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A novel algorithm that is used to lift the vanishing moments of the wavelet from general wavelet, and not only from the Lazy wavelet, is proposed.
Existing approaches consider only the risks of the permissions requested by an app and ignore both the benefits and what permissions are requested by other apps, thus having a limited effect.
Considering that seismic trace singularities are associated with acoustic impedance contrasts, and can be characterized by wavelet transform modulus maxima lines (WTMML), we show how to improve seismic resolution by using the wavelet transform.
Our approaches achieved better results in both accuracy and time performance with a reduced number of permissions.
Therefore, it is essential to find the significant combinations of the permissions that can be dangerous.
While we do not suggest that this conclusion be generalized, we do recommend that this exercise be carried out on a test volume to select the best mother wavelet to be used in the spectral decomposition.
The results of this article give us confidence that wavelet analysis can be used on experimental data, with lower signal-to-noise ratio, too.
The results indicate that reverse biorthogonal wavelet family can give better results for all (Compression Ratio's)CRs compared to other families.
Proceedings ArticleDOI
E.J. Bolster, Y.F. Zheng, R.L. Ewing 
17 Nov 2003
12 Citations
Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients.

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How can we use wavelet transform to enhance impaired speech?3 answersWavelet transform can be used to enhance impaired speech by applying denoising techniques. One approach is to use the Bionic Wavelet Transform (BWT) and mean square error to denoise speech signals corrupted by noise. Another approach is to use wavelet thresholding technique to decompose the noisy signal into different frequency bands and then apply a threshold to the coefficients to filter out noise. Additionally, fractional wavelet transforms (FrWT) can be used to minimize noise and enhance the desired signal. Furthermore, wavelet transform can be combined with neural networks, such as the radial basis function network (RBFN), to enhance speech quality by reducing noise and improving signal-to-noise ratio.
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