Computers & Electrical Engineering最新文献

{"title":"Outage probability and diversity analysis of OTFS in AF and DF cooperative communication systems","authors":"Annette James,&nbsp;Ananth A.","doi":"10.1016/j.compeleceng.2025.110516","DOIUrl":"10.1016/j.compeleceng.2025.110516","url":null,"abstract":"<div><div>The outage probability of orthogonal time frequency space (OTFS) in amplify and forward (AF) and decode and forward (DF) cooperative communication is investigated in this paper. The system setup involves a source node (S), an intermediate node (I), and a destination node (D). A half-duplex forwarding protocol is utilized, featuring two communication links: one direct link between the source and the destination, and one indirect link through the intermediate node. Closed-form expressions for the outage probability are derived for both AF and DF schemes. Additionally, an analysis of their diversity order is also conducted. The diversity order for both AF and DF schemes is found to be <span><math><mi>K</mi></math></span>, where <span><math><mi>K</mi></math></span> represents the number of multipaths. Thus, OTFS-based cooperative transmissions are capable of achieving complete channel diversity. Furthermore, the performance of OTFS AF and DF is compared with that of OFDM, GFDM and AFDM for AF and DF, demonstrating that OTFS outperforms OFDM and GFDM in terms of outage probability. The theoretical results are validated through Monte Carlo simulations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110516"},"PeriodicalIF":4.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144344634","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}

{"title":"Ultra-fast approximation of minimum cut by condensing traversal trees in large-scale graphs","authors":"Ronghua Ma","doi":"10.1016/j.compeleceng.2025.110527","DOIUrl":"10.1016/j.compeleceng.2025.110527","url":null,"abstract":"<div><div>Minimum cut (min-cut) algorithms are of great significance in numerous applications, making the need for rapid solutions highly pressing. Current acceleration techniques mainly focus on two aspects: optimizing the algorithm’s logical structure and reducing the size of graph data. Considering that each traversal tree corresponds to a particular cut, we approach the problem from the perspective of cut enumeration. Our proposed algorithm conducts cut enumeration by leveraging the depth-first traversal tree. For each node, it identifies the optimal tree with the smallest local cut and then performs condensing operations to achieve acceleration. After condensing, any pair of nodes can be separated by the condensed trees that contain only one of them. In a graph having <span><math><mi>M</mi></math></span> edges, the time complexity of the preprocessing step in the serial algorithm version is as low as <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>M</mi><mo>)</mo></mrow></mrow></math></span>. It can accurately determine the min-cut for over 99.9% of node pairs. The calculation time for each pair is remarkably short, being only a thousandth of that of the most efficient existing methods. Furthermore, when dealing with a graph having tens of millions of edges on a common computing node, the time consumption is just a few microseconds. As a result, this algorithm can serve as an effective heuristic method for min-cut approximation.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110527"},"PeriodicalIF":4.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144344635","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}

{"title":"A non-monotone proximal point method for image reconstruction using non-convex total variation models","authors":"R.A.L. Rabelo ,&nbsp;P.H.A. Ribeiro ,&nbsp;W.M.S. Santos ,&nbsp;R.C.C. Silva ,&nbsp;J.C.O. Souza","doi":"10.1016/j.compeleceng.2025.110491","DOIUrl":"10.1016/j.compeleceng.2025.110491","url":null,"abstract":"<div><div>Reconstructing images contaminated by noise is of fundamental importance in the data preprocessing stages, especially in digital image processing applications. In most practical applications involving image acquisition, the noises introduced in this process are of a known nature, with the most common being additive white Gaussian noise. In this context, continuous optimization algorithms have gained importance, such as the proximal point method (PPM) when applied to image denoising and filtering tasks. In this work, we propose a boosted version of the PPM for image denoising, called nmPPMDC, using a non-convex Total Variation model. The results obtained show that, with black and white images, nmPPMDC recovers images with less CPU time than PPM and that the convex model and, regarding SSIM and PSNR, have similar performance to known techniques such as DCA, BDCA and nmBDCA. nmPPMDC has the best CPU time, outperforming DCA and PPM in 83.33% of the experiments and the FISTA and BDCA techniques in all tests. The tests with medical images show that nmPPMDC with a non-convex model is more likely to obtain good results than the convex model, in addition to showing the superiority of nmPPMDC in relation to PPM, both in quality and CPU time.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110491"},"PeriodicalIF":4.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144344631","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}

