B-spline

Summary

In the mathematical subfield of numerical analysis, a B-spline or basis spline is a spline function that has minimal support with respect to a given degree, smoothness, and domain partition. Any spline function of given degree can be expressed as a linear combination of B-splines of that degree. Cardinal B-splines have knots that are equidistant from each other. B-splines can be used for curve-fitting and numerical differentiation of experimental data. In computer-aided design and computer graphics, spline functions are constructed as linear combinations of B-splines with a set of control points. Introduction The term "B-spline" was coined by Isaac Jacob Schoenberg and is short for basis spline. A spline function of order n is a piecewise polynomial function of degree n - 1 in a variable x. The places where the pieces meet are known as knots. The key property of spline functions is that they and their derivatives may be continuous, d

Official source

https://en.wikipedia.org/wiki/B-spline

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Mohamed Taher Nguira

This document presents a mathematical framerwork for delay retrieval between two signals. It treats Global and Local delay cases. It's based on E-splines and Exponential reproducing kernels properties. LCAV1601050917

2015

Geometrical reconstruction of curves and surfaces using NURBS

Sylvain Rouge

The aim of this project is the analysis of different approaches for curve and surface reconstruction and the development of a related MATLAB library. We initially study the Lagrange polynomial interpolation, then the Bezier, the B-splines and the NURBS curves and finally we introduce the NURBS surfaces. At each step we list the useful properties of the different approaches, the details of the mathematical representation and the evolution from simpler to more complicated representations.

2008

Application of optimal spline subspaces for the removal of spurious outliers in isogeometric discretizations

Espen Sande

We show that isogeometric Galerkin discretizations of eigenvalue problems related to the Laplace operator subject to any standard type of homogeneous boundary conditions have no outliers in certain optimal spline subspaces. Roughly speaking, these optimal subspaces are obtained from the full spline space defined on certain uniform knot sequences by imposing specific additional boundary conditions. The spline subspaces of interest have been introduced in the literature some years ago when proving their optimality with respect to Kolmogorov n-widths in L2-norm for some function classes. The eigenfunctions of the Laplacian - with any standard type of homogeneous boundary conditions - belong to such classes. Here we complete the analysis of the approximation properties of these optimal spline subspaces. In particular, we provide explicit L2 and H1 error estimates with full approximation order for Ritz projectors in the univariate and in the multivariate tensor-product setting. Besides their intrinsic interest, these estimates imply that, for a fixed number of degrees of freedom, all the eigenfunctions and the corresponding eigenvalues are well approximated, without loss of accuracy in the whole spectrum when compared to the full spline space. Moreover, there are no spurious values in the approximated spectrum. In other words, the considered subspaces provide accurate outlier-free discretizations in the univariate and in the multivariate tensor-product case. This main contribution is complemented by an explicit construction of B-spline-like bases for the considered spline subspaces. The role of such spaces as accurate discretization spaces for addressing general problems with non-homogeneous boundary behavior is discussed as well. c 2021 Elsevier B.V. All rights reserved.

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