检索结果-内蒙古大学图书馆

AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging

IEEE Transactions on Mobile computing 2025年

作者： Hu, Senkang Fang, Zhengru Fang, Zihan Deng, Yiqin Chen, Xianhao Fang, Yuguang Kwong, Sam Tak Wu City University of Hong Kong Department of Computer Science Hong Kong JC STEM Lab of Smart City Kowloon Hong Kong University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Lingnan University Department of Computing and Decision Sciences Hong Kong

Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue and enhance the safety and efficiency of connected and autonomous vehicles (CAVs) at multi-lane merging zones, we propose a novel collaborative decision-making framework, named AgentsCoMerge, to leverage large language models (LLMs). Specifically, we first design a scene observation and understanding module to allow an agent to capture the traffic environment. Then we propose a hierarchical planning module to enable the agent to make decisions and plan trajectories based on the observation and the agent's own state. In addition, in order to facilitate collaboration among multiple agents, we introduce a communication module to enable the surrounding agents to exchange necessary information and coordinate their actions. Finally, we develop a reinforcement reflection guided training paradigm to further enhance the decision-making capability of the framework. Extensive experiments are conducted to evaluate the performance of our proposed method, demonstrating its superior efficiency and effectiveness for multi-agent collaborative decision-making under various ramp merging scenarios. © 2002-2012 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Integrating Blockchain Technology with PKI for Secure and Interoperable Communication in 5G and Beyond Vehicular Networks

Integrating Blockchain Technology with PKI for Secure and In...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Fetulhak Abdurahman Shewajo Abdelwahab Boualouache Sidi Mohammed Senouci Ines El-Korbi Bouziane Brik Kinde Anlay Fante DRIVE Lab University of Burgundy Nevers France Faculty of Electrical and Computer Engineering Jimma Institute of Technology Jimma University Jimma FSTM University of Luxembourg Luxembourg Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE

Security and privacy are crucial in V2X networks due to sensitive user information. Public Key Infrastructure (PKI) is widely used in C-ITS to ensure security and privacy. However, the practical implementation of PKI faces challenges in achieving seamless communication across diverse ITS projects worldwide. The absence of interoperability between PKI systems and unre-solved issues in existing PKI standards hinder global adoption. Despite available security standardizations using centralized PKI technology for V2X, a universally adopted PKI-based security architecture is necessary. Furthermore, the progress made in 5G V2X technology has demonstrated significant potential for revolutionizing V2X communication in the future. Enhancing the level of trust through integration of the 5G Core Network (5GC) into the PKI security mechanism can lead to more secure and efficient V2X communication. To address these challenges, we propose a blockchain-based architecture that integrates the 5GC network with the PKI infrastructure, aiming to enhance privacy and security in 5G V2X communication. Our solution is designed to be distributed and interoperable, aligned with existing ETSI ITS PKI standard. By utilizing Hyperledger Fabric (HLF) platform, a permissioned blockchain framework, we present the architecture and conduct a comprehensive security analysis to ensure compliance with security and privacy requirements of V2X communications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

引用

computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

引用

IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Learning Photometric Feature Transform for Free-Form Object Scan

引用

IEEE Transactions on visualization and computer Graphics 2024年 PP卷 PP页

作者： Feng, Xiang Kang, Kaizhang Pei, Fan Ding, Huakeng You, Jinjiang Tan, Ping Zhou, Kun Wu, Hongzhi Zhejiang University State Key Lab of CAD & CG Hangzhou310058 China King Abdullah University of Science and Technology Visual Computing Center Thuwal23955-6900 Saudi Arabia The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong

We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. The effectiveness of the system is demonstrated with a lightweight prototype, consisting of a camera and an array of LEDs, as well as an off-the-shelf tablet. Our results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques. © 1995-2012 IEEE.

关键词： Photometry

来源：评论

学校读者我要写书评

暂无评论

Secrecy Analysis of UAV Control Information Transmission via NOMA

Secrecy Analysis of UAV Control Information Transmission via...

引用

IEEE Conference on Wireless Communications and Networking

作者： Zhaoxin Feng Huabing Lu Nan Zhao Zhaoyuan Shi Yunfei Chen Xianbin Wang School of Information and Communication Engineering Dalian University of Technology Dalian China Key Lab. of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Department of Engineering University of Durham Durham UK Department of Electrical and Computer Engineering Western University London ON Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicle (UAV) assisted wireless communication is essential for the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the design of control information transmission is essential, requiring ultra reliability, low latency, and high security. In this paper, considering both the large-scale path loss and the Nakagami-m small-scale fading, we investigate the secrecy performance of UAV control information transmission in a NOMA ground-air network with an external flying eavesdropper. A spherical secrecy protection zone is set, and the closed-form expressions for average secure BLER and average achievable secrecy throughput are derived. After that, the asymptotic performance in the high SNR regime is analyzed to get more insights. Ultimately, simulation results verify the accuracy of analysis.

关键词： Wireless communication NOMA Accuracy Simulation Information processing Autonomous aerial vehicles Throughput

来源：评论

学校读者我要写书评

暂无评论

A Survey on Decentralized Identifiers and Verifiable Credentials

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro University of Salerno Department of Information and Electrical Engineering and Applied Mathematics Fisciano84084 Italy Florida International University Cyber-Physical Systems Security Lab School of Computing and Information Science MiamiFL33174 United States University of Bologna Department of Computer Science and Engineering Bologna40136 Italy University of Padua Department of Mathematics Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by theWorldWideWeb Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 1998-2012 IEEE.

关键词： Decentralized control

来源：评论

学校读者我要写书评

暂无评论

Deep Video Representation Learning: A Survey

arXiv

引用

arXiv 2024年

作者： Ravanbakhsh, Elham Liang, Yongqing Ramanujam, J. Li, Xin Division of Electrical & Computer Engineering Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Section of Visual Computing and Interactive Media Texas A&M University College StationTX77843 United States

This paper provides a review on representation learning for videos. We classify recent spatio-temporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Building effective features for videos is a fundamental problem in computer vision tasks involving video analysis and understanding. Existing features can be generally categorized into spatial and temporal features. Their effectiveness under variations of illumination, occlusion, view and background are discussed. Finally, we discuss the remaining challenges in existing deep video representation learning studies. © 2024, CC BY-NC-ND.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach

arXiv

引用

arXiv 2024年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In School of Computer and Information Engineering Henan Normal University Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China College of Computing and Data Science Nanyang Technological University Singapore School of Electrical Engineering and Telecommunications University of New South Wales NSW2052 Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem. In the formulated problem, beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices are jointly optimized subject to constraints on total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, to further simplify the design, we adopt the maximum ratio transmission (MRT) and zero-forcing (ZF) as beamforming vectors in the action space. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach. Moreover, implementing the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2024, CC BY.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

HY-Deepnet: A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer's disease detection and diagnosis system

引用

engineering Science and Technology, an International Journal 2025年 68卷

作者： Veluchamy, S. Bhuvaneswari, R. Ashwini, K. Alshathri, Samah El-Shafai, Walid Department of Electronics and Communication Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Nagercoil India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Automated Systems and Computing Lab (ASCL) Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

A progressive neurological disorder, Alzheimer's disease (AD) predominantly impacts memory and cognitive functions of diseased victims. The disease is usually identified by the buildup of abnormal protein deposits in the brain, resulting in the development of plaques and tangles that interfere with communication between nerve cells. Over time, those affected by Alzheimer's undergo a diminishing mental capacity, affecting their daily activities and ultimately resulting in a loss of independence. While there is currently no remedy to reverse the advancement of Alzheimer's disease, identifying the initiation of AD can prove highly beneficial within the medical field. This study presents an innovative framework for the detection and diagnosis of Alzheimer's disease, employing deep transfer models and COATI optimization techniques. The proposed hybrid Deepnet (HY-Deepnet) framework consists of two main phases: detection and diagnosis. The detection phase aims at identifying the presence of disease with the aid of MRI images. This phase evaluates the performance of various deep learning models, including AlexNet, GoogLenet, SqueezeNet, VGGNet, ResNet and explores their combinations. Experimental results reveal that the combination of AlexNet, GoogLenet, and VGGNet outperforms other networks and their combinations, achieving an accuracy of 77.03% without optimization. The second phase focuses on the diagnosis of the detected disease into three different stages. The diagnosis phase is improved from COATI optimization techniques. The proposed HY-Deepnet thus attains an impressive overall accuracy of 97.6%, accompanied by precision, recall, and F1 scores of 0.978, 0.976, and 0.974, respectively. These results underscore the effectiveness of the framework in enhancing Alzheimer's detection and diagnosis, particularly when leveraging the synergy of deep transfer models and COATI optimization techniques. © 2025 The Authors

关键词： Alzheimer COATI optimization Deep learning Feature extraction Pre-trained networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：