2021
DOI: 10.1088/1757-899x/1012/1/012033
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Adapted wavelets by parameterization of wavelet bases for estimates of feature extraction of signals

Abstract: The work demonstrates the parameterization of filters coefficients of compactly supported wavelet to implement in the design of the best wavelet selection. The technique determines the best wavelet by means of a mathematical description and gives a representation in a single parameter for wavelets of finite length. The approach takes an input wavelet that is approximated to the best match. Through a choice of the parameter to adapt to the wavelet coefficients the perfect adaptation of the wavelet is achieved. … Show more

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Cited by 1 publication
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“…If the absolute value of the difference between adjacent data points D(j) and D(j + 1) exceeds the threshold, point D(j + 1) is considered noise and assigned the previous data value. The signal was then denoised and smoothed using a four-layer wavelet decomposition that selected the Daubechies mother wavelet on the order of 10 [28]. Data were extracted using a sensor embedded in the composite material.…”
Section: Data Processingmentioning
confidence: 99%
“…If the absolute value of the difference between adjacent data points D(j) and D(j + 1) exceeds the threshold, point D(j + 1) is considered noise and assigned the previous data value. The signal was then denoised and smoothed using a four-layer wavelet decomposition that selected the Daubechies mother wavelet on the order of 10 [28]. Data were extracted using a sensor embedded in the composite material.…”
Section: Data Processingmentioning
confidence: 99%