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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: May 2017
Next issue: Aug 2017
Avg review time: 76 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  4/2014 - 17
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High Performance Processing and Analysis of Geospatial Data Using CUDA on GPU

STOJANOVIC, N. See more information about STOJANOVIC, N. on SCOPUS See more information about STOJANOVIC, N. on IEEExplore See more information about STOJANOVIC, N. on Web of Science, STOJANOVIC, D. See more information about STOJANOVIC, D. on SCOPUS See more information about STOJANOVIC, D. on SCOPUS See more information about STOJANOVIC, D. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (810 KB) | Citation | Downloads: 312 | Views: 1,210

Author keywords
high performance computing, geographic information systems, multiprocessing systems, parallel programming, performance analysis

References keywords
parallel(6), data(6), graphics(5), cuda(5), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 109 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04017
Web of Science Accession Number: 000348772500017
SCOPUS ID: 84921662038

Abstract
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In this paper, the high-performance processing of massive geospatial data on many-core GPU (Graphic Processing Unit) is presented. We use CUDA (Compute Unified Device Architecture) programming framework to implement parallel processing of common Geographic Information Systems (GIS) algorithms, such as viewshed analysis and map-matching. Experimental evaluation indicates the improvement in performance with respect to CPU-based solutions and shows feasibility of using GPU and CUDA for parallel implementation of GIS algorithms over large-scale geospatial datasets.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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