Skip to main content
SpringerLink
Log in

Blind Extraction of Chaotic Sources from White Gaussian Noise Based on a Measure of Determinism

  • Conference paper
Independent Component Analysis and Signal Separation (ICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5441))

Abstract

This work presents a new method to perform blind extraction of chaotic signals mixed with stochastic sources. The technique makes use of the features underlying the generation of chaotic sources to recover a signal that is “as deterministic as possible”. The method is applied to invertible and underdertemined mixture models and illustrates the potential of incorporating such a priori information about the nature of the sources in the process of blind extraction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abarbanel, D.I.: Analysis of Observed Chaotic Data. Springer, New York (1996)

    Book  MATH  Google Scholar 

  2. Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Physica D 9, 189–208 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  3. Eckmann, J.-P., Kamphorst, S.O., Ruelle, D.: Recurrence Plots of Dynamical Systems. Europhysics Letters 4, 973–977 (1987)

    Article  Google Scholar 

  4. Landa, P.S., Rosenblum, M.G.: Time Series Analysis for System Identification and Diagnostics. Physica D 48, 232–254 (1991)

    Article  MATH  Google Scholar 

  5. Badii, R., Broggi, G., Derighetti, B., Ravani, M., Rubio, M.A.: Dimension Increase in Filtered Chaotic Signals. Physical Review Letters 11, 979–982 (1988)

    Article  Google Scholar 

  6. Kostelich, E.J., Schreiber, T.: Noise reduction in chaotic time-series data: a survey of common methods. Physical Review E 48(3), 1752–1763 (1993)

    Article  MathSciNet  Google Scholar 

  7. Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Physics Reports 438, 237–329 (2007)

    Article  MathSciNet  Google Scholar 

  8. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New-York (2001)

    Book  Google Scholar 

  9. Haykin, S.: Adaptive Filter Theory. Prentice Hall, New Jersey (2002)

    MATH  Google Scholar 

  10. Darbellay, G.A., Vajda, I.: Estimation of the Information by Adaptive Partitioning of the Observation Space. IEEE Transactions on Information Theory 45, 1315–1321 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  11. Rohde, G.K., Nichols, J.M., Dissinger, B.M., Bucholtz, F.: Stochastic analysis of recurrence plots with applications to the detection of deterministic signals. Physica D 237, 619–629 (2008)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering (FEEC), University of Campinas, (Unicamp), Caixa Postal 6101, CEP 13083-970, Campinas, SP, Brazil

    Diogo C. Soriano & Romis Attux

  2. Dept. of Microwave and Optics School of Electrical and Computer Engineering (FEEC), University of Campinas, (Unicamp), Caixa Postal 6101, CEP 13083-970, Campinas, SP, Brazil

    Ricardo Suyama

  3. Lab. of Signal Processing for Communications (DSPCom) School of Electrical and Computer Engineering (FEEC), University of Campinas, (Unicamp), Caixa Postal 6101, CEP 13083-970, Campinas, SP, Brazil

    Diogo C. Soriano, Ricardo Suyama & Romis Attux

Authors
  1. Diogo C. Soriano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricardo Suyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Romis Attux
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Electrical Engineering, ITE 324, University of Maryland, Baltimore County, 1000 Hilltop Circle, MD 21250, Baltimore, USA

    Tülay Adali

  2. Domaine Universitaire, GIPSA-lab, BP 46, 38402, Saint Martin d’Hères Cedex, France

    Christian Jutten

  3. Departamento de Microonda e Óptica (DMO), FEEC / Unicamp, Avenida Albert Einstein 400, 13083-852, Campinas, Sao Paulo, Brazil

    João Marcos Travassos Romano

  4. Centro Tecnológico, Curso de Engenharia Elétrica, Universidade Federal do Maranhão, Avenida dos Portugueses, s/n, Bacanga, 65080-040, São Luís, MA, Brazil

    Allan Kardec Barros

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Soriano, D.C., Suyama, R., Attux, R. (2009). Blind Extraction of Chaotic Sources from White Gaussian Noise Based on a Measure of Determinism. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00599-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00598-5

  • Online ISBN: 978-3-642-00599-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation