Wavelets and Fourier transform

In summary, the main disadvantage of FourierTransform is that you cannot use it on a non-uniform signal. Wavelets overcome this limitation by using a mother wavelet that expands or compresses the signal according to certain frequencies. This gives the user several results, with HF only, LF only components of the original signal. After this, the user is left with the decision of whether to use a filter or wavelet packets.
  • #1
likephysics
636
2
I am new to wavelets.
I was reading about wavelets and Fourier transforms. So the main disadvantage of Fourier Transform is that you cannot use it on a non-uniform signal.
Even though you use it you have to use a window and select your region of interest.
If the window is small enough you can see the high frequency components, but not the low frequency components. But if the window is large, then you see the LF components but not the HF components.
[Small window:good time resolution, bad freq res. Large window: bad time resolution, good freq resolution.]
So why not use both windows on the same signal. Maybe even use a lot of windows from narrow to wide.
Wouldn't this give us all the LF,HF information about the signal?

In wavelets, how do you decide on a mother wavelet?
If I have understood correctly, wavelet transform is similar to sampling a signal. In sampling you multiply the signal with a delta function. In WT, you multiply the signal with a mother wavelet. Instead of doing it once, you change the frequency of(expand/compress) the mother wavelet and multiply it with the signal each time. correct?
This will give us several results with HF only, LF only components of the original signal. So what after this?
Isn't this similar to sampling a signal(say well beyond Nyquist rate) and then decimating it in steps to get different frequency components?
 
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  • #2
Wow lots of questions.

Look, in general, you start off with fourier -(gives you all frequencies in your signal)
Then, you go to STFT (short time Fourier transform) in other words divide your time/distance into blocks and each time calculate the frequancy components in each block.
Heisenberg (Hope I spelt his name right) sais that looking at finite blocks is going to smear your freaquancies (-Uncertainty principle) depending on the size of your block=window.
apparently, STFT is not very good becuase areas with hight freq. and areas with low freq. will demand different sized windows. Here comes in the Wavelet principle.

From your questions it is clear you know some stu but it is all muddled up in your head. So in this case I suggest you read the tutorial of a man named Dr. Polikar Robi which will start right from the beginig up to DWT (Discrete Wavelet Transform).

http://users.rowan.edu/~polikar/WAVELETS/WTtutorial.html"

Good luck
 
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  • #3
John, thanks for the reply. A day after posting I read the Robi Polikar's tutorial. Really neat.
Cleared up most of my doubts. I could not understand the basys vectors and the reason wavelets are better for de-noising compared to a filter.
Now I am trying to analyse a non-uniform signal to see all the different frequencies. STFT gives good results, but when I apply CWT I don't see anything close to what I see in STFT.
I am trying wavelet packets, but still no luck.
 
  • #4
Well, I am working on the Non uniform fast Fourier transform, which can be use on non uniform signal now days, and i am stuck some where...I am trying to produce the same results as from
A.J.W.Duijndam and M.A.Schonewillie but i am not able to produce my time signal back anyone interested
 
  • #5
likephysics said:
John, thanks for the reply. A day after posting I read the Robi Polikar's tutorial. Really neat.
Cleared up most of my doubts. I could not understand the basys vectors and the reason wavelets are better for de-noising compared to a filter.
Now I am trying to analyse a non-uniform signal to see all the different frequencies. STFT gives good results, but when I apply CWT I don't see anything close to what I see in STFT.
I am trying wavelet packets, but still no luck.

I don't know what you are working with, I work with Matlab, but I can give you a few tutorials on wavelets in MATLAB http://visl.technion.ac.il/documents/wavelet_ug.pdf" for example.

I myself am trying to understand the cwt and dwt (there is also wavedec) right know.
Good luck
 
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  • #6
Thanks. I am using MATLAB too. I followed the MATLAB example from their help docs. Didn't help much. I'll go thru your examples. What is a wavedec?
 
  • #7
Does anyone know if i can construct-compose time-series consist of wavlets? e.g. if i have as data a characteristic wave height and a wave length i can produce time-series with Fourier transform which equals of a superposition of sinus waves. i want to do the same but with wavelets. possible?
 

1. What are wavelets and Fourier transform?

Wavelets and Fourier transform are mathematical tools used for analyzing signals and data in the time and frequency domains. They are used to decompose a signal into its component frequencies and to identify patterns and features in the signal.

2. How are wavelets and Fourier transform different?

Wavelets and Fourier transform are different in the way they analyze signals. Fourier transform decomposes a signal into its component frequencies, while wavelets decompose a signal into different scales or resolutions. Wavelets also have the ability to analyze localized features in a signal, unlike Fourier transform which is limited to global features.

3. What are some real-world applications of wavelets and Fourier transform?

Wavelets and Fourier transform have a wide range of applications in various fields such as signal processing, image and video compression, data analysis, and pattern recognition. They are used in industries like telecommunications, medicine, and finance for tasks such as noise reduction, feature extraction, and data compression.

4. What are the advantages of using wavelets and Fourier transform?

One of the main advantages of using wavelets and Fourier transform is their ability to analyze signals and data in both the time and frequency domains. This allows for a more comprehensive understanding of the signal and its features. They are also efficient and versatile tools that can handle a wide range of signals and data types.

5. Are there any limitations to using wavelets and Fourier transform?

While wavelets and Fourier transform are powerful tools, they do have some limitations. One limitation is that they assume the signal is stationary, meaning it does not change over time. They also require specific mathematical assumptions to be met in order to accurately analyze a signal. Additionally, they may not be suitable for analyzing signals with very high or very low frequencies.

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