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The name "butterfly" comes from the shape of the data-flow diagram in the radix-2 case, as described below. [1] The earliest occurrence in print of the term is thought to be in a 1969 MIT technical report. [2] [3] The same structure can also be found in the Viterbi algorithm, used for finding the most likely sequence of hidden states.
Transposed Block Face-splitting product in the model of a Multi-Face radar with DAA, proposed by V. Slyusar in 1996 [5]. The main approach to digital signal processing in DAA is the "digital beamforming" after Analog-to-digital converters (ADC) of receiver channels or before Digital-to-analog converters (DAC) by transmission.
A useful property of the DFT is that the inverse DFT can be easily expressed in terms of the (forward) DFT, via several well-known "tricks". (For example, in computations, it is often convenient to only implement a fast Fourier transform corresponding to one transform direction and then to get the other transform direction from the first.)
The most common example of Bloch's theorem is describing electrons in a crystal, especially in characterizing the crystal's electronic properties, such as electronic band structure. However, a Bloch-wave description applies more generally to any wave-like phenomenon in a periodic medium.
Analysis of plane wave scattering from a subwavelength plasmonic grating with RCWA method. Rigorous coupled-wave analysis (RCWA), also known as Fourier modal method (FMM), [1] is a semi-analytical method in computational electromagnetics that is most typically applied to solve scattering from periodic dielectric structures.
A twiddle factor, in fast Fourier transform (FFT) algorithms, is any of the trigonometric constant coefficients that are multiplied by the data in the course of the algorithm. This term was apparently coined by Gentleman & Sande in 1966, and has since become widespread in thousands of papers of the FFT literature.
When the DFT and IDFT are implemented by the FFT algorithm, the pseudocode above requires about N (log 2 (N) + 1) complex multiplications for the FFT, product of arrays, and IFFT. [B] Each iteration produces N-M+1 output samples, so the number of complex multiplications per output sample is about:
A special case occurs when, by design, the length of the blocks is an integer multiple of the interval between FFTs. Then the FFT filter bank can be described in terms of one or more polyphase filter structures where the phases are recombined by an FFT instead of a simple summation.