Proving Parseval's Theorem for Schwartz Functions with Compact Support

In summary, we can use the Parseval theorem and sampling theorem to show that \int_{\mathbb{R}}|f(x)|^2dx = \int_{\mathbb{R}}|\hat{f}(\omega)|^2d\omega = \sum_{n=-\infty}^\infty |f(n)|^2.
  • #1
Sonifa
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How to prove the following:

Suppose f is in the Schwartz Space ( smooth function with very fast decay). Its Fourier transform is smooth and has compact support contained in the interval (1/2,-1/2)

Show,

∫ (|f(x)|^2) dx = ∑ (|f(n)|^2) (where integral over R and sum up over n for all intergers)
 
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  • #2
Sonifa said:
How to prove the following:

Suppose f is in the Schwartz Space ( smooth function with very fast decay). Its Fourier transform is smooth and has compact support contained in the interval (1/2,-1/2)

Show,

∫ (|f(x)|^2) dx = ∑ (|f(n)|^2) (where integral over R and sum up over n for all intergers)
I would start by using the Parseval theorem \(\displaystyle \int_{\mathbb{R}}|f(x)|^2dx = \int_{\mathbb{R}}|\hat{f}(\omega)|^2d\omega.\)

The sampling theorem (taking the sampling interval $\delta$ to be $\delta = 1$) says that \(\displaystyle f(x) = \sum_{n=-\infty}^\infty f(n)\phi(x-n),\) where $\phi$ is a smooth function whose Fourier transform $\hat{\phi}$ is identically $1$ on $\bigl(-\frac12,\frac12\bigr)$ and has support in $(-\pi,\pi)$. Then \(\displaystyle \hat{f}(\omega) = \sum_{n=-\infty}^\infty f(n)\hat{\phi}(\omega -n)\). Can you use that to show that \(\displaystyle \int_{\mathbb{R}}|\hat{f}(\omega)|^2d\omega = \sum_{n=-\infty}^\infty |f(n)|^2\)?

Edit. Correction: the Fourier transform of $\phi(x-n)$ is not $\hat{\phi}(\omega -n)$, but something like $e^{in\omega}\hat{\phi}(\omega) .$
 
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FAQ: Proving Parseval's Theorem for Schwartz Functions with Compact Support

What is the Shannon Sampling theorem?

The Shannon Sampling theorem, also known as the Nyquist-Shannon sampling theorem, is a fundamental theorem in the field of signal processing. It states that in order to accurately reconstruct a continuous signal from its samples, the sampling rate must be at least twice the highest frequency component of the signal.

Why is the Shannon Sampling theorem important?

The Shannon Sampling theorem is important because it provides a mathematical basis for understanding how digital signals are created and processed. It also ensures that the reconstructed signal will be a faithful representation of the original continuous signal, without any loss of information.

What happens if the sampling rate is not high enough?

If the sampling rate is not high enough, a phenomenon called aliasing can occur. This means that higher frequency components of the signal will be misrepresented as lower frequency components, resulting in a distorted and inaccurate reconstruction of the original signal.

Can the Shannon Sampling theorem be applied to all types of signals?

Yes, the Shannon Sampling theorem can be applied to all types of signals, including audio, video, and images. It is a fundamental concept in the field of signal processing and is used in various applications such as telecommunications, digital audio and video recording, and medical imaging.

Are there any limitations to the Shannon Sampling theorem?

The Shannon Sampling theorem assumes that the signal being sampled is band-limited, meaning that it has a finite highest frequency component. This may not always be the case in real-world signals, and in these situations, the theorem may not be applicable. Additionally, the theorem does not take into account other factors such as noise and distortion, which can affect the accuracy of the reconstructed signal.

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