Electronics

16 pages, 919 KB

Open AccessEditor’s ChoiceArticle

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 362

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 Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set [...]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 Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set of attack behaviors.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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37 pages, 7662 KB

Open AccessEditor’s ChoiceArticle

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 218

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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24 pages, 1664 KB

Open AccessEditor’s ChoiceArticle

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 377

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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35 pages, 57348 KB

Open AccessEditor’s ChoiceArticle

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 328

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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28 pages, 1445 KB

Open AccessEditor’s ChoiceArticle

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

Viewed by 585

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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24 pages, 3589 KB

Open AccessEditor’s ChoiceArticle

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 260

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 (

\vec{C}

) and the reference pattern (

\vec{R}

). To minimize communication, we define non-periodic modes that restrict

\vec{C}

updates and eliminate

\vec{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 (

\vec{R}

and only sign of

\vec{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

Open AccessEditor’s ChoiceArticle

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 413

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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33 pages, 8263 KB

Open AccessEditor’s ChoiceArticle

Semantic Graphs of Learning Activities from LLM Embeddings: A Lightweight and Explainable Approach for Smart Learning Systems

by Javier García-Sigüenza, Alberto Real-Fernández, Faraón Llorens-Largo, Jose F. Vicent and Rafael Molina-Carmona

Electronics 2026, 15(7), 1414; https://doi.org/10.3390/electronics15071414 - 28 Mar 2026

Viewed by 525

Abstract

Smart learning systems are designed to analyze the context, needs, and progress of each student. These are becoming increasingly common, but they present challenges, such as predicting student performance and automatically managing learning activities. In this context, Large Language Models (LLMs) can be [...] Read more.

Smart learning systems are designed to analyze the context, needs, and progress of each student. These are becoming increasingly common, but they present challenges, such as predicting student performance and automatically managing learning activities. In this context, Large Language Models (LLMs) can be useful, as they are capable of understanding word relationships and analyzing their context. They are often associated with chatbots, which are computationally expensive, thereby complicating their integration. Instead, in this work, we propose to leverage the capabilities of LLMs through a semantic graph of activities created from sentence embeddings. This representation is a lightweight and explainable alternative. On the one hand, it requires a lower computational cost. On the other hand, it allows us to observe which activities are most similar directly. On this basis, we propose two problems to validate our proposal. In the first, we use the graph to classify new activities. In the second, we extend this representation with the temporal dimension to formulate a spatio-temporal problem and predict student performance. The results show that the semantic graph not only provides an accurate representation for the organization and classification of activities, but also offers practical advantages and improves explainability. Full article

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

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42 pages, 2464 KB

Open AccessEditor’s ChoiceArticle

Energy-Aware Multilingual Evaluation of Large Language Models

by I. de Zarzà, Mauro Liz, J. de Curtò and Carlos T. Calafate

Electronics 2026, 15(7), 1395; https://doi.org/10.3390/electronics15071395 - 27 Mar 2026

Viewed by 674

Abstract

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly [...] Read more.

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (

χ^{2} = 49.42

,

p = 4.78 \times 10^{- 10}

, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis (

r_{s} = 0.109

,

p = 0.386

). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

(This article belongs to the Special Issue Data-Related Challenges in Machine Learning: Theory and Application)

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26 pages, 16104 KB

Open AccessEditor’s ChoiceArticle

Multi-Slot Attention with State Guidance for Egocentric Robotic Manipulation

by Sofanit Wubeshet Beyene and Ji-Hyeong Han

Electronics 2026, 15(7), 1365; https://doi.org/10.3390/electronics15071365 - 25 Mar 2026

Viewed by 601

Abstract

Visual perception is fundamental to robotic manipulation for recognizing objects, goals, and contextual details. Third-person cameras provide global views but can miss contact-rich interactions and require calibration. Wrist-mounted egocentric cameras reduce these limitations but introduce occlusion, motion blur, and partial observability, which complicate [...] Read more.

Visual perception is fundamental to robotic manipulation for recognizing objects, goals, and contextual details. Third-person cameras provide global views but can miss contact-rich interactions and require calibration. Wrist-mounted egocentric cameras reduce these limitations but introduce occlusion, motion blur, and partial observability, which complicate visuomotor learning. Furthermore, existing perception modules that rely solely on pixels or fuse imagery with proprioception as flat vectors do not explicitly model structured scene representations in dynamic egocentric views. To address these challenges, a multi-slot attention fusion encoder for egocentric manipulation is introduced. Learnable slot queries extract localized visual features from image tokens, and Feature-wise Linear Modulation (FiLM) conditions each slot on the robot’s joint states, producing a structured slot-based latent representation that adapts to viewpoint and configuration changes without requiring object labels or external camera priors. The resulting structured slot-based latent representation is used as input to a Soft Actor–Critic (SAC) agent, which achieves a higher mean cumulative return than pixel-only CNN/DrQ and state-only baselines on a ManiSkill3 egocentric manipulation task. Probing experiments and real-camera evaluation further show that the learned representation remains stable under egocentric viewpoint shifts and partial occlusions, indicating robustness in practical manipulation settings. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 635 KB

Open AccessEditor’s ChoiceArticle

Conformal Prediction for Counterfactual Detection in Concept Learning from Synthetic Visual Patterns

by Ulf Norinder, Stephanie Lowry, Heimo Müller and Andreas Holzinger

Electronics 2026, 15(7), 1346; https://doi.org/10.3390/electronics15071346 - 24 Mar 2026

Viewed by 567

Abstract

Reliable detection of previously unseen classes under distributional shift remains a central challenge in concept learning and explainable artificial intelligence. In particular, high-performance deep learning models often lack statistically grounded mechanisms to signal when an instance deviates from learned concepts. This paper addresses [...] Read more.

Reliable detection of previously unseen classes under distributional shift remains a central challenge in concept learning and explainable artificial intelligence. In particular, high-performance deep learning models often lack statistically grounded mechanisms to signal when an instance deviates from learned concepts. This paper addresses this limitation by investigating whether conformal prediction can be effectively combined with a YOLOv5 deep learning classifier to enable principled counterfactual detection without prior exposure to the counterfactual class. As a controlled testbed, we employ Kandinsky patterns, a structured benchmark widely used in explainable AI research due to its rule-based generative transparency and suitability for concept learning studies. The proposed framework first classifies valid and invalid patterns and subsequently applies inductive conformal prediction to obtain calibrated prediction sets at a user-defined significance level. Counterfactual instances are, at start, identified based solely on information from known true and false patterns, without explicit training examples of the counterfactual class. Experimental results demonstrate that the conformalized detector reliably identifies a substantial proportion of previously unseen counterfactual patterns while maintaining statistical validity. In addition, the method flags unlabeled (“empty”) instances, thereby providing a principled signal for the emergence of new concepts. By conformalizing YOLOv5 outputs, the approach establishes a statistically sound mechanism for uncertainty-aware detection of divergent classes, contributing to robust and explainable concept learning in structured visual pattern recognition. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Image Perception, Recognition and Understanding)

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26 pages, 2609 KB

Open AccessEditor’s ChoiceArticle

Scale, Structure, and Stability: When Does LLM-Based Data Augmentation Improve Temporal Robustness in Web Intrusion Detection?

by Jun yeop Lee and Hee Cheol Kim

Electronics 2026, 15(7), 1344; https://doi.org/10.3390/electronics15071344 - 24 Mar 2026

Viewed by 515

Abstract

We investigate when LLM-based data augmentation can mitigate temporal collapse in web intrusion detection under extreme cross-temporal distribution shift. Under a strict hold-out protocol—training on CSIC-2010 and evaluating exclusively on the temporally separated SRBH-2020 golden set—the legacy-trained baseline exhibits near-collapse in balanced correlation [...] Read more.

We investigate when LLM-based data augmentation can mitigate temporal collapse in web intrusion detection under extreme cross-temporal distribution shift. Under a strict hold-out protocol—training on CSIC-2010 and evaluating exclusively on the temporally separated SRBH-2020 golden set—the legacy-trained baseline exhibits near-collapse in balanced correlation (MCC ≈ 0) despite retaining high recall, revealing severe false-positive bias under drift. Rather than assuming uniform benefits from synthetic data, we analyze how augmentation effects vary with model scale. Using a fixed DeBERTa-v3-base backbone and five random seeds, we compare synthetic training corpora generated by multiple LLMs under identical schema-guided structured decoding and filtering constraints. The results reveal a scale-dependent threshold effect. Models below 12B parameters (i.e., the 4–8B settings in our experiments) frequently introduce structural artifacts that amplify false-positive bias and further destabilize cross-temporal performance. In contrast, models at or above 12B parameters consistently produce modest but statistically reliable recovery from correlation collapse (p < 0.001), with balanced metrics shifting toward the target-domain distribution. Although the absolute performance remains limited under extreme temporal separation, a confusion-matrix analysis shows that large-scale generation reduces false-positive skew and moves decision-boundary behavior closer to the modern-domain regime. These findings indicate that LLM-based augmentation is not inherently robustness-enhancing; rather, its effect depends critically on model scale and disciplined generation control. When properly constrained, ≥12B-scale models can partially stabilize cross-temporal behavior, whereas smaller-scale generation may exacerbate distributional fragility. Full article

(This article belongs to the Special Issue AI and Cybersecurity: Emerging Trends and Key Challenges)

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10 pages, 2043 KB

Open AccessEditor’s ChoiceArticle

Vortex Wave Generation at E-Band Using a TWT Source and Metasurface

by Haojie Zhu, Jinjun Feng, Pan Pan, Shishuo Liu, Yueyi Zhang and Chaohai Du

Electronics 2026, 15(7), 1348; https://doi.org/10.3390/electronics15071348 - 24 Mar 2026

Viewed by 357

Abstract

In this paper, a novel scheme is introduced that combines a traveling wave tube (TWT) with a metasurface to generate high-power E-band vortex electromagnetic waves. The TE₁₀ mode electromagnetic wave emitted by the TWT is initially converted into a plane wave via [...] Read more.

In this paper, a novel scheme is introduced that combines a traveling wave tube (TWT) with a metasurface to generate high-power E-band vortex electromagnetic waves. The TE₁₀ mode electromagnetic wave emitted by the TWT is initially converted into a plane wave via a horn antenna and subsequently transformed into a vortex electromagnetic wave by the metasurface. The metasurface is designed and simulated, and the results show that this approach can convert the TE₁₀ mode from the TWT into vortex electromagnetic waves with a specific topological charge of

l = + 1

within the 71–76 GHz frequency range, achieving a remarkable mode purity of up to 97%. The experiment at 73.5 GHz was successfully carried out, generating vortex electromagnetic waves with the designated topological charge of

l = + 1

using this method. Although the experimentally measured mode purity was limited to 30.6%, this outcome confirms the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advanced RF, Microwave, and Millimeter-Wave Antennas, Devices, and Systems)

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31 pages, 4949 KB

Open AccessEditor’s ChoiceArticle

Attention Distribution-Aware Softmax for NPU-Accelerated On-Device Inference of LLMs: An Edge-Oriented Approximation Design

by Sanoop Sadheerthan, Min-Jie Hsu, Chih-Hsiang Huang and Yin-Tien Wang

Electronics 2026, 15(6), 1312; https://doi.org/10.3390/electronics15061312 - 20 Mar 2026

Viewed by 915

Abstract

Low-power NPUs enable on-device LLM inference through efficient integer and fixed-point algebra, yet their lack of native exponential support makes Transformer softmax a critical performance bottleneck. Existing NPU kernels approximate

e^{x}

using uniform piecewise polynomials to enable O(1) SIMD indexing, but this [...] Read more.

Low-power NPUs enable on-device LLM inference through efficient integer and fixed-point algebra, yet their lack of native exponential support makes Transformer softmax a critical performance bottleneck. Existing NPU kernels approximate

e^{x}

using uniform piecewise polynomials to enable O(1) SIMD indexing, but this wastes computation by applying high-degree arithmetic indiscriminately in every segment. Conversely, fully adaptive approaches maximize statistical fidelity but introduce pipeline stalls due to comparator-based boundary search. To bridge this gap, we propose an attention distribution-aware softmax that uses Particle Swarm Optimization (PSO) to define non-uniform segments and variable polynomial degrees, prioritizing finer granularity and lower arithmetic complexity in attention-dense regions. To ensure efficiency, we snap boundaries into a 128-bin LUT, enabling O(1) retrieval of segment parameters without branching. Inference measurements show that this favors low-degree execution, minimizing exp-kernel overhead. Using TinyLlama-1.1B-Chat as a testbed, the proposed weighted design reduces cycles per call exp kernel (CPC) by 18.5% versus an equidistant uniform Degree-4 baseline and 13.1% versus uniform Degree-3, while preserving ranking fidelity. These results show that grid-snapped, variable-degree approximation can improve softmax efficiency while largely preserving attention ranking fidelity, enabling accurate edge LLM inference. Full article

(This article belongs to the Special Issue Emerging Applications of FPGAs and Reconfigurable Computing System)

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38 pages, 3950 KB

Open AccessEditor’s ChoiceArticle

Investigating Post-Quantum Cryptography to Secure Transmitted Data via Mobile Communication

by Rongjie Zhou, Huaqun Guo and Francis Ee Cheok Teo

Electronics 2026, 15(6), 1275; https://doi.org/10.3390/electronics15061275 - 18 Mar 2026

Viewed by 1129

Abstract

The advent of quantum computing poses significant challenges to traditional cryptographic systems, threatening the confidentiality, integrity and authenticity of digital communications. This paper investigates the integration of post-quantum cryptography (PQC) algorithms into mobile communication systems to address these challenges. The study focuses on [...] Read more.

The advent of quantum computing poses significant challenges to traditional cryptographic systems, threatening the confidentiality, integrity and authenticity of digital communications. This paper investigates the integration of post-quantum cryptography (PQC) algorithms into mobile communication systems to address these challenges. The study focuses on evaluating key PQC algorithms shortlisted by the National Institute of Standards and Technology (NIST), including CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon and SPHINCS+, within the context of 5G and future mobile network architectures. The research encompasses the design and implementation of an experimental framework involving mobile devices, servers, and cloud-based infrastructure to simulate real-world communication scenarios. Performance metrics such as key generation time, signature generation, encryption and decryption speed, and resource consumption were analyzed across various devices to identify algorithms suitable for mobile environments. The findings reveal that lattice-based algorithms, such as Kyber and Dilithium, offer a promising balance between security and efficiency, making them ideal for resource-constrained devices. In contrast, hash-based algorithms like SPHINCS+ exhibit higher computational demands, limiting their practicality in certain applications. This work highlights the importance of algorithm selection and hardware optimization in ensuring secure and efficient communications in the quantum era. By integrating theoretical advancements in PQC with practical applications, this research lays the foundation for quantum-resistant security in mobile networks, ensuring secure and future-ready digital communications. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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16 pages, 6917 KB

Open AccessEditor’s ChoiceArticle

Design of a Receiver Path with Self-Developed Limiter MMIC of X-Band for AESA Radar Systems

by Yuseok Jeon, Jaejin Koo, Minseok Ahn and Youngoo Yang

Electronics 2026, 15(6), 1272; https://doi.org/10.3390/electronics15061272 - 18 Mar 2026

Viewed by 1148

Abstract

In the present study, the limiter component with excellent low insertion loss and leakage power characteristics is used at the beginning of the receiving path, mounted at the rear end of the antenna of the AESA radar system, to protect the low noise [...] Read more.

In the present study, the limiter component with excellent low insertion loss and leakage power characteristics is used at the beginning of the receiving path, mounted at the rear end of the antenna of the AESA radar system, to protect the low noise amplifier (LNA) from excessive input power. The main components required for the X-band transmit/receive module are designed and manufactured mainly using bare-type components to reduce the module size. In this paper, we develop the limiter component, which is a key component, and verify whether it can secure performance that can be operated from the system perspective by mounting it on the receiving path of the transmit/receive module. The performance results of the limiter component unit obtained insertion loss of less than 0.615 dB at 10 GHz and leakage power of less than +16.8 dBm in the X-band. The main performance of the receiving path in the transmit/receive module unit obtained results of a noise figure of less than 3.2 dB and a gain of more than 37 dB (including two stages of LNA). Full article

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

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27 pages, 3484 KB

Open AccessEditor’s ChoiceArticle

Enhancing RMF and ATT&CK Mapping Accuracy Through Integration of Sentence-BERT and Mitigation Parameters

by Hanhee Lee, Sukjoon Yoon, Yunkyung Lee and Jiwon Kang

Electronics 2026, 15(6), 1248; https://doi.org/10.3390/electronics15061248 - 17 Mar 2026

Viewed by 539

Abstract

To minimize cybersecurity risks in weapon systems, the implementation of the Korean Risk Management Framework (K-RMF) has become imperative. However, a significant “strategic gap” exists between high-level RMF controls and technical MITRE ATT&CK techniques, rendering manual mapping labor-intensive. This study proposes an automated [...] Read more.

To minimize cybersecurity risks in weapon systems, the implementation of the Korean Risk Management Framework (K-RMF) has become imperative. However, a significant “strategic gap” exists between high-level RMF controls and technical MITRE ATT&CK techniques, rendering manual mapping labor-intensive. This study proposes an automated mitigation-driven pipeline that integrates Sentence-BERT (SBERT) with the structural defense relationships of the ATT&CK knowledge graph. To address the data coverage limitations of the Center for Threat-Informed Defense (CTID) silver standard, we introduce Recall@restricted as a calibrated performance metric. Experimental evaluations demonstrate that the proposed ensemble framework achieves a Recall@restricted of 0.74, significantly outperforming baseline SBERT-only models. These findings suggest that deterministic mitigation relationships effectively complement semantic representations, providing a robust framework for aligning RMF controls with adversarial behaviors. 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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21 pages, 656 KB

Open AccessEditor’s ChoiceArticle

Acoustic Violence Detection Using Cascade Strategy for Computationally Constrained Scenarios

by Fangfang Zhu-Zhou, Diana Tejera-Berengué, Roberto Gil-Pita, Manuel Utrilla-Manso and Manuel Rosa-Zurera

Electronics 2026, 15(6), 1227; https://doi.org/10.3390/electronics15061227 - 16 Mar 2026

Viewed by 965

Abstract

Detecting violent content in audio recordings is crucial for public safety, autonomous surveillance, and content moderation, particularly when visual cues are unreliable or unavailable. A resource-aware two-stage cascade system is proposed for acoustic violence detection that combines a lightweight Least Squares Linear Detector [...] Read more.

Detecting violent content in audio recordings is crucial for public safety, autonomous surveillance, and content moderation, particularly when visual cues are unreliable or unavailable. A resource-aware two-stage cascade system is proposed for acoustic violence detection that combines a lightweight Least Squares Linear Detector (LSLD) as a first-stage screener with a trimmed version of YAMNet as a second-stage classifier. A percentile-based forwarding rule controls the fraction of segments routed to the deep stage, turning the accuracy–cost trade-off into an explicit operating parameter for always-on deployment. The approach is evaluated on a publicly released dataset of real-world violent audio augmented with background noise and artificial reverberation. The results in the low-false-alarm regime show that the proposed cascade preserves performance close to a Stage 2-only baseline while substantially reducing average deep-inference workload. An ablation study validates the role of the LSLD as an inexpensive pre-filter, and robustness is assessed under clean, reverberant, and 12 dB noise conditions. Finally, an analytic energy consumption model is provided, which links computational workload to daily energy demand and photovoltaic sizing on ultra-low-power hardware, supporting sustainable off-grid deployment. Full article

(This article belongs to the Special Issue Implementation of Neural Network Models on Resource-Constrained Devices)

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24 pages, 10576 KB

Open AccessEditor’s ChoiceArticle

Accurate Road User Position Estimation for V2I Using Point Clouds from Mobile Mapping Systems

by Ju Hee Yoo, Ho Gi Jung and Jae Kyu Suhr

Electronics 2026, 15(6), 1238; https://doi.org/10.3390/electronics15061238 - 16 Mar 2026

Viewed by 323

Abstract

Accurate detection and positioning of road users are essential for vehicle-to-infrastructure (V2I)-assisted autonomous driving. For this purpose, the road user’s ground contact point is usually detected in a monocular camera image. Then, a homography-based method is used to convert this detected point into [...] Read more.

Accurate detection and positioning of road users are essential for vehicle-to-infrastructure (V2I)-assisted autonomous driving. For this purpose, the road user’s ground contact point is usually detected in a monocular camera image. Then, a homography-based method is used to convert this detected point into its corresponding map position. However, the homography-based method assumes that the ground is planar, which leads to significant positioning errors in real-world environments. This limitation degrades the reliability of V2I-assisted autonomous driving, particularly in environments with complex road geometries. This study presents a method for accurately estimating the positions of road users using 3D point clouds generated by a Mobile Mapping System (MMS) for map construction without incurring additional costs. Moreover, since surveillance cameras are typically installed in urban areas, point clouds for these regions are often already available. The proposed method uses a pre-generated Look-Up Table (LUT), which is created by projecting MMS-based 3D point clouds onto the image coordinate system, so that each pixel in the image stores its corresponding 3D map position. Once the ground contact points of road users are detected in the image, the corresponding 3D positions on the map can be directly obtained by referencing the LUT. In the experiments, the proposed method was evaluated using surveillance camera images and MMS-based point clouds collected from various real-world environments. The results show that the proposed method reduces positioning errors of road users by an average of 61.4% compared to the conventional homography-based method. The improvement is particularly significant in environments with ground slope variations. In addition, the proposed method demonstrates real-time feasibility on an embedded camera, achieving low latency and power-efficient performance suitable for V2I edge deployment. Full article

(This article belongs to the Special Issue Autonomous Vehicles: Sensing, Mapping, and Positioning)

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24 pages, 905 KB

Open AccessEditor’s ChoiceArticle

Neural Encoding Strategies for Neuromorphic Computing

by Michael Liu, Honghao Zheng and Yang Yi

Electronics 2026, 15(6), 1221; https://doi.org/10.3390/electronics15061221 - 14 Mar 2026

Viewed by 789

Abstract

Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). [...] Read more.

Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). This paper provides a comprehensive overview of major neural encoding schemes used in neuromorphic systems, including rate and temporal encoding, as well as latency, interspike interval, phase, and multiplexed encoding. The purpose of this paper is to explore the use of encoding techniques for deep learning applications. We discussed the underlying principles of spike encoding approaches, their biological inspiration, computational efficiency, power consumption, integrated circuit design and implementation, and suitability for various neuromorphic applications. We also presented our research on a hardware-and-software co-design platform for different encoding schemes and demonstrated their performance. By comparing their strengths, limitations, and implementation challenges, we aim to provide insights that will guide the development of more efficient and application-specific neuromorphic systems. We also performed an encoder performance analysis via Python 3.12 simulations to compare classification accuracies across these spike encoders on three popular image and video datasets. The performance of neural encoders working with both deep neural networks (DNNs) and SNNs is analyzed. Our performance data is largely consistent with the benchmark data on image classification from other papers, while limited performance data on the University of Central Florida’s 101 (UCF-101) video dataset were found in comparable studies on spike encoders. Based on our encoder performance data, the Interspike Interval (ISI) encoder performs well across all three datasets, preserving continuous, detailed spike timing and richer temporal information for standard classification tasks. Further, for image classification, multiplexing encoders outperform other spike encoders as they simplify timing patterns by enforcing phase locking and improve stability and robustness to noise. Within the SNN testbenches, the ISI-Phase encoder achieved the highest accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, surpassing the Time-To-First Spike (TTFS) encoder by 1.9%. On the Canadian Institute For Advanced Research (CIFAR-10) dataset, the ISI encoder achieved the highest accuracy. This ISI encoder had 22.7% higher accuracy than the TTFS encoder on the CIFAR-10 dataset. The ISI encoder performed best on the UCF-101 dataset, achieving 12.7% better performance than the TTFS encoder. Full article

(This article belongs to the Section Artificial Intelligence)

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24 pages, 14412 KB

Open AccessEditor’s ChoiceArticle

Modeling and Trajectory Tracking Control for Double- Steering Wheeled Climbing Robot Based on Adaptive Dynamic Programming

by Zhentao Du, Shiqiang Zhu, Cheng Wang and Wei Song

Electronics 2026, 15(6), 1193; https://doi.org/10.3390/electronics15061193 - 13 Mar 2026

Viewed by 406

Abstract

A model and tracking control method for a double-steering wheeled climbing robot (DSWCR) are presented in this article. The dynamic model of the DSWCR system is established using the Lagrange equation, considering the effects of slipping, variations in gravity and the friction coefficient, [...] Read more.

A model and tracking control method for a double-steering wheeled climbing robot (DSWCR) are presented in this article. The dynamic model of the DSWCR system is established using the Lagrange equation, considering the effects of slipping, variations in gravity and the friction coefficient, and wall/wheel interaction forces. During wall motion, the DSWCR is subject to uncertainties introduced from both the state and model. To address the tracking problem of the DSWCR under state and model uncertainties, an adaptive dynamic programming (ADP) controller based on zero-sum theory is proposed. The stability of the DSWCR tracking system and the convergence of the weights in a neural network are demonstrated. Finally, simulations and a prototype experiment are conducted to verify the optimality and robustness of the proposed control method. Full article

(This article belongs to the Section Systems & Control Engineering)

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20 pages, 1396 KB

Open AccessEditor’s ChoiceArticle

A Cascaded Framework for Vehicle Detection in Low-Resolution Traffic Surveillance Videos

by Tao Yu and Laura Sevilla-Lara

Electronics 2026, 15(5), 1119; https://doi.org/10.3390/electronics15051119 - 8 Mar 2026

Viewed by 529

Abstract

Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and [...] Read more.

Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and false detections in traditional object detection algorithms trained on high-resolution data. To address this issue, this study proposes a cascaded collaborative framework that integrates video super-resolution (VSR) and object detection for robust perception in low-quality traffic surveillance scenarios. First, a transformer-based VSR model with masked intra- and inter-frame attention (MIA-VSR) is employed to reconstruct temporally coherent high-resolution video sequences from degraded inputs. A domain-specific super-resolved dataset is subsequently constructed to train a lightweight one-stage detector (You Only Look One-level Feature, YOLOF) for efficient vehicle localisation. Extensive experiments on public datasets (REDS, Vimeo90k, UA-DETRAC) demonstrate that the proposed framework achieved a 56.89 mAP@0.5 on low-resolution UA-DETRAC, outperforming both direct low-resolution inference (39.17 mAP@0.5) and conventional fine-tuning strategies (45.70 mAP@0.5) by 17.72 and 11.19 points, respectively. These findings indicate that super-resolution-driven data reconstruction provides an effective pathway for mitigating feature degradation in low-quality surveillance environments, offering both theoretical insight and practical value for intelligent transportation perception systems. Full article

(This article belongs to the Special Issue Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation)

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24 pages, 790 KB

Open AccessEditor’s ChoiceArticle

Maturity-Aware Cyber Insurance Optimization in IoT Networks

by Bishwa Bhusal, Delong Li, Xu Wang and Guangsheng Yu

Electronics 2026, 15(5), 1038; https://doi.org/10.3390/electronics15051038 - 2 Mar 2026

Viewed by 413

Abstract

As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific [...] Read more.

As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific characteristics, relying on uniform or simplified risk assumptions that fail to capture real-world vulnerabilities. To address this gap, this paper presents a maturity-aware cyber insurance optimization framework tailored for interconnected IoT environments. The framework integrates organizational security maturity, interdependent risk propagation modeled through a modified Susceptible–Infected–Susceptible (SIS) process, and a Stackelberg game formulation that captures strategic interactions between the insurer and the defender. Through numerical studies on representative IoT topologies, we demonstrate that maturity-aware, risk-sensitive premium structures quantitatively outperform uniform pricing baselines in cost-efficiency and insurer sustainability. Specifically, our experimental results reveal that operating at an optimal intermediate maturity level (

M = 3

) reduces the defender’s total expected cost by approximately

40 %

(from

255.38

k to

152.36

k) compared to the baseline state (

M = 1

). Furthermore, this structural hardening triggers an

88.3 %

reduction in full-coverage insurance premiums (from

225.38

k to

26.36

k). In contrast, our uniform-pricing baseline exhibits reduced profitability in our experiments due to cross-subsidization effects, reinforcing the value of tiered, risk-proportional pricing for mitigating adverse-selection incentives. In summary, this work establishes a tractable, economically viable framework for cyber insurance in IoT ecosystems and provides a foundation for future extensions to richer network settings. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Next-Generation Network and Cybersecurity)

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21 pages, 4260 KB

Open AccessEditor’s ChoiceArticle

CMCLTrack: Reliability-Modulated Cross-Modal Adapter and Cross-Layer Mamba Fusion for RGB-T Tracking

by Pengfei Li, Xiaohe Li and Zide Fan

Electronics 2026, 15(5), 989; https://doi.org/10.3390/electronics15050989 - 27 Feb 2026

Viewed by 515

Abstract

Single-object tracking has progressed rapidly, yet it remains fragile under low illumination, occlusion, and background clutter. RGB-Thermal (RGB-T) tracking improves robustness via modality complementarity, yet many existing trackers do not dynamically switch the dominant modality as sensing quality changes and often rely on [...] Read more.

Single-object tracking has progressed rapidly, yet it remains fragile under low illumination, occlusion, and background clutter. RGB-Thermal (RGB-T) tracking improves robustness via modality complementarity, yet many existing trackers do not dynamically switch the dominant modality as sensing quality changes and often rely on simple late fusion at a single stage, underutilizing multi-level features across the backbone. To address these challenges, we propose CMCLTrack, a unified framework that integrates the Reliability-Modulated Cross-Modal Adapter (RMCA) and the Cross-Layer Mamba Fusion (CLMF). Specifically, RMCA performs reliability-aware bidirectional cross-modal interaction by dynamically weighting modality contributions, while CLMF efficiently aggregates complementary cues from multiple encoder layers to exploit multi-level representations. To stabilize the learning of layer-wise modality reliability, we additionally incorporate a cross-layer reliability smoothness regularization. Extensive experiments on multiple RGB-T tracking benchmarks demonstrate that CMCLTrack achieves competitive performance compared to existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Advances in Multitarget Tracking and Applications)

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15 pages, 7961 KB

Open AccessEditor’s ChoiceArticle

A Compact Single-Resonator Dual-Port Circularly Polarized MIMO Dielectric Resonator Antenna for 28 GHz Applications

by Sumer Singh Singhwal and Ladislau Matekovits

Electronics 2026, 15(5), 977; https://doi.org/10.3390/electronics15050977 - 27 Feb 2026

Viewed by 465

Abstract

A compact dual-port circularly polarized (CP) multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) for 28 GHz applications is presented. A single cross-shaped dielectric resonator is excited by two orthogonal microstrip feeds, supporting hybrid orthogonal modes that enable CP radiation at both ports without [...] Read more.

A compact dual-port circularly polarized (CP) multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) for 28 GHz applications is presented. A single cross-shaped dielectric resonator is excited by two orthogonal microstrip feeds, supporting hybrid orthogonal modes that enable CP radiation at both ports without requiring perturbation cuts, parasitic elements, or decoupling structures. The fabricated prototype exhibits a measured 10 dB impedance bandwidth and 3 dB axial ratio bandwidth that fully cover the Federal Communications Commission (FCC)-allocated 28 GHz band (27.5–28.35 GHz). Port isolation remains better than 15 dB, and the antenna exhibits a peak gain of approximately 7.6 dBi with radiation efficiency exceeding 93%, within a compact 40 × 47 mm² footprint. MIMO performance is verified through envelope correlation coefficient (ECC), diversity gain (DG), and total active reflection coefficient (TARC). The results demonstrate that the proposed single-resonator dual-port CP DRA provides an efficient and integration-friendly solution for compact mmWave MIMO applications in next-generation 5G/6G terminals. Full article

(This article belongs to the Special Issue Next-Generation Antenna Technologies for Wireless Communications and Beyond)

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22 pages, 1795 KB

Open AccessEditor’s ChoiceArticle

PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing

by Wei Feng, Jingbo Zhang, Qiong Wu, Pingyi Fan and Qiang Fan

Electronics 2026, 15(5), 936; https://doi.org/10.3390/electronics15050936 - 25 Feb 2026

Viewed by 444

Abstract

To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless connectivity and semantic communication to minimize latency by [...] Read more.

To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless connectivity and semantic communication to minimize latency by transmitting semantic features. We formulate a comprehensive joint optimization problem by optimizing offloading ratios, the number of semantic symbols, and RIS phase shifts. Considering the problem’s high dimensionality and non-convexity, we propose a two-tier hybrid scheme that employs Proximal Policy Optimization (PPO) for discrete decision-making and Linear Programming (LP) for offloading optimization. The simulation results have validated the proposed framework’s superiority over existing methods. Specifically, the proposed PPO-based hybrid optimization scheme reduces the average end-to-end latency by approximately 40% to 50% compared to Genetic Algorithm (GA) and Quantum-behaved Particle Swarm Optimization (QPSO). Moreover, the system demonstrates strong scalability by maintaining low latency even in congested scenarios with up to 30 vehicles. Full article

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

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14 pages, 2793 KB

Open AccessEditor’s ChoiceArticle

A Cross-Domain Authentication Key Agreement Protocol for Edge Computing

by Zhaobo Wang, Wen Feng, Yifeng Yin and Zhiyong Jing

Electronics 2026, 15(5), 946; https://doi.org/10.3390/electronics15050946 - 25 Feb 2026

Viewed by 536

Abstract

With the rapid development of edge computing in the Industrial Internet, data sharing schemes among edge users require reliable cross-domain authentication and key agreement mechanisms to guarantee the security and reliability of inter-device communication. To tackle the deficiencies of existing group key agreement [...] Read more.

With the rapid development of edge computing in the Industrial Internet, data sharing schemes among edge users require reliable cross-domain authentication and key agreement mechanisms to guarantee the security and reliability of inter-device communication. To tackle the deficiencies of existing group key agreement schemes, including dependence on trusted third parties, high computational overhead, and the difficulty of achieving both privacy preservation and attack resistance, this paper presents a cross-domain authenticated key agreement protocol designed for edge computing environments. This protocol supports anonymous identity authentication between cross-domain users, and innovatively constructs a multi-dimensional virtual iterative cyberspace model to generate massive secure keys via the collaborative iteration of multi-user key sequences. The proposed protocol is decentralized, lightweight, and resistant to replay attacks and man-in-the-middle attacks, while satisfying forward and backward secrecy. Security analysis and performance comparison experiments illustrate that the protocol significantly reduces computational and communication overhead, matches the resource-constrained characteristics of edge devices, and can be widely deployed in large-scale data encryption and sharing scenarios under edge computing environments. Full article

(This article belongs to the Section Networks)

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35 pages, 1070 KB

Open AccessEditor’s ChoiceArticle

Adaptive Deep Learning Framework for Emotion Recognition in Social Robots: Toward Inclusive Human–Robot Interaction for Users with Special Needs

by Eryka Probierz and Adam Gałuszka

Electronics 2026, 15(5), 924; https://doi.org/10.3390/electronics15050924 - 25 Feb 2026

Viewed by 734

Abstract

Emotion recognition is a key capability of social robots operating in real-world human-centered environments, especially when interacting with users with special needs. Such users may express emotions in atypical, subtle, or strongly context-dependent ways. These characteristics pose significant challenges for conventional emotion recognition [...] Read more.

Emotion recognition is a key capability of social robots operating in real-world human-centered environments, especially when interacting with users with special needs. Such users may express emotions in atypical, subtle, or strongly context-dependent ways. These characteristics pose significant challenges for conventional emotion recognition systems. This paper proposes an adaptive deep learning framework for emotion recognition in social robots. The framework is designed to support inclusive and accessible human–robot interaction. It combines region-based convolutional neural networks with adaptive learning mechanisms. These mechanisms explicitly model individual variability, contextual information, and interaction dynamics. Multiple deep architectures are evaluated to assess robustness across diverse emotional expressions, including those influenced by cognitive, sensory, or developmental differences. Rather than relying on fixed emotion models, the proposed approach emphasizes adaptability. The system dynamically adjusts its perception strategies to user-specific expressive patterns. Experimental validation is conducted using context-aware emotion datasets. Performance is evaluated in terms of detection accuracy, robustness to variability, and generalization across emotion categories. The results show that adaptive mechanisms improve recognition performance in scenarios characterized by non-standard or low-intensity expressions, compared to static baseline models. This study highlights the importance of flexible, context-sensitive perception for inclusive social robotics. It also discusses design implications for deploying emotion-aware robots in assistive, educational, and therapeutic settings. Overall, the proposed framework represents a step toward socially intelligent robots capable of engaging more effectively with users with special needs. Full article

(This article belongs to the Special Issue Research on Deep Learning and Human-Robot Collaboration)

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20 pages, 3629 KB

Open AccessEditor’s ChoiceArticle

HS-FP and SS-FP: Fine-Pruning-Based Backdoor Elimination for Spiking Neural Networks on Neuromorphic Event Data

by Ki-Ho Kim and Eun-Kyu Lee

Electronics 2026, 15(5), 937; https://doi.org/10.3390/electronics15050937 - 25 Feb 2026

Viewed by 497

Abstract

Spiking Neural Networks (SNNs) have attracted increasing attention due to their energy efficiency and suitability for neuromorphic data processing. Despite these advantages, the security of SNNs—particularly their robustness against backdoor attacks—remains underexplored. This study revisits fine-pruning, a widely adopted backdoor defense technique in [...] Read more.

