Computers & Electrical Engineering最新文献

{"title":"Remote heart rate measurement based on spatial–temporal self-attention","authors":"Ning Sun ,&nbsp;Ningbin Wang ,&nbsp;Jixin Liu ,&nbsp;Lei Chai ,&nbsp;Haian Sun","doi":"10.1016/j.compeleceng.2025.110557","DOIUrl":"10.1016/j.compeleceng.2025.110557","url":null,"abstract":"<div><div>Remote photoplethysmography (rPPG), a non-contact technique for measuring heart rate, has gained significant traction due to its convenience and non-invasive nature. However, rPPG signals are inherently weak and exhibit regional variations across facial areas. Accurate heart rate estimation necessitates analysis of extended-duration video sequences (exceeding 100 frames). To address these challenges, this paper proposes a novel deep neural network (DNN) based on self-attention mechanism, named spatial–temporal self-attention network (STSA-Net). This convolution-free DNN primarily adopts a transformer architecture. Initially, differential processing is applied to the input video to accentuate frame-to-frame variations and amplify subtle rPPG signals. These differential frames are subsequently passed through a spatial self-attention encoding module. This module models spatial dependencies within facial regions, allowing the network to focus on informative areas while suppressing irrelevant noise. Following spatial encoding, the features are processed by a temporal self-attention module, which captures long-term dependencies across frames using transformer-based techniques. The proposed method is rigorously evaluated through comprehensive experiments, including ablation studies, intra-database evaluations, and cross-database comparisons, using three benchmark databases: UBFC-RPPG, PURE, and MAHNOB-HCI. Our model demonstrates performance on par with state-of-the-art methods for remote heart rate measurement, achieving a MAE of 0.4 bpm on PURE, 0.48 bpm on UBFC, and 3.75 bpm on MAHNOB-HCI in intra-database experiments, and 1.36 bpm (UBFC<span><math><mo>→</mo></math></span> PURE) and 1.27 bpm (PURE<span><math><mo>→</mo></math></span> UBFC) in cross-database experiments. Additionally, an outlier in the PURE database is identified, and its cause and impact are analyzed.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110557"},"PeriodicalIF":4.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633993","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 power quality disturbance classification method based on multi-level optimized adaptive time-frequency analysis and deep learning","authors":"Shaoyang Wang ,&nbsp;Qiang Zhang ,&nbsp;Menghan Li ,&nbsp;Zekai Zhao ,&nbsp;Ce Cao","doi":"10.1016/j.compeleceng.2025.110548","DOIUrl":"10.1016/j.compeleceng.2025.110548","url":null,"abstract":"<div><div>With the increasing integration of distributed generation in the power grid and the growing power quality challenges caused by various high-frequency power electronic devices, the stability of the grid is increasingly affected. In this paper, a power quality disturbance (PQD) recognition method based on a combination of time-frequency analysis and Deep Convolutional Neural Networks (DCNN) is proposed. First, the Variational Mode Decomposition (VMD) parameters are optimized using the Mean Filter Envelope Extremum Method (FE) and the Sparrow Search Algorithm (SSA). This is followed by the screening and removal of noise components from the modal components obtained through VMD decomposition. Secondly, for the selected multi-scale modal components, the Multi-Resolution S-Transform (MST) is applied to obtain the time-frequency feature maps of multiple scales, which are then input into an improved Inception-ResNet model for PQD recognition. Additionally, the Convolutional Block Attention Module (CBAM) attention mechanism is introduced to improve the model performance in capturing key information. The proposed method obtained a classification accuracy of 99.31 % in a 20–40 dB random noise environments and is validated through measured PQD data, demonstrating its reliability.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110548"},"PeriodicalIF":4.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632220","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":"From classical to intelligent control: Evolving trends in robotic manipulator technology","authors":"Saba Waseem ,&nbsp;Muhammad Adnan ,&nbsp;Muhammad Sajid Iqbal ,&nbsp;Arslan Ahmed Amin ,&nbsp;Anwar Shah ,&nbsp;Muhammad Tariq","doi":"10.1016/j.compeleceng.2025.110559","DOIUrl":"10.1016/j.compeleceng.2025.110559","url":null,"abstract":"<div><div>Numerous industrial, medical, and scientific applications currently depend on robotic manipulators, which are essential to the expanding robotics sector. These manipulators cannot operate well without advanced control algorithms that consider precision, adaptability, and flexibility. This work is focused on listing many types, applications, and functions of control strategies for robotic manipulators. The review commences with a broad overview of manipulators, including subjects such as cable-driven, parallel, and serial models. It subsequently contextualizes the unique mechanical and functional characteristics of each type. The next