检索结果-内蒙古大学图书馆

Content-Aware Joint Knob Configuration and Resource Allocation for Edge Video Analytics

IEEE Transactions on Mobile Computing 2025年第6期24卷 5536-5550页

作者： Bai, Tong Hou, Bo Wang, Zhipeng Liu, Dong Nallanathan, Arumugam Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science E1 4NS United Kingdom

Characterized by its ease of low-latency response, edge computing is capable of supporting real-time video analytics applications, constituting an edge video analytics paradigm, where the joint knob configuration and network scheduling design has drawn ever-escalating research attention. However, the potential of edge video analytics has not been fully exploited, owing to the limitations of the state-of-the-art as follows. i) The eminent impact of video content on accuracy performance has been ignored. ii) The variables that can be tuned are not fully considered in scheduling. iii) The heuristic algorithm-based solutions are far from the optimal. To fill in this gap, in this paper, we conceive a content-aware joint knob configuration and resource allocation scheme for edge video analytics. Concretely, fed with the features extracted from the video content, a deep neural network (DNN)-based predictor is proposed to predict the configuration-accuracy performance in a real-time manner. With an aid of the predictive results, we formulate an accuracy-maximization problem as an integer programming problem, by optimizing the variables, including resolution, frame rate, video analytic model, network bandwidth, and computational resource subject to the latency constraints. To solve this problem in an efficient manner, we devise a novel low-complexity dynamic programming method. Simulation results verify the efficiency of our content-aware joint knob configuration and resource allocation scheme. Quantitatively, a 3.3% gap is attained towards the upper bound in terms of the accuracy in an object detection scenario, relying on the scheme proposed. © 2002-2012 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Hardware-friendly rate estimation algorithm and architecture design for AVS3

引用

Multimedia Tools and Applications 2025年 1-19页

作者： Yan, Yunyao Xiang, Guoqing Chen, Jie Huang, Xiaofeng Zhang, Peng Jia, Huizhu Xie, Xiaodong School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

To enhance the capabilities of advanced video coding for emerging applications, the AVS3 standard has been introduced to double the coding efficiency compared to its predecessor, the AVS2 standard. It incorporates sophisticated coding tools, such as advanced rate-distortion optimization (RDO), to select the optimal coding mode. The AVS3 standard further refines bit rate compression through Advanced Entropy Coding (AEC) within the RDO process. However, AEC demands significant computational resources due to its high data dependency and involves complex steps like binarization, context modeling, interval subdivision, renormalization, handling of outstanding bits, and context updating. Notably, the time complexity of AEC is predominantly due to context updating and interval subdivision, which are recursive and thus pose challenges for real-time applications, particularly in hardware implementations. Addressing these challenges, this paper introduces an adaptive rate estimation algorithm using a hardware-friendly piece-wise linear function to expedite bit rate calculations for AVS3 applications. Initially, we develop a linear rate estimation method based on the statistics of bin numbers. To preserve high coding performance, we further propose an adaptive model parameter updating method that adjusts weights dynamically based on varying video content. Building on this fast rate estimation algorithm, we propose a high-throughput hardware architecture that incorporates pipelining and parallelization to meet the stringent real-time requirements for 4K@120fps ultra-high-definition video at 200 MHz, with only a minimal increase in BD-rate by 1.06% under the All-Intra (AI) configuration. To our knowledge, this is the pioneering study focusing on hardware-oriented rate estimation for the AVS3 standard, marking a significant advancement in the field of video coding technology. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature

关键词： Video signal processing

来源：评论

学校读者我要写书评

暂无评论

HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection

引用

IEEE Access 2025年 13卷 72314-72328页

作者： Ding, Lei Tang, Chunhui Fang, Yi University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

