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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Transactions on...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
IEEE Transactions on Computers
Article . 1991 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2017
Data sources: DBLP
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Improved diagnosability algorithms

Authors: Vijay Raghavan 0002; Anand R. Tripathi;

Improved diagnosability algorithms

Abstract

The concepts of the PMC and BGM self-diagnosing system models of F. P. Preparata et al. (1967) and F. Barsi et al. (1976), respectively, including the notions of fault sets, consistency, and diagnosability number, are reviewed. Two one-step diagnosability algorithms are applied, one to the PMC model and the other to the BGM model. In both models, one-step diagnosability refers to a system's ability to determine all the faulty units from single collection of test results. Using the letters n, m, and tau to denote the number of units, the number of tests, and the diagnosability number, respectively, it is shown that in the BGM model the algorithm has a complexity of O(n tau /sup 2//log tau ), and, in the PMC model, the algorithm has a complexity of O(n tau /sup 2.5/). >

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Top 10%
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