Electronics

32 pages, 7924 KiB

Open AccessArticle

A Multi-Feature Stock Index Forecasting Approach Based on LASSO Feature Selection and Non-Stationary Autoformer

by Zibin Sheng, Qingyang Liu, Yanrong Hu and Hongjiu Liu

Electronics 2025, 14(10), 2059; https://doi.org/10.3390/electronics14102059 - 19 May 2025

The Chinese stock market, one of the largest and most dynamic emerging economies, is characterized by individual investor dominance and strong policy influence, resulting in high volatility and complex dynamics. These distinctive features pose substantial challenges for accurate forecasting. Existing models like RNNs, [...] Read more.

The Chinese stock market, one of the largest and most dynamic emerging economies, is characterized by individual investor dominance and strong policy influence, resulting in high volatility and complex dynamics. These distinctive features pose substantial challenges for accurate forecasting. Existing models like RNNs, LSTMs, and Transformers often struggle with non-stationary data and long-term dependencies, limiting their forecasting effectiveness. This study proposes a hybrid forecasting framework integrating the Non-stationary Autoformer (NSAutoformer), LASSO feature selection, and financial sentiment analysis. LASSO selects key features from diverse structured variables, mitigating multicollinearity and enhancing interpretability. Sentiment indices are extracted from investor comments and news articles using an expanded Chinese financial sentiment dictionary, capturing psychological drivers of market behavior. Experimental evaluations on the Shanghai Stock Exchange Composite Index show that LASSO-NSAutoformer outperforms the NSAutoformer, reducing MAE by 8.75%. Additional multi-step forecasting and time-window analyses confirm the method’s effectiveness and stability. By integrating multi-source data, feature selection, and sentiment analysis, this framework offers a reliable forecasting approach for investors and researchers in complex financial environments. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

27 pages, 5866 KiB

Open AccessArticle

Modeling and Analysis in the Industrial Internet with Dual Delay and Nonlinear Infection Rate

by Jun Wang, Jun Tang, Changxin Li, Zhiqiang Ma, Jie Yang and Qiang Fu

Electronics 2025, 14(10), 2058; https://doi.org/10.3390/electronics14102058 - 19 May 2025

Abstract

This study proposes a novel virus propagation model designed explicitly for SCADA(supervisory Control and Data Acquisition) industrial networks. It addresses a critical limitation in existing models applied to the Internet and Industrial Internet of Things (IIoT)—their failure to account for inter-node information exchange [...] Read more.

This study proposes a novel virus propagation model designed explicitly for SCADA(supervisory Control and Data Acquisition) industrial networks. It addresses a critical limitation in existing models applied to the Internet and Industrial Internet of Things (IIoT)—their failure to account for inter-node information exchange processes. The model is inspired by the phenomenon that “immune” nodes in real-world and biological systems inhibit the spread of viruses by exchanging information. This model incorporates isolation strategies to curb virus transmission, considering the uncertainty of vulnerable device behavior. Central to this research are the assumptions of a nonlinear infection rate and dual delays, which better mirror the real-world conditions of industrial control networks. This approach diverges significantly from prior studies that relied on bilinear infection rate assumptions. This study constructed an SMIQR model through theoretical derivation and experimental validation. The model enables nodes to autonomously enhance their defenses after receiving risk information while accounting for the impact of inter-node information exchange. Experiments based on real-world data demonstrated the model’s effectiveness in simulating virus propagation and evaluating defense strategies, overcoming the limitations of traditional bilinear infection rate assumptions. Comparative experiments show that the SMIQR model significantly reduces the number of infected nodes in SCADA industrial networks, demonstrating its superior effectiveness in curbing virus spread. Furthermore, the research proposed dynamic isolation tactics that balance industrial operational continuity, providing SCADA industrial networks with a theoretical framework (incorporating nonlinear infection rates and dual delay characteristics) and practical defense solutions to curb malware spread without disrupting production. Full article

(This article belongs to the Special Issue Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends)

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32 pages, 1103 KiB

Open AccessArticle

Utilisation of Artificial Intelligence and Cybersecurity Capabilities: A Symbiotic Relationship for Enhanced Security and Applicability

by Ed Kamya Kiyemba Edris

Electronics 2025, 14(10), 2057; https://doi.org/10.3390/electronics14102057 - 19 May 2025

Abstract

The increasing interconnectivity between physical and cyber-systems has led to more vulnerabilities and cyberattacks. Traditional preventive and detective measures are no longer adequate to defend against adversaries. Artificial Intelligence (AI) is used to solve complex problems, including those of cybersecurity. Adversaries also utilise [...] Read more.

