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

2,878 篇 会议
929 篇 期刊文献
500 册 图书
29 篇 学位论文
1 篇 科技报告

馆藏范围

4,332 篇 电子文献
6 种 纸本馆藏

日期分布

学科分类号

1,740 篇 工学
- 1,080 篇 计算机科学与技术...
- 873 篇 电气工程
- 402 篇 信息与通信工程
- 371 篇 软件工程
- 130 篇 控制科学与工程
- 104 篇 电子科学与技术（可...
- 87 篇 仪器科学与技术
- 35 篇 机械工程
- 29 篇 航空宇航科学与技...
- 22 篇 测绘科学与技术
- 17 篇 材料科学与工程（可...
- 15 篇 核科学与技术
- 14 篇 交通运输工程
- 12 篇 石油与天然气工程
- 11 篇 网络空间安全
- 10 篇 土木工程
- 10 篇 生物医学工程（可授...
- 7 篇 建筑学
224 篇 理学
- 68 篇 物理学
- 47 篇 数学
- 44 篇 系统科学
- 34 篇 生物学
- 17 篇 化学
- 12 篇 地球物理学
- 8 篇 天文学
- 6 篇 地理学
129 篇 管理学
- 115 篇 管理科学与工程(可...
- 17 篇 图书情报与档案管...
59 篇 医学
- 44 篇 临床医学
- 19 篇 基础医学(可授医学...
13 篇 农学
9 篇 教育学
6 篇 文学
5 篇 经济学
3 篇 军事学
2 篇 法学
1 篇 哲学
1 篇 艺术学

主题

4,337 篇 distributed proc...
432 篇 nasa technical r...
432 篇 (ntrs)
402 篇 conferences
320 篇 distributed comp...
314 篇 computer archite...
274 篇 application soft...
228 篇 computer science
224 篇 parallel process...
213 篇 computational mo...
196 篇 computer network...
163 篇 hardware
160 篇 concurrent compu...
160 篇 resource managem...
154 篇 fault tolerance
154 篇 laboratories
149 篇 costs
136 篇 scalability
123 篇 protocols
104 篇 software enginee...

机构

19 篇 national laborat...
12 篇 univ calif los a...
8 篇 ibm thomas j. wa...
8 篇 gmd fokus
7 篇 department of ch...
7 篇 argonne national...
7 篇 institute of inf...
7 篇 school of cyber ...
7 篇 university of to...
6 篇 aalborg univ dep...
6 篇 carnegie mellon ...
6 篇 university of ch...
6 篇 hong kong univer...
5 篇 shanghai jiao to...
5 篇 huazhong univers...
5 篇 college of compu...
5 篇 lawrence berkele...
5 篇 pacific northwes...
5 篇 south asian univ...
5 篇 univ tennessee d...

作者

15 篇 sayed ali h.
12 篇 schizas ioannis ...
11 篇 nael h. el-farra
11 篇 ji wang
10 篇 oki eiji
10 篇 minyi guo
10 篇 kawabata akio
9 篇 koji nakano
8 篇 chatterjee bijoy...
8 篇 hai jin
7 篇 yoshihisa tomoki
7 篇 teranishi yuuich...
7 篇 kawakami tomoya
7 篇 laurence t. yang
7 篇 gao yunjun
6 篇 ishi yoshimasa
6 篇 akio kawabata
6 篇 chu ww
6 篇 eiji oki
6 篇 masaru fukushi

语言

4,167 篇 英文
109 篇 其他
56 篇 中文
2 篇 俄文
1 篇 法文
1 篇 日文

检索条件"主题词=distributed processing"

共 4337 条记录，以下是31-40 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：

Scaling Deep Learning-Based Analysis of High-Resolution Satellite Imagery with distributed processing

Scaling Deep Learning-Based Analysis of High-Resolution Sate...

引用

IEEE International Conference on Big Data (Big Data)

作者： Nguyen, Mai H. Li, Jiaxin Crawl, Daniel Block, Jessica Altintas, Ilkay Univ Calif San Diego San Diego Supercomp Ctr La Jolla CA 92093 USA Univ Calif San Diego Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Qualcomm Inst La Jolla CA 92093 USA

ISBN: (纸本)9781728108582

High-resolution satellite imagery is a rich source of data applicable to a variety of domains, ranging from demographics and land use to agriculture and hazard assessment. We have developed an end-to-end analysis pipeline that uses deep learning and unsupervised learning to process high-resolution satellite imagery and have applied it to various applications in previous work. As high-resolution satellite imagery is large volume data, scalability is important to be able to analyze data from large geographical areas. To add scalability to our process, we converted our original pipeline, implemented using the Caffe deep learning library and the Python machine learning library Scikit-Learn, to other platforms that make use of distributed computation. Specifically, to add scalability, we use Keras for deep learning, and evaluate two different distributed platforms, Spark and Dask, 14 unsupervised learning. We report on results in scaling up our satellite analysis pipeline.

关键词： satellite image analysis deep learning unsupervised learning distributed processing Keras Spark Dask

来源：评论

学校读者我要写书评

暂无评论

Implementation of distributed processing Using a PC-FPGA Hybrid System 18

Implementation of Distributed Processing Using a PC-FPGA Hyb...

引用

International Conference on Field-Programmable Technology (ICFPT)

作者： Takano, Keisuke Oda, Tetsuya Ozaki, Ryo Uejima, Akira Kohata, Masaki Okayama Univ Sci Grad Sch Engn Okayama Japan Okayama Univ Sci Fac Engn Okayama Japan

ISBN: (纸本)9781728129433

Recent research has focused on decreasing processing times and power consumption by combined use PCs and FPGAs. Here describe a system that performs distributed processing across multiple PCs and FPGAs using ethernet for PC connections and an in-house designed FPGA network to connect FPGAs. For processing software, we used Apache Spark on the PCs and an in-house application programming interface (API) for the FPGAs. Experimental results show that both PC and FPGA are effectively used for image compression calculation.

关键词： PC-FPGA Hybrid System FPGA distributed processing Cluster System Dynamic Partial Reconfiguration

来源：评论

学校读者我要写书评

暂无评论

Optimized distributed processing in a Vehicular Cloud Architecture

Optimized Distributed Processing in a Vehicular Cloud Archit...

引用

International Conference on Transparent Optical Networks

作者： Fatemah S. Behbehani Mohamed Musa Taisir Elgorashi J. M. H. Elmirghani School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom

ISBN: (数字)9781728184234

ISBN: (纸本)9781728184241

The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users, which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small-distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often un-used, in their on-board-units (OBUs). These can be used to augment the network edge processing capabilities. In this paper, we extend our previous work and develop a mixed integer linear programming (MILP) formulation that optimizes the allocation of networking and processing resources to minimize power consumption. Our edge processing architecture includes vehicular processing nodes, edge processing and cloud infrastructure. Furthermore, in this paper our optimization formulation includes delay. Compared to power minimization, our new formulation reduces delay significantly, while resulting in a very limited increase in power consumption.

关键词： Delays Power demand Minimization Energy efficiency Computer architecture Wireless fidelity distributed processing

来源：评论

学校读者我要写书评

暂无评论

distributed processing and Data Fusion In Dynamic Systems With Microsystem Sensor Networks 38

Distributed Processing and Data Fusion In Dynamic Systems Wi...

引用

IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)

作者： Lyshevski, Sergey E. Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA

ISBN: (纸本)9781538663837

distributed processing and control are critical to supports distributed intelligence and autonomy of multi-degree-of-freedom motion systems. Measurements and fusion of spatiotemporal physical quantities imply data-intensive computing and spatial distribution of computing resources to enable control and processing of large data sets from image and inertial sensors. We examine distributed and asynchronous processing nodes which process information independently deriving partial solutions. There are multiple sensing-and-processing nodes in each individual agent. Each node comprises solid-state or MEMS multi-degree-of-freedom sensors with ASICs which process and fuse data. On-node computing supports distributed processing. Adaptive bottom-up organization ensures data aggregation and data management with operation on sub-samples or hashed sets of large source datasets. Cooperative distributed processing is essential in centralized, decentralized and behavioral coordination. In the centralized organization, a central processor may not ensure adequacy. A network of semi-autonomous on-device processing sensors may interact to solve specific tasks and validate solutions. Problem allocation, partitioning, coordination and other tasks are implemented using software- and hardware-supported algorithms and protocols. This paper contributes to design of next generation of systems with distributed multi-node processing capabilities.

