforming. To tackle this mixed discrete–continuous non-convex problem, a reconfiguration-aware alternating optimization algorithm is developed by combining weighted minimum mean-square error (WMMSE)-based beamforming, local orientation search, projected position update, and restricted user scheduling. Simulation results show that the proposed scheme achieves higher effective spectral efficiency than representative fixed-configuration, sum-rate-oriented, and joint-search schemes. These results indicate that reconfiguration overhead should be considered, since instantaneous-rate-oriented designs may cause excessive PA movement or rotation and degrade effective spectral efficiency. Full article

(This article belongs to the Special Issue Advanced MIMO Technologies in Wireless Communications: Innovations and Future Prospects)

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22 pages, 2027 KB

Open AccessArticle

A Multi-Information Fusion Unsupervised Entity Alignment Model for Knowledge Graphs in Oil and Gas Pipeline Safety

by Wangweiyi Shan, Heng Duan, Weichun Chang, Kewen Li and Guangyue Zhou

Electronics 2026, 15(13), 2964; https://doi.org/10.3390/electronics15132964 - 7 Jul 2026

Viewed by 362

Abstract

Targeting the joint challenges posed by sparse graph topology, limited semantic expressiveness, and scarce annotation resources that commonly afflict knowledge graphs in the oil and gas pipeline safety domain, this paper presents a Multi-Information Fusion Unsupervised Entity Alignment model (MIF-UEA). The proposed method [...] Read more.

Targeting the joint challenges posed by sparse graph topology, limited semantic expressiveness, and scarce annotation resources that commonly afflict knowledge graphs in the oil and gas pipeline safety domain, this paper presents a Multi-Information Fusion Unsupervised Entity Alignment model (MIF-UEA). The proposed method constructs high-quality initial alignment pairs by integrating multi-source similarity computation with a structure-aware seed generation mechanism and performs representation learning by fusing structural features and semantic attribute information. Furthermore, a pseudo-label augmentation and denoising strategy is introduced to enhance the effectiveness of self-training. Finally, entity matching is achieved through an optimal transport model. Experimental results confirm that MIF-UEA surpasses existing baselines across both the specialized oil and gas pipeline safety dataset and multiple general-domain benchmarks, demonstrating its effectiveness and generalization capability. Full article

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29 pages, 19630 KB

Open AccessArticle

Single-Image 3D Mesh Reconstruction for Stylized Side-Face Characters via Prompt-Driven Multi-View Diffusion and Consistency Optimization

by Ke Zhang, Jiayi Lin, Zhixiang Zhang and Junghyun Heo

Electronics 2026, 15(13), 2963; https://doi.org/10.3390/electronics15132963 - 6 Jul 2026

Viewed by 795

Abstract

Single-image 3D reconstruction of stylized side-face characters remains challenging because profile-view inputs contain severe self-occlusion, missing frontal geometry, and stylized appearance cues that differ from the assumptions of generic reconstruction models. Because the unseen facial geometry cannot be uniquely determined from a single [...] Read more.

Single-image 3D reconstruction of stylized side-face characters remains challenging because profile-view inputs contain severe self-occlusion, missing frontal geometry, and stylized appearance cues that differ from the assumptions of generic reconstruction models. Because the unseen facial geometry cannot be uniquely determined from a single profile-view input, this study focuses on generating plausible and visually consistent 3D completions rather than uniquely recovering the unobserved geometry. When CRM is directly applied to stylized profile inputs, the outputs often exhibit unstable facial completion, local mesh collapse, UV misalignment, texture discontinuity, and other reconstruction artifacts. Rather than introducing a new reconstruction backbone, this study first diagnoses the task-specific limitations of CRM in this setting. We identify eight characteristic failure modes that occur when CRM is directly applied to stylized profile inputs and use this diagnosis to guide a retraining-free inference-time intervention strategy. The proposed strategy combines reconstruction-compatible auxiliary-view generation with failure-mode-oriented CRM refinement, including candidate verification, adaptive facial cropping, geometric stabilization, local detail enhancement, normal correction, UV repair, and texture continuity improvement. Experiments on a rendered stylized-character dataset and a cross-style adaptation set show that the proposed intervention improves frontal-view plausibility, mesh usability, texture continuity, and rendered appearance compared with direct reconstruction baselines. The seven-configuration progressive ablation and parameter sensitivity analyses further support the complementary role of the main intervention stages and the stability of the selected settings. These findings suggest that systematic failure-mode diagnosis, followed by task-specific inference-time intervention, provides a practical way to extend public image-to-3D models to stylized profile reconstruction scenarios, within the scope of the evaluated stylized-character datasets, while extreme viewpoints and highly abstract styles remain challenging. Full article

(This article belongs to the Special Issue Image/Video Processing and Computer Vision)

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24 pages, 1431 KB

Open AccessArticle

On Sampled Sequence Representations at Discontinuities and Their Impact on Discrete Convolution

by Chiman Kwan

Electronics 2026, 15(13), 2962; https://doi.org/10.3390/electronics15132962 - 6 Jul 2026

Viewed by 294

Abstract

If one compares the continuous-time convolution outputs with their sampled discrete counterparts, one may observe slight differences even when the sampling process itself is otherwise straightforward. This issue becomes noticeable when one or both continuous-time signals have a discontinuity at the sampling instant, [...] Read more.

If one compares the continuous-time convolution outputs with their sampled discrete counterparts, one may observe slight differences even when the sampling process itself is otherwise straightforward. This issue becomes noticeable when one or both continuous-time signals have a discontinuity at the sampling instant, such as t = 0. In this paper, we revisit this issue and explain its root causes: the treatment of midpoint values at discontinuities and the sampling-period scaling that appears when a continuous-time convolution is approximated in discrete time. Although the midpoint rule is not new, we show how this classical result can be used systematically to construct sampled sequence representations that are consistent with inverse-transform reconstruction at discontinuities. Based on this viewpoint, we derive midpoint-consistent sampled sequence representations for quite a few representative functions, and we show the corresponding implications for sampled convolution formulae. Several examples are used to compare conventional discrete formulae with midpoint-consistent sampled formulae and with samples of the continuous-time results. The proposed formulation is intended for sampled continuous-time signals at discontinuities; it is not meant to replace standard native discrete-time conventions used in digital signal processing. Full article

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13 pages, 10291 KB

Open AccessArticle

An Efficient Two-Stage Method for Correcting 3-D Positioning Errors of the Measuring Probe in a Non-Redundant Spherical Scan

by Francesco D’Agostino, Flaminio Ferrara, Claudio Gennarelli, Rocco Guerriero, Massimo Migliozzi and Luigi Pascarella

Electronics 2026, 15(13), 2961; https://doi.org/10.3390/electronics15132961 - 6 Jul 2026

Viewed by 230

Abstract

A robust procedure for compensating for inaccuracies caused by 3-D positioning errors in the measurement of the near-field (NF) data required by the non-redundant (NR) spherical near-to-far-field (NtFF) transformations for long antennas is presented in this article. These errors may arise from hardware [...] Read more.

A robust procedure for compensating for inaccuracies caused by 3-D positioning errors in the measurement of the near-field (NF) data required by the non-redundant (NR) spherical near-to-far-field (NtFF) transformations for long antennas is presented in this article. These errors may arise from hardware defects and positioners’ controlling inaccuracies, which may cause the probe to deviate from the intended spherical scan surface and prevent it from reaching the NR sampling points required by either of the two NR representations for long antennas. To account for these errors, the method proceeds through two steps. The first step, called spherical wave correction, compensates for the phase shifts due to radial displacements from the intended scanning sphere. As a result of this correction, the NF samples belong to the intended scanning sphere, but at points different from those required by the adopted NR representation, thus impairing the subsequent NF reconstruction via the optimal sampling interpolation (OSI) algorithm. Such an algorithm enables one to efficiently build the iterative scheme used in the second step, which makes it possible to effectively retrieve the NF samples at the prescribed NR positions. Test results are shown to numerically validate the capability of the developed two-step compensation technique to correct even significant and pessimistic 3-D positioning errors affecting the collection of the NF data. Full article

(This article belongs to the Special Issue Recent Advancements in Antenna Technologies: Novel Design Techniques and Emerging Applications)

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48 pages, 5522 KB

Open AccessReview

High-Frequency Resonators for Dielectric Characterization: A Review of Design Techniques, Performance Trade-Offs, and Future Directions

by Asma Benhamza, Nadhir Djeffal, Mounir Amir, Salem Titouni, Abdallah Hedir, Mellissa Amazouz, Idris Messaoudene and Hakim Achour

Electronics 2026, 15(13), 2960; https://doi.org/10.3390/electronics15132960 - 6 Jul 2026

Viewed by 825

Abstract

The rapid expansion of microwave and millimeter-wave telecommunication systems has intensified the need for precise dielectric material characterization at high frequencies. As operating frequencies increase, small uncertainties in permittivity and loss tangent significantly degrade resonance stability, bandwidth control, and quality factor, directly affecting [...] Read more.

