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Oberve :
 
Oberve :
  
<math> X(k.2pi/N) = \sum_{n=0}^{N-1} x_{p}[n].e^{-J.2pi.kn/N}</math>, where <math> x_{p}[n] = \sum_{-\infty}^{\infty} x[n-lN]</math>  
+
<math> X(kx2pi/N) = \sum_{n=0}^{N-1} x_{p}[n].e^{-J.2pi.kn/N}</math>, where <math> x_{p}[n] = \sum_{-\infty}^{\infty} x[n-lN]</math>  
 
is periodic with N.
 
is periodic with N.
  
 
This is because  
 
This is because  
  
math> X(k.2p/N) = \sum_{n =-\infty}^{\infty} x[n].e^{-J.2pi.kn/N}</math>
+
<math> X(k.2p/N) = \sum_{n =-\infty}^{\infty} x[n].e^{-J.2pi.kn/N}</math>
 
            
 
            
 
         <math> = . . . + \sum_{n = -N}^{-1} x[n].e^{-J2pi.k.n/N} + \sum_{n = 0}^{N-1} x[n].e^{-J.2pi.kn/N}</math>
 
         <math> = . . . + \sum_{n = -N}^{-1} x[n].e^{-J2pi.k.n/N} + \sum_{n = 0}^{N-1} x[n].e^{-J.2pi.kn/N}</math>
  
 
         <math> = \sum_{l=-\infty}^{\infty} \sum_{n=lN}^{lN+N-1} x[n].e^{-J.2pi.kn/N}</math>
 
         <math> = \sum_{l=-\infty}^{\infty} \sum_{n=lN}^{lN+N-1} x[n].e^{-J.2pi.kn/N}</math>

Revision as of 21:12, 22 September 2009

Discrete Fourier Transform

definition

Let X[n] be a DT signal with period N

DFT

$ X [k] = \sum_{k=0}^{N-1} x[n].e^{-J.2pi.kn/N} $

IDFT

$ x [n] = (1/N) \sum_{k=0}^{N-1} X[k].e^{J.2pi.kn/N} $


Derivation

Digital signals are 1) finite duration 2)discrete

want F.T. discrete and finite duration

Idea : discretize (ie. sample) the F.T.

$ X(w) = \sum_{n=-\infty}^{\infty} x[n].e^{-Jwn} >^{sampling}> X(k.2pi/N) = \sum x[n].e^{-J2pi.n.k/N} $

note : if X(w) band limited can reconstruct X(w) if N big enough.

Oberve :

$ X(kx2pi/N) = \sum_{n=0}^{N-1} x_{p}[n].e^{-J.2pi.kn/N} $, where $ x_{p}[n] = \sum_{-\infty}^{\infty} x[n-lN] $ is periodic with N.

This is because

$ X(k.2p/N) = \sum_{n =-\infty}^{\infty} x[n].e^{-J.2pi.kn/N} $

        $  = . . . + \sum_{n = -N}^{-1} x[n].e^{-J2pi.k.n/N} + \sum_{n = 0}^{N-1} x[n].e^{-J.2pi.kn/N} $
        $  = \sum_{l=-\infty}^{\infty} \sum_{n=lN}^{lN+N-1} x[n].e^{-J.2pi.kn/N} $

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