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Volume 14
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Electronics, Volume 14, Issue 15 (August-1 2025) – 239 articles

Cover Story (view full-size image): In this study, we present a real-time 3D visualization solution for digital twins, addressing the limitations of slow 3D simulations and non-intuitive 2D systems. The proposed method projects sensor data onto 3D CAD models to provide an intuitive, simulation-like view without the high computational overhead. By integrating GPU acceleration, geodesic distance calculations for surface phenomena, and Physically Based Rendering (PBR) for realism, the system achieves high performance. Experiments confirmed tens to hundreds of FPS even with complex models and numerous sensors, proving the system's ability to enhance data analysis through an intuitive, realistic, and real-time interface. View this paper
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21 pages, 3338 KiB  
Article
Novel Adaptive Intelligent Control System Design
by Worrawat Duanyai, Weon Keun Song, Min-Ho Ka, Dong-Wook Lee and Supun Dissanayaka
Electronics 2025, 14(15), 3157; https://doi.org/10.3390/electronics14153157 - 7 Aug 2025
Viewed by 112
Abstract
A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is [...] Read more.
A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is designed with a simple structure, consisting of two main subsystems: a meta-learning-triggered mechanism-based physics-informed neural network (MLTM-PINN) for plant identification and a self-tuning neural network controller (STNNC). This structure, featuring the triggered mechanism, facilitates a balance between high controllability and control efficiency. The MLTM-PINN incorporates the following: (I) a single self-supervised physics-informed neural network (PINN) without the need for labelled data, enabling online learning in control; (II) a meta-learning-triggered mechanism to ensure consistent control performance; (III) transfer learning combined with meta-learning for finely tailored initialization and quick adaptation to input changes. To resolve the conflict between streamlining the AICS’s structure and enhancing its controllability, the STNNC functionally integrates the nonlinear controller and adaptation laws from the MRAC system. Three STNNC design scenarios are tested with transfer learning and/or hyperparameter optimization (HPO) using a Gaussian process tailored for Bayesian optimization (GP-BO): (scenario 1) applying transfer learning in the absence of the HPO; (scenario 2) optimizing a learning rate in combination with transfer learning; and (scenario 3) optimizing both a learning rate and the number of neurons in hidden layers without applying transfer learning. Unlike scenario 1, no quick adaptation effect in the MLTM-PINN is observed in the other scenarios, as these struggle with the issue of dynamic input evolution due to the HPO-based STNNC design. Scenario 2 demonstrates the best synergy in controllability (best control response) and efficiency (minimal activation frequency of meta-learning and fewer trials for the HPO) in control. Full article
(This article belongs to the Special Issue Nonlinear Intelligent Control: Theory, Models, and Applications)
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23 pages, 3155 KiB  
Article
Construction of a Machining Process Knowledge Graph and Its Application in Process Route Recommendation
by Liang Li, Jiaxing Liang, Chunlei Li, Zhe Liu, Yingying Wei and Zeyu Ji
Electronics 2025, 14(15), 3156; https://doi.org/10.3390/electronics14153156 - 7 Aug 2025
Viewed by 178
Abstract
This paper proposes a knowledge graph (KG) construction method for a part machining process in response to the low degree of structuring of historical process data association relationships within the enterprise in the field of part machining, which makes it difficult to reuse [...] Read more.
This paper proposes a knowledge graph (KG) construction method for a part machining process in response to the low degree of structuring of historical process data association relationships within the enterprise in the field of part machining, which makes it difficult to reuse effectively. The part types are mainly shafts, gears, boxes and other common parts. First, the schema layer of the process knowledge graph was constructed using a top-down approach. Second, deep learning techniques were employed for entity extraction, while knowledge fusion and ontology relationship establishment methods were combined to build the data layer of the process knowledge graph (PKG) from the bottom up. Third, the mapping between the schema layer and data layer was implemented in the Neo4j graph database. Based on the constructed process KG, process route recommendation and rapid retrieval of process information were thus accomplished. Finally, a shaft part was used as the target part to verify the effectiveness of the proposed method. In over 300 trials, the similarity-based recommendation model achieved a hit rate of 91.7% (the target part’s route appeared in the recommended list in 91.7% of cases). These results indicate that the proposed machining PKG construction is feasible and can assist in process planning, potentially improving the efficiency of retrieving and reusing machining knowledge. Full article
(This article belongs to the Special Issue Human Robot Interaction: Techniques, Applications, and Future Trends)
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18 pages, 1572 KiB  
Article
A Distributed Multi-Microgrid Cooperative Energy Sharing Strategy Based on Nash Bargaining
by Shi Su, Qian Zhang and Qingyang Xie
Electronics 2025, 14(15), 3155; https://doi.org/10.3390/electronics14153155 - 7 Aug 2025
Viewed by 103
Abstract
With the rapid development of energy transformation, the proportion of new energy is increasing, and the efficient trading mechanism of multi-microgrids can realize energy sharing to improve the consumption rate of new energy. A distributed multi-microgrid cooperative energy sharing strategy is proposed based [...] Read more.
With the rapid development of energy transformation, the proportion of new energy is increasing, and the efficient trading mechanism of multi-microgrids can realize energy sharing to improve the consumption rate of new energy. A distributed multi-microgrid cooperative energy sharing strategy is proposed based on Nash bargaining. Firstly, by comprehensively considering the adjustable heat-to-electrical ratio, ladder-type positive and negative carbon trading, peak–valley electricity price and demand response, a multi-microgrid system with wind–solar-storage-load and combined heat and power is constructed. Then, a multi-microgrid cooperative game optimization framework is established based on Nash bargaining, and the complex nonlinear problem is decomposed into two stages to be solved. In the first stage, the cost minimization problem of multi-microgrids is solved based on the alternating direction multiplier method to maximize consumption rate and protect privacy. In the second stage, through the established contribution quantification model, Nash bargaining theory is used to fairly distribute the benefits of cooperation. The simulation results of three typical microgrids verify that the proposed strategy has good convergence properties and computational efficiency. Compared with the independent operation, the proposed strategy reduces the cost by 41% and the carbon emission by 18,490 kg, thus realizing low-carbon operation and optimal economic dispatch. Meanwhile, the power supply pressure of the main grid is reduced through energy interaction, thus improving the utilization rate of renewable energy. Full article
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18 pages, 4155 KiB  
Article
Economic-Optimal Operation Strategy for Active Distribution Networks with Coordinated Scheduling of Electric Vehicle Clusters
by Guodong Wang, Huayong Lu, Xiao Yang, Haiyang Li, Xiao Song, Jiapeng Rong and Yi Wang
Electronics 2025, 14(15), 3154; https://doi.org/10.3390/electronics14153154 - 7 Aug 2025
Viewed by 81
Abstract
With the continuous increase in the proportion of distributed energy output in the distribution network and the limited equipment on the management side of the active distribution network, it is very important to give full play to the regulating role of the dispatchable [...] Read more.
