Lattice Boltzmann Method for Solving Poiseuille Flow in Fluid Dynamics
Lattice Boltzmann Method simulation of Poiseuille flow in fluid dynamics with iterative computation inputs generating dynamic velocity profile visualizations
A Simplex Method Implementation Program
A simplex method implementation that displays optimal solutions, optimal values, and iteration counts with detailed algorithmic progression tracking
Solving Lid Driven Flow Problem in Fluid Mechanics Using LBM Method
Implementation of LBM method for lid driven flow simulation with iterative computation and velocity vector field visualization output.
Arnold Cat Map Experiment: Iteration Effects on Image Scrambling
This experiment explores the Arnold cat map algorithm, investigating the relationship between iteration count and scrambling effectiveness in digital image encryption - a classic technique for secure image processing with code implementation insights.
Total Variation Denoising
Total Variation Denoising Function: J = tv(I, iter, dt, ep, lam, I0) Input Parameters: I - Grayscale image, iter - Number of iterations [Default: 1], dt - Time step size [Default: 0.2], ep - Epsilon enhancement parameter [Default: 1], lam - Fidelity term lambda [Default: 0], I0 - Input noisy image [Default: I0=I] (Values in brackets indicate default parameters)
Original Code for Solving Function Optimization Problems Using Genetic Algorithm
Source code implementing genetic algorithm for function optimization, calculates optimal solution and iteration count with detailed algorithmic implementation
Adaptive Thresholding Image Processing
Adaptive thresholding image processing with iterative calculation implementation
Image Restoration Using the Lucy-Richardson Method
Image restoration performed with the Lucy-Richardson method, comparing results after 5 and 15 iterations
Genetic Algorithm for Function Optimization Problems
MATLAB implementation of genetic algorithm for function optimization, featuring source code that calculates optimal solutions and iteration counts with performance enhancement strategies.
2D Matching Pursuit Algorithm
The 2D Matching Pursuit algorithm achieves precise image reconstruction using fewer subspace comparisons, with progressively improved approximation to the source image as iteration count increases. Implementation typically involves greedy iterative selection of optimal dictionary atoms and residual updates.