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 f...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 im- plemented 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.展开更多
基金Project supported by the National Natural Science Foundation of China （Nos. 61301027, 61375015, and 11274226）
文摘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 im- plemented 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.
