Abstract
This paper describes a method for artefact detection and suppression based on a-Stable distributions. The reason for choosing the a-stable distribution is, that it is heavy-tailed distribution ideal for modeling of data polluted by outliers. A method for on-line data processing is emphasized. The artefact suppression is based on the idea that data are modeled by a Symmetric α-Stable distribution, parameters of which are estimated. Then the data are regenerated from the Gaussian distribution with parameters, that correspond to the original parameters of the α-Stable distribution. The new data is free of any outliers.
Supported by European project ProDaCtool, IST-1999-12058
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
E. E. Kuruoğlu, W. J. Fitzgerald, and P. J. Rayner, “Near optimal deteetion of signals in impulsive noise modeled with a symmetric alpha-stable distribution” IEEE Comm. Letters, vol. 2, pp. 1–14, October 1998.
E. E. Kuruoğlu, P. J. Rayner, and W. J. Fitzgerald, “Least ℓp-norm impulsive noise eaneellation with polynomial filters” Signal Processing, vol. 69, pp. 1–14, 1998.
C. L. Nikias and M. Shao, Signal processing with alpha-stable distributions and applications. Adaptive and learning systems for signal processing, communications, and control, Chicester: Wiley, 1995.
G. A. Tsihrintzis and C. L. Nikias, “Fast estimation of the parameters of alpha-stable impulsive interference” IEEE Transactions on Signal Processing, vol. 44, pp. 1492–1503, June 1996.
X. Ma and C. L. Nikias, “Parameter estimation and blind channel identification in impulsive signal environments” IEEE Transactions on Signal Processing, vol. 43, pp. 2884–2897, December 1995.
T. Bayes, “An essay towards solving a problem on the doctrine of chances” Philosophical Transactions of the Royal Society of London, vol. 53, pp. 370–418, 1763.
V. Peterka, “Bayesian approach to system identification” in Trends and Progress in System Identification (P. Eykhoff, ed.), ch. 8, pp. 239–304, Oxford: Pergamon Press, 1981.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Wien
About this paper
Cite this paper
Tesař, L., Quinn, A. (2001). Method for Artefact Detection and Suppression Using Alpha-Stable Distributions. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_103
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6230-9_103
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
eBook Packages: Springer Book Archive