The rapid expansion of microwave and millimeter-wave telecommunication systems has intensified the need for precise dielectric material characterization at high frequencies. As operating frequencies increase, small uncertainties in permittivity and loss tangent significantly degrade resonance stability, bandwidth control, and quality factor, directly affecting RF system reliability and performance. However, the growing diversity of resonator architectures and extraction methodologies has led to fragmentation in the literature, making it difficult to identify optimal solutions for telecommunication-oriented applications. This review provides a structured and application-driven assessment of high-frequency resonator-based dielectric characterization techniques relevant to modern telecommunication systems. Resonator topologies—including cavity, planar, substrate-integrated, metamaterial-inspireds—are systematically classified and critically compared. Their sensing mechanisms and parameter-extraction approaches are analyzed in terms of frequency-shift sensitivity, Q-factor performance, scalability toward millimeter-wave bands, integration capability, and measurement robustness. By synthesizing performance trade-offs, practical limitations, and emerging research directions, this review establishes clear design guidelines and a forward-looking framework for advancing dielectric metrology in next-generation high-frequency telecommunication technologies. Full article

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

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52 pages, 3539 KB

Open AccessArticle

An Interpretable Vision-Language Framework for Evaluating the Uncanny Valley Effect of XR Humanoid Characters

by Xiner Li, Yi Xiao, Jinhao Qiao, Yan Zheng and Chi-Sing Leung

Electronics 2026, 15(13), 2959; https://doi.org/10.3390/electronics15132959 - 6 Jul 2026

Viewed by 481

Abstract

As AI-generated humanoid characters are increasingly used in virtual, augmented, and mixed reality applications, evaluating the Uncanny Valley Effect (UVE) is crucial for immersive user experience. Existing evaluation methods map visual features to affective scores, offering limited interpretability regarding which visual cues are [...] Read more.

As AI-generated humanoid characters are increasingly used in virtual, augmented, and mixed reality applications, evaluating the Uncanny Valley Effect (UVE) is crucial for immersive user experience. Existing evaluation methods map visual features to affective scores, offering limited interpretability regarding which visual cues are associated with affinity judgments. Among the theoretical perspectives proposed to explain the UVE, perceptual conflict provides a visual-cue-oriented perspective for analyzing whether local-feature realism supports a coherent overall human-likeness impression and how this is reflected in affinity judgments, yet this perspective is rarely incorporated into interpretable UVE assessment. Thus, we propose UVE-Perception Chain-of-Thought (UVE-PCoT), a vision-language framework for interpretable UVE evaluation from a perceptual-conflict-oriented perspective. UVE-PCoT organizes assessment through a structured perceptual decomposition, including assessments of overall human-likeness, local-feature realism, perceptual conflict, and affinity. To provide supervision, we construct UVE-R, a structured rationale dataset with image-grounded, rating-consistent rationales linking visual cue observations, cue-level inconsistency analysis, and affinity judgments. Results show that UVE-PCoT improves affinity prediction and cue-level explanation over general-purpose multimodal large language models and ablations. Our approach operationalizes this perceptual-conflict-oriented perspective into an interpretable framework, advancing UVE evaluation from black-box scoring to explanatory analysis and providing cue-level insights for XR character assessment and revision. Full article

(This article belongs to the Special Issue Artificial Intelligence for Virtual, Mixed, and Augmented Reality Content and Interaction)

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17 pages, 2824 KB

Open AccessArticle

Projection-Based Strain–Excitation Mapping Model for Beam Recovery of Arbitrarily Deformed Phased Array Antennas

by Bo Tang, Jinzhu Zhou, Le Kang, Xinrui Fang and Qingdong Zhang

Electronics 2026, 15(13), 2958; https://doi.org/10.3390/electronics15132958 - 6 Jul 2026

Viewed by 550

Abstract

Surface deformation of a phased array antenna (PAA) induced by external loads can degrade its radiation performance. To restore the beam of a deformed PAA, this paper proposes a new strain–excitation mapping model (SEMM) capable of rapidly calculating excitation adjustments based on measured [...] Read more.

