Computers & Electrical Engineering最新文献

英文中文

Resampling video super-resolution based on multi-scale guided optical flow

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110176

Puying Li, Fuzhen Zhu, Yong Liu, Qi Zhang

Existing video super-resolution (VSR) methods are inadequate for dealing with inter-frame motion and spatial distortion problems, especially in high-motion scenes, which tend to lead to loss of details and degradation of reconstruction quality. To address these challenges, this paper puts forward a resampling video super-resolution algorithm based on multiscale guided optical flow. The method combines multi-scale guided optical flow estimation to address the issue of inter-frame motion and a resampling deformable convolution module to address the issue of spatial distortion. Specifically, features are first extracted from low-quality video frames using a convolutional layer, followed by feature extraction with Residual Swin Transformer Blocks (RSTBs). In the feature alignment module, a multiscale-guided optical flow estimation approach is employed, which addresses the inter-frame motion problem across different video segments and performs video frame interpolation and super-resolution reconstruction simultaneously. Furthermore, spatial alignment is achieved by integrating resampling into the deformable convolution module, mitigating spatial distortion. Finally, multiple Residual Swin Transformer Blocks (RSTBs) are used to extract and fuse features, and pixel rearrangement layers are employed to reconstruct high-quality video frames. The experimental results on the REDS, Vid4, and UDM10 datasets show that our method significantly outperforms current state-of-the-art (SOTA) techniques, with improvements of 0.61 dB in Peak Signal-to-Noise Ratio (PSNR) and 0.0121 in Structural Similarity (SSIM), validating the effectiveness and superiority of the method.

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引用次数: 0

Novel single diode solar cell nonlinear model: Optimization and validation

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110150

Martin Calasan, Snezana Vujosevic, Mihailo Micev

This paper focuses on nonlinear modeling of solar cells. In contrast to traditional methods that assume that the series and parallel resistances of solar cells are either constant or linearly dependent on the supply voltage, this paper introduces five nonlinear forms of this dependence. For each proposed form, the paper derives an analytical formula for the current expressed through the Lambert W function. To estimate the parameters of solar cells, a hybrid variant of the Pelican Optimization Algorithm (POA) and chaotic sequences, marked as C-POA, is proposed. The efficiency and applicability of the proposed algorithm, as well as the rationale for using the proposed nonlinear models of solar cells, were tested by observing the RTC France solar cell and two modules, the MSX 60 and the Photowat PWP 201 solar module. The justification for applying the proposed approach (new algorithm and proposed solar cell models) was also tested on the KC200GT solar module for different weather conditions. The numerical results indicate that the accuracy of solar cell modeling, as expressed through the root mean square error (RMSE) value, can be significantly improved by applying any of the five proposed models, sometimes by more than 50%. The calculation accuracy is significantly better than that of the most accurate model, i.e., the three-diode model of solar cells. Therefore, the presented research offers a new perspective in solar cell modeling.

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引用次数: 0

3D pedestrian detection based on hybrid multi-scale cascade fusion network

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110139

Yang Chen, Yan Mu, Rongrong Ni, Biao Yang

Improving pedestrian safety on the road is one of the essential tasks of autonomous driving. LiDAR-based intelligent perception systems can provide necessary guarantees for pedestrian safety in autonomous driving by accurately detecting pedestrians in real time. However, the detection performance suffers from the small-scale issue and blurred boundary of pedestrian point clouds. This work proposes a novel PillarHMCNet, which focuses on enhancing the feature representation of pedestrian point clouds to improve the 3D detection performance, tackling the issues mentioned above. Concretely, a Hybrid Encoder (HE) module is proposed to extract sparse and dense features of pedestrians through customized encoders, enhancing the feature representation of small-scale objects. Afterward, a Multi-scale Cascaded Feature Fusion (MCFF) module is introduced to fuse multi-layer sparse and dense features, improving the pedestrian contour representation. Finally, a dense head is used to conduct 3D detection based on the output of the MCFF module. Moreover, a direction-sensitive loss is leveraged to improve the model’s positioning accuracy by introducing the angle and distance-IoU (DIOU) losses. Quantitative and qualitative evaluations are conducted on the KITTI dataset, and in the detection of pedestrians and cyclists in 3D mode, our model outperforms PillarNet by 4.22% and 1.17%. The results verify the effectiveness and universality of the proposed method in intelligent perception of autonomous driving. The code will be available at https://github.com/CCZU-Myan/PillarHMCNet.

