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

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


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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
Issue 4/2016



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 "Big Data - " before the paper title in OpenConf.

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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.

Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  4/2014 - 11

Visual Peoplemeter: A Vision-based Television Audience Measurement System

SKELIN, A. K. See more information about SKELIN, A. K. on SCOPUS See more information about SKELIN, A. K. on IEEExplore See more information about SKELIN, A. K. on Web of Science, SUPUK, T. G. See more information about  SUPUK, T. G. on SCOPUS See more information about  SUPUK, T. G. on SCOPUS See more information about SUPUK, T. G. on Web of Science, BONKOVIC, M. See more information about BONKOVIC, M. on SCOPUS See more information about BONKOVIC, M. on SCOPUS See more information about BONKOVIC, M. on Web of Science
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Download PDF pdficon (835 KB) | Citation | Downloads: 223 | Views: 1,132

Author keywords
consumer behavior, machine vision, object detection, image motion analysis, video surveillance

References keywords
vision(13), recognition(13), tracking(9), pattern(9), attention(9), cvpr(7), machine(6), detection(6), visual(5), television(5)
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): 73 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04011
Web of Science Accession Number: 000348772500011
SCOPUS ID: 84921682412

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Visual peoplemeter is a vision-based measurement system that objectively evaluates the attentive behavior for TV audience rating, thus offering solution to some of drawbacks of current manual logging peoplemeters. In this paper, some limitations of current audience measurement system are reviewed and a novel vision-based system aiming at passive metering of viewers is prototyped. The system uses camera mounted on a television as a sensing modality and applies advanced computer vision algorithms to detect and track a person, and to recognize attentional states. Feasibility of the system is evaluated on a secondary dataset. The results show that the proposed system can analyze viewer's attentive behavior, therefore enabling passive estimates of relevant audience measurement categories.

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

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References Weight

Web of Science® Citations for all references: 9,058 TCR
SCOPUS® Citations for all references: 5,579 TCR

Web of Science® Average Citations per reference: 252 ACR
SCOPUS® Average Citations per reference: 155 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 background updated on 2017-02-17 20:58 in 186 seconds.

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

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