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Erschienen in:
2013 | OriginalPaper | Buchkapitel
8. Real-Time Noise Cancellation Using Wavelet Transforms
verfasst von : Ehsan Sheybani
Erschienen in: Advances in Interdisciplinary Mathematical Research
Verlag: Springer New York
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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.