Spiking Neural Networks (SNNs) have attracted increasing attention due to their energy efficiency and suitability for neuromorphic data processing. Despite these advantages, the security of SNNs—particularly their robustness against backdoor attacks—remains underexplored. This study revisits fine-pruning, a widely adopted backdoor defense technique in deep neural networks, and adapts it to the unique spatio-temporal characteristics of SNNs. We propose two SNN-specific fine-pruning methods: Hook–Surrogate Gradient-based fine-pruning (HS-FP) and Spike–STDP-based fine-pruning (SS-FP). HS-FP leverages hook-based activation analysis with surrogate gradient learning, while SS-FP integrates total spike activity with hybrid STDP and surrogate gradient fine-tuning. We evaluate both methods against static, moving, and smart backdoor attacks on two neuromorphic benchmarks, N-MNIST and DVS128-Gesture. Experimental results show that both approaches reduce the attack success rate down to approximately 10% while preserving model accuracy above 99% on N-MNIST and achieving substantial recovery on DVS128-Gesture. Moreover, our analysis reveals that several phenomena observed in fine-pruning-based defenses for deep neural networks—such as mixed-function neurons and backdoor reactivation during fine-tuning—also manifest in SNNs. These findings highlight both the effectiveness and limitations of fine-pruning in the SNN domain and suggest promising directions for extending existing DNN security methodologies to neuromorphic systems. Full article

(This article belongs to the Special Issue Innovative Architectures and Advanced Solutions for Network Security in the Era of Emerging Technologies)

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15 pages, 6200 KB

Open AccessEditor’s ChoiceArticle

A Beam-Splitter-Free Terahertz Receiver with Independent Antenna-Fed Local Oscillator for Enhanced Efficiency

by Pengfei Zhao, Dabao Wang, Xinyu Yao, Ning Liu, Xiaochun Jiao and Jing Cao

Electronics 2026, 15(5), 919; https://doi.org/10.3390/electronics15050919 - 24 Feb 2026

Viewed by 321

Abstract

This paper presents the design, fabrication, and experimental characterization of a novel terahertz receiver comprising two high-performance receiving antennas and a combiner. The low efficiency of local oscillator (LO) power utilization, caused by conventional beam splitters, presents a major bottleneck for large-array terahertz [...] Read more.

This paper presents the design, fabrication, and experimental characterization of a novel terahertz receiver comprising two high-performance receiving antennas and a combiner. The low efficiency of local oscillator (LO) power utilization, caused by conventional beam splitters, presents a major bottleneck for large-array terahertz receivers. By eliminating the conventional beam splitter, the proposed system allows the terahertz signal and LO power to be directly and independently received by two dedicated antennas, thereby significantly enhancing LO power efficiency. The receiver is successfully fabricated using micromachining technology into a compact 2.5-dimensional multilayered structure measuring 9 mm × 16 mm × 7.2 mm. Key performance metrics, including the waveguide port S-parameters, radiation patterns, and gains of the two horn antennas, were measured. The experimental results show close agreement with simulations, validating the system’s accuracy and reliability. Furthermore, the system’s equivalent noise temperature was measured to be 395 K, indicating excellent thermal stability and sensitivity. This study concludes that the proposed terahertz receiver design is both feasible and efficient for high-resolution applications, showing great potential for use in satellite-based space observation systems or base stations requiring advanced terahertz signal processing. Full article

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

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26 pages, 9016 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Integration of Hybrid Prefilter and Corner Trajectory Planning for Simultaneously Suppressing Residual Vibration and Reducing Cornering Error of SCARA Robots

by Syh-Shiuh Yeh and Ming-Han You

Electronics 2026, 15(4), 900; https://doi.org/10.3390/electronics15040900 - 23 Feb 2026

Viewed by 452

Abstract

During high-speed cornering, the motion accuracy and efficiency of SCARA robots are often compromised by residual vibrations and cornering errors. Conventional control methods often fail to address these two coupled problems simultaneously. Therefore, this study developed an integrated design strategy to simultaneously suppress [...] Read more.

During high-speed cornering, the motion accuracy and efficiency of SCARA robots are often compromised by residual vibrations and cornering errors. Conventional control methods often fail to address these two coupled problems simultaneously. Therefore, this study developed an integrated design strategy to simultaneously suppress residual vibrations and restrict cornering errors for improving the cornering performance of the SCARA robot. The core of this design strategy is to develop a hybrid prefilter via the convolution of an input shaper and a finite impulse response filter, thereby creating a prefilter with robust, high-performance residual vibration suppression. Subsequently, to accommodate the asymmetric acceleration and deceleration generated by the hybrid prefilter, this study developed a systematic corner trajectory planning method that can calculate the cornering trajectory parameters based on a preset value of the cornering error to restrict the cornering error and ensure the cornering accuracy of the SCARA robot. Experimental results indicated that under the condition of a restricted cornering error, the developed hybrid prefilter can reduce residual vibration by >85%. Thus, the hybrid prefilter designed with the corner trajectory planning method can mitigate the coupled problem of residual vibration and cornering error, suppressing the residual vibration without compromising cornering accuracy. Full article

(This article belongs to the Special Issue Intelligent Perception and Control for Robotics)

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22 pages, 1271 KB

Open AccessEditor’s ChoiceArticle

Leveraging MCP and Corrective RAG for Scalable and Interoperable Multi-Agent Healthcare Systems

by Dimitrios Kalathas, Andreas Menychtas, Panayiotis Tsanakas and Ilias Maglogiannis

Electronics 2026, 15(4), 888; https://doi.org/10.3390/electronics15040888 - 21 Feb 2026

Viewed by 986

Abstract

The rapid evolution of Generative AI (GenAI) has created the conditions for developing innovative solutions that disrupt all fields of human-related activities. Within the healthcare sector, numerous AI-driven applications have emerged, offering comprehensive health-related insights and addressing user questions in real time. Nevertheless, [...] Read more.

The rapid evolution of Generative AI (GenAI) has created the conditions for developing innovative solutions that disrupt all fields of human-related activities. Within the healthcare sector, numerous AI-driven applications have emerged, offering comprehensive health-related insights and addressing user questions in real time. Nevertheless, most of them use general-purpose Large Language Models (LLMs); consequently, the responses may not be as accurate as required in clinical settings. Therefore, the research community is adopting efficient architectures, such as Multi-Agent Systems (MAS) to optimize task allocation, reasoning processes, and system scalability. Most recently, the Model Context Protocol (MCP) has been introduced; however, very few applications apply this protocol within a healthcare MAS. Furthermore, Retrieval-Augmented Generation (RAG) has proven essential for grounding AI responses in verified clinical literature. This paper proposes a novel architecture that integrates these technologies to create an advanced Agentic Corrective RAG (CRAG) system. Unlike standard approaches, this method incorporates an active evaluation layer that autonomously detects retrieval failures and triggers corrective fallback mechanisms to ensure safety and accuracy. A comparative analysis was conducted for this architecture against Typical RAG and Cache-Augmented Generation (CAG), demonstrating that the proposed solution improves workflow efficiency and enables more accurate, context-aware interventions in healthcare. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)

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16 pages, 1038 KB

Open AccessFeature PaperEditor’s ChoiceArticle

The Agency-First Framework: Operationalizing Human-Centric Interaction and Evaluation Heuristics for Generative AI

by Christos Troussas, Christos Papakostas, Akrivi Krouska and Cleo Sgouropoulou

Electronics 2026, 15(4), 877; https://doi.org/10.3390/electronics15040877 - 20 Feb 2026

Viewed by 1311

Abstract

Current generative AI systems primarily utilize a prompt–response interaction model that restricts user intervention during the creative process. This lack of granular control creates a significant disconnect between user intent and machine output, which we define as the “Agency Gap”. This paper introduces [...] Read more.

Current generative AI systems primarily utilize a prompt–response interaction model that restricts user intervention during the creative process. This lack of granular control creates a significant disconnect between user intent and machine output, which we define as the “Agency Gap”. This paper introduces the Agency-First Framework (AFF), which combines cognitive engineering and co-active design approaches to formally define human-AI collaboration. This is operationalized through the development of ten Generative AI Agency (GAIA) Heuristics, a systematic method for evaluating agency-centric interactions within stochastic generative settings. By translating the theoretical layers of the AFF into measurable criteria, the GAIA heuristics provide the necessary instrument for the empirical auditing of existing systems and the guidance of agency-centric redesigns. Unlike existing assistive AI guidelines that focus on output-level usability, the AFF establishes agency as a first-class design construct, enabling mid-process intervention and the steering of the model’s latent reasoning trajectory. Validation of the AFF was conducted through a two-tiered empirical evaluation: (1) an expert heuristic audit of state-of-the-art platforms, such as ChatGPT-o1 and Midjourney v6, which achieved high inter-rater reliability, and (2) a controlled redesign study. The latter demonstrated that agency-centric interfaces significantly enhance the Sense of Agency and Intent Alignment Accuracy compared to baseline prompt-response models, even when introducing a deliberate increase in task completion time—a phenomenon we describe as “productive friction” or an intentional interaction slowdown designed to prioritize cognitive engagement and user control over raw speed. Overall, the findings suggest that the restoration of meaningful user agency requires a shift from “seamless” system efficiency towards “productive friction”, where controllability and transparency within the generative process are prioritized. The major contribution of this work is the provision of a scalable, empirically validated framework and set of heuristics that equip designers to move beyond prompt-centric interaction, establishing a methodological foundation for agency-preserving generative AI systems. Full article

(This article belongs to the Special Issue The Role of Artificial Intelligence in Enhancing Human-Computer Interaction)

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16 pages, 2588 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Smart Home IoT Forensics in Matter Ecosystems: A Data Extraction Method Using Multi-Admin

by Sungbum Kim, Sungmoon Kwon and Taeshik Shon

Electronics 2026, 15(4), 884; https://doi.org/10.3390/electronics15040884 - 20 Feb 2026

Viewed by 699

Abstract

As the smart home ecosystem expands with the adoption of Matter, a wide variety of Internet of Things (IoT) devices are entering the market, and these devices are becoming more complex, as they support diverse functionalities. Consequently, smart home forensics often requires data [...] Read more.

As the smart home ecosystem expands with the adoption of Matter, a wide variety of Internet of Things (IoT) devices are entering the market, and these devices are becoming more complex, as they support diverse functionalities. Consequently, smart home forensics often requires data extraction procedures that are specific to each device and platform, which increases the technical burden and time costs for investigators. To address these challenges, this study proposes a method that leverages Matter Multi-Admin support for multiple fabrics to enable efficient data acquisition from Matter-enabled IoT devices, regardless of the underlying smart home platform. This method configures a forensic Matter controller using chip-tool and commissions IoT devices that have already been commissioned to a smart home platform into a secondary fabric via Multi-Admin. The forensic controller then performs data extraction using standardized Matter interfaces. The proposed approach was validated on our smart home testbed by targeting a Matter smart bulb commissioned to the SmartThings platform and successfully extracting data generated by the platform, thereby demonstrating the utility of the method. The results indicate that the method enables nondestructive and efficient evidence acquisition from smart home IoT devices and can support future research and real-world investigations. Full article

(This article belongs to the Special Issue New Challenges in IoT Security)

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42 pages, 1277 KB

Open AccessEditor’s ChoiceArticle

A Hybrid Time Series Forecasting Model Combining ARIMA and Decision Trees to Detect Attacks in MITRE ATT&CK Labeled Zeek Log Data

by Raymond Freeman, Sikha S. Bagui, Subhash C. Bagui, Dustin Mink, Sarah Cameron and Germano Correa Silva De Carvalho

Electronics 2026, 15(4), 871; https://doi.org/10.3390/electronics15040871 - 19 Feb 2026

Viewed by 747

Abstract

Intrusion detection systems face challenges in processing high-volume network traffic while maintaining accuracy across diverse low volume attack types. This study presents a hybrid approach combining ARIMA time series forecasting with Decision Tree classification to detect attacks in Zeek network flow data labeled [...] Read more.

Intrusion detection systems face challenges in processing high-volume network traffic while maintaining accuracy across diverse low volume attack types. This study presents a hybrid approach combining ARIMA time series forecasting with Decision Tree classification to detect attacks in Zeek network flow data labeled with MITRE ATT&CK tactics, leveraging PySpark for scalability. ARIMA identifies temporal anomalies which Decision Trees then classify by attack type. The ARIMA model was evaluated across 13 MITRE ATT&CK tactics, though only 7 maintained sufficient class balance for valid assessment. Results are reported at three evaluation levels: Baseline (Decision Tree only), ARIMA-DT (Decision Tree tested on ARIMA-filtered anomalies), and End-to-End (pipeline performance measured against the original test population). The hybrid model demonstrated two distinct benefits: performance improvement for detectable attacks and detection enablement for previously undetectable attacks. For high-volume attacks with existing baseline detection, ARIMA preprocessing substantially improved performance, for example, Reconnaissance achieved an ARIMA-DT F1 score of 99.71% (from a baseline of 80.88%) with End-to-End metrics confirming this improvement at 97.59% F1-score. Credential Access reached a perfect 100% precision and recall on the ARIMA-filtered subset (from a baseline recall of 7.48%); however, End-to-End evaluation revealed that ARIMA filtering removed the vast majority of Credential Access attacks, resulting in a 1.28% End-to-End F1-score—worse than the baseline F1-score of 7.41%—demonstrating that the hybrid pipeline is counterproductive for attack types whose flow characteristics closely resemble legitimate traffic. More significantly, ARIMA preprocessing enabled detection where traditional Decision Trees completely failed (0% recall) for four stealthy attack types: Defense Evasion (ARIMA-DT recall of 93.22%, End-to-End 67.83%), Discovery (ARIMA-DT recall of 100%, End-to-End 63.43%), Persistence (ARIMA-DT recall of 86.92%, End-to-End 73.38%), and Privilege Escalation (ARIMA-DT recall of 89.93%, End-to-End 64.68%). These results demonstrate that ARIMA-based statistical anomaly detection is particularly effective for attacks involving subtle, low-volume activities that blend with legitimate operations, while also improving classification accuracy for high-volume reconnaissance activities. Full article

(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)

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30 pages, 8046 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Progressive Evaluation of MIMO Techniques in LoRa-Type Wireless Sensor Networks Under Imperfect Channel State Information

by Nikolaos Mouziouras, Andreas Tsormpatzoglou and Constantinos T. Angelis

Electronics 2026, 15(4), 867; https://doi.org/10.3390/electronics15040867 - 19 Feb 2026

Viewed by 429

Abstract

Low-Power Wide-Area Network (LPWAN) technologies play a central role in large-scale wireless sensor network (WSN) deployments, where energy efficiency, coverage and reliability dominate over throughput. Among them, Long Range (LoRa) technology has emerged as a widely adopted physical-layer solution due to its ability [...] Read more.

Low-Power Wide-Area Network (LPWAN) technologies play a central role in large-scale wireless sensor network (WSN) deployments, where energy efficiency, coverage and reliability dominate over throughput. Among them, Long Range (LoRa) technology has emerged as a widely adopted physical-layer solution due to its ability to operate at extremely low signal-to-noise ratios (SNRs). While multi-antenna techniques can potentially enhance link performance, their applicability in LoRa-type systems is constrained by low-SNR operation, strict energy budgets and the quality of channel state information (CSI). This paper presents a systematic and progressively structured evaluation of multiple-input multiple-output (MIMO) techniques in LoRa-type systems under representative operating conditions. A multi-stage simulation framework, implemented using the Vienna SLS v2.0 (Q3) simulator and adapted to LoRa-like waveforms, is employed to isolate the impact of large-scale propagation, small-scale fading, antenna configuration and CSI quality. The analysis starts from a system-level coverage baseline and advances to link-level evaluations of diversity-oriented MIMO schemes and spatial multiplexing configurations under both ideal and imperfect CSI. The results demonstrate that spatial diversity techniques are well aligned with the operational characteristics of LoRa links, offering robust performance in low-SNR regimes and under limited CSI accuracy. In contrast, spatial multiplexing exhibits higher sensitivity to channel estimation errors, with its practical benefits becoming apparent primarily when evaluated using throughput-oriented metrics such as packet error rate and normalized goodput. Overall, the study highlights the fundamental trade-off between reliability and capacity in LoRa MIMO systems and provides design-oriented insights for wireless sensor network deployments. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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25 pages, 1932 KB

Open AccessEditor’s ChoiceArticle

Blockchain-Enabled Governance for Health IoT Data Access via Interpretable Multi-Objective Optimization and Bargaining Under Privacy–Latency–Robustness Trade-Offs

by Farshid Keivanian, Yining Hu and Saman Shojae Chaeikar

Electronics 2026, 15(4), 864; https://doi.org/10.3390/electronics15040864 - 18 Feb 2026

Viewed by 681

Abstract

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework [...] Read more.

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework that separates on-chain accountability from off-chain decision intelligence. Off-chain, fuzzy context inference parameterizes scenario priorities, Pareto-based multi-objective search generates candidate governance policies, an emergency-aware feasibility guard filters unsafe trade-offs, and a bargaining-based selector chooses a single deployable policy. On chain, the blockchain layer records consent commitments, access events, and hashes of the selected policy and decision trace, serving as an immutable audit and accountability substrate rather than an online decision or optimization engine, while raw health data remain off-chain. Using simulation studies of home remote monitoring, clinic telehealth, and emergency triage under stochastic network variation and adversarial device behavior, FiB-MOBA-EAFG improves robustness and yields more repeatable policy selection than rule-based control and scalarized baselines within the evaluated simulation scenarios, while maintaining latency within ranges compatible with modeled edge deployment constraints through explicit emergency-aware feasibility constraints. A budget-matched random-search ablation further indicates that structured Pareto exploration is needed to reliably obtain robust, low-risk governance policies. Full article

(This article belongs to the Special Issue Blockchain-Enabled Management Systems in Health IoT)

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22 pages, 2506 KB

Open AccessEditor’s ChoiceArticle

CycleGAN-Based Data Augmentation for Scanning Electron Microscope Images to Enhance Integrated Circuit Manufacturing Defect Classification

by Andrew Yen, Nemo Chang, Jean Chien, Lily Chuang and Eric Lee

Electronics 2026, 15(4), 803; https://doi.org/10.3390/electronics15040803 - 13 Feb 2026

Viewed by 571

Abstract

Semiconductor defect inspection is frequently hindered by data scarcity and the resulting class imbalance in supervised learning. This study proposes a CycleGAN-based data augmentation pipeline designed to synthesize realistic defective CD-SEM images from abundant normal patterns, incorporating a quantitative quality control mechanism. Using [...] Read more.

Semiconductor defect inspection is frequently hindered by data scarcity and the resulting class imbalance in supervised learning. This study proposes a CycleGAN-based data augmentation pipeline designed to synthesize realistic defective CD-SEM images from abundant normal patterns, incorporating a quantitative quality control mechanism. Using an ADI CD-SEM dataset, we conducted a sensitivity analysis by cropping original 1024 × 1024 micrographs into 512 × 512 and 256 × 256 inputs. Our results indicate that increasing the effective defect-area ratio is critical for improving generative stability and defect visibility. To ensure data integrity, we applied a screening protocol based on the Structural Similarity Index (SSIM) and a median absolute deviation noise metric to exclude low-fidelity outputs. When integrated into the training of XceptionNet classifiers, this filtered augmentation strategy yielded substantial performance gains on a held-out test set, specifically improving the Recall and F1 score while maintaining a near-ceiling AUC. These results demonstrate that controlled CycleGAN augmentation, coupled with objective quality filtering, effectively mitigates class imbalance constraints and significantly enhances the robustness of automated defect detection. Full article

(This article belongs to the Special Issue Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions, 2nd Edition)

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28 pages, 2899 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Design of Secure Communication Networks for UAV Platform Empowered by Lightweight Authentication Protocols

by Muhammet A. Sen, Saba Al-Rubaye and Antonios Tsourdos

Electronics 2026, 15(4), 785; https://doi.org/10.3390/electronics15040785 - 12 Feb 2026

Viewed by 681

Abstract

Flying Ad Hoc Networks (FANETs) formed by cooperative Unmanned Aerial Vehicles (UAVs) require formally proven secure and resource-efficient authentication because open wireless channels allow active adversaries to inject commands, replay traffic, and impersonate nodes. Conventional certificate-based mechanisms impose key management overhead and remain [...] Read more.

Flying Ad Hoc Networks (FANETs) formed by cooperative Unmanned Aerial Vehicles (UAVs) require formally proven secure and resource-efficient authentication because open wireless channels allow active adversaries to inject commands, replay traffic, and impersonate nodes. Conventional certificate-based mechanisms impose key management overhead and remain vulnerable under device capture, while existing lightweight and Physical Unclonable Function (PUF)-assisted proposals commonly assume stable connectivity, lack formal adversarial verification, or are evaluated only through simulation. This paper presents a lightweight PUF-assisted authentication protocol designed for dynamic multi-hop FANET operation. The scheme provides mutual UAV–Ground Station (GS) authentication and session key establishment and further enables secure UAV–UAV communication using an off-path ticket mechanism that eliminates continuous infrastructure dependence. The protocol is constructed through verification-driven refinement and formally analysed under the Dolev–Yao model, establishing authentication and session key secrecy and resistance to replay and impersonation attacks. Implementation-oriented latency measurements on Raspberry-Pi-class embedded platforms demonstrate that cryptographic processing time can be further reduced with hardware improvements, while the overall end-to-end delay is still largely determined by channel conditions and connection behaviour. Comparative evaluation shows reduced communication cost and broader security coverage relative to existing UAV authentication schemes, indicating practical deployability in large-scale FANET environments. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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16 pages, 46885 KB

Open AccessEditor’s ChoiceArticle

Monolithic Integration of a Dual-Mode On-Chip Antenna with a Ferroelectric Hafnium Zirconium Oxide Varactor for Reprogrammable Radio-Frequency Front Ends

by Samuel Quaresima, Nicolas Casilli, Sherif Badran, Onurcan Kaya, Vitaly Petrov, Luca Colombo, Benyamin Davaji, Josep Miquel Jornet and Cristian Cassella

Electronics 2026, 15(4), 792; https://doi.org/10.3390/electronics15040792 - 12 Feb 2026

Viewed by 697

Abstract

In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using complementary metal-oxide semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the [...] Read more.

In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using complementary metal-oxide semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the extremely high frequency (EHF) range. The SHF mode resonates at 8.5 GHz and exhibits ultrawideband (UWB) behavior, while the EHF mode resonates at 36.6 GHz. Both resonance frequencies can be tuned in a non-volatile fashion by controlling the ferroelectric polarization state of a Hafnium Zirconium Oxide (HZO) varactor monolithically integrated into the feed line. This programmability arises from the ferroelectric switching of the embedded HZO film, which results in a non-volatile variation of its permittivity upon application of a voltage pulse. Ferroelectric switching occurs at approximately ±3 V and induces maximum resonance frequency shifts of 381 MHz for the SHF mode and 3 GHz for the EHF mode, corresponding to fractional frequency changes of 4.5% and 8.3%, respectively. Unlike previously reported ferroelectrically tunable antennas, our reported antenna combines full integration, CMOS compatibility, higher operating frequency, compact footprint, and non-volatile programmability. Full article

(This article belongs to the Special Issue Feature Papers in 'Microwave and Wireless Communications' Section, 2nd Edition)

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16 pages, 1372 KB

Open AccessEditor’s ChoiceArticle

Spatio-Temporal Deep Learning-Assisted Multi-Period AC Optimal Power Flow

by Jihun Kim, Sojin Park, Dongwoo Kang and Hunyoung Shin

Electronics 2026, 15(4), 761; https://doi.org/10.3390/electronics15040761 - 11 Feb 2026

Viewed by 486

Abstract

The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more comprehensive framework by incorporating inter-temporal constraints and resource flexibility, but its high [...] Read more.

The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more comprehensive framework by incorporating inter-temporal constraints and resource flexibility, but its high computational complexity and strong temporal coupling make large-scale applications challenging, often causing scalability issues and convergence difficulties in conventional solvers. We address these issues with a spatio-temporal deep learning model that combines a Graph Attention Network (GAT) for topology-aware feature learning with a Temporal Convolutional Network (TCN) for multi-period temporal modeling. The proposed model is trained on large-scale 500-bus and 1354-bus systems under both 8-period and 24-period settings, and it achieves robust scalability with consistently high prediction accuracy. Using the model’s predictions, we construct an initial solution and provide it to a conventional OPF solver, which improves convergence performance and demonstrates the model’s effectiveness as an auxiliary tool for complex MP-ACOPF problems. Full article

(This article belongs to the Special Issue Edge-Intelligent Sustainable Cyber-Physical Systems)

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28 pages, 635 KB

Open AccessEditor’s ChoiceArticle

Harmonizing Supervised Fine-Tuning and Reinforcement Learning with Reward-Based Sampling for Continual Machine Unlearning

by Jiaqi Lang, Jiahao Zhao, Linjing Li and Daniel Dajun Zeng

Electronics 2026, 15(4), 771; https://doi.org/10.3390/electronics15040771 - 11 Feb 2026

Viewed by 786

Abstract

Large language models (LLMs) are pretrained on massive internet data and inevitably memorize sensitive or copyrighted content. This continually raises privacy, legal, and security concerns. Machine unlearning has been proposed as an approach to remove the influence of undesired data while maintaining model [...] Read more.

Large language models (LLMs) are pretrained on massive internet data and inevitably memorize sensitive or copyrighted content. This continually raises privacy, legal, and security concerns. Machine unlearning has been proposed as an approach to remove the influence of undesired data while maintaining model utility. However, in real-world scenarios, unlearning requests continuously emerge, and existing approaches often struggle to handle these sequential requests, leading to utility degradation. To address this challenge, we propose the harmonization of Supervised fine-tuning and Reinforcement learning with Reward-based Sampling (SRRS) framework, which dynamically harmonizes supervised fine-tuning (SFT) and reinforcement learning (RL) via reward signals: SFT ensures forgetting efficacy, while RL preserves utility under continual adaptation. By harmonizing these paradigms, SRRS achieves reliable forgetting and sustained utility across sequential unlearning tasks, demonstrating competitive performance compared to baseline methods on TOFU and R-TOFU datasets. Full article

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

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19 pages, 3302 KB

Open AccessEditor’s ChoiceArticle

Empirical Analysis of Heterogeneous Multi-Orbit Satellite Networks for Communication Resilience in Island Regions

by Yi-Cheng Lin, Tuck Wai Choong, Zheng Cheng Pang, Ping-Hsiang Chuang, Yao-Ching Huang, Ming-Te Chen and Jenq-Shiou Leu

Electronics 2026, 15(4), 773; https://doi.org/10.3390/electronics15040773 - 11 Feb 2026

Viewed by 600

Abstract

Integrating Geostationary (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO) satellite systems offers a promising solution for enhancing communication resilience in disaster-prone island regions. However, effective integration via Software-Defined Wide Area Networks (SD-WANs) faces challenges due to the heterogeneous stochastic characteristics [...] Read more.

Integrating Geostationary (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO) satellite systems offers a promising solution for enhancing communication resilience in disaster-prone island regions. However, effective integration via Software-Defined Wide Area Networks (SD-WANs) faces challenges due to the heterogeneous stochastic characteristics of these links. This study presents a comprehensive performance benchmark of GEO, MEO, and LEO satellite links based on long-duration empirical campaigns conducted in Taiwan. Our findings quantify critical integration hurdles, specifically the “long-tail” latency distribution in LEO links induced by frequent handovers and significant TCP throughput degradation modeled by the Mathis equation. Furthermore, empirical tests demonstrate that simplistic link aggregation across these heterogeneous orbits results in severe packet reordering and goodput collapse. Based on these results, we propose a conceptual resilience-oriented SD-WAN architecture incorporating intelligent failover thresholds and application-aware routing policies. This work provides foundational data and a design framework to guide the future development of robust multi-layered satellite communication systems for disaster management. Full article

(This article belongs to the Section Networks)

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20 pages, 554 KB

Open AccessEditor’s ChoiceArticle

Balancing Long–Short-Term User Preferences via Multilevel Sequential Patterns for Review-Aware Recommendation

by Li Jin, Xinzhe Li, Suji Kim and Jaekyeong Kim

Electronics 2026, 15(4), 753; https://doi.org/10.3390/electronics15040753 - 10 Feb 2026

Viewed by 511

Abstract

Personalized recommender systems play an essential role in enhancing user experience by accurately predicting user preferences. Previous approaches mainly focus on modeling long-term preferences or capturing short-term dynamics through sequential patterns, while few achieve an effective balance between the two. This study proposes [...] Read more.

