Sparse fast Clifford Fourier transform
| Autor: | Yi-xuan Zhou, Yan-liang Jin, Rui Wang, Wenming Cao |
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| Rok vydání: | 2017 |
| Předmět: |
ComputerSystemsOrganization_COMPUTERSYSTEMIMPLEMENTATION
Computer Networks and Communications Discrete-time Fourier transform Prime-factor FFT algorithm Fast Fourier transform Short-time Fourier transform 020207 software engineering 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Fractional Fourier transform Algebra Cyclotomic fast Fourier transform Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering 0101 mathematics Electrical and Electronic Engineering Harmonic wavelet transform Algorithm Constant Q transform Mathematics |
| Zdroj: | Frontiers of Information Technology & Electronic Engineering. 18:1131-1141 |
| ISSN: | 2095-9230 2095-9184 |
| DOI: | 10.1631/fitee.1500452 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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