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Advances in Interdisciplinary Mathematical Research pp 153–166Cite as

Real-Time Noise Cancellation Using Wavelet Transforms

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 37))

Abstract

In general, most acquired datasets suffer from the effects of acquisition noise, channel noise, fading, and fusion nodes. At the decision-making stage, where these data are fused together, any deviation from the real values of these data could affect the decisions made. We use a wavelet transform approach to develop computationally low-power, low bandwidth, and low-cost filters that will remove the noise effectively so that a decision can be made at the node level. This wavelet-based method is guaranteed to converge to a stationary point for both uncorrelated and correlated datasets. Presented here is an overview of the theoretical background illustrated with some experimental results showing the performance and merits of this novel approach.

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Acknowledgements

This is to thank all of the anonymous reviewers and referees who with their constructive comments made this a better chapter for publication.

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Authors and Affiliations

  1. Virginia State University, Petersburg, VA, USA

    Ehsan Sheybani

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  1. Ehsan Sheybani
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Correspondence to Ehsan Sheybani .

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Editors and Affiliations

  1. , Dept of Mathematics & Computer Sciences, Virginia State University, P.O. Box 9068, Petersburg, 23806, Virginia, USA

    Bourama Toni

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Sheybani, E. (2013). Real-Time Noise Cancellation Using Wavelet Transforms. In: Toni, B. (eds) Advances in Interdisciplinary Mathematical Research. Springer Proceedings in Mathematics & Statistics, vol 37. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6345-0_8

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