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Titlebook: Geometric Computing with Clifford Algebras; Theoretical Foundati Gerald Sommer Book 2001 Springer-Verlag Berlin Heidelberg 2001 Algebra.Alg

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樓主: Indigent
21#
發(fā)表于 2025-3-25 05:05:18 | 只看該作者
Gathering and integrating feedback,at the CFT yields an extended and more efficient multi-dimensional signal theory compared to the theory based on complex numbers. Though the CFT of a . signal does not include new information (the complex Fourier transform is a . transform in the mathematical sense), the Clifford spectrum is a riche
22#
發(fā)表于 2025-3-25 10:52:23 | 只看該作者
Prioritize Documentation Requests,commutative hypercomplex Fourier transform (HFT). We can cover all these transforms with the term ., since all mentioned algebras are hypercomplex algebras (see Cha. 9). In order to have a numerical way to evaluate these transforms, we introduce the corresponding discrete transforms by sampling the
23#
發(fā)表于 2025-3-25 12:29:27 | 只看該作者
‘The Form of Faustus’ Fortunes’real signal directly accessible. Regrettably, there is no straightforward extension of this concept to multidimensional signals, yet. There are rather different approaches to an extension which have different drawbacks. In the first part of this chapter we will review the main approaches and introdu
24#
發(fā)表于 2025-3-25 17:35:53 | 只看該作者
25#
發(fā)表于 2025-3-25 22:10:54 | 只看該作者
Susan Carter,Cally Guerin,Claire Aitchisonld” has to be understood by a computer. This may be with regard to control movement (robots), to survey a scene for later interpretation (medicine), or to create and mix artificial with real environments (special effects).
26#
發(fā)表于 2025-3-26 02:46:56 | 只看該作者
27#
發(fā)表于 2025-3-26 08:23:53 | 只看該作者
28#
發(fā)表于 2025-3-26 10:49:48 | 只看該作者
29#
發(fā)表于 2025-3-26 16:21:55 | 只看該作者
30#
發(fā)表于 2025-3-26 16:56:22 | 只看該作者
Clifford Algebra Multilayer PerceptronsMultilayer Perceptrons (MLPs) are one of the most common and popular neural architectures. They are widely used in many different areas like handwriting recognition, speech recognition, and time series prediction for instance. In this chapter, we will extend MLPs from the domain of real numbers to Clifford algebra domains.
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