关键词： distributed processing inertial sensors MEMS microsystems smart sensors

来源：评论

学校读者我要写书评

暂无评论

In-Memory Indexed Caching for distributed Data processing

In-Memory Indexed Caching for Distributed Data Processing

引用

International Symposium on Parallel and distributed processing (IPDPS)

作者： Alexandru Uta Bogdan Ghit Ankur Dave Jan Rellermeyer Peter Boncz LIACS Leiden University Databricks UC Berkeley TU Delft CWI

Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we introduce the Indexed DataFrame, an in-memory cache that supports a dataframe abstraction which incorporates indexing capabilities to support fast lookup and join operations. Moreover, it supports appends with multi-version concurrency control. We implement the Indexed DataFrame as a lightweight, standalone library which can be integrated with minimum effort in existing Spark programs. We analyze the performance of the Indexed DataFrame in cluster and cloud deployments with real-world datasets and benchmarks using both Apache Spark and Databricks Runtime. In our evaluation, we show that the Indexed DataFrame significantly speeds-up query execution when compared to a non-indexed dataframe, incurring modest memory overhead.

关键词： distributed processing Runtime Data analysis Cluster computing Relational databases Libraries Concurrency control

来源：评论

学校读者我要写书评

暂无评论

Duality-Based Locality-Aware Stream Partitioning in distributed Stream processing Engines 25th

Duality-Based Locality-Aware Stream Partitioning in Distribu...

引用

25th International Conference on Parallel and distributed Computing (Euro-Par)

作者： Son, Siwoon Moon, Yang-Sae Kangwon Natl Univ Chunchon South Korea

ISBN: (纸本)9783030483401;9783030483395

In this paper, we propose duality-based locality-aware stream partitioning (LSP) in distributed stream processing engines (DSPEs). In general, LSP directly uses the locality concept of distributed batch processing engines (DBPEs). This concept does not fully take into account the characteristics of DSPEs and therefore does not maximize cluster resource utilization. To solve this problem, we first explain the limitations of existing LSP, and we then propose a duality relationship between DBPEs and DSPEs. We finally propose a simple but efficient ping-based mechanism to maximize the locality of DSPEs based on the duality. The insights uncovered in this paper can maximize the throughput and minimize the latency in stream partitioning.

关键词： distributed processing Data stream Locality Duality

来源：评论

学校读者我要写书评

暂无评论

FastDRC: Fast and Scalable Genome Compression Based on distributed and Parallel processing 19th

FastDRC: Fast and Scalable Genome Compression Based on Distr...

引用

19th International Conference on Algorithms and Architectures for Parallel processing (ICA3PP)

作者： Ji, Yimu Fang, Houzhi Yao, Haichang He, Jing Chen, Shuai Li, Kui Liu, Shangdong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Nanjing Inst Ind Technol Sch Comp & Software Nanjing 210023 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Nanjing Univ Posts & Telecommun Inst High Performance Comp & Big Data Nanjing 210003 Peoples R China Nanjing Ctr HPC China Nanjing 210003 Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

With the advent of next-generation sequencing technology, sequencing costs have fallen sharply compared to the previous sequencing technologies. Genomic big data has become the significant big data application. In the face of growing genomic data, its storage and migration face enormous challenges. Therefore, researchers have proposed a variety of genome compression algorithms, but these algorithms cannot meet the processing requirements for large amount of biological data and high processing speed. This manuscript proposes a parallel and distributed referential genome compression algorithm-Fast distributed Referential Compression (FastDRC). This algorithm compresses a large number of genomic sequences in parallel under the Apache Hadoop distributed computing framework. Experiments show that the compression efficiency of the FastDRC is greatly improved when it compresses large quantities of genomic data. Moreover, FastDRC leads to the only distributed computing method known to us in the field of genome compression. The source code for FastDRC can be obtained from this link: https://***/GhostCCCatHenry/FastDRC.

关键词： Genome compression distributed processing Apache Hadoop FASTA

来源：评论

学校读者我要写书评

暂无评论

Fault Detection and Monitoring in Secondary Electric Distribution Network based on distributed processing 20

Fault Detection and Monitoring in Secondary Electric Distrib...

