IEEE Access最新文献_第10页

Proof-of-Diversity (PoD): A Framework for Equitable Blockchain Governance 多样性证明（PoD）：公平的bb0治理框架

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562391

M. Erdem Isenkul

{"title":"Proof-of-Diversity (PoD): A Framework for Equitable Blockchain Governance","authors":"M. Erdem Isenkul","doi":"10.1109/ACCESS.2025.3562391","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562391","url":null,"abstract":"This paper introduces the Proof-of-Diversity (PoD) protocol, a new consensus mechanism that enhances decentralization, security, and energy efficiency using demographic, geographic, and computational diversity in validator selection. By using a multi-dimensional entropy-based approach, PoD shows high resistance to Sybil attacks, fosters inclusion, and ensures fair participation. Comparative analysis with Tendermint Proof-of-Stake (PoS) and Algorand Proof-of-Stake (Algorand) shows that PoD is more effective in various key metrics, including transaction finality, validator engagement, diversity entropy, energy use, and adaptability. In particular, PoD achieves a shortest average transaction finality time of 72.84 ms over a given period, a notable improvement compared to both Algorand at 215.37 ms and Tendermint PoS at 278.42 ms. In addition, PoD achieves a validator engagement of 85.42%, strengthening its ability to maintain decentralization. PoD also achieves a diversity score of 0.79, better than Tendermint PoS and Algorand, indicating a more fair and inclusive validator selection process. In terms of energy use, PoD achieves a mere 0.0132 kWh per transaction per second (TPS), a considerable improvement compared to its counterparts. In addition, PoD shows better adaptability to changes in step parameters and changes in benefit-cost ratios, further improving validator selection and network optimization. Overall, these results make PoD a scalable and sustainable consensus system that balances diversity, security, and performance in blockchain networks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"69116-69128"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Technology Anxiety in Virtual Reality Adoption: Examining the Impact of Age, Past Experience, and Cybersickness 技术焦虑在虚拟现实的采用：检查年龄，过去的经验，和晕屏的影响

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562383

Eman Al Khalifah;Ramy Hammady;Mahmoud Abdelrahman;Ons Al-Shamaileh;Mostafa Marghany;Hatana El-Jarn;Alyaa Darwish;Yusuf Kurt

{"title":"Technology Anxiety in Virtual Reality Adoption: Examining the Impact of Age, Past Experience, and Cybersickness","authors":"Eman Al Khalifah;Ramy Hammady;Mahmoud Abdelrahman;Ons Al-Shamaileh;Mostafa Marghany;Hatana El-Jarn;Alyaa Darwish;Yusuf Kurt","doi":"10.1109/ACCESS.2025.3562383","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562383","url":null,"abstract":"This study examines the role of Technology Anxiety (TA), age, past use, and cybersickness in the adoption of Virtual Reality (VR) technology. Using an extended Technology Acceptance Model (TAM), the research integrates age and past use as antecedents of TA and evaluates their influence on perceived ease of use (PEoU), perceived enjoyment (PENJ), and user attitudes. Data from 206 participants were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) following a VR pilgrimage experience. The findings challenge conventional assumptions, revealing that past VR use increased TA, contradicting prior studies that associate familiarity with reduced anxiety. Additionally, older users exhibited lower TA levels than younger participants, highlighting a potential shift in how age influences technology adoption. TA significantly enhanced PENJ, indicating that anxiety may amplify emotional engagement in immersive settings, rather than solely acting as a barrier. While TA enhanced PEoU, it had a negative correlation with cybersickness, suggesting that anxious users might interact with VR more cautiously, thereby limiting sensory mismatches. Moreover, cybersickness did not significantly influence attitudes toward the system, emphasizing the dominance of engagement over physical discomfort in emotionally significant experiences. Attitude toward the system strongly predicted use intention, highlighting the necessity of designing VR experiences that balance usability with emotional engagement. This study provides new insights into the psychological and demographic factors influencing VR adoption and offers practical strategies for optimizing user experience, particularly in religious and cultural applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71858-71879"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multilevel Inverter With a Single Battery Source and a High-Frequency Link for Electric Vehicles 电动汽车用单电池源和高频链路的多电平逆变器

