This course covers lessons on Adaptive Filters,Stochastic Processes ,Correlation Structure,Convergence Analysis,LMS Algorithm,Vector Space Treatment to Random Variables,Gradient Adaptive Lattice, Recursive Least Squares,Systolic Implementation & Singular Value Decomposition.
Course topics:
- Introduction to Adaptive Filters
- Introduction to Stochastic Processes
- Stochastic Processes
- Correlation Structure
- FIR Wiener Filter (Real)
- Steepest Descent Technique
- LMS Algorithm
- Convergence Analysis
- Convergence Analysis (Mean Square)
- Misadjustment and Excess MSE
- Sign LMS Algorithm
- Block LMS Algorithm
- Fast Implementation of Block LMS Algorithm
- Vector Space Treatment to Random Variables
- Orthogonalization and Orthogonal Projection
- Orthogonal Decomposition of Signal Subspaces
- Introduction to Linear Prediction
- Lattice Filter
- Lattice Recursions
- Lattice as Optimal Filter
- Linear Prediction and Autoregressive Modeling
- Gradient Adaptive Lattice
- Introduction to Recursive Least Squares
- RLS Approach to Adaptive Filters
- RLS Adaptive Lattice
- RLS Lattice Recursions
- RLS Lattice Algorithm
- RLS Using QR Decomposition
- Givens Rotation
- Givens Rotation and QR Decomposition
- Systolic Implementation
- Singular Value Decomposition
