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Defamiliarization Attack: Literary Theory Enabled Discussion of LLM Safety
Advances in Closed-Loop Artificial Intelligence for Healthcare
Securing Unmanned Devices in Critical Infrastructure: A Survey of Hardware, Network, and Swarm Intelligence
Model-Based Global Path Planning for Mobile Robots with Different Kinematic Structures Under Path Length and Energy Efficiency Criteria: A Case Study

Journal Description

Electronics

Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and its members receive a discount on article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Signal Processing)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Electronics include: Magnetism, Microwave, Network, Signals, Software, Microelectronics, Semiconductors and Heterogeneous Integration and Analog.
Journal Cluster of Electronic Engineering and Hardware Systems: Chips, Electronics, Hardware, Journal of Low Power Electronics and Applications, Microelectronics and Microwave.

Impact Factor: 2.9 (2025); 5-Year Impact Factor: 2.8 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

51 pages, 5501 KB

Open AccessReview

State of the Art in AI-Based Visual Inspection for Industrial Quality Control: Methods, Benchmarks, Challenges, and Autonomous Systems

by Amal Jayawardena, Jung-Hoon Sul, Diluka Moratuwage, Jaliya L. Wijayaraja and Lasitha Piyathilaka

Electronics 2026, 15(12), 2727; https://doi.org/10.3390/electronics15122727 (registering DOI) - 20 Jun 2026

Industrial quality control is a critical component of modern manufacturing, as defects can lead to significant economic losses and safety risks. Traditional inspection methods, largely reliant on human operators or rule-based systems, often suffer from inconsistency, limited scalability, and reduced accuracy in complex [...] Read more.

Industrial quality control is a critical component of modern manufacturing, as defects can lead to significant economic losses and safety risks. Traditional inspection methods, largely reliant on human operators or rule-based systems, often suffer from inconsistency, limited scalability, and reduced accuracy in complex environments. Recent advances in artificial intelligence (AI), particularly in deep learning and computer vision, have enabled automated defect detection and classification with unprecedented performance. This paper provides a comprehensive review of AI-based image processing techniques for industrial quality control, covering classification, detection, and segmentation approaches. Key applications across manufacturing sectors are discussed, alongside current challenges such as data scarcity, real-time implementation, and model generalisation. Furthermore, this paper explores emerging trends toward autonomous inspection systems, integrating real-time analytics, edge computing, and intelligent decision making. The insights presented aim to guide future research toward robust, scalable, and fully automated quality control solutions in smart manufacturing environments. Full article

(This article belongs to the Special Issue AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches: Second Edition)

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18 pages, 9812 KB

Open AccessArticle

AI-Assisted Circuit Digital Twin Reproducing Ultrasound Waves in Human Tissues

by Alessandro Massaro

Electronics 2026, 15(12), 2726; https://doi.org/10.3390/electronics15122726 (registering DOI) - 20 Jun 2026

The paper proposes a Digital Twin (DTw) framework, constructing a circuit model replicating the pulse transmission and reception processes for devices with high sensitivity to noises, such as wearable ultrasound transducers. The model is suitable to train supervised AI algorithms denoising the noisy [...] Read more.

The paper proposes a Digital Twin (DTw) framework, constructing a circuit model replicating the pulse transmission and reception processes for devices with high sensitivity to noises, such as wearable ultrasound transducers. The model is suitable to train supervised AI algorithms denoising the noisy ultrasound signal received. The DTw combines the circuit simulations with the AI data processing by training the model with the cleaned pulsed signals and by correcting the noises modeled by ‘white-noise’ voltage generators. Specifically, the voltage outputs of the circuit simulations are used to train the AI models and to test noisy signals for reconstruction. The DTw model is based on the transmission line theory combined with the perturbation impedance approach, supporting human body tissue discrimination based on noises. Two open-source tools are used for the DTw construction, the LTSpice and the Orange Mining tool, which are used for the circuit simulation and for the AI data processing, respectively. The theoretical work proves that the methodology is able to reconstruct correctly, with a good performance in the time domain and the frequency domain, noisy voltage signals, by addressing the analysis on cancer detection by combining circuit, AI and Monte Carlo approaches. Full article

(This article belongs to the Special Issue Wearable and Intelligent (Bio)Electronics for Healthcare: From Sensors to AI)

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22 pages, 6659 KB

Open AccessArticle

Active Resonance Suppression Strategy for Hybrid Multi-Infeed HVDC Receiving-End Grid with LCC and MMC

by Wen Hua, Chengming Zhang, Tian Hou, Guoteng Wang and Ying Huang

Electronics 2026, 15(12), 2725; https://doi.org/10.3390/electronics15122725 (registering DOI) - 20 Jun 2026

As renewable energy is increasingly integrated via high-voltage direct current (HVDC) transmission, hybrid multi-infeed receiving-end grids containing both line-commutated converters (LCC) and modular multilevel converters (MMC) have become common, and wideband resonance problems in power-electronized networks are growing more prominent. This paper proposes [...] Read more.

