MATLAB Implementation of Cubic Spline Interpolation
MATLAB program for performing cubic spline interpolation with sample code and visualization. Demonstrates using built-in spline() function for smooth curve fitting between data points.
Data Fitting and interp1 - Univariate Function Interpolation
This collection includes code implementations for data fitting, interp1 - univariate function interpolation, spline - spline interpolation, polyfit - polynomial interpolation or fitting, curvefit - curve fitting, caspe - spline interpolation with various boundary conditions, casps - spline fitting (not available), interp2 - bivariate function interpolation, griddata - bivariate interpolation for irregular data, interp - non-monotonic point interpolation, and lagrange - Lagrange interpolation method.
Cubic Spline Interpolation Code Implementation
Curve fitting using cubic spline interpolation with algorithm and implementation considerations
Metal Artifact Reduction via Segmentation and Interpolation
MATLAB implementation for metal artifact removal using mean shift segmentation and spline interpolation techniques. This algorithm processes medical images by first partitioning metal-affected regions then reconstructing missing data through smooth interpolation.
Data Fitting Techniques
Data Fitting % interp1 - 1D Interpolation % spline - Spline Interpolation % polyfit - Polynomial Interpolation and Fitting % curvefit - Curve Fitting % caspe - Spline Interpolation with Various Boundary Conditions % casps - Spline Fitting (Not Available) % interp2 - 2D Interpolation % griddata - 2D Interpolation for Irregular Data % *interp - Non-monotonic Node Interpolation % *lagrange - Lagrange Interpolation Method
MATLAB Implementation of Cubic Spline Interpolation with Algorithm Explanation
This MATLAB .m program implements cubic spline interpolation using a 2D input array A(Nx2). The interpolation method follows the formula: S(x) = A(J) + B(J)*(x - x(J)) + C(J)*(x - x(J))**2 + D(J)*(x - x(J))**3 for x(J) <= x < x(J+1), with detailed coefficient calculation and boundary handling.
Solving Ordinary Differential Equations Using the 4th-Order Classical Runge-Kutta Method
Implementation and Applications of Classical 4th-Order Runge-Kutta Method for ODE Initial Value Problems