Electronics

18 pages, 2103 KiB

Open AccessArticle

Towards a Unified Quantum Risk Assessment

by Šarūnas Grigaliūnas and Rasa Brūzgienė

Electronics 2025, 14(17), 3338; https://doi.org/10.3390/electronics14173338 (registering DOI) - 22 Aug 2025

Quantum computing poses an unprecedented threat to classical cryptography, requiring new risk assessment paradigms. This paper proposes a Quantum-Adjusted Risk Score (QARS) model, a theoretical and methodological innovation within the EU’s PAREK framework (Post-quantum asset and algorithm inventory, risk assessment, road mapping, execution, [...] Read more.

Quantum computing poses an unprecedented threat to classical cryptography, requiring new risk assessment paradigms. This paper proposes a Quantum-Adjusted Risk Score (QARS) model, a theoretical and methodological innovation within the EU’s PAREK framework (Post-quantum asset and algorithm inventory, risk assessment, road mapping, execution, key governance). QARS extends Mosca’s inequality—which defines a quantum threat timeline threshold—into a multi-factor risk scoring formula. We formalise QARS with mathematical expressions incorporating timeline, sensitivity, and exposure dimensions, each calibrated by factor weights and scaling functions. The design motivations for including these dimensions are discussed in depth. We present method for model calibration (including sector-specific weight adjustments) and outline validation strategies combining quantitative analysis and expert judgement. The proposed QARS model is situated in the context of the EU’s coordinated roadmap for post-quantum cryptography and cybersecurity regulations, illustrating how QARS supports compliance and strategic migration prioritisation. A prototype tool implementing QARS model is also provided to demonstrate practical applicability. Our contributions provide a unified approach to quantum risk assessment, marrying theoretical rigour with policy-relevant risk management needs to help organizations proactively address the quantum threat. Full article

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

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19 pages, 2247 KiB

Open AccessArticle

Fast Identification of Series Arc Faults Based on Singular Spectrum Statistical Features

by Dezhi Xiong, Shuai Yang, Yang Xue, Penghe Zhang, Runan Song and Jian Song

Electronics 2025, 14(16), 3337; https://doi.org/10.3390/electronics14163337 - 21 Aug 2025

Abstract

Series arc faults are a major cause of electrical fires, posing significant risks to life and property. Their negative-resistance characteristics make fault features difficult to detect, and the existing methods often suffer from high false-alarm rates, poor adaptability, and reliance on high sampling [...] Read more.

Series arc faults are a major cause of electrical fires, posing significant risks to life and property. Their negative-resistance characteristics make fault features difficult to detect, and the existing methods often suffer from high false-alarm rates, poor adaptability, and reliance on high sampling rates and long sampling windows. To enhance the accuracy and efficiency of series AC arc fault detection, this paper proposes a rapid identification method based on singular spectrum statistical features and a differential evolution-optimized XGBoost classifier. The approach first constructs the singular spectrum of current waveforms via a Hankel matrix singular value decomposition and extracts nine statistical features. It then optimizes seven XGBoost hyperparameters using differential evolution to build an efficient classification model. The experiments on 18,240 current samples covering 16 load conditions (including eight arc fault types) show that the method achieves an average identification accuracy of 98.90% using only three nominal cycles (60 ms) of current waveform. Even with a training set ratio as low as 5%, it maintains 97.11% accuracy, outperforming Back-propagation Neural Network, Support Vector Machine, and Recurrent Neural Network methods by up to three percentage points. The method avoids the need for high sampling rates or complex time–frequency transformations, making it suitable for resource-constrained embedded platforms and offering a generalizable solution for series arc fault detection. Full article

(This article belongs to the Special Issue Data Analytics for Power System Operations)

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14 pages, 1636 KiB

Open AccessArticle

Optimization of Thermal Stress in High-Power Semiconductor Laser Array Packaging

by Lei Cheng, Bingxing Wei, Xuanjun Dai, Yanan Bao and Huaqing Sun

Electronics 2025, 14(16), 3336; https://doi.org/10.3390/electronics14163336 - 21 Aug 2025

Abstract

To suppress the thermal stress in high-power semiconductor laser array packaging, the classic asymmetric heat dissipation structure of the array packaging was transformed into a symmetric one by incorporating microchannel heat sinks. This effectively reduced the maximum temperature, maximum thermal stress, thermal resistance, [...] Read more.

To suppress the thermal stress in high-power semiconductor laser array packaging, the classic asymmetric heat dissipation structure of the array packaging was transformed into a symmetric one by incorporating microchannel heat sinks. This effectively reduced the maximum temperature, maximum thermal stress, thermal resistance, and maximum vertical displacement of the semiconductor laser array. Using the response surface methodology, mathematical models were established to correlate the maximum temperature, maximum thermal stress, and maximum vertical displacement of the semiconductor laser array with the radius, height, and spacing of circular micro-pin fins. A genetic algorithm was then employed to perform multi-objective optimization of these parameters. The results demonstrate that, compared to the original packaging configuration, the optimized semiconductor laser array exhibits a maximum temperature reduction of 16.56 °C, a maximum thermal stress decrease of 24.01 MPa, and a reduction in the maximum vertical displacement of the chip by 0.77 μm. Full article

(This article belongs to the Topic Wide Bandgap Semiconductor Electronics and Devices)

23 pages, 5636 KiB

Open AccessArticle

Design and Implementation of Novel DC-DC Converter with Step-Up Ratio and Soft-Switching Technology

by Kuei-Hsiang Chao and Thi-Thanh-Truc Bau

Electronics 2025, 14(16), 3335; https://doi.org/10.3390/electronics14163335 - 21 Aug 2025

Abstract

This paper focuses on the development of a high-conversion-efficiency DC/DC boost converter, which features high-voltage boost ratio conversion and employs soft-switching technology to reduce conversion losses. In the proposed design, the conventional energy storage inductor used in traditional boost converters is replaced with [...] Read more.

This paper focuses on the development of a high-conversion-efficiency DC/DC boost converter, which features high-voltage boost ratio conversion and employs soft-switching technology to reduce conversion losses. In the proposed design, the conventional energy storage inductor used in traditional boost converters is replaced with a coupled inductor, and an additional boost circuit is introduced. This configuration allows the converter to achieve a higher voltage conversion ratio under the same duty cycle, thereby enhancing the voltage gain of the converter. Additionally, a resonance branch is incorporated into the converter, and by applying a simple switching signal control, zero-voltage switching (ZVS) of the main switch is realized. To decrease the switching losses typically found in hard-switching high-voltage boost ratio converters, the proposed design enhances overall power conversion efficiency. The operation principle of this novel high-voltage boost ratio soft-switching converter is first examined, followed by the component design process. The converter’s effectiveness is then confirmed through simulation in PSIM. Finally, experimental testing using the TMS320F2809 digital signal processor demonstrates that the main switch achieves ZVS, validating the practical viability of the design. The converter operates under a full load of 340 W, achieving a conversion efficiency of 92.7%, demonstrating the excellent conversion performance of the developed converter. Full article

(This article belongs to the Special Issue New Horizons and Recent Advances of Power Electronics)

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16 pages, 4854 KiB

Open AccessArticle

AI-Based Multi-Target Localization with Multi-Tx and Single-Rx Frequency Diverse Array Radar

by Jimyung Kang, Geon U Kim, Jeong-Phill Kim, Soonwoo Lee and Sang-Hwa Yi

Electronics 2025, 14(16), 3334; https://doi.org/10.3390/electronics14163334 - 21 Aug 2025

Abstract

Recently, frequency diverse array (FDA) systems have gained attention in target localization due to their time-varying and range-angle-dependent beam-focusing characteristics, which are different from those of conventional phased array (PA) systems. However, analysis of the received signal is challenging due to its time-varying [...] Read more.

Recently, frequency diverse array (FDA) systems have gained attention in target localization due to their time-varying and range-angle-dependent beam-focusing characteristics, which are different from those of conventional phased array (PA) systems. However, analysis of the received signal is challenging due to its time-varying nature. In this paper, an artificial intelligence (AI)-based multi-target localization system is proposed, which works with a simple multi-Tx (Transmitter) and single-Rx (Receiver) FDA system. The AI-based model can find the relationship between the locations of the targets and the received signal, since all the necessary information is contained in the time-varying reflected signal. With a simple multi-Tx, single-Rx model, the system can be implemented in the real world. It is verified that the proposed system can locate at least two targets simultaneously with reasonable performance. Full article

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36 pages, 14083 KiB

Open AccessFeature PaperArticle

Workload Prediction for Proactive Resource Allocation in Large-Scale Cloud-Edge Applications

by Thang Le Duc, Chanh Nguyen and Per-Olov Östberg

Electronics 2025, 14(16), 3333; https://doi.org/10.3390/electronics14163333 - 21 Aug 2025

Abstract

Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive [...] Read more.

Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive feedback loop. The framework is evaluated using 18 months of real traffic traces from a production multi-tier CDN, capturing realistic workload seasonality, cache–tier interactions, and propagation delays. Unlike generic cloud-edge predictors, our design incorporates CDN-specific features and model-switching mechanisms to balance prediction accuracy with computational cost. Seasonal ARIMA (S-ARIMA), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Online Sequential Extreme Learning Machine (OS-ELM) are combined to support both short-horizon scaling and longer-term capacity planning. The predictions drive a queue-based resource-estimation model, enabling proactive cache–server scaling with low rejection rates. Experimental results demonstrate that the framework maintains high accuracy while reducing computational overhead through adaptive model selection. The proposed approach offers a practical, production-tested solution for predictive autoscaling in CDNs and can be extended to other latency-sensitive edge-cloud services with hierarchical architectures. Full article

(This article belongs to the Special Issue Next-Generation Cloud–Edge Computing: Systems and Applications)

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Graphical abstract

19 pages, 2604 KiB

Open AccessArticle

Bayesian-Optimized GCN-BiLSTM-Adaboost Model for Power-Load Forecasting

by Jiarui Li, Jian Li, Jiatong Li and Guozheng Zhang

Electronics 2025, 14(16), 3332; https://doi.org/10.3390/electronics14163332 - 21 Aug 2025

Abstract

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes, this study proposes a Bayesian-optimized GCN-BiLSTM-Adaboost model [...] Read more.

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes, this study proposes a Bayesian-optimized GCN-BiLSTM-Adaboost model (abbreviated as GCN-BiLSTM-AB). It combines Graph Convolutional Networks (GCN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and a Bayesian-optimized AdaBoost framework. Firstly, the GCN is employed to capture the spatial correlation features of the input data. Then, the BiLSTM is employed to extract the long-term dependencies of the data time series. Finally, the AdaBoost framework is used to dynamically adjust the base learner weights, and a Bayesian method is employed to optimize the weight adjustment process and prevent overfitting. The experiment results on actual load data from a regional power grid show the GCN-BiLSTM-AB outperforms other compared models in prediction error metrics, with

M A E

,

M A P E

, and

R M S E

values of 1.86, 3.13%, and 2.26, respectively, which improve the prediction robustness during load change periods. Therefore, the proposed method shows that the synergistic effect of spatiotemporal feature extraction and dynamic weight adjustment improves prediction accuracy and robustness, which provides a new forecasting model with high precision and reliability for power system dispatch decisions. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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21 pages, 9325 KiB

Open AccessArticle

Lightweight Model Improvement and Application for Rice Disease Classification

by Tonglai Liu, Mingguang Liu, Chengcheng Yang, Ancong Wu, Xiaodong Li and Wenzhao Wei

Electronics 2025, 14(16), 3331; https://doi.org/10.3390/electronics14163331 - 21 Aug 2025

Abstract

The timely and correct identification of rice diseases is essential to ensuring rice productivity. However, many methods have drawbacks such as slow recognition speed, low recognition accuracy and overly complex models that are unfavorable for portability. Therefore, this study proposes an improved model [...] Read more.