As renewable energy is increasingly integrated via high-voltage direct current (HVDC) transmission, hybrid multi-infeed receiving-end grids containing both line-commutated converters (LCC) and modular multilevel converters (MMC) have become common, and wideband resonance problems in power-electronized networks are growing more prominent. This paper proposes an active resonance analysis and suppression strategy for such systems. First, a wideband current source converter model and a wideband voltage source converter model are adopted to describe the LCC and MMC, respectively, and a positive-sequence s-domain model of the system is established. A two-stage s-domain nodal admittance matrix method is then applied to efficiently determine the wideband resonance modes and the corresponding mode shape eigenvectors. A dual criterion combining the matching degree between resonance frequencies and LCC characteristic harmonics with the modal damping ratio identifies high-risk resonance modes. On this basis, an active damping strategy that realizes a parallel virtual resistance on the AC side through MMC supplementary control is proposed, together with a quantitative design method for the virtual conductance. At the control implementation level, a modulation wave reconstruction bypass injection scheme superimposes the high-frequency damping command directly in the

α β

stationary reference frame, thereby bypassing the PI controller and reducing the amplitude attenuation and phase distortion caused by the high-frequency limitation of the integral path. PSCAD/EMTDC simulation results on an IEEE 9-bus test system demonstrate that the proposed strategy effectively suppresses resonance amplification and wideband power oscillations excited by LCC characteristic harmonics without affecting the fundamental power transmission. Full article

(This article belongs to the Special Issue Advanced Power Converter Technologies for Smart Grids)

24 pages, 2535 KB

Open AccessArticle

RASC: Region-Aware Self-Calibration for Dense 2D Sensor Arrays

by Yinglei Ma and Fei Xiao

Electronics 2026, 15(12), 2724; https://doi.org/10.3390/electronics15122724 (registering DOI) - 19 Jun 2026

Bipolar junction transistor (BJT)-based 2D temperature-sensor arrays are factory-calibrated to ±0.1 °C, but post-deployment thermal and mechanical stresses drift their per-sensor gain–offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes [...] Read more.

Bipolar junction transistor (BJT)-based 2D temperature-sensor arrays are factory-calibrated to ±0.1 °C, but post-deployment thermal and mechanical stresses drift their per-sensor gain–offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes the global ill-posed problem into local cluster-level problems, runs robust alternating estimation (trimmed-mean field reconstruction + Huber iteratively reweighted least squares (IRLS)) inside each cluster, and reconciles overlapping estimates by linear consensus on the cluster-overlap graph with provable exponential convergence. On 7632 frames from a deployed 16 × 16 array exhibiting ≈5× factory-spec non-uniformity, RASC cuts the locally non-smooth fixed-pattern residual by 71 ± 5% (10-fold cross-validation (CV)), reducing this residual to a level comparable to the ±0.1 °C factory specification (as assessed by local-smoothness residual metrics, not independent absolute-temperature validation) while perturbing the calibrated field by only 0.041 °C RMSE; reduction concentrates at the edges (78% vs. 55% interior). In simulations on 8 × 8 to 32 × 32 arrays, RASC matches an oracle centralised extended Kalman filter (EKF) within 0.10 °C with ≈4× lower bandwidth. The real-data evaluation is a single-deployment proof of concept on one array and one host PCB; broader, longitudinal validation remains future work. Full article

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

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31 pages, 22229 KB

Open AccessArticle

Robust and Interpretable Anomaly Detection in Automotive Test Recordings Using Denoising Autoencoders with Adaptive Thresholding

by Mohammad Abboush, Franck Andy Dzoupet Yimtchi, Ömer Tan, Hamza Ouarrad and Andreas Rausch

Electronics 2026, 15(12), 2723; https://doi.org/10.3390/electronics15122723 (registering DOI) - 19 Jun 2026

The growing complexity of software-defined automotive systems generates massive heterogeneous sensor and ECU data during real and virtual validation, and conventional rule-based analysis of such multivariate time series struggles under dynamic operating conditions, noise, and diverse fault scenarios. Deep learning-based anomaly detection has [...] Read more.

