Advanced Computational Applications of Geometric Algebra
Springer International Publishing (Verlag)
978-3-031-55984-6 (ISBN)
How Geometric Algebra can naturally serve for constructing solutions for pattern recognition, machine learning, data compression, games, robotics, quantum computing, data encoding, to cite a few. Moreover, there is ample evidence that further research on GA and related areas can significantly expand the number of real-world applications in a wide variety of areas. A mathematical system that is very easy to handle, highly robust and superior performance for engineering applications. Good thematic introduction for engineers and researchers new to the subject. Extensive illustrations and code examples. Thematically well structured with many hands on examples. Learning about GA and how to use it for daily tasks in engineering research and development.
E. Hitzer, D. Hildenbrand, Introduction to Geometric Algebra.- L. Dorst and S. De Keninck, Physical Geometry by Plane-based Geometric Algebra.- E. Hitzer, Inner product of two oriented points in conformal geometric algebra in detail.- H. Yao, S. Mann, Line-Cyclide Intersection and Colinear Point Quadruples in the Double Conformal Model.- C. Matsantonis and J. Lasenby, A Geometric Algebra Solution to the Absolute Orientation Problem.- A. Pepe, J. Lasenby and P. Chacon: Geometric Algebra Models of Proteins for Three-Dimensional Structure Prediction: a detailed analysis.- K. Neumann et al: GAAlign: Robust Sampling-based Point Cloud Registration using Geometric Algebra.- W. Luo et al,Geometric algebra: a possible foundation for Digital twin modeling and analysis - a case study with PIR scene.- A. Arsenovic, A Spinor Model for Cascading Two-port Networks In Conformal Geometric Algebra.- G. Vieira Neto et al, Clifford Convolutional Neural Networks: Concepts, Implementation, and an Application for Lymphoblast Image Classification.- D. Hildenbrand+Ed Saribatir et al, Geometric Algebra algorithm code optimised by GAALOP executing on a simulated memristor crossbar array.
| Erscheinungsdatum | 05.06.2024 |
|---|---|
| Reihe/Serie | Springer Proceedings in Mathematics & Statistics |
| Zusatzinfo | X, 257 p. 83 illus., 73 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Schlagworte | algorithm optimization • Big Data • Computer Graphics • computing • Data Compression • Deep learning • Electric Power Engineering • Encryption • Engineering • Games • Geometric Algebra • Lidar • Navigation • Networks and Graphs • Quantum Computing • Robotics |
| ISBN-10 | 3-031-55984-3 / 3031559843 |
| ISBN-13 | 978-3-031-55984-6 / 9783031559846 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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