Fast Fourier Transform: Difference between revisions

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 $O(n^{2})\,$ computations, FFT takes an order of $O(n\,\log \,n)$ , 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 $n=64,128,256,512,1024,2048$ etc.

External links

Fast Fourier transform on Wikipedia

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-Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). Reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of o(n²) computations, FFT takes an order of o(n lg n), and is definitely the preferred algorithm to used in all applications. The disadvantage of the FFT is that the number of samples must be exactly a power of 2.
+'''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==
+* {{wikipedia|Fast Fourier transform}}
+[[Category:Signal Processing]]
+[[Category:Technical]]