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Optimal Codes for Strong Identification

Optimal codes for strong identification
Authors: Tero Laihonen;

Optimal Codes for Strong Identification

Abstract

Codes for identification (introduced in [\textit{M. G. Karpovsky, K. Chakrabarty} and \textit{L. B. Levitin}, On a new class of codes for identifying vertices in graphs, IEEE Trans. Inf. Theory 44, 599-611 (1998; Zbl 1105.94342)]) locate faulty processors in a multi-processor system. The idea is as follows. Let \(n\) processors be labelled with binary vectors of length \(n.\) A processor is able to check the processors whose label differs in at most \(t\) positions from its own. It reports a `1' to a central controller if something is wrong in this neighbourhood, and a `0' otherwise. The aim is to find a (small !) code \(C \subset \{0,1\}^n\) such that the reports from the processors from \(C\) allows localisation of the faulty processors if at most \(l\) processors are simultaneously malfunctioning. For a strongly \((t,\leq l)\) identifying code, we require more: it should also allow localisation of the faulty processors if these can give incorrect reports. The author presents, for each \(l\geq 3,\) infinite sequences of strongly \((1,\leq l)\) identifying codes of minimum cardinality.

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Keywords

Other types of codes, Fault detection; testing in circuits and networks, Reliability, testing and fault tolerance of networks and computer systems, fault detection, Theoretical Computer Science, faulty processors, Computational Theory and Mathematics, strongly identifying code, Geometry and Topology, Combinatorial aspects of packing and covering

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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!
13
Average
Top 10%
Top 10%
hybrid