The growing complexity of software-defined automotive systems generates massive heterogeneous sensor and ECU data during real and virtual validation, and conventional rule-based analysis of such multivariate time series struggles under dynamic operating conditions, noise, and diverse fault scenarios. Deep learning-based anomaly detection has shown promising performance, yet existing approaches remain limited by static thresholds, insufficient robustness, and reduced interpretability. This study proposes an adaptive framework for intelligent fault detection in test recordings of automotive software systems (ASSs), integrating deep denoising autoencoders (DAEs), adaptive Gaussian thresholding, and explainable artificial intelligence (XAI) techniques. Four DAE architectures (ANN-, RNN-, GRU-, and LSTM-DAE) are systematically evaluated under different noise levels, system versions, and fault conditions, with detection thresholds that adapt dynamically to the statistical behavior of the reconstructed signals, thereby reducing false alarms under varying operating conditions. The framework was evaluated using real-world test recordings from IAV and Hardware-in-the-Loop (HIL)-based digital test drives, where ANN-DAE achieved the most robust detection performance, with F1-scores of 93.91% and 96.39% on the real and virtual test-drive data, respectively. Furthermore, the integration of XAI improved the transparency of anomaly interpretation at the signal level. Overall, the proposed framework shows strong potential for intelligent anomaly detection and quality assurance in safety-critical automotive systems. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Fault Detection and Diagnosis for Automotive Software Systems: From Machine Learning to Foundation Models)

21 pages, 4540 KB

Open AccessArticle

Online Parameter Identification for Sensorless PMSM Drives with Inverter Nonlinearity Compensation

by Fuyuan Xiang, Zitong Zhou and Zuo Wang

Electronics 2026, 15(12), 2722; https://doi.org/10.3390/electronics15122722 (registering DOI) - 19 Jun 2026

Online parameter identification is important for sensorless permanent magnet synchronous motor (PMSM) drives because motor parameter variation can reduce the accuracy of the controller and observer. However, in the background of sensorless control, the accuracy of online parameter identification is significantly affected by [...] Read more.

Online parameter identification is important for sensorless permanent magnet synchronous motor (PMSM) drives because motor parameter variation can reduce the accuracy of the controller and observer. However, in the background of sensorless control, the accuracy of online parameter identification is significantly affected by rotor position estimation errors and inverter nonlinearity. To address these problems, this paper proposes a high-frequency d-axis voltage injection-based online parameter identification method with inverter nonlinearity compensation. The proposed online identification method can identify the stator resistance and d-axis inductance independently. It not only overcomes the rank-deficiency problem in conventional voltage-equation-based identification, but also shows through theoretical analysis that the identification results are insensitive to rotor position estimation errors. To improve the identification accuracy, the influence and importance of inverter nonlinearity on parameter identification are analyzed, and a compensation method based on zero-sequence voltage characteristics and a feedforward neural network is developed. The identified voltage error is compensated through equivalent dead-time correction. Simulation and experimental results verify the advantages of the proposed method under different operating conditions. Full article

(This article belongs to the Special Issue Advancements in Electric Motors, Drives, Power Converters and Related Systems, 3rd Edition)

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12 pages, 3399 KB

Open AccessArticle

Investigation on Degradation of Switching Characteristics in SiC MOSFETs Under Repetitive Surge Current

by Zhichao Cheng, Ling Sang, Feng He, Yawei He, Zheyang Li, Rui Jin and Peng Cui

Electronics 2026, 15(12), 2721; https://doi.org/10.3390/electronics15122721 (registering DOI) - 19 Jun 2026

Surge reliability is a crucial aspect of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) reliability. This study investigates the degradation behavior and mechanisms of switching characteristics in 1.2 kV planar-gate SiC MOSFETs under repetitive surge current. A surge current test platform is established [...] Read more.

Surge reliability is a crucial aspect of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) reliability. This study investigates the degradation behavior and mechanisms of switching characteristics in 1.2 kV planar-gate SiC MOSFETs under repetitive surge current. A surge current test platform is established to conduct surge tests on the device, while monitoring the evolution of its switching characteristics. The results indicate that after 4000 surge current cycles, the device’s turn-on delay time (t_d(on)), rise time (t_r), and turn-on loss (E_ON) show no significant changes. In contrast, the turn-off delay time (t_d(off)), fall time (t_f), and turn-off loss (E_OFF) increase by 9%, 7.5%, and 8.3%, respectively. Switching characteristics variations are closely linked to the reduction in threshold voltage (V_TH) and the increase in gate-source capacitance (C_GS) and gate-drain capacitance (C_GD). The degradation of these parameters stems from the accumulation of positive trapped charge in the gate oxide layer above the channel and junction field-effect transistor (JFET) region. The increase in charges results from the combined effects of negative gate bias and cyclic high temperature induced by repetitive surge current. This study provides a theoretical basis for the comprehensive understanding of the impact of surge current on SiC MOSFET performance. Full article

(This article belongs to the Section Power Electronics)

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17 pages, 2753 KB

Open AccessArticle

KoSim-GL: A Large-Scale Simulation-Based Dataset for UAV Cross-View Geo-Localization in Korean Urban Environments

by Heejin Ahn, Changhwan Lee, Sangwook Lee, HyeonJoong Wi, Insung Jang and Dong-Geol Choi

Electronics 2026, 15(12), 2720; https://doi.org/10.3390/electronics15122720 (registering DOI) - 19 Jun 2026

We propose KoSim-GL, a large-scale vision-based geo-localization dataset for drone positioning in GPS-denied environments. Geo-localization estimates a drone’s location by matching drone-view imagery against a geo-referenced satellite image database, offering a reliable alternative to GPS under conditions such as signal jamming, spoofing, or [...] Read more.

We propose KoSim-GL, a large-scale vision-based geo-localization dataset for drone positioning in GPS-denied environments. Geo-localization estimates a drone’s location by matching drone-view imagery against a geo-referenced satellite image database, offering a reliable alternative to GPS under conditions such as signal jamming, spoofing, or degradation in dense urban canyons. Although this task is challenging due to the domain gap between drone-view and satellite-view imagery, existing benchmarks are built predominantly around urban environments in the United States and China, leaving South Korea largely unrepresented, despite its distinctive landscape in which mountainous terrain coexists with dense high-rise districts and low-rise residential neighborhoods. To address this gap, we introduce KoSim-GL, constructed from drone-view images captured via an AirSim- and ROS-based flight simulator and satellite images collected through the Google Maps Tile API, covering the urban area of Daejeon, South Korea. Its key feature is a multi-view configuration that simultaneously captures five views, one nadir and four oblique, at each flight position across altitudes from 100 m to 600 m, enabling robust localization even in feature-sparse environments where nadir-only matching is prone to fail. In total, KoSim-GL comprises 2,450,315 drone images and 1704 satellite images. We further provide systematic comparisons against five existing benchmarks and baseline evaluations of ten representative geo-localization models under single- and multi-view settings. Experimental results show that the multi-view configuration substantially improves localization performance; for example, FSRA improves Recall@1 from 44.08% (single-view) to 65.37% (multi-view), a gain of 21.29 percentage points. The dataset is publicly available. Full article

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

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32 pages, 3279 KB

Open AccessArticle

Energy-Constrained Hybrid Repair for Lifelong Multi-Agent Path Finding in Smart Warehouses

by Riyang Luo, Can Lu and Jin He

Electronics 2026, 15(12), 2719; https://doi.org/10.3390/electronics15122719 (registering DOI) - 19 Jun 2026

Smart warehouses require autonomous mobile robots to complete lifelong tasks while avoiding conflicts, respecting battery constraints, and sharing charging stations. Existing MAPF methods provide strong conflict resolution, but energy, charging, and online action repair are commonly evaluated separately. We present ECR-HR, an energy-constrained [...] Read more.

Smart warehouses require autonomous mobile robots to complete lifelong tasks while avoiding conflicts, respecting battery constraints, and sharing charging stations. Existing MAPF methods provide strong conflict resolution, but energy, charging, and online action repair are commonly evaluated separately. We present ECR-HR, an energy-constrained hybrid repair framework that combines a normalized energy model, charging-aware goals, risk-informed priorities, and bounded local conflict repair. The scientific contribution is a coupled execution and evaluation interface rather than a new complete MAPF solver or a claim of dominance over MAPF-LNS2. In reproducible simulation, we compare ECR-HR with classical, repair-based, lazy-search, conflict-based, and learning-based baselines. In 40-seed nominal evaluation, ECR-HR reduces candidate conflict rate relative to WHCA* from 0.0479 to 0.0255 (

p = 3.89 \times 10^{- 6}

) while MAPF-LNS2 achieves the strongest raw success. A 30-seed study using MovingAI map geometry, priority and repair comparisons, module-level runtime profiling, simulated disturbance tests, 25-seed energy coefficient sensitivity, and preference weight sensitivity further define the framework’s operating boundary. Enhanced GNN-PPO-HR increases held-out success from the original 0.188 to

0.753 \pm 0.174

but remains below mature search baselines. All evidence is simulation-based, the energy coefficients are normalized rather than hardware-calibrated, and real-robot validation remains necessary. Full article

(This article belongs to the Section Artificial Intelligence)

20 pages, 2974 KB

Open AccessArticle

Modified Artificial Hummingbird Algorithm for Determining Optimal Location of EVCS in Power Grid

by Sravan Kumar Dumpeti and Mohd. Hasan Ali

Electronics 2026, 15(12), 2718; https://doi.org/10.3390/electronics15122718 - 19 Jun 2026

The rapid increase in the adoption of electric vehicles (EVs) in recent years is leading to a significant impact on the electric grid. To ensure sufficient power to these EVs, multiple electric vehicle charging stations (EVCSs) need to be deployed strategically in the [...] Read more.