Surface deformation of a phased array antenna (PAA) induced by external loads can degrade its radiation performance. To restore the beam of a deformed PAA, this paper proposes a new strain–excitation mapping model (SEMM) capable of rapidly calculating excitation adjustments based on measured structural strains. In the derivation of the SEMM, an analytical formula establishing the relationship between antenna excitations and the element positions and orientations for a PAA with an arbitrary surface shape is derived using the projection principle. Subsequently, the positions and orientations of the elements are expressed as functions of a limited number of strain measurements from the deformed antenna structure. An X-band PAA experimental system, equipped with a deformable mechanism and strain measurement capabilities, was developed. Two typical deformations were taken as examples to validate the proposed SEMM. Experimental results demonstrate that the SEMM can effectively recover the distorted pattern across the observation region. Compared with existing models, the proposed model achieves better sidelobe recovery. The rapid computation capability and analytical formulation of the SEMM make it highly suitable for developing an adaptive PAA that can autonomously preserve radiation beam quality under in-service deformations. Full article

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42 pages, 11388 KB

Open AccessArticle

Leader-Following Cluster Consensus of Heterogeneous Multi-Agent Systems with Disturbances and Weighted Cooperative-Competitive Networks

by Yufeng Pan and Liyun Zhao

Electronics 2026, 15(13), 2957; https://doi.org/10.3390/electronics15132957 - 6 Jul 2026

Viewed by 333

Abstract

With the rapid development of networked cyber-physical systems, the coordinated control of heterogeneous multi-agent systems has attracted increasing attention in applications such as autonomous vehicles, robotic arms, and distributed sensor networks. This paper investigates the leader-following cluster consensus problem for heterogeneous multi-agent systems [...] Read more.

With the rapid development of networked cyber-physical systems, the coordinated control of heterogeneous multi-agent systems has attracted increasing attention in applications such as autonomous vehicles, robotic arms, and distributed sensor networks. This paper investigates the leader-following cluster consensus problem for heterogeneous multi-agent systems over weighted cooperative–competitive networks with matched disturbances generated by linear exosystems. Unlike purely cooperative or binary signed networks, the considered network allows interaction weights to take arbitrary positive or negative values, thereby describing both the type and intensity of cooperative or competitive interactions. To handle heterogeneous agent dynamics and matched disturbances, a disturbance-observer-based distributed control protocol is developed for both first-order and second-order followers. Based on path-product-based coordinate transformations and Lyapunov stability analysis, sufficient conditions are derived to guarantee topology-dependent scaled leader-following cluster consensus under interactively balanced and interactively sub-balanced topologies. For interactively unbalanced topologies, a structurally selected pinning control strategy is introduced to compensate for sign conflicts caused by unbalanced directed cycles and ensure global asymptotic convergence. Numerical simulations verify the effectiveness of the proposed protocol under heterogeneous dynamics, weighted cooperative–competitive interactions, and matched disturbances. Full article

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30 pages, 19341 KB

Open AccessArticle

Computationally Efficient Deep Learning Approach Using IQ-MobNet for Radar DoA Estimation in Limited Snapshot Conditions

by Neeraja P. Kovilakam, Bindiya T. Sambasivan and Raghu C. Variyam

Electronics 2026, 15(13), 2956; https://doi.org/10.3390/electronics15132956 - 6 Jul 2026

Viewed by 429

Abstract

This paper presents a computationally efficient deep learning framework for accurate direction-of-arrival (DoA) estimation in portable radar applications. Leveraging a MobileNet architecture, the proposed model directly processes raw in-phase and quadrature-phase (IQ) data, enabling more effective learning of both spatial and temporal features. [...] Read more.

This paper presents a computationally efficient deep learning framework for accurate direction-of-arrival (DoA) estimation in portable radar applications. Leveraging a MobileNet architecture, the proposed model directly processes raw in-phase and quadrature-phase (IQ) data, enabling more effective learning of both spatial and temporal features. This direct input approach enhances DoA estimation accuracy, particularly under challenging conditions such as low signal-to-noise ratio (SNR) and limited snapshot scenarios. A unified training strategy is adopted for both single-source and multi-source target detection, ensuring consistency and robustness. Comprehensive simulation experiments demonstrate the proposed model’s competitive and robust performance across various conditions, including different SNR levels, closely spaced targets, and random off-grid angles. It also shows that our method achieves performance comparable to or better than recent deep learning approaches in several challenging scenarios, establishing its potential for resource-constrained environments where only low snapshot data are available. The proposed IQ-MobNet DoA estimation model achieves this competitive performance with substantially lower computational complexity, requiring only 0.24 million parameters and 0.42 million Floating Point Operations (FLOPs), representing a reduction of over 96% compared to the recent neural network models. To ensure practical applicability, the proposed IQ-MobNet framework is validated using real-world measured data, confirming its robustness beyond simulated environments. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing: Methods and Applications)

