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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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  2/2008 - 9

Static Test Compaction for VLSI Tests An Evolutionary Approach

LOGOFATU, D. See more information about LOGOFATU, D. on SCOPUS See more information about LOGOFATU, D. on IEEExplore See more information about LOGOFATU, D. on Web of Science
Click to see author's profile in 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 (929 KB) | Citation | Downloads: 760 | Views: 3,509

Author keywords
evolutionary algorithms, digital circuit design, test compaction problem, set coverage problem, test generation, greedy algorithm, optimization, don't care value

References keywords
algorithms(11), drechsler(6), test(5), logofatu(5), genetic(5), vlsi(4), systems(4), problems(4), design(4), data(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2008-06-02
Volume 8, Issue 2, Year 2008, On page(s): 49 - 53
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.02009
Web of Science Accession Number: 000264815000009
SCOPUS ID: 77955593111

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The test compaction is one of most important requirement regarding the large scale integration (LSI) testing. The overall cost of a VLSI circuit's testing depends on the length of its test sequence; therefore the reduction of this sequence, keeping the coverage of error prone points, will lead to a reduction of used resources in the testing process. This problem is NP-complete. Consequently an optimal algorithm doesn't have applicability in practice. In this paper we describe an evolutionary algorithm (GATC) and we introduce the term of compaction factor (cf), i.e. the expected percentage of compacted test sequence. GATC provides in praxis better results than a greedy approach (GR) for many configurations. This improvement comes from the freedom to merge randomly pairs of compatible tests for different candidates to solution and keeps the ones with more Don't care positions, thus there is an increased probability to find for them compatible tests in the next stage. Also the C++ implementation was optimized, using compact data structures and the Standard Template Library.

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

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[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 44]

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[23] Logofatu, D., "Algoritmi fundamentali in Java. Aplicaþii", Editura Polirom, Iasi, 2007.

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[CrossRef] [Web of Science Times Cited 394] [SCOPUS Times Cited 491]

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[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 96]

References Weight

Web of Science® Citations for all references: 1,935 TCR
SCOPUS® Citations for all references: 2,828 TCR

Web of Science® Average Citations per reference: 72 ACR
SCOPUS® Average Citations per reference: 105 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 2019-02-20 21:42 in 64 seconds.

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