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562351

S. A. Kannan;G. Jagadanand;Nikhil Sasidharan

{"title":"A Multilevel Inverter With a Single Battery Source and a High-Frequency Link for Electric Vehicles","authors":"S. A. Kannan;G. Jagadanand;Nikhil Sasidharan","doi":"10.1109/ACCESS.2025.3562351","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562351","url":null,"abstract":"Multilevel inverter topologies with cascaded H-bridges fed by asymmetrical direct-current (DC) voltage sources have higher output voltage levels than symmetrical ones and are preferred in electric vehicles (EVs). However, these converters are difficult to incorporate in electric vehicles because the system requires a significant number of isolated DC supplies. This study presents a novel multilevel inverter drive topology, which is powered by a single battery source and uses a small, affordable high-frequency link (HFL) to generate isolated DC sources across H-bridges. The HFL consists of a Single-Input Multiple-Output (SIMO) flyback converter and a Bidirectional DC-DC (BDC) converter, which enables dynamic voltage control with a finite number of levels. This study focuses on a 27-level inverter fed induction motor drive with a cross-regulated DC link. In addition, the proposed multilevel drive system enables a smooth transition from motoring to regenerative charging of the battery with three-level rectifier operation of the cascaded H-bridge converter. The hybrid nearest level control (HNLC) modulation scheme is deployed in the proposed drive to control the inverter voltage over a wide range of speed variations without compromising the number of voltage levels. The proposed topology was simulated using MATLAB/Simulink and validated using hardware experiments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"70964-70979"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TouchWIM: Object Manipulation in AR Spatial Design With World in Miniature and Hybrid User Interface TouchWIM：对象操作在AR空间设计与世界在微型和混合用户界面

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562253

Shintaro Imatani;Kensuke Tobitani;Kyo Akabane

引用次数: 0

Innovative Filter for Nonlinear Multitarget Tracking: Improved SCKF-GM-DLPMBM Filter and Its Implementation 非线性多目标跟踪的创新滤波器：改进的SCKF-GM-DLPMBM滤波器及其实现

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562215

Yubin Zhou;Bo Li;Jinyu Zhang;Zhikang Li

{"title":"Innovative Filter for Nonlinear Multitarget Tracking: Improved SCKF-GM-DLPMBM Filter and Its Implementation","authors":"Yubin Zhou;Bo Li;Jinyu Zhang;Zhikang Li","doi":"10.1109/ACCESS.2025.3562215","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562215","url":null,"abstract":"The Poisson multi-Bernoulli mixture (PMBM) filter is capable of estimating the states of multiple targets based on available measurements. To address the limitations of the traditional PMBM filter, which involves the enumeration of assumptions that increases computational time and leads to inaccurate state estimates under noisy conditions, we propose the dual-label PMBM (DLPMBM) filter. This paper enhances the PMBM filter by incorporating labels for both measurements and targets. In the prediction and update phases, the filter is divided into a labeled Poisson point process (LPPP) and a labeled multi-Bernoulli mixture (LMBM) process, which predict and update undetected targets, potential targets, and surviving targets. During the measurement generation phase, each measurement is assigned a unique label, and an improved elliptical gate is used to filter the measurements, embedding them into the LPPP and LMBM measurement update processes. This approach reduces the enumeration of global hypotheses. Furthermore, to address the imprecise estimates of the conventional PMBM filter, an optimization method and its implementation are proposed in this study. To mitigate the uncertainties of conventional filters under nonlinear conditions, we develop an implementation of the Gaussian mixture DLPMBM filter using the square-root cubature Kalman filter (SCKF). The covariance matrix of unknown process noise is improved by integrating the Sage-Husa filter. To ensure the positive definiteness of the estimated covariance, Cholesky decomposition is employed in both the prediction and update phases of the DLPMBM filter. Finally, multitarget tracking experiments are conducted to demonstrate the performance of the proposed DLPMBM filter.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72603-72619"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation FlexiNet：一种自适应特征综合网络，用于实时自我车辆速度估计

