IEEE Access最新文献

{"title":"A Power Control Algorithm for V2V Communication Networks Based on Dynamic Multi-Objective Optimization","authors":"Mengyu Ma;Zihao Gu;Chao Wang;Zuxing Li;Jianyao Hu;Fuqiang Liu","doi":"10.1109/ACCESS.2025.3554402","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554402","url":null,"abstract":"Vehicular communication networks have the nature of high dynamics and consist of communication links with different or even conflicting transmission objectives and quality of service (QoS) requirements. Therefore, it is extremely challenging to make optimal transmission decisions in vehicular communication networks. In this paper, we study a sequential transmission design problem in a typical vehicle-to-vehicle (V2V) communication network and formulate the problem as a dynamic multi-objective optimization (DMO) problem with the aim to trade-off transmission objectives and guarantee QoS requirements through power control. We propose a prediction-based dynamic multi-objective optimization evolutionary algorithm (DMOEA) that facilitates the evolution of the solution population by predicting the centroid of the power allocation decision set in a new environment, so that transmission decisions can be made to adapt to the highly dynamic environment. Extensive simulation experiments demonstrate the effectiveness and advantages of the proposed algorithm.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54823-54835"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761574","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}

{"title":"Few-Shot Transfer Learning-Based Fault Classification in Wireless Sensor Networks","authors":"Nouman Ijaz;Md. Nazmul Hasan;Insoo Koo","doi":"10.1109/ACCESS.2025.3554748","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554748","url":null,"abstract":"This paper introduces a few-shot transfer learning approach to fault classification in wireless sensor networks (WSNs) with a minimal number of fault samples. WSNs are susceptible to various faults, such as drift, stuck, bias, and spike faults, erratic behavior, and data loss, which can compromise system reliability. Conventional deep learning fault diagnosis methods have achieved promising results, however, the majority of these approaches require a substantial amount of labeled training data, which is not available in real-world scenarios. To address this, we propose a novel method that combines convolutional neural networks (ResNet-18, VGG-16, and MobileNetV2 backbone architectures) with prototypical networks for fault diagnosis in WSNs with only a few fault samples. By transforming time-series data into Gramian Angular Field images, our approach leverages pre-trained deep learning models to extract feature-rich embeddings. These embeddings are then classified using a prototypical network, which enhances the system’s ability to diagnose faults even with a limited amount of labeled data. The proposed model is lightweight and deployable on Internet of Things (IoT) devices, ensuring efficient fault classification with minimal computational resources. Experimental results demonstrate the model’s high accuracy and robustness across various fault types, highlighting its potential for scalable and adaptive IoT applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"55017-55033"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761373","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}

{"title":"Experimental Study of the Stability of a Low-Cost C-Band Active Reflector Using Sentinel-1 Imagery","authors":"Guido Luzi;Qi Gao;Pedro Espín-López","doi":"10.1109/ACCESS.2025.3554305","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554305","url":null,"abstract":"The application of Synthetic Aperture Radar (SAR) interferometry (InSAR) techniques when in the imaged areas the coherence is low and persistent scatterers are missing often demands the installation of artificial reflectors, usually represented by passive corner reflectors (PCR). For missions based on C band sensors such as Sentinel-1, passive corner reflectors capable of providing a high phase accuracy are cumbersome and heavy, and their installation in hard places can be difficult and costly. The installation of active reflectors, compact and smaller with respect to passive corner reflectors, can sometimes represent a valid alternative but the use of these devices has not yet largely spread due to their high sensitivity to the seasonal temperature variation which can reduce their reliability both in amplitude and phase stability. This paper focuses on the analysis of the performance of a low-cost active reflector designed to operate with C band spaceborne radars, tested in a real field campaign using Sentinel-1 imagery. The goal is to assess through an experimental based approach the stability of this device, using data acquired during the monitoring campaign of a landslide based on InSAR. The stability of the Active Reflector (AR) installed in a stable area is analyzed to evaluate its performance when used as a reference in interferometric processing. The results of this study show a stability over a temporal lapse of almost three years equivalent in deformation measurement to a few millimeters accuracy, which can be considered a satisfactory goal for this InSAR application.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"55202-55210"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761575","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}

{"title":"The Role of Artificial Intelligence in Boosting Cybersecurity and Trusted Embedded Systems Performance: A Systematic Review on Current and Future Trends","authors":"Ahmed Oun;Kaden Wince;Xiangyi Cheng","doi":"10.1109/ACCESS.2025.3554739","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554739","url":null,"abstract":"As technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) Distributed Denial-of-Service (DDoS) Detection and Prevention. We investigated current trends in integrating AI into security domains by summarizing the hardware used, the AI methodologies adopted, and the statistical distribution by publication year and region. The key findings of our review indicate that AI significantly enhances security measures by enabling capabilities such as detection, classification, feature selection, data privacy preservation, model combination, data generation, output interpretation, optimization, and adaptation. In addition, the benefits and challenges identified in these studies provide insight into the future potential of AI integration in security. Suggested directions for future work include improving generalization and scalability, exploring continuous or real-time monitoring, and improving AI model performance. This analysis serves as a foundation for advancing AI applications in the effective securing of cyber and embedded systems effectively.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"55258-55276"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942377","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761302","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}

{"title":"Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”","authors":"Oriane Pierrès;Markus Christen;Felix M. Schmitt-Koopmann;Alireza Darvishy","doi":"10.1109/ACCESS.2025.3534338","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534338","url":null,"abstract":"Presents corrections to the paper, (Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"50556-50558"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706625","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}

{"title":"SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation","authors":"Chong-Xiao Peng;Zhi-Jun Gao;Jin-Huan Wang;Xin Yue;Yi Li;Li-Li Sun;Yin-Huan Sun;Fu-Quan Du","doi":"10.1109/ACCESS.2025.3554487","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554487","url":null,"abstract":"In the domain of Traditional Chinese Medicine, accurately segmenting tongue images is fundamental for computer-assisted diagnosis. Yet, current models often falter with images of diverse scales and clarity, impeding their widespread application. To address this challenge, we propose SHPNeXt, an innovative network designed to accurately segment tongue images across different scales and resolutions. This model blends PoolFormer and Hire-MLP to adeptly discern both overarching and nuanced details, ensuring accurate segmentation across varying tongue image sizes. Furthermore, SHPNeXt’s precision was further enhanced by integrating a Nuclear-Norm Non-negative Matrix Factorization (NMF) approach, which robustly counters noise in lower quality images. Experiments on three benchmark datasets demonstrate SHPNeXt’s superior performance, achieving mean Intersection over Union (mIoU) scores of 99.64%, 97.05%, and 96.82%. Balancing efficiency and accuracy, SHPNeXt’s architecture comprises 5.984 million parameters and operates at 1.22 GFLOPs, rendering it an exceptionally effective tool for real-world tongue diagnosis in TCM. The code has been released on github: (<uri>https://github.com/Kuanzhaipcx/SHPNeXt.git</uri>).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54504-54516"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748726","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}

{"title":"Revisiting the Control Systems of Autonomous Vehicles in the Agricultural Sector: A Systematic Literature Review","authors":"Vinayambika S. Bhat;Yong Wang","doi":"10.1109/ACCESS.2025.3555142","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3555142","url":null,"abstract":"The primary objective of this article is to systematically review and categorize the diverse control algorithms applied in autonomous vehicles within the agricultural sector from 2000 to 2023. This systematic literature review (SLR) was conducted using Scopus and Web of Science databases to ensure a comprehensive coverage of peer-reviewed research. The geographical scope of this review is global, encompassing studies from various regions to present a holistic perspective on the technological advancements in autonomous agricultural vehicles. By employing a systematic literature review (SLR) methodology, this study meticulously analyzed published articles to identify, extract, and synthesize data on various control algorithms, which include their application and effectiveness in enhancing agricultural productivity and sustainability. The findings reveal a significant evolution in autonomous vehicle control systems, highlighting a trend towards integrating artificial intelligence (AI)-based control algorithms. These advancements suggest potential navigation and operational efficiency improvements, contributing towards sustainable development goals (SDGs) related to sustainable agriculture. This research presents a novel systematic categorization of control algorithms for autonomous agricultural vehicles by integrating control strategies into a multi-dimensional framework based on algorithmic type (linear, nonlinear, AI-based), application context (path tracking, stability control, obstacle avoidance), and agricultural field type (dry, paddy). Unlike previous reviews that primarily classify algorithms based on technical specifications alone, this study uniquely maps these algorithms to real-world agricultural challenges, providing a structured framework that aligns control methodologies with practical implementation scenarios. This approach enhances clarity in understanding algorithm suitability, adaptability, and scalability across different agricultural settings. The study’s broad implications suggest that enhanced control systems could revolutionize the agricultural sector by improving precision farming techniques. Future research directions include further exploration of AI and machine learning integration with control algorithms and their scalability across various agricultural settings. This SLR provides foundational knowledge and direction for future innovation in the farming sector’s autonomous vehicle technology.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54686-54721"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748836","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}

{"title":"Learning Observers’ Gaze Dynamics: An Efficient and Mobile Sport Scenery Recognition Pipeline","authors":"Huiting Lv;Jiashun Gao;Yu Li;Hongcheng Li","doi":"10.1109/ACCESS.2025.3554704","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554704","url":null,"abstract":"This study addresses the challenge of semantically sorting complex scenes in a mobile environment by processing multimodal visual inputs to create detailed landscape representations. Central to the approach is a streamlined multi-layer hierarchical model that mimics human attention dynamics, using the BING objectness metric to quickly identify significant areas by recognizing objects across different scales and contexts. To enhance feature extraction, time-sensitive and manifold-guided selectors are employed to prioritize high-quality visual features, while a low-rank active learning (LAL) algorithm simulates human-like focus on key visual zones, specifically in sports scenes. The model generates a Gaze Shift Path (GSP), which directs the collection of composite CNN features, ultimately classifying the scenes into distinct landscape types using a support vector machine (SVM). Experimental results on seven scene image sets have shown that our method outperforms the others by <inline-formula> <tex-math>$2% sim 5%$ </tex-math></inline-formula>. Additionally, our calculated deep GSP features can greatly facilitate image clustering. Last but not least, our visualized GSPs are over 90% consistent with real-world human gaze behaviors, which explains the competitiveness of our method.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53188-53202"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761592","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}

{"title":"Zero-Shot Prediction of Conversational Derailment With Large Language Models","authors":"Kenya Nonaka;Mitsuo Yoshida","doi":"10.1109/ACCESS.2025.3554548","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554548","url":null,"abstract":"Online discussion platforms often show a tendency for conversations to stray from the topic and devolve into personal attacks. Previous studies have trained machine learning algorithms to detect conversational derailment using supervised methods. However, creating the datasets required for supervised training is very costly. To address this challenge, we focus on the zero-shot performance of large language models (LLMs), which have advanced rapidly in recent years. This study aims to evaluate the zero-shot prediction performance of conversational derailment using LLMs. First, we measured the performance of the most commonly used LLMs in predicting conversational derailments and found that the zero-shot prediction performance is comparable to that of traditional fine-tuning approaches. Secondly, we explored the effect of inserting prior knowledge into the prompts on the behavior of the LLMs. We discovered that this practice does not necessarily improve the performance of LLMs, resulting in unexpected changes in prediction timing. This study’s contributions are as follows: We demonstrate that zero-shot inference with LLMs is an effective method for predicting conversational derailment in the absence of training data. Additionally, we found that altering the prompt to include prior knowledge leads to unintended results in the conversational derailment prediction task. This observation emphasizes the need to evaluate the impact of prompt changes from various perspectives, going beyond simple performance metrics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"55081-55093"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761348","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}

{"title":"Opacity Verification for a Class of Modular Discrete Event Systems","authors":"Jingkai Yang;Weilin Deng","doi":"10.1109/ACCESS.2025.3554636","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554636","url":null,"abstract":"The verification of opacity of discrete event systems (DESs) is subjected to the curse of dimensionality because this issue has been proven to be EXPSPACE-complete. Therefore, how to improve the efficiency of opacity verification in DESs is crucial. We discuss in this paper the verification of several classes of opacity in modular discrete event systems (modular DESs) formalized as Cartesian composition of deterministic finite state automata. We derive the sufficient and necessary conditions for these opacity notions in modular DESs. Many examples are also used to explain the results obtained in this article. These results greatly reduce the computational complexity of verifying the opacity for modular DESs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54080-54089"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748829","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}