Abstract
A method to optimize the parameters used in signal denoising in the wavelet domain is presented. The method, which is based on cross-validation (CV) procedure, permits to select the best decomposition level and the best wavelet filter function to denoise a signal in the discrete wavelet domain. The procedure was validated by using computer generated signals to which white noise was added. Signals having different features and a range of signal to noise ratios were explored. The method was shown to give reliable results for all cases studied. The proposed method was applied to experimental gravitation field flow fractionation records, and the results were compared with classical low pass filtering in the Fourier domain.
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