Probability & Random Variables by M. Chakraborty (IIT Kharagpur)

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source: nptelhrd    2008年7月9日
Electronics - Probability & Random Variables by Prof. M. Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.

Lecture - 1 Introduction to the Theory of Probability 59:50
Lecture - 2 Axioms of Probability 59:49
Lecture - 3 Axioms of Probability (Contd.) 59:51
Lecture - 4 Introduction to Random Variables 59:37
Lecture - 5 Probability Distributions and Density Functions 59:48
Lecture - 6 Conditional Distribution and Density Functions 59:55
Lecture - 7 Function of a Random Variable 59:53
Lecture - 8 Function of a Random Variable (Contd.) 59:50
Lecture - 9 Mean and Variance of a Random Variable 59:54
Lecture - 10 Moments 59:47
Lecture - 11 Characteristic Function 59:55
Lecture - 12 Two Random Variables 59:53
Lecture - 13 Function of Two Random Variables 1:02:50
Lecture - 14 Function of Two Random Variables (Contd.) 59:47
Lecture - 15 Correlation Covariance and Related Innver 59:54
Lecture - 16 Vector Space of Random Variables 59:47
Lecture - 17 Joint Moments 59:54
Lecture - 18 Joint Characteristic Functions 59:48
Lecture - 19 Joint Conditional Densities 59:49
Lecture - 20 Joint Conditional Densities (Contd.) 59:57
Lecture - 21 Sequences of Random Variables 59:57
Lecture - 22 Sequences of Random Variables (Contd.) 59:45
Lecture - 23 Correlation Matrices and their Properties 59:53
Lecture - 24 Correlation Matrices and their Properties 59:50
Lecture - 25 Conditional Densities of Random Vectors 59:56
Lecture - 26 Characteristic Functions and Normality 1:00:58
Lecture - 27 Thebycheff Inquality and Estimation 59:55
Lecture - 28 Central Limit Theorem 59:55
Lecture - 29 Introduction to Stochastic Process 59:56
Lecture - 30 Stationary Processes 59:49
Lecture - 31 Cyclostationary Processes 59:52
Lecture - 32 System with Random Process at Input 1:00:01
Lecture - 33 Ergodic Processes 59:57
Lecture - 34 Introduction to Spectral Analysis 59:51
Lecture - 35 Spectral Analysis Contd. 59:57
Lecture - 36 Spectrum Estimation - Non Parametric Methods 59:50
Lecture - 37 Spectrum Estimation - Parametric Methods 1:00:04
Lecture - 38 Autoregressive Modeling and Linear Prediction 59:50
Lecture - 39 Linear Mean Square Estimation - Wiener (FIR) 59:57
Lecture - 40 Adaptive Filtering - LMS Algorithm 59:47

Electronics - Adaptive Signal Processing by M. Chakraborty (IIT Kharagpur)

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source: nptelhrd     2009年1月9日
Electronics - Adaptive Signal Processing by Prof. M. Chakraborty, Department of E and ECE, IIT Kharagpur.

Lecture - 1 Introduction to Adaptive Filters 53:57
Lecture - 2 Introduction to Stochastic Processes 59:47
Lecture - 3 Stochastic Processes 59:46
Lecture - 4 Correlation Structure 57:04
Lecture - 5 FIR Wiener Filter (Real) 57:42
Lecture - 6 Steepest Descent Technique 55:27
Lecture - 7 LMS Algorithm 55:30
Lecture - 8 Convergence Analysis 59:25
Lecture - 9 Convergence Analysis (Mean Square) 54:42
Lecture - 10 Convergence Analysis (Mean Square) 53:24
Lecture - 11 Misadjustment and Excess MSE 56:13
Lecture - 12 Misadjustment and Excess MSE 56:47
Lecture - 13 Sign LMS Algorithm 54:45
Lecture - 14 Block LMS Algorithm 54:17
Lecture - 15 Fast Implementation of Block LMS Algorithm 1:00:49
Lecture - 16 Fast Implementation of Block LMS Algorithm 54:43
Lecture - 17 Vector Space Treatment to Random Variables 51:48
Lecture - 18 Vector Space Treatment to Random Variables 1:00:14
Lecture - 19 Orthogonalization and Orthogonal Projection 55:48
Lecture - 20 Orthogonal Decomposition of Signal Subspaces 54:59
Lecture - 21 Introduction to Linear Prediction 58:30
Lecture - 22 Lattice Filter 54:16
Lecture - 23 Lattice Recursions 55:41
Lecture - 24 Lattice as Optimal Filter 52:57
Lecture - 25 Linear Prediction and Autoregressive Modeling 55:57
Lecture - 26 Gradient Adaptive Lattice 51:51
Lecture - 27 Gradient Adaptive Lattice 56:20
Lecture - 28 Introduction to Recursive Least Squares 55:21
Lecture - 29 RLS Approach to Adaptive Filters 56:14
Lecture - 30 RLS Adaptive Lattice 1:01:43
Lecture - 31 RLS Lattice Recursions 54:40
Lecture - 32 RLS Lattice Recursions 55:16
Lecture - 33 RLS Lattice Algorithm 56:02
Lecture - 34 RLS Using QR Decomposition 59:52
Lecture - 35 Givens Rotation 1:00:16
Lecture - 36 Givens Rotation and QR Decomposition 59:48
Lecture - 37 Systolic Implementation 56:34
Lecture - 38 Systolic Implementation 48:52
Lecture - 39 Singular Value Decomposition 57:01
Lecture - 40 Singular Value Decomposition 55:49
Lecture - 41 Singular Value Decomposition 59:44