检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Lv, Shaohe Lu, Yong Dong, Mianxiong Wang, Xiaodong Dou, Yong Zhuang, Weihua National Laboratory of Parallel and Distributed Processing National University of Defense Technology ChangshaHunan China Department of Information and Electronic Engineering Muroran Institute of Technology 27-1 Mizumoto-cho Muroran Hokkaido050-8585 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

—Human-machine systems required a deep understanding of human behaviors. Most existing research on action recognition has focused on discriminating between different actions, however, the quality of executing an action has received little attention thus far. In this paper, we study the quality assessment of driving behaviors and present WiQ, a system to assess the quality of actions based on radio signals. This system includes three key components, a deep neural network based learning engine to extract the quality information from the changes of signal strength, a gradient based method to detect the signal boundary for an individual action, and an activity-based fusion policy to improve the recognition performance in a noisy environment. By using the quality information, WiQ can differentiate a triple body status with an accuracy of 97%, while for identification among 15 drivers, the average accuracy is 88%. Our results show that, via dedicated analysis of radio signals, a fine-grained action characterization can be achieved, which can facilitate a large variety of applications, such as smart driving assistants. Copyright © 2017, The Authors. All rights reserved.

关键词： Man machine systems

来源：评论

学校读者我要写书评

暂无评论

A performance study of CSMA in wireless networks with successive interference cancellation

A performance study of CSMA in wireless networks with succes...

引用

IEEE International Conference on Communications (ICC)

作者： Shaohe Lv Weihua Zhuang Xiaodong Wang Chi Liu Xiaofeng Hu Yipin Sun Xingming Zhou Institute of Network Information and Security National University of Defense Technology Changsha China Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada Central South University Changsha China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

Successive interference cancellation (SIC) is an effective way of multipacket reception to combat interference. As conventional CSMA (Carrier Sense Multiple Access) is designed for single packet reception, it is unclear whether or not CSMA performs well to exploit the SIC capability. In this paper, we analyze the performance of a simple CSMA protocol in a network with SIC. For a given link, we derive the residing areas of an interfering node when simultaneous transmission is allowed and when the interference is harmful, respectively. We show that, though SIC provides many new transmission opportunities, CSMA cannot effectively exploit them. There is a fundamental tradeoff in a CSMA protocol between exploiting the transmission opportunities from SIC and capturing the harmful interference. In many cases, when CSMA achieves its best performance, almost all new transmission opportunities are not exploited. It is therefore very necessary to design a new distributed access protocol in wireless networks with SIC.

关键词： Silicon carbide Multiaccess communication Interference Receivers Propagation losses Protocols Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Dynamic scheduling algorithm for real-time multiprocessor systems based on grouping and properly choosing policies

引用

Jisuanji Xuebao/Chinese Journal of Computers 2006年第1期29卷 81-91页

作者： Bin, Xue-Lian Yang, Yu-Hai Jin, Shi-Yao Department of Information and Command Automation Air Force Radar Academy Wuhan 430019 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

Dynamic scheduling algorithms for real-time multiprocessor are important components of real-time systems. In this paper, a new algorithm, named Group-Proper (GP), is presented based on analysis of the shortcomings of myopic algorithm and thrift algorithm. GP algorithm includes two policies: grouping policy and properly choosing policy (PCP). Grouping policy is aimed to increase the utilization of resources. PCP is aimed to enlarge the utilization of CPU. On the condition of ensuring not missing the deadlines of tasks with smaller heuristic value, grouping policy prior chooses the task accessing resources in the shared mode to extend. If a task needn't access resources or the earliest available times of resources required by it are earlier than that of the CPU, PCP assigns the task to the CPU with the earliest available time. Otherwise, PCP assigns the task to the CPU with the closest available times to that of the resources required by it. Simulation results show that acceptance ratio of GP algorithm is higher than that of myopic and thrift.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Accelerating MatLab code using GPU: A review of tools and strategies

Accelerating MatLab code using GPU: A review of tools and st...

