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Topology-based Signal Separation

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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.date.issued2004
dc.date.updated2015-12-11T09:47:09Z
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.identifier.issn1054-1500
dc.identifier.urihttp://hdl.handle.net/1885/77658
dc.publisherAmerican Institute of Physics (AIP)
dc.sourceChaos An Interdisciplinary Journal of Nonlinear Science
dc.titleTopology-based Signal Separation
dc.typeJournal article
local.bibliographicCitation.issue2
local.bibliographicCitation.lastpage316
local.bibliographicCitation.startpage305
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.contributor.authoruidRobins, Vanessa, u9213671
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010109 - Ordinary Differential Equations, Difference Equations and Dynamical Systems
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub6478
local.identifier.citationvolume14
local.identifier.doi10.1063/1.1705852
local.identifier.scopusID2-s2.0-3042589915
local.type.statusPublished Version

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