Double integration problem for IDSFT

In summary: So in summary, we calculate each term of the inverse DSFT using double integration and then substitute the limits of integration. In this specific problem, we get a sinc function which equals 1 at n1 = 0. This explains the final result of zero.
  • #1
Kurd
3
0

Homework Statement


[/B]
The 2D Discrete Space Fourier transform (DSFT) X(w1,w2) of the sequence x(n1,n2) is given by,

$$X(w_1,w_2) = 5 + 2j sin(w_2) + cos(w_1) + 2e^{(-jw1-jw2)}$$

determine x(n1,n2)

Homework Equations



By definition inverse DSFT is,

$$x(n_1,n_2) = \dfrac{1}{(2π)^2} \int_{-π}^{π}\int_{-π}^{π} X(w_1,w_2) e^{(jw_1n_1+jw_2n_2)} dw_1dw_2$$

The Attempt at a Solution


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I got zero as a final answer.

Solving the double integral for each term I get zero when substituting pi, is it correct or did I made a mistake somewhere, what gets me confused is that when doing DSFT for some simple problem I can get cos(w1) for example but if I did the inverse DSFT I will get zero. can someone help.

Thanks in advance.
 
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  • #2
Kurd said:
I got zero as a final answer
Please post your working.
 
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Likes Kurd
  • #3
Taking each term,

$$X_1(w_1,w_2) = 5$$
$$X_2(w_1,w_2) = 2jsin(w_2)$$
$$X_3(w_1,w_2) = cos(w_1)$$
$$X_4(w1,w2) = 2e^{(-jw_1-jw_2)}$$

then,

$$ x_1(n_1,n_2) = \dfrac{5}{(2π)^2} \int_{-π}^{π}\int_{-π}^{π} e^{jw_1n_1+jw_2n_2} dw_1dw_2$$
$$x_1(n_1,n_2) = \dfrac{5}{(2π)^2} \int_{-π}^{π}e^{jw_2n_2}[ \int_{-π}^{π} e^{jw_1n_1} dw_1]dw_2$$
$$x_1(n_1,n_2) = \dfrac{5}{(2π)^2} \int_{-π}^{π}e^{jw_2n_2} [\dfrac{e^{jw_1n_1}}{jn_1}]_{-π}^{π} dw_2$$
$$x_1(n_1,n_2) = \dfrac{5}{(2π)^2} \int_{-π}^{π}e^{jw_2n_2} [\dfrac{1}{jn_1}(e^{jπn_n1} - e^{-jπn_1})] dw_2$$

since n1 and n2 are discrete, then
$$(e^{jπn_1} - e^{-jπn_1})] = cos(jπn_1) + jsin(jπn_1) - cos(-jπn_1) + jsin(-jπn_1) = 0$$

I solved the remaining terms in a similar fashion, do you find anything wrong here.
 
  • #4
Ok I got it.

I didn't notice that

$$\dfrac{sin(πn_1)}{πn_1}$$

which is a sinc function and equals 1 at n1 = 0.
 
  • #5
Kurd said:
Ok I got it.

I didn't notice that

$$\dfrac{sin(πn_1)}{πn_1}$$

which is a sinc function and equals 1 at n1 = 0.
Well done.
 

FAQ: Double integration problem for IDSFT

1. What is double integration problem for IDSFT?

The double integration problem for IDSFT refers to the process of using double integration techniques to solve for the inverse discrete short-time Fourier transform (IDSFT). This is a mathematical problem that involves finding the original signal from its frequency domain representation, which is obtained through the discrete short-time Fourier transform (DSTFT).

2. Why is the double integration problem for IDSFT important?

The double integration problem for IDSFT is important because it allows us to analyze and manipulate signals in the time domain, which is often more intuitive and easier to interpret than the frequency domain. It also plays a crucial role in various applications such as signal processing, communications, and image and audio processing.

3. What is the difference between single and double integration for IDSFT?

The main difference between single and double integration for IDSFT is the number of integrations performed. In single integration, only one integration is performed to obtain the original signal from its DSTFT. In double integration, two integrations are performed, which allows for a more accurate reconstruction of the original signal.

4. What are some common methods for solving the double integration problem for IDSFT?

There are various methods for solving the double integration problem for IDSFT, including numerical integration techniques such as Simpson's rule and the trapezoidal rule, as well as analytical methods such as contour integration and residue calculus. Depending on the specific problem, some methods may be more efficient or accurate than others.

5. Are there any challenges or limitations to solving the double integration problem for IDSFT?

Yes, there are some challenges and limitations to solving the double integration problem for IDSFT. One of the main challenges is the computational complexity, as double integration can be a time-consuming process for large signals. Additionally, the accuracy of the solution can be affected by factors such as signal noise and the choice of integration method.

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