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lisyarus.github.io | ||
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thenumb.at
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| | | | | [AI summary] This text provides an in-depth exploration of how functions can be treated as vectors, particularly in the context of signal and geometry processing. It discusses the representation of functions as infinite-dimensional vectors, the use of Fourier transforms in various domains (such as 1D, spherical, and mesh-based), and the application of linear algebra to functions for tasks like compression and smoothing. The text also touches on the mathematical foundations of these concepts, including the Laplace operator, eigenfunctions, and orthonormal bases. It concludes with a list of further reading topics and acknowledges the contributions of reviewers. | |
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marc-b-reynolds.github.io
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| | | | | using visualizating as an exercise to note some quaternion properties | |
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liorsinai.github.io
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| | | | | A detailed foundation of quaternion mathematics. | |
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www.johndcook.com
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| | | You can use quaternions to describe rotations and quaternion products to carry out these rotations. These products have the form qpq* where q represents a rotation, q* is its conjugate, and p is the the vector being rotated. (That's leaving out some details that we'll get to shortly.) The primary advantage of using quaternions to | ||
