Rui Wang, Yi-xuan Zhou, Yan-liang Jin, Wen-ming Cao, Sparse fast Clifford Fourier transform, Frontiers of Information Technology & Electronic Engineering 2017, Vol. 18, No. 8, pp.1131-1141, DOI: 10.1631/FITEE.1500452. Free download: http://www.jzus.zju.edu.cn/oldversion/opentxt.php?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.
Source: Email from Zhai Ziyang, Editor of JZUS & FITEE, jzus_zzy_AT_zju.edu.cn, 09 Sep. 2017, http://www.jzus.zju.edu.cn/oldversion/opentxt.php?doi=10.1631/FITEE.1500452