Algorithm/algorithmus 42 an algorithm for cubic spline fitting with convexity constraints

Computing ◽  
1980 ◽  
Vol 24 (4) ◽  
pp. 349-371 ◽  
Author(s):  
P. Dierckx
Keyword(s):  
Cubic Spline ◽  
Spline Fitting ◽  
Convexity Constraints ◽  
Cubic Spline Fitting

Efficient 4D shape completion from sparse samples via cubic spline fitting in linear rotation-invariant space

2019 ◽  
Vol 82 ◽  
pp. 129-139 ◽  
Author(s):  
Qing Xia ◽  
Chengju Chen ◽  
Jiarui Liu ◽  
Shuai Li ◽  
Aimin Hao ◽  
...  
Keyword(s):  
Cubic Spline ◽  
Invariant Space ◽  
Rotation Invariant ◽  
Spline Fitting ◽  
Shape Completion ◽  
Sparse Samples ◽  
Fitting In ◽  
Cubic Spline Fitting

scholarly journals Displacement Response Reconstruction of Slender Flexible Structures Based on Cubic Spline Fitting Method

2019 ◽  
Vol 33 (2) ◽  
pp. 226-236
Author(s):  
Qing-hua Han ◽  
Ye-xuan Ma ◽  
Xin-xin Feng ◽  
Wan-hai Xu
Keyword(s):  
Flexible Structures ◽  
Cubic Spline ◽  
Displacement Response ◽  
Spline Fitting ◽  
Fitting Method ◽  
Response Reconstruction ◽  
Cubic Spline Fitting

Bi-cubic spline fitting and display of tooth external surface and canal

2003 ◽  
Author(s):  
Y.C. Pao ◽  
P.-Y. Qin ◽  
Q.-S. Yuan
Keyword(s):  
External Surface ◽  
Cubic Spline ◽  
Spline Fitting ◽  
Cubic Spline Fitting

Parametric cubic spline-fitting programs for open and closed curves

1992 ◽  
Vol 6 (5) ◽  
pp. 472 ◽  
Author(s):  
D. A. Smith
Keyword(s):  
Cubic Spline ◽  
Closed Curves ◽  
Spline Fitting ◽  
Cubic Spline Fitting ◽  
Parametric Cubic Spline

A coupled bend-twist deformation monitoring method based on inclination measurement and rational cubic spline fitting

2021 ◽  
Vol 147 ◽  
pp. 107084
Author(s):  
Haoyu Yu ◽  
Xiaolong Chen ◽  
Mengyuan Ren ◽  
Likang Yin ◽  
Qian Wu ◽  
...  
Keyword(s):  
Cubic Spline ◽  
Deformation Monitoring ◽  
Monitoring Method ◽  
Spline Fitting ◽  
Cubic Spline Fitting

Cubic spline fitting using data dependent triangulations

1990 ◽  
Vol 7 (1-4) ◽  
pp. 293-301 ◽  
Author(s):  
Ewald Quak ◽  
Larry L. Schumaker
Keyword(s):  
Cubic Spline ◽  
Spline Fitting ◽  
Using Data ◽  
Cubic Spline Fitting

Research on Non-Circular Gear Hobbing Simulation Based on Piecewise Cubic Spline Fitting

2014 ◽  
Vol 620 ◽  
pp. 357-362 ◽  
Author(s):  
Da Zhu Li ◽  
Lian Xia ◽  
You Yu Liu ◽  
Jiang Han
Keyword(s):  
Cubic Spline ◽  
Parameters Optimization ◽  
Secondary Development ◽  
Design Parameters ◽  
Three Dimension ◽  
Spline Fitting ◽  
Gear Hobbing ◽  
Polar Coordinate ◽  
Pitch Curve ◽  
Cubic Spline Fitting

In order to solve the hobbing machining data calculation and hobbing simulation of non-circular gear with various shapes, the piecewise cubic spline fit method has been adopted to unify the polar coordinate expression of non-circular pitch curve. The method to generate hobbing machining data was deduced based on hobbing linkage mathematical model, and the machining data error caused by piecewise cubic spline fitting method was analyzed. The three-dimension software has been used as simulation tool to make secondary development, and then non-circular gear hobbing simulation was realized based on machining data, which can verify the validity of machining data and the relevant design drawbacks of non-circular gear. The work of this paper lay a good foundation for design parameters optimization and hobbing of non-circular gear.

Fourier Transformation of X-Ray Rocking Curves from Interferometer Structures

1993 ◽  
Vol 37 ◽  
pp. 135-144
Author(s):  
J. M. Hudson ◽  
B. K. Tanner ◽  
R. Blunt
Keyword(s):  
Layer Thickness ◽  
Bragg Peak ◽  
Noisy Data ◽  
X Ray Diffraction ◽  
Interference Structure ◽  
X Ray ◽  
Auto Correlation ◽  
Interference Fringes ◽  
Spline Fitting ◽  
Cubic Spline Fitting

AbstractWe discuss the use of Fourier transform techniques to extract layer thickness from the interference fringes observed in high resolution X-ray diffraction rocking curves of pseudomorphic HEMT structures. The interference structure is extracted by cubic spline fitting to the extrema of the data, thereby obtaining a background envelope which is used to normalise the data. The resulting constant background is subtracted from the data and the residual Fourier transformed. Auto correlation of the residual significantly improves the result from noisy data. Satisfactory results are obtained only when the Bragg peak from the substrate is windowed out. With a limited dynamic and angular range, there is often insufficient data to separate the two closely spaced periods arising from the total layer thickness and that excluding the quantum well. The result then corresponds to the average of these two thicknesses.

Spline Fitting

2011 ◽  
pp. 166-171
Author(s):  
Michael Wodny
Keyword(s):  
Optimization Problems ◽  
Smoothing Splines ◽  
Cubic Spline ◽  
Smoothing Parameter ◽  
Spline Functions ◽  
System Of Equations ◽  
Linear System Of Equations ◽  
Spline Fitting ◽  
Natural Cubic Spline ◽  
The Given

Given are the m points (xi,yi), i=1,2,…,m. Spline functions are introduced, and it is noticed that the interpolation task in the case of natural splines has a unique solution. The interpolating natural cubic spline is constructed. For the construction of smoothing splines, different optimization problems are formulated. A selected problem is looked at in detail. The construction of the solution is carried out in two steps. In the first step the unknown Di=s(xi) are calculated via a linear system of equations. The second step is the construction of the interpolating natural cubic spline with respect to these (xi,Di), i=1,2,…,m. Every optimization problem contains a smoothing parameter. A method of estimation of the smoothing parameter from the given data is motivated briefly.

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