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Lec 24 Linear Prediction of Signals 1

Lec 1 Overview of Statistical Signal Processing

Lec 2 Probability and Random Variables

Lec 3 Linear Algebra of Random Variables

Lec 4 Random Processes

Lec 5 Linear Shift Invariant Systems with Random Inputs

Lec 6 White Noise and Spectral Factorization Theorem

Lec 7 Linear Models of Random Signals 1

Lec 8 Linear Models of Random Signals 2

Lec 9 Estimation Theory 1

Lec 10 Estimation Theory 2 MVUE and Cramer Rao Lower Bound I

Lec 11 Estimation Theory 3 Cramer Rao Lower Bound II

Lec 12 MVUE through Sufficient Statistic 1

Lec 13 MVUE through Sufficient Statistic 2

Lec 14 Method of Moments and Maximum Likelihood Estimators

Lec 15 Properties of Maximum Likelihood Estimator MLE

Lec 16 Bayesian Estimators 1

Lec 17 Bayesian Estimators 2

Lec 18 Optimal linear filters Wiener Filter

Lec 19 Review Assignment 1

Lec 20 FIR Wiener filter

Lec 21 Non Causual IIR Wiener Filter

Lec 22 Causal IIR Wiener Filter

Lec 23 Review Assignment 2

Lec 24 Linear Prediction of Signals 1

Lec 25 Linear Prediction of Signals 2

Lec 26 Linear Prediction of Signals 3

Lec 27 Solution to Review Assignment 3

Lec 28 Solution to Review Assignment 4

Lec 29 Adaptive Filters 1

Lec 30 Adaptive Filters 2 LMS algorithm

Lec 31 Adaptive Filters 3 Convergence of the LMS algorithm

Lec 32 Adaptive Filters 4 Variants of the LMS algorithm

Lec 33 Least squares Estimator

Lec 34 Properties of Least squares Estimator

Lec 35 Recursive Least Squares RLS Adaptive Filter 2

Lec 36 Kalman Filter 1

Lec 37 Vector Kalman Filter

Lec 38 Review

Cabinetry Basics

MFT and MFT related accessories

hinges workshops

CNC Computer numerical control

Jointing Systems

Cabinet tips

Festool Domino

Bandsaws

doors building

Communication Circuits Experiments

Experiments with PlutoSDR

Electric Motors

Energy Engineering

Digital Electronics

Engineering Hydrology

Engineering Mechanics