CLC number: TP391
On-line Access: 2017-09-08
Received: 2015-12-09
Revision Accepted: 2016-08-23
Crosschecked: 2017-08-24
Cited: 0
Clicked: 19680
Rui Wang, Yi-xuan Zhou, Yan-liang Jin, Wen-ming Cao. Sparse fast Clifford Fourier transform[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1131-1141.
@article{title="Sparse fast Clifford Fourier transform",
author="Rui Wang, Yi-xuan Zhou, Yan-liang Jin, Wen-ming Cao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="8",
pages="1131-1141",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500452"
}
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%A Rui Wang
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%A Yan-liang Jin
%A Wen-ming Cao
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 8
%P 1131-1141
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%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500452
TY - JOUR
T1 - Sparse fast Clifford Fourier transform
A1 - Rui Wang
A1 - Yi-xuan Zhou
A1 - Yan-liang Jin
A1 - Wen-ming Cao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 8
SP - 1131
EP - 1141
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500452
Abstract: The clifford Fourier transform (CFT) can be applied to both vector and scalar fields. However, due to problems with big data, CFT is not efficient, because the algorithm is calculated in each semaphore. The sparse fast Fourier transform (sFFT) theory deals with the big data problem by using input data selectively. This has inspired us to create a new algorithm called sparse fast CFT (SFCFT), which can greatly improve the computing performance in scalar and vector fields. The experiments are implemented using the scalar field and grayscale and color images, and the results are compared with those using FFT, CFT, and sFFT. The results demonstrate that SFCFT can effectively improve the performance of multivector signal processing.
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