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- Thread starter DavidLiew
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MATLAB is your best bet to play around with the wavelet transform (i think they even have a toolbox)

-IEEE access and do some digging on the transforms (esp if someone used the output of them as the input to a neural network simulation) and how others have applied it..

I used the wavelet transform coefficients (daubechies) for an undergrad signal detection project....

depends on the input data (ours looked like the letter N (gunshots) with noise on it) the type and level of decomposition needed would vary (of course computational horsepower as well more levels would be slow basically u are splitting the signal by sending it thru low pass and bp filters and looking at it more closely...)

We kinda did a Normalized cross correlation (NCC) on the wavelet coefficients (incoming signal versus our database signature) and used some statistical estimation to classify when they would match (detecting the high peaks generated due to NCC)

The Wiki page is free :) (compared to IEEE and others) http://en.wikipedia.org/wiki/Daubechies_wavelet

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http://en.wikipedia.org/wiki/R_(programming_language)

with Wavelet packet

http://cran.r-project.org/web/packages/wavelets/index.html

Good starter book about wavelets with R language

http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-75960-9

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Maybe this would help you?

http://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf

And yes, you can use cofficients or sums of cofficients as a input data. You could use also a wavelet network where neurons activation functions are wavelet functions. See:

http://maths.york.ac.uk/www/sites/default/files/Veitch.pdf

http://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf

And yes, you can use cofficients or sums of cofficients as a input data. You could use also a wavelet network where neurons activation functions are wavelet functions. See:

http://maths.york.ac.uk/www/sites/default/files/Veitch.pdf

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