Lane detection is a crucial component of autonomous driving perception systems. Existing methods still encounter performance limitations in complex situations like occlusions and extreme illumination. To address these challenges, this paper introduces a novel Hybrid Global Semantic and Local Detail Feature Network (HGLFNet), designed to enhance lane detection accuracy and robustness in complex scenarios. Technically, we propose a Global Semantic Feature Extraction Module (GSFEM), integrating a Long-Range Information Capture Unit (LRICU) and a Multi-Scale Aggregation Unit (MSAU). GSFEM innovatively utilizes large-kernel convolutions to efficiently capture global contextual information, significantly improving the model’s perception of complete lane line shapes and overcoming the limited receptive field of conventional methods. Furthermore, we develop a Semantic Feature Fusion Module (SFFM) to effectively bridge the semantic gap between global semantic features and local detailed features. SFFM incorporates a Deformable Feature-based Attention Mechanism (DFAM) to adaptively enhance the representational power of fused features and highlight crucial lane line characteristics. HGLFNet effectively integrates global semantic context with local detailed information, enhancing the network’s ability to detect thin and occluded lane line structures. In quantitative evaluation, HGLFNet is compared with state-of-the-art methods. On the VIL-100 dataset, the F1 score and accuracy are improved by 0.75% and 0.38%, respectively, compared to ADNet. On the Tusimple dataset, the F1 score and accuracy are improved by 0.49% and 0.11%, respectively. On the more challenging CULane dataset, the F1 score is improved by 0.03%. The experimental results comprehensively demonstrate that HGLFNet surpasses the existing state-of-the-art techniques in both accuracy and efficiency, providing a novel and effective solution for lane detection in complex scenarios and showing significant potential in pra

关键词： Semantics Feature extraction Lane detection Shape Attention mechanisms Accuracy Kernel Lighting Autonomous vehicles Roads

来源：评论

学校读者我要写书评

暂无评论

Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

引用

China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

来源：评论

学校读者我要写书评

暂无评论

Movable-Antenna-Assisted Covert Communications With Reconfigurable Intelligent Surfaces

引用

IEEE Internet of Things Journal 2025年第9期12卷 12369-12382页

作者： Xie, Wenwu Li, Zilong Yu, Chao Xu, Hongbo Wang, Ji Wu, Weimin Li, Xingwang Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate. © 2014 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Feature selection for determining input parameters in antenna modeling

引用

Journal of Systems engineering and electronics 2025年第1期36卷 15-23页

作者： LIU Zhixian SHAO Wei CHENG Xi OU Haiyan DING Xiao School of Physics University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer and Information Engineering Xinjiang Agricultural UniversityUrumqi 830052China

In this paper,a feature selection method for determining input parameters in antenna modeling is *** antenna modeling,the input feature of artificial neural network(ANN)is geometric *** selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)*** information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear *** EM response range is utilized to evaluate the *** wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is *** the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly *** modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed *** number of input parameters decreases from8 to *** testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.

关键词： antenna modeling artificial neural network(ANN) feature selection maximal information coefficient(MIC)

来源：评论

学校读者我要写书评

暂无评论

Towards an Explainable Artificial Intelligence approach for smart grid systems

引用

Discover Artificial Intelligence 2025年第1期5卷 1-20页

作者： Alfayan, Mahmoud Hagras, Hani School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

As global energy demands escalate, effective management of electrical grids and reducing carbon emissions have become critical objectives. This paper proposes a novel system which employs Explainable Artificial Intelligence (XAI) to enhance the operational efficiency of smart grids by predicting energy consumption and optimising resource allocation accordingly. The integration of XAI addresses the complexities of grid management by providing transparency into AI-driven predictions, thus fostering user trust and facilitating informed decision-making. The main contributions of this work include evaluating the status of smart energy grid management and presenting a pathway for integrating XAI to enhance these practices. Using a real-world dataset with variables related to operations, environment, and time, our system employs a type-2 fuzzy logic XAI based system to generate clear, interpretable predictions of energy demand. The transition from a Type-1 to a Type-2 fuzzy logic system resulted in enhanced prediction performance, as evidenced by a reduction in the root mean square error (RMSE) from 8.7734 to 5.9422, resulting in 32.2% enhancement in RMSE. The proposed system demonstrated comparable performance compared to conventional black-box models, including neural network. It also shows stakeholders how to interpret these predictions across a wide range of consumption scenarios, which emphasizes the importance of predictions in making the best use of resources. Ultimately, this paper presents a first step for the employment of XAI within smart grid management. © The Author(s) 2025.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Two-Timescale Hierarchical Contract for Joint Computation Offloading and Energy Management in Edge Computing System

引用

IEEE Transactions on Network science and engineering 2025年第3期12卷 1745-1760页

作者： Yan, Min Wang, Li Xu, Lianming Hou, Luyang Han, Zhu Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

To mitigate the rising energy costs in edge computing, edge servers (ESs) can receive revenues from reducing their energy usage by contracting with virtual power plant (VPP). ESs also respond to user equipment (UE) by providing computation offloading services. However, such two-layer coordinated trading of computation offloading and energy management in a VPP-ES-UE architecture should address the information asymmetry issue and uncertain task arrivals, posing challenges to maximizing the stakeholders' utility. In this paper, we formulate the two-layer coordinated trading as a hierarchical contracting problem, which addresses the information asymmetry using two contract models. We design a VPP-ES energy contract on a large timescale and an ES-UE computation contract on a small timescale, where ESs need to coordinate the future task arrivals under the energy reduction target assigned by VPP. We construct a two-timescale virtual queue to achieve the energy reduction goal by the long-term queue stability constraint and employ Lyapunov optimization to transform the original problem into an online optimization problem without requiring future information. An online two-timescale hierarchical contract optimization algorithm is proposed to solve the transformed problem. The simulation results demonstrate that our method achieves higher social welfare compared to other benchmarks. © 2013 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

引用

Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Time-Domain Modeling And Prediction of Global Ionospheric Total Electron Content: Applications of Attention Mechanisms

Time-Domain Modeling And Prediction of Global Ionospheric To...

引用

2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Liu, Xingyu Wang, Yufei Yang, Kunlin Liu, Yang School of Computer Science and Engineering Beihang University China School of Instrumentation and Opto-electronic Engineering Beihang University China

ISBN: (纸本)9780936406404

The global Total Electron Content (TEC) is a critical parameter to present ionosphere morphology, at the same time it significantly affects the propagation of trans-ionosphere radio waves, especially the global navigation satellite signals. The variations in ionosphere can cause satellite signal delays. To address the problem, it is necessary to make accurate prediction of ionosphere TEC so as to strengthen navigation performance. With the rapid development of deep learning techniques, time-series prediction models based on deep learning networks have demonstrated great potential in TEC prediction. Compared to traditional methods, deep learning models have many advantages in capturing complex spatiotemporal dependencies in TEC series. To address the problem, this work compares the performance of five deep learning models in application of ionosphere TEC prediction, focusing particularly on the usage of attention mechanisms on improvement for prediction accuracy. In the experiments, a number of leading deep learning models are evaluated and tested for short-term ionosphere TEC prediction, including the long-short term memory (LSTM), those attention-based networks like CNN-BiLSTM-Attention, Transformer, PatchTST, the TimesNet based on convolutional kernel was also considered. Two years of TEC series are selected for testing, the year 2014 as the solar maximum in solar cycle 24, and the year 2017 in the solar descending phase of solar cycle 24. The advantage of attention-mechanism are analyzed and compared. The results show that the PatchTST model has significant improvement in prediction accuracy, effectively capturing spatiotemporal variations in TEC. Models with attention mechanisms show better performance in prediction accuracy and reliability. By dynamically adjusting the hyper-parameters in different parts of the input sequence, the attention mechanism helps to capture variation features that have greater impact on prediction in time series, thus effectively impr

关键词： Ionosphere

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：