A complete CD for learning Python Language in a powerful and easy way.

**Download Here**

**RAR Password :**omati.net

A complete CD for learning Python Language in a powerful and easy way.

This is a full animation course for GSM from Motorola with good animation, nice photos and sound.

you can download this CD from the following link

Note: you will need Virtual CD to run it

Video Lectures Series on Adaptive Signal Processing by Prof.M.Chakraborty, Department of E and ECE, IIT Kharagpur.

Lecture - 1 Introduction to Adaptive Filters.

Lecture - 2 Introduction to Stochastic Processes.

Lecture - 3 Stochastic Processes.

Lecture - 4 Correlation Structure.

Lecture - 5 FIR Wiener Filter (Real).

Lecture - 6 Steepest Descent Technique.

Lecture - 7 LMS Algorithm

**Please feel free to leave a comment if there is a dead link or a problem with the links.**
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Lecture - 1 Introduction to Adaptive Filters.

Lecture - 2 Introduction to Stochastic Processes.

Lecture - 3 Stochastic Processes.

Lecture - 4 Correlation Structure.

Lecture - 5 FIR Wiener Filter (Real).

Lecture - 6 Steepest Descent Technique.

Lecture - 7 LMS Algorithm

Lecture - 8 Convergence Analysis

Lecture - 9 Convergence Analysis (Mean Square) (P1)

Lecture - 10 Convergence Analysis (Mean Square) (P2)

Lecture - 11 Misadjustment and Excess MSE (P1)

Lecture - 12 Misadjustment and Excess MSE (P2)

Lecture - 13 Sign LMS Algorithm

Lecture - 14 Block LMS Algorithm

Lecture - 15 Fast Implementation of Block LMS Algorithm (P1)

Lecture - 16 Fast Implementation of Block LMS Algorithm (P2)

Lecture - 17 Vector Space Treatment to Random Variables (P1)

Lecture - 18 Vector Space Treatment to Random Variables (P2)

Lecture - 19 Orthogonalization and Orthogonal Projection

Lecture - 20 Orthogonal Decomposition of Signal Subspaces

Lecture - 21 Introduction to Linear Prediction

Lecture - 22 Lattice Filter

Lecture - 23 Lattice Recursions

Lecture - 24 Lattice as Optimal Filter

Lecture - 25 Linear Prediction and Autoregressive Modeling

Lecture - 26 Gradient Adaptive Lattice

Lecture - 27 Gradient Adaptive Lattice Contd.

Lecture - 28 Introduction to Recursive Least Squares

Lecture - 29 RLS Approach to Adaptive Filters

Lecture - 30 RLS Adaptive Lattice

Lecture - 31 RLS Lattice Recursions (P1)

Lecture - 32 RLS Lattice Recursions (P2)

Lecture - 33 RLS Lattice Algorithm

Lecture - 34 RLS Using QR Decomposition

Lecture - 35 Givens Rotation

Lecture - 36 Givens Rotation and QR Decomposition

Lecture - 37 Systolic Implementation

Lecture - 38 Systolic Implementation Contd.

Lecture - 39 Singular Value Decomposition (P1)

Lecture - 40 Singular Value Decomposition (P2)

Lecture - 41 Singular Value Decomposition (P3)

These Videos are available at the following link

This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

Lecture 1: The geometry of linear equations (Watch Here)

Lecture 2: Elimination with matrices (Watch Here)

Lecture 3: Multiplication and inverse matrices (Watch Here)

Lecture 4: Factorization into A = LU (Watch Here)

Lecture 5: Transposes, permutations, spaces R^n (Watch Here)

Lecture 6: Column space and nullspace (Watch Here)

Lecture 7: Solving Ax = 0: pivot variables, special solutions (Watch Here)

Lecture 8: Solving Ax = b: row reduced form R (Watch Here)

Lecture 9: Independence, basis, and dimension (Watch Here)

Lecture 10: The four fundamental subspaces (Watch Here)

Lecture 11: Matrix spaces; rank 1; small world graphs (Watch Here)

Lecture 12: Graphs, networks, incidence matrices (Watch Here)

Lecture 13: Quiz 1 review (Watch Here)

Lecture 14: Orthogonal vectors and subspaces (Watch Here)

Lecture 15: Projections onto subspaces (Watch Here)

Lecture 16: Projection matrices and least squares (Watch Here)

Lecture 17: Orthogonal matrices and Gram-Schmidt (Watch Here)

Lecture 18: Properties of determinants (Watch Here)

Lecture 19: Determinant formulas and cofactors (Watch Here)

Lecture 20: Cramer's rule, inverse matrix, and volume (Watch Here)

Lecture 21: Eigenvalues and eigenvectors (Watch Here)

Lecture 22: Diagonalization and powers of A (Watch Here)

Lecture 23: Differential equations and exp(At) (Watch Here)

Lecture 24: Markov matrices; fourier series (Watch Here)

Lecture 24b: Quiz 2 review (Watch Here)

Lecture 25: Symmetric matrices and positive definiteness (Watch Here)

Lecture 26: Complex matrices; fast fourier transform (Watch Here)

Lecture 27: Positive definite matrices and minima (Watch Here)

Lecture 28: Similar matrices and jordan form (Watch Here)

Lecture 29: Singular value decomposition (Watch Here)

Lecture 30: Linear transformations and their matrices (Watch Here)

Lecture 31: Change of basis; image compression (Watch Here)

Lecture 32: Quiz 3 review (Watch Here)

Lecture 33: Left and right inverses; pseudoinverse (Watch Here)

Lecture 34: Final course review (Watch Here)

Please feel free to leave a comment if there is a dead link or a problem with the links.

By the end of this free course you will be able to get the basic and advanced knowledge of the programming using MATLAB . Starting from introducing the variables and the vectors until you will be able to build your own program like “The Angry Birds” program.

Title | Duration |

01 Introduction Desktop, Arithmetic | 9 min. 56 sec. |

02 Introduction Variables, mathematical functions, vectors, plotting | 12 min. 5 sec. |

03 Introduction systems of equations, demo, Mexican hat | 9 min. 14 sec. |

04 Introduction Programs, m files | 10 min 23 sec. |

05 Variables Scalars and Vectors | 11 min. 4 sec. |

06 Matrices. Simple Program | 10 min. 12 sec. |

07 Numbers and Operators | 9 min. 25 sec. |

08 Operators and Operations on Arrays | 12 min. 5 sec. |

09 Formula Vectorization, output, format | 15 min. 28 sec. |

10 Introducing for loops | 23 min. 31 sec. |

11 Introducing if statements | 13 min. 12 sec. |

12 Logical operators and if statements | 17 min. 57 sec. |

13 Complex numbers | 6 min. 37 sec. |

14 Output with fprintf | 5 min. 1 sec. |

15 Input function | 1 min. 42 sec. |

16 Programming Steps | 5 min. 13 sec. |

17 Angry Birds Program | 23 min. 17 sec. |

18 Common Functions | 9 min. 56 sec. |

19 Beam Deflection Problem | 5 min. 58 sec. |

20 Logical Vectors | 8 min. 35 sec. |

Please feel free to leave a comment if there is a dead link or a problem with the links.

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