Fast Fourier Transform: Difference between revisions

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'''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT|discrete Fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.


 
==External links==
Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc. 
* {{wikipedia|Fast Fourier transform}}


[[Category:Signal Processing]]
[[Category:Signal Processing]]
[[Category:Technical]]

Latest revision as of 12:45, 18 August 2023

Fast Fourier transform (FFT) is an efficient algorithm for calculating the discrete Fourier transform (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of computations, FFT takes an order of , and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a FFT Radix 2 algorithm where etc.

External links