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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.

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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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  1/2015 - 8

Computer Vision Based Measurement of Wildfire Smoke Dynamics

BUGARIC, M. See more information about BUGARIC, M. on SCOPUS See more information about BUGARIC, M. on IEEExplore See more information about BUGARIC, M. on Web of Science, JAKOVCEVIC, T. See more information about  JAKOVCEVIC, T. on SCOPUS See more information about  JAKOVCEVIC, T. on SCOPUS See more information about JAKOVCEVIC, T. on Web of Science, STIPANICEV, D. See more information about STIPANICEV, D. on SCOPUS See more information about STIPANICEV, D. on SCOPUS See more information about STIPANICEV, 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 (882 KB) | Citation | Downloads: 279 | Views: 1,324

Author keywords
image motion analysis, computer vision, computer aided analysis, virtual reality, pattern analysis

References keywords
smoke(16), detection(14), fire(8), wildfire(6), visual(5), computational(5), video(4), spatial(4), image(4), forest(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 55 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01008
Web of Science Accession Number: 000352158600008
SCOPUS ID: 84924804457

Abstract
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This article presents a novel method for measurement of wildfire smoke dynamics based on computer vision and augmented reality techniques. The aspect of smoke dynamics is an important feature in video smoke detection that could distinguish smoke from visually similar phenomena. However, most of the existing smoke detection systems are not capable of measuring the real-world size of the detected smoke regions. Using computer vision and GIS-based augmented reality, we measure the real dimensions of smoke plumes, and observe the change in size over time. The measurements are performed on offline video data with known camera parameters and location. The observed data is analyzed in order to create a classifier that could be used to eliminate certain categories of false alarms induced by phenomena with different dynamics than smoke. We carried out an offline evaluation where we measured the improvement in the detection process achieved using the proposed smoke dynamics characteristics. The results show a significant increase in algorithm performance, especially in terms of reducing false alarms rate. From this it follows that the proposed method for measurement of smoke dynamics could be used to improve existing smoke detection algorithms, or taken into account when designing new ones.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 1]


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[CrossRef] [SCOPUS Times Cited 19]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 6]


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[CrossRef]


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References Weight

Web of Science® Citations for all references: 1,441 TCR
SCOPUS® Citations for all references: 1,771 TCR

Web of Science® Average Citations per reference: 55 ACR
SCOPUS® Average Citations per reference: 68 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-07-22 20:21 in 124 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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


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