Wavelet decomposition is a new approach to understanding and modelling turbulence, based on phase-space structures, allowing the determination of “local” spectra1. The overall objective of the proposed work is to develop a tool to identify “wavelet patterns” and their role in turbulence dynamics...
In this work, the noisy-S1 heart signal was investigated to separate M1 and T1 components of it by making the comprehensive analysis of the determination of wavelet function-level pair for the decomposition and reconstruction of artificial S1 by using multiresolution analysis (MRA) and discrete ...
Through wavelet decomposition and re-construction of surface signal, surface features, such as form error, waviness and roughness are separated rationally. Some fundamental issues, such as the selection of wavelet bases and the determination of wavelet decomposition level are discussed in detail. The ...
Due to the frequency decomposition of the wavelet analysis all these methods can handle a high noise level. Of course, the quality of the solution still depends on the noise level. The current practice of verifying the results are based on the known parameters or those obtained by other ...
Moreover, Fejér-Korovkin wavelets demonstrated strong performance in capturing the essential features of input groundwater level signals. This determination was made through a comparison of various mother wavelet approaches, considering different filter lengths (up to 22) and decomposition levels (up to...
However, wavelet transforms may not be sensitive enough to minor changes and complex local structures of the signal, especially when an inappropriate wavelet base or decomposition level is chosen, potentially leading to information loss. Therefore, CEEMDAN can initially be used to decompose the signal...
Precise determination of onset times of dynamical eventsas observed by coronagraphs requires a high-degree-of-accuracytracking of the structures involved. In particular, early steps inthe evolution turn to be of utmost importance. However,corresponding f
This paper implements wavelet decomposition with radial basis function artificial neural network for identifying fingerprints. Sample finger prints are taken from data base from the internet resource. The fingerprints are decomposed using daubauchi wavelet 1(db1) to 5 levels. The coefficients of approx...
The first stage of decomposition will give the first level approximation (a1) which, if decomposed, will give the second level approximation (a2) and so on. Detail analysis is performed with a contracted, high-frequency version of the mother wavelet, while approximation analysis is performed with...
To overcome the limitations of traditional evaluation indicators in determining the optimal wavelet decomposition level, this paper proposes an adaptive method for selecting the best decomposition level by combining the Jarque鈥揃era test and a composite weighting approach. Firstly, in the noise ...