Electronics

27 pages, 2134 KB

Open AccessArticle

Adaptive SOC Estimation of Reconfigurable Battery Modules Based on a Hybrid Deep Learning Model

by Qiang Zhao, Fanqi Tang and Bing Zhang

Electronics 2026, 15(10), 2208; https://doi.org/10.3390/electronics15102208 - 20 May 2026

Reconfigurable battery modules can dynamically adjust the connection topology among battery cells, significantly improving the energy utilization efficiency of battery energy storage systems. However, existing state estimation methods focus primarily on individual battery cells. Frequent topology changes cause traditional State of Charge (SOC) [...] Read more.

Reconfigurable battery modules can dynamically adjust the connection topology among battery cells, significantly improving the energy utilization efficiency of battery energy storage systems. However, existing state estimation methods focus primarily on individual battery cells. Frequent topology changes cause traditional State of Charge (SOC) estimation algorithms to accumulate large errors due to mismatches in equivalent capacity and internal resistance, making them ineffective for reconfigurable battery modules. To address this limitation, this paper proposes a Gated Recurrent Unit–Transformer architecture for precise SOC estimation in reconfigurable battery modules. The model uses a Gated Recurrent Unit to capture the temporal continuity of electrochemical evolution and employs the Transformer’s self-attention mechanism to analyze discrete topology changes. Experimental results show excellent estimation accuracy across different initial SOC levels, with a mean absolute error as low as 0.085% at full charge and 0.035% at 50% SOC. The architecture demonstrates strong topology self-identification capability and maintains high robustness even under abrupt voltage steps caused by reconfiguration. This method provides accurate and reliable state estimation for large-scale two-layer reconfigurable battery systems while reducing control complexity and improving operational efficiency. Full article

(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

29 pages, 1438 KB

Open AccessArticle

Stability-Driven Feature Extraction–Kolmogorov–Arnold Network-Driven Ensemble Framework for Reliable Breast Cancer Detection

by Abdul Rahaman Wahab Sait and Yazeed Alkhurayyif

Electronics 2026, 15(10), 2207; https://doi.org/10.3390/electronics15102207 - 20 May 2026

Abstract

Breast cancer screening is a fundamentally probabilistic diagnostic task that requires precise identification of complex imaging characteristics from diverse patient cohorts. Despite improvements in deep learning techniques, current automatic tools are typically trained on well-curated datasets and do not generalize to heterogeneous data, [...] Read more.

Breast cancer screening is a fundamentally probabilistic diagnostic task that requires precise identification of complex imaging characteristics from diverse patient cohorts. Despite improvements in deep learning techniques, current automatic tools are typically trained on well-curated datasets and do not generalize to heterogeneous data, thereby limiting their application. This study aims to address these shortcomings by introducing a more effective and generalizable framework for breast cancer classification that focuses on the stability of features, the learning of complementary representations, and improved decision modeling. The proposed methodology incorporates stability-driven feature extraction (SDFE) with a multi-branch architecture that consists of EfficientNetV2 (Convolutional neural networks (CNNs)), EfficientFormer (Vision transformers (ViTs)), and multi-layer perceptron (MLP)-Mixer models to extract various feature representations. To improve non-linear decision boundaries, it uses a Kolmogorov–Arnold Network (KAN)-based classification head and selects the most credible prediction via an adaptive voting mechanism. This model is trained using patient-level splitting on the VinDr-Mammo dataset, evaluated using five-fold cross-validation, and subsequently externally validated on the CBIS-DDSM dataset. Experimental findings demonstrate the consistent performance of the proposed model, with accuracies of 94.5% in cross-validation, 93.3% on the VinDr-Mammo test set, and 94.6% on CBIS-DDSM, surpassing other recent state-of-the-art solutions. It demonstrates enhanced robustness and cross-dataset generalization, offering a scalable, consistent framework for breast cancer classification that supports the development of computer-aided diagnostic systems. Full article

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

14 pages, 2569 KB

Open AccessArticle

Cyanate Ester–Lunar Regolith Composites for In Situ Fabrication of Structural Electronics on the Moon

by Guancheng Li, Batuhan Mirac Alasahin, Mark Mirotznik and Robert L. Opila

Electronics 2026, 15(10), 2206; https://doi.org/10.3390/electronics15102206 - 20 May 2026

Abstract

The development of electronic substrates from locally available materials is critical for sustainable lunar infrastructure. This work investigates the synthesis, processing, and characterization of cyanate ester–lunar regolith simulant (CE-LRS) composites designed specifically for the extreme lunar environment. LRS were evaluated as functional fillers [...] Read more.

