检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,405 篇 期刊文献
996 篇 会议
7 篇 学位论文

馆藏范围

2,408 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,357 篇 工学
- 1,701 篇 计算机科学与技术...
- 1,373 篇 电气工程
- 1,310 篇 信息与通信工程
- 529 篇 软件工程
- 160 篇 交通运输工程
- 155 篇 电子科学与技术（可...
- 139 篇 控制科学与工程
- 133 篇 动力工程及工程热...
- 83 篇 仪器科学与技术
- 53 篇 环境科学与工程（可...
- 45 篇 机械工程
- 36 篇 生物工程
- 26 篇 航空宇航科学与技...
- 21 篇 土木工程
- 18 篇 网络空间安全
- 12 篇 测绘科学与技术
- 11 篇 石油与天然气工程
308 篇 理学
- 202 篇 数学
- 69 篇 生物学
- 48 篇 化学
- 40 篇 统计学（可授理学、...
- 34 篇 系统科学
- 21 篇 物理学
- 15 篇 大气科学
207 篇 管理学
- 196 篇 管理科学与工程(可...
- 102 篇 工商管理
- 19 篇 图书情报与档案管...
18 篇 经济学
- 18 篇 应用经济学
15 篇 法学
- 15 篇 社会学
13 篇 医学
- 11 篇 临床医学
5 篇 艺术学
1 篇 教育学

主题

2,408 篇 computation offl...
450 篇 mobile edge comp...
364 篇 edge computing
342 篇 task analysis
278 篇 resource allocat...
268 篇 servers
229 篇 resource managem...
200 篇 deep reinforceme...
191 篇 cloud computing
186 篇 computational mo...
185 篇 energy consumpti...
164 篇 optimization
147 篇 internet of thin...
135 篇 delays
106 篇 mobile cloud com...
104 篇 multi-access edg...
95 篇 fog computing
86 篇 mobile edge comp...
77 篇 wireless communi...
73 篇 energy efficienc...

机构

40 篇 beijing univ pos...
31 篇 nanjing univ sta...
26 篇 carleton univ de...
26 篇 nanjing univ inf...
25 篇 xidian univ stat...
23 篇 southeast univ n...
22 篇 tsinghua univ de...
21 篇 sun yat sen univ...
21 篇 beijing jiaotong...
19 篇 cent south univ ...
19 篇 shenzhen univ co...
19 篇 univ british col...
18 篇 nanyang technol ...
17 篇 chongqing univ p...
16 篇 univ chinese aca...
16 篇 peng cheng lab p...
15 篇 kyung hee univ d...
14 篇 univ houston dep...
14 篇 beijing univ pos...
14 篇 huaqiao univ col...

作者

30 篇 xu xiaolong
27 篇 han zhu
27 篇 leung victor c. ...
27 篇 yu f. richard
24 篇 yang yang
21 篇 peng kai
20 篇 yuan haitao
20 篇 bi jing
20 篇 chen xu
19 篇 li keqin
18 篇 guizani mohsen
17 篇 zhao liang
16 篇 min geyong
16 篇 ji hong
16 篇 du jianbo
15 篇 li xi
15 篇 wu yuan
15 篇 guo songtao
15 篇 qi lianyong
15 篇 chu xiaoli

语言

2,305 篇 英文
93 篇 其他
11 篇 中文
1 篇 法文

检索条件"主题词=computation offloading"

共 2408 条记录，以下是2201-2210 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

computation Resource Allocation Based on Particle Swarm Optimization for LEO Satellite Networks

Computation Resource Allocation Based on Particle Swarm Opti...

引用

作者： Shan Lu Fei Zheng Wei Si Mengqi Liu The 54th Research Institute of China Electronics Technology Group Corporation Guilin University of Electronic Technology Jilin University

A distributed computing architecture for computation offloading and data processing in low earth orbit(LEO) satellite networks without transmitting data back to the ground station,so that the task execution time is minimized,is introduced in this *** computational resource allocation schemes are not applicable for the LEO satellite networks and can be classified as an optimal solution problem with *** obtain the optimal solution of this problem,group intelligent optimization algorithm is utilized to approach the minimum task execution time and the influence of CPU frequency,transmission rate,and channel delay is considered ***,considering the task execution time is mainly determined by the processing power,the processing power of each satellite is evaluated and transformed into weight to adjust the velocity of particle in particle swarm optimization(PSO) *** results demonstrate that the improved algorithm has faster convergent speed than PSO algorithm and costs less time for task execution.

关键词： LEO Satellite Networks computation offloading Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing 1

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud...

