Very fast computation of the radix-2 discrete Fourier transform

Preuss, R.

doi:10.1109/tassp.1982.1163935

Cited by 32 publications

(5 citation statements)

References 10 publications

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“…Another interesting remark is as follows: the same number of multiplications as in SRFFT could also be obtained by so-called 'real factor radix-2 FFTs' [24,42,44] (which were, on another respect, somewhat numerically ill-conditioned and needed about 20% more additions). They were obtained by making use of some computational trick to replace the complex twiddle factors by purely real or purely imaginary ones.…”

Section: Signal Processingmentioning

confidence: 89%

“…This reduction in the number of multiplications was obtained at the cost of an increase in the number of additions, and a greater sensitivity to roundoff noise. Hence, further developments of these 'real factor' FFTs appeared in [24,42], reducing these problems. Bruun also proposed an original scheme [22] particularly suited for real data.…”

Section: Signal Processing Tutorial On Fast Fourier Transformsmentioning

confidence: 99%

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Fast fourier transforms: A tutorial review and a state of the art

Duhamel¹,

1990

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Rrsum4. La publication de l'algorithme de Cooley-Tukey pour la transformation de Fourier rapide a ouvert une nouvelle ~re dans le traitement num6rique des signaux, en r4duisant l'ordre de complexit6 de probl~mes cruciaux, comme la transformation de Fourier ou la convolution, de N 2 ~ N log2 N (oh N est la taille du probl~me). Le drvelopment des algorithmes principaux (Cooley-Tukey, split-radix FFT, algorithmes des facteurs premiers, et transform6e rapide de Winograd) est drcrit. Ensuite, l'&at de l'art est donn4, et on parle des probl~mes ouverts et des implantations.

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Section: Signal Processingmentioning

confidence: 89%

Section: Signal Processing Tutorial On Fast Fourier Transformsmentioning

confidence: 99%

Fast fourier transforms: A tutorial review and a state of the art

Duhamel¹,

1990

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“…Numerous efficient implementations of the discrete Fourier transform are known in literature [14,18,58,62], the so-called fast Fourier transforms (FFTs). Three implementations are taken into consideration.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

Power-Aware Architecting for data-dominated applications

Ditzel¹,

Otten²,

Serdijn³

2007

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“…The numbers shown in Table I are obtained by actual counting. Also shown in Table I are the numbers of multiplications using the Rader-Brenner algorithm [6,7]. We see that the MSDFFT shows a considerable saving over the SDFFT, although their asymptotic behaviors are the same.…”

Section: Computational Complexitymentioning

confidence: 95%

Two dimensional DFT using mixed time and frequency decimations

Caraiscos

Liu²

ICASSP '82. IEEE International Conference on Acoustics, Speech, and Signal Processing

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The well-known decimation-in-time and decimation-infrequency FFT algorithms have recently been combined into a single and more efficient one, the Mixed Decimation FFT algorithm. On the other hand, an efficient way to perform a two dimensional DFT computation is to use decimation-in-time or decimation-infrequency in both dimensions simultaneously. In this paper the bove two ideas are combined to give a new two dimensional DFT algorithm, the Mixed Simultaneous Decimation FFT algorithm.

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Very fast computation of the radix-2 discrete Fourier transform

Cited by 32 publications

References 10 publications

Fast fourier transforms: A tutorial review and a state of the art

Fast fourier transforms: A tutorial review and a state of the art

Power-Aware Architecting for data-dominated applications

Two dimensional DFT using mixed time and frequency decimations

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