A fast fault-identification algorithm for bijective connection graphs using the PMC model-Reference-Cited by-同舟云学术

A fast fault-identification algorithm for bijective connection graphs using the PMC model

Published:2012-03 Issue: Volume:187 Page:291-297
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Li Tseng-Kuei,Tsai Chang-Hsiung,Hsu Hong-Chun

Funder

National Science Council

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference33 articles.

1. S. Akers, D. Harel, B. Krishnamurthy, The star graph: an attractive alternative to the n-cube, in: Proceedings of International Conference of Parallel Processing, 1987, pp. 393–400.

2. (t,k)-Diagnosis for matching composition networks;Chang;IEEE Trans. Comput.,2006

3. Conditional diagnosability of augmented cubes under the PMC model;Chang;IEEE Trans. Dependable Secure Comput.,2010

4. The Möbius cubes;Cull;IEEE Trans. Comput.,1995

5. An O(n2.5) fault identification algorithm for diagnosable systems;Dahbura;IEEE Trans. Comput.,1984

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