引用

1st International Conference on Advances in Science, Engineering and Robotics Technology, ICASERT 2019

作者： Sarker, V.K. Pena Queralta, J. Gia, T.N. Tenhunen, H. Westerlund, T. University of Turku Department of Future Technologies Turku Finland KTH Royal Institute of Technology Department of Electronics Stockholm Sweden

ISBN: (纸本)9781728134451

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant percentage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power consumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer. © 2019 IEEE.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient computation offloading model for mobile phone environment

Energy-efficient computation offloading model for mobile pho...

引用

1st IEEE International Conference on Cloud Networking (CloudNet)

作者： Fekete, Krisztian Csorba, Kristof Forstner, Bertalan Feher, Marcell Vajk, Tamas Budapest Univ Technol & Econ Dept Automat & Appl Informat Budapest Hungary

ISBN: (纸本)9781467327985;9781467327978

The popularity of smartphones is growing every day. Thanks to the more powerful hardware the applications can run more tasks and use broadband network connection, however there are several known issues. For example, under typical usage (messaging, browsing, and gaming) a smartphone can be discharged in one day. This makes the battery life one of the biggest problems of the mobile devices. That is a good motivation to find energy-efficient solutions. One of the possible methods is the "computation offloading" mechanism, which means that some of the tasks are uploaded to the cloud. In this paper we are going to present a new energy-efficient job scheduling model and a measurement infrastructure which is used to analyze the energy consumption of smartphones. Our results are going to be demonstrated through some scenarios where the goal is to save energy. The offloading task is based on LP and scheduling problems.

关键词： computation offloading Energy Consumption Mobile Computing Cloud Job Scheduling ILP Simulation

来源：评论

学校读者我要写书评

暂无评论

Markov Based computational Tasks offloading Decision for Face Detection 14

Markov Based Computational Tasks Offloading Decision for Fac...

引用

14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Thu, Mi Swe Zar Htoon, Ei Chaw University of Information Technology Yangon Myanmar

ISBN: (纸本)9781728123486

Smart mobile devices are essential for technology trend of modern lifestyles. Mobile applications are getting more diverse and complex with an increasing use of mobile devices. At mobile environment, resource limitation is irresistible extremely;it is a key challenge and can impact on mobile computing performance. Mobile Cloud computing (MCC) is one of the eventual gold computing executions of rich mobile application on an abundance of mobile devices. Limited computational power, storage, and energy are necessary for hardware limitations of offloading. So, the proposed system aims to avoid limitation of devices starting in case of intensive tasks by using dynamic computation offloading of Markov process. The system reduces energy consumption of resource hungry device as the way of taking decision to offload the computing intensive tasks to the remote cloud by the result of our cost model. The experiment shows that the proposed offloading decision solver can reduce not only computing time but also battery usage of mobile device for face detection application compared to the dynamic off loading MAUI decision framework. © 2019 IEEE.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Optimal joint power allocation and task splitting in wireless distributed computing 11

Optimal joint power allocation and task splitting in wireles...

引用

11th International ITG Conference on Systems, Communications and Coding, SCC 2017

作者： Le, Hong Quy Al-Shatri, Hussein Klein, Anja Communications Engineering Lab Technische Universität Darmstadt Merckstrasse 25 Darmstadt64283 Germany

ISBN: (纸本)9783800743629

The mobile application market is growing very fast. More and more applications require intensive computations. However, computation power of mobile devices is limited and does not catch up with the growth of computation demand of applications. computation offloading is a promising approach for reducing the computation load and reducing execution time and energy consumption. In this work, we investigate a wireless distributed computing scenario where a mobile device exploits the computation resources of the nearby devices to reduce execution time. The main goal is to study the gain of computation offloading among heterogeneous devices. We formulate the problem of optimal offloading as a joint resource allocation problem consisting of power allocation and task splitting. The resulting problem is non-linear and non-convex. By exploiting the unique characteristics of the problem, we can transform it into a convex problem. The optimal solution of the original problem can then be determined by performing a bisection search over one parameter. Using numerical simulation, we can show that by offloading computation load to nearby devices, the execution time is reduced substantially. Moreover, the gain of offloading in comparison with computing locally increases with the ratio of the computation load to the size of input data of the task as well as with increasing number of nearby devices. © VDE Verlag GMBH. Berlin. Offenback. All rights reserved.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Optimizing Task Execution for Mobile Edge Computing 2019

Optimizing Task Execution for Mobile Edge Computing

引用

Proceedings of the 2019 8th International Conference on Networks, Communication and Computing

