Computers & Electrical Engineering最新文献

{"title":"PTSRDet: End-to-End Super-Resolution and object-detection approach for small defect detection of power transmission lines","authors":"Shahrzad Falahatnejad ,&nbsp;Azam Karami ,&nbsp;Hossein Nezamabadi-pour","doi":"10.1016/j.compeleceng.2025.110374","DOIUrl":"10.1016/j.compeleceng.2025.110374","url":null,"abstract":"<div><div>Detecting small defects in power transmission lines is crucial for ensuring the safety and reliability of power grids. Unmanned Aerial Vehicle (UAV) imagery can be utilized for this purpose; however, the small size of defects and the low resolution of images make this task challenging. In this paper, we introduce a novel model that combines super-resolution and object detection techniques to address this issue. Our approach employs a Power Transmission Lines Single Image Super-Resolution Generative Adversarial Network (PTSRGAN) for super-resolution, and a new architecture for object detection that includes the HorNet backbone, a Super-Resolution based Path Aggregation Feature Pyramid Network (SR-PAFPN) neck, and a You Only Look Once-X (YOLOX) decoupled head.</div><div>The SR-PAFPN neck enhances feature quality and diversity, particularly for small defects, by integrating feature super-resolution during training. To further improve the accuracy of small defect detection, our model is trained end-to-end, allowing the super-resolution model to receive feedback from the object detection model and adapt accordingly. Extensive experiments demonstrate the effectiveness and efficiency of our Power Transmission Lines Super Resolution Defect Detection (PTSRDet) method. Our model achieves a precision of 92.87% and a recall of 96.32%, processing each image in just 0.34 s. These results highlight the model’s capability to accurately detect small defects in power transmission lines, making it a valuable contribution to the field.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110374"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886383","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":"Lightweight RFID-enabled authentication protocol in post-quantum environment","authors":"Haradhan Ghosh ,&nbsp;Pramod Kumar Maurya ,&nbsp;Satya Bagchi ,&nbsp;Ashutosh Dhar Dwivedi","doi":"10.1016/j.compeleceng.2025.110367","DOIUrl":"10.1016/j.compeleceng.2025.110367","url":null,"abstract":"<div><div>RFID technology is widely used for contactless identification and tracking across various sectors. However, the rise of quantum computing threatens classical cryptographic protocols. To address this, we propose a lightweight RFID protocol based on the Ring Learning With Errors (RLWE) problem, ensuring post-quantum security while maintaining efficiency for resource-constrained environments. The RLWE problem provides a strong foundation for constructing quantum-resistant cryptographic schemes. Formal security proofs using the random oracle model and Scyther tool confirm its robustness against attacks. Additionally, we evaluate the protocol’s security, computation, and communication costs, demonstrating its feasibility for RFID systems. This work advances post-quantum cryptographic solutions for the Internet of Things (IoT) and other ubiquitous computing environments, ensuring long-term security and privacy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110367"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891663","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":"Multi-objective optimization for balanced Q-coverage problem in under-provisioned directional sensor networks","authors":"Rajib Kumar Mondal ,&nbsp;Tandra Pal ,&nbsp;Sanghita Bhattacharjee","doi":"10.1016/j.compeleceng.2025.110376","DOIUrl":"10.1016/j.compeleceng.2025.110376","url":null,"abstract":"<div><div>This study investigates the target <span><math><mi>Q</mi></math></span>-coverage problem in under-provisioned directional sensor network (DSN). The coverage imbalance is a serious issue in under-provisioned networks. In <span><math><mi>Q</mi></math></span>-coverage, some targets may get the required coverage while others may be partially covered or even not covered. We have proposed a new balancing index <span><math><mrow><msub><mrow><mi>Q</mi></mrow><mrow><mi>b</mi></mrow></msub><mi>I</mi></mrow></math></span> to measure the balanced coverage of the network. In this study, we have modified four existing multi-objective genetic algorithms (MOGAs), strength Pareto evolutionary algorithm 2 (SPEA2), nondominated sorting genetic algorithm II (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), and two-stage evolutionary strategy based MOEA/D (MOEA/D-TS), where the objectives are maximization of the balanced coverage based on the proposed <span><math><mrow><msub><mrow><mi>Q</mi></mrow><mrow><mi>b</mi></mrow></msub><mi>I</mi></mrow></math></span> and minimization of the number of active sensors in the DSN. Keeping their generic structures the same, we have modified the MOGAs to make them suitable for implementing the proposed <span><math><mi>Q</mi></math></span>-coverage problem. For this purpose, a new mutation operator is also designed. As per our limited knowledge, no work in the literature considered the target <span><math><mi>Q</mi></math></span>-coverage problem in multi-objective paradigm. We have analyzed the impact of five different network parameters on the two objectives mentioned above: the number of targets, the number of sensors, the number of orientations, the sensing radius, and the coverage requirement. To compare the performances among the MOGAs, we have considered three different performance metrics: Hypervolume (HV), Inverted generational distance (IGD), and spread. The sensitivity analysis is done on three different network parameters to show the robustness of the modified MOGAs. Additionally, the performances of four MOGAs are compared with a genetic algorithm, existing in the literature, for the <span><math><mi>Q</mi></math></span>-coverage problem. The modified MOGAs are also tested on large scale, very large scale, and real networks, and the results show the effectiveness of the proposed MOGAs on the <span><math><mi>Q</mi></math></span>-coverage problem. Finally, statistical tests are performed on the three performance metrics to validate the results. The modified MOGAs improve the overall coverage and <span><math><mrow><msub><mrow><mi>Q</mi></mrow><mrow><mi>b</mi></mrow></msub><mi>I</mi></mrow></math></span> value by at least 13% and 21%, respectively compared to the existing algorithm.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110376"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894902","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 efficient anonymous certificateless authentication and key agreement scheme for smart grids","authors":"Qingfeng Cheng ,&nbsp;Yuqian Ma ,&nbsp;Fushan Wei ,&nbsp;Xinghua Li","doi":"10.1016/j.compeleceng.2025.110369","DOIUrl":"10.1016/j.compeleceng.2025.110369","url":null,"abstract":"<div><div>With the vibrant development of the Internet, smart grids have been provided with a suitable environment to flourish. Smart meters record and transmit electricity consumption information and send it to gateways and service providers. Power suppliers process the data to evaluate and predict the frequency of electricity consumption by users, to save resources. However, the power consumption information contained in the data may reveal users’ identity, community address or the frequency at home. If malicious attackers get these messages, the residential safety will be greatly threatened. Therefore, data needs to be protected. Authentication and key agreement protocol is a promising solution, which first realizes mutual authentication between communication parties, and then establishes a session key between them to protect transmitted data. Recently, Chai et al. proposed an authentication scheme based on SM2 authentication key exchange (AKE) protocol. Unfortunately, after our analysis, it is difficult to achieve forward security as they stated. Specifically, if the long term key of the communicating smart meter is leaked, the adversary can recover the session keys established before. Further, we propose a provable secure certificateless authentication and key agreement scheme. The security of the proposed scheme is analyzed by provable security and BAN logic. Compared with the existing scheme, our proposed scheme can achieve a better balance from the security properties, communication cost, and computation cost three aspects.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110369"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887244","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":"Biometric and smart contract enabled secure data sharing in drone-assisted battlefield systems","authors":"Neeraj Kumar,&nbsp;Rifaqat Ali","doi":"10.1016/j.compeleceng.2025.110407","DOIUrl":"10.1016/j.compeleceng.2025.110407","url":null,"abstract":"<div><div>Surveillance drones in intelligent battlefield systems capture sensitive data but face severe security threats, including eavesdropping, tampering, Global Positioning System (GPS) spoofing, and drone hijacking. To counter these, we propose a secure data-sharing protocol integrating smart contracts and biometric-based multi-factor authentication within a drone-assisted Internet of Things (IoT) environment. The protocol leverages decentralized fog servers for real-time, low-latency communication while ensuring robust defense against attacks like impersonation and replay. It supports dynamic drone integration and secure credential updates, addressing key limitations in existing schemes. Security is rigorously validated via the Real-or-Random (RoR) model, Scyther tool, and informal analysis. Comparative results confirm that our protocol outperforms prior solutions in terms of security, scalability, energy efficiency, and computational overhead.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110407"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935711","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":"Phishing email detection using vector similarity search leveraging transformer-based word embedding","authors":"Chanchal Patra ,&nbsp;Debasis Giri ,&nbsp;Sutanu Nandi ,&nbsp;Ashok Kumar Das ,&nbsp;Mohammed J.F. Alenazi","doi":"10.1016/j.compeleceng.2025.110403","DOIUrl":"10.1016/j.compeleceng.2025.110403","url":null,"abstract":"<div><div>As cybercrime increases, using email cautiously is crucial. Phishing emails are a major threat, often exploited to steal sensitive data and cause financial losses. While anti-phishing techniques exist, evolving phishing tactics make countering them challenging. This study proposes a phishing detection system using transformer-based word embedding and vector similarity search. Pre-trained models like Dense Passage Retrieval (DPR) create high-dimensional vector embeddings from emails, stored in a vector database for real-time similarity searches. The proposed approach outperforms traditional machine learning by automating feature extraction and improving similarity search efficiency, making it more effective in detecting phishing emails. Empirical evaluation has been conducted using three publicly available datasets Enron, Nazario phishing corpora, and the Phishing validation emails dataset. The system demonstrates the superior performance, achieving 98.43% accuracy, 98.44% precision, 98.38% recall, 98.41% F1-score, and an area under the curves (AUC) of 0.984 using cosine similarity.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110403"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946475","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":"Prior-guided attention network for underwater image enhancement","authors":"Zhe Chen ,&nbsp;Gaohui Chen ,&nbsp;Yipin Shen","doi":"10.1016/j.compeleceng.2025.110361","DOIUrl":"10.1016/j.compeleceng.2025.110361","url":null,"abstract":"<div><div>Underwater images often suffer from color deviation, low contrast, blurring, and other degradation issues due to the attenuation characteristics of water and the presence of particles in the aquatic environment. In this paper, we propose an underwater image enhancement method based on the transformer architecture and underwater prior knowledge to achieve visually improved results. Specifically, we introduce a prior-guided attention network (PGANet), which comprises a prior-guided block (PGB) and a multi-feature attention block (MFAB). On the one hand, considering the varying degrees of color degradation, we employ the PGB to direct the network in capturing features that are significantly degraded. On the other hand, the multi-feature attention block is incorporated to explore rich-feature information at multiple scales in the underwater image. Experimental results demonstrate that our method effectively corrects color biases and removes haze across diverse underwater datasets.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110361"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946476","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":"Corrigendum to “On-chip kernel-based spatial filter for CMOS image sensor” [Computers and Electrical Engineering volume 123 (2025), p. 110218]","authors":"Bárbaro Maykel López-Portilla Vigil ,&nbsp;Wladimir Valenzuela ,&nbsp;Payman Zarkesh-Ha ,&nbsp;Miguel Figueroa","doi":"10.1016/j.compeleceng.2025.110409","DOIUrl":"10.1016/j.compeleceng.2025.110409","url":null,"abstract":"","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110409"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922802","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":"CTWR: A congestion, temperature and wear-aware routing algorithm for partially-connected 3D network-on-chip","authors":"Mojtaba Asadboland,&nbsp;Amin Mehranzadeh,&nbsp;Mohammad Mosleh","doi":"10.1016/j.compeleceng.2025.110421","DOIUrl":"10.1016/j.compeleceng.2025.110421","url":null,"abstract":"<div><div>Three-dimensional Network-on-Chip (3D-NoC) is an efficient solution to overcome communication limitations in complex System-on-Chip (SoC) architectures. However, challenges such as increased temperature, traffic congestion, and link wear-out significantly impact network performance and lifespan. In this study, we propose an adaptive routing algorithm named CTWR (Congestion, Temperature and Wear-aware Routing), which simultaneously considers temperature, congestion, and wear-out while utilizing both intra-layer and inter-layer routing approaches to enhance network performance. The algorithm employs a dynamic approach to assess the real-time status of vertical links to control and reduce wear-out, selecting paths that mitigate thermal hotspots, balance traffic distribution, and extend the lifespan of interconnects. Extensive assessments and simulations performed under diverse traffic scenarios and multiple vertical link or elevator layout configurations indicate that the CTWR algorithm outperforms ETW, EF, HE, and Nezarat routing methods in reducing average packet delay by 92.71 %, 67.84 %, 56.33 %, and 26.91 %, respectively. Furthermore, our proposed approach enhances average network throughput by 9.88 %, 4.38 %, 2.64 %, and 1.66 % compared to these methods. Thermal analysis of the chip surface also reveals a lower overall temperature and a more balanced heat distribution than competing techniques.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110421"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143950544","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":"E-GAP: Evolutionary Gradient Attack on Privacy","authors":"Yuvraj Singh Chaudhry,&nbsp;Rammohan Mallipeddi","doi":"10.1016/j.compeleceng.2025.110399","DOIUrl":"10.1016/j.compeleceng.2025.110399","url":null,"abstract":"<div><div>Collaborative learning, particularly in Federated Learning, has revolutionized the industry by enabling models to be trained collectively by a group while preserving participants’ data privacy. Such networks operate by sharing only local updates with a global model, preventing direct exposure of raw data. However, vulnerabilities such as optimization-based gradient attacks have demonstrated the potential to reconstruct raw data from shared updates, exposing critical privacy risks and questioning the robustness of these frameworks. In this paper, we propose a privacy attack referred to as Evolutionary Gradient Attack on Privacy (E-GAP), an enhancement of the Recursive Gradient Attack on Privacy (R-GAP). E-GAP integrates Differential Evolution (DE) which belongs to the class of evolutionary algorithms, to optimize reconstructed gradients, diverging from traditional gradient descent approaches that rely on mean squared error (MSE). Since evolutionary approach allows us to examine the non-uniqueness of gradient weights, E-GAP not only improves reconstruction efficacy but also offers more profound insights into how these weights facilitate data reconstruction in weight-sharing networks. This study presents advances to an existing privacy attack, highlighting the inherent vulnerabilities of Federated Learning, and sheds light on the urgent need to reassess privacy safeguards in such frameworks. The implementation of E-GAP is publicly available at <span><span>https://github.com/yuvrajchaudhry/egap</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110399"},"PeriodicalIF":4.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929283","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}