The rapid increase in the adoption of electric vehicles (EVs) in recent years is leading to a significant impact on the electric grid. To ensure sufficient power to these EVs, multiple electric vehicle charging stations (EVCSs) need to be deployed strategically in the electrical power network. Randomly adding these EVCSs can cause potential power quality problems and necessitate additional infrastructure like new distribution/transmission lines, transformers and sub-stations. This can be overcome by optimal deployment of EVCSs. Many existing optimization techniques suffer from premature convergence, sensitivity to initial parameters, the curse of dimensionality and not performing well on non-linear problems. This leads to suboptimal results. To address these drawbacks, a novel method, based on the Artificial Hummingbird Algorithm (AHA), has been developed to identify the optimal location of EVCSs. The novel method, the Modified Artificial Hummingbird Algorithm (MAHA), has been applied to the standard power network–IEEE-57 bus system to find the optimal placement of EVCSs. When compared to existing methods of AHA, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO), the results show that MAHA is more effective in determining the optimal placement of EVCSs. Full article

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18 pages, 2634 KB

Open AccessArticle

An Intelligent Wireless Sensor Network for Real-Time Kimchi Fermentation Monitoring and Early Abnormality Detection

by Jihyun Byun, Jooho Lee, Seongju Woo and Sangoh Kim

Electronics 2026, 15(12), 2717; https://doi.org/10.3390/electronics15122717 - 19 Jun 2026

Kimchi fermentation involves dynamic physicochemical and microbial changes; however, conventional monitoring methods are generally dependent on intermittent measurements, resulting in limitations in the real-time detection of abnormal fermentation. In this study, a Wireless Sensor Network (WSN)-based Fermentation Monitoring System (WFMS) and a Long [...] Read more.

Kimchi fermentation involves dynamic physicochemical and microbial changes; however, conventional monitoring methods are generally dependent on intermittent measurements, resulting in limitations in the real-time detection of abnormal fermentation. In this study, a Wireless Sensor Network (WSN)-based Fermentation Monitoring System (WFMS) and a Long Short-Term Memory (LSTM)-based Anomaly Detection System (LADS) were developed to continuously monitor internal pressure changes during kimchi fermentation. Kimchi samples were prepared under normal fermentation conditions (CON) and glucose-added conditions (GLU-6). Pressure data were collected at 10 min intervals using 15 psi and 30 psi pressure sensors connected to an Arduino Nano 33 IoT board and were transmitted to the ThingSpeak platform. During the fermentation period, pressure data were collected stably, while the external temperature was maintained at approximately 25 °C. Both CON and GLU-6 samples exhibited a rapid increase in internal pressure during the early fermentation stage, followed by a gradual decrease. However, relatively larger pressure fluctuations were observed in the middle and late fermentation stages of the GLU-6 samples. An LSTM autoencoder model trained using CON data established a reconstruction error-based threshold of 0.0025 and successfully detected anomalies in the GLU-6 samples. Anomalies were mainly identified during the initial fermentation stage and between fermentation days 2 and 4. These results demonstrate that pressure-based real-time monitoring combined with LSTM autoencoder analysis can be effectively applied for the non-destructive tracking of kimchi fermentation and the early detection of abnormal fermentation patterns. Full article

(This article belongs to the Special Issue Towards Intelligent Wireless Sensor Networks)

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23 pages, 602 KB

Open AccessArticle

A Decentralized Framework to Gather and Certify Green Energy Data in Demand Response Programs

by Daniele Marletta, Alessandro Midolo and Emiliano Tramontana

Electronics 2026, 15(12), 2716; https://doi.org/10.3390/electronics15122716 - 19 Jun 2026

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The [...] Read more.

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants. Full article

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

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13 pages, 3658 KB

Open AccessArticle

TR-ABFT: Tile-Resilient Fault Detection for Neural Processing Units

by Yang Hua, Yunhong Bai, Bo Wang, Wei Zhuang and Yuanfu Zhao

Electronics 2026, 15(12), 2715; https://doi.org/10.3390/electronics15122715 - 19 Jun 2026

Spaceborne neural processing units (NPUs) increasingly support real-time deep-learning inference, but their dense multiply-accumulate arrays are vulnerable to radiation-induced soft errors. Conventional radiation-hardening methods improve reliability through hardware redundancy, but they incur substantial area, performance and compiler-mapping overheads. This paper proposes tile-resilient algorithm-based [...] Read more.