作者： Muhammad Fayyaz Cao Bin Waleed Almughalles Yun Li Liaqat Ali Key Lab of Mobile Communication Technology Chongqing University of Posts and Telecommunications

ISBN: (纸本)9781450377027

computation-intensive applications can be enabled by mobile edge computing (MEC) in 5G networks because MEC carries cloud computing almost near to smart devices. In this paper, we study a multi-user MEC system, where several smart devices (SDs) can fulfill computation offloading over wireless channels to a MEC server. we study the minimization of a total sum cost which is energy consumption and time delay for all the smart devices (where smart devices can choose one out of three scenarios to execute the task, i.e., full local computing scenario, full offloading execution scenario, and partial offloading execution scenario) as our objective function optimization. We mutually optimize task partition, offloading decision and computation resource sharing to reduce the total cost of the MEC system. We used an extensive search method and Lagrange method to solve these problems. Statistical results prove the effectiveness of our proposed scheme.

关键词： computation offloading computing mode Mobile edge computing execution scenario task partition

来源：评论

学校读者我要写书评

暂无评论

Optimizing Task Execution for Mobile Edge Computing 8

Optimizing Task Execution for Mobile Edge Computing

引用

8th International Conference on Networks, Communication and Computing, ICNCC 2019

作者： Fayyaz, Muhammad Bin, Cao Almughalles, Waleed Li, Yun Ali, Liaqat Key Lab of Mobile Communication Technology Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9781450377027

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Cuckoo: A computation offloading framework for smartphones 2nd

Cuckoo: A computation offloading framework for smartphones

引用

2nd International Conference on Mobile Computing, Applications, and Services, MobiCASE 2010

作者： Kemp, Roelof Palmer, Nicholas Kielmann, Thilo Bal, Henri Vrije Universiteit De Boelelaan 1081A Amsterdam Netherlands

ISBN: (纸本)9783642293351

offloading computation from smartphones to remote cloud resources has recently been rediscovered as a technique to enhance the performance of smartphone applications, while reducing the energy usage. In this paper we present the first practical implementation of this idea for Android: the Cuckoo framework, which simplifies the development of smartphone applications that benefit from computation offloading and provides a dynamic runtime system, that can, at runtime, decide whether a part of an application will be executed locally or remotely. We evaluate the framework using two real life applications. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2012.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Poster abstract: On-device training from sensor data on batteryless platforms 18

Poster abstract: On-device training from sensor data on batt...

引用

18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019

作者： Islam, Bahsima Luo, Yubo Lee, Seulki Nirjon, Shahriar University of North Carolina Chapel Hill United States

ISBN: (纸本)9781450362849

In this paper, we argue that the fusion of machine learning (ML) and batteryless computing systems enables true lifelong learning in mobile devices. The lack of learning from experience in current batteryless systems makes them ignorant of changes in their operating environment. Due to high communication cost, latency, privacy, and dependency issues of offloading computation to an edge device, on-device training is a solution for batteryless systems to learn and adapt in dynamically changing environments. Combining batteryless systems and ML is however a challenging task. Sporadic energy supply and limited resources in a batteryless system cause execution-discontinuity and data-constraints in ML processes. To understand these challenges, we identify suitable ML tasks for such systems and study the energy producers, i.e., harvesters, and consumers, i.e., intermittently executable tasks in a ML pipeline. Using a trace-driven simulation, we demonstrate the feasibility of on-device training of a batteryless learner. © 2019 Copyright held by the owner/author(s).

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Joint offloading and frequency scaling technology for mobile edge computing 21

Joint offloading and frequency scaling technology for mobile...

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： Liang, Jie Liu, Chubo Tan, Guanghua Yang, Lei College of Information Science and Engineering Hunan University China National Supercomputing Center in Changsha Changsha Hunan410082 China

ISBN: (纸本)9781728120584

Mobile-edge computing (MEC) is a prominent technique to support computation-intensive tasks for mobile devices. Currently, almost all works on MEC offloading focus on independent tasks, and most works involve MEC center with only one powerful server. However, actually, there are usually precedence constraints among tasks and a MEC center is equipped with multiple heterogeneous servers. In this paper, we refer to a makespan minimization problem for tasks offloading with precedence constraints in MEC system. The problem is NP hard. To address such a problem, we propose a joint scheduling algorithm that consists of two sub-algorithms optimization, i.e., task offloading scheduling and execution frequency selection. Finally, we conduct some experiments. The results demonstrate the capability of the proposed algorithms in achieving a smaller makespan compared to several other common algorithms in scheduling literature. © 2019 IEEE.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共241页 << < 217 218 219 220 221 222 223 224 225 226 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：