Search Results for "wavelet"
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Showing 8 open source projects for "wavelet"
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wmtsa-python
Discrete wavelet methods for time series analysis using python
Several python libraries implement discrete wavelet transforms. However, none of them, or at least none that I know, is aimed at scientific use. This library aims at filling this gap, in particular considering discrete wavelet transform as described by Percival and Walden. This module started as translation of the wmtsa Matlab toolbox (http://www.atmos.washington.edu/~wmtsa/), so most naming conventions and most of the code structure follows their choices. -
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smooth
Wavelet smoothing in a data stream
It is often necessary to smooth high frequency fluctuations out of data streams sequenced by time, position, etc. The 'smooth' utility applies such smoothing using the wavelet algorithm. This implementation of wavelet smoothing was optimized for use in a data stream. It was adapted from HMMSeg Wavelet.Java by Thomas E. Wilson, University of Michigan. HMMSeg Wavelet.Java was written by Andrew Hemmaplardh, University of Washington. http://noble.gs.washington.edu/proj/hmmseg/ Unsupervised segmentation of continuous genomic data, Bioinformatics 2007 23:1424-1426 See the above references for a more thorough description of the principles behind wavelet smoothing. -
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blitzwave
C++ wavelet library
blitzwave is a c++ wavelet library building on the blitz++ array classes. NOTE: blitzwave has moved to GitHub - the new project URL is http://github.com/oschulz/blitzwave/ . -
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Adaptive Wavelet Applications on general domains - the goal still far away. But the first step is done. This project aims at introducing wavelet methods realized in C++. As building block it uses FLENS.
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wxMorlet implements the new algorithm (see "Documentation") for the computation of the continuous wavelet transform with the Morlet wavelet. wxMorlet calculates it as a numerical solution of the proper PDE and represents the results in a graphical form
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Feature extraction toolkit based on a wavelet multiscale vision model. Includes 1D signal processing sample application for line identification and classification of astronomical spectra.
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ObjC framework for mathematical operations. Various DSP (inc. FFT + wavelet), statistical operations and more on vector and array types. Optimized for SIMD and multi-threading where possible.
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