sections encompass a range of control procedures, from conventional techniques such as Proportional-Integral-Derivative (PID) and linear control to complex systems that integrate hybrid control frameworks like machine learning (ML), and artificial intelligence (AI). This comprehensive analysis addresses some enduring issues like task flexibility, computational efficiency, and environmental uncertainty, together with the advantages and disadvantages of each approach. This study also emphasizes emergent domains, including federated learning (FL), blockchain integration, and quantum computing, while also identifying prospective future research topics. By combining current information and outlining potential developments, the study provides researchers and engineers with a reference for improving the control of robotic manipulators, hence improving performance and reliability in challenging working environments. The findings of this work demonstrate the efficacy of advanced control methodologies and pave the way for advances that could transform robotic manipulation in the future.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110559"},"PeriodicalIF":4.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614101","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 VCNN models with RVFLwoDL to boost the parking space classification","authors":"Navpreet,&nbsp;Rajendra Kumar Roul,&nbsp;Rinkle Rani","doi":"10.1016/j.compeleceng.2025.110444","DOIUrl":"10.1016/j.compeleceng.2025.110444","url":null,"abstract":"<div><div>Parking space classification is critical in elevating traffic congestion, reducing air pol- lution, and enhancing drivers’ convenience. This study introduces a robust model for parking space classification, ingeniously combining variants of the Convolutional Neu- ral Network (VCNN) with the Incremental Random Vector Function Link without di- rect link (I-RVFLwoDL). The primary innovation consists of substituting the fully connected layer of the VCNN with I-RVFLwoDL. This eliminates the need for a costly backpropagation procedure, resulting in a substantial decrease in training time. The integration of VCNN with I-RVFLwoDL utilizes the I-RVFLwoDL’s rapid learning efficency and robust generalization capabilities. I-RVFLwoDL simplifies the network structure by eliminating the complex neuron pathways typically found in other established methodologies. The proposed hybrid model’s effectiveness is rigorously as- sessed utilizing three established datasets: PKLot, CNRPark, and CNRPark+EXT. The system’s ability to distinguish between occupied and vacant parking spaces is demon- strated through various performance metrics used in machine learning. The proposed model’s performance is also evaluated against existing deep learning models to illustrate its superiority. This research presents significant potential for intelligent transportation systems, providing an efficient solution for parking space classification.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110444"},"PeriodicalIF":4.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614102","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":"Image encryption scheme based on 2D-ICCM and bit-planes cross permutation-diffusion using parallel computing","authors":"Xingbin Liu,&nbsp;Shuyi Zheng,&nbsp;Jing Yang","doi":"10.1016/j.compeleceng.2025.110569","DOIUrl":"10.1016/j.compeleceng.2025.110569","url":null,"abstract":"<div><div>With the increasing reliance on digital images for communication and storage, image security has become a critical concern. Traditional encryption schemes often face challenges regarding efficiency and robustness against modern cryptographic attacks. In this paper, a novel image encryption scheme is proposed based on an innovative chaotic system named 2D-ICCM and cross bit-plane permutation, enhanced by parallel computing for improved efficiency. The 2D-ICCM is designed to exhibit complex dynamic properties and high sensitivity to initial conditions, the chaotic behaviors of which are verified through the Lyapunov exponent, bifurcation diagrams, sample entropy, and 0-1 test. In addition, a cross bit-plane permutation method is proposed to rearrange the bits in high four bit-planes and low four bit-planes, which further increases the complexity of the encryption and provides stronger protection against attacks. In the diffusion process, an independent bit-plane multi-direction diffusion method using a Zigzag scan is proposed. To address the issue of encryption speed, the parallel computing technique is applied for the diffusion process. Experimental results show that the proposed scheme offers high encryption quality, robust resistance to statistical analysis attacks, and significantly improved encryption speed compared to traditional methods.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110569"},"PeriodicalIF":4.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614100","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 enhanced uncertainty and disturbance estimator based on Bi-LSTM-OTC-LADRC of grid-connected wind energy conversion system","authors":"AL-Wesabi Ibrahim ,&nbsp;Abdullrahman A. Al-Shamma’a ,&nbsp;Jiazhu Xu ,&nbsp;Imad Aboudrar ,&nbsp;Khaled Ameur ,&nbsp;Riadh Al Dawood ,&nbsp;Hassan M. Hussein Farh ,&nbsp;Grant Charles Mwakipunda","doi":"10.1016/j.compeleceng.2025.110534","DOIUrl":"10.1016/j.compeleceng.2025.110534","url":null,"abstract":"<div><div>Power restriction and load reduction are key challenges for large wind turbines in high wind speeds. Controller design is crucial to handle system nonlinearities and unpredictable wind for stable, eco-friendly power generation without oscillations. In this context, this study introduces both linear and nonlinear control algorithms that might be implemented on a grid-connected wind energy conversion system (G-CWECS) to optimize the extraction of the global maximum power point (GMPP) and improve active and reactive power regulation. The foundation of these strategies lies in linear active disturbance rejection control (LADRC), which is well-known for its capability to handle uncertainties and disturbances, relying on its observer. The current-based bidirectional LSTM (CBi-LSTM) and optimum torque control (OTC) MPPT method integrated with LADRC are employed to extract GMP from WECS. A powerful metaheuristic technique, named catch fish algorithm (CFA), is utilized to update the Bi-LSTM weights. The LADRC approach is applied to regulate both active and reactive power by controlling grid currents, ensuring a unity power factor. Matlab simulation and Hardware-In-the-Loop (HIL) experiment are carried out to verify the feasibility of the implementation. Comparing with well-known-MPPT methods, the output outcomes prove the efficiency of the recommended Bi-LSTM-OTC-LADRC regarding GMPP extraction during wind speed variation. Additionally, it's proved that the proposed LADRC approach is robustness in terms of managing the uncertainties and disturbances compared to PI, PID and SMC controllers.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110534"},"PeriodicalIF":4.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632221","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 review of machine learning and IoT-based energy management systems for AC microgrids","authors":"Hanane Tasmant ,&nbsp;Badre Bossoufi ,&nbsp;Chakib Alaoui ,&nbsp;Pierluigi Siano","doi":"10.1016/j.compeleceng.2025.110563","DOIUrl":"10.1016/j.compeleceng.2025.110563","url":null,"abstract":"<div><div>Global energy is in a disruptive shift into the demanded sustainable, efficient, and decentralized energy approach. Microgrids have emerged as key innovations as they can accommodate renewable energy sources, advanced storage solutions, and intelligent controls. This review provides insight into the critical role that Energy Management Systems (EMS) play in optimizing microgrid operation, providing stability, and improving energy use. This includes the added features of infusing new Technologies like Machine Learning (ML) and the Internet of Things (IoTs), which have changed the outlook for Energy Management Systems through predictive analytics, real-time optimization, and greater reliability. Key challenges of microgrid deployment are addressed - renewable energy variability, cybersecurity concerns-in addition to future trends like digital twins and blockchain applications. This thorough analysis will further reinforce that microgrids represent a point crucial in the solutions offered to the demands of the world in energy for resilient and sustainable power systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110563"},"PeriodicalIF":4.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604477","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":"Improved Signcryption algorithm for cloud virtual machines","authors":"Yogesh Gurav ,&nbsp;Mukil Alagirisamy ,&nbsp;Sathish Kumar Selvaperumal ,&nbsp;Suresh Shirbahadurkar ,&nbsp;Bankat Patil","doi":"10.1016/j.compeleceng.2025.110499","DOIUrl":"10.1016/j.compeleceng.2025.110499","url":null,"abstract":"<div><div>Over the past few years, cloud computing has grown in popularity across all corporate sectors worldwide. However, security continues to be a major roadblock in the dependability of the cloud computing platform. As a result, tenant data security isolation is crucial for protecting sensitive tenant data. This paper's main goal is to present a new decentralized information flow control system for cloud virtual machines with multiple tenants. Three main elements make up the cloud computing environment: (a) Cloud Service Provider (CSP), (b) Data Store (DS), and (c) User. The Cloud Service Provider (CSP) is embedded with the newly proposed decentralized information flow control, and it enables the flow of control of data policies for regulating the flow of data between the cloud users within the cloud. The proposed technique involves three primary stages (i) data identification (ii) trust evaluation (iii) optimal key-based cipher-text information flow security. Initially, according to the security values, tactful information is derived from the input data, which is secured in the CA of CSP to safeguard the labels and keys authentically, subsequently, the decryption process takes place. Then the EP exhibit the flow of ciphertext data based on the proposed estimation of trust. The proposed improved Signcryption algorithm is employed to cipher the user's information and the optimal key is generated by using the proposed improved Sun Flow Optimization Algorithm, which ensures that data remains confidential and tamper-proof. Ultimately, based on the proposed trust value, a decryption key will be offered to users like direct users and indirect users, in which the direct users are the old users already having access rights whereas the indirect users are the new users. The proposed model is examined over various existing techniques, in which the cost function of the proposed work for the lung cancer dataset is 14.2%, 15.7%, 13.6%, 12.1%, 11.5% and 15.2% greater than the current methods like WOA, LA, SSA, ABC-GOA, FSO and SFO respectively. Therefore, the proposed ISFO has been proven to be very concurrent with the traditional optimization approaches, and this aids in increasing the security of the data.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110499"},"PeriodicalIF":4.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604268","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":"Stabilizing DC bus voltage using CEEMDAN-XGBoost based adaptive filtering technique in EV chargers","authors":"Gaurav Yadav,&nbsp;Poonam Dhaka","doi":"10.1016/j.compeleceng.2025.110547","DOIUrl":"10.1016/j.compeleceng.2025.110547","url":null,"abstract":"<div><div>The need for clean AC current, transient-free DC bus voltage, and reactive power support (RPS) in EV chargers has increased due to the widespread use of power electronics for current loads. Addressing these challenges, this manuscript proposes an adaptive filtering technique (AFT) that synergizes Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and XGBoost, an AI-driven framework, to stabilize DC-link voltage under nonlinear and dynamic load conditions. The CEEMDAN-XGBoost approach leverages CEEMDAN’s robust signal decomposition capability to isolate transient disturbances, while XGBoost’s machine learning prowess adaptively optimizes voltage regulation and transient response. This integration achieves enhanced grid stability and power quality, reducing total harmonic distortion (THD) to 2.44% in grid-to-vehicle (G2V) and 3.83% in vehicle-to-grid (V2G) modes. Further, a fourth-order Quadrature Signal Generator (QSG) filter is embedded within EV chargers to augment harmonic attenuation, suppress DC offsets, and accelerate settling times during abrupt load transitions. The efficacy of the proposed control strategy is rigorously validated through MATLAB/Simulink simulations and experimental testing on a laboratory-scale prototype. Results demonstrate superior DC bus voltage stabilization, improved dynamic performance, and compliance with power quality standards, underscoring the viability of CEEMDAN-XGBoost as a transformative solution for next-generation EV charging systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110547"},"PeriodicalIF":4.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604478","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":"Secure key agreement in IoT: A systematic literature review and taxonomy analysis","authors":"Rasoul Rasta ,&nbsp;Hamid Haj Seyyed Javadi ,&nbsp;Midida Reshadi","doi":"10.1016/j.compeleceng.2025.110480","DOIUrl":"10.1016/j.compeleceng.2025.110480","url":null,"abstract":"<div><div>The increasing reliance on smart systems and their remote management has led to the widespread adoption of the Internet of Things (IoT). Despite its benefits, IoT introduces significant communication security challenges due to its heterogeneous components. Key agreement techniques, which establish symmetric encryption keys between communicating parties, are crucial for secure communications. However, the diversity and resource constraints of IoT devices present ongoing challenges. Existing studies on key agreement often focus on devices with specific conditions and specialized communication models. Their broader applicability is limited by that reason. This study addresses this gap by identifying and compiling the necessary features and conditions for key agreement algorithms in IoT environments. We survey key agreement methods to understand the different approaches in IoT using the Systematic Literature Review (SLR) method. This paper aims to categorize, analytically and statistically, the current research techniques on key agreement approaches in IoT published from 2016 to 2024. We present three technical taxonomies for the key agreement approaches in IoT based on the content of current studies. Our findings reveal that crucial parameters such as communication models, essential resources, and required tools are often overlooked in current IoT key management approaches. Notably, only 2% of the reviewed algorithms are lightweight and suitable for device-to-device communication models without prerequisites. This highlights a significant gap, as over 98% of existing algorithms either fail to meet the current requirements of IoT devices or are not easily implementable in the desired environment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110480"},"PeriodicalIF":4.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597099","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}