{"title":"Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids","authors":"Athanasios Ioannis Arvanitidis ,&nbsp;Paul Talbot ,&nbsp;Nikolaos Gatsis ,&nbsp;Miltiadis Alamaniotis","doi":"10.1016/j.compeleceng.2025.110528","DOIUrl":"10.1016/j.compeleceng.2025.110528","url":null,"abstract":"<div><div>As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110528"},"PeriodicalIF":4.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144344632","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}

{"title":"A lightweight small object detection network inspired by the visual area V2","authors":"Dandan Zhang,&nbsp;Chuan Lin,&nbsp;Yongcai Pan","doi":"10.1016/j.compeleceng.2025.110471","DOIUrl":"10.1016/j.compeleceng.2025.110471","url":null,"abstract":"<div><div>In high-resolution aerial images, due to the low feature density and small pixels of the objects to be detected, it is difficult to capture subtle details during feature extraction, which can easily lead to missed detections and false detections. To address this problem, this paper proposes a small object detection algorithm inspired by the information processing mechanism of visual area V2, in order to improve the network’s ability to extract detailed features such as the edge and direction of small objects. Inspired by the information processing mechanism of complex cells and hypercomplex cells in biological visual area V2, we designed a complex cell module (CCM) that mimics the edge-sensitive properties of complex cells and a hypercomplex cell module (HCM) that simulates the edge and direction-sensitive properties of hypercomplex cells. By simulating the sensitive characteristics of complex cells and hypercomplex cells to edge and direction information, the model’s ability to extract edge and direction features of small objects is enhanced. In addition, inspired by the bottom-up attention mechanism between visual cortexes, this paper designs a spatial enhanced attention module (SEAM) between Neck and Head, which uses shallow features to modulate deep features to retain key shallow information while focusing on small objects. The results show that on the UAV small object dataset VisDrone2019 and the remote sensing small object AITODv2, the network we designed achieved an accuracy index (mAP50) score of 48.9% and 49.1% with 1.3M parameters, successfully achieving a good balance between lightweight network and detection accuracy, achieving the best performance of lightweight models, and effectively reducing the occurrence of missed detections and false detections. The code will be available online at <span><span>https://github.com/Dzzz614/V2</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110471"},"PeriodicalIF":4.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335987","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}

{"title":"Technical study of innovative strategies of direct torque control for doubly fed induction motor — A review","authors":"Said Mahfoud ,&nbsp;Najib El Ouanjli ,&nbsp;Aziz Derouich","doi":"10.1016/j.compeleceng.2025.110498","DOIUrl":"10.1016/j.compeleceng.2025.110498","url":null,"abstract":"<div><div>Direct Torque Control (DTC) is widely recognized for its simplicity in both modeling and implementation. However, it still suffers from the major drawback of torque ripples. To address this limitation, numerous innovative solutions have been proposed in the literature. This paper provides an in-depth review of recent methods aimed at enhancing the performance of DTC, with a particular focus on optimizing Doubly-Fed Induction Motors (DFIM) in terms of torque and speed control. This review begins with a detailed presentation of the DFIM model, followed by a mathematical analysis of basic DTC control. The main challenges associated with this approach are identified and explained. The core section of the review explores a range of traditional techniques (such as backstepping and space vector modulation) as well as innovative methods based on optimization algorithms (genetic algorithm, ant colony optimization, and rooted tree optimization) and artificial intelligence, including fuzzy logic and neural networks, all aimed at improving DTC control efficiency. The objective is to reduce torque ripples while optimizing speed dynamics. Each of these techniques is analyzed in terms of its advantages and disadvantages, providing a critical perspective on their potential to enhance the performance of DTC control systems. This work stands out for its in-depth comparative study of these techniques based on major criteria (torque ripples and complexity), classification study and proposing actionable recommendations. This analysis aims to identify the most effective control strategies in the literature. The techniques that demonstrated the highest efficiency in this study are FL-DTC and ANN-DTC, which reduced torque ripples from 2.445 Nm for standard DTC to 1.14 Nm for FL-DTC and 1.08 Nm for ANN-DTC. Additionally, ANN-DTC offers the added benefit of lower complexity, providing a simpler yet equally effective solution compared to FL-DTC.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110498"},"PeriodicalIF":4.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329996","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}

{"title":"A novel intrusion detection framework for industrial IoT: GCN-GRU architecture optimized with ant colony optimization","authors":"Mahdi Mir ,&nbsp;Mohammad Trik","doi":"10.1016/j.compeleceng.2025.110541","DOIUrl":"10.1016/j.compeleceng.2025.110541","url":null,"abstract":"<div><div>The swift proliferation of IIoT ecosystems has highlighted the essential requirement for effective Intrusion Detection System (IDS) to protect crucial infrastructures. This research presents a novel hybrid IDS that combines Graph Convolutional Networks (GCN) with Gated Recurrent Units (GRU), optimized by the Ant Colony Optimization (ACO) method, a bio-inspired meta-heuristic based on ant foraging behavior. This method automates hyperparameter adjustment, overcoming the constraints of conventional human optimization techniques. The proposed system utilizes GCN for structural feature extraction and GRU for sequential pattern analysis, facilitating thorough anomaly detection in IIoT traffic. The ACO-optimized IDS surpasses traditional optimization methods, including Genetic Algorithms, by attaining quicker convergence and enhanced performance metrics. Notwithstanding its effectiveness, the computational burden of the optimization approach necessitates additional enhancement. Experimental assessments of the EDGE-IIOTSET, CICAPT-IIoT, and WUSTL-IIoT datasets reveal detection accuracies of 97 % for the majority of attack scenarios, alongside improved scalability and diminished processing requirements. This study emphasizes the capability of integrating sophisticated neural architectures with nature-inspired optimization to enhance Industrial Internet of Things (IIoT) security against advancing cyber threats.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110541"},"PeriodicalIF":4.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329995","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}

{"title":"An optimized ECG copyright protection technique and its feature authentication","authors":"Ranjana Dwivedi ,&nbsp;Divyanshu Awasthi ,&nbsp;Vinay Kumar Srivastava","doi":"10.1016/j.compeleceng.2025.110546","DOIUrl":"10.1016/j.compeleceng.2025.110546","url":null,"abstract":"<div><div>The safe and dependable transfer of biomedical signals through smart medical devices is made possible by digital watermarking. This work proposes a reliable, secure, optimized watermarking technique for Electrocardiogram (ECG) data. Patient ID is used as watermark and pre-processed with 1-level lifting wavelet transform (LWT) before embedding. The suitable scaling factor is obtained using the Harris Hawks optimization (HHO) technique to balance the trade-off between robustness and imperceptibility. Watermark is encrypted using Henon map before embedding to provide security. QR decomposition and randomized singular value decomposition (RSVD) are applied to host ECG's horizontal and vertical sub-bands. Principal components (PC’s) are modified to embed the watermark instead of singular values, and thus, the proposed technique is free from false positive problems (FPP). The maximum Peak signal to noise ratio (PSNR) value obtained is 50.05 dB, and normalized correlation coefficient (NC) is 0.9975. The average percentage improvement in imperceptibility is 6.72 %, and in time complexity is 76.26 %. Comparison with existing ECG watermarking techniques establishes that the proposed ECG watermarking scheme outperforms in terms of robustness, imperceptibility, capacity, and computational complexity. Binary Robust Invariant Scalable Keypoints (BRISK) are used to verify the vital features of input ECG image to detect any undesired alteration.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110546"},"PeriodicalIF":4.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329997","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}

{"title":"Deception and cloud integration: A multi-layered approach for DDoS detection, mitigation, and attack surface minimization in SD-IoT networks","authors":"Ameer El-Sayed ,&nbsp;Ahmed A. Toony ,&nbsp;Amr Tolba ,&nbsp;Fayez Alqahtani ,&nbsp;Yasser Alginahi ,&nbsp;Wael Said","doi":"10.1016/j.compeleceng.2025.110543","DOIUrl":"10.1016/j.compeleceng.2025.110543","url":null,"abstract":"<div><div>Detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Internet of Things (SD-IoT) networks is challenging due to vulnerabilities in single-controller architectures, the limitations of the OpenFlow protocol, evolving DDoS strategies, and resource constraints. This research proposes a multi-layered security framework that integrates deception-based security, cloud-integrated machine learning (ML), a new hierarchically distributed multi-controller (HDMC) architecture, P4-enabled real-time traffic monitoring, and adaptive mitigation. The framework includes dynamic time-based windowing for enhanced detection, a decoy network to divert attackers, and a cloud-based multi-task ML model (MT-EDD) for attack classification. It also features a synchronized multi-control design for secure communication and coordinated actions among multiple controllers and a dynamic monitoring algorithm for real-time traffic analysis. P4 switches extract features from network traffic and send them to a cloud-based server for preprocessing and analysis by a pre-trained ensemble learning model (MT-EDD), which predicts attack states and communicates results to the central controller for mitigation. The controller then enforces appropriate mitigation actions on P4 switches. This approach offloads computationally intensive tasks to the cloud, improving scalability and detection accuracy. Evaluations show the framework achieves an average accuracy of 98.42%, precision of 96.17%, recall of 94.72%, F1-score of 95.39%, and specificity of 98.22%. The proposed P4-enabled solution consumes 30% less bandwidth and 25% less CPU, reduces detection times by 54.3%, and improves detection accuracy by 5.2% compared to the OpenFlow-enabled method. The HDMC architecture, evaluated against a single-controller setup, demonstrated 40% higher throughput and 32% lower latency, confirming its superior performance across multiple metrics.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110543"},"PeriodicalIF":4.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321265","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}

{"title":"Integration of the layer 2 protection extended application for Open RAN: A security by design approach","authors":"Jihye Kim,&nbsp;Jaehyoung Park,&nbsp;Jong-Hyouk Lee","doi":"10.1016/j.compeleceng.2025.110479","DOIUrl":"10.1016/j.compeleceng.2025.110479","url":null,"abstract":"<div><div>The Open Radio Access Network (Open RAN) paradigm is progressively evolving to deliver open, intelligent, and innovative solutions for next-generation networks, particularly in 5G-Advanced and 6G. Spearheading this effort, the O-RAN Alliance has established a comprehensive framework for Open RAN and continues to lead standardization efforts in this domain. However, an analysis of the O-RAN security standard specifications reveals a primary focus on layer 3 and above, with an emphasis on IPsec protocols, leaving layer 2 security inadequately addressed. This gap exposes the E2 interface – a critical component enabling the openness and intelligence of Open RAN – to potential Man-in-the-Middle attacks at the layer 2. To mitigate this vulnerability, we propose a novel E2 protection xApp designed to enhance the security of the E2 interface. The proposed xApp detects and prevents layer 2 attacks, such as Address Resolution Protocol spoofing, ensuring robust protection for Open RAN environments. Experimental evaluations demonstrate that the proposed solution significantly improves network resilience, outperforming existing methods in mitigating layer 2 security vulnerability.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110479"},"PeriodicalIF":4.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321267","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}