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Thanks for the reply! So do u mean that the further we do the decomposition the higher gets the scale! The downsampling changes the scale,right! So what is affecting the resolution then, in the DWT? Actually this Nyquist rule refers especially to the discrete signal that we are using and i think it makes sense, since we are sampling it according the Nyquist theorem!! ************************************************************ >Consider an analogy: Take a photograph of the ground when you're standing on >it. Your photograph covers 1 m^2. Now go up x meters and take another photo >and the new image cover 4 m^2. Because the resolution of the image stays >the same, you have discarded every other pixel from rows and every other >from the columns of the image. But now you see the ground in a bigger >scale. > >Oh, and there's no need for wavelets to comply with Nyquist rule. And they >usually don't (really short FIRs). Aliasing happens but it cancels out at >the syntesis end. > >-- >---- >Jani Huhtanen >Tampere University of Technology, Pori >

Umutesi Faith wrote: > Thanks for the reply! > So do u mean that the further we do the decomposition the higher gets the > scale! The downsampling changes the scale,right! So what is affecting the > resolution then, in the DWT? Downsampling changes the scale, right. I'm not exactly sure what you mean by the term scale. In my previous post scale meant "how much more you see" when you downsample. So in this sence scale doubles when you downsample by two. You "see" twice as long signal. But in wavelet literature the term scale often refers to the level of decomposition and there is different ways of numbering the different scales. One may start with scale 0 as the original signal and scale 1 as the first decomposition level. Or scale K as original and K-1 as first level and 0 as the final level of decomposition. Resolution clearly gets lower each time we do the downsampling. This is because we loose the details in favor of seeing more of the signal. From dyadic decomposition (the basic DWT), we get "details" from every scale (level) and if the decomposition is carried infinitely far we could reconstruct the original signal from the details. > Actually this Nyquist rule refers especially to the discrete signal that > we are using and i think it makes sense, since we are sampling it > according the Nyquist theorem!! > The downsampling by two in DWT doesn't require that the incoming coefficients (or the signal at scale j) are bandlimited to fs/4, where fs is the sampling rate before downsampling. Take for example the simplest possible wavelet transform, lazy wavelet (use monospace font): x --->|D2|--w1-->|U2|--- | | |z^-1| |z^-1| | | -->|D2|--w2-->|U2|-> + -----> x D2 is downsampling by two (ie. discard every other sample) and U2 is upsampling by two. The diagram shows wavelet transform (analysis and synthesis) with only one level. Filters are only unit-delays so it's clear that frequency content stays intact. Signals w1 and w2 are highly aliased due to the downsampling. But at the summation alias components cancel out, resulting in perfect reconstruction altough the signals were not bandlimited. -- Jani Huhtanen Tampere University of Technology, Pori

Sorry if this post comes twice. I sent the same post yesterday but it never appeared on the comp.dsp group, so perhaps I have better luck this time. Umutesi Faith wrote: > Thanks for the reply! > So do u mean that the further we do the decomposition the higher gets the > scale! The downsampling changes the scale,right! So what is affecting the > resolution then, in the DWT? Downsampling changes the scale, right. I'm not exactly sure what you mean by the term scale. In my previous post scale meant "how much more you see" when you downsample. So in this sence scale doubles when you downsample by two. You "see" twice as long signal. But in wavelet literature the term scale often refers to the level of decomposition and there is different ways of numbering the different scales. One may start with scale 0 as the original signal and scale 1 as the first decomposition level. Or scale K as original and K-1 as first level and 0 as the final level of decomposition. Resolution clearly gets lower each time we do the downsampling. This is because we loose the details in favor of seeing more of the signal. From dyadic decomposition (the basic DWT), we get "details" from every scale (level) and if the decomposition is carried infinitely far we could reconstruct the original signal from the details. > Actually this Nyquist rule refers especially to the discrete signal that > we are using and i think it makes sense, since we are sampling it > according the Nyquist theorem!! > The downsampling by two in DWT doesn't require that the incoming coefficients (or the signal at scale j) are bandlimited to fs/4, where fs is the sampling rate before downsampling. Take for example the simplest possible wavelet transform, lazy wavelet (use monospace font): x --->|D2|--w1-->|U2|--- | | |z^-1| |z^-1| | | -->|D2|--w2-->|U2|-> + -----> x D2 is downsampling by two (ie. discard every other sample) and U2 is upsampling by two. The diagram shows wavelet transform (analysis and synthesis) with only one level. Filters are only unit-delays so it's clear that frequency content stays intact. Signals w1 and w2 are highly aliased due to the downsampling. But at the summation alias components cancel out, resulting in perfect reconstruction altough the signals were not bandlimited. -- Jani Huhtanen Tampere University of Technology, Pori

In my previous post scale meant "how much more you see" >when you downsample. So in this sence scale doubles when you downsample - - - - - - - - - - - - - - - - - - - - - - - - - - - - Exactly, this was the point i wanted to understand,and i think you explained it well but it is really hard to see it when having 1D signal for instance of 64 samples and the downsampling gives 32 samples, this means then that we see " twice" as long signal, How?? The downsampling by two in DWT doesn't require that the incoming >coefficients (or the signal at scale j) are bandlimited to fs/4, where fs >is the sampling rate before downsampling. - - - - - - - - - - - - - - - - - - - - - As far as i know, while doing 1D DWT ,the original signal has to be sampled according the Nyquist theorem ( fs = 2fmax) - - - - -- - - - -- - - - - - - -- - - - - By the way, i would like to load the daubechies decomposition filters coefficients, i have perfomed the following command load db4 ( in matlab command windonw) db4 Columns 1 through 7 0.1629 0.5055 0.4461 -0.0198 -0.1323 0.0218 0.0233 Column 8 -0.0075 I got the result above. Are these coefficients for lowpass or highpass filters and how to find out what are the decomposition and synthesis coefficients. Is it correct to say the the lowpass filter is actually the scaling function and the highpass filter the wavelet function?? Kiitos!

>More info: http://www.math.hmc.edu/faculty/ward/wavelets/pdfs/m185l2.pdf I have been at this link, well i guess the calculations of these coefficients are not as easy as i thought. It shows quite simple way of getting the scaling and wavelet coefficients instead S = (h0,h1,h2,h3) W = (h3,-h2,h1,-h0) So, does it mean that when i perform load dbN in matlab command window , what i get are the coefficients of the scaling function and from this i can obtain the coefficients of the wavelet function just according to the statement above.i have checked the freq(db4) it is a lowpass filter as you said! Once we have obtained a plot of DWT of a certain signal, where actually do we see this time-frequency representation? I hope you don't get bored with my questions. you really have a good knowledge about the wavelets! Is it possible for me to come to Tampere university to learn more about the wavelets? kiitos again!

Umutesi Faith wrote: >>More info: http://www.math.hmc.edu/faculty/ward/wavelets/pdfs/m185l2.pdf > > I have been at this link, well i guess the calculations of these > coefficients are not as easy as i thought. It shows quite simple way of > getting the scaling and wavelet coefficients instead > > S = (h0,h1,h2,h3) > W = (h3,-h2,h1,-h0) > I assumed that you were familiar with z-transform. Sorry about that. I'll try to explain what I meant. Here are the equations again (without the typo in H1): H0(z), analysis scaling filter (ie. S) H1(z) = (-z)^-N*H0(-z^-1), analysis wavelet filter (ie. W) F0(z) = H1(-z), synthesis scaling filter F1(z) = -H0(-z), synthesis wavelet filter Now lets say that the scaling coefficients are h0,h1,h2, and h3. Then the z-transform of the filter is H0(z) = h0 + h1*z^-1 + h2*z^-2 + h3*z^-3. OK, not let's use the equations above to derive the coefficients for other filters. First analysis wavelet: In our case N is 3 so, H1(z) = (-z)^-3 * H0(-z^-1) = (-z)^-3 * (h0 - h1*z^1 + h2*z^2 - h3*z^3) = -h0*z^-3 + h1*z^-2 - h2*z^-1 + h3. If you wonder why the signs change like that, it is simply because for example (-z)^-2 = z^-2 and (-z)^-1 = -z^-1. The exponent of the z tells us the place of the coefficient (0 corresponds to first coefficient and -1 to the second). => H1 = (h3, -h2, h1, -h0). Which is exactly the same that you said. > So, does it mean that when i perform load dbN in matlab command window , > what i get are the coefficients of the scaling function and from this i > can obtain the coefficients of the wavelet function just according to the > statement above Yes. Oh, and if you're confused why db4 has more than 4 coefficients (as Daubechies 4 should have) it is because in matlab the number after db refers to the number of vanishing moments of the wavelet. > Once we have obtained a plot of DWT of a certain signal, where actually do > we see this time-frequency representation? I hope you don't get bored with > my questions. I assume you use dyadic-decomposition. Something like this (use monospace font): x---->|S|--a0------>|S|---a1------>|S|-->a2 | | | -->|W|->b0 -->|W|-->b1 -->|W|-->b2 That should look like three level dyadic decomposition. S is scaling filter and W is wavelet filter. For clarity I didn't include downsampling to the ASCII art. Output from the filter bank are the signals b0,b1,b2 and a2. If x is N samples long, then there is N/2 b0 samples, N/4 b1 samples, B/8 b2 and a2 samples (in total N samples of output). Samples of b0 are in time order (not in any mixed order). So are b1, b2 and a2. So clearly you have time representation. Further every time the signal goes through S it gets lowpass filtered. That is, it loses high-frequencies. This means that a2 contains low-frequency information from x. Another way of saying is that a2 is an approximation of x. b2 contains high frequency info of a1 which in turn is low frequency info of x. So b2 is a frequency band and so is b1. b0 has gone trough only highpass filter so it's the high-frequency info of x. One can think bs as the details of the x, which are added in synthesis to the approximation aN. Note that because of downsampling the bandwidth of b0 is fs/4 (where fs is sampling rate of x) and b1 is fs/8, b2 and a2 are fs/16. This is why this tree is also called octave decomposition (or something close to that, i'm not sure about the exact english term). So in this case when you plot the signals you should have plot like this: ------------------------- b0: | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ------------------------- b1: | | | | | | | | | | | | | | ------------------------- b2: | | | | ------------------------- a2: | | | | ------------------------- The | marks are borders of one sample and - are borders of one "band". Now the time goes from left to right and frequency from top to bottom. Now its also easy to verify that low-frequency components (b2,a2) have lower time resolution than b1 or b2 but higher frequency resolution and vice-versa. > Is it possible for me > to come to Tampere university to learn more about the wavelets? Yes, if you are a citizen of Finland. Perhaps through some exchange program otherwise (or if you are planning on moving to Finland). Check from here to be sure: http://www.tut.fi/public/index.cfm?MainSel01&Sel01&Show97&Siteid2 -- Jani Huhtanen Tampere University of Technology, Pori

>http://www.tut.fi/public/index.cfm? Thanks for the link , i'll try to get more information about it! I have found some good books about the wavelets, maybe they are already familiar to you : Wavelet applications in Engineering Electromagnetics by Tapan K. Sarkar... Wavelet and filters banks by Gilbert Strang /Truong Nguyen I will now keep reading...