The timely and correct identification of rice diseases is essential to ensuring rice productivity. However, many methods have drawbacks such as slow recognition speed, low recognition accuracy and overly complex models that are unfavorable for portability. Therefore, this study proposes an improved model for accurately classifying rice diseases based on a two-level routing attention mechanism and dynamic convolution based on the above difficulties. The model employs Alterable Kernel Convolution with dynamic, irregularly shaped convolutional kernels and Bi-level Routing Attention that utilizes sparsity to reduce parameters and involves a GPU-friendly dense matrix multiplication, which can achieve high-precision rice disease recognition while ensuring lightweight and recognition speed. The model successfully classified 10 species, including nine diseased and healthy rice, with 97.31% accuracy and a 97.18% F1-score. Our proposed method outperforms MobileNetV3-large, EfficientNet-b0, Swin Transformer-tiny and ResNet-50 by 1.73%, 1.82%, 1.25% and 0.67%, respectively. Meanwhile, the model contains only

4.453 \times 10^{6}

parameters and achieves an inference time of 6.13 s, which facilitates deployment on mobile devices.The proposed MobileViT_BiAK method effectively identifies rice diseases while providing a lightweight and high-performance classification solution. Full article

(This article belongs to the Special Issue Target Tracking and Recognition Techniques and Their Applications)

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15 pages, 1970 KiB

Open AccessArticle

Transmission Control for Space–Air–Ground Integrated Multi-Hop Networks in Millimeter-Wave and Terahertz Communications

by Liang Zong, Yun Cheng, Zhangfeng Ma, Han Wang, Zhan Liu and Yinqing Tang

Electronics 2025, 14(16), 3330; https://doi.org/10.3390/electronics14163330 - 21 Aug 2025

Abstract

Millimeter-wave (mmWave) and terahertz (THz) communications are susceptible to frequent link disruptions and severe performance degradation due to high directionality, significant path loss, and sensitivity to blockages. These challenges are particularly acute in highly dynamic and densely populated user environments. The issues present [...] Read more.

Millimeter-wave (mmWave) and terahertz (THz) communications are susceptible to frequent link disruptions and severe performance degradation due to high directionality, significant path loss, and sensitivity to blockages. These challenges are particularly acute in highly dynamic and densely populated user environments. The issues present significant obstacles to ensuring reliability and quality of service (QoS) in future space–air–ground integrated networks. To address these challenges, this paper proposes an adaptive transmission control scheme designed for space–air–ground integrated multi-hop networks operating in the mmWave/THz bands. By analyzing the intermittent connectivity inherent in such networks, the proposed scheme incorporates an incremental factor and a backlog indicator into its congestion control mechanism. This allows for the accurate differentiation between packet losses resulting from network congestion and those caused by channel blockages, such as human body occlusion or beam misalignment. Furthermore, the scheme optimizes the initial congestion window during the slow-start phase and dynamically adapts its transmission strategy during the congestion avoidance phase according to the identified cause of packet loss. Simulation results demonstrate that the proposed method effectively mitigates throughput degradation from link blockages, improves data transmission rates in highly dynamic environments, and sustains more stable end-to-end connectivity. Our proposed scheme achieves a 35% higher throughput than TCP Hybla, 40% lower latency than TCP Veno, and maintains 99.2% link utilization under high mobility. Full article

(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications)

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25 pages, 1078 KiB

Open AccessArticle

Road Accident Analysis and Prevention Using Autonomous Vehicles with Application for Montreal

by Manmeet Singh and Anjali Awasthi

Electronics 2025, 14(16), 3329; https://doi.org/10.3390/electronics14163329 - 21 Aug 2025

Abstract

Road safety in cities is becoming a bigger concern worldwide. As more people own cars and traffic congestion increases on old roads, the risk of accidents also grows, which severely affects victims and their families. In 2023, data from the Société de l’Assurance [...] Read more.

Road safety in cities is becoming a bigger concern worldwide. As more people own cars and traffic congestion increases on old roads, the risk of accidents also grows, which severely affects victims and their families. In 2023, data from the Société de l’Assurance Automobile du Québec (SAAQ) reported that 380 people died in traffic accidents in Quebec. A study of road accidents in Montreal between 2012 and 2021 looked at the most dangerous locations, times, and traffic patterns. In this paper, we investigate the role of autonomous vehicles (AVs) vs human-driven vehicles (HDVs) in reducing road accidents in mixed traffic situations. The reaction time of human drivers to road accidents at signalized intersections affects safety and is used to compare the difference between the two situations. Microscopic traffic simulation models (MTMs) namely the Krauss car-following model is developed using SUMO to assess the vehicles performance. Case study 1 assesses the effect of reaction time on human-driven vehicles. The findings show that longer reaction times lead to more collisions. Case study 2 looks at autonomous vehicles and how human-driven vehicles interact in mixed traffic. The simulations tested various levels of AV penetration (0%, 25%, 50%, 75%, and 100%) in mixed traffic and found that more AVs on the road improve safety and reduce the number of accidents. Full article