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33 pages, 1098 KB

Open AccessArticle

Comparative Evaluation of Transformer-Based Models for Plain Language Classification in Hungarian Legal–Administrative Texts

by István Üveges

Electronics 2026, 15(13), 2955; https://doi.org/10.3390/electronics15132955 - 6 Jul 2026

Viewed by 578

Abstract

Plain Language seeks to enhance the clarity and comprehensibility of legal and administrative communication; while Natural Language Processing (NLP) offers promising tools for assessing text complexity, most Plain Language classification studies focus exclusively on English, leaving low-resource languages underexplored. This study presents the [...] Read more.

Plain Language seeks to enhance the clarity and comprehensibility of legal and administrative communication; while Natural Language Processing (NLP) offers promising tools for assessing text complexity, most Plain Language classification studies focus exclusively on English, leaving low-resource languages underexplored. This study presents the first systematic evaluation of transformer-based models for sentence-level Plain Language classification in Hungarian tax administrative texts. We benchmarked zero-shot prompting with GPT-4o against fine-tuned open-weight and proprietary models, including huBERT, XLM-RoBERTa, GPT-4o-mini, and Gemini 1.0 Pro, and contextualized these results against previously established lightweight machine learning baselines based on term frequency-inverse document frequency with a support vector machine (TF-IDF + SVM) and fastText. To address data scarcity, we applied translation-based data augmentation using parallel Hungarian–English corpora. The best-performing model achieved a macro-average F1-score of 0.79. Mid-sized models also delivered competitive results, combining accuracy with feasible inference speed and deployment flexibility. Beyond classification performance, we conducted local and aggregated interpretability analysis based on Shapley-values to identify linguistic patterns influencing model decisions. This revealed alignment with known Plain Language features, such as nominalizations and syntactic complexity, as well as biases introduced by frequent domain-specific terms. Our findings demonstrate that Plain Language classifiers can be effectively adapted to low-resource legal–administrative domains. The results support the development of real-time feedback tools that promote linguistic accessibility and contribute to the broader goal of Access to Justice. Full article

(This article belongs to the Special Issue Machine Learning Applications in Computer Vision, Data Modeling, and Natural Language Processing)

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34 pages, 13638 KB

Open AccessArticle

Optimization Method for Transient Characteristics of Multi-Infeed DC Systems Based on Minimum Energy Accumulation

by Ying Xu, Ming Li, Zheng Zhao, Xuezhi Deng and Jing Ma

Electronics 2026, 15(13), 2954; https://doi.org/10.3390/electronics15132954 - 6 Jul 2026

Viewed by 309

Abstract

To address the difficulty in quantitatively analyzing the impact of various control loops on the transient stability of DC sending-end systems under N–m contingencies, this paper proposes a transient stability analysis and transient performance optimization method for multi-source DC systems. First, detailed transient [...] Read more.

To address the difficulty in quantitatively analyzing the impact of various control loops on the transient stability of DC sending-end systems under N–m contingencies, this paper proposes a transient stability analysis and transient performance optimization method for multi-source DC systems. First, detailed transient energy models of the source side and the DC side are established, through which the evolution laws of transient energy in each subsystem are revealed. Based on this, interaction energy components that characterize the influence of different control loops on system stability are extracted. Then, the effect of variations in key control parameters on system stability is quantitatively evaluated using the transient energy interaction intensity. Furthermore, combined with parameter sensitivity analysis, a parameter optimization strategy is developed with the objective of minimizing energy accumulation, subject to constraints on stability requirements and non-degradation of transient performance. The global optimal control parameters are obtained using the Improved Butterfly Optimization Algorithm (IBOA). Finally, real-time hardware-in-the-loop simulations on the RT-LAB platform demonstrate that the proposed method effectively suppresses energy accumulation and achieves simultaneous improvement in transient performance and system stability. Full article

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16 pages, 5589 KB

Open AccessArticle

A High-Energy-Efficiency, Tunable-Bandwidth, and OOK IR-UWB Transmitter for Implantable Brain–Computer Interfaces

by Wenjun Zou, Razieh Eskandari, Jie Yang and Mohamad Sawan

Electronics 2026, 15(13), 2953; https://doi.org/10.3390/electronics15132953 - 6 Jul 2026

Viewed by 501

Abstract

We present in this paper an ultra-low-power impulse radio ultra-wideband (IR-UWB) transmitter intended for short-range, highly energy-efficient, and compact silicon-area applications, such as implantable brain–computer interfaces (iBCIs). The proposed transmitter features effective spectrum tunability, enabling independent adjustments to both the center frequency and [...] Read more.

