By D. Lee Fugal
I'm a working towards sign processing engineer and feature been operating with wavelets given that they have been first invented. My purposes were in modulation (US Robotics), audio/image/video processing/compression/recognition, hybrid algorithms, predictive algorithms, circulation prediction for a magnetically levitated synthetic middle (spin-off from the collage of Utah), x-ray florescence spectroscopy (XRF) detectors, and wavelet/neural community algorithms for gasoline chromatography.
The technology of wavelets used to be invented by means of mathematicians -- so, the entire early books have been written by means of mathematicians and intensely tricky to navigate. the ability of those new wavelet tools past Fourier Transforms was once visible, yet rather obscure and use.
I have an important choice of books on wavelets from many of the mathematicians. whilst I got Lee Fugal's publication, i used to be very happy to work out anything that used to be written from a realistic standpoint. i discovered the booklet effortless to learn and comprehend, or even with a good looking heavy heritage in wavelets, I realized alot. additionally as a MatLAB person, it was once really nice to discover precious code examples besides wavelet toolbox services defined in detail.
Thank you Lee Fugal for taking forever to write down this e-book. every thing else on my bookshelf is amassing dirt -- Lee's publication, 'Conceptual Wavelets' is what i take advantage of each day in my work.
W. Kurt Dobson, CEO
Sigma know-how Holdings
Salt Lake urban, UT
Read Online or Download Conceptual wavelets in digital signal processing: an in-depth, practical approach for the non-mathematician PDF
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Extra resources for Conceptual wavelets in digital signal processing: an in-depth, practical approach for the non-mathematician
The 2 best known and most utilized of these are the conventional (decimated) discrete wavelet transform or DWT and the undecimated discrete wavelet transform or UDWT. A quick note about terminology is in order here. The DWT is actually more complicated than the UDWT and an argument could be made that it should be called the decimated or downsampled discrete wavelet transform, leaving the shorter name to the simpler UDWT discussed in this chapter. However, the more complicated form is better known and we thus follow convention calling it the DWT and requiring the simpler form to add the descriptor “undecimated”.
2–1 Single-level Undecimated Discrete Wavelet Transform (UDWT) filter bank shown at left (a). The functional equivalent of this single-level UDWT is shown in the right diagram (b). We saw in the last section that filtering the signal, S, by H and then by H’ means convolving S with H to produce cD1 and then convolving cD1 with H’ to produce D1. Since the order of convolution doesn’t matter, we can first convolve H with H’ and then convolve the result with S. For the simple Haar filters we have conv(H, H’) = conv([–1 1], [1 1]) = [–1 2 –1] = Php (a result easily verifiable by hand).
Notice that the highpass and lowpass filters (a and b) are vertical and horizontal mirror images of the other. In each frequency graph we can see perfect symmetry around the superimposed dotted lines. Notice also that the phase (lower 2 graphs) is linear. © 2005 Space & Signals Technologies LLC, All Rights Reserved. 8 Normalized frequency (Nyquist == 1) 1 0 -50 -100 -150 -200 Frequency characteristics of the halfband highpass and lowpass filters. We have learned that the short 2-point Haar filters have great time resolution and are suited for tasks such as edge detection.