Computation of Fourier Transform

In summary, the conversation discusses using properties of Fourier transform to find the answer for X(jw), which involves dealing with the presence of u(t-1) in the expression. The approach suggested is to split x(t) into two parts and use properties to find the transforms for each part separately. This will result in the final transform for X(jw).
  • #1
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Homework Statement


x(t) = t*exp(a)*exp(-a*t)*u(t-1) - exp(a)*exp(-a*t)*u(t-1)

I need to find X(jw)...


Homework Equations


how to apply properties of Fourier transform to get an answer? Because i know that the only effective method for this..


The Attempt at a Solution



For example from properties i know that exp(-a*t)u(t) is 1/(a+jw) and t*exp(-a*t)*u(t) is 1/(a+jw)^2..

now... i can think of my X(jw) consisting of 2 parts.. and i need to apply properties to each of those parts... however... properties are given in case if i have u(t) in expression... but in my problem i have u(t-1).. which kind of spoils things...

How should i deal with that properly?
 
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  • #2
ok i'v been thinking more of this problem, now here is what i decided to do:

lets say I am concentrating my attention on the 1st part of x(t) which is: t*exp(a)*exp(-a*t)*u(t-1), and i will compute X1(jw) for it first.

i am changing t-1 to Tao, => Tao = t - 1, t=Tao+1, then i have the following:

(Tao+1) *exp(a) * exp(-a*(Tao+1)) * u(Tao) = Tao*exp(a)*exp(-a*Tao)*exp(-a)*u(Tao) + exp(a)*exp(-a*Tao)*exp(-a)*u(Tao).

Now, recognizing mentioned properties above, i see that the last expression has transform:

1/(a+jw) + 1/(a+jw)^2 ... and this will be the transform for my X1(jw)..

Was the thinking correct? if yes then simililar stuff applied to second part of x(t) and problem solved..

thanks..
 

FAQ: Computation of Fourier Transform

What is the Fourier Transform?

The Fourier Transform is a mathematical operation that decomposes a signal into its constituent frequencies. It is commonly used in signal processing and image processing to analyze and manipulate signals in the frequency domain.

What is the difference between the Fourier Transform and the Inverse Fourier Transform?

The Fourier Transform converts a signal from the time domain to the frequency domain, while the Inverse Fourier Transform converts a signal from the frequency domain back to the time domain. Essentially, the Fourier Transform shows the frequency components of a signal, while the Inverse Fourier Transform reconstructs the original signal using those frequency components.

What is the relationship between the Fourier Transform and the Fast Fourier Transform (FFT)?

The Fast Fourier Transform (FFT) is an algorithm that efficiently calculates the Discrete Fourier Transform (DFT), which is a discrete version of the Fourier Transform. The FFT is a more efficient way of calculating the Fourier Transform for discrete signals, and it is commonly used in digital signal processing.

What types of signals can be analyzed using the Fourier Transform?

The Fourier Transform can be applied to any signal that is periodic or can be represented as a sum of sinusoidal functions. This includes signals in the time domain such as audio signals, images, and time series data.

What are some real-world applications of the Fourier Transform?

The Fourier Transform has many practical applications in various fields, including signal processing, image processing, data compression, audio and video encoding, and solving differential equations. It is also used in technologies such as MRI and radar for signal analysis and filtering.

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