Electronics

33 pages, 8263 KB

Open AccessArticle

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 (registering DOI) - 28 Mar 2026

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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18 pages, 1930 KB

Open AccessArticle

Frequency Error Analysis and Optimization in UXB Satellite TT&C Systems

by Haozhe Zhang, Ziyue Song, Min Wu, Wen Zhang, Guangzu Liu and Jun Zou

Electronics 2026, 15(7), 1413; https://doi.org/10.3390/electronics15071413 (registering DOI) - 28 Mar 2026

Abstract

High-precision Doppler measurement is essential for deep-space Unified X-band (UXB) tracking systems, yet digital implementations suffer from finite word-length quantization errors that degrade performance. This study analyzes frequency offset errors in UXB transponder systems, focusing on the phase-locked loop (PLL) and system-level digital [...] Read more.

High-precision Doppler measurement is essential for deep-space Unified X-band (UXB) tracking systems, yet digital implementations suffer from finite word-length quantization errors that degrade performance. This study analyzes frequency offset errors in UXB transponder systems, focusing on the phase-locked loop (PLL) and system-level digital processing. A digital system model is presented, featuring an FFT-based coarse acquisition and a digital Costas loop for carrier synchronization. The simulation results reveal that 32-bit quantization yields unacceptable frequency offset errors. By extending critical paths to 48 bits, the system reduces frequency offset error by approximately 2¹⁶ and achieves sub-0.01 mm/s velocity accuracy, significantly improving coherence and meeting deep-space measurement requirements. Full article

29 pages, 930 KB

Open AccessArticle

Stateful Order-Preserving Encryption for Secure Cloud Databases

by Nam-Su Jho and Taek-Young Youn

Electronics 2026, 15(7), 1412; https://doi.org/10.3390/electronics15071412 (registering DOI) - 28 Mar 2026

Abstract

We propose stateful order-preserving encryption (SOPE), a novel framework designed to realize human-centric data security and privacy, the fundamental values of the Fifth Industrial Revolution. Conventional order-preserving encryption supports efficient queries in cloud databases but fundamentally leaks plaintext distributions, leaving data vulnerable to [...] Read more.

We propose stateful order-preserving encryption (SOPE), a novel framework designed to realize human-centric data security and privacy, the fundamental values of the Fifth Industrial Revolution. Conventional order-preserving encryption supports efficient queries in cloud databases but fundamentally leaks plaintext distributions, leaving data vulnerable to inference attacks. To mitigate this vulnerability while maintaining query efficiency, SOPE introduces a partition-based dynamic density adjustment mechanism under an honest-but-curious threat model. This mechanism offsets density imbalances between partitions in real time by inserting decoy ciphertexts, thereby limiting the leakage scope to the order of data while obscuring frequency information. Our analysis and empirical evaluations demonstrate that SOPE’s ciphertexts consistently approach a uniform distribution by adaptively compensating for the underlying plaintext distribution through decoy insertion. While the continuous insertion of decoy ciphertexts inevitably incurs additional storage overhead (controlled by a tunable parameter

λ

), our evaluations demonstrate practical performance. By striking an optimal balance between efficiency and human privacy rights, SOPE provides a trustworthy infrastructure for secure data utilization. Full article

(This article belongs to the Special Issue Securing Tomorrow: Human-Centric Security and Privacy in the Fifth Industrial Revolution)

27 pages, 26535 KB

Open AccessArticle

Audio Adversarial Example Detection Scheme via Re-Attack

by Yanru Feng, Qingjie Liu and Jing Li

Electronics 2026, 15(7), 1411; https://doi.org/10.3390/electronics15071411 (registering DOI) - 28 Mar 2026

Abstract

Adversarial examples, created by adding small distortions to audio, can fool neural network models and cause automatic speech recognition (ASR) systems to produce incorrect outputs. Most current detection methods rely on the recognition capabilities of ASR systems. As a result, they often fail [...] Read more.

Adversarial examples, created by adding small distortions to audio, can fool neural network models and cause automatic speech recognition (ASR) systems to produce incorrect outputs. Most current detection methods rely on the recognition capabilities of ASR systems. As a result, they often fail to detect such examples when ASR performance degrades or when facing an evasion attack—referred to in this paper as a “partial adversarial attack”—that is specifically designed to bypass ASR models. In this paper, we identify a distinct noise energy difference between adversarial examples and their original audio. Moreover, this noise energy difference is typically greater than that between adversarial examples and their re-attacked examples. This finding leads us to propose a novel detection method that fundamentally departs from traditional approaches by operating independently of ASR systems. The proposed method employs a detection strategy that involves re-attacking the input audio and identifies adversarial examples by characterizing the noise energy difference before and after re-attack without relying on ASR systems. The experimental results demonstrate that the proposed method detects various state-of-the-art adversarial attacks. Compared with the baselines, the proposed method achieves substantially better detection performance across standard adversarial examples, noisy adversarial examples, and partial adversarial examples. Full article

(This article belongs to the Special Issue Intelligent Detection of Internet Threats and Human-Centric Security)

20 pages, 2227 KB

Open AccessArticle

Multi-Target Sensing for UAV System-Enabled ISAC Network

by Nuo Chen, Jianwei Zhao, Weimin Jia, Wei Jin, Fang He, Haojie Hu and Fenggan Zhang

Electronics 2026, 15(7), 1410; https://doi.org/10.3390/electronics15071410 (registering DOI) - 28 Mar 2026

Abstract

6G has introduced integrated sensing and communication (ISAC) technology, which is one of the core technologies for the next-generation communication networks. In this paper, we propose an efficient sensing method for multiple moving targets in a 6G ISAC unmanned aerial vehicle (UAV) system. [...] Read more.

6G has introduced integrated sensing and communication (ISAC) technology, which is one of the core technologies for the next-generation communication networks. In this paper, we propose an efficient sensing method for multiple moving targets in a 6G ISAC unmanned aerial vehicle (UAV) system. Firstly, we establish an integrated channel model for multi-user communication and multi-moving-target sensing. Secondly, we design the ISAC signal and analyze the target echo signal. We propose a multiple moving-target sensing method to achieve the joint accurate estimation of the target angle of departure (AOD), radial velocity, and distance. Specifically, to solve the problems of limited-angle scanning resolution and grid error in parameter estimation, we adopt the weighted fusion interpolation algorithm and the iteratively reweighted compressed sensing (CS) algorithm to optimize the parameter estimation performance of multiple moving targets. Finally, extensive simulation results verify the effectiveness of the proposed method. Full article

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51 pages, 1921 KB

Open AccessReview

Federated Retrieval-Augmented Generation for Cybersecurity in Resource-Constrained IoT and Edge Environments: A Deployment-Oriented Scoping Review

by Hangyu He, Xin Yuan, Kai Wu and Wei Ni

Electronics 2026, 15(7), 1409; https://doi.org/10.3390/electronics15071409 (registering DOI) - 27 Mar 2026

Abstract

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by [...] Read more.

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes 82 studies published between 2020 and 2026, identified through keyword search and citation snowballing over OpenAlex, arXiv, and Crossref. We develop a taxonomy that clarifies the components of federated systems, deployment locations, trust boundaries, and protected assets. We further map the combined RAG+FL attack surface, summarize practical defenses and system patterns, and distill actionable guidance for secure, privacy-preserving, and efficient FedRAG deployment in real-world IoT and edge scenarios. Our synthesis highlights recurring trade-offs among robustness, privacy, latency, communication overhead, and maintainability, and identifies open research priorities in benchmark design, governance mechanisms, and cross-silo evaluation protocols for practical deployment. Full article

(This article belongs to the Special Issue Novel Approaches for Deep Learning in Cybersecurity)

14 pages, 2326 KB

Open AccessArticle

Steel Surface Defect Detection Based on Improved YOLOv8 with Multi-Scale Feature Fusion and Attention Mechanism

by Yalei Jia, Xian Zhang, Jianhui Meng and Jisong Zang

Electronics 2026, 15(7), 1408; https://doi.org/10.3390/electronics15071408 - 27 Mar 2026

Abstract

Identifying microscopic textural anomalies and filtering out complicated industrial background noise remain significant hurdles in inspecting metallic surfaces. To tackle these operational bottlenecks, our research introduces a refined multi-scale detection framework built upon the YOLOv8l architecture. Specifically, we engineer a fine-grained detection pathway [...] Read more.

