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Created by:
Last updated:
September 21, 2023
Duration:
Unlimited Duration
FREE
This course includes:
Unlimited Duration
Badge on Completion
Certificate of completion
Unlimited Duration
Description
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)
Course Curriculum
- Lecture 01 – Introduction to Adaptive Filters Unlimited
- Lecture 02 – Introduction to Stochastic Processes Unlimited
- Lecture 03 – Stochastic Processes (cont.) Unlimited
- Lecture 04 – Correlation Structure Unlimited
- Lecture 05 – FIR Wiener Filter (Real) Unlimited
- Lecture 06 – Steepest Descent Technique Unlimited
- Lecture 07 – LMS Algorithm Unlimited
- Lecture 08 – Convergence Analysis (in Mean) Unlimited
- Lecture 09 – Convergence Analysis (Mean Square) Unlimited
- Lecture 10 – Convergence Analysis (Mean Square) (cont.) Unlimited
- Lecture 11 – Misadjustment and Excess MSE Unlimited
- Lecture 12 – Misadjustment and Excess MSE (cont.) Unlimited
- Lecture 13 – Sign LMS Algorithm Unlimited
- Lecture 14 – Block LMS Algorithm Unlimited
- Lecture 15 – Fast Implementation of Block LMS Algorithm Unlimited
- Lecture 16 – Fast Implementation of Block LMS Algorithm (cont.) Unlimited
- Lecture 17 – Vector Space Treatment to Random Variables Unlimited
- Lecture 18 – Vector Space Treatment to Random Variables (cont.) Unlimited
- Lecture 19 – Orthogonalization and Orthogonal Projection Unlimited
- Lecture 20 – Orthogonal Decomposition of Signal Subspaces Unlimited
- Lecture 21 – Introduction to Linear Prediction Unlimited
- Lecture 22 – Lattice Filter Unlimited
- Lecture 23 – Lattice Recursions Unlimited
- Lecture 24 – Lattice as Optimal Filter Unlimited
- Lecture 25 – Linear Prediction and Autoregressive Modeling Unlimited
- Lecture 26 – Gradient Adaptive Lattice Unlimited
- Lecture 27 – Gradient Adaptive Lattice (cont.) Unlimited
- Lecture 28 – Introduction to Recursive Least Squares (RLS) Unlimited
- Lecture 29 – RLS Approach to Adaptive Filters Unlimited
- Lecture 30 – RLS Adaptive Lattice Unlimited
- Lecture 31 – RLS Lattice Recursions Unlimited
- Lecture 32 – RLS Lattice Recursions (cont.) Unlimited
- Lecture 33 – RLS Lattice Algorithm Unlimited
- Lecture 34 – RLS using QR Decomposition Unlimited
- Lecture 35 – Givens Rotation Unlimited
- Lecture 36 – Givens Rotation and QR Decomposition Unlimited
- Lecture 37 – Systolic Implementation Unlimited
- Lecture 38 – Systolic Implementation (cont.) Unlimited
- Lecture 39 – Singular Value Implementation Unlimited
- Lecture 40 – Singular Value Implementation (cont.) Unlimited
- Lecture 41 – Singular Value Implementation (cont.) Unlimited
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