We present in this paper an ultra-low-power impulse radio ultra-wideband (IR-UWB) transmitter intended for short-range, highly energy-efficient, and compact silicon-area applications, such as implantable brain–computer interfaces (iBCIs). The proposed transmitter features effective spectrum tunability, enabling independent adjustments to both the center frequency and −10 dB bandwidth. Fabricated in TSMC 40 nm CMOS technology, the chip occupies a core area of just 0.001 mm². Experimental results demonstrate an energy efficiency of 2.45 pJ/b across a data rate range of 10 to 200 Mbps. The peak-to-peak output voltage amplitude is approximately 310 mV when driving a 50 Ω load. Furthermore, in vitro wireless measurements demonstrate reliable through-tissue transmission at an implantation depth of 18 mm and achieve a distance range exceeding 0.8 m. Full article

(This article belongs to the Section Bioelectronics)

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21 pages, 2166 KB

Open AccessArticle

Geo-Temporal EM-AMP for CSI Acquisition in FAS-Assisted Grant-Free Random Access with a Mobile Receiver

by Yiran Shi, Sen Chen, Beiping Zhou and Xiao Chen

Electronics 2026, 15(13), 2952; https://doi.org/10.3390/electronics15132952 - 6 Jul 2026

Viewed by 280

Abstract

Receiver mobility complicates channel state information (CSI) acquisition in fluid antenna system (FAS)-assisted grant-free random access (GFRA), because user activity and multi-port channels evolve across pilot frames. Existing FAS acquisition methods are mainly frame-wise, while temporal recovery schemes do not directly combine receiver [...] Read more.

Receiver mobility complicates channel state information (CSI) acquisition in fluid antenna system (FAS)-assisted grant-free random access (GFRA), because user activity and multi-port channels evolve across pilot frames. Existing FAS acquisition methods are mainly frame-wise, while temporal recovery schemes do not directly combine receiver geometry with Doppler information. This article proposes geo-temporal expectation-maximization approximate message passing (GT-EM-AMP), which transfers posterior information between frames and refines the channel prior using receiver trajectory, effective Doppler, and coarse geometry. The proposed recursion preserves the low-complexity structure of EM-AMP while introducing only limited additional state updates. Simulations over an SNR range from

- 14

to 8 dB show that GT-EM-AMP achieves lower channel-estimation error and a favorable activity-detection tradeoff relative to static, temporal-only, geometry-only, and greedy baselines. Ablation, robustness, mobility, scalability, and statistical evaluations characterize the operating range of GT-EM-AMP and show that its activity-detection advantage depends on the SNR regime. GT-EM-AMP introduces modest runtime and memory overhead relative to static EM-AMP. The evaluation focuses on short acquisition windows with coarse geometry information under a Jakes-type temporal model. Full article

(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications, 2nd Edition)

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18 pages, 17016 KB

Open AccessArticle

Multiple Geological Information Recognition Techniques for Tunnel Face Information Recognition and Engineering Use Using Convolutional Neural Networks

by Xinbo Jiang, Chuanyi Ma, Guiyang Zhang, Ning Zhang, Yuxue Chen, Yuanshang Cao, Hao Zou, Changyuan Chen, Wenfeng Tu and Hui Cai

Electronics 2026, 15(13), 2951; https://doi.org/10.3390/electronics15132951 - 6 Jul 2026

Viewed by 354

Abstract

The BQ method, the most widely used technique for classifying tunnel surrounding rocks, requires correction factors including groundwater conditions, structural surface characteristics, and initial ground stress. However, existing deep learning approaches address only a single parameter and rely heavily on empirical judgment, lacking [...] Read more.