Identifying microscopic textural anomalies and filtering out complicated industrial background noise remain significant hurdles in inspecting metallic surfaces. To tackle these operational bottlenecks, our research introduces a refined multi-scale detection framework built upon the YOLOv8l architecture. Specifically, we engineer a fine-grained detection pathway utilizing the P2 layer, which aims to preserve critical details of miniature flaws that are otherwise discarded during feature extraction. Furthermore, a Bi-directional Feature Pyramid Network model is embedded to reconstruct the feature fusion path, balancing the preservation of shallow geometric textures with enhanced multi-scale representation capabilities. To bolster anti-interference performance, a Convolutional Block Attention Module (CBAM) is integrated prior to the detection head, employing adaptive channel and spatial weighting to suppress unstructured background noise. Experimental results utilizing TTA demonstrate that the mAP@0.5 reached 76.3%. Detection accuracies for patches and inclusions reached 93.1% and 85.3%. Full article

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18 pages, 1265 KB

Open AccessArticle

Robust Trajectory Tracking Control of Underactuated Overhead Cranes via Time Delay Estimation and the Sliding Mode Technique

by Ziyuan Lin and Xianqing Wu

Electronics 2026, 15(7), 1407; https://doi.org/10.3390/electronics15071407 - 27 Mar 2026

Abstract

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. [...] Read more.

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

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24 pages, 1020 KB

Open AccessArticle

Research on the Diagnosis of Abnormal Sound Defects in Automobile Engines Based on Fusion of Multi-Modal Images and Audio

by Yi Xu, Wenbo Chen and Xuedong Jing

Electronics 2026, 15(7), 1406; https://doi.org/10.3390/electronics15071406 - 27 Mar 2026

Abstract

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. [...] Read more.

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

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46 pages, 2125 KB

Open AccessReview

Big Data and Graph Deep Learning for Financial Decision Support from Social Networks: A Critical Review

by Leonidas Theodorakopoulos and Alexandra Theodoropoulou

Electronics 2026, 15(7), 1405; https://doi.org/10.3390/electronics15071405 - 27 Mar 2026

Abstract

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the [...] Read more.

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Applications in Social Networks)

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16 pages, 1544 KB

Open AccessArticle

Evaluating and Enhancing YOLOv8’s Soft Error Resilience

by Aonan Yang, Zhi Liu, Yihao Guan, Tan Tan and Jinrong Shen

Electronics 2026, 15(7), 1404; https://doi.org/10.3390/electronics15071404 - 27 Mar 2026

Abstract

Soft errors pose a critical reliability threat to deep neural networks in safety-critical systems. We present YOLO-FI, a fault injection framework for YOLO models that supports module-wise analysis, multiple formats, and customizable fault models. Using YOLO-FI, we evaluate YOLOv8 in floating- and fixed-point [...] Read more.

Soft errors pose a critical reliability threat to deep neural networks in safety-critical systems. We present YOLO-FI, a fault injection framework for YOLO models that supports module-wise analysis, multiple formats, and customizable fault models. Using YOLO-FI, we evaluate YOLOv8 in floating- and fixed-point forms on COCO. Results show that floating-point YOLOv8 is vulnerable across modules, while fixed-point models are resilient except for the final module. Range restriction improves resilience in most floating-point modules but fails in the final one. To address this, we propose selective hardening of the vulnerable module, achieving effective fault mitigation with modest overhead. Full article

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

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19 pages, 1357 KB

Open AccessArticle

Clinically Aligned Long-Context Transformers for Cross-Platform Mental Health Risk Detection

by Aditya Tekale and Mohammad Masum

Electronics 2026, 15(7), 1403; https://doi.org/10.3390/electronics15071403 - 27 Mar 2026

Abstract

Social media platforms contain rich but noisy narratives of psychological distress, creating opportunities for early mental health risk detection. However, existing datasets capture heterogeneous constructs such as suicide risk severity, depression diagnosis, and DSM-5 symptom presence, and most prior models are trained and [...] Read more.

Social media platforms contain rich but noisy narratives of psychological distress, creating opportunities for early mental health risk detection. However, existing datasets capture heterogeneous constructs such as suicide risk severity, depression diagnosis, and DSM-5 symptom presence, and most prior models are trained and evaluated on a single corpus, limiting their clinical alignment and cross-dataset generalizability. In this study, we fine-tune a domain-specific long-document transformer, AIMH/Mental-Longformer-base-4096, for binary mental health risk detection (risk vs. no risk) using two clinically aligned Reddit datasets: the C-SSRS Reddit corpus and the eRisk 2025 depression dataset. To handle long user histories, we introduce an LLM-based summarization pipeline that compresses posts exceeding 2000 tokens while preserving mental health-relevant information. We also conduct a seven-configuration ablation study across combinations of three corpora (C-SSRS, eRisk, and ReDSM5) to examine how dataset semantics influence model performance. On a held-out C-SSRS + eRisk test set (n = 279), the proposed model achieves a mean balanced accuracy of 0.89 ± 0.01 across five random seeds, with a best run of 0.90 and a 5.74 percentage point improvement over the strongest baseline (TF-IDF + Random Forest). The model also shows strong cross-platform generalization, achieving BA = 0.78 on the depression-reddit-cleaned dataset (n = 7731) and BA = 0.85 (ROC-AUC = 0.92) on a Twitter suicidal-intention dataset (n = 9119) without additional fine-tuning. The ablation analysis shows that although a three-dataset configuration (C-SSRS + eRisk + ReDSM5) maximizes aggregate performance, the ReDSM5 labels encode symptom presence rather than clinical risk, creating a semantic mismatch. This finding highlights the importance of label compatibility when combining heterogeneous mental health corpora. Explainability analysis using Integrated Gradients and attention visualization shows that the model focuses on clinically meaningful expressions such as therapy references, diagnosis, and hopelessness rather than isolated keywords. These results demonstrate that clinically aligned long-context transformers can provide accurate and interpretable mental health risk detection from social media while emphasizing the critical role of dataset semantics in multi-corpus training. Full article

(This article belongs to the Special Issue Role of Artificial Intelligence in Natural Language Processing)

13 pages, 44672 KB

Open AccessArticle

ARMANI: Dictionary-Learning-Inspired Data-Free Deep Generative Modeling with Meta-Attention and Implicit Preconditioning for Compressively Sampled Magnetic Resonance Imaging

by Ming Wu, Jing Cheng, Qingyong Zhu and Dong Liang

Electronics 2026, 15(7), 1402; https://doi.org/10.3390/electronics15071402 - 27 Mar 2026

Abstract

Magnetic resonance imaging (MRI) reconstruction from undersampled k-space data enables accelerated acquisition but leads to a severely ill-posed inverse problem. Although supervised deep learning methods have achieved strong performance, they typically rely on large paired datasets that are difficult to obtain in clinical [...] Read more.

Magnetic resonance imaging (MRI) reconstruction from undersampled k-space data enables accelerated acquisition but leads to a severely ill-posed inverse problem. Although supervised deep learning methods have achieved strong performance, they typically rely on large paired datasets that are difficult to obtain in clinical practice. To address these limitations, we propose a dictionary-learning-inspired dAta-fRee deep generative modeling with Meta-Attention and implicit precoNditIoning for compressively sampled MRI (CS-MRI), termed ARMANI. Specifically, a meta-attention-augmented deep image prior (MA-DIP) generator performs a joint optimization over the latent input

