Computers & Electrical Engineering最新文献_第10页

A rippling surveillance model with subsequent tracking in UAV-enabled spaces 涟漪监视模型，在无人机启用的空间中进行后续跟踪

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-09-02 DOI: 10.1016/j.compeleceng.2025.110654

Minsoo Kim, Hyunbum Kim

{"title":"A rippling surveillance model with subsequent tracking in UAV-enabled spaces","authors":"Minsoo Kim, Hyunbum Kim","doi":"10.1016/j.compeleceng.2025.110654","DOIUrl":"10.1016/j.compeleceng.2025.110654","url":null,"abstract":"<div><div>A surveillance has emerged as a critical research since a critical security surveillance system can affect various applications including transportation services, smart cities, mobile computing, etc. Existing surveillance models primarily focus on how to perform initial or preliminary detection against intruders into the target spaces. Also, existing surveillance systems had limitations in detecting intruders following nonlinear paths by relying on static sensors, but this study introduced a method of tracking the intrusion path by activating dynamic sensors after detection. In particular, while existing studies have focused on detection at the moment of intrusion, this study is differentiated in that it attempted to strengthen security through tracking after detection. In this paper, we introduce a rippling surveillance model to provide sustainable surveillance with subsequent tracking after initial detection in UAV-enabled applications. The proposed model performs a cooperation of a static configuration and a dynamic formation deployed in a k-means clustering method to strengthen the surveillance and tracking function in the difficult-to-predict intrusion path. The system evaluated dynamic sensing radius and intruder speed as variables, and as a result, the tracking accuracy improves as the radius increases, but the resource efficiency decreases when the radius becomes too large. In addition, as the intruder speed increases, the tracking accuracy tends to decrease significantly in the linear path. The system combines the stability of static sensors with the flexibility of dynamic sensors to achieve high tracking accuracy across different intrusion paths, emphasizing that the optimization of dynamic sensing radius and sensor placement is an important factor.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110654"},"PeriodicalIF":4.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CropCapsNet: Enhanced capsule network for crop disease classification CropCapsNet：用于作物病害分类的增强胶囊网络

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-09-02 DOI: 10.1016/j.compeleceng.2025.110635

Juan Qin , Linfan Deng , Cong Li , Junjie He , Haibo Pen , Zhaoxia Wang

{"title":"CropCapsNet: Enhanced capsule network for crop disease classification","authors":"Juan Qin , Linfan Deng , Cong Li , Junjie He , Haibo Pen , Zhaoxia Wang","doi":"10.1016/j.compeleceng.2025.110635","DOIUrl":"10.1016/j.compeleceng.2025.110635","url":null,"abstract":"<div><div>The prevention and treatment of crop diseases are crucial for the development of smart agriculture. The classification of crop diseases based on deep learning for early disease monitoring and control has become the mainstream direction of research. This paper proposes a novel deep learning model called ”CropCapsNet”, which combines Squeeze-and-Excitation Inception (SE-Inception) module and has improved capsule structure for crop disease classification. The network first extracts shallow features of input samples through double-layer convolution, then uses SE-Inception to achieve deep multi-scale feature acquisition, and finally outputs classification results through an improved capsule structure. SE-Inception adds Squeeze-and-Excitation(SE) attention after each multi-scale feature extraction block to improve the model’s perception of diseases without increasing the number of parameters. The improved capsule structure is embedded with a parameter grouping strategy, which can control trainable parameters by adjusting the number of capsule groups to adapt to different application scenarios. To verify the generalization of the network, this paper uses three datasets containing different experimental scenarios (PlantVillage, Xinong Apple Dataset, and FGVC8) to evaluate the performance of CropCapsNet. The results show that CropCapsNet has achieved classification accuracies of 99.99%, 98.18%, and 98.09% in the three datasets, respectively. Compared with methods such as ConvNeXt, RegNet, and ResNeSt, CropCapsNet performs excellently. In addition, this paper uses image reconstruction networks and heatmaps to visualize CropCapsNet, improving the interpretability of the model.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110635"},"PeriodicalIF":4.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero-day attack detection with a Dynamic-Weighted Contractive Autoencoder and GAN-based evaluation 基于动态加权压缩自编码器和gan评估的零日攻击检测

