1,721,168 research outputs found

    Haar wavelet-based technique for sharp jumps classification

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    A wavelet-based technique is proposed for analysing ocalized significant changes in observed data, in the presence of noise. The main tasks of the proposed technique are: (a) denoising the observed data without removing localized significant changes, (b) classifying the detected sharp jumps (sèikes), and (c) obtaining a smooth trend (deterministic function) to represent the time-series evolution. By using the Haar discrete wavelet transform, the sequence of data is transformed into a sequence of wavelet coefficients. The Haar wavelet coefficients together with their rate of change, represent local changes and local correlation of data, therfore, their analysis gives rise to multi-dimensional thresholds and constraints which allow both the denoising and the sorting of data in a suitable space

    The Wavelet-based technique in dispersive wave propagation

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    The paper expounds a wavelet-based technique. The techniques is applied to the dispersive wave equation by describing wave propagation by a composition of fundamental (harmonic) wavelets. The linear Klein–Gordon equation is analyzed and associated approximate wavelet solutions are considered for fixed resolution levels. Discretized wavelet families are computed explicitly using a basis of time harmonic wavelets. Some applications at various low resolution levels show that the technique proposed provides new opportunities for wave propagation analysis

    Harmonic Wavelet Solution ofPoisson's Problem

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    The multiscale (wavelet) decomposition of the solution is proposed for the analysis of the Poisson problem. The approximate so- lution is computed with respect to a ̄nite dimensional wavelet space [4, 5, 7, 8, 16, 15] by using the Galerkin method. A fundamental role is played by the connection coe±cients [2, 7, 11, 9, 14, 17, 18], expressed by some hypergeometric series. The solution of the Poisson problem is compared with the approach based on Daubechies wavelets [18]
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