Search results
Results from the WOW.Com Content Network
The Bailey's FFT (also known as a 4-step FFT) is a high-performance algorithm for computing the fast Fourier transform (FFT). This variation of the Cooley–Tukey FFT algorithm was originally designed for systems with hierarchical memory common in modern computers (and was the first FFT algorithm in this so called "out of core" class).
The Cooley–Tukey algorithm, named after J. W. Cooley and John Tukey, is the most common fast Fourier transform (FFT) algorithm. It re-expresses the discrete Fourier transform (DFT) of an arbitrary composite size = in terms of N 1 smaller DFTs of sizes N 2, recursively, to reduce the computation time to O(N log N) for highly composite N (smooth numbers).
An example FFT algorithm structure, using a decomposition into half-size FFTs A discrete Fourier analysis of a sum of cosine waves at 10, 20, 30, 40, and 50 Hz A fast Fourier transform ( FFT ) is an algorithm that computes the Discrete Fourier Transform (DFT) of a sequence, or its inverse (IDFT).
Rader's algorithm (1968), [1] named for Charles M. Rader of MIT Lincoln Laboratory, is a fast Fourier transform (FFT) algorithm that computes the discrete Fourier transform (DFT) of prime sizes by re-expressing the DFT as a cyclic convolution (the other algorithm for FFTs of prime sizes, Bluestein's algorithm, also works by rewriting the DFT as a convolution).
This category is for fast Fourier transform (FFT) algorithms, i.e. algorithms to compute the discrete Fourier transform (DFT) in O(N log N) time (or better, for approximate algorithms), where is the number of discrete points.
The latest version of Smaart 8 runs under Windows 7 or newer, and Mac OSX 10.7 or newer, including 32- and 64-bit versions. A computer having a dual-core processor with a clock rate of at least 2 GHz is recommended. [5] Smaart can be set to sample rates of 44.1 kHz, 48 kHz or 96 kHz, and to bit depths of 16 or 24.
The Schönhage–Strassen algorithm is based on the fast Fourier transform (FFT) method of integer multiplication. This figure demonstrates multiplying 1234 × 5678 = 7006652 using the simple FFT method. Base 10 is used in place of base 2 w for illustrative purposes. Schönhage (on the right) and Strassen (on the left) playing chess in ...
The corresponding FFT algorithm would consist of first computing x k (z) = x(z) mod F k (z), then computing x k,j (z) = x k (z) mod F k,j (z), and so on, recursively creating more and more remainder polynomials of smaller and smaller degree until one arrives at the final degree-0 results.