Sparse fast Clifford Fourier transform
2017
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.
Keywords:
- Harmonic wavelet transform
- Discrete Fourier transform (general)
- Constant Q transform
- Mathematical optimization
- Fourier inversion theorem
- Prime-factor FFT algorithm
- Fourier transform on finite groups
- Discrete mathematics
- Mathematics
- Discrete Fourier transform
- Cyclotomic fast Fourier transform
- Fractional Fourier transform
- Computer science
- Algorithm
- Discrete-time Fourier transform
- Algebra
- Fast Fourier transform
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
35
References
2
Citations
NaN
KQI