Topology-based signal separation

V. Robins; N. Rooney; E. Bradley

doi:10.1063/1.1705852

Topology-based signal separation

V. Robins^*, N. Rooney, E. Bradley

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	305-316
Number of pages	12
Journal	Chaos
Volume	14
Issue number	2
DOIs	https://doi.org/10.1063/1.1705852
Publication status	Published - Jun 2004

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title = "Topology-based signal separation",

abstract = "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.",

author = "V. Robins and N. Rooney and E. Bradley",

year = "2004",

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AU - Robins, V.

AU - Rooney, N.

AU - Bradley, E.

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AB - 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.

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JO - Chaos

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Topology-based signal separation

Abstract

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