With the continuous increase in the proportion of distributed energy output in the distribution network and the limited equipment on the management side of the active distribution network, it is very important to give full play to the regulating role of the dispatchable potential of large-scale electric vehicles for the economic operation of the distribution network. To deal with this issue, this paper proposes an optimal dispatching model of the distribution network considering the combination of the dispatchable potential of electric vehicle clusters and demand response. Firstly, the active distribution network dispatching model with the demand response is introduced, and the equipment involved in the active distribution network dispatching is modeled. Secondly, the bidirectional long short-term memory network algorithm is used to process the historical data of electric vehicles to reduce the uncertainty of the model. Then, the shared energy-storage characteristics based on the dispatchable potential of electric vehicle clusters are fully explored and the effect of peak shaving and valley filling after the demand response is fully explored. This approach significantly reduces the network loss and operating cost of the active distribution network. Finally, the modified IEEE-33 bus test system is utilized for test analysis in the case analysis, and the test results show that the established active distribution network model can reduce the early construction cost of the system’s energy-storage equipment, improve the energy-utilization efficiency, and realize the economic operation of the active distribution network. Full article
(This article belongs to the Section Circuit and Signal Processing)
25 pages, 1436 KiB  
Review
Large Language Models for Structured and Semi-Structured Data, Recommender Systems and Knowledge Base Engineering: A Survey of Recent Techniques and Architectures
by Alma Smajić, Ratomir Karlović, Mieta Bobanović Dasko and Ivan Lorencin
Electronics 2025, 14(15), 3153; https://doi.org/10.3390/electronics14153153 - 7 Aug 2025
Viewed by 127
Abstract
Large Language Models (LLMs) are reshaping recommendation systems through enhanced language understanding, reasoning, and integration with structured data. This systematic review analyzes 88 studies published between 2023 and 2025, categorized into three thematic areas: data processing, technical identification, and LLM-based recommendation architectures. Following [...] Read more.
Large Language Models (LLMs) are reshaping recommendation systems through enhanced language understanding, reasoning, and integration with structured data. This systematic review analyzes 88 studies published between 2023 and 2025, categorized into three thematic areas: data processing, technical identification, and LLM-based recommendation architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review highlights key trends such as the use of knowledge graphs, Retrieval-Augmented Generation (RAG), domain-specific fine-tuning, and robustness improvements. Findings reveal that while LLMs significantly advance semantic reasoning and personalization, challenges remain in hallucination mitigation, fairness, and domain adaptation. Technical innovations, including graph-augmented retrieval methods and human-in-the-loop validation, show promise in addressing these limitations. The review also considers the broader macroeconomic implications associated with the deployment of LLM-based systems, particularly as they relate to scalability, labor dynamics, and resource-intensive implementation in real-world recommendation contexts, emphasizing both productivity gains and potential labor market shifts. This work provides a structured overview of current methods and outlines future directions for developing reliable and efficient LLM-based recommendation systems. Full article
(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)
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21 pages, 737 KiB  
Article
RiscADA: RISC-V Extension for Optimized Control of External D/A and A/D Converters
by Cosmin-Andrei Popovici, Andrei Stan, Nicolae-Alexandru Botezatu and Vasile-Ion Manta
Electronics 2025, 14(15), 3152; https://doi.org/10.3390/electronics14153152 - 7 Aug 2025
Viewed by 93
Abstract
The increasing interest shared by academia and industry in the development of RISC-V cores, extensions and accelerators becomes fructified by collaborative efforts, like the EU’s ChipsJU, which leverages the design of building blocks, IPs and cores based on RISC-V architecture. A domain capable [...] Read more.
The increasing interest shared by academia and industry in the development of RISC-V cores, extensions and accelerators becomes fructified by collaborative efforts, like the EU’s ChipsJU, which leverages the design of building blocks, IPs and cores based on RISC-V architecture. A domain capable of benefiting from the RISC-V extensibility is the control of external DACs and ADCs. The proposed solution is an open-source RISC-V extension for optimized control of external DACs and ADCs called RiscADA. The extension supports a parametrizable number of DACs and ADCs, is integrated as a coprocessor beside CVA6 in a SoC by using the CV-X-IF interface, deployed on a Kintex UltraScale+ FPGA and implements ISA extension instructions. After benchmarks with commercial solutions, the results show that CVA6 using RiscADA extension configures external DACs 38.6 × and 10.9× times faster than MicroBlaze V and simple CVA6, both using AXI SPI peripherals. The proposed extension achieves 5.35× and 3.05× times higher sample rates of external ADCs than the two configurations mentioned above. RiscADA extension performs digital signal conditioning 4.52× and 3.1× times faster than the MicroBlaze V and CVA6, both using AXI SPI peripherals. It computes statistics for external ADC readings (minimum, maximum, simple-moving average and over-threshold duration). Full article
(This article belongs to the Section Computer Science & Engineering)
45 pages, 3787 KiB  
Review
Electromigration Failures in Integrated Circuits: A Review of Physics-Based Models and Analytical Methods
by Ping Cheng, Ling-Feng Mao, Wen-Hao Shen and Yu-Ling Yan
Electronics 2025, 14(15), 3151; https://doi.org/10.3390/electronics14153151 - 7 Aug 2025
Viewed by 271
Abstract
Electromigration (EM), current-driven atomic diffusion in interconnect metals, critically threatens integrated circuit (IC) reliability via void-induced open circuits and hillock-induced short circuits. This review examines EM’s physical mechanisms, influencing factors, and advanced models, synthesizing seven primary determinants: current density, temperature, material properties, microstructure, [...] Read more.
Electromigration (EM), current-driven atomic diffusion in interconnect metals, critically threatens integrated circuit (IC) reliability via void-induced open circuits and hillock-induced short circuits. This review examines EM’s physical mechanisms, influencing factors, and advanced models, synthesizing seven primary determinants: current density, temperature, material properties, microstructure, geometry, pulsed current, and mechanical stress. It dissects the coupled contributions of electron wind force (dominant EM driver), thermomigration (TM), and stress migration (SM). The review assesses four foundational modeling frameworks: Black’s model, Blech’s criterion, atomic flux divergence (AFD), and Korhonen’s theory. Despite advances in multi-physics simulation and statistical EM analysis, achieving predictive full-chip assessment remains computationally challenging. Emerging research prioritizes the following: (i) model order reduction methods and machine-learning solvers for verification of EM in billion-scale interconnect networks; and (ii) physics-informed routing optimization to inherently eliminate EM violations during physical design. Both are crucial for addressing reliability barriers in IC technologies and 3D heterogeneous integration. Full article
(This article belongs to the Section Electronic Materials, Devices and Applications)
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12 pages, 492 KiB  
Article
AFJ-PoseNet: Enhancing Simple Baselines with Attention-Guided Fusion and Joint-Aware Positional Encoding
by Wenhui Zhang, Yu Shi and Jiayi Lin
Electronics 2025, 14(15), 3150; https://doi.org/10.3390/electronics14153150 - 7 Aug 2025
Viewed by 82
Abstract
Simple Baseline has become a dominant benchmark in human pose estimation (HPE) due to its excellent performance and simple design. However, its “strong encoder + simple decoder” architectural paradigm suffers from two core limitations: (1) its non-branching, linear deconvolutional path prevents it from [...] Read more.
Simple Baseline has become a dominant benchmark in human pose estimation (HPE) due to its excellent performance and simple design. However, its “strong encoder + simple decoder” architectural paradigm suffers from two core limitations: (1) its non-branching, linear deconvolutional path prevents it from leveraging the rich, fine-grained features generated by the encoder at multiple scales and (2) the model lacks explicit prior knowledge of both the absolute positions and structural layout of human keypoints. To address these issues, this paper introduces AFJ-PoseNet, a new architecture that deeply enhances the Simple Baseline framework. First, we restructure Simple Baseline’s original linear decoder into a U-Net-like multi-scale fusion path, introducing intermediate features from the encoder via skip connections. For efficient fusion, we design a novel Attention Fusion Module (AFM), which dynamically gates the flow of incoming detailed features through a context-aware spatial attention mechanism. Second, we propose the Joint-Aware Positional Encoding (JAPE) module, which innovatively combines a fixed global coordinate system with learnable, joint-specific spatial priors. This design injects both absolute position awareness and statistical priors of the human body structure. Our ablation studies on the MPII dataset validate the effectiveness of each proposed enhancement, with our full model achieving a mean PCKh of 88.915, a 0.341 percentage point improvement over our re-implemented baseline. On the more challenging COCO val2017 dataset, our ResNet-50-based AFJ-PoseNet achieves an Average Precision (AP) of 72.6%. While this involves a slight trade-off in Average Recall for higher precision, this result represents a significant 2.2 percentage point improvement over our re-implemented baseline (70.4%) and also outperforms other strong, publicly available models like DARK (72.4%) and SimCC (72.1%) under comparable settings, demonstrating the superiority and competitiveness of our proposed enhancements. Full article
(This article belongs to the Section Computer Science & Engineering)
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20 pages, 859 KiB  
Article
MultiHeart: Secure and Robust Heartbeat Pattern Recognition in Multimodal Cardiac Monitoring System
by Hossein Ahmadi, Yan Zhang and Nghi H. Tran
Electronics 2025, 14(15), 3149; https://doi.org/10.3390/electronics14153149 - 7 Aug 2025
Viewed by 158
Abstract
The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of [...] Read more.
The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. Full article
(This article belongs to the Special Issue New Technologies in Applied Cryptography and Network Security)
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20 pages, 105195 KiB  
Article
Filter-Based Tchebichef Moment Analysis for Whole Slide Image Reconstruction
by Keun Woo Kim, Wenxian Jin and Barmak Honarvar Shakibaei Asli
Electronics 2025, 14(15), 3148; https://doi.org/10.3390/electronics14153148 - 7 Aug 2025
Viewed by 168
Abstract
In digital pathology, accurate diagnosis and prognosis critically depend on robust feature representation of Whole Slide Images (WSIs). While deep learning offers powerful solutions, its “black box” nature presents significant challenges to clinical interpretability and widespread adoption. Handcrafted features offer interpretability, yet orthogonal [...] Read more.
In digital pathology, accurate diagnosis and prognosis critically depend on robust feature representation of Whole Slide Images (WSIs). While deep learning offers powerful solutions, its “black box” nature presents significant challenges to clinical interpretability and widespread adoption. Handcrafted features offer interpretability, yet orthogonal moments, particularly Tchebichef moments (TMs), remain underexplored for WSI analysis. This study introduces TMs as interpretable, efficient, and scalable handcrafted descriptors for WSIs, alongside a novel two-dimensional digital filter architecture designed to enhance numerical stability and hardware compatibility during TM computation. We conducted a comprehensive reconstruction analysis using H&E-stained WSIs from the MIDOG++ dataset to evaluate TM effectiveness. Our results demonstrate that lower-order TMs accurately reconstruct both square and rectangular WSI patches, with performance stabilising beyond a threshold moment order, confirmed by SNIRE, SSIM, and BRISQUE metrics, highlighting their capacity to retain structural fidelity. Furthermore, our analysis reveals significant computational efficiency gains through the use of pre-computed polynomials. These findings establish TMs as highly promising, interpretable, and scalable feature descriptors, offering a robust alternative for computational pathology applications that prioritise both accuracy and transparency. Full article
(This article belongs to the Special Issue Image Fusion and Image Processing)
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22 pages, 6051 KiB  
Article
Research on GNSS Spoofing Detection and Autonomous Positioning Technology for Drones
by Jiawen Zhou, Mei Hu, Chao Zhou, Zongmin Liu and Chao Ma
Electronics 2025, 14(15), 3147; https://doi.org/10.3390/electronics14153147 - 7 Aug 2025
Viewed by 159
Abstract
With the rapid development of the low-altitude economy, the application of drones in both military and civilian fields has become increasingly widespread. The safety and accuracy of their positioning and navigation have become critical factors in ensuring the successful execution of missions. Currently, [...] Read more.
With the rapid development of the low-altitude economy, the application of drones in both military and civilian fields has become increasingly widespread. The safety and accuracy of their positioning and navigation have become critical factors in ensuring the successful execution of missions. Currently, GNSS spoofing attack techniques are becoming increasingly sophisticated, posing a serious threat to the reliability of drone positioning. This paper proposes a GNSS spoofing detection and autonomous positioning method for drones operating in mission mode, which is based on visual sensors and does not rely on additional hardware devices. First, during the deception detection phase, the ResNet50-SE twin network is used to extract and match real-time aerial images from the drone’s camera with satellite image features obtained via GNSS positioning, thereby identifying positioning anomalies. Second, once deception is detected, during the positioning recovery phase, the system uses the SuperGlue network to match real-time aerial images with satellite image features within a specific area, enabling the drone’s absolute positioning. Finally, experimental validation using open-source datasets demonstrates that the method achieves a GNSS spoofing detection accuracy of 89.5%, with 89.7% of drone absolute positioning errors controlled within 13.9 m. This study provides a comprehensive solution for the safe operation and stable mission execution of drones in complex electromagnetic environments. Full article
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23 pages, 4024 KiB  
Article
WaveCORAL-DCCA: A Scalable Solution for Rotor Fault Diagnosis Across Operational Variabilities
by Nima Rezazadeh, Mario De Oliveira, Giuseppe Lamanna, Donato Perfetto and Alessandro De Luca
Electronics 2025, 14(15), 3146; https://doi.org/10.3390/electronics14153146 - 7 Aug 2025
Viewed by 198
Abstract
This paper presents WaveCORAL-DCCA, an unsupervised domain adaptation (UDA) framework specifically developed to address data distribution shifts and operational variabilities (OVs) in rotor fault diagnosis. The framework introduces the novel integration of discrete wavelet transformation for robust time–frequency feature extraction and an enhanced [...] Read more.
This paper presents WaveCORAL-DCCA, an unsupervised domain adaptation (UDA) framework specifically developed to address data distribution shifts and operational variabilities (OVs) in rotor fault diagnosis. The framework introduces the novel integration of discrete wavelet transformation for robust time–frequency feature extraction and an enhanced deep canonical correlation analysis (DCCA) network with correlation alignment (CORAL) loss for superior domain-invariant representation learning. This combination enables more effective alignment of source and target feature distributions without requiring any labelled data from the target domain. Comprehensive validation on both experimental and numerically simulated rotor datasets across three health conditions—i.e., normal, unbalanced, and misaligned—demonstrates that WaveCORAL-DCCA achieves an average diagnostic accuracy of 95%. Notably, it outperforms established UDA benchmarks by at least 5–17% in cross-domain scenarios. These results confirm that WaveCORAL-DCCA provides robust generalisation across machines, fault severities, and operational conditions, even with scarce target domain samples, offering a scalable and practical solution for industrial rotor fault diagnosis. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Signal Processing for Fault Detection)
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20 pages, 983 KiB  
Article
A Library-Oriented Large Language Model Approach to Cross-Lingual and Cross-Modal Document Retrieval
by Wang Yi, Xiahuan Cai, Hongtao Ma, Zhengjie Fu and Yan Zhan
Electronics 2025, 14(15), 3145; https://doi.org/10.3390/electronics14153145 - 7 Aug 2025
Viewed by 230
Abstract
Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates [...] Read more.
Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates visual features from images, layout-aware optical character recognition (OCR) text, and bilingual semantic representations in Chinese and English. This framework aims to construct a shared semantic embedding space that mitigates semantic discrepancies across modalities and resolves inconsistencies in cross-lingual mappings. The architecture incorporates three main components: a visual encoder, a structure-aware OCR module, and a multilingual Transformer. Furthermore, a joint contrastive learning loss has been introduced to enhance alignment across both modalities and languages. The proposed method has been evaluated on three core tasks: a single-modality retrieval task from image → OCR, a cross-lingual retrieval task between Chinese and English, and a joint multimodal retrieval task involving image, OCR, and language inputs. Experimental results demonstrate that, in the joint multimodal setting, the proposed model achieved a Precision@10 of 0.693, Recall@10 of 0.684, nDCG@10 of 0.672, and F1@10 of 0.685, substantially outperforming established baselines such as CLIP, LayoutLMv3, and UNITER. Ablation studies revealed that removing either the structure-aware OCR module or the cross-lingual alignment mechanism resulted in a decrease in mean reciprocal rank (MRR) to 0.561, thereby confirming the critical role of these components in reinforcing semantic consistency across modalities. This study highlights the powerful potential of large language models in multimodal semantic fusion and retrieval tasks, providing robust solutions for large-scale semantic understanding and application scenarios in multilingual and multimodal contexts. Full article
(This article belongs to the Section Artificial Intelligence)
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23 pages, 1115 KiB  
Article
Research on Mongolian–Chinese Neural Machine Translation Based on Implicit Linguistic Features and Deliberation Networks
by Qingdaoerji Ren, Shike Li, Xuerong Wei, Yatu Ji and Nier Wu
Electronics 2025, 14(15), 3144; https://doi.org/10.3390/electronics14153144 - 7 Aug 2025
Viewed by 220
Abstract
Sequence-to-sequence neural machine translation (NMT) has achieved great success with many language pairs. However, its performance remains constrained in low-resource settings such as Mongolian–Chinese translation due to its strong reliance on large-scale parallel corpora. To address this issue, we propose ILFDN-Transformer, a Mongolian–Chinese [...] Read more.
Sequence-to-sequence neural machine translation (NMT) has achieved great success with many language pairs. However, its performance remains constrained in low-resource settings such as Mongolian–Chinese translation due to its strong reliance on large-scale parallel corpora. To address this issue, we propose ILFDN-Transformer, a Mongolian–Chinese NMT model that integrates implicit language features and a deliberation network to improve translation quality under limited-resource conditions. Specifically, we leverage the BART pre-trained language model to capture deep semantic representations of source sentences and apply knowledge distillation to integrate the resulting implicit linguistic features into the Transformer encoder to provide enhanced semantic support. During decoding, we introduce a deliberation mechanism that guides the generation process by referencing linguistic knowledge encoded in a multilingual pre-trained model, therefore improving the fluency and coherence of target translations. Furthermore, considering the flexible word order characteristics of the Mongolian language, we propose a Mixed Positional Encoding (MPE) method that combines absolute positional encoding with LSTM-based dynamic encoding, enabling the model to better adapt to complex syntactic variations. Experimental results show that ILFDN-Transformer achieves a BLEU score improvement of 3.53 compared to the baseline Transformer model, fully demonstrating the effectiveness of our proposed method. Full article
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13 pages, 1329 KiB  
Article
The Complex Interaction Between the Sense of Presence, Movement Features, and Performance in a Virtual Reality Spatial Task: A Preliminary Study
by Tommaso Palombi, Andrea Chirico, Laura Mandolesi, Maurizio Mancini, Noemi Passarello, Erica Volta, Fabio Alivernini and Fabio Lucidi
Electronics 2025, 14(15), 3143; https://doi.org/10.3390/electronics14153143 - 7 Aug 2025
Viewed by 187
Abstract
The present study explores the innovative application of virtual reality (VR) in conducting the Radial Arm Maze (RAM) task, a performance-based test traditionally utilized for assessing spatial memory. This study aimed to develop a gamified version of the RAM implemented in immersive VR [...] Read more.
The present study explores the innovative application of virtual reality (VR) in conducting the Radial Arm Maze (RAM) task, a performance-based test traditionally utilized for assessing spatial memory. This study aimed to develop a gamified version of the RAM implemented in immersive VR and investigate the interaction between the sense of presence, movement features, and performance within the RAM. We developed software supporting a head-mounted display (HMD), addressing prior limitations in the scientific literature concerning user interaction, data collection accuracy, operational flexibility, and immersion level. This study involved a sample of healthy young adults who engaged with the immersive VR version of the RAM, examining the influence of VR experience variables (sense of presence, motion sickness, and usability) on RAM performance. Notably, it also introduced the collection and analysis of movement features within the VR environment to ascertain their impact on performance outcomes and their relationship with VR experience variables. The VR application developed is notable for its user-friendliness, adaptability, and integration capability with physiological monitoring devices, marking a significant advance in utilizing VR for cognitive assessments. Findings from our study underscore the importance of VR experience factors in RAM performance, highlighting how a heightened sense of presence can predict better performance, thereby emphasizing engagement and immersion as crucial for task success in VR settings. Additionally, this study revealed how movement parameters within the VR environment, specifically speed and directness, significantly influence RAM performance, offering new insights into optimizing VR experiences for enhanced task performance. Full article
(This article belongs to the Special Issue Augmented Reality, Virtual Reality, and 3D Reconstruction)
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27 pages, 4681 KiB  
Article
Gecko-Inspired Robots for Underground Cable Inspection: Improved YOLOv8 for Automated Defect Detection
by Dehai Guan and Barmak Honarvar Shakibaei Asli
Electronics 2025, 14(15), 3142; https://doi.org/10.3390/electronics14153142 - 6 Aug 2025
Viewed by 216
Abstract
To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and [...] Read more.
To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article
(This article belongs to the Special Issue Robotics: From Technologies to Applications)
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18 pages, 1085 KiB  
Article
Enhancing Real-Time Anomaly Detection of Multivariate Time Series Data via Adversarial Autoencoder and Principal Components Analysis
by Alaa Hussien Ali, Hind Almisbahi, Entisar Alkayal and Abeer Almakky
Electronics 2025, 14(15), 3141; https://doi.org/10.3390/electronics14153141 - 6 Aug 2025
Viewed by 185
Abstract
Rapid data growth in large systems has introduced significant challenges in real-time monitoring and analysis. One of these challenges is detecting anomalies in time series data with high-dimensional inputs that contain complex inter-correlations between them. In addition, the lack of labeled data leads [...] Read more.
Rapid data growth in large systems has introduced significant challenges in real-time monitoring and analysis. One of these challenges is detecting anomalies in time series data with high-dimensional inputs that contain complex inter-correlations between them. In addition, the lack of labeled data leads to the use of unsupervised learning that relies on daily system data to train models, which can contain noise that affects feature extraction. To address these challenges, we propose PCA-AAE, a novel anomaly detection model for time series data using an Adversarial Autoencoder integrated with Principal Component Analysis (PCA). PCA contributes to analyzing the latent space by transforming it into uncorrelated components to extract important features and reduce noise within the latent space. We tested the integration of PCA into the model’s phases and studied its efficiency in each phase. The tests show that the best practice is to apply PCA to the latent code during the adversarial training phase of the AAE model. We used two public datasets, the SWaT and SMAP datasets, to compare our model with state-of-the-art models. The results indicate that our model achieves an average F1 score of 0.90, which is competitive with state-of-the-art models, and an average of 58.5% faster detection speed compared to similar state-of-the-art models. This makes PCA-AAE a candidate solution to enhance real-time anomaly detection in high-dimensional datasets. Full article
(This article belongs to the Section Artificial Intelligence)
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15 pages, 871 KiB  
Article
Analogical Reasoning with Multimodal Knowledge Graphs: Fine-Tuning Model Performance Based on LoRA
by Zhenglong Zhang, Sijia Zhang, Zongshi An, Zhenglin Li and Chun Zhang
Electronics 2025, 14(15), 3140; https://doi.org/10.3390/electronics14153140 - 6 Aug 2025
Viewed by 112
Abstract
Multimodal knowledge graphs have recently been successfully applied to tasks such as those relating to information retrieval, question and answer, and recommender systems. In this study, we propose a dual-path fine-tuning mechanism technique with a low-rank adapter and an embedded cueing layer, aiming [...] Read more.
Multimodal knowledge graphs have recently been successfully applied to tasks such as those relating to information retrieval, question and answer, and recommender systems. In this study, we propose a dual-path fine-tuning mechanism technique with a low-rank adapter and an embedded cueing layer, aiming to improve the generalization and accuracy of the model in analogical reasoning tasks. The low-rank fine-tuning (LoRA) technique with rank-stable scaling factor is used to fine-tune the MKGformer model, and a cue-embedding layer is innovatively added to the input layer, which enables the model to better grasp the scale of the relationship between entities according to the dynamic cue vectors during the fine-tuning process and ensures that the model achieves the best results during training. The experimental results show that the R-MKG model improves several evaluation indexes by more than 20%, which is significantly better than the traditional DoRA and FA-LoRA methods. This research provides technical support for multimodal knowledge graph analogical reasoning. We hope that our work will bring benefits and inspire future research. Full article
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36 pages, 3039 KiB  
Article
Decision Tree Pruning with Privacy-Preserving Strategies
by Yee Jian Chew, Shih Yin Ooi, Ying Han Pang and Zheng You Lim
Electronics 2025, 14(15), 3139; https://doi.org/10.3390/electronics14153139 - 6 Aug 2025
Viewed by 213
Abstract
Machine learning techniques, particularly decision trees, have been extensively utilized in Network-based Intrusion Detection Systems (NIDSs) due to their transparent, rule-based structures that enable straightforward interpretation. However, this transparency presents privacy risks, as decision trees may inadvertently expose sensitive information such as network [...] Read more.
Machine learning techniques, particularly decision trees, have been extensively utilized in Network-based Intrusion Detection Systems (NIDSs) due to their transparent, rule-based structures that enable straightforward interpretation. However, this transparency presents privacy risks, as decision trees may inadvertently expose sensitive information such as network configurations or IP addresses. In our previous work, we introduced a sensitive pruning-based decision tree to mitigate these risks within a limited dataset and basic pruning framework. In this extended study, three privacy-preserving pruning strategies are proposed: standard sensitive pruning, which conceals specific sensitive attribute values; optimistic sensitive pruning, which further simplifies the decision tree when the sensitive splits are minimal; and pessimistic sensitive pruning, which aggressively removes entire subtrees to maximize privacy protection. The methods are implemented using the J48 (Weka C4.5 package) decision tree algorithm and are rigorously validated across three full-scale NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. To ensure a realistic evaluation of time-dependent intrusion patterns, a rolling-origin resampling scheme is employed in place of conventional cross-validation. Additionally, IP address truncation and port bilateral classification are incorporated to further enhance privacy preservation. Experimental results demonstrate that the proposed pruning strategies effectively reduce the exposure of sensitive information, significantly simplify decision tree structures, and incur only minimal reductions in classification accuracy. These findings reaffirm that privacy protection can be successfully integrated into decision tree models without severely compromising detection performance. To further support the proposed pruning strategies, this study also includes a comprehensive review of decision tree post-pruning techniques. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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19 pages, 8091 KiB  
Article
Leveraging Synthetic Degradation for Effective Training of Super-Resolution Models in Dermatological Images
by Francesco Branciforti, Kristen M. Meiburger, Elisa Zavattaro, Paola Savoia and Massimo Salvi
Electronics 2025, 14(15), 3138; https://doi.org/10.3390/electronics14153138 - 6 Aug 2025
Viewed by 168
Abstract
Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline [...] Read more.
Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline simulating common artifacts in dermatological images, including blur, noise, downsampling, and compression. This synthetic degradation approach enabled effective training of DermaSR-GAN, a super-resolution generative adversarial network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH2) using structural similarity and no-reference quality metrics. DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches, including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for enhancing teledermatology applications and computer-aided diagnosis systems. Full article
(This article belongs to the Section Computer Science & Engineering)
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13 pages, 3394 KiB  
Article
Design of a Wideband Loaded Sleeve Monopole Embedded with Filtering High–Low Impedance Structure
by Jiansen Ma, Weiping Cao and Xinhua Yu
Electronics 2025, 14(15), 3137; https://doi.org/10.3390/electronics14153137 - 6 Aug 2025
Viewed by 175
Abstract
In this paper, a compact wideband filtering monopole is presented for remote terrestrial omnidirectional communication systems. The presented antenna features a sleeve monopole structure integrating with two key components: the lumped parallel RLC circuits and an embedded high–low impedance structure within the sleeve [...] Read more.
In this paper, a compact wideband filtering monopole is presented for remote terrestrial omnidirectional communication systems. The presented antenna features a sleeve monopole structure integrating with two key components: the lumped parallel RLC circuits and an embedded high–low impedance structure within the sleeve section. The integrated high–low impedance structure enables the monopole to achieve excellent filtering characteristics while maintaining the monopole compactly. Meanwhile, the combination of the RLC loads and the sleeve monopole ensures wideband omnidirectional radiation performance. To validate the design, a prototype operating from 200 to 1500 MHz is fabricated and tested. The measurement results demonstrate that the monopole achieves a VSWR below 3 across the entire operating band and a measured gain exceeding 0 dB. Furthermore, the monopole exhibits satisfactory out-of-band rejection from 1700 to 4000 MHz, confirming its effective filtering capability. Full article
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27 pages, 8913 KiB  
Article
Laser Radar and Micro-Light Polarization Image Matching and Fusion Research
by Jianling Yin, Gang Li, Bing Zhou and Leilei Cheng
Electronics 2025, 14(15), 3136; https://doi.org/10.3390/electronics14153136 - 6 Aug 2025
Viewed by 259
Abstract
Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering [...] Read more.
Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering polarized image and a laser LiDAR point cloud, and the corresponding system is constructed. Based on the extraction of pixel coordinates from the 3D LiDAR point cloud, the method adds information on the polarization degree and polarization angle of the micro-light polarization image, as well as on the reflective intensity of each point of the LiDAR. The mapping matrix of the radar point cloud to the pixel coordinates is made to contain depth offset information and show better fitting, thus optimizing the 3D point cloud converted from the micro-light polarization image. On this basis, algorithms such as 3D point cloud fusion and pseudo-color mapping are used to further optimize the matching and fusion procedures for the micro-light polarization image and the radar point cloud, so as to successfully realize the alignment and fusion of the 2D micro-light polarization image and the 3D LiDAR point cloud. The experimental results show that the alignment rate between the 2D micro-light polarization image and the 3D LiDAR point cloud reaches 74.82%, which can effectively detect the target hidden behind the glass under the low illumination condition and fill the blind area of the LiDAR point cloud data acquisition. This study verifies the feasibility and advantages of “polarization + LiDAR” fusion in low-light glass scene reconnaissance, and it provides a new technological means of covert target detection in complex environments. Full article
(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)
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21 pages, 3869 KiB  
Article
Research on Optimal Scheduling of the Combined Cooling, Heating, and Power Microgrid Based on Improved Gold Rush Optimization Algorithm
by Wei Liu, Zhenhai Dou, Yi Yan, Tong Zhou and Jiajia Chen
Electronics 2025, 14(15), 3135; https://doi.org/10.3390/electronics14153135 - 6 Aug 2025
Viewed by 226
Abstract
To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling [...] Read more.
To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article
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35 pages, 29912 KiB  
Article
Hybrid Analysis Model for Detecting Fileless Malware
by Syed Noman Ali Sherazi and Amna Qureshi
Electronics 2025, 14(15), 3134; https://doi.org/10.3390/electronics14153134 - 6 Aug 2025
Viewed by 354
Abstract
Fileless malware is a type of malware that does not rely on executable files to persist or propagate. Unlike traditional file-based malware, fileless malware is more difficult to detect and remove, posing a significant threat to organizations. This paper introduces a novel hybrid [...] Read more.
Fileless malware is a type of malware that does not rely on executable files to persist or propagate. Unlike traditional file-based malware, fileless malware is more difficult to detect and remove, posing a significant threat to organizations. This paper introduces a novel hybrid analysis model that combines static and dynamic analysis techniques to identify fileless malware. Applied to four real-world and two custom-created fileless malware samples, the proposed model demonstrated its qualitative effectiveness in uncovering complex behaviors and evasion tactics, such as obfuscated macros, process injection, registry persistence, and covert network communications, which often bypass single-method analyses. While the analysis reveals the potential for significant damage to organizational reputation, resources, and operations, the paper also outlines a set of mitigation measures that cybersecurity professionals and researchers can adopt to protect users and organizations against threats posed by fileless malware. Overall, this research offers valuable insights and a novel analysis model to better address and understand fileless malware threats. Full article
(This article belongs to the Section Networks)
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14 pages, 24112 KiB  
Article
ImpactAlert: Pedestrian-Carried Vehicle Collision Alert System
by Raghav Rawat, Caspar Lant, Haowen Yuan and Dennis Shasha
Electronics 2025, 14(15), 3133; https://doi.org/10.3390/electronics14153133 - 6 Aug 2025
Viewed by 166
Abstract
The ImpactAlert system is a chest-mounted system that detects objects that are likely to hit a pedestrian and alerts that pedestrian. The primary use cases are visually impaired pedestrians or pedestrians who need to be warned about vehicles or other pedestrians coming from [...] Read more.
The ImpactAlert system is a chest-mounted system that detects objects that are likely to hit a pedestrian and alerts that pedestrian. The primary use cases are visually impaired pedestrians or pedestrians who need to be warned about vehicles or other pedestrians coming from unseen directions. This paper argues for the need for such a system, the design and algorithms of ImpactAlert, and experiments carried out in varied urban environments, ranging from densely crowded to semi-urban in the United States, India and China. ImpactAlert makes use of a LiDAR camera found on a commercial wireless phone, processes the data over several frames to evaluate the time to impact and speed of potential threats. When ImpactAlert determines a threat meets the criteria set by the user, it sends warning signals through an output device to warn a pedestrian. The output device can be an audible warning and/or a low-cost smart cane that vibrates when danger approaches. Our experiments in urban and semi-urban environments show that (i) ImpactAlert can avoid nearly all false negatives (when an alarm should be sent and it isn’t) and (ii) enjoys a low false positive rate. The net result is an effective low cost system to alert pedestrians in an urban environment. Full article
(This article belongs to the Special Issue Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions)
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26 pages, 1698 KiB  
Article
Photoplethysmography-Based Blood Pressure Calculation for Neonatal Telecare in an IoT Environment
by Camilo S. Jiménez, Isabel Cristina Echeverri-Ocampo, Belarmino Segura Giraldo, Carolina Márquez-Narváez, Diego A. Cortes, Fernando Arango-Gómez, Oscar Julián López-Uribe and Santiago Murillo-Rendón
Electronics 2025, 14(15), 3132; https://doi.org/10.3390/electronics14153132 - 6 Aug 2025
Viewed by 197
Abstract
This study presents an algorithm for non-invasive blood pressure (BP) estimation in neonates using photoplethysmography (PPG), suitable for resource-constrained neonatal telecare platforms. Using the Windkessel model, the algorithm processes PPG signals from a MAX 30102 sensor, (Analog Devices (formerly Maxim Integrated), based in [...] Read more.
This study presents an algorithm for non-invasive blood pressure (BP) estimation in neonates using photoplethysmography (PPG), suitable for resource-constrained neonatal telecare platforms. Using the Windkessel model, the algorithm processes PPG signals from a MAX 30102 sensor, (Analog Devices (formerly Maxim Integrated), based in San Jose, CA, USA) filtering motion noise and extracting cardiac cycle time and systolic time (ST). These parameters inform a derived blood flow signal, the input for the Windkessel model. Calibration utilizes average parameters based on the newborn’s post-conceptional age, weight, and gestational age. Performance was validated against readings from a standard non-invasive BP cuff at SES Hospital Universitario de Caldas. Two parameter estimation methods were evaluated. The first yielded root mean square errors (RMSEs) of 24.14 mmHg for systolic and 19.13 mmHg for diastolic BP. The second method significantly improved accuracy, achieving RMSEs of 2.31 mmHg and 5.13 mmHg, respectively. The successful adaptation of the Windkessel model to single PPG signals allows for BP calculation alongside other physiological variables within the telecare program. A device analysis was conducted to determine the appropriate device based on computational capacity, availability of programming tools, and ease of integration within an Internet of Things environment. This study paves the way for future research that focuses on parameter variations due to cardiovascular changes in newborns during their first month of life. Full article
(This article belongs to the Section Circuit and Signal Processing)
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30 pages, 2873 KiB  
Article
Quasar—A Process Variability-Aware Radiation Robustness Evaluation Tool
by Bernardo Borges Sandoval, Lucas Yuki Imamura, Ana Flávia D. Reis, Leonardo Heitich Brendler, Rafael B. Schvittz and Cristina Meinhardt
Electronics 2025, 14(15), 3131; https://doi.org/10.3390/electronics14153131 - 6 Aug 2025
Viewed by 214
Abstract
This work presents Quasar, an open-source tool developed to boost the characterization of how variability effects impact radiation sensitivity in digital circuits. Quasar receives a SPICE netlist as input and automatically determines robustness metrics, such as the critical Linear Energy Transfer, for every [...] Read more.
This work presents Quasar, an open-source tool developed to boost the characterization of how variability effects impact radiation sensitivity in digital circuits. Quasar receives a SPICE netlist as input and automatically determines robustness metrics, such as the critical Linear Energy Transfer, for every configuration in which a Single Event Transient fault can propagate an error. The tool can handle ranges from small basic cells to median multi-gate circuits in a few seconds, speeding up the traditional fault injection mechanism based on a large number of electrical simulations. The tool’s workflow explores logical masking to reduce the design space exploration, i.e., reducing the necessary number of electrical simulations, as well as regression methods to speed up variability evaluations. Quasar already has shown the potential to provide useful results, and a prototype has also been published. This work presents a more polished and complete version of the tool, one that optimizes the tool’s search process and allows not only for a fast evaluation of the radiation robustness of a circuit, but also for an analysis of how fabrication process metrics impact this robustness, such as Work Function Fluctuation. Full article
(This article belongs to the Special Issue Single-Event Effects: Modeling, Prediction, Testing and Radiation Hardening)
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21 pages, 5215 KiB  
Article
A Cyber-Physical Integrated Framework for Developing Smart Operations in Robotic Applications
by Tien-Lun Liu, Po-Chun Chen, Yi-Hsiang Chao and Kuan-Chun Huang
Electronics 2025, 14(15), 3130; https://doi.org/10.3390/electronics14153130 - 6 Aug 2025
Viewed by 156
Abstract
The traditional manufacturing industry is facing the challenge of digital transformation, which involves the enhancement of intelligence and production efficiency. Many robotic applications have been discussed to enable collaborative robots to perform operations smartly rather than just automatically. This article tackles the issues [...] Read more.
The traditional manufacturing industry is facing the challenge of digital transformation, which involves the enhancement of intelligence and production efficiency. Many robotic applications have been discussed to enable collaborative robots to perform operations smartly rather than just automatically. This article tackles the issues of intelligent robots with cognitive and coordination capability by introducing cyber-physical integration technology. The authors propose a system architecture with open-source software and low-cost hardware based on the 5C hierarchy and then conduct experiments to verify the proposed framework. These experiments involve the collection of real-time data using a depth camera, object detection to recognize obstacles, simulation of collision avoidance for a robotic arm, and cyber-physical integration to perform a robotic task. The proposed framework realizes the scheme of the 5C architecture of Industry 4.0 and establishes a digital twin in cyberspace. By utilizing connection, conversion, calculation, simulation, verification, and operation, the robotic arm is capable of making independent judgments and appropriate decisions to successfully complete the assigned task, thereby verifying the proposed framework. Such a cyber-physical integration system is characterized by low cost but good effectiveness. Full article
(This article belongs to the Topic Innovation, Communication and Engineering)
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20 pages, 394 KiB  
Article
Feedback Linearization for a Generalized Multivariable T-S Model
by Basil Mohammed Al-Hadithi, Javier Blanco Rico and Agustín Jiménez
Electronics 2025, 14(15), 3129; https://doi.org/10.3390/electronics14153129 - 6 Aug 2025
Viewed by 133
Abstract
This study presents a novel optimal fuzzy logic control (FLC) strategy based on feedback linearization for the regulation of multivariable nonlinear systems. Building upon an enhanced Takagi–Sugeno (T-S) model previously developed by the authors, the proposed method incorporates a refined parameter-weighting scheme to [...] Read more.
This study presents a novel optimal fuzzy logic control (FLC) strategy based on feedback linearization for the regulation of multivariable nonlinear systems. Building upon an enhanced Takagi–Sugeno (T-S) model previously developed by the authors, the proposed method incorporates a refined parameter-weighting scheme to optimize both local and global approximations within the T-S framework. This approach enables improved selection and minimization of the performance index. The effectiveness of the control strategy is validated through its application to a two-link serial robotic manipulator. The results demonstrate that the proposed FLC achieves robust performance, maintaining system stability and high accuracy even under the influence of noise and load disturbances, with well-damped system behavior and negligible steady-state error. Full article
(This article belongs to the Section Systems & Control Engineering)
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21 pages, 3918 KiB  
Article
Design of BPC LF Time Code Signal Generator Based on ARM Architecture Microcontroller and FPGA
by Hongzhen Cao, Jianfeng Wu, Xiaolong Guan, Dangli Zhao, Yan Xing, Zhibo Zhou, Yuji Li and Kexin Yin
Electronics 2025, 14(15), 3128; https://doi.org/10.3390/electronics14153128 - 6 Aug 2025
Viewed by 221
Abstract
Low-frequency (LF) time code timing technology holds significant importance in civilian applications such as radio-controlled clocks. This study focuses on the design and implementation of a high-precision Binary Phase Code (BPC) LF time code signal generator. A generator system was constructed, demonstrating good [...] Read more.
Low-frequency (LF) time code timing technology holds significant importance in civilian applications such as radio-controlled clocks. This study focuses on the design and implementation of a high-precision Binary Phase Code (BPC) LF time code signal generator. A generator system was constructed, demonstrating good stability, superior resolution, and flexible adjustment capabilities for both amplitude and phase. The system employs an ARM + FPGA cooperative architecture: the ARM processor is responsible for parsing and scheduling the time code data, while the FPGA implements carrier wave generation and high-precision digital modulation. This digital processing is combined with analog circuitry to achieve digital-to-analog (D/A) signal conversion. Compared to traditional methods, carrier generation is achieved using Direct Digital Synthesis (DDS) technology. Digital modulation techniques enable the precise control of the modulation depth (adjustable between 70% and 90%) and phase (with a resolution of 1 ns). A sliding window algorithm was utilized for time difference calculation and compensation. Testing confirmed the stability of key signal parameters, including integrity, carrier frequency and modulation depth. These results validate the feasibility and superiority of the digital LF time code generation technology proposed in this study, providing a valuable reference for the development of next-generation timing equipment. Full article
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