A Survey on Quaternion Algebra and Geometric Algebra Applications in Engineering and Computer Science 1995-2020

被引:31
作者
Bayro-Corrochano, Eduardo [1 ]
机构
[1] CINVESTAV Guadalajara, Dept Elect Engn & Comp Sci, Guadalajara Campus, Zapopan 45119, Jalisco, Mexico
关键词
Algebra; Quaternions; Rotors; Three-dimensional displays; Robots; Computer science; Calculus; Geometric algebra; Clifford algebra; quaternion algebra; screw theory; signal processing; electrical engineering and power systems; geometric and quantum computing; image processing; computer vision; graphic engineering; artificial intelligence; machine learning; neural networks; control engineering; robotics; biomedical engineering; and biotechnology; INVARIANT BODY KINEMATICS; SUPPORT VECTOR MACHINES; MOTOR ALGEBRA; ARTIFICIAL-INTELLIGENCE; NONSINUSOIDAL SOURCES; ROBOT MANIPULATORS; FOURIER-TRANSFORM; CLIFFORD-ALGEBRA; COMPLEX NUMBERS; NEURAL-NETWORKS;
D O I
10.1109/ACCESS.2021.3097756
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in image processing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.
引用
收藏
页码:104326 / 104355
页数:30
相关论文
共 251 条
[1]  
Ablamowicks R., ECLIFFORD SOFTWARE P
[2]  
Abou El Dahab E., 2000, Adv. Appl. Clifford Algebras, V10, P217
[3]   A probabilistic neural network for earthquake magnitude prediction [J].
Adeli, Hojjat ;
Panakkat, Ashif .
NEURAL NETWORKS, 2009, 22 (07) :1018-1024
[4]  
Al-Nuaimi A., 2016, 2016 INT C IND POS I, P1
[5]  
Altamirano-Gómez GE, 2014, LECT NOTES COMPUT SC, V8827, P802, DOI 10.1007/978-3-319-12568-8_97
[6]  
[Anonymous], 1871, Proc. Lond. Math. Soc., P381, DOI DOI 10.1112/PLMS/S1-4.1.381
[7]  
[Anonymous], 2015, Space-Time Algebra
[8]  
[Anonymous], 1995, PROC SCANDINAVIAN C
[9]  
[Anonymous], 2010, Geometric algebra for physicists
[10]  
[Anonymous], P 41 N AM POW S OCT