Fourier transform Discreet time-shift

In summary, we are dealing with a discreet function that is shifted forward by N/2. To find the Fourier transform, we need to remember that a positive phase shift of p corresponds to a shift of the transform by e^{\frac{2 \pi i k p}{N}}. We must also ensure that N is even for the function to have a solution. Once we have found the Fourier transform for the non-shifted function, we can use the formula X(f)*e^(j*pi*k) to find the transform for the shifted function.
  • #1
electronic engineer
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suppose we have this discreet function:

x(k)=rect(k+N/2)= 1 ; when -N/2=<K<=N/2-1
x(k)=0; otherwise

This is discreet function(not continuous) of k shifted forward by N/2, we need to find Fourier transform for it ..

anyway let N=6 for simplicity, then:

x(k)=rect(k+3)= 1 ; when -3=<K<=2
x(k)=0; otherwise

i think there's a law for finding FT of shifted signals but i can't remember ,and i need a guidance to get the solution

thanks
 
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  • #2
First you must be careful and assert that N is even, because the function would be nothing if N was odd and it was shifted over by a fractional amount, which you did specify.

To answer your question though, a positive phase shift of p corresponds to a shift of the transform by [tex]e^{\frac{2 \pi i k p}{N}}[/tex] where k is the counting variable, N is the period, so if x[n] [tex]\rightarrow X[k][/tex] then [tex]x[n+\frac{p}{2}] \rightarrow X[k] e^{\frac{\pi i k p}{N}}[/tex]. See http://en.wikipedia.org/wiki/Discrete_Fourier_transform

Well I shouldn't have used the same counting variables for both x and its transform. Let me clean this up some.
 
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  • #3
so the solution would be like this:

x(k)<<DFT...<< X(f)
x(K+N/2) <<DFT...<< X(f).e^{(j*2*pi*k*N/2)/N}=X(f)*e^(j*pi*k)

where k is counting variable,N number of samples, j: imaginary unit

so the next step is to find X(f) of non-shifted disctreet function x(k)

if you have more comments or corrections about the problem , please do post again here

regards
 

FAQ: Fourier transform Discreet time-shift

What is a Fourier transform Discreet time-shift?

A Fourier transform Discreet time-shift is a mathematical operation that converts a signal from the time domain to the frequency domain. It is used to analyze and understand the frequency components of a signal and is commonly used in signal processing and data analysis.

How does a Fourier transform Discreet time-shift work?

A Fourier transform Discreet time-shift works by decomposing a signal into its individual frequency components. It uses complex exponential functions to represent the signal in the frequency domain, with each function corresponding to a specific frequency. The resulting representation can then be used to analyze the signal and extract useful information.

What is the difference between Fourier transform Discreet time-shift and continuous Fourier transform?

The main difference between Fourier transform Discreet time-shift and continuous Fourier transform is that the former is used for signals that are discrete and sampled at specific time intervals, while the latter is used for signals that are continuous and have no defined sampling rate. Additionally, the Fourier transform Discreet time-shift produces a periodic spectrum, while the continuous Fourier transform produces a continuous spectrum.

What are the applications of Fourier transform Discreet time-shift?

Fourier transform Discreet time-shift has a wide range of applications in various fields such as signal processing, image and audio compression, and data analysis. It is also used in engineering, physics, and mathematics for analyzing and understanding the frequency components of different signals.

Are there any limitations to using Fourier transform Discreet time-shift?

One limitation of using Fourier transform Discreet time-shift is that the signal must be finite and discrete. It also assumes that the signal is periodic, which may not always be the case. Additionally, the Fourier transform Discreet time-shift may not be suitable for analyzing signals with sudden changes or sharp edges, as it can result in spectral leakage and distort the frequency representation of the signal.

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