Adaptive Signal Processing

Adaptive Signal Processing
Lecture Series on Adaptive Signal Processing by Prof.M.Chakraborty, Department of E and ECE, IIT Kharagpur.
Lecture - 1 Introduction to Adaptive Filters
Lecture - 2 Introduction to Stochastic Processes
Lecture - 3 Stochastic Processes
Lecture - 4 Correlation Structure
Lecture - 5 FIR Wiener Filter (Real)
Lecture - 6 Steepest Descent Technique
Lecture - 7 LMS Algorithm
Lecture - 8 Convergence Analysis
Lecture - 9 Convergence Analysis (Mean Square)
Lecture - 10 Convergence Analysis (Mean Square)
Lecture - 11 Misadjustment and Excess MSE
Lecture - 12 Misadjustment and Excess MSE
Lecture - 13 Sign LMS Algorithm
Lecture - 14 Block LMS Algorithm
Lecture - 15 Fast Implementation of Block LMS Algorithm
Lecture - 16 Fast Implementation of Block LMS Algorithm
Lecture - 17 Vector Space Treatment to Random Variables
Lecture - 18 Vector Space Treatment to Random Variables
Lecture - 19 Orthogonalization and Orthogonal Projection
Lecture - 20 Orthogonal Decomposition of Signal Subspaces
Lecture - 21 Introduction to Linear Prediction
Lecture - 22 Lattice Filter
Lecture - 23 Lattice Recursions
Lecture - 24 Lattice as Optimal Filter
Lecture - 25 Linear Prediction and Autoregressive Modeling
Lecture - 26 Gradient Adaptive Lattice
Lecture - 27 Gradient Adaptive Lattice
Lecture - 28 Introduction to Recursive Least Squares
Lecture - 29 RLS Approach to Adaptive Filters
Lecture - 30 RLS Adaptive Lattice
Lecture - 31 RLS Lattice Recursions
Lecture - 32 RLS Lattice Recursions
Lecture - 33 RLS Lattice Algorithm
Lecture - 34 RLS Using QR Decomposition
Lecture - 35 Givens Rotation
Lecture - 36 Givens Rotation and QR Decomposition
Lecture - 37 Systolic Implementation
Lecture - 38 Systolic Implementation
Lecture - 39 Singular Value Decomposition
Lecture - 40 Singular Value Decomposition