Spaceborne neural processing units (NPUs) increasingly support real-time deep-learning inference, but their dense multiply-accumulate arrays are vulnerable to radiation-induced soft errors. Conventional radiation-hardening methods improve reliability through hardware redundancy, but they incur substantial area, performance and compiler-mapping overheads. This paper proposes tile-resilient algorithm-based fault tolerance (TR-ABFT), a software-scheduled, detection-oriented scheme for quantized NPU inference. TR-ABFT generates checksum information at tile granularity and maps checking tasks onto the original processing element (PE) array without changing the hardware topology. To make ABFT compatible with INT8 datapaths, we design two checksum-coding strategies: checksum decomposition and modulo-239 checksum coding. The modulo-239 scheme removes structural missed detections for two-bit flips with bit-position spacings in (1, 31), while preserving compatibility with signed INT8 inputs. Evaluations on ResNet, YOLOv8, and RT-DETR show that, on a

16 \times 16

array, TR-ABFT introduces only 6.37% to 24.61% additional computational overhead. By converting spatial redundancy into schedulable temporal redundancy, TR-ABFT preserves systolic-array regularity and provides a low-overhead reliability-enhancement mechanism for space-grade neural-network accelerators. Full article

(This article belongs to the Special Issue Artificial Intelligence and Microsystems)

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33 pages, 685 KB

Open AccessArticle

A Secure and Lightweight Authentication and Key Agreement Protocol for Blockchain-Assisted IoT Collaboration Environments

by Dalhae Kim, Hyewon Park and Yohan Park

Electronics 2026, 15(12), 2714; https://doi.org/10.3390/electronics15122714 - 18 Jun 2026

Blockchain-assisted authentication frameworks have been introduced to mitigate the single point-of-failure problem in centralized IoT collaboration environments. Recently, a lightweight trust management framework based on a permissioned blockchain was proposed for distributed authentication and interaction traceability. However, our analysis shows that this protocol [...] Read more.

Blockchain-assisted authentication frameworks have been introduced to mitigate the single point-of-failure problem in centralized IoT collaboration environments. Recently, a lightweight trust management framework based on a permissioned blockchain was proposed for distributed authentication and interaction traceability. However, our analysis shows that this protocol is vulnerable to offline password guessing, terminal device impersonation, session-key disclosure, and user traceability attacks. It also fails to provide perfect forward secrecy. Accordingly, we propose a secure and lightweight authentication and key agreement protocol for blockchain-assisted IoT collaboration environments. The proposed scheme integrates Physically Unclonable Functions to improve resistance against physical capture and device cloning attacks. It also uses a fuzzy extractor to support biometric-based authentication and a dynamic pseudo-identity update mechanism managed through a consortium blockchain to protect user anonymity and untraceability. The proposed protocol is verified using the Real-or-Random model, BAN logic, and AVISPA simulations. Full article

(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems, 3rd Edition)

26 pages, 6047 KB

Open AccessArticle

Analysis of Memristor-Based Neural Networks and Logic Circuits for Artificial Intelligence Using Standard and Improved Memristor Models

by Stoyan Kirilov, Georgi Tsenov and Valeri Mladenov

Electronics 2026, 15(12), 2713; https://doi.org/10.3390/electronics15122713 - 18 Jun 2026

Memristors are state-of-the-art electronic elements with nano sizes, about 3 nm dimensions, with very good nano-second switching and memory properties, low power usage of about 100 µW, and good compatibility with the current technology of CMOS-integrated chips and circuits. These components are potentially [...] Read more.

Memristors are state-of-the-art electronic elements with nano sizes, about 3 nm dimensions, with very good nano-second switching and memory properties, low power usage of about 100 µW, and good compatibility with the current technology of CMOS-integrated chips and circuits. These components are potentially applicable in T-byte memory arrays, artificial neural networks, logic gates and many other digital and analog electronic schemes and devices for artificial intelligence. This paper presents the application of some simple and fast-operating modified memristor models with activation thresholds in neural networks and logic circuits. MATLAB ver. 2016a and LTSPICE ver. XVII products are used for the analysis of memristor neural nets and logical circuits for artificial intelligence. Several simple, accurate and fast-operating existing modified memristor models, together with several frequently used standard memristor models, are utilized for the associated analyses and simulations. A comparison between the used memristor models is conducted. The considered memristor models are tuned, using experimentally recorded i-v relations of tungsten-sulfide Knowm memristors. An accurate functioning of the analyzed neural nets and logic functions is confirmed by the derived results. The considered modified memristor models, neural networks and logic schemes are important in modeling and analysis of memristor-based circuits for ultra-high-density artificial intelligence-integrated chips. Full article

(This article belongs to the Special Issue Memristors—Building Blocks of Future Electronics: 6G, AI, and Beyond)

34 pages, 614 KB

Open AccessArticle

A Verification-Table-Free Post-Quantum Authenticated Key Agreement Scheme via ML-DSA-Based Subliminal Message Recovery

by Ming-Hsien Lu and Tzung-Her Chen

Electronics 2026, 15(12), 2712; https://doi.org/10.3390/electronics15122712 - 18 Jun 2026

In user–server authentication environments, persistent server-side verification tables, such as password verifiers, shared authentication records, or per-user secret tables, may become a critical point of failure once leaked. To address this problem in the post-quantum setting, this paper proposes an ML-DSA-specific verification-table-free authenticated [...] Read more.

