# InfoCoBuild

## Adaptive Signal Processing

Adaptive Signal Processing. Instructor: Prof. Mrityunjoy Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. 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 and Singular Value Decomposition. (from nptel.ac.in)

 Introduction to Adaptive Filters

 Lecture 01 - Introduction to Adaptive Filters Lecture 02 - Introduction to Stochastic Processes Lecture 03 - Stochastic Processes (cont.) Lecture 04 - Correlation Structure Lecture 05 - FIR Wiener Filter (Real) Lecture 06 - Steepest Descent Technique Lecture 07 - LMS Algorithm Lecture 08 - Convergence Analysis (in Mean) Lecture 09 - Convergence Analysis (Mean Square) Lecture 10 - Convergence Analysis (Mean Square) (cont.) Lecture 11 - Misadjustment and Excess MSE Lecture 12 - Misadjustment and Excess MSE (cont.) 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 (cont.) Lecture 17 - Vector Space Treatment to Random Variables Lecture 18 - Vector Space Treatment to Random Variables (cont.) 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 (cont.) Lecture 28 - Introduction to Recursive Least Squares (RLS) Lecture 29 - RLS Approach to Adaptive Filters Lecture 30 - RLS Adaptive Lattice Lecture 31 - RLS Lattice Recursions Lecture 32 - RLS Lattice Recursions (cont.) 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 (cont.) Lecture 39 - Singular Value Implementation Lecture 40 - Singular Value Implementation (cont.) Lecture 41 - Singular Value Implementation (cont.)

 References Adaptive Signal Processing Instructor: Prof. Mrityunjoy Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. This course covers lessons on adaptive signal processing.