检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Chen, Zhimin Chen, Peng Guo, Ziyu Zhang, Yudong Wang, Xianbin The School of Electronic and Information Shanghai Dianji University Shanghai201306 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The School of Information Science and Technology Fudan University Shanghai200433 China The State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://***/chenpengseu/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

arXiv

引用

arXiv 2021年

作者： Liang, Xin Zhao, Kai Di, Sheng Li, Sihuan Underwood, Robert Gok, Ali M. Tian, Jiannan Deng, Junjing Calhoun, Jon C. Tao, Dingwen Chen, Zizhong Cappello, Franck Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States Department of Computer Science & Engineering University of California Riverside RiversideCA92521 United States Facebook Inc. Menlo ParkCA94025 United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL60439 United States Cerebras Systems SunnyvaleCA94085 United States Department of Computer Science Washington State University PullmanWA99164 United States School of Computing Clemson University ClemsonSC29634 United States Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Today’s scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compressor has been considered one of the most effective solutions to the above problem. In practice, however, the best-fit compression method often needs to be customized/optimized in particular because of diverse characteristics in different datasets and various user requirements on the compression quality and performance. In this paper, we develop a novel modular, composable compression framework (namely SZ3), which involves three significant contributions. (1) SZ3 features a modular abstraction for the prediction-based compression framework such that the new compression modules can be plugged in easily. (2) SZ3 supports multi algorithm predictors and can automatically select the best-fit predictor for each data block based on the designed error estimation criterion. (3) SZ3 allows users to easily compose different compression pipelines on demand, such that both compression quality and performance can be significantly improved for their specific datasets and requirements. (4) In addition, we evaluate several lossy compressors composed from SZ3 using the real-world datasets. Specifically, we leverage SZ3 to improve the compression quality and performance for different use-cases, including GAMESS quantum chemistry dataset and Advanced Photon Source (APS) instrument dataset. Experiments show that our customized compression pipelines lead to up to 20% improvement in compression ratios under the same data distortion compared with the state-of-the-art approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressors

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey of Deep Reinforcement Learning in UAV-Assisted IoT Data Collection

引用

Vehicular Communications 2025年

作者： Oluwatosin Ahmed Amodu Huda Althumali Zurina Mohd Hanapi Chedia Jarray Raja Azlina Raja Mahmood Mohammed Sani Adam Umar Ali Bukar Nor Fadzilah Abdullah Nguyen Cong Luong Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia Computer Science Department College of Science and Humanities Imam Abdulrahman Bin Faisal University P.O. Box 31961 Jubail Saudi Arabia MACS Research Laboratory National Engineering School of Gabes University of Gabes Gabes 6029 Tunisia Department of Electrical Electronics & Systems Engineering Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia UKM Bangi 43600 Selangor Malaysia Department of Computer Science Faculty of Computing and Artificial Intelligence Taraba State University ATC Jalingo 660213 Nigeria Faculty of Computer Science Phenikaa University Hanoi 12116 Viet Nam

Unmanned Aerial Vehicles (UAVs) play a critical role in data collection for a wide range of Internet of Things (IoT) applications across remote, urban, and marine environments. In large-scale deployments, UAVs often face complex decision-making challenges, for which Deep Reinforcement Learning (DRL) has emerged as a promising solution. This paper presents a comprehensive review of UAV-assisted IoT applications utilizing DRL, covering key research questions, DRL algorithm variants, deployment objectives, architectural features, integrated technologies, UAV roles, optimization constraints, energy management strategies, and performance metrics. Findings indicate that value-based and actor-critic algorithms are the most commonly employed, targeting objectives such as path planning, transmit power control, scheduling, velocity and altitude control, and charging optimization. Architectural considerations include clustering, security, obstacle avoidance, buffered sensors, and multi-UAV coordination. Beyond data collection, UAVs are also used for tasks such as device selection, data aggregation, and sensor charging, with energy management primarily achieved through charging and energy harvesting techniques. Performance is typically assessed using metrics like energy efficiency, throughput, latency, packet loss, and Age of Information (AoI). The paper concludes by outlining several promising research directions and open challenges critical to the successful deployment of UAVs in IoT data collection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Carrying out CNN Channel Pruning in a White Box

arXiv

引用

arXiv 2021年

作者： Zhang, Yuxin Lin, Mingbao Lin, Chia-Wen Chen, Jie Wu, Yongjian Tian, Yonghong Ji, Rongrong The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan The Peng Cheng Laboratory Shenzhen518000 China Department of Computer Science and Technology Peking University Beijing100871 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this paper, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner w.r.t. the input image’s category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts. For instance, on CIFAR-10, it reduces 65.23% FLOPs with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOPs reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://***/zyxxmu/White-Box. © 2021, CC BY.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Mobile data offloading in the vehicular networks: A truthful incentive mechanism 21

Mobile data offloading in the vehicular networks: A truthful...