引用

International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC)

作者： Baida Zhang Shuai Xu Feng Zhang Yuan Bi Linqi Huang National laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Information Engineering Academy of Armored Forces Engineering Beijing China Inspur (Beijing) Electronic Information Industry Company Limited Beijing China Naval Academy of Armament Beijing China School of Resources and Safety Engineering Central South University Changsha China

Lots of toolboxes of accelerating MatLab using GPU are available now[1], but, users are confused by which toolbox is best suitable for a particular task. Three toolboxes-Jacket, GPUmat, and parallel Computing Toolbox of MatLab are selected. For each toolbox, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which toolbox is appropriate for a given task. Strategies of whether a function should execute on GPU are given after a formula analysis. The analysis is also a framework for program automatically decides which function is cost-efficient to execute on GPU. A series of benchmark of different types of computing, including data transfer between GPU and CPU, data matrix Generation, matrix operation and GPU functions were tested in all three toolboxes. And the results show that Jacket is the best one. Some advices to improve the performance of toolboxes are given in the end.

关键词： Graphics processing unit Acceleration parallel processing Computer architecture Conferences Licenses MATLAB

来源：评论

学校读者我要写书评

暂无评论

Correntropy induced metric based graph regularized non-negative matrix factorization

Correntropy induced metric based graph regularized non-negat...

引用

International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bin Mao Naiyang Guan Dacheng Tao Xuhui Huang Zhigang Luo College of Science National University of Defense Technology Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia

Non-negative matrix factorization (NMF) is an efficient dimension reduction method and plays an important role in many pattern recognition and computer vision tasks. However, conventional NMF methods are not robust since the objective functions are sensitive to outliers and do not consider the geometric structure in datasets. In this paper, we proposed a correntropy graph regularized NMF (CGNMF) to overcome the aforementioned problems. CGNMF maximizes the correntropy between data matrix and its reconstruction to filter out the noises of large magnitudes, and expects the coefficients to preserve the intrinsic geometric structure of data. We also proposed a modified version of our CGNMF which construct the adjacent graph by using sparse representation to enhance its reliability. Experimental results on popular image datasets confirm the effectiveness of CGNMF.

关键词： Robustness Vectors Clustering algorithms Mutual information Measurement Computer integrated manufacturing

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

arXiv

引用

arXiv 2024年

作者： Li, Xin Yuan, Kun Pei, Yajing Lu, Yiting Sun, Ming Zhou, Chao Chen, Zhibo Timofte, Radu Sun, Wei Wu, Haoning Zhang, Zicheng Jia, Jun Zhang, Zhichao Cao, Linhan Chen, Qiubo Min, Xiongkuo Lin, Weisi Zhai, Guangtao Sun, JianHui Wang, Tianyi Li, Lei Kong, Han Wang, Wenxuan Li, Bing Luo, Cheng Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Kong, Fangyuan Fan, Haotian Xu, Yifang Xu, Haoran Yang, Mengduo Zhou, Jie Li, Jiaze Wen, Shijie Xu, Mai Li, Da Yao, Shunyu Du, Jiazhi Zuo, Wangmeng Li, Zhibo He, Shuai Ming, Anlong Fu, Huiyuan Ma, Huadong Wu, Yong Xue, Fie Zhao, Guozhi Du, Lina Guo, Jie Zhang, Yu Zheng, Huimin Chen, Junhao Liu, Yue Zhou, Dulan Xu, Kele Xu, Qisheng Sun, Tao Ding, Zhixiang Hu, Yuhang University of Science and Technology of China China Kuaishou Technology China University of Würzburg Germany Shanghai Jiao Tong University China Nanyang Technological University Singapore Xiaohongshu China WeChat China Tencent China Wuhan University China ByteDance Shenzhen China Zhejiang University China Department of Electronic and Information Engineering Beihang University Beijing China Southern University of Science and Technology China Harbin Institute of Technology China Beijing University of Posts and Telecommunications China China Merchants Bank China Shandong Jianzhu University China Key Laboratory for Parallel and Distributed Processing Changsha China Institute of Automation CAS China University of Chinese Academy of Sciences China

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-CVPR-NTIRE2024. Copyright © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Journal of information Science and Engineering: Editorial notice

引用

Journal of information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract available

关键词：

来源：评论

学校读者我要写书评

暂无评论

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

引用

arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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：