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Comparison of the DFT and FFT via Matrices

   The purpose of this article is to illustrate the differences of the Discrete Fourier Transform (DFT) versus the Fast Fourier Transform (FFT). Please note, the following explanation of the FFT will use the "divide and conquer" method. 
   To start, we will define the DFT as, 

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



x[n] = n2(u[n + 3] − u[n − 1])

x[n] = n2(δ(n + 3) + δ(n + 2) + δ(n + 1) + δ(n))

$ X(z) = \sum_{n=-\infty}^{+\infty} x[n] z^{-n} $

$ X(z) = \sum_{n=-\infty}^{+\infty} n^2(\delta(n+3)+\delta(n+2)+\delta(n+1)+\delta(n)) z^{-n} $

X(z) = 9z3 + 4z2 + z + 1 for all z in complex plane

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva