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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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24 pages, 6185 KB  
Article
PILOT: A Replay-Free Continual Learning Approach for Real-Time Semantic Segmentation via Boundary Guidance
by Yujing Zhou, Prashant Shekhar, Thomas Yang and Yongxin Liu
Electronics 2026, 15(13), 2833; https://doi.org/10.3390/electronics15132833 - 29 Jun 2026
Viewed by 173
Abstract
Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real-world environments often requires the ability to learn novel classes incrementally without retraining on the entire dataset. This capability is known as continual learning. [...] Read more.
Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real-world environments often requires the ability to learn novel classes incrementally without retraining on the entire dataset. This capability is known as continual learning. In this regard, standard fine-tuning methods often suffer from catastrophic forgetting, where the model learns new information but loses accuracy on previously learned classes. The severity of this effect depends on the incremental setup, the available data, and the fine-tuning strategy. Contributing to this crucial domain, this paper proposes a novel continual learning framework tailored for PIDNet, which is a widely cited state-of-the-art real-time semantic segmentation model. Our method, PILOT (Parallel Incremental Learning Over Time), introduces a real-time and lightweight strategy by implementing a parallel Derivative branch (D-branch) designed to capture the high-frequency boundary information of novel classes while freezing the trained parameters of the original segmentation network. This novel setup allows the model to adapt to new semantic categories while preserving the knowledge of previously learned classes. By using only data associated with the new class, our model significantly reduces training overhead. Experimental results demonstrate that our approach successfully segments new classes while maintaining a high mean Intersection over Union (mIoU) on the original base classes, thereby outperforming prior continual learning approaches in this real-time segmentation setting. Overall, PILOT is shown to effectively mitigate catastrophic forgetting with minimal impact on inference latency, adding fewer than 5% additional parameters and reducing the frame rate by only about 9%, thus maintaining real-time performance. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Recent Developments and Emerging Trends)
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24 pages, 10388 KB  
Article
Adaptive Content and Style Fusion for Text-to-Image Generations
by Yi-Fang Lee, Chun-Chieh Lee, Chi-Hung Chuang, Chih-Lung Lin and Kuo-Chin Fan
Electronics 2026, 15(13), 2800; https://doi.org/10.3390/electronics15132800 - 25 Jun 2026
Viewed by 221
Abstract
Text-to-image generation aims to produce images that match the semantic content of a text prompt. In style transfer tasks, the model must further integrate reference styles while preserving prompt semantics. However, balancing semantic consistency and style fidelity remains challenging. Existing methods commonly rely [...] Read more.
Text-to-image generation aims to produce images that match the semantic content of a text prompt. In style transfer tasks, the model must further integrate reference styles while preserving prompt semantics. However, balancing semantic consistency and style fidelity remains challenging. Existing methods commonly rely on fixed feature weights and lack adaptive control, which often leads to style over-injection and content distortion. To address these issues, we propose a novel framework that performs dynamic regulation at both the feature and temporal levels. At the feature level, we propose an Entropy-Aware Adaptive Fusion (EAAF) module. It incorporates a bidirectional distribution transformation mechanism to enhance the statistical correlation between content and style features. The module further uses information entropy as a dynamic control signal to adaptively adjust the strength of style injection, thereby achieving a balance between semantic consistency and style fidelity. At the temporal level, we design a Progressive Feature Reweighting (PFR) strategy. By applying stage-wise weighting to content and style features at different diffusion steps, this strategy effectively improves structural stability and color consistency. In addition, our framework is modular and can be integrated into existing diffusion-based style transfer models without additional fine-tuning or retraining. Experimental results demonstrate that applying our approach to current state-of-the-art models, such as StyleStudio and CSGO, significantly enhances their performance, particularly in maintaining strong prompt alignment while achieving high-fidelity style transfer. Full article
(This article belongs to the Special Issue Recent Advances in Object Detection and Computer Vision)
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35 pages, 919 KB  
Article
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
Viewed by 179
Abstract
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)
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21 pages, 964 KB  
Article
HySV: A Hymba-Inspired Hybrid-Head Framework for Quality-Aware and Deployment-Aware Speaker Verification in Intelligent Embedded Systems
by Sundareswari Thiyagarajan and Deok-Hwan Kim
Electronics 2026, 15(12), 2676; https://doi.org/10.3390/electronics15122676 - 17 Jun 2026
Viewed by 204
Abstract
Speaker verification is an important biometric technology for secure and personalized human–computer interaction in intelligent embedded systems. However, deploying deep speaker verification models on edge devices remains challenging because of restricted computational resources and strict real-time latency requirements. Existing systems commonly rely on [...] Read more.
Speaker verification is an important biometric technology for secure and personalized human–computer interaction in intelligent embedded systems. However, deploying deep speaker verification models on edge devices remains challenging because of restricted computational resources and strict real-time latency requirements. Existing systems commonly rely on convolutional, time-delay, or Transformer-based encoders. Although ECAPA-TDNN-based models provide strong verification performance, their temporal modeling mainly depends on convolutional and TDNN-style operations. Transformer-based models can capture broader temporal patterns, but they often require high computational and memory costs, making them less suitable for embedded deployment. To address these limitations, this paper proposes HySV, a Hymba-inspired hybrid attention and state-space encoder for deployment-aware speaker verification. Rather than directly employing the original Hymba language model, HySV adapts its hybrid-head principle to speaker embedding extraction. Specifically, conventional ECAPA-TDNN-style encoder blocks are replaced with three stacked Hymba context blocks. Each block contains an attention branch for local speaker-discriminative cue modeling and a state-space branch for efficient temporal context summarization. In addition, a quality-aware decision support module is introduced after cosine similarity scoring to improve reliability using utterance duration, voice activity ratio, and embedding confidence. The proposed system is evaluated using both speaker verification and deployment-oriented metrics, including EER, minDCF, FLOPs, and latency. Full article
(This article belongs to the Special Issue Intelligent Embedded Systems: Latest Advances and Applications)
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30 pages, 21671 KB  
Article
Semantic Translation and LLM-RAG Fusion of Multi-Source Heterogeneous Data for Production Cognition in Discrete Manufacturing
by Pingwen Zheng, Liping Wang, Changchun Liu and Dunbing Tang
Electronics 2026, 15(12), 2692; https://doi.org/10.3390/electronics15122692 - 17 Jun 2026
Viewed by 187
Abstract
Multi-source heterogeneous data in discrete manufacturing shop floors, including vibration signals, equipment logs, visual monitoring data, and handwritten production reports, exhibit significant differences in modality and semantic representation. Traditional fusion methods often fail to bridge the semantic gap between low-level sensing signals and [...] Read more.
Multi-source heterogeneous data in discrete manufacturing shop floors, including vibration signals, equipment logs, visual monitoring data, and handwritten production reports, exhibit significant differences in modality and semantic representation. Traditional fusion methods often fail to bridge the semantic gap between low-level sensing signals and high-level manufacturing cognition, limiting intelligent anomaly analysis and decision-making capability. To address this issue, this paper proposes a semantic translation and fusion framework for industrial heterogeneous data based on Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs). First, a unified semantic translation mechanism is developed to convert multimodal industrial data into structured semantic representations for cross-modal alignment. Second, an industrial knowledge graph and RAG mechanism are introduced to integrate process knowledge, maintenance manuals, and historical fault records into the reasoning process. Third, an LLM-driven reasoning framework is designed for multimodal semantic fusion, anomaly identification, causal analysis, and optimization recommendation generation. In addition, a digital twin-based visualization interface is constructed to realize real-time interaction between production lines, industrial data, and intelligent cognitive reports. Experimental results demonstrate that the proposed framework significantly improves industrial reasoning accuracy, anomaly analysis correctness, and response efficiency compared with general-purpose LLMs, providing an effective solution for intelligent cognition and decision-making in discrete manufacturing systems. Full article
(This article belongs to the Section Computer Science & Engineering)
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22 pages, 2560 KB  
Article
An Open Hardware ML-KEM Polynomial Ring Accelerator on Chipyard RISC-V SoC: System-Level Integration and Evaluation
by Yi-Chang Tsai, Yu-Han Lin and Wen-Jyi Hwang
Electronics 2026, 15(12), 2511; https://doi.org/10.3390/electronics15122511 - 7 Jun 2026
Viewed by 338
Abstract
With the standardization of the Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) in NIST FIPS 203 (2024), efficient hardware support for polynomial ring operations has become critical for practical post-quantum cryptography deployment. The dominant computational workload of ML-KEM arises from matrix–vector multiplications over polynomial rings, [...] Read more.
With the standardization of the Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) in NIST FIPS 203 (2024), efficient hardware support for polynomial ring operations has become critical for practical post-quantum cryptography deployment. The dominant computational workload of ML-KEM arises from matrix–vector multiplications over polynomial rings, which involve repeated Number Theoretic Transform (NTT), pointwise multiplication, and modular addition operations. This work proposes an ML-KEM polynomial ring accelerator leveraging Open Intellectual Property (Open IP) and integrates it into an open hardware Chipyard RISC-V System on Chip (SoC) via a Memory-Mapped I/O (MMIO) interface. The design incorporates an NTT-based datapath with multiplier and adder arrays, and employs a scratchpad memory to enable intermediate data reuse and reduce memory access overhead. The proposed architecture is implemented on a Genesys 2 FPGA development board featuring a Kintex-7 XC7K325T Field Programmable Gate Array (FPGA) (Digilent Inc., Pullman, WA, USA) and evaluated at both kernel and system levels. Experimental results show that the accelerator reduces matrix–vector multiplication latency to 7372 cycles, achieving up to 40× speedup over a software baseline. At the SoC level, the complete ML-KEM implementation achieves performance improvements of 1.6× to 2.1× across different parameter sets. These results demonstrate that integrating Open IP within an open hardware SoC provides an effective and reproducible approach for accelerating ML-KEM. Full article
(This article belongs to the Special Issue New Trends in Cybersecurity and Hardware Design for IoT)
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18 pages, 294 KB  
Article
A Vulnerability Taxonomy for Tor-Based Hidden Services: Toward a De-Anonymization Framework for Cybercrime Investigation
by Jiho Shin and Inkyoung Shin
Electronics 2026, 15(11), 2370; https://doi.org/10.3390/electronics15112370 - 31 May 2026
Viewed by 454
Abstract
Tor-based hidden services host substantial criminal infrastructure, yet de-anonymization research remains fragmented across heterogeneous techniques. No prior work has organized these techniques into a unified taxonomy oriented toward forensic investigation. This paper proposes a five-layer vulnerability taxonomy for Tor hidden services, distinguishing network-level [...] Read more.
Tor-based hidden services host substantial criminal infrastructure, yet de-anonymization research remains fragmented across heterogeneous techniques. No prior work has organized these techniques into a unified taxonomy oriented toward forensic investigation. This paper proposes a five-layer vulnerability taxonomy for Tor hidden services, distinguishing network-level (L1), application-level (L2), side-channel (L3), operational-security-failure (L4), and ecosystem-level (L5) categories. The taxonomy is derived from a structured review of literature published between 2002 and 2024. We further propose a Traceability Evaluation Framework (TEF) that scores 11 vulnerability types along three dimensions: Applicability, Technical Difficulty, and Legal Admissibility. The TEF dimension weights are derived through Analytic Hierarchy Process elicitation from a five-member expert panel of cybercrime investigators, digital forensics researchers, and a legal scholar. The resulting weights of (0.385, 0.204, 0.412) for Applicability, inverted Technical Difficulty, and Legal Admissibility prove robust to ±0.10 perturbations in sensitivity analysis. Under this framework, four application-layer (L2) and operational-security-failure (L4) vulnerabilities receive the highest traceability scores (TS ≥ 2.80), while two network-level (L1) attacks and one side-channel (L3) technique fall to the lowest tier. The framework integrates technical exploitability with legal admissibility constraints across U.S., EU, and other evidentiary regimes, providing a structured reference for investigators and a methodological foundation for case-based empirical validation in future work. Full article
(This article belongs to the Special Issue Advances in Cybersecurity, Data Privacy, Robotics, and Cloud and Service Computing)
18 pages, 18915 KB  
Article
A 140 GHz Two-Channel Transmitter in 40 nm Bulk CMOS
by Junkyu Lee, Changjung Lee, Jaegwan Kim and Munkyo Seo
Electronics 2026, 15(11), 2349; https://doi.org/10.3390/electronics15112349 - 28 May 2026
Viewed by 248
Abstract
This paper presents a 140 GHz two-channel transmitter in 40 nm bulk CMOS technology for D-band wireless communication systems. The transmitter employs a direct upconversion architecture with IQ Gilbert cell mixers and a shared ×9 frequency multiplier for local oscillator (LO) generation. [...] Read more.
This paper presents a 140 GHz two-channel transmitter in 40 nm bulk CMOS technology for D-band wireless communication systems. The transmitter employs a direct upconversion architecture with IQ Gilbert cell mixers and a shared ×9 frequency multiplier for local oscillator (LO) generation. The Lange coupler generates quadrature LO signals for I and Q paths, while the two-way four-stage differential power amplifier with cascade topology provides high output power. On-wafer measurement at 140 GHz LO frequency demonstrates a 9.9 dB conversion gain with a 5.5–6.1 GHz 3 dB bandwidth. The measured saturated output power is 10.1 dBm with an output 1 dB compression point of 6.5 dBm. The IQ imbalance remains within 2 dB across the 3 dB bandwidth. The fabricated transmitter occupies a chip area of 1.68 mm2 and consumes 435 mW from a 1 V supply. The power density of 6.09 mW/mm2 is the highest among reported CMOS-based D-band transmitters. The dual-channel architecture with shared LO generation enables MIMO transmission, spatial multiplexing, and diversity techniques while maintaining compact size and competitive power efficiency for high data rate wireless applications in the D-band frequency range. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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31 pages, 13410 KB  
Article
Early Detection of Distributed Denial of Service in Cloud Computing Using Quantum-Enhanced Knowledge Distillation Framework
by Bhargavi Krishnamurthy, Saikat Das and Sajjan G. Shiva
Electronics 2026, 15(11), 2327; https://doi.org/10.3390/electronics15112327 - 27 May 2026
Viewed by 261
Abstract
Cloud computing is one of the essential computing platforms for modern enterprises. About 98 percent of large businesses will use cloud computing services in 2025 to enable remote working. The highly distributed structures of cloud computing are prone to attacks starting from weakened [...] Read more.
Cloud computing is one of the essential computing platforms for modern enterprises. About 98 percent of large businesses will use cloud computing services in 2025 to enable remote working. The highly distributed structures of cloud computing are prone to attacks starting from weakened access control to data breaches. The sources making cloud systems vulnerable to attacks are public accessibility, auto scaling, and shared form of network architecture. Distributed Denial of Service (DDoS) is one of the most serious forms of attacks where multiple botnets get created simultaneously and flood massive requests for the cloud services. If the DDoS attack is not identified early it leads to the unavailability of cloud services, increased cost of migration, exhaustion of resources, and frequent violations of Service Level Agreements (SLAs). Hence, there is a need to detect DDoS at an early stage. Traditional machine learning models demand high computational power and larger memory capacity which make it unsuitable for a real-time cloud environment. This limitation is overcome by presenting a novel Quantum-Enhanced Knowledge Distillation framework (QKD) to detect DDoS attacks in cloud systems. QKD is a highly potential form of architecture which uses quantum computing to enhance the knowledge transfer between teacher and student models. The knowledge is extracted from the teacher model and quantum encoding of knowledge is performed. The complex correlation between the features of the traffic is extracted by applying the entanglement gates. The student model is trained considering the distillation loss and optimized until convergence. The simulation of the QKD is performed using DynamicCloudSim 3.0.3 simulator considering benchmark dataset CIC-DDoS2019and the performance is further validated using expected value analysis methodology. The performance of QKD is found to be promising toward performance metrics such as packet loss rate, attack detection time, attack recovery ratio, bandwidth utilization, and response time. Full article
(This article belongs to the Special Issue Artificial Intelligence/Machine Learning (AI/ML)-Based Tools for Cyber Security)
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32 pages, 3646 KB  
Article
Client-Side Continuous Authentication Using Keystroke Dynamics: A Lightweight Pipeline and Cross-Session Evaluation
by Zhanhe Zhang, Maria Papaioannou, Gaurav Choudhary and Nicola Dragoni
Electronics 2026, 15(11), 2325; https://doi.org/10.3390/electronics15112325 - 27 May 2026
Viewed by 344
Abstract
Post-login threats such as device sharing and session takeover motivate continuous authentication with behavioral signals. This paper studies a lightweight keystroke-dynamics pipeline designed for strict cross-session evaluation and browser-side scoring. Using the fixed-text and free-text tracks of the public KeyRecs dataset, we extract [...] Read more.
Post-login threats such as device sharing and session takeover motivate continuous authentication with behavioral signals. This paper studies a lightweight keystroke-dynamics pipeline designed for strict cross-session evaluation and browser-side scoring. Using the fixed-text and free-text tracks of the public KeyRecs dataset, we extract compact repetition-level and sliding-window digraph-timing features and train per-user one-vs-rest Logistic Regression verifiers on Session 1 (S1). Thresholds are selected only on S1 and transferred unchanged to Session 2 (S2), preventing test-set tuning and exposing operating-point instability under session drift. Fixed-text achieves S2 AUC mean/median 0.895/0.918 with a half total error rate (HTER) around 0.19, while free-text reaches AUC mean/median 0.884/0.899 with a similar transferred-threshold HTER. Personal thresholds and a pooled-S1 global threshold perform similarly on average, suggesting that global thresholding can simplify deployment without replacing per-user scoring models. A scaler-only warm-up update yields limited and inconsistent gains, showing that mean/variance adaptation alone is insufficient. Finally, compact JSON artifacts and replay-based browser benchmarks demonstrate deterministic client-side scoring with very small per-sample latency. Overall, the results show that useful threshold-free separability does not by itself guarantee stable operating-point transfer under cross-session drift. Full article
(This article belongs to the Special Issue Improving IoT Security and Efficiency Through Advanced Data Analysis Method)
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24 pages, 2369 KB  
Article
A Single-Link Propagation-Driven Performance Study of IEEE 802.11be Wi-Fi 7 in Complex Indoor Environments
by Nurul I. Sarkar and Rashid Mustafa
Electronics 2026, 15(11), 2324; https://doi.org/10.3390/electronics15112324 - 27 May 2026
Viewed by 363
Abstract
IEEE 802.11be, commercially known as Wi-Fi 7, extends wireless local area network (WLAN) capability through wider channel bandwidths, higher-order modulation, and tri-band operation. However, realised indoor performance is still strongly affected by radio propagation conditions. This study presents a controlled empirical assessment of [...] Read more.
IEEE 802.11be, commercially known as Wi-Fi 7, extends wireless local area network (WLAN) capability through wider channel bandwidths, higher-order modulation, and tri-band operation. However, realised indoor performance is still strongly affected by radio propagation conditions. This study presents a controlled empirical assessment of Wi-Fi 7 behaviour in a multi-storey university building by examining throughput and received signal strength (RSS) across the 2.4 GHz, 5 GHz, and 6 GHz bands using a single-link measurement setup. Six experimental scenarios were used to examine distance variation, wall penetration, line-of-sight (LOS) obstruction, floor separation, antenna orientation, and microwave interference. The measured RSS values were compared with the free-space, two-ray ground reflection, and log-distance shadowing models using mean absolute error (MAE). Six experimental scenarios were designed to isolate dominant indoor impairments, including distance variation, wall penetration, line-of-sight obstruction, floor separation, antenna orientation, and microwave interference. Measured RSS values were evaluated against free-space, two-ray, and log-distance shadowing models using mean absolute error as the comparison metric. Results show that 2.4 GHz retains greater penetration at lesser capacity, while 6 GHz offers the maximum short-range throughput under clear line-of-sight conditionsbut rapidly deteriorates with structural attenuation. Performance in all bands is greatly diminished by multi-wall blockage and line-of-sight loss. A single propagation model cannot adequately capture the divergence introduced by increasing distance and indoor attenuation, while short-range line-of-sight conditions more closely resemble deterministic predictions in terms of measured RSS alignment. Overall, the results highlight the trade-off between Wi-Fi 7’s capacity and coverage, and provide helpful advice for choosing frequencies, positioning access points, and organizing indoor coverage. The research findings provide insights into the practical deployment of next-generation Wi-Fi in multi-story buildings and residential houses. Full article
(This article belongs to the Special Issue Wireless Artificial Intelligent Computing Systems and Applications (WASA))
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37 pages, 2001 KB  
Article
Spec2SeqFuzz: A Category Prediction-Guided Approach for Stateful Multi-Step REST API Fuzzing
by Zhuofeng He, Sunpei Shang, Yumeng Guo and Aojie Zhou
Electronics 2026, 15(11), 2309; https://doi.org/10.3390/electronics15112309 - 26 May 2026
Viewed by 281
Abstract
REST APIs have become a dominant interface for modern web applications and cloud services, and a growing body of work has studied automated testing and reproducible error discovery for such systems. Prior approaches have explored dependency inference, cross-request value reuse, and, more recently, [...] Read more.
REST APIs have become a dominant interface for modern web applications and cloud services, and a growing body of work has studied automated testing and reproducible error discovery for such systems. Prior approaches have explored dependency inference, cross-request value reuse, and, more recently, learning- or LLM-based test generation. However, deep stateful multi-step reproducible error discovery remains difficult in practice because sequence construction is still often performed directly in the endpoint space, reusable runtime artifacts are not always tightly coupled with sequence expansion, and online LLM-driven generation may introduce cost and instability. We present Spec2SeqFuzz, a stateful multi-step fuzzing framework for REST API systems. The central idea is to guide online exploration in a compact category space rather than directly in the full endpoint space. Spec2SeqFuzz uses LLMs only in an offline pre-processing stage to normalize public multi-step PoCs, classify OpenAPI endpoints into a transferable category taxonomy, and construct training data for next-category prediction. During online fuzzing, the framework predicts the next likely API category from the executed prefix and observed response feedback, maps the predicted categories back to concrete endpoints, and combines this guidance with black-box endpoint fuzzing, proxy-based payload collection, and snapshot-assisted state restoration. We implemented a prototype and evaluated it on GitLab and WordPress, using MINER as the primary reproduced baseline in our current study. The results show that Spec2SeqFuzz is promising for both multi-step and single-endpoint error discovery on these two targets. Following the terminology used in MINER, we report reproducible errors rather than treating every triggered failure as a confirmed security vulnerability. Across the two targets, Spec2SeqFuzz discovers more reproducible multi-step errors than MINER, while the ablation results further suggest that category guidance, payload reuse, and depth-first stateful exploration are important to the final error-discovery performance. Full article
(This article belongs to the Special Issue Advances in Cyber-Physical Networks: Intelligence, Control and Security)
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31 pages, 917 KB  
Article
X-GATE: Attribution-Aware Distillation and Hardening for Compressed Edge-IIoT Intrusion Detection
by Tran Duc Le, Yida Bao and Mohammad Arifuzzaman
Electronics 2026, 15(11), 2284; https://doi.org/10.3390/electronics15112284 - 25 May 2026
Viewed by 310
Abstract
Industrial Internet of Things (IIoT) intrusion detection requires compact, latency-efficient models whose behavior remains assessable under adversarial stress, yet compression can alter the feature-attribution structure learned by a full-precision model. This paper presents X-GATE (eXplanation-Guided Adversarial Training Engine), an attribution-aware training framework for [...] Read more.
Industrial Internet of Things (IIoT) intrusion detection requires compact, latency-efficient models whose behavior remains assessable under adversarial stress, yet compression can alter the feature-attribution structure learned by a full-precision model. This paper presents X-GATE (eXplanation-Guided Adversarial Training Engine), an attribution-aware training framework for compressed Edge-IIoT intrusion detection. X-GATE combines Explanation-Consistency Distillation (ECD), which aligns Teacher–Student feature-attribution rankings with a differentiable soft-rank Spearman penalty, and Explanation-Guided Adversarial Training (EGAT), which hardens the Student on Teacher-salient feature coordinates. On the full Edge-IIoTset 2022 benchmark, the latest three-seed ablation gives Full X-GATE 89.30 ± 3.89% F1-Macro with 0.617 M parameters, within approximately 0.6 percentage points of the full-precision Teacher; a Random Forest model remains a stronger clean-F1 reference, so X-GATE is not framed as the clean-accuracy optimum. In a separate deployment-subset rerun, X-GATE obtains 78.83 ± 5.83% float F1-Macro and 79.11 ± 5.47% INT8 F1-Macro, reduces the adversarial false-positive rate from 0.46 ± 0.08% for KD-only to 0.16 ± 0.09% under the evaluated single-step white-box explanation-evasion protocol, and reduces CPU latency from 4.16 to 1.25 ms/sample. Component ablation further shows that ECD reduces Logical Drift by 17.24%, while EGAT improves adversarial F1 by 10.57 percentage points. Taken together, these benchmark- and protocol-bounded results position X-GATE as a compact neural operating point for the Edge-IIoT setting studied here, balancing attribution consistency, targeted hardening, and CPU-side efficiency. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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16 pages, 412 KB  
Article
Exploring the Effects of Data Volume and Transfer-Language Choice on Transfer Learning with Application to Polish
by Juuso Eronen, Zhenzhen Liu, Michal Ptaszynski, Karol Nowakowski and Fumito Masui
Electronics 2026, 15(11), 2254; https://doi.org/10.3390/electronics15112254 - 22 May 2026
Viewed by 272
Abstract
Transfer learning offers a practical way to improve neural machine translation in low-resource settings, but its effectiveness depends on both the choice of transfer language and the amount of target-language data available for adaptation. In this study, we examine these factors specifically for [...] Read more.
Transfer learning offers a practical way to improve neural machine translation in low-resource settings, but its effectiveness depends on both the choice of transfer language and the amount of target-language data available for adaptation. In this study, we examine these factors specifically for Polish–English translation using mBART. We evaluate Czech, Russian, and German as parent languages and extend the analysis with a combined Slavic parent model trained on Czech and Russian. The models are compared across 0-shot, 10-shot, 100-shot, 1k-shot, and 10k-shot settings. Within this Polish–English mBART setting, Czech provides the strongest zero-shot performance, while Russian and German improve substantially as Polish fine-tuning data increases and achieve the strongest results at higher shot levels. The paper therefore analyzes selected transfer-language configurations rather than a formally measured similarity variable. The results suggest that, in this setup, transfer-language choice matters most when no Polish supervision is available, whereas larger amounts of Polish data can compensate for weaker initial transfer alignment. Full article
(This article belongs to the Special Issue Natural Language Processing: From Deep Learning to Real-World Solutions)
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37 pages, 2822 KB  
Article
A Real-Time Sensor-Driven Multi-Agent Navigation System with Reinforcement Learning for Blind and Visually Impaired Users in Urban Environments
by Pilar Herrero-Martin and Álvaro García-Ballestero
Electronics 2026, 15(11), 2250; https://doi.org/10.3390/electronics15112250 - 22 May 2026
Viewed by 329
Abstract
Urban navigation in dynamic environments remains a challenging problem for blind and visually impaired users due to the presence of unpredictable obstacles and the limitations of conventional navigation systems, which rely primarily on static map-based information and lack real-time environmental awareness. This paper [...] Read more.
Urban navigation in dynamic environments remains a challenging problem for blind and visually impaired users due to the presence of unpredictable obstacles and the limitations of conventional navigation systems, which rely primarily on static map-based information and lack real-time environmental awareness. This paper presents a real-time sensor-driven navigation system based on a multi-agent architecture incorporating a reinforcement-learning navigation policy for assistive mobility in urban environments. The proposed system integrates GPS-based global localization with vision-based perception to enable continuous fusion of global route planning and local obstacle detection. This integration allows the system to dynamically adjust navigation strategies in response to changing environmental conditions. The architecture is designed as a modular multi-agent system comprising agents for perception, navigation, sensor fusion, personalization, safety arbitration, interface management, and system monitoring. The reinforcement learning component formulates local navigation as a sequential decision-making problem, where the navigation policy is trained to balance path efficiency, obstacle avoidance, and safety constraints through interaction with simulated environments. Prototype implementation is developed and evaluated in both simulation and controlled real-world scenarios. Experimental results demonstrate that the proposed system shows improved obstacle avoidance performance and navigation stability under the evaluated conditions while maintaining low-latency responsiveness compared to baseline navigation approaches. The system also exhibits robust behaviour under varying environmental conditions, supporting its potential applicability to assistive navigation tasks in controlled urban environments. The proposed approach contributes to a scalable architecture that integrates a reinforcement-learning navigation policy within a multi-agent coordination framework and real-time sensor perception, providing a foundation for the development of intelligent and deployable assistive navigation systems. Full article
(This article belongs to the Special Issue Wearable and Intelligent (Bio)Electronics for Healthcare: From Sensors to AI)
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19 pages, 2536 KB  
Article
A Lightweight Network for Encrypted Traffic Classification Based on Convolutional Positional Encoding and Efficient Multi-Scale Attention
by Yuan Feng, Yifan Ren, Jianwei Zhang, Zengyu Cai, Juncheng Yang and Liang Zhu
Electronics 2026, 15(11), 2248; https://doi.org/10.3390/electronics15112248 - 22 May 2026
Viewed by 269
Abstract
Network traffic classification is a cornerstone of network management and security. Addressing the challenges of feature extraction in encrypted traffic and the deployment limitations of traditional deep learning models on resource-constrained edge devices due to their large parameter sizes, this paper proposes a [...] Read more.
Network traffic classification is a cornerstone of network management and security. Addressing the challenges of feature extraction in encrypted traffic and the deployment limitations of traditional deep learning models on resource-constrained edge devices due to their large parameter sizes, this paper proposes a lightweight network for encrypted traffic classification, termed CEMA-Net (Convolutional Positional Encoding and Efficient Multi-scale Attention Network). Specifically, the proposed model integrates an Efficient Multi-scale Attention (EMA) mechanism with a Convolutional Positional Encoding (CPE) strategy to jointly capture global dependencies and local contextual information. To enable efficient adaptation to traffic data, an Efficient Multi-scale Attention Adapter (EMAAdapter) is designed, which reconstructs one-dimensional traffic sequences into a pseudo-2D representation and extracts horizontal, vertical, and local features in parallel. This design facilitates effective modeling of complex cross-scale dependencies in encrypted traffic with minimal computational overhead. Experimental results on three public datasets demonstrate that the proposed method, with only 0.66 M parameters, achieves superior classification performance compared with mainstream vision-based models such as ResNet-101, while significantly reducing computational cost. These results highlight the effectiveness of combining convolutional positional encoding with multi-scale attention mechanisms and provide an efficient solution for encrypted traffic classification in resource-constrained environments. Full article
(This article belongs to the Section Networks)
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26 pages, 2578 KB  
Article
Ontological Representation of Cyber–Physical Systems for Knowledge-Based Production
by Kathrin Gorgs, Tom Löhnert, Tobias Vogel and Matthias L. Hemmje
Electronics 2026, 15(11), 2235; https://doi.org/10.3390/electronics15112235 - 22 May 2026
Viewed by 323
Abstract
This paper presents a process-centric ontology for the semantic representation of cyber–physical systems (CPSs) within knowledge-based production planning (KPP). The approach integrates physical systems (PSs), cyber systems (CSs), and CPSs into a unified semantic model based on a three-layer classification. The ontology was [...] Read more.
This paper presents a process-centric ontology for the semantic representation of cyber–physical systems (CPSs) within knowledge-based production planning (KPP). The approach integrates physical systems (PSs), cyber systems (CSs), and CPSs into a unified semantic model based on a three-layer classification. The ontology was implemented using OWL and integrated into a Neo4j-based graph architecture to support semantic querying and process modeling. The evaluation was conducted using prototypical manufacturing scenarios, including semiconductor and mechanical engineering domains. Validation included (i) consistency checking using the HermiT reasoner, (ii) execution of SPARQL queries for retrieving CPS-related process information, and (iii) integration into a three-stage planning model. The results show that the ontology enables consistent semantic representation and cross-domain querying of CPS-based production processes. The work provides a validated proof-of-concept and establishes a foundation for future research on ontology-based production systems. Full article
(This article belongs to the Section Computer Science & Engineering)
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19 pages, 692 KB  
Article
A Hierarchical Key Management Scheme for Efficient Outsourced Computation in Cloud Storage Services
by Nan-I Wu, Tsuei-Hung Sun, Cheng-Ying Yang and Min-Shiang Hwang
Electronics 2026, 15(10), 2185; https://doi.org/10.3390/electronics15102185 - 19 May 2026
Viewed by 252
Abstract
This study addresses the high computational overhead associated with updating encryption keys and authentication information by delegating these tasks to cloud storage services. This approach reduces the burden on data owners and facilitates the establishment of a robust cloud storage ecosystem. Our evaluation [...] Read more.
This study addresses the high computational overhead associated with updating encryption keys and authentication information by delegating these tasks to cloud storage services. This approach reduces the burden on data owners and facilitates the establishment of a robust cloud storage ecosystem. Our evaluation focuses on three critical performance metrics for cloud security mechanisms: 1. Computational Complexity: The processing time and frequency required for encryption, decryption, and verification. 2. Communication Complexity: The volume and length of message exchanges. 3. Storage Overhead: The space required to maintain a comprehensive cloud storage system. We propose a hierarchical key management scheme that enables data owners to update file encryption keys efficiently without impacting other users within the Cloud Storage Service (CSS) environment. By eliminating the need for owners to store all keys in a hierarchical tree, our scheme minimizes the computational cost of key updates. Ultimately, this approach enhances efficiency for both data owners and receivers, making it particularly suitable for resource-constrained mobile devices. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 3rd Edition)
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29 pages, 2292 KB  
Article
EcoInfer: Optimizing Energy Efficiency with Latency Guarantees Through Iteration-Level GPU Frequency Control in LLM Serving
by Qingyuan Hu and Jian Li
Electronics 2026, 15(10), 2139; https://doi.org/10.3390/electronics15102139 - 16 May 2026
Viewed by 583
Abstract
Large language model (LLM) serving has emerged as a major source of energy consumption in modern AI infrastructure. In current deployments, graphics processing units (GPUs) are typically operated at default high-frequency settings to maximize performance. However, under practical service-level objectives (SLOs), peak performance [...] Read more.
Large language model (LLM) serving has emerged as a major source of energy consumption in modern AI infrastructure. In current deployments, graphics processing units (GPUs) are typically operated at default high-frequency settings to maximize performance. However, under practical service-level objectives (SLOs), peak performance is often unnecessary, especially during the memory-bound decode stage, resulting in substantial power redundancy and avoidable energy waste. Existing studies that apply GPU dynamic voltage and frequency scaling (DVFS) to improve the energy efficiency of LLM serving have shown promising results. However, they generally rely on coarse-grained control, accurate output length prediction, or request-level resource management, which limits their effectiveness under highly dynamic workloads and strict SLO constraints. We present EcoInfer, a fine-grained DVFS framework for energy-efficient LLM serving. EcoInfer performs iteration-level, workload-aware GPU frequency control that adapts to the current inference phase and system state while preserving latency guarantees. It comprises three tightly integrated modules: a machine-learning-based frequency–latency predictor that estimates iteration latency across candidate GPU frequencies using lightweight iteration-level features; an SLO-aware frequency controller that selects the minimum feasible frequency within a sweet-spot-guided candidate range; and a low-overhead runtime optimization layer that combines adaptive decision caching with asynchronous execution to reduce and hide the overhead of online control. Implemented on top of vLLM, EcoInfer achieves up to 25.4% energy savings and 21.5% average energy savings and improves energy efficiency by 1.28× on average in terms of Tokens/J while maintaining a nearly unchanged SLO attainment rate compared with the default vLLM baseline. Full article
(This article belongs to the Special Issue High-Performance Computing for AI: Architecture, Systems, and Algorithms)
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18 pages, 429 KB  
Article
Evaluating Distributed Communication Architectures for GPU-Accelerated Image Encoding
by Haojie Zheng, Carlos Reaño and Juan F. Ariño-Sales
Electronics 2026, 15(10), 2137; https://doi.org/10.3390/electronics15102137 - 16 May 2026
Viewed by 379
Abstract
Artificial intelligence (AI) has transformed how we engage with visual data, particularly within the context of enterprises. Multi-modal codification systems enable the creation of semantic connections between text and visual data using AI models. This opens new markets for businesses by enabling visual [...] Read more.
Artificial intelligence (AI) has transformed how we engage with visual data, particularly within the context of enterprises. Multi-modal codification systems enable the creation of semantic connections between text and visual data using AI models. This opens new markets for businesses by enabling visual search engines, recommendation systems, and automatic tagging of visual data. However, implementing these systems presents significant technical challenges. The typical workflow involves encoding images using an AI model, converting these representations into semantic vectors, and inserting them into databases optimized for fast searches. This not only affects technical efficiency but also impacts the ability of companies to scale these systems to a commercial level. This paper presents a comprehensive comparative analysis of communication architectures for large-scale image encoding systems, evaluating gRPC, RabbitMQ, serverless Lambda, and SageMaker approaches across performance and resource efficiency dimensions. Through controlled experiments processing up to 18,000 images using the SigLIP model, we establish clear performance–architecture relationships that inform system design decisions for visual content-based search applications. Full article
(This article belongs to the Special Issue AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches)
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30 pages, 2091 KB  
Article
MOSAIC: A Cognitively Motivated Multi-Agent Framework for Interpretable and Training-Free Empathetic Dialogue
by Kai Liu, Hangyu Xiong, Jinyi Zhang and Min Peng
Electronics 2026, 15(10), 2078; https://doi.org/10.3390/electronics15102078 - 13 May 2026
Viewed by 329
Abstract
Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without architecturally enforced intermediate representations, forfeiting intermediate-state transparency and long-horizon personalization. Drawing on [...] Read more.
Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without architecturally enforced intermediate representations, forfeiting intermediate-state transparency and long-horizon personalization. Drawing on neuroscientific and cognitive–psychological evidence that human empathy is functionally dissociable, we present MOSAIC (Multi-agent Orchestration with Structured Affective memory for Interpretable empathiC dialogue), a training-free framework that operationalizes empathetic dialogue as a four-stage cognitive pipeline: affective perception, causal appraisal, episodic memory retrieval, and response synthesis. Three innovations distinguish MOSAIC from prior work: (1) a cognitively motivated modular architecture whose functionally dissociable stages enable post hoc failure attribution through logged intermediate states; (2) a hierarchical three-tier emotional memory—perceptual, semantic, and episodic—coupled with adaptive three-dimensional retrieval over emotion, situation, and coping-strategy cues; and (3) a heterogeneous model orchestration strategy coordinating open-source and API-accessible models through role-specific chain-of-thought prompts, requiring no task-specific fine-tuning. We note that the EmpatheticDialogues evaluation pre-populates the memory store with 200 training-split episodes prior to test-set interaction, a data-access asymmetry relative to single-model baselines that must be borne in mind when interpreting comparative results. Experiments on EmpatheticDialogues and ESConv show that MOSAIC achieves a 76.4% weighted F1 and an empathy score of 3.87 (on a 1–5 Likert scale) and that it improves over single-model, training-free baselines on aggregate empathy and—most prominently—on human-rated personalization (3.67 vs. 3.24 against Claude-3.5 five-shot, d=0.48). We caution that the comparison against training-free baselines is not data access-controlled (see the cold-start discussion in Methods); the personalization advantage, supported by the ablation without the Event Agent, is the result we treat as the primary practical contribution of this work. Full article
(This article belongs to the Special Issue Affective Computing in Human–Robot Interaction)
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16 pages, 26838 KB  
Article
Experimental Evaluation and Theoretical Analysis of I/Q Imbalance in Direct Millimeter-Wave Six-Port QPSK Demodulators
by Chaouki Hannachi, Matthieu Egels, Phillipe Pannier and Serioja Ovidiu Tatu
Electronics 2026, 15(10), 2072; https://doi.org/10.3390/electronics15102072 - 13 May 2026
Viewed by 326
Abstract
This paper presents a comprehensive investigation of the impact of I/Q (In-phase/Quadrature) imbalance on the performance of a six-port receiver operating in the millimeter-wave band, specifically in the 60–65 GHz frequency range. Unlike traditional heterodyne architectures, the six-port junction offers a low-cost and [...] Read more.
This paper presents a comprehensive investigation of the impact of I/Q (In-phase/Quadrature) imbalance on the performance of a six-port receiver operating in the millimeter-wave band, specifically in the 60–65 GHz frequency range. Unlike traditional heterodyne architectures, the six-port junction offers a low-cost and low-power alternative for direct conversion; however, it is highly sensitive to hardware imperfections. This study demonstrates that manufacturing tolerances in passive components, such as 90° hybrid couplers and power dividers, introduce significant amplitude and phase disparities. These imbalances geometrically distort the ideal QPSK constellation, transforming the circular decision boundaries into an elliptical profile. The research methodology employs a robust co-simulation approach in Advanced Design System (ADS), integrating measured S-parameters with mathematical analysis to quantify signal degradation. Performance is evaluated using the Error Vector Magnitude (EVM) metric. The experimental findings reveal that even at the higher end of the spectrum (65 GHz), where the amplitude imbalance reaches 0.7 dB and the phase error is approximately 5°, the six-port QPSK receiver maintains an EVM of 8.7%. This result is comfortably below the 17.5% limit mandated by modern wireless communication standards, such as LTE and 5G. These results confirm the architectural resilience of the six-port receiver, validating its effectiveness as a reliable solution for high-speed, short-range data transmission in future ultra-wideband telecommunication infrastructures. Full article
(This article belongs to the Special Issue Advances in 6G Wireless Communication Technologies)
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22 pages, 477 KB  
Article
Distributed Disco Intelligent Reflecting Surfaces-Based Fully Passive Jamming for MU-MISO Systems
by Yitian Wang, Sitian Li, Huan Huang, Yanan Zhang, Luyao Sun, Yongxing Song, Jide Yuan, Tianqi Yu and Yi Cai
Electronics 2026, 15(10), 2033; https://doi.org/10.3390/electronics15102033 - 10 May 2026
Viewed by 350
Abstract
Maliciously deployed disco intelligent reflecting surfaces (DIRSs) introduce active channel aging (ACA) to achieve fully passive jamming without requiring channel state information or jamming power. To enhance this capability, we propose a distributed DIRS framework for downlink multi-user multiple-input single-output (MU-MISO) systems. By [...] Read more.
Maliciously deployed disco intelligent reflecting surfaces (DIRSs) introduce active channel aging (ACA) to achieve fully passive jamming without requiring channel state information or jamming power. To enhance this capability, we propose a distributed DIRS framework for downlink multi-user multiple-input single-output (MU-MISO) systems. By distributing multiple panels, this framework increases independent reflection paths and introduces inter-panel cascaded reflections, severely exacerbating precoder mismatch. We develop a comprehensive near- and far-field cascaded channel model, deriving closed-form expressions for the interference variance and a sum-rate lower bound in the large-antenna regime. Both pilot training (PT) phase-on and phase-off scenarios are investigated to evaluate the jamming impact under different operational states. Analytical and simulation results reveal that DIRS-induced interference scales with transmit power, imposing a strict rate ceiling. Specifically, at 10 dBm transmit power per LU, the proposed framework not only reduces the achievable sum-rate by over 57% relative to the interference-free scenario, but also improves the jamming impact by approximately 36% compared to the conventional single-panel DIRS, demonstrating superior and robust fully passive jamming capability. Full article
(This article belongs to the Special Issue Massive MIMO and Other Advanced Antenna Techniques for 5G/6G Wireless Systems)
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20 pages, 1186 KB  
Article
Radio Frequency Resonate and Fire (RF-RAF) Neurons Supporting Device Classification
by David L. Weathers, Michael A. Temple and Brett J. Borghetti
Electronics 2026, 15(10), 2023; https://doi.org/10.3390/electronics15102023 - 9 May 2026
Viewed by 365
Abstract
Radio Frequency Fingerprinting (RFF) enables passive physical-layer device authentication by exploiting unintentional hardware variations in wireless transmitters. Neuromorphic implementations are attractive, given their potential for low-latency, energy-efficient inference capability under Size, Weight, and Power (SWaP) constraints at the edge. A new RFF capability [...] Read more.
Radio Frequency Fingerprinting (RFF) enables passive physical-layer device authentication by exploiting unintentional hardware variations in wireless transmitters. Neuromorphic implementations are attractive, given their potential for low-latency, energy-efficient inference capability under Size, Weight, and Power (SWaP) constraints at the edge. A new RFF capability is demonstrated here using recently introduced Radio Frequency Resonate-and-Fire (RF-RAF) neurons and eight WirelessHART devices. Performance is evaluated for RF-RAF-generated fingerprints against the established Gabor Transform (GTX) baseline using three classifier architectures: Random Forest (RndF), Convolutional Neural Network (CNN), and a Time-Incremented Spiking Neural Network (TI-SNN). The results show that RF-RAF fingerprints achieve an average classification accuracy of 96.7% across all three classifier types and consistently outperform GTX fingerprints at all evaluated fingerprint sizes. This performance persists under time-span-matched conditions, and the RF-RAF versus GTX benefit is not solely attributable to input data utilization. The TI-SNN surpasses 94% classification accuracy using M = 4 time step RF-RAF fingerprints with approximately 100 spikes per inference—a 4× larger GTX fingerprint requires approximately 1000 spikes to achieve the same classification accuracy. RF-RAF fingerprints offer two additional benefits: they are natively non-negative, which supports efficient neuromorphic hardware implementation, and they provide greater flexibility in fingerprint size selection. It is concluded that RF-RAF neurons provide an efficient neuromorphic-native encoding pathway for device RFF discrimination and offer improved accuracy–efficiency tradeoffs in training and inference for various classifier architectures. Full article
(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)
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37 pages, 2297 KB  
Review
Space Photovoltaics: Materials, Device Concepts and Operational Challenges
by Anna Drabczyk, Paweł Uss, Katarzyna Bucka, Wojciech Bulowski, Patryk Kasza, Grzegorz Putynkowski and Robert P. Socha
Electronics 2026, 15(10), 1978; https://doi.org/10.3390/electronics15101978 - 7 May 2026
Viewed by 1090
Abstract
Space photovoltaics remains the primary power source for satellites and spacecraft, where high efficiency, radiation resistance, and low mass are essential requirements. While conventional III–V multijunction solar cells currently represent the technological benchmark, recent advances in materials science and device architectures have significantly [...] Read more.
Space photovoltaics remains the primary power source for satellites and spacecraft, where high efficiency, radiation resistance, and low mass are essential requirements. While conventional III–V multijunction solar cells currently represent the technological benchmark, recent advances in materials science and device architectures have significantly expanded the design space of space photovoltaic systems. This review provides a comprehensive overview of the fundamental physical principles, material platforms, and device concepts relevant to photovoltaic operation under space conditions, with particular emphasis on the AM0 spectrum, radiation effects, and thermal cycling. Special attention is devoted to advanced architectures, including inverted metamorphic multijunction solar cells, concentrator photovoltaic systems, and emerging tandem concepts such as perovskite/silicon and all-perovskite devices. The review highlights the growing importance of system-level metrics, particularly specific power and integration flexibility, which increasingly complement efficiency as key performance indicators. Although emerging technologies offer unprecedented opportunities for lightweight and high-efficiency photovoltaic systems, challenges related to long-term stability, defect control, and scalability remain critical for their practical implementation. Overall, the future of space photovoltaics lies in the development of application-specific solutions that balance efficiency, durability, mass, and cost, enabling next-generation space missions and energy systems. Full article
(This article belongs to the Special Issue Recent Advances in Emerging Semiconductor Devices)
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22 pages, 597 KB  
Article
Scaling Computer Vision: A Comparative Analysis of Cloud Infrastructures for AI-Based Image Processing and Classification Applications
by Haojie Zheng, Carlos Reaño, Alberto Castillo, Juan F. Ariño-Sales, Álvaro Igual and Carles Igual
Electronics 2026, 15(9), 1953; https://doi.org/10.3390/electronics15091953 - 5 May 2026
Viewed by 453
Abstract
Artificial intelligence-driven computer vision has undergone rapid expansion in recent years, largely propelled by progress in deep learning techniques and the availability of extensive annotated datasets. Nevertheless, the large-scale adoption of such systems remains challenging for many organizations due to financial constraints and [...] Read more.
Artificial intelligence-driven computer vision has undergone rapid expansion in recent years, largely propelled by progress in deep learning techniques and the availability of extensive annotated datasets. Nevertheless, the large-scale adoption of such systems remains challenging for many organizations due to financial constraints and technological complexity. In this context, cloud computing has become an appealing alternative, as it offers elastic, on-demand resources under a pay-as-you-go model. Despite these advantages, the use of cloud platforms also introduces specific challenges for computer vision applications. One of the key open issues concerns the assessment of whether it is better to use classical Infrastructure (IaaS) or Containers (CaaS) to build applications. In this paper, we evaluated and compared these two models by using a real-world use case: an AI-based image processing and classification application. The best-performing model achieved speed-ups of up to 2.12× and reduced resource consumption and costs by up to 22% compared with the other evaluated alternatives. Full article
(This article belongs to the Special Issue AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches)
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16 pages, 919 KB  
Article
A Comparative Performance Study of Host-Based Intrusion Detection Using TextRank-Based System Call Preprocessing and Deep Learning Models
by Hyunwook You, Chulgyun Park, Dongkyoo Shin and Dongil Shin
Electronics 2026, 15(9), 1856; https://doi.org/10.3390/electronics15091856 - 27 Apr 2026
Viewed by 497
Abstract
Host-based intrusion detection systems (HIDSs) can address the limitations of network-based detection by analyzing system calls and other low-level events. Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set [...] Read more.
Host-based intrusion detection systems (HIDSs) can address the limitations of network-based detection by analyzing system calls and other low-level events. Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set of attack behaviors. To address this gap, this study builds a TextRank-based preprocessing pipeline on the LID-DS 2021 dataset and compares five end-to-end pipelines: Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network(CNN) + LSTM, LSTM, Bidirectional LSTM (BiLSTM), and CNN + Bidirectional Gated Recurrent Unit (BiGRU). Of the 15 scenarios in the dataset, six multi-stage attacks were excluded, and three representative scenarios were selected based on attack-category coverage and suitability for single-chunk host-level detection. Within these three selected scenarios and same-scenario file-level splits, the deep learning pipelines achieved F1-scores of 0.90–0.94, whereas RF ranged from 0.55 to 0.63. Among the evaluated pipelines, CNN + BiGRU produced the strongest overall results. These findings indicate that, under this constrained evaluation setting, sequential deep learning pipelines can be effective for scenario-specific system-call-based HIDS; however, broader generalization to unseen attacks or to the full LID-DS 2021 scenario set remains unverified. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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14 pages, 1630 KB  
Article
Broadband Stepped-Impedance Wilkinson Power Divider with Improved Performance
by Stelios Tsitsos, Maria Prousali and Hristos T. Anastassiu
Electronics 2026, 15(9), 1839; https://doi.org/10.3390/electronics15091839 - 26 Apr 2026
Viewed by 519
Abstract
Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was [...] Read more.
Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was performed to extract the design equations, followed by simulation and optimization to enhance impedance matching and output-port isolation over a broad bandwidth. Finally, the proposed divider was fabricated using microstrip-line technology, and experimental measurements were conducted using the Agilent E5071C vector network analyzer. The simulation and measurement results showed efficient wideband operation over the 1–4 GHz frequency range. Specifically, the measured return loss at the input port was <−10 dB; the corresponding return loss at the output ports was <−15 dB. The measured insertion loss was −3.73 ± 0.42 dB. The isolation between the output ports was <−10 dB, reaching approximately −30 dB at 2.1 GHz and −25 dB at the center operating frequency (f0 = 2.5 GHz). The amplitude and phase imbalances were 0 ± 0.2 dB and 0o ± 0.8o, respectively. Furthermore, the overall size of the proposed wideband Wilkinson power divider was 0.35λg × 0.21λg. Compared to previous designs, the divider proposed in this study exhibits an improved and more symmetric frequency response, as well as a substantially reduced size, making it suitable for several modern wireless technologies such as Wi-Fi, Bluetooth, GPS, DCS, WCDMA, and sub-6 GHz 5G communication systems. Full article
(This article belongs to the Special Issue High-Frequency Electromagnetic Technologies for 5G/6G Communications: Antenna Design and Signal Optimization)
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47 pages, 5474 KB  
Review
Bias in Large Language Models: Origin, Evaluation, and Mitigation
by Yufei Guo, Muzhe Guo, Juntao Su, Zhou Yang, Mengqiu Zhu, Hongfei Li, Mengyang Qiu and Shuo Shuo Liu
Electronics 2026, 15(9), 1824; https://doi.org/10.3390/electronics15091824 - 24 Apr 2026
Cited by 3 | Viewed by 1206
Abstract
Large language models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their [...] Read more.
Large language models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various natural language processing (NLP) tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible artificial intelligence (AI) systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies. Full article
(This article belongs to the Special Issue Data Mining in Natural Language Processing: Latest Advances and Prospects)
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37 pages, 7662 KB  
Article
Joint Congestion Control Evaluation for MPTCP and MPQUIC over Multi-Link Backhauls with eMBB and mMTC-like Traffic
by Roberto Picchi and Daniele Tarchi
Electronics 2026, 15(9), 1797; https://doi.org/10.3390/electronics15091797 - 23 Apr 2026
Viewed by 360
Abstract
Multi-link terrestrial backhauls create a shared transport environment in which heterogeneous multipath protocols compete for the same forwarding resources while reacting to congestion with different control logics. In this paper, we investigate this problem in a 5G Integrated Access and Backhaul (IAB) scenario [...] Read more.
Multi-link terrestrial backhauls create a shared transport environment in which heterogeneous multipath protocols compete for the same forwarding resources while reacting to congestion with different control logics. In this paper, we investigate this problem in a 5G Integrated Access and Backhaul (IAB) scenario where an IAB node aggregates traffic from multiple User Equipments (UEs) and forwards it toward the core network over two terrestrial backhaul paths. We focus on the coexistence of Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC), evaluating how cross-protocol Congestion Control (CC) pairings affect performance. Specifically, all feasible BBR, CUBIC, and Reno cross-pairings are assessed under symmetric and asymmetric dual-backhaul conditions, considering Enhanced Mobile Broadband (eMBB) and dense low-rate traffic regimes representative of mMTC-like operation. The analysis considers throughput, Jain’s fairness index, jitter, and packet loss to identify the trade-offs of each CC pairing. Results show that CC selection is a first-order design factor in MPTCP/MPQUIC coexistence over shared backhauls. No single pairing is uniformly optimal across all metrics: some configurations provide more balanced throughput sharing, others improve fairness, while the most favorable solutions for jitter do not necessarily maximize transport efficiency. These findings identify CC pairing as a tuning dimension for multi-link backhaul systems based on heterogeneous multipath transports. Full article
(This article belongs to the Section Computer Science & Engineering)
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21 pages, 66333 KB  
Review
Diffusion Models: Unlocking the “4 Secrets” of High-Quality Image Generation
by Tao Zhou, Zhe Zhang, Mingzhe Zhang, Wenwen Chai, Yong Xia and Fuyuan Hu
Electronics 2026, 15(8), 1755; https://doi.org/10.3390/electronics15081755 - 21 Apr 2026
Viewed by 2035
Abstract
The diffusion model (DM) is a hot topic in deep generative models and is widely applied in image generation. In diffusion models, there are four main “secrets” that affect high-quality image generation: constructing the diffusion model, improving the sampling velocity, designing the diffusion [...] Read more.
The diffusion model (DM) is a hot topic in deep generative models and is widely applied in image generation. In diffusion models, there are four main “secrets” that affect high-quality image generation: constructing the diffusion model, improving the sampling velocity, designing the diffusion process, and guiding diffusion models. How should one construct the diffusion model? How can one improve the sampling velocity? How should one design the diffusion process? How should one guide diffusion models? These questions are critical to enhancing diffusion model performance. However, most existing review papers focus on applications, while discussion of the four key technical aspects remains limited. In response, this paper summarizes four key technologies and six representative application directions. First, the basic principles of diffusion models are reviewed from three perspectives: denoising diffusion probabilistic models, noise conditional score network models, and stochastic differential equation models. Second, key techniques for improving sampling velocity are summarized from three perspectives: non-Markovian sampling, knowledge distillation sampling, and discrete optimization sampling. Third, the diffusion process design is summarized from three perspectives: latent space, Transformer-based diffusion, and non-Euclidean space. Fourth, guidance strategies are summarized from three perspectives: classifier guidance, classifier-free guidance, and multimodal guidance. Fifth, the advantages and applications of diffusion models are discussed in high-quality text-to-image generation, high-quality text-to-video generation, and high-quality image-to-image generation. Finally, this paper discusses the challenges faced by diffusion models in image generation. Overall, this review systematically discusses the four “secrets” of diffusion models for image generation and provides a useful reference for future research in this field. Full article
(This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing)
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24 pages, 1664 KB  
Article
Optimizing Influence Maximization in Social Networks via Centrality-Driven Discrete Particle Swarm Optimization (DPSO)
by John Titos Papadakis and Haridimos Kondylakis
Electronics 2026, 15(8), 1730; https://doi.org/10.3390/electronics15081730 - 19 Apr 2026
Viewed by 512
Abstract
Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the [...] Read more.
Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the IM problem is NP-hard, making exact solutions computationally infeasible for large-scale networks. Existing approximation methods typically rely either on static centrality heuristics, which often ignore global network structure, or on metaheuristic algorithms, which may suffer from slow convergence due to random initialization. This paper proposes a novel approach, termed Advanced Centrality-Driven Discrete Particle Swarm Optimization (DPSO), which integrates a weighted hybrid centrality score combining Degree, PageRank, and Betweenness centrality to guide the stochastic search process. In addition, a systematic grid search methodology is employed to determine the optimal weight configuration of the hybrid score. Experiments conducted on three real-world datasets (Twitter, ego-Facebook, and ca-HepTh) demonstrate that the optimal seeding strategy is strongly dependent on network topology. The results show that dense social networks favor popularity-based metrics such as Degree and PageRank, whereas sparse collaboration networks benefit significantly from bridge-oriented metrics such as Betweenness centrality. Overall, the proposed method achieves consistent improvements in influence spread across different network types, with the largest gains (up to 70%) observed in sparse network settings. Full article
(This article belongs to the Special Issue Advances in Web Data Management)
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17 pages, 6497 KB  
Article
Optimization Trade-Offs in Memristor-Based Crossbar Arrays for MAC Acceleration
by Hassen Aziza, Hanzhi Xun, Moritz Fieback, Mottaqiallah Taouil and Said Hamdioui
Electronics 2026, 15(8), 1710; https://doi.org/10.3390/electronics15081710 - 17 Apr 2026
Viewed by 658
Abstract
Vector–matrix multiplication (VMM), implemented through multiply–accumulate (MAC) operations, represents the dominant computational primitive in many artificial intelligence (AI) workloads. When executed on conventional von Neumann architectures, VMM operations suffer from important energy consumption and latency due to the separation between memory and processing [...] Read more.
Vector–matrix multiplication (VMM), implemented through multiply–accumulate (MAC) operations, represents the dominant computational primitive in many artificial intelligence (AI) workloads. When executed on conventional von Neumann architectures, VMM operations suffer from important energy consumption and latency due to the separation between memory and processing units. To overcome these limitations, crossbar arrays built from Resistive Random Access Memory (RRAM) cells have been proposed for accelerating VMM computations. In this work, we investigate the key optimization trade-offs associated with implementing RRAM-based neural networks for classification applications. A simple two-layer neural network is first defined and trained in software to generate the weight matrices and bias parameters. Next, three hardware implementation scenarios are evaluated depending on whether negative floating-point numbers are used: Positive Weights Only (PWO), Positive and Negative Weights Only (PNWO), and Positive and Negative Weights with Biases (PNWB). The different implementations are analyzed at the hardware level by examining classification accuracy, energy efficiency, latency, and area overhead. The study further incorporates important RRAM limitations, including restricted conductance range and device variability. Hardware results show that the PWO scenario offers the lowest energy consumption (189 fJ/MAC) and area overhead but results in the lowest accuracy. PNWO and PNWB significantly improve accuracy (+177% and +180%) but increase energy consumption (+63% and +87%) and area (×2 and ×2.1). Under variability effects, PWO achieves better accuracy (94.65%), followed by PNWO (93.11%) and PNWB (92.11%). Full article
(This article belongs to the Special Issue Prospective of Semiconductor Memory Devices)
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16 pages, 2379 KB  
Article
A Novel Standard Cell Structure and Physical Design Methodology to Enhance Routability
by Seongjun Lee and Changho Han
Electronics 2026, 15(8), 1690; https://doi.org/10.3390/electronics15081690 - 17 Apr 2026
Viewed by 846
Abstract
In the era of highly integrated circuits, continuous miniaturization has significantly increased routing complexity, thereby directly impacting circuit performance. As process scaling advances and the number of on-chip metal layers increases, conventional standard cell libraries face limitations that cause severe routing bottlenecks. To [...] Read more.
In the era of highly integrated circuits, continuous miniaturization has significantly increased routing complexity, thereby directly impacting circuit performance. As process scaling advances and the number of on-chip metal layers increases, conventional standard cell libraries face limitations that cause severe routing bottlenecks. To overcome these limitations, this paper proposes a dual-component approach. First, we introduce a novel standard cell structure that improves routing flexibility by expanding the degrees of freedom for pin access, particularly in highly congested regions. Second, we present a physical design methodology specifically designed to ensure seamless integration with existing electronic design automation (EDA) tools, allowing new cells to be effectively placed and routed without major modifications to current flows. The proposed approach was validated using the open-source ASAP7 process design kit (PDK). Experimental results confirm significant reductions in via count and total wirelength, leading to improved routability, reduced power consumption, and enhanced performance. These findings demonstrate that combining the new cell architecture with a tailored design methodology provides a practical alternative to conventional solutions, enabling more efficient and scalable circuit designs for future technology nodes. Full article
(This article belongs to the Section Circuit and Signal Processing)
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14 pages, 810 KB  
Article
TRIDENT: Efficient Small-Large Model Collaboration via Heterogeneous Expert Decoupling
by Guangyu Dai, Siliang Tang and Yueting Zhuang
Electronics 2026, 15(8), 1699; https://doi.org/10.3390/electronics15081699 - 17 Apr 2026
Viewed by 414
Abstract
The burgeoning scale of Pre-trained Large Models (PLMs) has intensified the demand for efficient inference without compromising performance, while existing large model collaborative frameworks have shown promise, they often suffer from functional redundancy among experts and limited robustness in complex cross-domain scenarios. In [...] Read more.
The burgeoning scale of Pre-trained Large Models (PLMs) has intensified the demand for efficient inference without compromising performance, while existing large model collaborative frameworks have shown promise, they often suffer from functional redundancy among experts and limited robustness in complex cross-domain scenarios. In this paper, we propose Tri-gate Routing for Inference via Decoupled Efficient Network Technologies (TRIDENT), a highly efficient and robust heterogeneous collaborative inference framework. TRIDENT leverages the complementary inductive biases of MLP (for statistical patterns) and KAN (for symbolic logic) to maximize reasoning potential with minimal parametric overhead. To address feature homogenization in traditional distillation, we introduce Orthogonal Feature Decoupling Distillation, utilizing an orthogonality loss Lorth for functional decoupling and a reconstruction loss Lrecon to anchor decoupled features to the PLM knowledge manifold. During inference, a Dual-Threshold Arbiter effectively detects expert hallucinations by integrating individual confidence τcon and heterogeneous consistency τagree. Extensive experiments on CIFAR-100-LT, XNLI, and GSM8K demonstrate that TRIDENT significantly reduces the Invocation Rate (IR) of PLMs while maintaining high accuracy. Our findings reveal a distinct Pareto optimal balance and validate the spontaneous division of labor between heterogeneous experts. By transcending the limitations of single-architecture systems, TRIDENT provides a robust and interpretable pathway for efficient collaborative intelligence. Full article
(This article belongs to the Section Artificial Intelligence)
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10 pages, 3121 KB  
Article
Study of Gate Leakage Current and Failure Mechanism for Schottky-Type p-GaN Gate of GaN HEMTs
by Cristina Miccoli, Marcello Cioni, Giacomo Cappellini, Alberto Millefanti, Alessio Pirani, Giansalvo Pizzo, Viviana Fezzi, Maurizio Moschetti, Maria Eloisa Castagna, Ferdinando Iucolano, Giovanni Giorgino and Alessandro Chini
Electronics 2026, 15(8), 1698; https://doi.org/10.3390/electronics15081698 - 17 Apr 2026
Cited by 1 | Viewed by 1343
Abstract
In this work, a novel understanding of the main failure mechanism of a Schottky p-GaN gate AlGaN/GaN HEMT subject to forward gate stress is reported. First an experimental characterization of the gate leakage current (IGSS) at different temperatures is reported. Then, [...] Read more.
In this work, a novel understanding of the main failure mechanism of a Schottky p-GaN gate AlGaN/GaN HEMT subject to forward gate stress is reported. First an experimental characterization of the gate leakage current (IGSS) at different temperatures is reported. Then, Technology Computer Aided Design (TCAD) simulations are used to reproduce the experimental IGSS thanks to the impact ionization model, also at different temperatures. Simulation results underline how the stressed regions for the Device Under Test (DUT) at high gate biases are the Schottky/p-GaN interface, the p-GaN/AlGaN barrier interface, and p-GaN sidewalls. Moreover, Time Dependent Gate Breakdown (TDGB) measurements were done, and the TEM analysis on the failed device showed the lattice crystal damage located at the p-GaN/AlGaN interface, in accordance with TCAD simulations’ current density distribution at high voltage gate stress. Full article
(This article belongs to the Special Issue Feature Papers in Semiconductor Devices, 2nd Edition)
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13 pages, 747 KB  
Article
Uplink-Centric DUDe for IoT and Industry 4.0
by Charalampos Chatzigeorgiou, Christos Bouras, Vasileios Kokkinos, Apostolos Gkamas and Philippos Pouyioutas
Electronics 2026, 15(8), 1680; https://doi.org/10.3390/electronics15081680 - 16 Apr 2026
Viewed by 355
Abstract
This study investigates Downlink/Uplink Decoupling (DUDe) in 5G networks, a framework that allows user equipment to select its uplink serving cell independently of the downlink anchor. This approach is designed to alleviate the “macro bias” and pathloss issues that typically degrade performance for [...] Read more.
This study investigates Downlink/Uplink Decoupling (DUDe) in 5G networks, a framework that allows user equipment to select its uplink serving cell independently of the downlink anchor. This approach is designed to alleviate the “macro bias” and pathloss issues that typically degrade performance for Internet of Things (IoT) traffic. We propose a framework managed by Mobile Edge Computing (MEC) that operates on a per-Transmission Time Interval (TTI) basis, incorporating stability mechanisms such as hysteresis and Time to Trigger to prevent frequent, unnecessary handovers. The performance is evaluated using a system-level simulator across two scenarios: a high-density urban IoT deployment and an Industry 4.0 smart factory environment. Our results demonstrate that the proposed framework significantly improves uplink throughput and reduces tail latency compared to traditional coupled association methods. Furthermore, an ablation study confirms that these performance gains are derived from the structural decoupling of links, providing a scalable path for improving connectivity in 5G and beyond. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
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21 pages, 1472 KB  
Article
Intelligence-Driven Leader Selection in PEGASIS: A Data-Driven Machine Learning Framework for Sustainable and Secure Wireless Sensor Networks
by Abdulla Juwaied and Andrzej Romanowski
Electronics 2026, 15(8), 1686; https://doi.org/10.3390/electronics15081686 - 16 Apr 2026
Cited by 1 | Viewed by 459
Abstract
Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, [...] Read more.
Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
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55 pages, 4195 KB  
Article
Multimodal Large Language Model-Based Explainable Boosting Machine Analysis for Interpretation of State-of-Health Prediction of Lithium-Ion Batteries
by Jaehyeok Lee, Jaeseung Lee and Jehyeok Rew
Electronics 2026, 15(8), 1675; https://doi.org/10.3390/electronics15081675 - 16 Apr 2026
Viewed by 528
Abstract
Accurate prediction of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety and reliability of electric vehicles and energy storage systems. While machine learning (ML)-based models have demonstrated strong predictive performance, their limited interpretability remains a major challenge [...] Read more.
Accurate prediction of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety and reliability of electric vehicles and energy storage systems. While machine learning (ML)-based models have demonstrated strong predictive performance, their limited interpretability remains a major challenge for deployment in safety-critical applications. Although explainable boosting machines (EBMs) provide an interpretable alternative through their additive structure, existing studies still rely on manual analysis of model outputs, which restricts scalability and reproducibility. To address this limitation, this study proposes a structured interpretation framework that integrates EBMs with multimodal large language models (MLLMs). The proposed framework employs EBMs to generate SOH predictions along with global feature importance and variable-level score-density visualizations. These outputs are subsequently processed by an MLLM to perform automated interpretation at both global and variable levels, followed by aggregation, cross-validation, and generation of a unified interpretation report. Experiments were conducted on a lithium-ion battery degradation dataset and the EBM achieved competitive predictive performance compared to baseline ML models. In addition, the quality of the generated interpretations was evaluated using both an MLLM-as-a-Judge and a user study. The evaluation results show that the generated interpretations consistently achieved high scores, with average ratings exceeding 4.5 out of 5 across key criteria such as interpretation accuracy and faithfulness, as assessed by both independent MLLMs and domain experts. These results demonstrate that the proposed framework enables reliable and scalable interpretation of battery SOH prediction models, providing a practical solution for explainable artificial intelligence in battery health management. Full article
(This article belongs to the Special Issue Trustworthy Decision Intelligence: Data-Centric AI, Foundation Models, and Real-World Impact)
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35 pages, 57348 KB  
Article
A Target-Oriented Shared-Control Framework for Adaptive Spatial and Kinematic Support in Mixed Reality Teleoperation
by Soma Okamoto and Kosuke Sekiyama
Electronics 2026, 15(8), 1653; https://doi.org/10.3390/electronics15081653 - 15 Apr 2026
Viewed by 537
Abstract
Mixed Reality (MR) teleoperation offers an intuitive interface for Human-Robot Collaboration (HRC), yet it often faces the “Embodiment Gap”—a physical and kinematic mismatch between human operators and robotic platforms. Existing MR systems primarily rely on a “direct mapping” approach, where user movements are [...] Read more.
Mixed Reality (MR) teleoperation offers an intuitive interface for Human-Robot Collaboration (HRC), yet it often faces the “Embodiment Gap”—a physical and kinematic mismatch between human operators and robotic platforms. Existing MR systems primarily rely on a “direct mapping” approach, where user movements are transferred directly to the robot. This forces operators to manually adapt to robotic constraints, such as singularities and joint limits, making task performance heavily dependent on individual skill. This study proposes Mixed reality Adaptive Spatial and Kinematic support (MASK), an adaptive shared-control framework designed to bridge the “Gulf of Execution” and “Gulf of Evaluation” by separating target selection from reachability and kinematic feasibility. The MASK system integrates three core modules: (1) Target Object Identification (TOI) based on body motion features to identify the intended manipulation target; (2) a Base Relocation Module (BRI) utilizing Inverse Reachability Maps to optimize the robot’s spatial configuration; and (3) a Kinematic Correction Module (KCM) that autonomously resolves kinematic constraints through pose blending and null-space optimization. Initial experimental results suggest that MASK reduces the operator’s cognitive and physical load by shifting the burden of kinematic resolution from the human to the system. This approach enables high-precision manipulation through an intuitive interface, potentially reducing the performance gap between different levels of operator proficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cyber-Physical Systems)
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23 pages, 1996 KB  
Article
Trustworthy Visual Privacy Auditing with Causal Governance and Resilient Federated Protection for NIST AI Risk Management Framework
by Ray-I Chang, Wei-Xun Lu and Chih Yang
Electronics 2026, 15(8), 1658; https://doi.org/10.3390/electronics15081658 - 15 Apr 2026
Viewed by 422
Abstract
Our previous visual privacy framework leveraging Graph Convolutional Networks (GCNs) and Federated Learning (FL) has been shown to achieve state-of-the-art (SOTA) predictive performance. However, it neglects the systemic requirements of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI [...] Read more.
Our previous visual privacy framework leveraging Graph Convolutional Networks (GCNs) and Federated Learning (FL) has been shown to achieve state-of-the-art (SOTA) predictive performance. However, it neglects the systemic requirements of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF). To address this critical gap, this paper proposes the Trustworthy Visual Privacy Auditing (TVPA) system, which transitions conventional static detection models into a dynamic and secure governance ecosystem. We first establish system resilience against adversarial threats by proposing an active auditing mechanism called Resilient Federated Protection (RFP) to embed unique model parameter watermarks within client-side updates. The RFP mechanism enables the federated aggregator to verify node legitimacy and automatically isolate malicious clients attempting poisoning attacks. Then, to ensure strict accountability, we design an immutable audit log mechanism in the RFP mechanism that utilizes a Cryptographic Hash Chain (CHC) to record and verify the provenance of every model update, creating a transparent chain of custody. Furthermore, the prediction mechanism is enhanced by Causal Governance (CG) that integrates causal inference to provide counterfactual reasoning for explaining the root causes of privacy risks rather than merely flagging associations. Experiments on the VISPR dataset demonstrate that our TVPA system can synthesize high-performance recognition with robust security, auditability, and causal explainability to provide trustworthy AI governance. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies, Volume II)
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28 pages, 4609 KB  
Review
Reconfigurable Antennas Enabled by Tunable Metasurfaces for Next-Generation Wireless Communications: A Review
by Zahra Hamzavi-Zarghani, Ladislau Matekovits and Wolfgang Bösch
Electronics 2026, 15(8), 1610; https://doi.org/10.3390/electronics15081610 - 13 Apr 2026
Viewed by 2344
Abstract
Reconfigurable antennas play a central role in next-generation wireless communication systems by enabling dynamic adaptation of operating frequency, radiation pattern, and polarization. Tunable metasurfaces have emerged as a powerful and compact approach to antenna reconfiguration, allowing electromagnetic wave manipulation through engineered, planar structures [...] Read more.
Reconfigurable antennas play a central role in next-generation wireless communication systems by enabling dynamic adaptation of operating frequency, radiation pattern, and polarization. Tunable metasurfaces have emerged as a powerful and compact approach to antenna reconfiguration, allowing electromagnetic wave manipulation through engineered, planar structures whose properties can be dynamically controlled. By embedding active devices or tunable materials within metasurface unit cells, antenna characteristics can be modified without altering the antenna geometry. This review provides a comprehensive overview of reconfigurable antennas enabled by tunable metasurfaces. We adopt a functionality-based classification that focuses on operating frequency, radiation pattern, polarization, and multifunction reconfiguration. An overview of major tunability technologies, including PIN diodes, varactors, MEMS, graphene and two-dimensional materials, and liquid crystal (LC) or phase-change materials, is first presented. Subsequently, metasurface-based reconfiguration strategies are discussed and compared for each antenna functionality, highlighting design principles, practical trade-offs, and limitations. The review concludes with an assessment of challenges and future research directions relevant to next-generation wireless communications and beyond. Full article
(This article belongs to the Special Issue Next-Generation Antenna Technologies for Wireless Communications and Beyond)
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21 pages, 1059 KB  
Article
Lightweight MLP-Based Feature Extraction with Linear Classifier for Intrusion Detection System in Internet of Things
by Jisi Chandroth and Jehad Ali
Electronics 2026, 15(8), 1604; https://doi.org/10.3390/electronics15081604 - 12 Apr 2026
Cited by 1 | Viewed by 636
Abstract
The Internet of Things (IoT) comprises diverse devices connected through heterogeneous communication protocols to deliver a wide range of services. However, the complexity and scale of IoT networks make them difficult to secure. Network intrusion detection systems (NIDSs) have therefore become essential for [...] Read more.
The Internet of Things (IoT) comprises diverse devices connected through heterogeneous communication protocols to deliver a wide range of services. However, the complexity and scale of IoT networks make them difficult to secure. Network intrusion detection systems (NIDSs) have therefore become essential for identifying malicious activities and protecting IoT environments across many applications. Although recent deep learning (DL)-based IDS approaches achieve strong detection performance, they often require substantial computation and storage, which limits their practicality on resource-constrained IoT devices. To balance detection accuracy with computational efficiency, we propose a lightweight deep learning model for IoT intrusion detection. Specifically, our method learns compact, intrusion-relevant representations from traffic features using a two-layer multi-layer perceptron (MLP) embedding backbone, followed by a linear SoftMax classification head for multi-class attack detection. We evaluate the proposed approach on three benchmark datasets, CICIDS2017, NSL-KDD, and CICIoT2023, and the results show strong performance, achieving 99.85%, 99.21%, and 98.45% accuracy, respectively, while significantly reducing model size and computational overhead. The experimental results demonstrate that the proposed method achieves excellent classification performance while maintaining a lightweight design, with fewer parameters and lower FLOPs than existing approaches. Full article
(This article belongs to the Special Issue Secure and Intelligent IoT & CPS: AI Driven Attack–Defense, Network Analysis and Smart Data Protection)
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28 pages, 1445 KB  
Article
Cost-Aware Lightweight Deep Learning for Intrusion Detection: A Comparative Study on UNSW-NB15 and CIC-IDS2017
by Marija Gombar, Amir Topalović and Mirjana Pejić Bach
Electronics 2026, 15(8), 1603; https://doi.org/10.3390/electronics15081603 - 12 Apr 2026
Cited by 1 | Viewed by 837
Abstract
Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet [...] Read more.
Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet, a convolutional model optimized for feature-centric detection, and SigNet, a gated recurrent model designed for sequence-oriented modeling of ordered flow-feature representations. Both models are trained with Cost-Robust Focal Loss (CRF-Loss), a cost-aware objective that penalizes false positives and false negatives according to deployment-specific risk preferences. We evaluate the models on the UNSW-NB15 and CIC-IDS2017 benchmarks using six standard metrics (accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC)), complemented by an analysis of false-positive behavior. On CIC-IDS2017, ForNet achieves precision up to 0.95 and MCC up to 0.93 with AUROC above 0.94, while SigNet shows a stronger recall-oriented profile on UNSW-NB15. In an ablation study, replacing Binary Cross-Entropy with CRF-Loss reduces the false-positive rate by approximately 15–20% and improves robustness-oriented metrics such as MCC by up to 12% on CIC-IDS2017. Rather than claiming universal state-of-the-art performance, the study focuses on performance–risk trade-offs under realistic operational constraints. The results highlight how architectural bias and cost-aware optimisation jointly shape IDS behaviour and offer benchmark-based guidance for interpreting performance–risk trade-offs in lightweight intrusion detection. Full article
(This article belongs to the Special Issue Recent Advances of Applied Data Science in Cybersecurity and Process Monitoring)
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28 pages, 5745 KB  
Article
FPGA-Based Design and Implementation of a High-Performance Telemetry Transmission Architecture for Satellite Communications
by Adriana N. Moreno Mercado and Víctor P. Gil Jiménez
Electronics 2026, 15(8), 1581; https://doi.org/10.3390/electronics15081581 - 10 Apr 2026
Viewed by 644
Abstract
This paper presents a high-performance and resource-efficient Field Programmable Gate Array (FPGA)-based architecture for satellite telemetry transmission systems. The proposed design implements a flexible channel coding chain, including Reed–Solomon (R-S) encoding, convolutional encoding, symbol interleaving, pseudo-randomization, and Attached Synchronization Marker (ASM) insertion, in [...] Read more.
This paper presents a high-performance and resource-efficient Field Programmable Gate Array (FPGA)-based architecture for satellite telemetry transmission systems. The proposed design implements a flexible channel coding chain, including Reed–Solomon (R-S) encoding, convolutional encoding, symbol interleaving, pseudo-randomization, and Attached Synchronization Marker (ASM) insertion, in accordance with CCSDS recommendations. The architecture is fully integrated and configurable, allowing dynamic selection of coding schemes without requiring structural modifications. The system is implemented on a modern FPGA platform with a 32-bit AXI4-Stream interface at 110 MHz, reaching an effective throughput of up to 1.76 Gbps. Experimental results demonstrate reliable timing with positive setup and hold margins, allowing the system to operate at approximately 130 MHz. Power consumption is measured using Switching Activity Interchange Format (SAIF)-based switching activity, providing a realistic estimate of programmable logic power consumption. The total on-chip power is about 1.77 W for individual coding modes. It rises to 1.91 W in the concatenated setup, which is the worst-case scenario. The results show that the proposed architecture efficiently uses resources, runs reliably at high speeds, and exhibits predictable power consumption. This makes it well suited for high-reliability and energy-constrained satellite communication systems. resources are used. Full article
(This article belongs to the Special Issue Advances in Satellite/UAV Communications)
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24 pages, 3589 KB  
Article
Impact of Optimization Goal Visibility on Inter-Cloud DTM Performance
by Grzegorz Rzym, Zbigniew Duliński, Rafał Stankiewicz and Piotr Wydrych
Electronics 2026, 15(8), 1576; https://doi.org/10.3390/electronics15081576 - 9 Apr 2026
Viewed by 298
Abstract
This work presents an enhancement to the Dynamic Traffic Management (DTM) framework aimed at reducing signaling overhead between SDN controllers in multi-domain cloud environments. This extension is based on the ability to transmit information regarding the amount of balanced traffic and the optimal [...] Read more.
This work presents an enhancement to the Dynamic Traffic Management (DTM) framework aimed at reducing signaling overhead between SDN controllers in multi-domain cloud environments. This extension is based on the ability to transmit information regarding the amount of balanced traffic and the optimal transfer pattern. In the baseline periodic mode, the system regularly exchanges the compensation vector (C→) and the reference pattern (R→). To minimize communication, we define non-periodic modes that restrict C→ updates and eliminate R→ transmission entirely. Within these restricted signaling modes, we further distinguish between reactive and proactive operational schemes. Our experimental results demonstrate that reducing the visibility of optimization goals (R→ and only sign of C→) and cutting signaling frequency in this manner maintains a comparable level of cost-efficiency. Specifically, the initial evaluation shows that DTM typically decreases transit costs by 8% to 15%, with maximum savings reaching up to 29% when compared to the worst-case default BGP path scenario. These findings suggest that the DTM mechanism can maintain its economic efficiency even with significantly reduced inter-domain coordination. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence, 2nd Edition)
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24 pages, 1821 KB  
Article
MAVIS: Multi-Stem Audio Visualisation in Immersive Spaces Framework
by Jethro Shell and Sophy Smith
Electronics 2026, 15(8), 1559; https://doi.org/10.3390/electronics15081559 - 8 Apr 2026
Viewed by 543
Abstract
The visualisation of music has gained traction in both research and musical composition in recent years. The increased accessibility to immersive technologies, such as virtual reality (VR) and other forms of mixed reality (MR), lend themselves to the examination of how visualisation can [...] Read more.
The visualisation of music has gained traction in both research and musical composition in recent years. The increased accessibility to immersive technologies, such as virtual reality (VR) and other forms of mixed reality (MR), lend themselves to the examination of how visualisation can impact the perception of audio virtual worlds. In this paper, we propose the MAVIS (Multi-stem Audio Visualisation in Immersive Spaces) design framework, an approach to generating a visualisation of multi-stem structured orchestral music in a virtual world. This research explores the impact on participants’ interaction with an orchestral musical composition through the use of a two framework iterations informed by use cases. The resulting final design structure outlined in this article points towards constructing multi-stem virtual orchestral experiences through three pillars: semantic consistency, spatial agency, and complexity control. Whilst this research serves to propose a design intervention, future work requires a more extensive participant testing approach, coupled with an exploration of additional multimodal analysis. Full article
(This article belongs to the Special Issue Applications of Virtual, Augmented, and Mixed Reality in Creative Technology)
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25 pages, 11063 KB  
Article
Tac-Mamba: A Pose-Guided Cross-Modal State Space Model with Trust-Aware Gating for mmWave Radar Human Activity Recognition
by Haiyi Wu, Kai Zhao, Wei Yao and Yong Xiong
Electronics 2026, 15(7), 1535; https://doi.org/10.3390/electronics15071535 - 7 Apr 2026
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Abstract
Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high [...] Read more.
Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high computational costs, unsuitable for edge devices. To address these challenges, we propose Tac-Mamba, a lightweight cross-modal state space model. First, we introduce a topology-guided distillation scheme that uses a Spatial Mamba teacher to extract structural priors from visual skeletons. These priors are then explicitly distilled into a Point Transformer v3 (PTv3) radar student with a modality dropout strategy. We also developed a Trust-Aware Cross-Modal Attention (TACMA) module to prevent negative transfer. It evaluates the reliability of visual features through a SiLU-activated cross-modal bilinear interaction, smoothly degrading to a pure radar-driven fallback projection when visual inputs are corrupted. Finally, a Lightweight Temporal Mamba Block (LTMB) with a Zero-Parameter Cross-Gating (ZPCG) mechanism captures long-range kinematic dependencies with linear complexity. Experiments on the public MM-Fi dataset under strict cross-environment protocols demonstrate that Tac-Mamba achieves competitive accuracies of 95.37% (multimodal) and 87.54% (radar-only) with only 0.86M parameters and 1.89 ms inference latency. These results highlight the model’s exceptional robustness to modality missingness and its feasibility for edge deployment. Full article
(This article belongs to the Section Artificial Intelligence)
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26 pages, 2454 KB  
Article
Evolving LLMs from Next-Token Prediction to Multi-Token Prediction via Self-Distillation
by Yang Xu and Wanxiang Che
Electronics 2026, 15(7), 1533; https://doi.org/10.3390/electronics15071533 - 6 Apr 2026
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Abstract
Mainstream Large Language Models (LLMs) work under the paradigm of Next-Token Prediction (NTP). Multi-Token Prediction (MTP) is motivated by higher decoding efficiency, extending NTP to enable LLMs to draft multiple tokens during each forward pass. However, existing MTP approaches pretrain MTP along with [...] Read more.
Mainstream Large Language Models (LLMs) work under the paradigm of Next-Token Prediction (NTP). Multi-Token Prediction (MTP) is motivated by higher decoding efficiency, extending NTP to enable LLMs to draft multiple tokens during each forward pass. However, existing MTP approaches pretrain MTP along with the target LLM, making it difficult to unlock MTP for LLMs without official support. In this work, we propose a post-hoc approach to training an MTP module for a target LLM, providing an efficient way to evolve the LLM from NTP to MTP. The proposed approach features two main characteristics. (1) No changes to the target LLM, since it is frozen during MTP training. (2) Efficient MTP training via self-distillation from the target LLM’s native NTP capability. Results show that our approach can post-hoc train a performant MTP module via lightweight pretraining. Full article
(This article belongs to the Special Issue Innovative Applications of Large Language Models in Natural Language Processing (NLP))
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23 pages, 2145 KB  
Article
Seeing Through Touch: A Stereo-Vision Vibrotactile Aid for Visually Impaired People
by Claudia Presicci, Giulia Ballardini, Giorgia Marchesi, Paolo Robutti, Matteo Moro, Camilla Pierella, Andrea Canessa and Maura Casadio
Electronics 2026, 15(7), 1511; https://doi.org/10.3390/electronics15071511 - 3 Apr 2026
Viewed by 609
Abstract
Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external [...] Read more.
Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article
(This article belongs to the Special Issue Feature Papers in Bioelectronics: 2025–2026 Edition)
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