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引用次数: 0

A dual-path interactive denoising autoencoder for removing mixed noise in multi-lead electrocardiogram signals

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110177

Xiuxin Zhang , Meng Chen , Yongjian Li , Jiahui Gao , Yiheng Sun , Feifei Liu , Shoushui Wei

Denoising of dynamic electrocardiography (ECG) is a hotspot in ECG processing. Existing denoising methods mainly target single-lead signals or single noise sources, with limited research addressing mixed noise across multiple leads. Based on the time-frequency characteristics of common noise in ECG acquisition, this study introduces the Dual-Path Interactive Denoising Autoencoder (DP-IDAE). One pathway utilizes multi-scale convolutional kernels to extract local features, while the other employs bidirectional long short-term memory to capture global features. An interactive transmission mechanism facilitates information exchange between pathways. Experimental results demonstrate DP-IDAE's superior denoising performance across seven types of noise, including single and mixed noise composed of baseline wander (BW), muscle artifact (MA), and electrode motion (EM). In the complex noise environment of BW+EM+MA at -6 dB, the

SN R_{imp}

and PRD still achieve 8.3 dB and 31.87 %, respectively. Additionally, the study investigates the relationship between denoising effects and signal similarity of different lead ECGs. It concludes that a higher similarity between lead signals leads to better denoising performance, especially for leads I, II, AVF, and V4-V6, where the denoising effect is more significant. From both time-domain and frequency-domain perspectives, DP-IDAE effectively removes local and global noise by leveraging the advantages of dual pathways.

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引用次数: 0

Cross-Modality Target Detection Using Infrared and Visible Image Fusion for Robust Objection recognition

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110133

Hang Yu , Jichen Gao , Suiping Zhou, Chenyang Li, Jiaqi Shi, Feng Guo

Visible-infrared cross-modal object detection aims to overcome the limitations of single modality highlighting in complex environments (rain, fog, weak light) by utilizing dual-modal images. Most existing methods typically use finite size convolution kernels to learn local features, and ignore the interaction of non-local feature dependencies between modalities such as the infrared and the visible modalities, resulting in unsatisfactory detection performance. To tackle the problem, we propose a multi-modal object detection algorithm that fuse visible and infrared modalities through cross enhancement and long-range guidance, effectively combining complementary information and shared collaborative information to enhance detection capabilities. In this paper, we first propose the cross-modality feature enhancement method that utilizes the difference between channel information and spatial information of each modality. Secondly, we use cross-attention layers on the basis of transformer to achieve long-range interactive information exchange, and add self-attention layers to enhance internal connections. Finally, we propose a feature enhancement module that enhances performance by utilizing a multi-branch structure composed of different convolutions. Experiments on three publicly available datasets have shown that our proposed approach achieves superior robustness and accuracy under all weather conditions and constantly changing lighting conditions.

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引用次数: 0

Self-supervised graph transformer networks for social recommendation

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110121

Qinyao Li , Qimeng Yang , Shengwei Tian , Long Yu

Social recommendation systems often use graph data to represent users, items, and their interactions. Graph Neural Networks (GNNs) are effective at analyzing the complex relationships among nodes. However, traditional GNN models tend to focus only on immediate neighbors during information propagation, limiting their ability to capture global information. To address this limitation, we propose a Self-Supervised Graph Transformer Network (SGTN) for social recommendation. SGTN applies the Transformer to process graph data, using multi-head attention mechanisms for global node information exchange. It also includes edge feature pipeline to fully utilize edge information in social networks, enhancing the model’s understanding of user preferences. Additionally, multi-head attention makes the learned representation multi-view. SGTN uses different user representations generated from user–item interactions and user–user relationships for contrastive learning, effectively integrating these two sources of information for more accurate user representations. Extensive experiments on two real-world datasets demonstrate the effectiveness of SGTN.

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引用次数: 0

Privacy and security vulnerabilities in edge intelligence: An analysis and countermeasures

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110146

Ahmed Shafee , S.R. Hasan , Tasneem A. Awaad

Recent advancements in deep learning have significantly accelerated the growth of artificial intelligence (AI) technologies, powering applications like the Metaverse, augmented reality (AR), virtual reality (VR), and tactile communications on emerging 6G networks. The proliferation of Internet of Things (IoT) devices and mobile computing has connected vast numbers of devices to the internet, generating enormous amounts of data at the network edge.

To harness the potential of this big data, extending AI capabilities to the network edge has become increasingly vital. Edge AI, or edge intelligence (EI), enables computing tasks to be performed closer to data sources, reducing latency and enhancing efficiency. However, this shift has amplified privacy concerns due to increased data sharing, compounded by the growing prevalence of data breaches. Research also reveals that sharing AI models instead of raw data does not fully safeguard privacy, as certain attacks can still compromise sensitive training information.

This paper reviews Edge Intelligence with a focus on privacy and security issues, identifying critical challenges and vulnerabilities in edge and cloud computing environments. It provides a comprehensive analysis of state-of-the-art solutions to address these concerns, offering valuable insights into enhancing privacy and security in distributed computing systems.

