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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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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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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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  3/2009 - 13

The Unit Histogram Concept for Scarce Statistical Information

RUGESCU, R. D. See more information about RUGESCU, R. D. on SCOPUS See more information about RUGESCU, R. D. on IEEExplore See more information about RUGESCU, R. D. on Web of Science
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Download PDF pdficon (480 KB) | Citation | Downloads: 718 | Views: 3,445

Author keywords
unit histogram, scarce population statistics, squeezed information, unit filter

References keywords
processing(6), histograms(6), time(4), statistics(4), signal(4), analysis(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 68 - 74
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03013
Web of Science Accession Number: 000271872000013
SCOPUS ID: 77954753836

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The new unit histogram concept is described as increasing with one order of magnitude the amount of statistical information extracted from experimental data. The new statistical technology is particularly suited for very scarce population samples, when it dramatically increases the information extracted from the available data. Otherwise, existing features of the population would remain inaccessible. The method could prove useful also for signal processing, when a high level of accuracy in detail rendition is required. The efficiency of the method is demonstrated in examples of experimental measurement of the combustion heat delivered by rocket propellants. Populations as small as of six readings, where the regular statistics is useless, are successfully processed and characterized.

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

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References Weight

Web of Science® Citations for all references: 4,316 TCR
SCOPUS® Citations for all references: 5,353 TCR

Web of Science® Average Citations per reference: 108 ACR
SCOPUS® Average Citations per reference: 134 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 2018-07-20 08:28 in 82 seconds.

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