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  2. Wavelet - Wikipedia

    en.wikipedia.org/wiki/Wavelet

    It is computationally impossible to analyze a signal using all wavelet coefficients, so one may wonder if it is sufficient to pick a discrete subset of the upper halfplane to be able to reconstruct a signal from the corresponding wavelet coefficients. One such system is the affine system for some real parameters a > 1, b > 0.

  3. Discrete wavelet transform - Wikipedia

    en.wikipedia.org/wiki/Discrete_wavelet_transform

    An example of computing the discrete Haar wavelet coefficients for a sound signal of someone saying "I Love Wavelets." The original waveform is shown in blue in the upper left, and the wavelet coefficients are shown in black in the upper right. Along the bottom are shown three zoomed-in regions of the wavelet coefficients for different ranges.

  4. Wavelet transform - Wikipedia

    en.wikipedia.org/wiki/Wavelet_transform

    Discrete wavelet transform has been successfully applied for the compression of electrocardiograph (ECG) signals [6] In this work, the high correlation between the corresponding wavelet coefficients of signals of successive cardiac cycles is utilized employing linear prediction. Wavelet compression is not effective for all kinds of data.

  5. Daubechies wavelet - Wikipedia

    en.wikipedia.org/wiki/Daubechies_wavelet

    Daubechies wavelet approximation can be used to analyze Griffith crack behavior in nonlocal magneto-elastic horizontally shear (SH) wave propagation within a finite-thickness, infinitely long homogeneous isotropic strip. [10] Daubechies wavelet cepstral coefficients can be useful in the context of Parkinson's disease detection.

  6. Coiflet - Wikipedia

    en.wikipedia.org/wiki/Coiflet

    Below are the coefficients for the scaling functions for C6–30. The wavelet coefficients are derived by reversing the order of the scaling function coefficients and then reversing the sign of every second one (i.e. C6 wavelet = {−0.022140543057, 0.102859456942, 0.544281086116, −1.205718913884, 0.477859456942, 0.102859456942}).

  7. Wavelet packet decomposition - Wikipedia

    en.wikipedia.org/wiki/Wavelet_packet_decomposition

    Wavelet packet decomposition over 3 levels. g[n] are the low-pass approximation coefficients, h[n] are the high-pass detail coefficients. For n levels of decomposition the WPD produces 2 n different sets of coefficients (or nodes) as opposed to (n + 1) sets for the DWT.

  8. Haar wavelet - Wikipedia

    en.wikipedia.org/wiki/Haar_wavelet

    The Haar wavelet. In mathematics, the Haar wavelet is a sequence of rescaled "square-shaped" functions which together form a wavelet family or basis. Wavelet analysis is similar to Fourier analysis in that it allows a target function over an interval to be represented in terms of an orthonormal basis. The Haar sequence is now recognised as the ...

  9. Lifting scheme - Wikipedia

    en.wikipedia.org/wiki/Lifting_scheme

    Lifting sequence consisting of two steps. The lifting scheme is a technique for both designing wavelets and performing the discrete wavelet transform (DWT). In an implementation, it is often worthwhile to merge these steps and design the wavelet filters while performing the wavelet transform.