引用

20th International Middle East Power Systems Conference (MEPCON)

作者： Mnyanghwalo, Daudi Kawambwa, Shamte Mwifunyi, Rukia Gilbert, Gilbert M. Makota, David Mvungi, Nerey Univ Dar Es Salaam Dept Comp Sci & Engn Dar Es Salaam Tanzania

ISBN: (纸本)9781538666548

Inefficient fault management is one of the major challenges facing the only electrical utility company, the Tanzania Electric Supply Company to ensure safe, reliable and efficient power supply in the electrical Secondary Distribution Network (SDN). Currently, faults management processes from detection to clearance are performed manually and therefore inefficient. There have been initiatives by utility electrical company to automate faults clearance in the transmission and primary distribution parts of the network using SCADA and DMS, which are largely centralized. However, due to the complexity and ubiquitous nature of the SDN, automation is still challenging. This study proposes a distributed processing architecture for automatic faults detection and monitoring for the Tanzanian electrical SDN as it has been identified to fit well in network monitoring compared to the centralized approach. The proposed architecture consists of remote sensing units, distributed central processing units, central monitoring and communication platform. The architecture was implemented and tested in live network at UDSM-Kijitonyama Campus and found to work well.

关键词： Fault Detection Fault Clearance distributed processing Secondary Distribution Network Remote Sensing Unit

来源：评论

学校读者我要写书评

暂无评论

Parallel and distributed processing in the Context of Fog Computing High Throughput Pattern Matching and distributed Monitoring

Parallel and Distributed Processing in the Context of Fog Co...

引用

作者： CHARALAMPOS STYLIANOPOULOS Chalmers University of Technology

学位级别：博士

With the introduction of the Internet of Things (IoT), physical objects now have cyber counterparts that create and communicate data. Extracting valuable information from that data requires timely and accurate processing, which calls for more efficient, distributed approaches. In order to address this challenge, the fog computing approach has been suggested as an extension to cloud pro- cessing. Fog builds on the opportunity to distribute computation to a wider range of possible platforms: data processing can happen at high-end servers in the cloud, at intermediate nodes where the data is aggregated, as well as at the resource-constrained devices that produce the data in the first place. In this work, we focus on efficient utilization of the diverse hardware re- sources found in the fog and identify and address challenges in computation and communication. To this end, we target two applications that are representa- tive examples of the processing involved across a wide spectrum of computing platforms. First, we address the need for high throughput processing of the in- creasing network traffic produced by IoT networks. Specifically, we target the processing involved in security applications and develop a new, data parallel algorithm for pattern matching at high rates. We target the vectorization capa- bilities found in modern, high-end architectures and show how cache locality and data parallelism can achieve up to three times higher processing throughput than the state of the art. Second, we focus on the processing involved close to the sources of data. We target the problem of continuously monitoring sensor streams -a basic building block for many IoT applications. We show how dis- tributed and communication-efficient monitoring algorithms can fit in real IoT devices and give insights of their behavior in conjunction with the underlying network stack.

关键词： fog computing resource-constrained devices high throughput pattern matching vectorization distributed processing distributed monitoring

来源：评论

学校读者我要写书评

暂无评论

Parallel and distributed processing Support for a Geospatial Data Visualization DSL 19

Parallel and Distributed Processing Support for a Geospatial...

引用

19th Symposium on High-Performance Computing Systems (WSCAD)

作者： Ewald, Endrius Vogel, Adriano Rista, Cassiano Griebler, Dalvan Manssour, Isabel Fernandes, Luiz Gustavo Pontifical Catholic Univ Rio Grande Sul PUCRS Sch Technol Porto Alegre RS Brazil

ISBN: (纸本)9781728137728

T he amount of data generated worldwide related to geolocalization has exponentially increased. However, the fast processing of this amount of data is a challenge from the programming perspective, and many available solutions require learning a variety of tools and programming languages. This paper introduces the support for parallel and distributed processing in a DSL for Geospatial Data Visualization to speed up the data pre-processing phase. The results have shown the MPI version with dynamic data distribution performing better under medium and large data set files, while MPI-I/O version achieved the best performance with small data set files.

关键词： Data Visualization distributed processing Domain Specific Language

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434页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：