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  2. Wavelet - Wikipedia

    en.wikipedia.org/wiki/Wavelet

    A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Usually one can assign a frequency range to each scale component. Each scale component can then be studied with a resolution that matches its scale. A wavelet transform is the representation of a function by wavelets.

  3. Scaling function - Wikipedia

    en.wikipedia.org/wiki/Scaling_function

    Download as PDF; Printable version; In other projects ... move to sidebar hide. Scaling function may refer to: Critical exponent § Scaling functions; Wavelet ...

  4. Wavelet transform - Wikipedia

    en.wikipedia.org/wiki/Wavelet_transform

    In mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform. [1] [2] [3] [4]

  5. Coiflet - Wikipedia

    en.wikipedia.org/wiki/Coiflet

    Both the scaling function (low-pass filter) and the wavelet function (high-pass filter) must be normalised by a factor /. 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 ...

  6. Ricker wavelet - Wikipedia

    en.wikipedia.org/wiki/Ricker_wavelet

    is the negative normalized second derivative of a Gaussian function, i.e., up to scale and normalization, the second Hermite function. It is a special case of the family of continuous wavelets (wavelets used in a continuous wavelet transform) known as Hermitian wavelets. The Ricker wavelet is frequently employed to model seismic data, and as a ...

  7. Daubechies wavelet - Wikipedia

    en.wikipedia.org/wiki/Daubechies_wavelet

    Daubechies wavelets are widely used in solving a broad range of problems, e.g. self-similarity properties of a signal or fractal problems, signal discontinuities, etc. The Daubechies wavelets are not defined in terms of the resulting scaling and wavelet functions; in fact, they are not possible to write down in closed form.