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-09-01 DOI: 10.1016/j.compeleceng.2025.110650

M. Franckie Singha, Ripon Patgiri, Zeba Shamsi, Laiphrakpam Dolendro Singh

{"title":"Zero-day attack detection with a Dynamic-Weighted Contractive Autoencoder and GAN-based evaluation","authors":"M. Franckie Singha, Ripon Patgiri, Zeba Shamsi, Laiphrakpam Dolendro Singh","doi":"10.1016/j.compeleceng.2025.110650","DOIUrl":"10.1016/j.compeleceng.2025.110650","url":null,"abstract":"<div><div>Anomaly detection, which has faced quite a challenge in zero-day attacks whose nature is novel and unpredictable, shall be addressed here. This research proposes a novel method for zero-day attacks with an adaptive loss-based Dynamic-Weighted Contractive Autoencoder (DW-CAE). The proposed method differs from the traditional autoencoder approach because it balances reconstruction and Contractive penalty and pays particular attention to features that are difficult to reconstruct. The training of DW-CAE on normal data learns invariant feature representations that enable the efficient detection of anomalies based on high reconstruction errors. The dynamic weighting mechanism further enhances the adaptive balancing of reconstruction and Contractive penalty to increase the model’s sensitivity and robustness against unseen attacks. Furthermore, we have utilized GANs to generate novel synthetic zero-day attack data for rigorous evaluation of the model. CAE and dynamic weight coordination introduce an innovative and robust model for detecting zero-day attacks. Experimental results are shown on the CICIoT2023, CICDDoS2019, ToN-IoT, and synthetic datasets, validating the performance of the proposed approach. The proposed DW-CAE demonstrates a significant performance gain over the fixed-weight CAE, achieving a significant improvement across the three benchmark datasets, highlighting its effectiveness across diverse intrusion detection scenarios.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110650"},"PeriodicalIF":4.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent target detection and encrypted transmission system based on FPGA 基于FPGA的智能目标检测与加密传输系统

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-09-01 DOI: 10.1016/j.compeleceng.2025.110653

Dongxu Liu, Yuzhuo Zhao, Zhiyan Ma, Qun Ding

引用次数: 0

Modeling impairments of computing systems considering hardware–software interaction failures and dependability quantification 考虑软硬件交互故障和可靠性量化的计算系统建模缺陷

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-08-31 DOI: 10.1016/j.compeleceng.2025.110632

Antony Gratas Varuvel , Rajendra Prasath

{"title":"Modeling impairments of computing systems considering hardware–software interaction failures and dependability quantification","authors":"Antony Gratas Varuvel , Rajendra Prasath","doi":"10.1016/j.compeleceng.2025.110632","DOIUrl":"10.1016/j.compeleceng.2025.110632","url":null,"abstract":"<div><div>The computing systems deployed in safety-critical applications often comprise complex hardware with highly intensive software/firmware. The development and validation of computing systems for safety-critical applications shall comply with the Design Assurance Level-A as per RTCA DO-254/DO-178 or Safety Integrity Level-4 as per IEC-61508 towards certification. However, adherence to this process does not assure dependability. Quantifying the reliability to assess the risk associated with using these systems for safety-critical applications is necessary. Hence, dependability quantification of the computing system has been undertaken in this research, with significant improvements over the conventional approaches. In the classical approach, hardware faults and software errors were treated as independent events, and failures arising from interactions were ignored. It is proposed to model dependent states arising due to hardware–software interaction, such as hardware-triggered software failure and software-triggered hardware failures, in addition to hardware and software failures, to model the failure characteristics of the system completely. A stochastic Petri Net (SPN) based methodology is adopted to model the error/fault propagation by considering all possible places, transitions, and tokens. SPN is then transformed into a Continuous-Time Markov Chain to quantify reliability analytically. This enhanced methodology enables more accurate dependability quantification and risk assessment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110632"},"PeriodicalIF":4.9,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preemptive crash risk reduction through a real-time cost-based safety prediction model (RECOSAM) for traffic signal control 基于实时成本安全预测模型（RECOSAM）的交通信号控制先发制人降低碰撞风险

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-08-31 DOI: 10.1016/j.compeleceng.2025.110639

Lok Sang Chan, Neema Nassir, Xiaocai Zhang, Mobin Yazdani, Majid Sarvi

{"title":"Preemptive crash risk reduction through a real-time cost-based safety prediction model (RECOSAM) for traffic signal control","authors":"Lok Sang Chan, Neema Nassir, Xiaocai Zhang, Mobin Yazdani, Majid Sarvi","doi":"10.1016/j.compeleceng.2025.110639","DOIUrl":"10.1016/j.compeleceng.2025.110639","url":null,"abstract":"<div><div>This paper proposes a novel real-time cost-based safety prediction model (RECOSAM) and incorporating it in intersection traffic signal control optimisation, complementing the recent advances in deep reinforcement learning (RL)-based adaptive traffic signal control (ATSC). The primary contribution is the development of RECOSAM, a model designed to predict traffic safety risks one step ahead of time, for various signal phase configurations at intersections. The proposed model offers a dynamic safety evaluation strategy, estimating near-future safety metrics for seamless integration into machine learning-based ATSC systems. Extensive experiments validate the model’s effectiveness, demonstrating its potential for adaptive adjustments to mitigate impending safety risks. Perhaps more importantly from an operational policy perspective, the proposed model is capable of finding an optimal and justifiable trade-off between the efficiency of traffic flow and its safety in real-time.</div><div>A case study showcases the integration of RECOSAM into deep RL for green time optimisation. Results suggest that extended dedicated right turn phases may reduce safety risks, while overly protected phases could lead to inefficiencies in green time allocation and increased congestion. The model’s adaptability across different scenarios is further illustrated, showing its capability to evaluate critical trade-offs between safety and efficiency especially for vehicles trying to make a right turn by finding gaps through traffic coming form the opposing direction (in left-hand-side driving countries—same applies for left turns in right-hand-side driving countries).</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110639"},"PeriodicalIF":4.9,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight spatial attention pyramid network-based image forgery detection optimized for real-time edge TPU deployment 针对实时边缘TPU部署优化的基于轻量级空间注意力金字塔网络的图像伪造检测

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-08-31 DOI: 10.1016/j.compeleceng.2025.110645

Baby Sree Gangarapu , Rama Muni Reddy Yanamala , Archana Pallakonda , Hindupur Raghavender Vardhan , Rayappa David Amar Raj

{"title":"Lightweight spatial attention pyramid network-based image forgery detection optimized for real-time edge TPU deployment","authors":"Baby Sree Gangarapu , Rama Muni Reddy Yanamala , Archana Pallakonda , Hindupur Raghavender Vardhan , Rayappa David Amar Raj","doi":"10.1016/j.compeleceng.2025.110645","DOIUrl":"10.1016/j.compeleceng.2025.110645","url":null,"abstract":"<div><div>The widespread accessibility of image editing software has made image forgery a considerable threat in journalism, legal contexts, and social media, requiring effective and precise detection techniques. The Authors propose a Spatial Attention Pyramid Network (SAPN) that integrates multi-scale residual feature extraction with an adaptive spatial attention mechanism to tackle the difficulties of identifying subtle and localized alterations. SAPN attains enhanced forgery detection performance and computational efficiency by utilizing hierarchical feature learning and selectively augmenting regions susceptible to manipulation. Extensive experiments conducted on four benchmark datasets illustrate the effectiveness and generalizability of SAPN. On the CASIA V1 dataset, SAPN attains an accuracy of 94% and an AUC of 0.99, outperforming 29 state-of-the-art models. An ablation study further supports the contributions of the pyramid feature extraction and spatial attention modules to the overall performance improvements. Moreover, a lightweight model architecture, containing merely 0.57 million parameters, enables efficient real-time deployment on Edge TPU devices, with an average inference latency of 1.17 s per image. These results proclaim SAPN as a scalable and robust framework for image forgery detection and localization in real-world applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110645"},"PeriodicalIF":4.9,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel framework for cross-platform malware detection via AFSP and ADASYN-based balancing 一种基于AFSP和ad异步平衡的跨平台恶意软件检测新框架

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-08-31 DOI: 10.1016/j.compeleceng.2025.110625

Tong Anh Tuan , Pham Sy Nguyen , Pham Ngoc Van , Nguyen Duc Hai , Pham Duy Trung , Nguyen Thi Kim Son , Hoang Viet Long

{"title":"A novel framework for cross-platform malware detection via AFSP and ADASYN-based balancing","authors":"Tong Anh Tuan , Pham Sy Nguyen , Pham Ngoc Van , Nguyen Duc Hai , Pham Duy Trung , Nguyen Thi Kim Son , Hoang Viet Long","doi":"10.1016/j.compeleceng.2025.110625","DOIUrl":"10.1016/j.compeleceng.2025.110625","url":null,"abstract":"<div><div>The rapid spread of malware and the growing complexity of attack methods demand accurate and scalable detection solutions, particularly in classification techniques in which both feature selection and model selection play a critical role. However, malware datasets are often high-dimensional and imbalanced, leading to biased models and suboptimal classification performance. This paper introduces CMF, a novel cross-platform malware detection framework that integrates Adaptive Feature Selection and Projection (AFSP) for dimensionality reduction, Adaptive Synthetic Sampling (ADASYN) for data balancing, and voting ensemble learning for classification. ADASYN consistently outperforms SMOTE by adaptively oversampling hard-to-learn boundary regions, improving minority class detection. Meanwhile, AFSP preserves feature structures while reducing dimensions, while PCA only retains maximal variance directions, making AFSP more effective for malware classification. Extensive experiments on four comprehensive available malware datasets demonstrate that CMF outperforms traditional and deep learning-based approaches, achieving superior accuracy and robustness. Notably, the highest improvement was close to 5% compared to the state-of-the-art on the CIC-MalMem-2022 (16 classes) dataset. CMF framework is highly effective detection of malware variants across multiple operating systems, for instance Windows, Linux, and Android, and heterogeneous cloud environments. This confirms CMF framework as a scalable and high-performance solution for real-world malware detection across environmental diversity.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110625"},"PeriodicalIF":4.9,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Range anxiety mitigation through blockchain-based mobile charging delivery service in Internet of Electric Vehicles 基于区块链的电动汽车互联网移动充电配送服务缓解里程焦虑

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-08-31 DOI: 10.1016/j.compeleceng.2025.110647

Brijmohan Lal Sahu, Preeti Chandrakar

引用次数: 0

Novel three-phase phase-locked loop design for microgrid inverter based on harmonic detection technology 基于谐波检测技术的微电网逆变器三相锁相环设计

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-08-31 DOI: 10.1016/j.compeleceng.2025.110642

Wansheng Jia , Xiaoxin Hou , Tianlei Zhang , Yongliang Hao , Ning Zhang

{"title":"Novel three-phase phase-locked loop design for microgrid inverter based on harmonic detection technology","authors":"Wansheng Jia , Xiaoxin Hou , Tianlei Zhang , Yongliang Hao , Ning Zhang","doi":"10.1016/j.compeleceng.2025.110642","DOIUrl":"10.1016/j.compeleceng.2025.110642","url":null,"abstract":"<div><div>As a key component in distributed generation systems, the inverter synchronization unit (Phase-Locked Loop, PLL) is critical for stable control system output. Under grid unbalanced conditions, the traditional Synchronous Reference Frame PLL(SRF-PLL) fails to lock phase accurately. Existing improved schemes integrating filters into PLLs suppress unbalanced interference but reduce bandwidth, degrade dynamic response (prolonging phase-locking time), and face challenging filter parameter tuning under diverse conditions. Additionally, frequency, as the derivative of phase angle, is significantly affected by phase changes. To address these issues, this paper introduces the Triple Fundamental Frequency concept for three-phase systems, proposing a novel orthogonal signal generation method and a corresponding Triple Fundamental Frequency PLL architecture, enabling stable frequency output. To further improve the performance of the phase - locked loop, a harmonic quantization method is integrated to quantify and offset harmonic impacts on the PLL by subtracting quantified data from dynamic outputs. Leveraging triple frequency advantages, this method markedly enhances system dynamic response. Simulation experiments verify the superiority of the proposed harmonic quantization triple fundamental frequency PLL in dynamic response.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110642"},"PeriodicalIF":4.9,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0