Convolution with a sinc gives uniform approximation to a function

In summary, Some students have asked about problem 2.13 in Mallat's book "A wavelet Tour of Signal Proccessing" which discusses the convergence of a continuous and bounded variation function with a sinc. After some consideration, it seems that the problem may not be entirely correct and some additional assumptions on the modulus of continuity are needed. This could potentially justify why we can truncate the spectrum of a signal and still recover a similar result for L2 signals. The bounded variation assumption provided in Mallat's book could also give us the necessary modulus of continuity. Overall, the result is significant as it shows the convergence of the sinc function to the delta function.
  • #1
Fernsanz
57
0
Hi everybody.
Some students have asked me about problem 2.13 in Mallat's book "A wavelet Tour of Signal Proccessing". After some work on it, I think is not completely correct. I think some hypostesis on modulus of continuity are needed.
I attach the statement.
mallat.jpg

Esentially, what it says, is that the convolution of a continuous and bounded variation function with a sinc converges UNIFORMLY to the function. I would need a kind of uniform Riemann-Lebesgue lemma to achieve that conclussion.
It is a relevant problem cause it could justify why we can truncate the spectrum of a signal and recover something not too diferent from that truncate spectra for L2 signals.
Let me know what do you think.
Thanks
 
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  • #2
You've probably had this addressed by now - but the bounded variation assumption that Mallat should provide you with the modulus of continuity that you need, because it says the difference in two values of the function are bounded.

The result is interesting for a deeper reason too ... it says that the sinc function converges to the delta function.
 

1. What is convolution with a sinc function?

Convolution with a sinc function is a mathematical operation that involves multiplying a function with a sinc function and integrating over a certain range. This results in a new function that is a weighted sum of the original function, giving a smoother and more uniform approximation.

2. How does a sinc function give a uniform approximation?

A sinc function has a special property where it has a value of 1 at the origin and decreases to 0 as the distance from the origin increases. This means that when convolving with a function, the sinc function will weigh the values of the function near the origin more heavily, resulting in a more uniform approximation.

3. What types of functions can be approximated using convolution with a sinc?

Convolution with a sinc function is most commonly used for approximating continuous functions, especially those with sharp edges or discontinuities. It is also commonly used for signal processing and filtering applications.

4. Are there any limitations to using convolution with a sinc function?

One limitation of using convolution with a sinc function is that it can only approximate continuous functions. It may also not be suitable for functions with very complex or oscillating behavior, as the sinc function may not be able to capture all the details.

5. How can I use convolution with a sinc function in my research or work?

If you are working with continuous functions or have a need for a smoother and more uniform approximation, convolution with a sinc function can be a useful tool. It is commonly used in fields such as signal processing, image processing, and data analysis. There are also many software packages and libraries available that can perform convolution with a sinc function for you.

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