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562229

Abdalrahaman Ibrahim;Kyandoghere Kyamakya;Wolfgang Pointner

{"title":"FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation","authors":"Abdalrahaman Ibrahim;Kyandoghere Kyamakya;Wolfgang Pointner","doi":"10.1109/ACCESS.2025.3562229","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562229","url":null,"abstract":"Ego vehicle speed estimation is critical for autonomous driving and advanced driver-assistance systems (ADAS), but traditional methods often fail in accuracy and computational efficiency under dynamic conditions. To address these challenges, we propose FlexiNet, a novel adaptive feature synthesis network that leverages monocular camera data to perform real-time speed estimation. FlexiNet integrates five key components, the Contextual Motion Analysis Block, Adaptive Feature Transformer, Spatial Feature Extraction Module, Motion Feature Extraction Module, and Dynamic Integration Gate, to effectively extract and fuse spatial and temporal features, thereby overcoming limitations of previous approaches by mitigating noise and capturing subtle motion dynamics. Comprehensive evaluations on the KITTI and nuImages datasets demonstrate FlexiNet’s superior performance. On the nuImages dataset, our model achieves an RMSE of 1.1358 m/s and an MAE of 0.9599 m/s, while on the KITTI dataset it records an RMSE of 1.9542 m/s and an MAE of 1.0610 m/s—reductions in error of up to 27.6% and 75.5% compared to baseline methods. These results validate the technical soundness and real-time capability of FlexiNet for deployment on embedded automotive platforms. By addressing critical gaps in previous research, FlexiNet makes a significant contribution toward the development of safer and more efficient autonomous vehicle technologies. The source code for FlexiNet is publicly available at here <uri>https://github.com/Geekgineer/FlexiNet</uri>","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71082-71100"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models 基于神经网络剪枝的轻质金属腐蚀图像分割模型

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562435

Vincent F. Yu;Gemilang Santiyuda;Shih-Wei Lin;Udjianna S. Pasaribu;Yuli Sri Afrianti

{"title":"Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models","authors":"Vincent F. Yu;Gemilang Santiyuda;Shih-Wei Lin;Udjianna S. Pasaribu;Yuli Sri Afrianti","doi":"10.1109/ACCESS.2025.3562435","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562435","url":null,"abstract":"Metal corrosion detection is essential for ensuring structural safety and minimizing economic losses. While deep learning (DL)-based image segmentation has improved corrosion detection accuracy and efficiency, its high computational demands hinder deployment on resource-constrained edge devices. This study investigates lightweight DL models for corrosion segmentation by applying structured pruning to reduce computational costs while maintaining accuracy. We evaluate five segmentation architectures (U-Net, U-Net++, FPN, LinkNet, and MA-Net) and three pruning strategies (linear, automated gradual pruning, and movement pruning) on two corrosion image datasets (NEA and SSCS). Detailed trade-off analysis between model size, computational cost (MAC), and segmentation performance (IoU) reveals that pruning up to 90% sparsity leads to a <inline-formula> <tex-math>$leq 10%$ </tex-math></inline-formula> IoU drop on SSCS and <inline-formula> <tex-math>$leq 5%$ </tex-math></inline-formula> on NEA, demonstrating that significant compression is possible with minimal accuracy loss. However, some architectures (e.g., LinkNet) and pruning strategies (e.g., movement pruning) show significant performance deterioration, suggesting that pruning effectiveness varies across models. These findings provide insights into optimizing corrosion segmentation models for efficient deployment on edge devices, balancing accuracy and resource constraints.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71673-71687"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel Local and Global Context Modeling of Deep Learning-Based Monaural Speech Source Separation Techniques 基于深度学习的单声源分离技术的并行局部和全局上下文建模

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562343

Swati Soni;Lalita Gupta;Rishav Dubey

{"title":"Parallel Local and Global Context Modeling of Deep Learning-Based Monaural Speech Source Separation Techniques","authors":"Swati Soni;Lalita Gupta;Rishav Dubey","doi":"10.1109/ACCESS.2025.3562343","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562343","url":null,"abstract":"The novel deep learning-based time domain single channel speech source separation methods have shown remarkable progress. Recent studies achieve either successful global or local context modeling for monaural speaker separation. Existing CNN-based methods perform local context modeling, and RNN-based or attention-based methods work on the global context of the speech signal. In this paper, we proposed two models which parallelly combine CNN-RNN-based and CNN-attention-based separation modules and perform parallel local and global context modeling. Our models keep maximum global or local context value at a particular time step. These values help our models to separate the speaker signals more accurately. We have conducted the experiments on Libri2mix and Libri3mix datasets. The experimental data demonstrates that our proposed models have outperformed the state-of-the-art methods. Our proposed models remarkably improve SDR and SI-SDR values on Libri2mix and Libri3mix datasets. The proposed parallel CNN-RNN-based and CNN-attention-based separation models achieve average SDR improvement of 2.10 dB and 2.21 dB, respectively, and SI-SDR improvement of 2.74 dB and 2.78 dB, respectively, on the Libri2mix dataset. However, on the Libri3mix dataset, the proposed models achieve 0.57 dB and 0.87 dB average SDR improvement for parallel CNN-RNN-based separation module, and 0.88 dB and 1.4 dB average SI-SDR improvement for CNN-attention-based separation models. Our work indirectly contributes to SDG Goal 10 (Reduced Inequalities) by improving communication tools for diverse linguistic communities. Furthermore, this technology aids SDG Goal 9 (Industry, Innovation, and Infrastructure) by advancing AI-powered assistive technologies, fostering innovation, and building resilient communication systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"68607-68621"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global-Local Ensemble Detector for AI-Generated Fake News 人工智能生成假新闻的全局-局部集成检测器

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562154

Yujia Wang;Wen Long

引用次数: 0

Efficient Real-Time Pathfinding for Visually Impaired Individuals 视障人士的高效实时寻路

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-18 DOI: 10.1109/ACCESS.2025.3562247

Tadeh Ghahremanians;Hossein Mahvash Mohammadi

{"title":"Efficient Real-Time Pathfinding for Visually Impaired Individuals","authors":"Tadeh Ghahremanians;Hossein Mahvash Mohammadi","doi":"10.1109/ACCESS.2025.3562247","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562247","url":null,"abstract":"This paper presents a novel computer vision system, which enables real-time pathfinding for individuals with visual impairments. The navigation experience for visually impaired individuals has significantly improved “in traditional segmentation methods and deep learning techniques”. Traditional methods usually focus on the detection of specific patterns or objects, requiring custom algorithms for each object of interest. In contrast, deep learning models such as instance segmentation and semantic segmentation allow for independent recognition of different elements within a scene. In this research, deep convolutional neural networks are employed to perform semantic segmentation of camera images, thereby facilitating the identification of patterns across the image’s feature space. Motivated by a unique concept of a two-branch core architecture, we propose utilizing semantic segmentation to support navigation for visually impaired individuals. The “demarcation path” captures spatial details with wide channels and shallow layers, while the “path with rich features” extracts categorical semantics using deep layers. By providing awareness of both “obstacles” and “paths” in the surrounding vicinity, this method enhances the perceptual understanding of visually impaired individuals. We try to prioritize real-time performance and low computational overhead to ensure timely and responsive assistance. With a wearable assistive system, we demonstrate that semantic segmentation provides a comprehensive understanding of the surroundings to those with visual impairments. The experimental results showcase an impressive accuracy of 72.6% in detecting paths, path objects, and path boundaries.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71323-71334"},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0