The BQ method, the most widely used technique for classifying tunnel surrounding rocks, requires correction factors including groundwater conditions, structural surface characteristics, and initial ground stress. However, existing deep learning approaches address only a single parameter and rely heavily on empirical judgment, lacking the capability for comprehensive multi-parameter intelligent assessment. To address these limitations, a large-scale database of 21,800 tunnel face images was constructed through on-site data collection, data aggregation, and image augmentation. Five convolutional neural network (CNN) models were trained and evaluated using a proposed multi-indicator scoring method comprising seven performance metrics: loss value, accuracy, precision, recall, confusion matrix, frames per second (FPS), and model size. EfficientNet-B2, ResNet101, and DenseNet121 achieved the highest scores of 100, 84, and 87 for groundwater classification, rock structure type classification, and weathering degree classification, respectively. These three optimized models were integrated into a unified software platform that recognizes multiple geological attributes from a single tunnel face image. Field validation across multiple sections of the Jiaozhou Bay Second Undersea Tunnel shows that the platform achieves a recognition accuracy of over 90%. The results demonstrate that the proposed multi-indicator evaluation method yields more-comprehensive model selection, and the integrated platform can directly support BQ value correction, contributing to intelligent surrounding rock classification in complex tunnel construction environments. Full article

(This article belongs to the Section Artificial Intelligence)

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27 pages, 602 KB

Open AccessArticle

NNFDA: A Digest-Based Integrity Verification Scheme for Enhancing Secure Queries in Loss-Tolerant TMWSNs

by Peng Li, Weipeng Wang, Wenxin Yang and Yang Pei

Electronics 2026, 15(13), 2950; https://doi.org/10.3390/electronics15132950 - 6 Jul 2026

Viewed by 328

Abstract

Tiered Mobile Wireless Sensor Networks (TMWSNs), consisting of mobile sensor nodes and storage nodes, are widely used in various fields due to their scalability, energy efficiency, and flexibility. Most existing secure query algorithms assume that data packets generated by sensor nodes can always [...] Read more.

Tiered Mobile Wireless Sensor Networks (TMWSNs), consisting of mobile sensor nodes and storage nodes, are widely used in various fields due to their scalability, energy efficiency, and flexibility. Most existing secure query algorithms assume that data packets generated by sensor nodes can always be delivered to storage nodes. This assumption does not hold in practice, where packets may be lost due to attacks or adverse communication conditions. This paper proposes a loss-tolerant wireless network model for TMWSNs and a novel threat model tailored to this scenario, in which packet-dropping attacks compromise the integrity of query results. To counter these attacks, we present a baseline integrity verification algorithm, the Neighbor Node-Forwarding Digest Algorithm (NNFDA). Each sensor generates a digest of its data and forwards it to neighboring nodes. These digests are then transmitted to storage nodes together with the neighbors’ data, thereby establishing a chained relationship among sensor data. The base station verifies query results using this relationship. The baseline algorithm, however, causes high communication overhead. To reduce this cost, we propose an improved version, NNFDA-BM (NNFDA with Bitmap), which optimizes digest generation and transmission. Experimental results show that NNFDA-BM verifies query result integrity effectively while achieving a significant reduction in communication overhead compared with the baseline algorithm. Full article

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

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25 pages, 1141 KB

Open AccessArticle

Local LLM-Based Cyber Incident Analysis in Air-Gapped Networks via Teacher–Student Knowledge Distillation and Agentic Orchestration

by Sunghun Jang, MyoungRak Lee and Taeshik Shon

Electronics 2026, 15(13), 2949; https://doi.org/10.3390/electronics15132949 - 6 Jul 2026

Viewed by 864

Abstract

Recent cyber incidents have become increasingly sophisticated through Living-off-the-Land (LotL) techniques that exploit legitimate behavior and multi-stage attacks. This requires advanced reasoning capabilities to discern the attack contexts within fragmented large-scale logs. However, closed network environments with physical network separation (air-gapped), such as [...] Read more.

Recent cyber incidents have become increasingly sophisticated through Living-off-the-Land (LotL) techniques that exploit legitimate behavior and multi-stage attacks. This requires advanced reasoning capabilities to discern the attack contexts within fragmented large-scale logs. However, closed network environments with physical network separation (air-gapped), such as national critical infrastructures, restrict the use of high-performance cloud large language models (LLMs), thereby limiting the adoption of cutting-edge artificial intelligence (AI)-based analysis technologies. To overcome these constraints, this study proposes a Local LLM-based intrusion analysis framework that operates independently within closed networks. The proposed framework combines (i) an Offline Knowledge Distillation technique that transfers the analytical reasoning process of external high-performance models to the Local LLM after a security review, and (ii) an AI agent orchestration structure that controls the analysis procedure step-by-step and suppresses hallucinations. Experiments and validation using a public dataset (Atomic Red Team) demonstrated that the proposed model achieved a consistently higher detection accuracy (88.4%) and MITRE Adversarial Tactics, Techniques, and Common Knowledge mapping performance (0.91 F1-Score) than existing general-purpose Local LLMs. Furthermore, the proposed model suppressed hallucination rates to 6.2% through an automated verification mechanism and significantly improved analysis efficiency by refining large-scale logs to focus on core events. This study quantitatively demonstrated that AI-based intrusion incident analysis can be automated using a single graphics processing unit server under controlled evaluation conditions. The proposed framework provides a practical prototype for intelligent security monitoring in closed-network environments. However, the operational performance must be validated in real-world deployments. Full article

