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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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About This Course

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.

  • 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 Curriculum

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)

    57m <?php echo  $alt; ?>
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    Lecture - 6 Steepest Descent Technique

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    Lecture - 7 LMS Algorithm

    55m <?php echo  $alt; ?>
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    Lecture - 8 Convergence Analysis

    59m <?php echo  $alt; ?>
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    Lecture - 9 Convergence Analysis (Mean Square)

    54m <?php echo  $alt; ?>
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    Lecture - 10 Convergence Analysis (Mean Square)

    53m <?php echo  $alt; ?>
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    Lecture - 11 Misadjustment and Excess MSE

    56m <?php echo  $alt; ?>
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    Lecture - 12 Misadjustment and Excess MSE

    56m <?php echo  $alt; ?>
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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

    51m <?php echo  $alt; ?>
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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

    58m <?php echo  $alt; ?>
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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

    51m <?php echo  $alt; ?>
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    Lecture - 27 Gradient Adaptive Lattice

    56m <?php echo  $alt; ?>
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    Lecture - 28 Introduction to Recursive Least Squares

    55m <?php echo  $alt; ?>
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    Lecture - 29 RLS Approach to Adaptive Filters

    56m <?php echo  $alt; ?>
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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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