Personalized recommender systems play an essential role in enhancing user experience by accurately predicting user preferences. Previous approaches mainly focus on modeling long-term preferences or capturing short-term dynamics through sequential patterns, while few achieve an effective balance between the two. This study proposes Rec-SSP, a novel review-aware recommendation model that integrates long-term and short-term preferences through a gated fusion mechanism. Long-term preferences are extracted from aggregated user reviews, whereas short-term preferences are modeled by identifying sequential patterns from recent interactions at both the review and category levels. This multilevel design captures fine-grained opinions across items, ensuring a more accurate understanding of the evolving user intent. This study conducted various experiments on real-world datasets, showing that Rec-SSP outperforms baseline models. These findings demonstrate that balancing long-term and short-term preferences with multilevel sequence modeling can significantly improve recommendation accuracy across diverse domains. Full article

(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)

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22 pages, 1612 KB

Open AccessEditor’s ChoiceArticle

Lightweight 1D-CNN-Based Battery State-of-Charge Estimation and Hardware Development

by Seungbum Kang, Yoonjae Lee, Gahyeon Jang and Seongsoo Lee

Electronics 2026, 15(3), 704; https://doi.org/10.3390/electronics15030704 - 6 Feb 2026

Cited by 1 | Viewed by 650

Abstract

This paper presents the FPGA implementation and verification of a lightweight one-dimensional convolutional neural network (1D-CNN) pipeline for real-time battery state-of-charge (SoC) estimation in automotive battery management systems. The proposed model employs separable 1D convolution and global average pooling, and applies aggressive structured [...] Read more.

This paper presents the FPGA implementation and verification of a lightweight one-dimensional convolutional neural network (1D-CNN) pipeline for real-time battery state-of-charge (SoC) estimation in automotive battery management systems. The proposed model employs separable 1D convolution and global average pooling, and applies aggressive structured pruning to reduce the number of parameters from 3121 to 358, representing an 88.5% reduction, without significant accuracy loss. Using quantization-aware training (QAT), the network is trained and executed in INT8, which reduces weight storage to one-quarter of the 32-bit baseline while maintaining high estimation accuracy with a Mean Absolute Error (MAE) of 0.0172. The hardware adopts a time-multiplexed single MAC architecture with FSM control, occupying 98,410 gates under a 28 nm process. Evaluations on an FPGA testbed with representative drive-cycle inputs show that the proposed INT8 pipeline achieves performance comparable to the floating-point reference with negligible precision drop, demonstrating its suitability for in-vehicle BMS deployment. Full article

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

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36 pages, 24812 KB

Open AccessEditor’s ChoiceReview

Artificial Intelligence-Enhanced Droop Control for Renewable Energy-Based Microgrids: A Comprehensive Review

by Michael Addai and Petr Musilek

Electronics 2026, 15(3), 707; https://doi.org/10.3390/electronics15030707 - 6 Feb 2026

Cited by 2 | Viewed by 1309

Abstract

The integration of renewable energy sources into modern power systems requires advanced control strategies to maintain stability, reliability, and efficiency. This paper presents a comprehensive review of the application of artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, in improving [...] Read more.

The integration of renewable energy sources into modern power systems requires advanced control strategies to maintain stability, reliability, and efficiency. This paper presents a comprehensive review of the application of artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, in improving droop control for renewable energy integration. These artificial intelligence-based methods address key challenges such as frequency and voltage regulation, power sharing, and grid compliance under conditions of high renewable penetration. Machine learning approaches, such as support vector machines, are used to optimize droop parameters for dynamic grid conditions, while deep learning models, including recurrent neural networks, capture complex system dynamics to enhance the stability of distributed energy systems. Reinforcement learning algorithms enable adaptive, autonomous control, improving multi-objective optimization within microgrids. In addition, emerging directions such as transfer learning and real-time data analytics are explored for their potential to enhance scalability and resilience. Overall, this review synthesizes recent advances to demonstrate the growing impact of artificial intelligence in droop control and outlines future pathways toward more intelligent and sustainable power systems. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems, 2nd Edition)

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25 pages, 7527 KB

Open AccessEditor’s ChoiceArticle

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

by Matthew Boeding, Michael Hempel, Hamid Sharif and Juan Lopez, Jr.

Electronics 2026, 15(3), 692; https://doi.org/10.3390/electronics15030692 - 5 Feb 2026

Cited by 1 | Viewed by 637

Abstract

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly [...] Read more.

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures. Full article

(This article belongs to the Special Issue AI-Powered Safeguards: Enhancing Security and Privacy in Smart Cities and IoT Ecosystems)

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37 pages, 501 KB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Attribute-Based Encryption Schemes for Special Internet of Things Applications

by Łukasz Pióro, Krzysztof Kanciak and Zbigniew Zieliński

Electronics 2026, 15(3), 697; https://doi.org/10.3390/electronics15030697 - 5 Feb 2026

Viewed by 868

Abstract

Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, [...] Read more.

Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, and context-aware security management in modern Internet of Things (IoT) applications. ABAC controls access based on attributes associated with users, devices, resources, and environmental conditions rather than fixed roles, making it highly adaptable to the complex and heterogeneous nature of IoT ecosystems. ABE can significantly improve the security and manageability of modern military IoT systems. Nevertheless, its practical implementation requires obtaining a range of performance data and assessing the additional overhead, particularly regarding data transmission efficiency. This paper provides a comparative analysis of the performance of two cryptographic schemes for attribute-based encryption in the context of special Internet of Things (IoT) applications. This applies to special environments, both military and civilian, where infrastructure is unreliable and dynamic and decisions must be made locally and in near-real time. From a security perspective, there is a need for strong authentication, precise access control, and a zero-trust approach at the network edge as well. The CIRCL scheme, based on traditional pairing-based ABE (CP-ABE), is compared with the newer Covercrypt scheme, a hybrid key encapsulation mechanism with access control (KEMAC) that provides quantum resistance. The main goal is to determine which scheme scales better and meets the performance requirements for two different scenarios: large corporate networks (where scalability is key) and tactical edge networks (where minimal bandwidth and post-quantum security are paramount). The benchmark results are used to compare the operating costs in detail, such as the key generation time, message encryption and decryption times, public key size, and cipher overhead, showing that Covercrypt provides a reduction in ciphertext overhead in tactical scenarios, while CIRCL offers faster decryption throughput in large-scale enterprise environments. It is concluded that the optimal choice depends on the specific constraints of the operating environment. Full article

(This article belongs to the Special Issue Computer Networking Security and Privacy)

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18 pages, 538 KB

Open AccessEditor’s ChoiceArticle

Enhancing Vehicle IoT Security with PQC: A Lightweight Approach for Encrypted Sensor Data Transmission

by Jackson Diaz-Gorrin and Candido Caballero-Gil

Electronics 2026, 15(3), 684; https://doi.org/10.3390/electronics15030684 - 4 Feb 2026

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Abstract

Cybersecurity threats are evolving constantly, and the arrival of quantum computing raises serious doubts about whether today’s cryptographic methods will hold up over time. This concern has motivated interest in algorithms designed to resist future attacks, with CRYSTALS-Kyber emerging as a practical candidate [...] Read more.

Cybersecurity threats are evolving constantly, and the arrival of quantum computing raises serious doubts about whether today’s cryptographic methods will hold up over time. This concern has motivated interest in algorithms designed to resist future attacks, with CRYSTALS-Kyber emerging as a practical candidate and forming the basis of an NIST post-quantum standard. This study focuses on protecting data exchanged between a vehicle sensor suite and cloud services over the Message Queuing Telemetry Transport protocol. Performance must remain acceptable; therefore, attention centers on lightweight and efficient execution while leveraging the board’s hardware capabilities to keep latency and resource usage low. Adding this layer of post-quantum encryption helps limit the exposure of critical telemetry and control data to sophisticated adversaries. It also aims to preserve integrity and confidentiality in vehicular communications as the Internet of Things becomes increasingly connected. This approach maintains a practical balance between forward-looking security and real-world deployability. Full article

(This article belongs to the Special Issue New Technologies in Applied Cryptography and Network Security)

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LiDAR–RADAR Sensor Fusion and Telemetry Data Integration for Obstacle Detection Along the UAS Navigation Trajectory

by Luigi Farina, Francesco Lo Caso, Gennaro Ariante, Aniello Menichino, Michele Inverno, Vittorio Di Vito, Salvatore Ponte and Giuseppe Del Core

Electronics 2026, 15(3), 685; https://doi.org/10.3390/electronics15030685 - 4 Feb 2026

Viewed by 1152

Abstract

Today, the use of UASs (unmanned aerial systems) is rapidly expanding across civil, military, and scientific applications. The deployment of drones in close proximity to urban areas is becoming increasingly common, particularly during missions conducted beyond visual line of sight (BVLOS) or in [...] Read more.

Today, the use of UASs (unmanned aerial systems) is rapidly expanding across civil, military, and scientific applications. The deployment of drones in close proximity to urban areas is becoming increasingly common, particularly during missions conducted beyond visual line of sight (BVLOS) or in fully autonomous modes. Advancements in technology have enabled the development of systems and platforms that no longer require a human operator onboard and are equipped with progressively higher levels of autonomy. Therefore, enhancing onboard systems is crucial to ensure a high level of operational safety, particularly during missions conducted in harsh and complex environments, such as urban and suburban areas, where the presence of a large number of static and dynamic obstacles, including pedestrians, vehicles, and other aircraft, is pervasive. In this context, the implementation and integration of multiple onboard devices and sensors represent the core focus of this work, with the objective of improving perception, navigation, and safety capabilities for autonomous UAV operations. In particular, communication channels, hardware integration, and data fusion techniques have been implemented and evaluated to improve system performance and situational awareness. This work presents the hardware and software integration of LiDAR and radar sensors with a Pixhawk autopilot and a Raspberry Pi companion computer, aimed at developing obstacle detection applications. Full article

(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, 2nd Edition)

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