(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)

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16 pages, 1750 KiB

Open AccessArticle

An Intelligent Educational System: Analyzing Student Behavior and Academic Performance Using Multi-Source Data

by Haifang Li and Zhandong Liu

Electronics 2025, 14(16), 3328; https://doi.org/10.3390/electronics14163328 - 21 Aug 2025

Abstract

Student behavior analysis plays a critical role in enhancing educational quality and enabling personalized learning. While previous studies have utilized machine learning models to analyze campus card consumption data, few have integrated multi-source behavioral data with large language models (LLMs) to provide deeper [...] Read more.

Student behavior analysis plays a critical role in enhancing educational quality and enabling personalized learning. While previous studies have utilized machine learning models to analyze campus card consumption data, few have integrated multi-source behavioral data with large language models (LLMs) to provide deeper insights. This study proposes an intelligent educational system that examines the relationship between student consumption behavior and academic performance. The system is built upon a dataset collected from students of three majors at Xinjiang Normal University, containing exam scores and campus card transaction records. We designed an artificial intelligence (AI) agent that incorporates LLMs, SageGNN-based graph embeddings, and time-series regularity analysis to generate individualized behavior reports. Experimental evaluations demonstrate that the system effectively captures both temporal consumption patterns and academic fluctuations, offering interpretable and accurate outputs. Compared to baseline LLMs, our model achieves lower perplexity while maintaining high report consistency. The system supports early identification of potential learning risks and enables data-driven decision-making for educational interventions. Furthermore, the constructed multi-source dataset serves as a valuable resource for advancing research in educational data mining, behavioral analytics, and intelligent tutoring systems. Full article

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30 pages, 3477 KiB

Open AccessArticle

Dynamic Task Scheduling Based on Greedy and Deep Reinforcement Learning Algorithms for Cloud–Edge Collaboration in Smart Buildings

by Ping Yang and Jiangmin He

Electronics 2025, 14(16), 3327; https://doi.org/10.3390/electronics14163327 - 21 Aug 2025

Abstract

Driven by technologies such as the Internet of Things and artificial intelligence, smart buildings have developed rapidly, and the demand for processing massive amounts of data has risen sharply. Traditional cloud computing is confronted with challenges such as high network latency and large [...] Read more.

Driven by technologies such as the Internet of Things and artificial intelligence, smart buildings have developed rapidly, and the demand for processing massive amounts of data has risen sharply. Traditional cloud computing is confronted with challenges such as high network latency and large bandwidth pressure. Although edge computing can effectively reduce latency, it has problems such as resource limitations and difficulties with cluster collaboration. Therefore, cloud–edge collaboration has become an inevitable choice to meet the real-time and reliability requirements of smart buildings. In view of the problems with the existing task scheduling methods in the smart building scenario, such as ignoring container compatibility constraints, the difficulty in balancing global optimization and real-time performance, and the difficulty in adapting to the dynamic environments, this paper proposes a two-stage cloud-edge collaborative dynamic task scheduling mechanism. Firstly, a task scheduling system model supporting container compatibility was constructed, aiming to minimize system latency and energy consumption while ensuring the real-time requirements of tasks were met. Secondly, for this task-scheduling problem, a hierarchical and progressive solution was designed: In the first stage, a Resource-Aware Cost-Driven Greedy algorithm (RACDG) was proposed to enable edge nodes to quickly generate the initial task offloading decision. In the second stage, for the tasks that need to be offloaded in the initial decision-making, a Proximal Policy Optimization algorithm based on Action Masks (AMPPO) is proposed to achieve global dynamic scheduling. Finally, in the simulation experiments, the comparison with other classical algorithms shows that the algorithm proposed in this paper can reduce the system delay by 26–63.7%, reduce energy consumption by 21.7–66.9%, and still maintain a task completion rate of more than 91.3% under high-load conditions. It has good scheduling robustness and application potential. It provides an effective solution for the cloud–edge collaborative task scheduling of smart buildings. Full article

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17 pages, 1594 KiB

Open AccessArticle

TransMODAL: A Dual-Stream Transformer with Adaptive Co-Attention for Efficient Human Action Recognition

by Majid Joudaki, Mehdi Imani and Hamid R. Arabnia

Electronics 2025, 14(16), 3326; https://doi.org/10.3390/electronics14163326 - 21 Aug 2025

Abstract

Human Action Recognition has seen significant advances through transformer-based architectures, yet achieving a nuanced understanding often requires fusing multiple data modalities. Standard models relying solely on RGB video can struggle with actions defined by subtle motion cues rather than appearance. This paper introduces [...] Read more.

Human Action Recognition has seen significant advances through transformer-based architectures, yet achieving a nuanced understanding often requires fusing multiple data modalities. Standard models relying solely on RGB video can struggle with actions defined by subtle motion cues rather than appearance. This paper introduces TransMODAL, a novel dual-stream transformer that synergistically fuses spatiotemporal appearance features from a pre-trained VideoMAE(Video Masked AutoEncoders) backbone with explicit skeletal kinematics from a state-of-the-art pose estimation pipeline (RT-DETR(Real-Time DEtection Transformer) + ViTPose++). We propose two key architectural innovations to enable effective and efficient fusion: a CoAttentionFusion module that facilitates deep, iterative cross-modal feature exchange between the RGB and pose streams, and an efficient AdaptiveSelector mechanism that dynamically prunes less informative spatiotemporal tokens to reduce computational overhead. Evaluated on three challenging benchmarks, TransMODAL demonstrates robust generalization, achieving accuracies of 98.5% on KTH, 96.9% on UCF101, and 84.2% on HMDB51. These results significantly outperform a strong VideoMAE-only baseline and are competitive with state-of-the-art methods, demonstrating the profound impact of explicit pose guidance. TransMODAL presents a powerful and efficient paradigm for composing pre-trained foundation models to tackle complex video understanding tasks by providing a fully reproducible implementation and strong benchmark results. Full article

(This article belongs to the Special Issue Real-Time Audio, Video and Image Processing: Latest Advances and Prospects)

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20 pages, 1818 KiB

Open AccessArticle

Image Captioning Model Based on Multi-Step Cross-Attention Cross-Modal Alignment and External Commonsense Knowledge Augmentation

by Liang Wang, Meiqing Jiao, Zhihai Li, Mengxue Zhang, Haiyan Wei, Yuru Ma, Honghui An, Jiaqi Lin and Jun Wang

Electronics 2025, 14(16), 3325; https://doi.org/10.3390/electronics14163325 - 21 Aug 2025

Abstract

To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and [...] Read more.

To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and external commonsense knowledge enhancement. The model employs a backbone architecture comprising CLIP’s ViT visual encoder, Faster R-CNN, BERT text encoder, and GPT-2 text decoder. It incorporates two core mechanisms: a multi-step cross-attention mechanism that iteratively aligns image and text features across multiple rounds, progressively enhancing inter-modal semantic consistency for more accurate cross-modal representation fusion. Moreover, the model employs Faster R-CNN to extract region-based object features. These features are mapped to corresponding entities within the dataset through entity probability calculation and entity linking. External commonsense knowledge associated with these entities is then retrieved from the ConceptNet knowledge graph, followed by knowledge embedding via TransE and multi-hop reasoning. Finally, the fused multimodal features are fed into the GPT-2 decoder to steer caption generation, enhancing the lexical richness, factual accuracy, and cognitive plausibility of the generated descriptions. In the experiments, the model achieves CIDEr scores of 142.6 on MSCOCO and 78.4 on Flickr30k. Ablations confirm both modules enhance caption quality. Full article

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19 pages, 9113 KiB

Open AccessArticle

DRA-Net: Dynamic Feature Fusion Upsampling and Text-Region Focus for Ancient Chinese Scene Text Detection

by Qiuyi Xin, Chu Zhang, Yihang Wang, Chuanhao Fan, Hao Yang, Qing Lang and Hengnian Qi

Electronics 2025, 14(16), 3324; https://doi.org/10.3390/electronics14163324 - 21 Aug 2025

Abstract

Ancient Chinese scene text detection, as an emerging interdisciplinary topic between computer vision and cultural heritage preservation, presents unique technical challenges. Compared with modern scene text, ancient Chinese text is characterized by complex backgrounds, diverse fonts, extreme aspect ratios, and a scarcity of [...] Read more.

Ancient Chinese scene text detection, as an emerging interdisciplinary topic between computer vision and cultural heritage preservation, presents unique technical challenges. Compared with modern scene text, ancient Chinese text is characterized by complex backgrounds, diverse fonts, extreme aspect ratios, and a scarcity of annotated data. Existing detection methods often perform poorly under these conditions. To address these challenges, this paper proposes a novel detection network based on dynamic feature fusion upsampling and text-region focus, named DRA-Net. The core innovations of the proposed method include (1) a dynamic fusion upsampling module, which adaptively assigns weights to effectively fuse multi-scale features while preserving critical information during feature propagation; (2) an adaptive text-region focus module that incorporates axial attention mechanisms to enhance the model’s ability to locate text regions and suppress background interference; and (3) the integration of deformable convolution, which improves the network’s capacity to model irregular text shapes and extreme aspect ratios. To tackle the issue of data scarcity, we construct a dataset named ACST, specifically for ancient Chinese text detection. This dataset includes a wide range of scene types, such as stone inscriptions, calligraphy works, couplets, and other historical media, covering various font styles from different historical periods, thus offering strong data support for related research. Experimental results demonstrate that DRA-Net achieves significantly higher detection accuracy on the ACST dataset compared to existing methods and performs robustly in scenarios with complex backgrounds and extreme text aspect ratios. It achieves an F1-score of 72.9%, a precision of 82.8%, and a recall of 77.5%. This study provides an effective technical solution for the digitization of ancient documents and the intelligent preservation of cultural heritage, with strong theoretical significance and practical potential. Full article

(This article belongs to the Special Issue Deep Learning-Based Object Detection/Classification)

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21 pages, 434 KiB

Open AccessFeature PaperArticle

Translution: A Hybrid Transformer–Convolutional Architecture with Adaptive Gating for Occupancy Detection in Smart Buildings

by Pratiksha Chaudhari, Yang Xiao and Tieshan Li

Electronics 2025, 14(16), 3323; https://doi.org/10.3390/electronics14163323 - 21 Aug 2025

Abstract

Occupancy detection is vital for improving energy efficiency, automation, and security in smart buildings. Reliable detection systems enable dynamic control of lighting, heating, ventilation, air conditioning, and security operations, leading to substantial cost savings and enhanced occupant comfort. However, accurately detecting occupancy using [...] Read more.

Occupancy detection is vital for improving energy efficiency, automation, and security in smart buildings. Reliable detection systems enable dynamic control of lighting, heating, ventilation, air conditioning, and security operations, leading to substantial cost savings and enhanced occupant comfort. However, accurately detecting occupancy using environmental sensor data remains challenging. Existing machine learning and deep learning models, such as Random Forests, convolutional neural networks, and recurrent neural networks, often struggle to capture both fine-grained local patterns and long-range temporal dependencies, limiting their generalization to complex, real-world occupancy patterns. To address these challenges, we propose Translution, a novel hybrid Transformer-based architecture specifically designed for occupancy detection from multivariate sensor time-series data. Translution combines multi-scale convolutional encoding to extract local temporal features, self-attention mechanisms to model long-range dependencies, and an adaptive gating mechanism that dynamically selects relevant features to improve robustness and generalization. We trained Translution on 8143 samples and evaluated it on two distinct subsets of the University of California, Irvine (UCI) Occupancy Detection Dataset: one with shorter, more consistent time spans (2804 samples) and another covering longer, more varied occupancy cycles with abrupt changes and different lighting/ventilation conditions (9752 samples). Evaluating these diverse subsets, which represent both typical and challenging real-world scenarios, explicitly strengthens Translution’s generalizability claim, demonstrating its ability to detect occupancy across varied temporal patterns and environmental conditions accurately. Our results demonstrate that Translution achieves 98.5% accuracy, 97.3% F1-score, and 98.55% area under the receiver operating characteristic curve, significantly outperforming traditional machine learning and deep learning baselines. These findings highlight Translution’s potential as a highly accurate and stable solution for real-time occupancy detection in diverse smart building environments. Full article

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

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21 pages, 3408 KiB

Open AccessArticle

Hot-Spot Temperature Reduction in Oil-Immersed Transformers via Kriging-Based Structural Optimization of Winding Channels

by Mingming Xu, Bowen Shang, Hengbo Xu, Yunbo Li, Shuai Wang, Jiangjun Ruan, Tao Liu, Deming Huang and Zhuanhong Li

Electronics 2025, 14(16), 3322; https://doi.org/10.3390/electronics14163322 - 21 Aug 2025

Abstract

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical [...] Read more.

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method. Full article

(This article belongs to the Section Computer Science & Engineering)

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14 pages, 730 KiB

Open AccessArticle

A Configurable Parallel Architecture for Singular Value Decomposition of Correlation Matrices

by Luis E. López-López, David Luviano-Cruz, Juan Cota-Ruiz, Jose Díaz-Roman, Ernesto Sifuentes, Jesús M. Silva-Aceves and Francisco J. Enríquez-Aguilera

Electronics 2025, 14(16), 3321; https://doi.org/10.3390/electronics14163321 - 21 Aug 2025

Abstract

Singular value decomposition (SVD) plays a critical role in signal processing, image analysis, and particularly in MIMO channel estimation, where it enables spatial multiplexing and interference mitigation. This study presents a configurable parallel architecture for computing SVD on 4 × 4 and 8 [...] Read more.

Singular value decomposition (SVD) plays a critical role in signal processing, image analysis, and particularly in MIMO channel estimation, where it enables spatial multiplexing and interference mitigation. This study presents a configurable parallel architecture for computing SVD on 4 × 4 and 8 × 8 correlation matrices using the Jacobi algorithm with Givens rotations, optimized via CORDIC. Exploiting algorithmic parallelism, the design achieves low-latency performance on a Virtex-5 FPGA, with processing times of 5.29 µs and 24.25 µs, respectively, while maintaining high precision and efficient resource usage. These results confirm the architecture’s suitability for real-time wireless systems with strict latency demands, such as those defined by the IEEE 802.11n standard. Full article

(This article belongs to the Special Issue Hardware/Algorithm Co-Design for Communication and Networking Acceleration)

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23 pages, 1562 KiB

Open AccessArticle

SCNOC-Agentic: A Network Operation and Control Agentic for Satellite Communication Systems

by Wenyu Sun, Chenhua Sun, Yasheng Zhang, Zhan Yin and Zhifeng Kang

Electronics 2025, 14(16), 3320; https://doi.org/10.3390/electronics14163320 - 20 Aug 2025

Abstract

Large language models (LLMs) have demonstrated powerful capability to solve practical problems through complex step-by-step reasoning. Specifically designed LLMs have begun to be integrated into terrestrial communication networks. However, relevant research in the field of satellite communications remains exceedingly rare. To address this [...] Read more.

Large language models (LLMs) have demonstrated powerful capability to solve practical problems through complex step-by-step reasoning. Specifically designed LLMs have begun to be integrated into terrestrial communication networks. However, relevant research in the field of satellite communications remains exceedingly rare. To address this gap, we introduce SCNOC-Agentic, a novel architecture especially designed to integrate the management and control of satellite communication systems in LLMs. SCNOC-Agentic incorporates four components tailored to the characteristics of satellite communications: intent refinement, multi-agent workflow, personalized long-term memory, and graph-based retrieval. Furthermore, we define four typical real-world scenarios that can be effectively addressed by integrating with LLMs: network task planning, carrier and cell optimization, fault analysis of satellites, and satellite management and control. Utilizing the SCNOC-Agentic framework, a series of open-source LLMs have achieved outstanding performance on the four tasks under various baselines, including zero-shot CoT, CoT-5, and self-consistency. For example, qwen2.5-70B with SCNOC-Agentic significantly improves the parameter generation accuracy in the network task planning task from 15.6% to 32.2%, while llama-3.3-70B increases from 16.2% to 29.0%. In addition, ablation studies were conducted to validate the importance of each proposed component within the SCNOC-Agentic framework. Full article

(This article belongs to the Special Issue Satellite Terrestrial Networks: Technologies, Security and Applications)

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