Inverse fourier transform

In summary, the limits of integration for finding the impulse response are 1 and 2, the variance of the output signal can be found using the given formula, and while the output process is stationary and continuous, it is not differentiable in the mean square sense due to a discontinuity in the impulse response.
  • #1
peter.a
21
0

Homework Statement


I can't figure out what the limits of integration should be;

if a transfer function is given as follows:
h(ω)=1 if 1<|ω|<2, 0 otherwise

1) find the impulse response

2) if the input is white noise of intensity σ² find the variance of the output signal

3)state with reasons if the output process is stationary and continuous and differentiable in the mean square sense

Homework Equations





The Attempt at a Solution

 
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  • #2
1) the impulse response is h(t)=e^|2-t|-e^|1-t|2) Var(x(t))=σ²∫h²(τ)dτ 3) The output process is stationary as it does not depend on time, and continuous as the impulse response is continuous, however it is not differentiable in the mean square sense as the impulse response has a discontinuity at τ=1 and τ=2.
 

Related to Inverse fourier transform

1. What is an inverse Fourier transform?

An inverse Fourier transform is a mathematical operation that converts a signal from the frequency domain back to the time domain. It essentially reverses the process of a regular Fourier transform, which converts a signal from the time domain to the frequency domain.

2. How does an inverse Fourier transform work?

An inverse Fourier transform uses complex numbers and trigonometric functions to convert a signal from the frequency domain back to the time domain. The resulting signal will contain information about both the amplitude and phase of each frequency component in the original signal.

3. What are the applications of an inverse Fourier transform?

An inverse Fourier transform has many applications in fields such as signal processing, image processing, and data analysis. It is commonly used to filter noise from signals, compress data, and extract useful information from complex signals.

4. Are there any limitations to using an inverse Fourier transform?

One limitation of an inverse Fourier transform is that it assumes the signal is periodic and infinite. This means that it may not accurately represent signals with sharp changes or discontinuities. Additionally, it can only be applied to signals that are continuous and have a finite energy.

5. Is there a difference between an inverse Fourier transform and an inverse discrete Fourier transform?

Yes, there is a difference between an inverse Fourier transform and an inverse discrete Fourier transform. An inverse Fourier transform is used for continuous signals, while an inverse discrete Fourier transform is used for discrete signals. The latter is often used in digital signal processing and has some additional limitations compared to the inverse Fourier transform.

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