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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)

Lecture 17 - Vector Space Treatment to Random Variables

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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.)