Solve Fourier Transform Homework: Find Variance & Covariance

In summary, the individual variances of the real and imaginary parts of Y, as well as the power spectral density |Y|^2, are undefined due to Y being a complex function known as a Cauchy distribution.
  • #1
peter.a
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Homework Statement



Just something I am working through and am a bit stuck on.

Homework Equations



I have taken the Fourier transform of an RC circuit which gives me :
Y(ω)=((X(ω))/(1+iωτ))
If i take the voltage across the circuit as white noise then i get:
Y(ω)=σ^²/2π/(1+iωτ))
How can i find the variance function and covariance of this Fourier transform

The Attempt at a Solution


I am not sure how to do this
 
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  • #2
First, welcome to PF. Regarding your questions, why are you trying to find these? Y is a complex function, so what do you even mean by variance? Individual variances of real and imaginary parts? Of its power spectral density |Y|^2?

The variances of Real(Y) and Imag(Y) are the third and second moments of a function known in statistics as a Cauchy distribution, and these moments are undefined. |Y|^2 is the Cauchy distribution itself, and it has undefined first and second moments (mean and variance).
 
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  • #3
Of the spectral density which is X(ω)/(1+(ωτ)^2)) i had incorectly stated it in the post.
 

FAQ: Solve Fourier Transform Homework: Find Variance & Covariance

What is a Fourier Transform?

A Fourier Transform is a mathematical tool used to analyze the frequency components of a signal or function. It transforms a time-domain signal into its frequency-domain representation, showing the amplitude and phase of each frequency component.

How is a Fourier Transform used to find variance?

A Fourier Transform can be used to find the variance of a signal by squaring the amplitude of each frequency component and then summing these values. The resulting sum is proportional to the variance of the original signal.

What is covariance and how is it related to Fourier Transform?

Covariance is a measure of the relationship between two variables. In the context of Fourier Transform, covariance is used to determine the relationship between two signals in the frequency domain. It is calculated by multiplying the amplitudes of corresponding frequency components and summing them.

Can Fourier Transform be used to find the covariance of non-stationary signals?

No, Fourier Transform is not suitable for finding the covariance of non-stationary signals. This is because it assumes that the signal is stationary, meaning that its statistical properties do not change over time. Covariance can only be accurately calculated for stationary signals.

What are some applications of finding variance and covariance using Fourier Transform?

Some applications of finding variance and covariance using Fourier Transform include signal processing, image processing, and data analysis. These techniques can be used to identify patterns, detect anomalies, and extract useful information from various types of data.

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