(This article belongs to the Special Issue New Insights into Natural Language Processing and Large Language Models)

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27 pages, 16380 KB

Open AccessArticle

YOLOv11-UAV: An Improved Deep Learning Algorithm for Small Maritime Target Detection

by Shicheng Li, Wentao Li, Tao Chen, Qinghua Liu and Mengdi Zhao

Electronics 2026, 15(13), 2948; https://doi.org/10.3390/electronics15132948 - 6 Jul 2026

Viewed by 468

Abstract

Maritime UAV surveillance requires rapid, accurate identification of small surface targets amidst volatile sea states. Conventional detectors typically degrade under intense wave clutter, variable lighting, and edge computing constraints. To address these limitations, this paper presents YOLOv11-UAV, a compact framework optimized for real-time [...] Read more.

Maritime UAV surveillance requires rapid, accurate identification of small surface targets amidst volatile sea states. Conventional detectors typically degrade under intense wave clutter, variable lighting, and edge computing constraints. To address these limitations, this paper presents YOLOv11-UAV, a compact framework optimized for real-time edge deployment. We introduce an SPPFLSC module integrating large separable kernel attention (LSKA-C) to extend the receptive field with minimal computational overhead. Additionally, an optimized C3k2-EVA block utilizing sparse decomposed large separable kernel attention (SDLSKA) improves feature representation and processing throughput. To refine localization for low-contrast objects, a high-resolution prediction head is integrated into the multi-scale pipeline. Quantitative evaluations on the SeaDronesSee benchmark demonstrate that YOLOv11-UAV yields substantial precision and recall gains, validating its efficacy for airborne maritime reconnaissance. Full article

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28 pages, 4041 KB

Open AccessArticle

Topology-Aware Hierarchical Attack Graph Optimization for Cyber-Physical Power Systems

by Mohamed Massaoudi, Thejas G.S., Maymouna Ez Eddin and Katherine R. Davis

Electronics 2026, 15(13), 2947; https://doi.org/10.3390/electronics15132947 - 6 Jul 2026

Viewed by 746

Abstract

Cyber-physical power systems face multi-stage attacks that exploit both communication-network topology and power-grid interdependencies to reach critical substations from low-security entry points. Attack graphs systematically enumerate multi-step attack paths. However, existing approaches either ignore physical network topology or separate attack-graph construction from defense [...] Read more.

Cyber-physical power systems face multi-stage attacks that exploit both communication-network topology and power-grid interdependencies to reach critical substations from low-security entry points. Attack graphs systematically enumerate multi-step attack paths. However, existing approaches either ignore physical network topology or separate attack-graph construction from defense placement, limiting operational usefulness. This paper presents an enhanced topology-aware greedy (TAG) framework that couples source-to-critical attack-path search with dual-mode cyber defense and explicit cyber-physical interdependency modeling. A hierarchical attack graph is constructed directly on the physical network graph, encoding compromise probabilities conditioned on both cyber vulnerability profiles and power-grid criticality. TAG employs topology-aware candidate screening, deterministic probabilistic propagation, beam search, and one-swap local refinement, followed by a dual-mode defense package combining node hardening, micro-segmentation, and monitored-neighbor shielding. Monte Carlo experiments on the 179-bus medium-voltage feeder, IEEE 39-bus New England, IEEE 118-bus, and RTS-96 benchmarks demonstrate that TAG reduces critical-reach probability by

54.3

–

82.0 %

versus no-defense baselines (mean

70.5 %

), and by

30.2

–

51.5 %

versus vanilla greedy placement. A cyber-physical impact-weighted risk analysis further shows that TAG’s structural defense placement yields proportional reductions in power-flow-consequence-weighted risk. Parameter sensitivity across the segmentation/containment factor

η \in [0.30, 0.80]

and the monitored-neighbor shielding factor

ρ \in [0.05, 0.30]

confirms robust method superiority (68–

72 %

risk reduction across all tested values). Full article

(This article belongs to the Special Issue Advanced Power Electronics and Sustainable Energy Systems: Recent Developments, Challenges and Future Perspectives)

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