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Topology-based Signal Separation

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Robins, Vanessa; Rooney, Niall; Bradley, Elizabeth

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Traditional noise-filtering techniques are known to significantly alter features of chaotic data. In this paper, we present a noncausal topology-based filtering method for continuous-time dynamical systems that is effective in removing additive, uncorrelated noise from time-series data. Signal-to-noise ratios and Lyapunov exponent estimates are dramatically improved following the removal of the identified noisy points.

dc.contributor.authorRobins, Vanessa
dc.contributor.authorRooney, Niall
dc.contributor.authorBradley, Elizabeth
dc.date.accessioned2015-12-13T22:39:09Z
dc.date.available2015-12-13T22:39:09Z
dc.identifier.issn1054-1500
dc.identifier.urihttp://hdl.handle.net/1885/77658
dc.description.abstractTraditional noise-filtering techniques are known to significantly alter features of chaotic data. In this paper, we present a noncausal topology-based filtering method for continuous-time dynamical systems that is effective in removing additive, uncorrelated noise from time-series data. Signal-to-noise ratios and Lyapunov exponent estimates are dramatically improved following the removal of the identified noisy points.
dc.publisherAmerican Institute of Physics (AIP)
dc.sourceChaos An Interdisciplinary Journal of Nonlinear Science
dc.titleTopology-based Signal Separation
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume14
dc.date.issued2004
local.identifier.absfor010109 - Ordinary Differential Equations, Difference Equations and Dynamical Systems
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub6478
local.type.statusPublished Version
local.contributor.affiliationRobins, Vanessa, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationRooney, Niall, University of Colorado
local.contributor.affiliationBradley, Elizabeth, University of Colorado
local.bibliographicCitation.issue2
local.bibliographicCitation.startpage305
local.bibliographicCitation.lastpage316
local.identifier.doi10.1063/1.1705852
dc.date.updated2015-12-11T09:47:09Z
local.identifier.scopusID2-s2.0-3042589915
CollectionsANU Research Publications

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