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dc.contributor.authorHunt, S.
dc.contributor.authorSun, Yi.
dc.contributor.authorShafarenko, A.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.contributor.authorSlater, B.
dc.contributor.authorBhamber, R.
dc.contributor.authorBoscolo, S.
dc.contributor.authorTuritsyn, S.K.
dc.identifier.citationHunt , S , Sun , Y , Shafarenko , A , Adams , R G , Davey , N , Slater , B , Bhamber , R , Boscolo , S & Turitsyn , S K 2010 , ' Correcting Errors in Optical Data Transmission Using Neural Networks ' , Lecture Notes in Computer Science , vol. 6353 , pp. 448-457 .
dc.identifier.otherPURE: 99308
dc.identifier.otherPURE UUID: 9c19038d-0fb0-4d20-9fd6-e4cf0a9a7892
dc.identifier.otherdspace: 2299/5500
dc.identifier.otherScopus: 78049388312
dc.description“The original publication is available at”. Copyright Springer [Full text of this article is not available in the UHRA]
dc.description.abstractOptical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.en
dc.relation.ispartofLecture Notes in Computer Science
dc.titleCorrecting Errors in Optical Data Transmission Using Neural Networksen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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