In user–server authentication environments, persistent server-side verification tables, such as password verifiers, shared authentication records, or per-user secret tables, may become a critical point of failure once leaked. To address this problem in the post-quantum setting, this paper proposes an ML-DSA-specific verification-table-free authenticated key agreement (AKA) scheme based on the NIST-standardized Module-Lattice-Based Digital Signature Algorithm (ML-DSA). The main contribution is a protocol-level use of the signer-recoverable masking vector in ML-DSA as an on-demand reconstruction mechanism for user-related authentication material. This enables the server to reconstruct the required user-related authentication material from its own signature and long-term secret key. This architecture reduces the exposure associated with centralized verification-table leakage, but it should be understood as a storage-relocation tradeoff rather than a storage-free design, because each user must retain the issued signature and the corresponding hash-derived authentication value. By combining the recovered value with identity information through a quantum-resistant one-way hash function, the server can authenticate the user and establish a session key. Its security is analyzed within a Canetti–Krawczyk-style adversarial model and further discussed in the random-oracle setting through a sequence-of-games argument. The analysis supports session-key indistinguishability under the stated freshness and exposure assumptions, while explicitly excluding full forward secrecy under compromise of the server’s long-term ML-DSA secret key. In addition, an operation-level comparison is provided to clarify computational, storage, and communication tradeoffs relative to representative post-quantum AKA schemes. Since the present work does not include implementation-level benchmarking, the performance discussion should be interpreted as analytical rather than empirical validation. The proposed scheme is therefore most suitable for account-login-oriented applications in which reducing centralized verification-table leakage is a primary design objective and where user-side credential storage can be securely managed. Full article

(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 3rd Edition)

32 pages, 3409 KB

Open AccessArticle

xServeNet: An Explainable Deep Neural Network for Web Services Classification

by Yilong Yang, Muhammad Ali Khan, Zhaotian Li and Weiru Wang

Electronics 2026, 15(12), 2711; https://doi.org/10.3390/electronics15122711 - 18 Jun 2026

Web service classification plays an important role in software reuse, service discovery, and automatic metadata organization. Although recent deep learning approaches have improved classification performance by using service names and natural-language descriptions, most existing methods still operate as black-box models and offer limited [...] Read more.

Web service classification plays an important role in software reuse, service discovery, and automatic metadata organization. Although recent deep learning approaches have improved classification performance by using service names and natural-language descriptions, most existing methods still operate as black-box models and offer limited insight into how different metadata sources influence classification decisions. This lack of transparency reduces their practical usefulness for developers who need to verify predicted categories, analyze incorrect classifications, and improve service metadata quality. A well-trained interpretable model can not only help developers choose more appropriate and reliable categories for each web service, but also help write a more reasonable service name and description. In this paper, we present xServeNet, an explainability-oriented extension of ServeNet for transparent web service classification. xServeNet preserves the BERT-based representation and CNN–BiLSTM feature extractor of ServeNet and introduces (i) an instance-wise dynamic source-fusion mechanism that adaptively combines service-name and service-description features according to their semantic contribution, and (ii) model-internal importance indicators at both the source and word levels that support inspection of classification decisions without introducing additional trainable parameters. We benchmark xServeNet against eleven machine learning baselines on two real-world ProgrammableWeb datasets of 10,943 and 14,086 services covering 50 categories. xServeNet reaches 71.08% Top-1/91.35% Top-5 accuracy on the original dataset and 74.10% Top-1/92.95% Top-5 accuracy on the updated dataset, consistently improving Top-1 accuracy over ServeNet while remaining competitive on Top-5, and achieving the lowest per-category Top-5 standard deviation among all twelve compared methods. In practice, the importance indicators support three concrete activities at the service registry: helping developers verify predicted categories at registration time, iterating on description wording when the predicted category looks wrong, and supporting registry curators in flagging likely mislabelled services for review. Full article

(This article belongs to the Special Issue New Trends in Machine Learning, System and Digital Twins)

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16 pages, 1199 KB

Open AccessArticle

Calibration-Block-Based Tilt-Pose Error Identification and Compensation for Line Confocal Sensors

by Yuan Fu, Ting Chen, Ning Chen, Bin Guo, Yinghui Wang, Yinbao Cheng and Chuan Ma

Electronics 2026, 15(12), 2710; https://doi.org/10.3390/electronics15122710 - 18 Jun 2026

Line confocal sensors provide non-contact, high-resolution, and high-efficiency measurement and can be integrated into optical measurement systems such as Photon for three-dimensional topography measurement of complex surfaces. However, installation-induced tilt-pose errors of the sensor can couple height information with lateral position, thereby reducing [...] Read more.