引用

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Enfang, Zhong Li, Peng Zhang, Tao Huang, Weiyi Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Mobile data offloading is a cost-effective and promising approach to alleviate the burden of the cellular network. In this paper, we focus on designing a truthful incentive mechanism that the auction cloud platform motivates mobile users with deadline constraints and capacity constraints to offload data. First, we formulate the mobile data offloading problem as a reverse auction. Then, we reduce this social welfare maximization reverse auction for data offloading as an integer linear programming (ILP) problem. To solve this problem, we propose a data offloading auction greedy algorithm to choose the most cost-effective bids. We decompose the data offload auction greedy algorithm into the winner determination problem (WDP) and the threshold payment scheme. Moreover, we propose a delay winner winning algorithm based on the data offloading auction greedy algorithm. Extensive simulation experiments demonstrate that the data offloading auction mechanism dramatically reduces the burden of the cellular network while meeting the offloading tasks deadline constraints and mobile user capacity constraints. © 2019 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Budget constraint roadside units placement for traffic flows monitoring system with reliability in vehicular networks 21

Budget constraint roadside units placement for traffic flows...

引用

作者： Jiang, Peng Li, Peng Zhang, Tao Huang, Weiyi He, Heng Nie, Lei Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

In Traffic Flows Monitoring System (TFMS), the key problem is how to install Roadside Units (RSUs) reliably to monitor traffic flows with the constrained budget. The reliability of Traffic Flows Monitoring System(TFMS) is determined by the number of RSUs each traffic flow pass through, and the RSUs placement number is determined by the budget and the placement strategy. In this paper, we investigate the budget constraint RSUs placement problem for traffic flows monitoring system with reliability in vehicular networks. Given a budget constraint, the problem is how to install RSUs to ensure the reliability of TFMS, that is each traffic flow pass through RSUs as many as possible with the limited budget. We prove that this problem is NP-hard. Then, we propose two greedy algorithms to solve the problem according to the pigeonhole principle. Moreover, approximation ratios of proposed algorithms are given. Finally, we verify the feasibility of proposed algorithms through extensive experiments, and the results show that the proposed algorithms are superior to the other algorithms. © 2019 IEEE.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

Minimum Trotterization Formulas for a time-Dependent Hamiltonian

arXiv

引用

arXiv 2022年

作者： Ikeda, Tatsuhiko N. Abrar, Asir Chuang, Isaac L. Sugiura, Sho RIKEN Center for Quantum Computing Saitama Wako351-0198 Japan Department of Physics Boston University BostonMA02215 United States Institute for Solid State Physics University of Tokyo Chiba Kashiwa277-8581 Japan Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Department of Physics Department of Electrical Engineering and Computer Science Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States

When a time propagator eδtA for duration δt consists of two noncommuting parts A = X + Y, Trotterization approximately decomposes the propagator into a product of exponentials of X and Y. Various Trotterization formulas have been utilized in quantum and classical computers, but much less is known for the Trotterization with the time-dependent generator A(t). Here, for A(t) given by the sum of two operators X and Y with time-dependent coefficients A(t) = x(t)X + y(t)Y, we develop a systematic approach to derive high-order Trotterization formulas with minimum possible exponentials. In particular, we obtain fourth-order and sixth-order Trotterization formulas involving seven and fifteen exponentials, respectively, which are no more than those for time-independent generators. We also construct another fourth-order formula consisting of nine exponentials having a smaller error coefficient. Finally, we numerically benchmark the fourth-order formulas in a Hamiltonian simulation for a quantum Ising chain, showing that the 9-exponential formula accompanies smaller errors per local quantum gate than the well-known Suzuki formula. © 2022, CC BY.

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

Minimum cost task assignment for vehicle-based crowdsensing with deterministic and nondeterministic trajectory 21

Minimum cost task assignment for vehicle-based crowdsensing ...

引用

作者： Tu, Meimei Li, Peng Zhang, Tao Zhou, Qiang Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Vehicle-based crowdsensing (VCS) is a special case in crowdsourcing, and task assignment is a basic and important problem. In this paper, we investigate the minimum cost task assignment (MCTA) problem for vehicle-based crowdsensing. The VCS platform hopes to recruit some vehicles to complete given spatial-temporal tasks with the minimum cost. As vehicle trajectories are dynamic, we divide the MCTA problem into two sub-problems, the deterministic trajectory (D-MCTA) problem, and nondeterministic trajectory (N-MCTA) problem. For D-MCTA problem, we prove that the D-MCTA problem is NP-hard, and we propose a greedy algorithm to solve this problem. For N-MCTA problem, firstly, we determine the probability of each vehicle's trajectory through the logistic regression method. Then, we exploit the semi-Markov method to calculate the probability of each vehicle executing the task. Moreover, a greedy algorithm is proposed and the theoretical analysis is given. Finally, extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms. © 2019 IEEE.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

引用

arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Recent progress of integrated circuits and optoelectronic chips

引用

science China(Information sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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：