η

and the network parameter

θ

, where

η

is regularized via gradient-domain sparsity and

θ

is constrained by a ridge penalty, mirroring the adaptive estimation of sparse coefficients and an empirical sparsifying dictionary. Furthermore, we integrate a single-step pseudo-orthogonal projection to achieve implicit preconditioning, which modulates the loss landscape and mitigates ill-conditioning of the forward operator. Experimental results demonstrate that ARMANI consistently outperforms existing SOTA data-free and self-supervised methods, and, with limited training data, achieves performance comparable to or slightly better than the supervised benchmark MoDL, with effective artifact suppression and faithful recovery of fine structural details. Overall, ARMANI shows strong scalability and potential for practical deployment in fully data-free CS-MRI reconstruction scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Medical Image Processing)

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15 pages, 796 KB

Open AccessArticle

An Action Potential Detector Based on a High-Order Nonlinear Energy Operator

by Tao Yang, Xiaolong Li and Wei Zheng

Electronics 2026, 15(7), 1401; https://doi.org/10.3390/electronics15071401 - 27 Mar 2026

Abstract

This paper presents an action potential detector (APD) based on a high-order non-linear energy operator (HONEO). The APD consists of a HONEO, a positive threshold generator, a negative threshold generator, and an XOR. The APD is capable of detecting the half-width of an [...] Read more.

This paper presents an action potential detector (APD) based on a high-order non-linear energy operator (HONEO). The APD consists of a HONEO, a positive threshold generator, a negative threshold generator, and an XOR. The APD is capable of detecting the half-width of an action potential since it can determine both the positive peak and the negative peak of the action potential by means of the HONEO and two threshold generators. In addition, the signal-to-noise ratio (SNR) of the APD can also be improved due to the two adaptive threshold generators. The circuit is designed in a standard 0.18

μ

m CMOS process with a 1.8 V supply voltage. Pre-layout simulations are performed under typical conditions (TT process corner, 1.8 V supply, 27

^{\circ}

C). The results show that the output amplitudes of the HONEO remain almost constant (

\pm 100

mV) when the amplitude of the source signal varies from −10 mV to 30 mV at 1 kHz. Across temperature variations from

- 20

^{\circ}

C to 80

^{\circ}

C, the output amplitude remains within

\pm 12 %

of the nominal value, demonstrating acceptable stability for the target implantable application. Compared to the conventional NEO, the APD achieves 14–

20 dB

SNR improvement, a detection accuracy of

97 %

. The power consumption of the APD is approximately

62 μ W

. Full article

33 pages, 3590 KB

Open AccessSystematic Review

Diffusion-Based Approaches for Medical Image Segmentation: An In-Depth Review

by Muhammad Yaseen, Maisam Ali, Sikandar Ali and Hee-Cheol Kim

Electronics 2026, 15(7), 1400; https://doi.org/10.3390/electronics15071400 - 27 Mar 2026

Abstract

Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant [...] Read more.

Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant attention in medical image analysis. This comprehensive review examines the current state of the art in diffusion models for medical image segmentation, covering theoretical foundations, methodological innovations, computational efficiency strategies, and clinical applications. We analyze recent advances in latent diffusion frameworks, transformer-based architectures, and ambiguous segmentation modeling while addressing the practical challenges of implementing these models in clinical environments. The review encompasses applications across multiple medical imaging modalities including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and X-ray imaging, providing insights into performance achievements and identifying future research directions. Through systematic analysis of publications mostly from 2019 to 2025, we demonstrate that diffusion models have achieved remarkable progress in addressing fundamental challenges including data scarcity, inter-observer variability, and uncertainty quantification. Notable achievements include inference time being reduced from 91.23 s to 0.34 s for echocardiogram segmentation (LDSeg, Echo dataset), DSC scores up to 0.96 for knee cartilage MRI segmentation, and a +13.87% DSC improvement over baseline methods for breast ultrasound segmentation. This review serves as a comprehensive resource for researchers and clinicians interested in leveraging diffusion models for medical image segmentation, providing a roadmap for future research and clinical translation. Full article

(This article belongs to the Special Issue Advanced Techniques in Real-Time Image Processing)

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30 pages, 8163 KB

Open AccessArticle

SDGR-Net: A Spatiotemporally Decoupled Gated Residual Network for Robust Multi-State HDD Health Prediction

by Zehong Wu, Jinghui Qin, Yongyi Lu and Zhijing Yang

Electronics 2026, 15(7), 1399; https://doi.org/10.3390/electronics15071399 - 27 Mar 2026

Abstract

Accurate prediction of hard disk drive (HDD) health states is critical for enabling proactive data maintenance and ensuring data reliability in large-scale data centers. However, conventional models often suffer from semantic entanglement among heterogeneous SMART attributes and from the masking of incipient failure [...] Read more.

Accurate prediction of hard disk drive (HDD) health states is critical for enabling proactive data maintenance and ensuring data reliability in large-scale data centers. However, conventional models often suffer from semantic entanglement among heterogeneous SMART attributes and from the masking of incipient failure signatures by stochastic noise. To address these challenges, we propose SDGR-Net, a spatiotemporally decoupled learning framework designed to model the complex degradation dynamics of HDDs. SDGR-Net introduces three synergistic innovations: (1) a spatiotemporally decoupled dual-branch encoder that disentangles longitudinal temporal evolution from cross-variable correlations via parameter-isolated branches, thereby reducing representational interference; (2) a parsimonious dual-view temporal extraction mechanism that captures early-stage anomalies through forward–reverse sequence concatenation, enabling high-fidelity preservation of non-stationary pre-failure patterns; and (3) a cross-branch dynamic gated residual fusion module that functions as an adaptive information bottleneck to emphasize failure-critical features while suppressing redundant noise. Extensive experiments conducted on three heterogeneous HDD datasets, ST4000DM000, HUH721212ALN604, and MG07ACA14TA, demonstrate that SDGR-Net consistently outperforms six state-of-the-art baselines. In particular, SDGR-Net achieves a peak fault detection rate (FDR) of 0.9898 and a 69.6% relative reduction in false alarm rate (FAR) under high-reliability operating conditions. These results, corroborated by comprehensive ablation studies, indicate that SDGR-Net effectively balances detection sensitivity and operational robustness, offering a practical solution for intelligent HDD health monitoring. Full article

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11 pages, 1347 KB

Open AccessArticle

Research on a Multimodal Fusion-Based Localization State Evaluation Algorithm for an Intelligent RF Power Supply

by Zhaolong Fan, Zhifeng Wang, Lang Hu, Long Yao, Yingbin Zhao, Siao Zeng, Jiaze Wu and Hanxiao Zhan

Electronics 2026, 15(7), 1398; https://doi.org/10.3390/electronics15071398 - 27 Mar 2026

Abstract

This paper proposes a multimodal fusion-based localization state evaluation algorithm for a mobile RF power supply. The algorithm computes the information entropy between point cloud data acquired by the laser sensor on the mobile RF power supply platform and a preconstructed prior grid [...] Read more.

This paper proposes a multimodal fusion-based localization state evaluation algorithm for a mobile RF power supply. The algorithm computes the information entropy between point cloud data acquired by the laser sensor on the mobile RF power supply platform and a preconstructed prior grid map; a higher entropy score indicates more accurate localization. Meanwhile, IMU/odometry data fusion is performed by estimating the platform pose from the localization system at consecutive timestamps, and then calculating the differences in Euclidean distance and rotational attitude between this estimated pose and the poses output by the IMU and odometer at the same timestamps. By jointly considering static and dynamic conditions, the laser point cloud matching scores are integrated with the IMU/odometry fusion results to obtain the final localization state evaluation. Experimental results demonstrate that the proposed algorithm can accurately characterize the localization state of the intelligent RF power supply positioning system, achieving an evaluation accuracy of 99%. Full article

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23 pages, 2175 KB

Open AccessArticle

Robust Long Short-Term Memory-Enabled Beamforming for Cell-Free Massive MIMOs in 6G Networks

by Tadele A. Abose and Thomas O. Olwal

Electronics 2026, 15(7), 1397; https://doi.org/10.3390/electronics15071397 - 27 Mar 2026

Abstract

This paper presents a performance evaluation of a long short-term memory (LSTM)-based precoder for cell-free (CF) massive multiple-input multiple-output (MIMO) systems in 6G networks operating under hardware impairments and imperfect channel state information (CSI). It also compares the proposed method with traditional Kalman, [...] Read more.