Line confocal sensors provide non-contact, high-resolution, and high-efficiency measurement and can be integrated into optical measurement systems such as Photon for three-dimensional topography measurement of complex surfaces. However, installation-induced tilt-pose errors of the sensor can couple height information with lateral position, thereby reducing the accuracy of profile reconstruction. To address this issue, this paper proposes a calibration-block-based tilt-pose error identification and compensation method for line confocal sensors. Using the known geometric features of the calibration block, the proposed method establishes a mapping relationship between sensor tilt-pose errors and measured profile distortion. Sensitivity analysis is performed to identify the dominant error components, and the tilt-pose errors are estimated in a single identification process, enabling quantitative compensation of the measured point cloud. Experimental results show that, after calibration and compensation, the maximum Z-direction height difference in the overlapping profile region of the calibration block is reduced from 12.782 μm to 0.307 μm. The proposed method requires no complex external alignment devices and provides an effective approach for high-precision integrated applications of line confocal sensors. Full article

18 pages, 1351 KB

Open AccessArticle

Threshold-Based Private Set Intersection Protocol for Secure Deconfliction in Multi-Jurisdictional Blockchain Investigations

by Ruslan Shevchuk, Bogdan Adamyk and Vladlena Benson

Electronics 2026, 15(12), 2709; https://doi.org/10.3390/electronics15122709 - 18 Jun 2026

Cross-border blockchain investigations frequently face data isolation challenges where multiple jurisdictions may conduct parallel inquiries into the same suspicious entities, leading to operational conflicts and redundant efforts. This paper presents a purpose-built t-out-of-n watchlist-anchored private set intersection (PSI) protocol, adapting established [...] Read more.

Cross-border blockchain investigations frequently face data isolation challenges where multiple jurisdictions may conduct parallel inquiries into the same suspicious entities, leading to operational conflicts and redundant efforts. This paper presents a purpose-built t-out-of-n watchlist-anchored private set intersection (PSI) protocol, adapting established threshold secret-sharing techniques for secure jurisdictional discovery, enabling agencies to identify overlapping investigative targets without prematurely disclosing sensitive case details. The methodology is built upon Shamir’s Secret Sharing (SSS) and polynomial interpolation over the

2^{127} - 1

Mersenne prime field. A deterministic dual-hash field mapping ensures statistical uniformity over the prime field. Experimental validation using the Elliptic++ dataset confirmed the system’s high efficiency. The protocol maintains linear communication complexity of

O (n \cdot | S_{0} |)

, where complexity scales with the watchlist size rather than the full participant dataset and remains stable under varying consensus requirements, where increasing the threshold t results in a marginal increase in total latency. Under the semi-honest adversarial model, the false-positive rate is cryptographically negligible at

2^{- 127}

. The protocol achieves a hybrid security model wherein share privacy is information-theoretic under SSS, while field mapping and share authentication rely on standard computational assumptions. By integrating native source traceability, this framework provides a practical technological foundation for initiating formal Mutual Legal Assistance Treaty (MLAT) requests based on confidential matches identified across independent investigative workflows. Full article

(This article belongs to the Special Issue Data Privacy Protection in Blockchain Systems)

23 pages, 1602 KB

Open AccessArticle

Research on a Three-Tier Joint Trading Coordination Framework and Benefit Allocation for Provincial Power Grids in Northeast China Under High-Penetration Renewable Energy

by Xiaotian Zhang, Zhen Huang, Qun Li, Linkun Man, Yang Yu, Donglin Lv, Jichun Liu and Yikai Wang

Electronics 2026, 15(12), 2708; https://doi.org/10.3390/electronics15122708 - 18 Jun 2026

To address supply–demand dynamics and inter-provincial resource mismatches under high-penetration renewable integration, this study proposes a tri-level collaborative trading framework for provincial power grids in Northeast China. The system integrates intra-provincial baselines, intra-regional synergy, and inter-regional optimized export through multi-stage optimization models. An [...] Read more.

To address supply–demand dynamics and inter-provincial resource mismatches under high-penetration renewable integration, this study proposes a tri-level collaborative trading framework for provincial power grids in Northeast China. The system integrates intra-provincial baselines, intra-regional synergy, and inter-regional optimized export through multi-stage optimization models. An improved Shapley value method is further introduced to ensure fair benefit allocation based on electricity contribution and transmission support. Simulation results for the Northeast region show that this collaborative approach reduces generation costs by 13.88% and enhances social welfare by 7.58% compared to independent trading. This framework effectively breaks inter-provincial barriers and provides a theoretical foundation for regional power market planning and security of supply. Full article

(This article belongs to the Special Issue AI-Enhanced Stability and Resilience in Modern Power Systems)

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