Abstract

All the biostatistics and biometric applications suffer from the effects of added noise due to their data dependency. The quality of data and impurities due to noise could affect the decisions made based on these datasets. Detecting anomalies caused by noisy datasets requires special preprocessing techniques that do not hurt the integrity of data. The authors have developed computationally low power, low bandwidth, and low-cost filters (DMAW) that will remove the noise, compress the dataset, and decompose the dataset so that a decision can be made by looking at different layers of data. This wavelet-based method is guaranteed to converge to a stationary point for both uncorrelated and correlated data. Presented here is the theoretical background with examples showing the performance and merits of this novel approach compared to other alternatives. Keywords:Biostatistics; Biometric; Discrete wavelet transform; Discrete meyer mdaptive wavelet

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