Solve Fourier Transform: f(t)=sin(2πt)/t

In summary, the rectangular function g(x) = \begin{cases}1&\text{if }|x|\leqslant 1, \\ 0&\text{otherwise.} \end{cases} can be transformed to the function f(t) = \frac{\sin t}t by applying the Fourier inversion theorem.
  • #1
lucad93
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I don't know if it is the right section to post in. I have a problem with a "simple" Fourier transform. This is the function to transform: \(\displaystyle f(t)=\frac{\sin\left({2\pi t}\right)}{t}\). My first idea was to write that as \(\displaystyle \sin\left({2\pi t}\right)\cdot\frac{1}{t}\) but then my fantasy crashed against a wall not finding the right transform for \(\displaystyle \frac{1}{t}\). Can you help me please?
 
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  • #2
lucad93 said:
I don't know if it is the right section to post in. I have a problem with a "simple" Fourier transform. This is the function to transform: \(\displaystyle f(t)=\frac{\sin\left({2\pi t}\right)}{t}\). My first idea was to write that as \(\displaystyle \sin\left({2\pi t}\right)\cdot\frac{1}{t}\) but then my fantasy crashed against a wall not finding the right transform for \(\displaystyle \frac{1}{t}\). Can you help me please?

The following may be useful,

\(\displaystyle \frac{sin(2\pi t)}{t} = 2\pi sinc(2\pi t)\).

and,

\(\displaystyle \int_{-\infty}^{\infty} \,2\pi sinc(2\pi t) = \pi\).

Also, if \(\displaystyle f(t) = f(-t)\) and \(\displaystyle f: \Bbb{R}\rightarrow \Bbb{R}\) then \(\displaystyle \mathbb{I}\mathbb{m}({{F}_{n}}) = 0\)

EDIT: I forgot to add that this function is not periodic (\(\displaystyle f(t) \ne f(t+T)\)). We can deal with this by taking the limit as \(\displaystyle T \rightarrow \infty\) or equivalently, the limit as \(\displaystyle f \rightarrow 0\). This is because, very loosely speaking, the function is periodic with \(\displaystyle \infty\).

Did you manage to solve this?
 
Last edited:
  • #3
lucad93 said:
I don't know if it is the right section to post in. I have a problem with a "simple" Fourier transform. This is the function to transform: \(\displaystyle f(t)=\frac{\sin\left({2\pi t}\right)}{t}\). My first idea was to write that as \(\displaystyle \sin\left({2\pi t}\right)\cdot\frac{1}{t}\) but then my fantasy crashed against a wall not finding the right transform for \(\displaystyle \frac{1}{t}\). Can you help me please?
This is not an easy problem, but there is a "back door" way to sneak up on the answer.

Start with the "rectangular" function \(\displaystyle g(x) = \begin{cases}1&\text{if }|x|\leqslant 1, \\ 0&\text{otherwise.} \end{cases}\) Its Fourier transform is given by $$\hat{g}(t) = \int_{\mathbb R}g(x)e^{-itx}dx = \int_{-1}^1e^{-itx}dx = \Bigl[\frac{e^{-itx}}{-it}\Bigr]_{-1}^1 = \frac{e^{-it} - e^{it}}{-it} = \frac{2\sin t}t.$$ So the Fourier transform of the rectangular function is your function (give or take a couple of constants). You can now apply the Fourier inversion theorem to deduce that the Fourier transform of your function is a rectiangular function.
 

Related to Solve Fourier Transform: f(t)=sin(2πt)/t

1. What is the Fourier Transform?

The Fourier Transform is a mathematical tool used to decompose a complex signal into its individual frequency components.

2. How do you solve the Fourier Transform?

To solve the Fourier Transform, you need to use the Fourier Transform formula, which involves integrating the function over all possible frequencies. This can be done analytically or using numerical methods.

3. What is the function f(t) in this Fourier Transform?

The function f(t) in this Fourier Transform is sin(2πt)/t, which represents a simple harmonic motion with a frequency of 2π and an amplitude of 1/t.

4. Why is the Fourier Transform important?

The Fourier Transform is important because it allows us to analyze complex signals and understand their frequency components. This is useful in fields such as signal processing, image processing, and data analysis.

5. What is the relationship between the time domain and frequency domain in the Fourier Transform?

The Fourier Transform converts a function from the time domain to the frequency domain, meaning it shows how much of each frequency is present in the original signal. This allows us to view the signal in a different perspective and extract useful information.

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