The development of electronic substrates from locally available materials is critical for sustainable lunar infrastructure. This work investigates the synthesis, processing, and characterization of cyanate ester–lunar regolith simulant (CE-LRS) composites designed specifically for the extreme lunar environment. LRS were evaluated as functional fillers at loadings up to 55 wt.% with CE binder selected for its thermal stability (T_g > 230 °C), vacuum compatibility, and known radiation resistance from prior literature. A vacuum-assisted curing procedure was developed that utilizes the lunar environment as a processing advantage, reducing porosity from approximately 7% to less than 1% as quantified by X-ray micro-computed tomography. Dynamic mechanical analysis revealed that increased filler loading and vacuum processing enhanced the storage modulus and T_g through constraining polymer chain mobility at the filler-binder interface, confirming effective stress transfer and interfacial adhesion. Scanning electron microscopy also verified intimate polymer–filler wetting. Waveguide measurements in the microwave frequency range demonstrated that the composites remain non-magnetic while exhibiting moderately increased permittivity and low dielectric loss, meeting the requirements for radio-frequency substrate applications. Through material selection and process design that embraces, rather than ignores, lunar environmental constraints, this work establishes the CE-LRS composites that represent a viable pathway for the in situ fabrication of structural electronics on the Moon. Full article

23 pages, 2348 KB

Open AccessArticle

The Study of UAV-Based Tea Shoots Detection with TSDet-UAV Method

by Kaihua Wei, Yulin Cai, Chengbo Lu, Jingcheng Zhang, Dong Ren, Shun Ren and Dongmei Chen

Electronics 2026, 15(10), 2205; https://doi.org/10.3390/electronics15102205 - 20 May 2026

Abstract

The picking of tea leaves in tea gardens requires multiple batches in the short and valuable tea harvest period. To realize timely and efficient tea plucking, it is feasible to use unmanned aerial vehicles (UAV) for tea shoot detection in large tea gardens. [...] Read more.

The picking of tea leaves in tea gardens requires multiple batches in the short and valuable tea harvest period. To realize timely and efficient tea plucking, it is feasible to use unmanned aerial vehicles (UAV) for tea shoot detection in large tea gardens. For the typical small targets of tea buds in unmanned aerial vehicle (UAV) aerial images, it is necessary to design an efficient tea buds detection model. In order to improve the accuracy and the speed of the tea buds detection in the UAV images, we designed the SH-CoordMapping hash space mapping algorithm to accelerate the remerging of the detection results into the original image. The C2PSA-BI module and the CARAFE upsampling module are applied to improve detail preservation during feature fusion. A lightweight detection head is further used to reduce redundant computation in the detection stage. By comparing with the traditional detection methods, it can be proved that the SWO sections are necessary for UAV-scale tea shoots detection. Based on the accuracy and the number of model parameters, the YOLO11n model with slice size as 640 and overlap rate as 0.1 performs the best. The TSDet-UAV was deployed on the NVIDIA Jetson Orin NX chip to construct an inspection system capable of real-time acquisition and detection. The experimental results demonstrate that the proposed TSDet-UAV achieves excellent performance, recording an mAP₅₀ of 52.9% on the constructed UAV-TS dataset while maintaining high efficiency. With a parameter size of 2.4 M and a total processing time of 1.32 s per high-resolution image under TensorRT FP16, the processing speed is highly suitable for real-time edge deployment on agricultural UAV platforms. The UAV image-based tea garden shoot inspection platform proposed in this paper can effectively confirm the growth status of tea shoots, assisting farm management in formulating precise picking plans. Full article

34 pages, 11332 KB

Open AccessArticle

Artificial Intelligence for Autonomous Vehicles: Robustness Analysis in Complex Urban Traffic Scenarios

by Brandon Quezada-Godoy, Antonio Guerrero-González, Francisco García-Córdova, Francisco Lloret-Abrisqueta and Antonio Martínez-Espinosa

Electronics 2026, 15(10), 2204; https://doi.org/10.3390/electronics15102204 - 20 May 2026

Abstract

Autonomous driving in complex urban environments remains challenging due to perception uncertainty, dynamic multi-agent interactions, and control instability under adverse conditions. Despite advances in individual components, systematic evaluations of fully integrated modular pipelines under compounded urban disturbances remain scarce. This work presents a [...] Read more.

Autonomous driving in complex urban environments remains challenging due to perception uncertainty, dynamic multi-agent interactions, and control instability under adverse conditions. Despite advances in individual components, systematic evaluations of fully integrated modular pipelines under compounded urban disturbances remain scarce. This work presents a modular autonomous driving framework in CARLA Town10HD, integrating Convolutional Neural Network (CNN)-based perception using ResNet-18, global path planning via A* algorithm, and two control strategies: a classical Proportional–Integral–Derivative (PID) controller and a Deep Q-Network (DQN) agent with adaptive geometric steering assistance. A structured protocol assessed robustness across five scenarios: Heavy Rain, Dense Fog, Nighttime Driving, Dense Traffic, and Combined Extreme Conditions. The perception module achieved F1-scores close to 0.99 for traffic-sign, pedestrian, and lane classification; results reflect synthetic CARLA data and should not be interpreted as real-world generalization. The PID controller produced smoother trajectories with lower steering oscillations, while the DQN agent achieved faster traversal times at the cost of higher control variability. Route efficiency remained around 0.96 under isolated disturbances and decreased to 0.52 under compounded conditions, confirming sensitivity to multi-factor complexity. This study contributes a reproducible multi-scenario benchmark quantifying stability–adaptability trade-offs between classical and learning-based control, identifying scenario generalization and simulation-to-reality transfer as key future directions. Full article

(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)

28 pages, 2401 KB

Open AccessArticle

Novel Positioning Scheme Based on Supervised Deep Reinforcement Learning for Indoor Wireless Localization

by Youngghyu Sun, Kyounghun Kim, Seongwoo Lee, Joonho Seon, Soohyun Kim and Jinyoung Kim

Electronics 2026, 15(10), 2203; https://doi.org/10.3390/electronics15102203 - 20 May 2026

Abstract

In this paper, a supervised deep reinforcement learning (SDRL)-based positioning scheme is proposed for indoor wireless localization. The proposed scheme formulates the positioning problem as a Markov decision process and introduces a target-aware reward design based on the artificial potential field (APF) to [...] Read more.

In this paper, a supervised deep reinforcement learning (SDRL)-based positioning scheme is proposed for indoor wireless localization. The proposed scheme formulates the positioning problem as a Markov decision process and introduces a target-aware reward design based on the artificial potential field (APF) to alleviate the sparse reward problem commonly encountered in search-based reinforcement learning. In the proposed scheme, supervision is provided at the reward level by incorporating the target position into the reward design, rather than at the action level via expert demonstrations. A multi-scale action set with 49 candidates is further adopted to provide a favorable trade-off between estimation accuracy and search efficiency. An anchor-based environment construction strategy is developed by selecting the four strongest reference points (RPs) and transforming their coordinates with respect to the strongest RP. Simulation results show that the proposed scheme achieves a mean absolute error (MAE) below 0.8 m and success rates above 99.1% within 1 m and 99.2% within 2 m under the default Bluetooth Low Energy setting, while the convex-valid rate of the anchor-based environment exceeds 99.5%. Compared with existing methods, the proposed scheme reduces the MAE by approximately 92.3%. Ablation studies confirm that multi-scale actions reduce the average search steps by approximately 69.5% compared with a single-scale baseline. The proposed scheme also retains stable performance across BLE, Wi-Fi, and Zigbee infrastructures when trained under a representative path-loss setting without retraining and maintains sub-meter accuracy under mild shadow fading. These results confirm that the proposed scheme can improve positioning accuracy and search efficiency for indoor wireless localization. Full article

(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)

► Show Figures

Figure 1

28 pages, 8595 KB

Open AccessArticle

A Reproducible FPGA-to-Silicon Verification Methodology for an Embedded SoC Platform in 28 nm CMOS

by Hyeseung Sun and Kwangki Ryoo

Electronics 2026, 15(10), 2202; https://doi.org/10.3390/electronics15102202 - 20 May 2026

Abstract

Many System-on-Chip (SoC) studies rely solely on simulation and tool-based results, encountering unexpected failures during post-silicon validation. In particular, silicon-level demonstrations of Hardware/Software (HW/SW) functional equivalence, which confirms that an FPGA-validated design operates identically on an ASIC with the same firmware, remain extremely [...] Read more.

Many System-on-Chip (SoC) studies rely solely on simulation and tool-based results, encountering unexpected failures during post-silicon validation. In particular, silicon-level demonstrations of Hardware/Software (HW/SW) functional equivalence, which confirms that an FPGA-validated design operates identically on an ASIC with the same firmware, remain extremely rare. This work proposes a reproducible FPGA-to-silicon verification methodology that establishes HW/SW functional equivalence at the silicon level by applying an identical firmware source code, device driver, and memory map to both platforms. The methodology is validated on an Arm Cortex-M0-based SoC platform fabricated in Samsung 28nm Low Power Plus (LPP) CMOS technology with a dual Inter-Integrated Circuit (I2C) interface. The fabricated chip integrates two 64KB on-chip memories within a core area of 653

μ

m × 769

μ

m, operates at 125 MHz, and consumes 17.5 mW at the optimal operating point of 1.0 V. The primary contributions are: (1) a reproducible FPGA-to-silicon HW/SW functional equivalence verification methodology based on shared firmware source code, device driver, and memory map across both platforms, (2) silicon-measurement-based performance characterization with verified experimental data, (3) a reproducible design methodology documenting the complete flow from FPGA verification through ASIC fabrication, including static timing closure, place-and-route, and physical verification, and (4) an extensible SoC platform architecture enabling researchers to integrate and validate their own Intellectual Property (IP) via Advanced High-performance Bus (AHB) and I2C interfaces. Full article

(This article belongs to the Topic Advanced Integrated Circuit Design and Application)

26 pages, 6226 KB

Open AccessArticle

Three-Stage Stochastic Optimal Operation and Game-Theoretic Benefit Allocation Strategy for a PV-Storage Virtual Power Plant Under Multi-Market Synergy

by Xiang Li, Gaoquan Ma, Bangcan Wang, Na Cai, Junwei Bao, Zishi Wang, Xuan Yang, Qian Ai and Chenyang Zhao

Electronics 2026, 15(10), 2201; https://doi.org/10.3390/electronics15102201 - 20 May 2026

Abstract

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs [...] Read more.

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article

► Show Figures

Figure 1

23 pages, 527 KB

Open AccessArticle

Regularizing Temporal Explanations in Dynamic Neural Networks

by Dalius Navakauskas and Martynas Dumpis

Electronics 2026, 15(10), 2200; https://doi.org/10.3390/electronics15102200 - 20 May 2026

Abstract

Using attribution-based priors to improve the temporal interpretability and robustness of dynamic neural networks provides a computationally efficient method that does not alter the model structure during inference. We explore explanation-guided training for timeseries classification through the introduction of attribution-sensitive loss terms that [...] Read more.

Using attribution-based priors to improve the temporal interpretability and robustness of dynamic neural networks provides a computationally efficient method that does not alter the model structure during inference. We explore explanation-guided training for timeseries classification through the introduction of attribution-sensitive loss terms that serve as regularizers for the evolution of input relevance over time. The main contributions are the Temporal Relevance Smoothness Index (TRSI) and a ratio-based loss that reduces irregular step-to-step changes in channel-aggregated absolute relevance. TRSI is compared against temporal total-variation penalties computed using Layer-wise Relevance Propagation Total Variation (LRP-TV) and Integrated Gradients Total Variation (IG-TV). Experiments on a controlled three-class subset of the Korean University Human Activity Recognition (KU-HAR) dataset using a finite impulse response neural network (FIRNN) show that TRSI yields the strongest smoothness improvement, reducing the total variation of the aggregated relevance signal from 0.768 to 0.447 (41.8%), compared with 0.667 (LRP-TV) and 0.677 (IG-TV). Robustness tests indicate a clear advantage for TRSI under impulsive and white Gaussian test-time noise. Full article

(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology, 2nd Edition)

► Show Figures

Figure 1

20 pages, 7044 KB

Open AccessArticle

Context-Aware Human Pose Estimation via Hierarchical Information Arbitration

by Jiayuan Wang, Jie Lv, Xiaoru Chen and Yong Yang

Electronics 2026, 15(10), 2199; https://doi.org/10.3390/electronics15102199 - 20 May 2026

Abstract

Human pose estimation requires accurate localization of body keypoints under complex backgrounds, occlusion, and diverse human postures. Existing high-resolution pose-estimation networks preserve spatial details effectively, but their static information flow limits their adaptability to different image contexts. To address this limitation, this paper [...] Read more.

Human pose estimation requires accurate localization of body keypoints under complex backgrounds, occlusion, and diverse human postures. Existing high-resolution pose-estimation networks preserve spatial details effectively, but their static information flow limits their adaptability to different image contexts. To address this limitation, this paper proposes a context-aware hierarchical information arbitration method that dynamically regulates feature interaction at both multi-resolution fusion and residual feature refinement levels. The proposed method achieves superior performance on COCO, reaching 77.0 average precision and improving the High-Resolution Network baseline by 3.6 percentage points, with only a minor increase in model parameters. These results demonstrate that adaptive information arbitration improves pose-estimation accuracy and robustness while maintaining computational efficiency. Full article

► Show Figures

Figure 1

34 pages, 5797 KB

Open AccessArticle

A Method for Identifying Single Faults in Nonlinear Circuits

by Stanisław Hałgas

Electronics 2026, 15(10), 2198; https://doi.org/10.3390/electronics15102198 - 20 May 2026

Abstract

The paper focuses on fault diagnosis in nonlinear analog circuits. Due to its complexity, the problem, which has been the subject of research for over 50 years, remains unsolved. The paper proposes a comprehensive method for identifying single hard and soft faults in [...] Read more.

The paper focuses on fault diagnosis in nonlinear analog circuits. Due to its complexity, the problem, which has been the subject of research for over 50 years, remains unsolved. The paper proposes a comprehensive method for identifying single hard and soft faults in nonlinear DC circuits. The dictionary-based method belongs to the simulation-before-test method group. During dictionary creation, the possibility of multiple equilibrium points in transistor circuits is considered. For this purpose, Monte Carlo simulations that account for the spread of circuit parameters within tolerance limits are preceded by circuit analyses using a method that guarantees the determination of all operating points. A method for selecting nodes for hard faults is proposed. Two dictionaries are formulated for soft and hard faults. After performing measurements in the circuit under test, the dictionaries are searched to determine the fault type. In the case of soft faults, unlike methods that use artificial intelligence tools, it is possible not only to identify the faulty element but also to determine its approximate range of values. Furthermore, the paper highlights several problems and risks at the dictionary-creation stage. The method was verified through simulation and on a test bench under various test conditions. Full article

(This article belongs to the Special Issue Advances in Fault Detection and Diagnosis)

► Show Figures

Figure 1

25 pages, 9954 KB

Open AccessArticle

Privacy-Preserving Federated Cybersecurity Analytics for Smart-Grid SCADA: Maintaining Controllability and Observability Under Coordinated Attacks

by Zachary Etinge, Annamalai Annamalai, Mohamed Chouikha and Samir Abood

Electronics 2026, 15(10), 2197; https://doi.org/10.3390/electronics15102197 - 20 May 2026

Abstract

Ensuring resilient controllability and observability in SCADA-based smart grids under coordinated cyberattacks remains a critical and unresolved challenge in modern cyber-physical power systems. This paper investigates the impact of coordinated cyberattacks on the stability and monitoring capabilities of SCADA-based smart-grid systems within a [...] Read more.

Ensuring resilient controllability and observability in SCADA-based smart grids under coordinated cyberattacks remains a critical and unresolved challenge in modern cyber-physical power systems. This paper investigates the impact of coordinated cyberattacks on the stability and monitoring capabilities of SCADA-based smart-grid systems within a controlled cyber-physical environment. An active cyber-physical testbed representing a multi-bus power system was created to analyze how attacks targeting communication channels affect controllability and observability. Several attack scenarios were implemented, including remote access attacks via Secure Shell (SSH), Modbus/TCP flooding, and ICMP-based attacks, to monitor their impact on control actions, communication reliability, and system responsiveness. To address these vulnerabilities, a SCADA-based cybersecurity monitoring system was implemented within the controlled testbed environment. The system analyzes SCADA operational logs from smart grid devices while packet-level network traffic is captured and examined using monitoring tools such as Wireshark. A central monitoring layer coordinates system-wide attack detection and response. System resilience was evaluated using controllability and observability matrix rank analysis, together with dynamic stability metrics during attack conditions. Experimental and simulation results show that coordinated cyberattacks significantly degrade system performance, with the average delay rising from 12 ms to 210 ms, the packet loss rate increasing to 15.5%, and the command execution error rate reaching 40%. Furthermore, the ranks of the controllability and observability matrices dropped from 4 to 2, indicating a critical partial loss of the system’s control and monitoring capabilities. In this work, the federated-learning-based component is explored as a distributed, privacy-preserving cybersecurity monitoring framework for anomaly detection and observability enhancement using SCADA-derived datasets, rather than as a fully integrated real-time SCADA operational control mechanism. At the same time, the attack’s impact on electrical properties remained limited to less than 2%. Full article

(This article belongs to the Special Issue Stability Analysis and Optimal Operation Strategy for Power Systems with High Renewable Energy Penetration)

► Show Figures

Figure 1

22 pages, 1557 KB

Open AccessArticle

A Culturally Aware LLM Framework for Analyzing Social Engineering Tactics in Korean Phishing Messages

by Kiho Lee, Yongjoon Lee, Jaeyeong Jeong, Yong-ha Choi and Dongkyoo Shin

Electronics 2026, 15(10), 2196; https://doi.org/10.3390/electronics15102196 - 20 May 2026

Abstract

Phishing messages have evolved from simple fraud templates into socially engineered texts that exploit anxiety, trust, relational obligation, and culturally embedded norms. In Korean phishing messages, attackers frequently combine institutional authority, family or acquaintance framing, requests for cooperation, and urgency cues to induce [...] Read more.

Phishing messages have evolved from simple fraud templates into socially engineered texts that exploit anxiety, trust, relational obligation, and culturally embedded norms. In Korean phishing messages, attackers frequently combine institutional authority, family or acquaintance framing, requests for cooperation, and urgency cues to induce concrete victim actions such as money transfer, link clicking, phone contact, app installation, or credential submission. However, prior studies have largely emphasized binary phishing detection while offering limited interpretability regarding how such messages mobilize social and cultural persuasion strategies. This study proposes a culturally aware large language model framework for analyzing social engineering tactics in Korean phishing messages. The framework is built on a multidimensional codebook that represents the message text, phishing label, tactic type, relation type, requested action, cultural lever, and evidence span, enabling structured and explainable analysis beyond simple classification. To operationalize this framework, an OpenChat-based model is fine-tuned with QLoRA to generate structured outputs that jointly predict the phishing status and socially relevant attributes, while evidence-span supervision is incorporated to improve grounding and explanation consistency. The evaluation examines not only phishing-detection performance but also attribute-level prediction accuracy, evidence alignment, parsing reliability, and human-rated usefulness and trustworthiness. By integrating the cultural context, relational framing, and evidence-grounded explanation into LLM-based phishing analysis, this study provides an interpretable analytical framework for Korean phishing messages and an evidence-grounded basis for analyst-supportive phishing triage. On the 82-sample authoritative clean hold-out split, Model D produced error-free label predictions and achieved 0.841 exact-match core and 0.886 span-F1. However, because the evaluation used a single 82-sample internal hold-out split and no independent external corpus, these results should be interpreted as feasibility evidence under leakage-controlled conditions rather than as proof of deployment-level robustness or cross-domain generalization. The main contribution of this study is therefore not improved binary detection over strong lexical baselines, but the structured and evidence-grounded representation of Korean phishing persuasion tactics for analyst-supportive triage. Full article

(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)

► Show Figures

Figure 1

26 pages, 10416 KB

Open AccessArticle

A Lightweight FFT-Domain Co-Channel Interference Detection Method for Narrowband Wireless Systems

by Yuqi Qin, Jinbai Zou, Lingxiao Chen and Qing Zhou

Electronics 2026, 15(10), 2195; https://doi.org/10.3390/electronics15102195 - 19 May 2026

Abstract

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or [...] Read more.

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article

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

► Show Figures

Figure 1

24 pages, 3623 KB

Open AccessArticle

Multi-ObjectiveOptimization of the Electro-Optical Performances of Fluorescent OLEDs Based on Defect-State and ETL/HTL Thickness Analysis

by Mohammed El Halaoui, Mustapha El Halaoui, Lahcen Amhaimar, Adel Asselman, Laurent Canale and Bousselham Samoudi

Electronics 2026, 15(10), 2194; https://doi.org/10.3390/electronics15102194 - 19 May 2026

Abstract

In scientific research, the optimization of organic light-emitting diodes (OLEDs) is generally achieved through a lengthy and expensive experimental process as new ideas and configurations are tested on real devices. Electro-optical simulation allows for the rapid evaluation of key performance parameters of device [...] Read more.

In scientific research, the optimization of organic light-emitting diodes (OLEDs) is generally achieved through a lengthy and expensive experimental process as new ideas and configurations are tested on real devices. Electro-optical simulation allows for the rapid evaluation of key performance parameters of device structures, thus reducing manufacturing time and costs. This paper presents an original contribution to the electro-optical modeling and optimization of multilayer OLED devices using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This optimization explicitly incorporates defect states within the ITO/NPB/Alq₃:C545T/Alq₃/LiF-Al structure. The simulated model is calibrated using experimental data by fitting the trap state distribution. The Pareto front resulting from the multi-objective optimization identifies a set of non-dominated configurations, including an optimal intermediate structure defined by an electron transport layer (ETL) thickness of approximately 42 nm and a hole transport layer (HTL) thickness of approximately 53 nm. This configuration leads to a limited reduction of 1.75–2% in current efficiency (

η_{c}

) while offering a remarkable improvement of 23–30% in power efficiency (

η_{p}

) compared to the extreme configurations of the optimal Pareto set. Thus, this solution represents an optimal Pareto trade-off between high current efficiency and improved power efficiency. This paper shows that combining defect modeling and thickness optimization provides a reliable framework for the electro-optical optimization of OLED devices. Future work will extend this approach to spectral and colorimetric analysis. Full article

(This article belongs to the Special Issue Feature Papers in Semiconductor Devices, 2nd Edition)

► Show Figures

Figure 1

16 pages, 2815 KB

Open AccessArticle

A Longitudinal Layer-Wise Strategy for Fabricating Tapered Micro-Cones by Ion-Beam Etching

by Jingyu Huang, Chenhui Deng, Pengfei Wang, Bohua Yin, Liping Zhang and Li Han

Electronics 2026, 15(10), 2193; https://doi.org/10.3390/electronics15102193 - 19 May 2026

Abstract

The controllable fabrication of tapered three-dimensional (3D) microstructures by ion-beam processing remains challenging, especially when both profile fidelity and geometric controllability are required. Tapered conical microstructures are of interest because they are relevant to a variety of applications, including micro-optical elements, functional textured [...] Read more.

The controllable fabrication of tapered three-dimensional (3D) microstructures by ion-beam processing remains challenging, especially when both profile fidelity and geometric controllability are required. Tapered conical microstructures are of interest because they are relevant to a variety of applications, including micro-optical elements, functional textured surfaces, biomimetic interfaces, and field-enhancing emitter-related structures, where taper angle, aspect ratio, and structural uniformity strongly influence the resulting performance. In this work, a longitudinal layer-wise strategy is proposed for tapered micro-cone fabrication by ion-beam etching. The core idea is to discretize a continuous cone profile along the vertical direction into a sequence of annular layers whose dimensions are determined by the local geometry of the target three-dimensional structure. After this geometric discretization step, each individual layer is executed using a conventional multi-pass strategy, thereby combining longitudinal profile construction with stabilized local material removal. A dedicated pattern-design software, EBWriter, was developed to automatically generate annular patterns and process files from user-defined geometric parameters. Experimental validation was carried out on single-crystal silicon substrates using a dual-beam microscope platform operated at 30 kV. The results show that increasing the longitudinal layer number effectively weakens the staircase effect and improves the continuity of the reconstructed cone profile. For positive micro-cones fabricated using annular patterns with a nominal outer processing diameter of 3 μm, the increasing-inner-radius strategy enables preservation of the cone apex and reconstruction of tapered morphologies with improved fidelity. Under the present processing conditions, an empirical correspondence between the target geometric ratio and the recommended layer number was further summarized: layer numbers of approximately 50, 100, and 300 support cone structures with base-diameter-to-height ratios close to 1:2, 1:3, and 1:4, respectively. In addition, a 3 × 3 positive micro-cone array was successfully fabricated, with a total processing time of about 80 s. The measured cone base diameter and height were 0.886 ± 0.005 μm and 2.354 ± 0.023 μm, respectively, with dimensional variations controlled within ±2%. These results demonstrate that the proposed method provides a feasible layer-wise ion-beam fabrication route for tapered microstructures and offers a useful process basis for future studies on micro-optical surfaces, functional textured interfaces, and emitter-related microstructures. Full article

► Show Figures

Figure 1

31 pages, 24265 KB

Open AccessArticle

Generalized Positive/Negative Floating Impedance Multiplier Circuit and Its Application

by Durmuş Ersoy, Fırat Kaçar, Metin Ozturk and Ali Ataş

Electronics 2026, 15(10), 2192; https://doi.org/10.3390/electronics15102192 - 19 May 2026

Abstract

Passive components in integrated circuits occupy significant areas and increase production costs, driving the demand for compact alternatives. This study presents a generalized, electronically controllable positive/negative floating impedance multiplier implemented in TSMC 180 nm CMOS technology. To achieve a compact layout, the architecture [...] Read more.