The increasing interconnectivity between physical and cyber-systems has led to more vulnerabilities and cyberattacks. Traditional preventive and detective measures are no longer adequate to defend against adversaries. Artificial Intelligence (AI) is used to solve complex problems, including those of cybersecurity. Adversaries also utilise AI for sophisticated and stealth attacks. This study aims to address this problem by exploring the symbiotic relationship of AI and cybersecurity to develop a new, adaptive strategic approach to defend against cyberattacks and improve global security. This paper explores different disciplines to solve security problems in real-world contexts, such as the challenges of scalability and speed in threat detection. It develops an algorithm and a detective predictive model for a Malicious Alert Detection System (MADS) that is an integration of adaptive learning and a neighbourhood-based voting alert detection framework. It evaluates the model’s performance and efficiency among different machines. The paper discusses Machine Learning (ML) and Deep Learning (DL) techniques, their applicability in cybersecurity, and the limitations of using AI. Additionally, it discusses issues, risks, vulnerabilities, and attacks against AI systems. It concludes by providing recommendations on security for AI and AI for security, paving the way for future research on enhancing AI-based systems and mitigating their risks. Full article

22 pages, 11774 KiB

Open AccessArticle

Classification of Whole-Slide Pathology Images Based on State Space Models and Graph Neural Networks

by Feng Ding, Chengfei Cai, Jun Li, Mingxin Liu, Yiping Jiao, Zhengcan Wu and Jun Xu

Electronics 2025, 14(10), 2056; https://doi.org/10.3390/electronics14102056 - 19 May 2025

Abstract

Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibility in feature integration and underutilize sequential information. To address [...] Read more.

Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibility in feature integration and underutilize sequential information. To address these limitations, this work proposes a novel MIL framework: Dynamic Graph and State Space Model-Based MIL (DG-SSM-MIL). DG-SSM-MIL combines graph neural networks and selective state space models, leveraging the former’s ability to extract local and spatial features and the latter’s advantage in comprehensively understanding long-sequence instances. This enhances the model’s performance in diverse instance classification, improves its capability to handle long-sequence data, and increases the precision and scalability of feature fusion. We propose the Dynamic Graph and State Space Model (DynGraph-SSM) module, which aggregates local and spatial information of image patches through directed graphs and learns global feature representations using the Mamba model. Additionally, the directed graph structure alleviates the unidirectional scanning limitation of Mamba and enhances its ability to process pathological images with dispersed lesion distributions. DG-SSM-MIL demonstrates superior performance in classification tasks compared to other models. We validate the effectiveness of the proposed method on features extracted from two pretrained models across four public medical image datasets: BRACS, TCGA-NSCLC, TCGA-RCC, and CAMELYON16. Experimental results demonstrate that DG-SSM-MIL consistently outperforms existing MIL methods across four public datasets. For example, when using ResNet-50 features, our model achieves the highest AUCs of 0.936, 0.785, 0.879, and 0.957 on TCGA-NSCLC, BRACS, CAMELYON16, and TCGA-RCC, respectively. Similarly, with UNI features, DG-SSM-MIL reaches AUCs of 0.968, 0.846, 0.993, and 0.990, surpassing all baselines. These results confirm the effectiveness and generalizability of our approach in diverse WSI classification tasks. Full article

(This article belongs to the Special Issue AI-Driven Medical Image/Video Processing)

24 pages, 633 KiB

Open AccessArticle

Physical Layer Authentication Exploiting Antenna Mutual Coupling Effects in mmWave Systems

by Mu Niu, Ayinuer Nuertai, Runqing Wang and Pinchang Zhang

Electronics 2025, 14(10), 2055; https://doi.org/10.3390/electronics14102055 - 19 May 2025

Abstract

Impersonation attacks pose a significant threat to millimeter wave (mmWave) wireless systems due to the unique characteristics (e.g., highly directional beams) of mmWave communications. To this end, this paper proposes a novel physical layer authentication (PLA) scheme that leverages the antenna array-specific mutual [...] Read more.

Impersonation attacks pose a significant threat to millimeter wave (mmWave) wireless systems due to the unique characteristics (e.g., highly directional beams) of mmWave communications. To this end, this paper proposes a novel physical layer authentication (PLA) scheme that leverages the antenna array-specific mutual coupling (MC) feature to validate the identity of the transmitter. In particular, we first demonstrate the authentication feasibility of the MC feature by modeling its amplitude and phase characteristics using generalized Gaussian and Laplace distributions, respectively. Then, based on the amplitude and phase of the MC feature, we design a kernel-based authentication scheme to further improve device distinguishability in mmWave systems. Moreover, we provide analytical expressions of the false alarm and detection probabilities, enabling a theoretical characterization of the proposed authentication scheme performance. Finally, numerical results are provided to verify the reliability and effectiveness of the proposed authentication scheme under various settings. The results indicate that the proposed scheme can provide performance gain compared to the single-dimensional feature-based schemes under different signal–noise ratio scenarios. Full article

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17 pages, 1419 KiB

Open AccessArticle

Point Cloud Semantic Segmentation with Transformer and Multi-Scale Feature Extraction

by Wenqing Zhao, Lyuchao Liao, Zhimin Wang, Sijing Cai and Yu Liang

Electronics 2025, 14(10), 2054; https://doi.org/10.3390/electronics14102054 - 19 May 2025

Abstract

To address the limitations of existing point cloud semantic segmentation methods in modeling long-range dependencies and adapting to multi-scale targets, we propose an improved Cylinder3D framework based on multi-scale feature extraction. Current voxel-based methods primarily rely on local 3D convolutions, limiting their effectiveness [...] Read more.

To address the limitations of existing point cloud semantic segmentation methods in modeling long-range dependencies and adapting to multi-scale targets, we propose an improved Cylinder3D framework based on multi-scale feature extraction. Current voxel-based methods primarily rely on local 3D convolutions, limiting their effectiveness in complex scenes, while fixed-receptive-field convolutions also limit model adaptability to multi-scale targets. To address these challenges, we propose two major improvements: first, we introduce a Transformer module for global modeling in high-level feature extraction, leveraging the self-attention mechanism to enable global information exchange between different voxels, overcoming the limitations of traditional convolution’s local receptive fields; second, we design a multi-scale feature extraction architecture with tri-directional dilated convolutions, namely the improved DDCM module, which combines standard convolution and dilated convolution, effectively expanding the receptive field while maintaining parameter efficiency and enhancing the processing capability for point clouds with heterogeneous density distributions. Evaluation on the SemanticKITTI dataset demonstrates that our improved model achieves an mIoU of 66.8%, a 2.4% increase over the original Cylinder3D. Our research indicates that the proposed enhancements significantly improve the accuracy of 3D point cloud semantic segmentation, though the computational complexity of the model requires further optimization, which will be addressed in future work. Full article

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23 pages, 4217 KiB

Open AccessArticle

Integrated Framework for Managing Childhood Obesity Based on Biobanks, AI Tools and Methods, and Serious Games

by Ioannis Vondikakis, Elena Politi, Dimitrios Goulis, George Dimitrakopoulos, Michael Georgoulis, George Saltaouras, Meropi Kontogianni, Theodora Brisimi, Marios Logothetis, Harry Kakoulidis, Marios Prasinos, Athanasios Anastasiou, Ioannis Kakkos, Eleftheria Vellidou, George Matsopoulos and Dimitris Koutsouris

Electronics 2025, 14(10), 2053; https://doi.org/10.3390/electronics14102053 - 19 May 2025

Abstract

The growing epidemic of childhood obesity is a major threat to their overall development and poses a number of challenges for health systems. We propose an integrated framework to comprehensively address childhood obesity. The proposed architecture addresses essential data management and pre-processing functionalities [...] Read more.

The growing epidemic of childhood obesity is a major threat to their overall development and poses a number of challenges for health systems. We propose an integrated framework to comprehensively address childhood obesity. The proposed architecture addresses essential data management and pre-processing functionalities to support scalable, secure, and privacy-preserving data processing in distributed environments. We are also incorporating a health data-driven AI approach for predictive analytics and decision support. There is additionally a User Engagement Layer, which serves as the main point of interaction for users. It connects individuals to system capabilities, facilitating data collection, progress monitoring, and insights. Finally, we present four serious games designed to address protective factors (such as physical activity and healthy eating) and mitigate risk factors (such as excessive screen time and unhealthy food choices). The identified educational objectives were translated into game elements including goal setting, social support, and positive reinforcement. In order to facilitate our approach, we have described the essential data flows and user interactions within our Biobank architecture. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)

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23 pages, 9051 KiB

Open AccessArticle

Predicting User Attention States from Multimodal Eye–Hand Data in VR Selection Tasks

by Xiaoxi Du, Jinchun Wu, Xinyi Tang, Xiaolei Lv, Lesong Jia and Chengqi Xue

Electronics 2025, 14(10), 2052; https://doi.org/10.3390/electronics14102052 - 19 May 2025

Abstract

Virtual reality (VR) devices that integrate eye-tracking and hand-tracking technologies can capture users’ natural eye–hand data in real time within a three-dimensional virtual space, providing new opportunities to explore users’ attentional states during natural 3D interactions. This study aims to develop an attention-state [...] Read more.

Virtual reality (VR) devices that integrate eye-tracking and hand-tracking technologies can capture users’ natural eye–hand data in real time within a three-dimensional virtual space, providing new opportunities to explore users’ attentional states during natural 3D interactions. This study aims to develop an attention-state prediction model based on the multimodal fusion of eye and hand features, which distinguishes whether users primarily employ goal-directed attention or stimulus-driven attention during the execution of their intentions. In our experiment, we collected three types of data—eye movements, hand movements, and pupil changes—and instructed participants to complete a virtual button selection task. This setup allowed us to establish a binary ground truth label for attentional state during the execution of selection intentions for model training. To investigate the impact of different time windows on prediction performance, we designed eight time windows ranging from 0 to 4.0 s (in increments of 0.5 s) and compared the performance of eleven algorithms, including logistic regression, support vector machine, naïve Bayes, k-nearest neighbors, decision tree, linear discriminant analysis, random forest, AdaBoost, gradient boosting, XGBoost, and neural networks. The results indicate that, within the 3 s window, the gradient boosting model performed best, achieving a weighted F1-score of 0.8835 and an Accuracy of 0.8860. Furthermore, the analysis of feature importance demonstrated that the multimodal eye–hand features play a critical role in the prediction. Overall, this study introduces an innovative approach that integrates three types of multimodal eye–hand behavioral and physiological data within a virtual reality interaction context. This framework provides both theoretical and methodological support for predicting users’ attentional states within short time windows and contributes practical guidance for the design of attention-adaptive 3D interfaces. In addition, the proposed multimodal eye–hand data fusion framework also demonstrates potential applicability in other three-dimensional interaction domains, such as game experience optimization, rehabilitation training, and driver attention monitoring. Full article

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19 pages, 13137 KiB

Open AccessArticle

Initial/Last-State-Correlated SAR Control with Optimized Trajectory to Reduce Reverse Overshoot and Smooth Current Switching of Hybrid LDOs

by Yinyu Wang, Jinkun Ke, Run Min, Hangyu Xu, Zhaoliang Guan, Shuo Zhang, Chang Liu and Jingbo Feng

Electronics 2025, 14(10), 2051; https://doi.org/10.3390/electronics14102051 - 18 May 2025

Abstract

Conventional successive approximation recursive (SAR) control hybrid low-dropout regulators (LDOs) have problems such as significant reverse overshoot and discontinuous current switching. Based on trajectory optimization in the phase plane, this paper presented initial/last-state-correlated SAR control to address the aforementioned issues. Firstly, the operating [...] Read more.

Conventional successive approximation recursive (SAR) control hybrid low-dropout regulators (LDOs) have problems such as significant reverse overshoot and discontinuous current switching. Based on trajectory optimization in the phase plane, this paper presented initial/last-state-correlated SAR control to address the aforementioned issues. Firstly, the operating principles of the conventional SAR controller are elucidated, which is depicted by a finite state machine (FSM). Secondly, large reverse overshoot and discontinuous current switching of the SAR control hybrid LDO are analyzed through the phase plane trajectory. Then, the initial/last-state SAR control is proposed by FSM to optimize the trajectory, thereby reducing the reverse overshoot and smoothing current switching. Finally, a series of design challenges of the initial/last-state-correlated SAR control are discussed. Implemented at 1.11 mm × 0.567 mm in a 180 nm bipolar-CMOS-DMOS (BCD) process, under a 0.1 nF output capacitor, the 6-bit hybrid LDO in this paper achieved an output voltage overshoot of 76 mV and a transient time of 18.8 μs at a 152 mA load current change. The experimental result demonstrates the distinct dynamic response advantages of the initial/last-state SAR control. Full article

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22 pages, 2307 KiB

Open AccessArticle

Channel Interaction Mamba-Guided Generative Adversarial Network for Depth-Image-Based Rendering 3D Image Watermarking

by Qingmo Chen, Zhongxing Sun, Rui Bai and Chongchong Jin

Electronics 2025, 14(10), 2050; https://doi.org/10.3390/electronics14102050 - 18 May 2025

Abstract

In the field of 3D technology, depth-image-based rendering (DIBR) has been widely adopted due to its inherent advantages including low data volume and strong compatibility. However, during network transmission of DIBR 3D images, both center and virtual views are susceptible to unauthorized copying [...] Read more.

In the field of 3D technology, depth-image-based rendering (DIBR) has been widely adopted due to its inherent advantages including low data volume and strong compatibility. However, during network transmission of DIBR 3D images, both center and virtual views are susceptible to unauthorized copying and distribution. To protect the copyright of these images, this paper proposes a channel interaction mamba-guided generative adversarial network (CIMGAN) for DIBR 3D image watermarking. To capture cross-modal feature dependencies, a channel interaction mamba (CIM) is designed. This module enables lightweight cross-modal channel interaction through a channel exchange mechanism and leverages mamba for global modeling of RGB and depth information. In addition, a feature fusion module (FFM) is devised to extract complementary information from cross-modal features and eliminate redundant information, ultimately generating high-quality 3D image features. These features are used to generate an attention map, enhancing watermark invisibility and identifying robust embedding regions. Compared to the current state-of-the-art (SOTA) 3D image watermarking methods, the proposed watermark model shows superior performance in terms of robustness and invisibility while maintaining computational efficiency. Full article

(This article belongs to the Collection Deep Learning for Computer Vision: Algorithms, Theory and Application)

19 pages, 2626 KiB

Open AccessArticle

GTSDC: A Graph Theory Subspace-Based Analytical Algorithm for User Behavior

by Jianping Li, Yubo Tan, Jing Wang, Junwei Yu and Qiuyuan Hu

Electronics 2025, 14(10), 2049; https://doi.org/10.3390/electronics14102049 - 18 May 2025

Abstract

The exponential growth of multi-modal behavioral data in campus networks poses significant challenges for clustering analysis, including high dimensionality, redundancy, and attribute heterogeneity, which lead to degraded accuracy in existing methods. To address these issues, this study proposes a graph-theoretic subspace deep clustering [...] Read more.

The exponential growth of multi-modal behavioral data in campus networks poses significant challenges for clustering analysis, including high dimensionality, redundancy, and attribute heterogeneity, which lead to degraded accuracy in existing methods. To address these issues, this study proposes a graph-theoretic subspace deep clustering framework that synergizes a deep sparse auto-encoder (DSAE) with a method of graph partitioning based on normalized cut. First, a four-layer DSAE is designed to extract discriminative features while enforcing sparsity constraints, effectively reducing data dimensionality and mitigating noise. Second, the refined subspace representations are transformed into a similarity graph, where normalized cut optimization partitions users into coherent behavioral clusters by balancing intra-cluster cohesion and inter-cluster separation. Experimental validation on three datasets—USER_DATA, MNIST, and COIL20—demonstrates the superiority of GTSDC. It achieves 91% accuracy on USER_DATA, outperforming traditional algorithms (e.g., CLIQUE, K-means) by 120% and advanced methods (e.g., deep subspace clustering) by 15%. The proposed framework not only enhances network resource allocation through behavior-aware analytics but also lays the groundwork for personalized educational services. This work bridges the gap between graph theory and deep learning, offering a scalable solution for high-dimensional behavioral pattern recognition. In simple terms, this new algorithm can more accurately analyze user behavior in campus networks. It helps universities better allocate network resources, such as ensuring smooth online classes, and can also provide personalized educational services to students according to their behavior patterns. Full article

(This article belongs to the Special Issue Application of Big Data Mining and Analysis)

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17 pages, 874 KiB

Open AccessArticle

Research on Stochastic Dual Model Predictive Control and Application to Solar Thermal Collector Field

by Xiaoyan Zhang, Diandian Wang, Chaobo Chen, Xiaohua Song and Suping Zhao

Electronics 2025, 14(10), 2048; https://doi.org/10.3390/electronics14102048 - 18 May 2025

Abstract

Uncertainty is inevitable in real-world systems. If uncertainty is not effectively addressed, it may degrade the performance of model predictive control (MPC). This paper proposes a stochastic dual model predictive control (SDMPC) method for linear systems with parameter uncertainty and measurement noise. The [...] Read more.

Uncertainty is inevitable in real-world systems. If uncertainty is not effectively addressed, it may degrade the performance of model predictive control (MPC). This paper proposes a stochastic dual model predictive control (SDMPC) method for linear systems with parameter uncertainty and measurement noise. The method not only actively explores uncertainty while optimizing control but also introduces probabilistic output constraints to expand the set of feasible solutions. Specifically, Kalman filtering is employed to construct a real-time parameter estimator. The future output errors are incorporated into the nominal cost function as exploration signals to balance exploration and exploitation. Simulation results in the field of solar collectors show that SDMPC can effectively track the temperature by varying the inlet flow under changing environmental and opportunity constraints. The cumulative performance index of SDMPC is 125.3, compared to 316.4 obtained by conventional MPC, validating its effectiveness. Full article

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

22 pages, 1231 KiB

Open AccessArticle

Research on the Double Frequency Suppression Strategy of DC Bus Voltage on the Rectification Side of a Power Unit in a New Type of Same Phase Power Supply System

by Jinghua Zhou and Yuchen Li

Electronics 2025, 14(10), 2047; https://doi.org/10.3390/electronics14102047 - 17 May 2025

Abstract

This work provides a new solution for high-power quality traction power systems. The rapid development of electrified railways not only promotes economic development, but also seriously restricts the improvement of electric locomotive operation performance due to power quality problems, such as second harmonic [...] Read more.

This work provides a new solution for high-power quality traction power systems. The rapid development of electrified railways not only promotes economic development, but also seriously restricts the improvement of electric locomotive operation performance due to power quality problems, such as second harmonic distortion and negative sequence in the power supply system. In view of the shortcomings of the traditional in-phase power supply system in DC bus voltage stability control, a new in-phase power supply topology based on a back-to-back H-bridge power supply unit is proposed in this study. By establishing the iterative analysis model of the rectifier side double closed-loop control system, the internal correlation mechanism between the DC bus voltage second harmonic fluctuation and the grid side current harmonic is deeply revealed. On this basis, a rectifier-side disturbance compensation control strategy with a second harmonic suppression function is designed. Through real-time detection and compensation of second harmonic components, the active stability control of DC bus voltage is realized. The simulation model of the new cophase power supply system based on the experimental platform shows that the strategy can reduce the ripple coefficient of the DC bus voltage and the total harmonic distortion of the grid side current, which effectively verifies the superiority of the second harmonic suppression strategy in improving the power quality of the cophase power supply system. This work provides a new solution for a high-power quality traction power system. Full article

28 pages, 1162 KiB

Open AccessReview

Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM)

by Talha Ahmed, Asad Ali, Ying Huang and Pan Lu

Electronics 2025, 14(10), 2046; https://doi.org/10.3390/electronics14102046 - 17 May 2025

Abstract

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also [...] Read more.

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also improving safety and flexibility and lowering vehicle emissions. Considering these facts, the purpose of this study is to assess the possible effects and advantages of AVs under diverse traffic situations in urban and rural environments. Knowledge of traffic behavior inside a certain road network is made easier by traffic microsimulation. PTV VISSIM (Verkehr In Städten—SIMulationsmodell) is among the microsimulation software programs that has attracted great interest because of its remarkable capacity to faithfully simulate traffic conditions. This review helps researchers choose the best methodological strategy for their individual study objectives and restrictions while using VISSIM. This research assesses the effect of AVs in different driving behavior and weather conditions in urban and rural situations using VISSIM and introduces traffic safety using the surrogate safety assessment model (SSAM). The study focuses on 10 parameters from the Wiedemann 99 car-following model and speed distribution to establish the correlation between weather conditions and surrogate safety measures (SSMs). The findings could lead to more accurate and authentic models of driving behavior and encourage the automotive industry to further equip AVs to operate efficiently in various environmental and driving conditions. Full article

(This article belongs to the Special Issue Featured Review Papers in Electrical and Autonomous Vehicles)

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23 pages, 12437 KiB

Open AccessArticle

Vision-Based Structural Adhesive Detection for Electronic Components on PCBs

by Ruzhou Zhang, Tengfei Yan and Jian Zhang

Electronics 2025, 14(10), 2045; https://doi.org/10.3390/electronics14102045 - 17 May 2025

Abstract

Structural adhesives or fixing glues are typically applied to larger components on printed circuit boards (PCBs) to increase mechanical stability and minimize damage from vibration. Existing work tends to focus on component placement verification and solder joint analysis, etc. However, the detection of [...] Read more.

Structural adhesives or fixing glues are typically applied to larger components on printed circuit boards (PCBs) to increase mechanical stability and minimize damage from vibration. Existing work tends to focus on component placement verification and solder joint analysis, etc. However, the detection of structural adhesives remains largely unexplored. This paper proposes a vision-based method for detecting structural adhesive defects on PCBs. The method uses HSV color segmentation to extract PCB regions, followed by Hough-transform-based morphological analysis to identify board features. The perspective transformation then extracts and rectifies the adhesive regions, and constructs an adhesive region template by detecting the standard adhesive area ratio in its corresponding adhesive region. Finally, template matching is used to detect the structural adhesives. The experimental results show that this approach can accurately detect the adhesive state of PCBs and identify the qualified/unqualified locations, providing an effective vision-based detection scheme for PCB manufacturing. The main contributions of this paper are as follows: (1) A vision-based structural adhesive detection method is proposed, and its detailed algorithm is presented. (2) The developed system includes a user-friendly visualization interface, streamlining the inspection workflow. (3) Actual experiments are performed to evaluate this study, and the results validate its effectiveness. Full article

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

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31 pages, 3425 KiB

Open AccessArticle

RPF-MAD: A Robust Pre-Training–Fine-Tuning Algorithm for Meta-Adversarial Defense on the Traffic Sign Classification System of Autonomous Driving

by Xiaoxu Peng, Dong Zhou, Jianwen Zhang, Jiaqi Shi and Guanghui Sun

Electronics 2025, 14(10), 2044; https://doi.org/10.3390/electronics14102044 - 17 May 2025

Abstract

Traffic sign classification (TSC) based on deep neural networks (DNNs) plays a crucial role in the perception subsystem of autonomous driving systems (ADSs). However, studies reveal that the TSC system can make dangerous and potentially fatal errors under adversarial attacks. Existing defense strategies, [...] Read more.

Traffic sign classification (TSC) based on deep neural networks (DNNs) plays a crucial role in the perception subsystem of autonomous driving systems (ADSs). However, studies reveal that the TSC system can make dangerous and potentially fatal errors under adversarial attacks. Existing defense strategies, such as adversarial training (AT), have demonstrated effectiveness but struggle to generalize across diverse attack scenarios. Recent advancements in self-supervised learning (SSL), particularly adversarial contrastive learning (ACL) methods, have demonstrated strong potential in enhancing robustness and generalization compared to AT. However, conventional ACL methods lack mechanisms to ensure effective defense transferability across different learning stages. To address this, we propose a robust pre-training–fine-tuning algorithm for meta-adversarial defense (RPF-MAD), designed to enhance the sustainability of adversarial robustness throughout the learning pipeline. Dual-track meta-adversarial pre-training (Dual-MAP) integrates meta-learning with ACL methods, which improves the generalization ability of the upstream model to different adversarial conditions. Meanwhile, adaptive variance anchoring robust fine-tuning (AVA-RFT) utilizes adaptive prototype variance regularization to stabilize feature representations and reinforce the generalizable defense capabilities of the downstream model. Leveraging the meta-adversarial defense benchmark (MAD) dataset, RPF-MAD ensures comprehensive robustness against multiple attack types. Extensive experiments across eight ACL methods and three robust fine-tuning (RFT) techniques demonstrate that RPF-MAD significantly improves both standard accuracy (

S A

) by 1.53% and robust accuracy (

R A

) by 2.64%, effectively enhances the lifelong adversarial resilience of TSC models, achieves a 13.77% improvement in the equilibrium defense success rate (

E D S R

), and reduces the attack success rate (

A S R

) by 9.74%, outperforming state-of-the-art (SOTA) defense methods. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicles: Motion Planning, Trajectory Prediction and Control, 2nd Edition)

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17 pages, 1345 KiB

Open AccessArticle

Wearable Sensor-Based Analysis of Human Biomechanics in Manual and Cobot-Assisted Agricultural Transplanting

by Yuetong Wu, Xiangrui Wang and Boyi Hu

Electronics 2025, 14(10), 2043; https://doi.org/10.3390/electronics14102043 - 17 May 2025

Abstract

Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative [...] Read more.

Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)

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27 pages, 5930 KiB

Open AccessArticle

How Do Incentive Policy and Benefit Distribution Affect the Cooperative Development Mechanism of Intelligent Connected Vehicles? A Tripartite Evolutionary Game Approach

by Rui Zhang, Yanxi Xie, Yuewen Li, Qingfeng Chen and Qiaosong Wang

Electronics 2025, 14(10), 2042; https://doi.org/10.3390/electronics14102042 - 17 May 2025

Abstract

The intelligent connected vehicle (ICV) industry encounters substantial challenges related to technology, policies, and funding. Its development relies not only on the close collaboration and technological innovation between carmakers and technology companies but also on the support of government’s incentive policies. Therefore, this [...] Read more.

The intelligent connected vehicle (ICV) industry encounters substantial challenges related to technology, policies, and funding. Its development relies not only on the close collaboration and technological innovation between carmakers and technology companies but also on the support of government’s incentive policies. Therefore, this paper establishes a tripartite evolutionary game model that involves governments, carmakers, and technology companies to investigate the stability equilibrium strategy of multi-party participation in promoting the development of the ICV industry. In addition, by analyzing relevant regulations and company annual reports, this paper conducts a simulation analysis to examine how government incentive policies and benefit distribution mechanisms impact the evolutionary trajectory. Several insightful and practical conclusions are drawn. First, in the early stages of industrial development, the government’s infrastructure investment could promote the cross-border innovation cooperation between carmakers and technology companies, thereby accelerating the advancement of ICVs; however, the long-term impact of the sustained investment remains limited. Second, the incremental government subsidies for carmakers and technology companies within limits could increase the probability of them choosing to cooperate and innovate with each other. Still, the excessive subsidies could result in unstable industry growth. Finally, the increase in the benefit distribution ratio for carmakers with professional technology in automotive technology and vehicle design has a positive effect on the development of the ICV industry. This paper expands the research scope of ICVs and provides theoretical insights for promoting the sustainable development of the ICV industry from policy and market viewpoints. Full article

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13 pages, 3477 KiB

Open AccessArticle

Cache-Based Design of Spaceborne Solid-State Storage Systems

by Chang Liu, Junshe An, Qiang Yan and Zhenxing Dong

Electronics 2025, 14(10), 2041; https://doi.org/10.3390/electronics14102041 - 17 May 2025

Abstract

To address the current limitations of spaceborne solid-state storage systems that cannot effectively support the parallel storage of multiple high-speed data streams, the throughput bottleneck of NAND FLASH-based solid-state storage systems was analyzed in relation to the high-speed data input requirements of payloads. [...] Read more.

To address the current limitations of spaceborne solid-state storage systems that cannot effectively support the parallel storage of multiple high-speed data streams, the throughput bottleneck of NAND FLASH-based solid-state storage systems was analyzed in relation to the high-speed data input requirements of payloads. A four-stage pipeline operation and bus parallel expansion scheme was proposed to enhance the throughput. Additionally, to support the parallel storage of multichannel data and continuity of pipeline loading, the shortcomings of existing caching schemes were analyzed, leading to the design of a storage system based on Synchronous Dynamic Random Access Memory (SDRAM). Model simulations indicate that, under extreme conditions, the proposed scheme could continuously receive and cache multiple high-speed file data streams into the SDRAM. File data were dynamically written into FLASH based on the priority and status of each partition cache autonomously, without overflow during caching. The system eventually entered a regular dynamic balance scheduling state to achieve parallel reception, caching, and autonomous scheduling of storage for multiple high-speed payload data streams. The data throughput rate of the storage system can reach 4 Gbps, thus satisfying future requirements for multichannel high-speed payload data storage in spaceborne solid-state storage systems. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing for Emerging Applications)

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