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2017, Volume 18, Issue 8

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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 8 doi: 10.1631/FITEE.1500452

Sparse fast Clifford Fourier transform

. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.. College of Information Engineering, Shenzhen University, Shenzhen 518060, China.. Department of Electrical and Computer Engineering, University of Missouri, Columbia 65211, USA

Available online: 2017-10-31
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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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