This paper presents a performance evaluation of a long short-term memory (LSTM)-based precoder for cell-free (CF) massive multiple-input multiple-output (MIMO) systems in 6G networks operating under hardware impairments and imperfect channel state information (CSI). It also compares the proposed method with traditional Kalman, minimum mean square error (MMSE), and zero forcing (ZF) precoders. Simulations conducted at 2.4 GHz show that the LSTM-based scheme offers improved spectral efficiency (SE) and energy efficiency (EE) while remaining computationally feasible. Specifically, the LSTM precoder achieves an average per-user SE of 1.74 bps/Hz, representing gains of about 1.15% over Kalman, 3.45% over MMSE, 4.6% over ZF, and 5.75% over MRT. Under severe hardware impairments, it provides a 2.94% improvement over Kalman and a 5.88% improvement over MMSE. The total SE reaches 17.4 bps/Hz, increasing the overall system capacity by approximately 2.87% over Kalman, 4.02% over MMSE, 6.32% over ZF, and 8.05% over MRT when the number of users (K) is 10. The LSTM-based precoder also achieves the highest peak EE, indicating that its learning-driven adaptability yields higher SE for comparable power usage. Despite a slight increase in power consumption, its inference time remains shorter than both MMSE and ZF, offering a favorable balance between performance and computational complexity. Overall, the results demonstrate that a learning-driven, impairment-aware precoding approach provides significant advantages in terms of robustness and scalability for next-generation 6G CF massive MIMO networks, particularly in non-ideal hardware environments. Full article

(This article belongs to the Special Issue Wireless Artificial Intelligent Computing Systems and Applications (WASA))

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28 pages, 1976 KB

Open AccessReview

Advances in Closed-Loop Artificial Intelligence for Healthcare

by Diba Das, Scott D. Adams, Dean M. Corva, Tracey K. Bucknall and Abbas Z. Kouzani

Electronics 2026, 15(7), 1396; https://doi.org/10.3390/electronics15071396 - 27 Mar 2026

Abstract

Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making through clinical decision support systems (CDSS). Human-in-the-loop (HITL) approaches introduce clinician oversight to improve model interpretability, reliability, and adaptability, while explainable AI (XAI) helps clinicians understand model behaviour. This review categorises [...] Read more.

Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making through clinical decision support systems (CDSS). Human-in-the-loop (HITL) approaches introduce clinician oversight to improve model interpretability, reliability, and adaptability, while explainable AI (XAI) helps clinicians understand model behaviour. This review categorises HITL AI approaches in healthcare into pre-deployment and post-deployment stages and provides a dedicated review focusing specifically on post-deployment HITL systems. It also introduces the concept of closed-loop AI, where real-time expert feedback can refine AI outputs without requiring model retraining. A systematic review following PRISMA guidelines was conducted using the Scopus and PubMed databases for studies published between 2020 and July 2025. From 3466 identified records, 3012 remained after duplicate removal. After title and abstract screening, 1630 articles were assessed through full-text review, and 15 studies met the predefined inclusion criteria related to HITL, post-deployment adaptation, and interactive XAI in healthcare. The selected studies indicate growing interest in post-deployment HITL systems that allow clinicians to refine AI outputs, provide real-time feedback, and support adaptive CDSS. These findings highlight a shift toward human-centred, closed-loop AI frameworks that integrate expert feedback into deployed systems to improve transparency, trust, and responsiveness in clinical decision-making. Full article

(This article belongs to the Special Issue Trustworthy Decision Intelligence: Data-Centric AI, Foundation Models, and Real-World Impact)

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Electronics

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