Passive components in integrated circuits occupy significant areas and increase production costs, driving the demand for compact alternatives. This study presents a generalized, electronically controllable positive/negative floating impedance multiplier implemented in TSMC 180 nm CMOS technology. To achieve a compact layout, the architecture utilizes custom-designed operational transconductance amplifiers (OTAs). The circuit operates on a lossless principle, scaling resistance, capacitance, and inductance values within a wide multiplication range of −100 to +100 using only a single base element. Comprehensive LTspice simulations including PVT, Monte Carlo, THD, and noise analyses verify the design’s stable operation, low distortion, and favorable noise characteristics across various filter configurations. Furthermore, practical feasibility is validated through SPICE simulations using commercial LM13700 OTA, confirming consistent behavior for real-world applications. The proposed active topology occupies a compact core area of only 5831 μm². By scaling down large passive components, this design decreases the overall system-level footprint, providing a versatile and area-efficient solution for tunable analog IC applications. It should be noted that the reported 5831 μm² corresponds to the active core only, while the effective system-level area benefit depends on the selected base impedance and the target application. Full article

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

22 pages, 1655 KB

Open AccessArticle

Dynamic Security Assessment and Security Region Construction Based on the Maximum Lyapunov Exponent Criterion

by Qiuquan Deng, Xikai Liu, Cuiyun Luo, Yin Wu, Guangming Li, Xiejin Ling, Zhencheng Liang, Junzhi Ren, Yuan Zeng and Chao Qin

Electronics 2026, 15(10), 2191; https://doi.org/10.3390/electronics15102191 - 19 May 2026

Abstract

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated [...] Read more.

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated by this capability, a rapid construction methodology for the practical dynamic security region (PDSR) is proposed based on the MLE criterion. Initially, through analyzing the dynamic characteristics of generator rotor angle trajectories after disturbances, the dynamic MLE characteristics of the generator’s angular velocity deviation trajectory are extracted to formulate the MLE-based stability criterion. Subsequently, a stability boundary function based on MLE trajectories is developed, and the linear relationship between the injection space parameters and the MLE stability boundary function is derived. Finally, leveraging the sensitivity of the stability boundary function to the variations in injection space parameters, the dynamic security region is constructed around the dominant instability critical point, thereby establishing a mapping function between transient stability and the injection space parameters. The effectiveness of the proposed method is demonstrated through simulations on the IEEE39 power system. Results show that the method exhibits promising performance in terms of speed and adaptability for transient stability analysis and boundary construction. Full article

27 pages, 2093 KB

Open AccessArticle

Wires, Patents and Growth: An Explainable Machine Learning Approach for What Drives Digital Competitiveness in the European Union

by Rareș Mihai Nițu, Raluca Iuliana Georgescu, Dumitru Alexandru Bodislav, Loredana Maria Popescu, Cristina Voicu and Andrei Josan

Electronics 2026, 15(10), 2190; https://doi.org/10.3390/electronics15102190 - 19 May 2026

Abstract

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on [...] Read more.

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on six World Bank indicators—fixed broadband subscriptions, internet users, mobile subscriptions, patent applications, R&D expenditure, and secure internet servers—achieves a training R² of 0.804 and a test R² of 0.430 under temporal out-of-sample validation spanning the COVID-19 structural break. TreeSHAP decomposition identifies fixed broadband as the strongest predictor of model-estimated GDP per capita growth (mean |SHAP| = 0.948; bootstrap rank 1 in 78% of 50 resamples; Friedman Chi-square (5) = 168.16, p < 0.001), providing predictive support for Hypothesis H1. Innovation indicators, represented by patent applications and R&D expenditure, exceed the pre-specified materiality threshold, providing predictive support for H2, while SHAP dependence plots reveal pronounced non-linear threshold patterns consistent with S-curve diffusion theory, supporting H3. Temporal SHAP decomposition identifies three structural phases: broadband dominance (1995–2007), crisis-induced reconfiguration (2008–2013), and quality convergence (2014–2024). The framework reconciles contradictory findings from prior literature by visualizing the complete functional form of the broadband–growth relationship without imposing a parametric specification. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

Journal Description

Electronics

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI