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Electronics and Communication
Adaptive Signal Processing
Adaptive Signal Processing
Curriculum
1 Section
41 Lessons
10 Weeks
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Adaptive Signal Processing
41
2.1
Lecture 1: Introduction to Adaptive Filters
2.2
Lecture 2: Introduction to Stochastic Processes
2.3
Lecture 3: Stochastic Processes
2.4
Lecture 4: Correlation Structure
2.5
Lecture 5: FIR Wiener Filter (Real)
2.6
Lecture 6: Steepest Descent Technique
2.7
Lecture 7: LMS Algorithm
2.8
Lecture 8: Convergence Analysis
2.9
Lecture 9: Convergence Analysis (Mean Square)
2.10
Lecture 10: Convergence Analysis (Mean Square)
2.11
Lecture 11: Misadjustment and Excess MSE – I
2.12
Lecture 12: Misadjustment and Excess MSE – II
2.13
Lecture 13: Sign LMS Algorithm
2.14
Lecture 14: Block LMS Algorithm
2.15
Lecture 15: Fast Implementation of Block LMS Algorithm – I
2.16
Lecture 16: Fast Implementation of Block LMS Algorithm – II
2.17
Lecture 17: Vector Space Treatment to Random Variables – I
2.18
Lecture 18: Vector Space Treatment to Random Variables – II
2.19
Lecture 19: Orthogonalization and Orthogonal Projection
2.20
Lecture 20: Orthogonal Decomposition of Signal Subspaces
2.21
Lecture 21: Introduction to Linear Prediction
2.22
Lecture 22: Lattice Filter
2.23
Lecture 23: Lattice Recursions
2.24
Lecture 24: Lattice as Optimal Filter
2.25
Lecture 25: Linear Prediction and Autoregressive Modeling
2.26
Lecture 26: Gradient Adaptive Lattice – I
2.27
Lecture 27: Gradient Adaptive Lattice – II
2.28
Lecture 28: Introduction to Recursive Least Squares (RLS)
2.29
Lecture 29: RLS Approach to Adaptive Filters
2.30
Lecture 30: RLS Adaptive Lattice
2.31
Lecture 31: RLS Lattice Recursions – I
2.32
Lecture 32: RLS Lattice Recursions – II
2.33
Lecture 33: RLS Lattice Algorithm
2.34
Lecture 34: RLS Using QR Decomposition
2.35
Lecture 35: Givens Rotation
2.36
Lecture 36: Givens Rotation and QR Decomposition
2.37
Lecture 37: Systolic Implementation – I
2.38
Lecture 38: Systolic Implementation – II
2.39
Lecture 39: Singular Value Decomposition – I
2.40
Lecture 40: Singular Value Decomposition – II
2.41
Lecture 41: Singular Value Decomposition – III
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