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Adaptive Signal Processing

Adaptive Signal Processing

Validity Unlimited Adaptive Signal Processing All Level Adaptive Signal Processing English

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Description

An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization algorithm. Because of the complexity of the optimization algorithms, almost all adaptive filters are digital filters. Adaptive filters are required for some applications because some parameters of the desired processing operation (for instance, the locations of reflective surfaces in a reverberant space) are not known in advance or are changing. The closed loop adaptive filter uses feedback in the form of an error signal to refine its transfer function.

Includes

  • Adaptive Signal Processing Total 1 Module
  • Adaptive Signal Processing 41 Videos
  • Adaptive Signal Processing Downloadable on mobile
  • Adaptive Signal Processing Published on 26 June, 2019

Course Content

Adaptive Signal Processing

Adaptive Signal Processing
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    Lecture - 1 Introduction to Adaptive Filters

    53m <?php echo  $alt; ?>
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    Lecture - 2 Introduction to Stochastic Processes

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    Lecture - 3 Stochastic Processes

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    Lecture - 4 Correlation Structure

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    Lecture - 5 FIR Wiener Filter (Real)

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    Lecture - 6 Steepest Descent Technique

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    Lecture - 7 LMS Algorithm

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    Lecture - 8 Convergence Analysis

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    Lecture - 9 Convergence Analysis (Mean Square)

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    Lecture - 10 Convergence Analysis (Mean Square)

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    Lecture - 11 Misadjustment and Excess MSE

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    Lecture - 12 Misadjustment and Excess MSE

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    Lecture - 13 Sign LMS Algorithm

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    Lecture - 14 Block LMS Algorithm

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    Lecture - 15 Fast Implementation of Block LMS Algorithm

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    Lecture - 16 Fast Implementation of Block LMS Algorithm

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    Lecture - 17 Vector Space Treatment to Random Variables

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    Lecture - 18 Vector Space Treatment to Random Variables

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    Lecture - 19 Orthogonalization and Orthogonal Projection

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    Lecture - 20 Orthogonal Decomposition of Signal Subspaces

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    Lecture - 21 Introduction to Linear Prediction

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    Lecture - 22 Lattice Filter

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    Lecture - 23 Lattice Recursions

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    Lecture - 24 Lattice as Optimal Filter

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    Lecture - 25 Linear Prediction and Autoregressive Modelin

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    Lecture - 26 Gradient Adaptive Lattice

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    Lecture - 27 Gradient Adaptive Lattice

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    Lecture - 28 Introduction to Recursive Least Squares

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    Lecture - 29 RLS Approach to Adaptive Filters

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    Lecture - 30 RLS Adaptive Lattice

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    Lecture - 31 RLS Lattice Recursions

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    Lecture - 32 RLS Lattice Recursions

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    Lecture - 33 RLS Lattice Algorithm

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    Lecture - 34 RLS Using QR Decomposition

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    Lecture - 35 Givens Rotation

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    Lecture - 36 Givens Rotation and QR Decomposition

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    Lecture - 37 Systolic Implementation

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    Lecture - 38 Systolic Implementation

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    Lecture - 39 Singular Value Decomposition

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    Lecture - 40 Singular Value Decomposition

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    Lecture - 41 Singular Value Decomposition

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