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引用次数: 0

Blockchain-based identity authentication and data interaction scheme for Industrial Internet of Things

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.compeleceng.2025.110143

Chao Geng , Yang Zhang , Xin Xu , Yingbiao Yao , Conghui Lu , Zhongqiang Zhi

There are numerous identities in the Industrial Internet of Things (IIoT), and the security of the industrial data generated by these identity devices is crucial. However, problems such as single point of failure caused by centralization and inefficiencies of participant identity authentication are still persistent in the current IIoT. To address these problems, we propose a blockchain-based identity authentication and data interaction scheme for IIoT. In our scheme, we first design a decentralized and staged key generation authentication method based on the Sign Identity Composite (SIC), which uses key generation in stages to realize efficient identity authentication based on Schnorr’s non-interactive zero-knowledge proof. Then, we propose asynchronous data interaction based on the Diffie–Hellman (DH) key negotiation algorithm and SIC. Finally, we prove the security of our scheme through security analysis, and the performance is evaluated and analyzed by simulation experiments. The results show that our scheme can perform identity authentication and asynchronous data interaction while maintaining security, outperforms the comparison scheme.

{"title":"Blockchain-based identity authentication and data interaction scheme for Industrial Internet of Things","authors":"Chao Geng , Yang Zhang , Xin Xu , Yingbiao Yao , Conghui Lu , Zhongqiang Zhi","doi":"10.1016/j.compeleceng.2025.110143","DOIUrl":"10.1016/j.compeleceng.2025.110143","url":null,"abstract":"<div><div>There are numerous identities in the Industrial Internet of Things (IIoT), and the security of the industrial data generated by these identity devices is crucial. However, problems such as single point of failure caused by centralization and inefficiencies of participant identity authentication are still persistent in the current IIoT. To address these problems, we propose a blockchain-based identity authentication and data interaction scheme for IIoT. In our scheme, we first design a decentralized and staged key generation authentication method based on the Sign Identity Composite (SIC), which uses key generation in stages to realize efficient identity authentication based on Schnorr’s non-interactive zero-knowledge proof. Then, we propose asynchronous data interaction based on the Diffie–Hellman (DH) key negotiation algorithm and SIC. Finally, we prove the security of our scheme through security analysis, and the performance is evaluated and analyzed by simulation experiments. The results show that our scheme can perform identity authentication and asynchronous data interaction while maintaining security, outperforms the comparison scheme.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110143"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378652","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

Increase efficiency of wireless power transmission with conical coil

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-10 DOI: 10.1016/j.compeleceng.2025.110165

Yuhui Xu

Wireless power transmission (WPT) is a widely adopted technology with promising applications. However, conventional WPT systems suffer from magnetic leakage, energy loss, and reduced efficiency. To address these challenges, this study proposes a conical primary coil and establishes a hardware experimental platform for evaluation. Comparative experiments between a conventional planar coil and the conical coil were conducted at various frequencies and distances. Experimental results demonstrate that the conical coil improves transmission efficiency by up to 10 % compared to the planar coil under identical conditions, achieving 91.8 % efficiency at 5 cm and 85.6 % at 8 cm, compared to 82.4 % and 76.8 % for the planar coil, respectively. To further analyze the magnetic field distribution, Maxwell software simulations were conducted. The results reveal that the conical coil enhances energy density, extends transmission range, and effectively reduces magnetic leakage. This study introduces a novel coil geometry that significantly improves WPT performance, providing valuable insights for future high-efficiency WPT systems.

引用次数: 0

A delayed charging enabled station for electric vehicles

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2025-02-10 DOI: 10.1016/j.compeleceng.2025.110166

Suraj S , Narayan S. Manjarekar , Soumyabrata Barik , Sudarshan Swain

The resurgence and adoption of Electric Vehicle (EV) technology have necessitated the establishment of more Electric Vehicle Charging Stations (EVCS) to mitigate driving range anxiety among EV owners. The battery, a critical component of EVs, has garnered significant research interest, particularly concerning its lifespan management. EV batteries experience cyclic and calendar aging, which can adversely impact their performance and longevity. While cyclic aging due to vehicle operation is unavoidable, measures can be taken to mitigate calendar aging, particularly when batteries remain idle at a high State of Charge (SoC). This paper introduces a delayed charging technique designed to minimize idle periods with an SoC above 85 %, thereby reducing calendar aging. The proposed technique, implemented at EVCS, is capable of charging multiple EVs simultaneously, and leverages Internet of Things (IoT) technology to collect vehicle departure times and optimize the charging schedule. The Delayed Charging Algorithm (DCA) determines the initial SoC using the Open Circuit Voltage (OCV) method and estimates the updated SoC during charging via the Coulomb Counting (CoC) approach. By calculating the required time to reach a full charge (100 % SoC), the DCA strategically delays charging to align with departure times, effectively mitigating calendar aging. A MATLAB-based simulation was performed for a 320 V, 94.5 Ah EV battery, and the proposed methodology was validated through a hardware setup charging two 3,000 mAh Lithium-Ion (Li-ion) cells of model 18,650 simultaneously. App-based results further confirm the effectiveness of the DCA in a charging station scenario. A comparative analysis with conventional charging modes shows that the DCA reduces calendar aging by 4.7 %.

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