Sunday, May 18

3:00 PM-5:00 PM Magpie A & B - Level B

MS8
Processing Signals from Noisy Chaotic Systems

Experimental observations are always subject to noise, yet most methods for their interpretation in terms of deterministic chaos were developed to work near the noise free limit; techniques based on Takens's embedding theorem and the various minimization criteria were designed to determine the underlying dynamics treat noise as an afterthought. Here we discuss new results for time series, considering large amplitude measurement (additive) noise as well as dynamical (multiplicative) noise. Talks will range from theory to application including an embedding theorem for noisy systems, a novel look at optimal modeling and prediction, and signal separation applied to noisy ECG data.

Organizers: Mark Muldoon, University of Manchester Institute of Science and Technology, United Kingdom; and Holger Kantz, Max Planck Institute for Physics of Complex Systems, Germany

3:00 Embedding in the Presence of Dynamical Noise
Mark Muldoon, Organizer; J. P. Huke and D. S. Broomhead, University of Manchester Institute of Science and Technology, United Kingdom
3:30 Markov Chain Monte Carlo Methods in Nonlinear Signal Processing
Michael E. Davies, University College London, United Kingdom
4:00 Modeling Noisy Chaotic Data
Holger Kantz, Organizer; and Lars Jaeger, Max Planck Institute for Physics of Complex Systems, Germany
4:30 Processing of Noisy Nonlinear Signals: The Fetal ECG
Thomas Schreiber and Marcus Richter, University of Wuppertal, Germany; and Daniel T. Kaplan, Macalester College

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TMP, 4/3/97 TJF, 4/15/97