Algorithm Collections for Digital Signal Processing Applications Using Matlab (eBook, PDF) - Gopi, E. S.
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The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are scattered in different fields. There is the need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in application point of view. Algorithm Collections for Digital Signal Processing Applications using MATLAB attempts to satisfy the above requirement. Also the algorithms are made clear using MATLAB programs. TOC:Preface. Acknowledgments. 1 Artificial Intelligence. 2 Probability And Random Process. 3 Numerical Linear Algebra. 4 Selected Applications. Appendix. Index.…mehr

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Produktbeschreibung
The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are scattered in different fields. There is the need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in application point of view. Algorithm Collections for Digital Signal Processing Applications using MATLAB attempts to satisfy the above requirement. Also the algorithms are made clear using MATLAB programs. TOC:Preface. Acknowledgments. 1 Artificial Intelligence. 2 Probability And Random Process. 3 Numerical Linear Algebra. 4 Selected Applications. Appendix. Index.

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  • Produktdetails
  • Verlag: Springer-Verlag GmbH
  • Erscheinungstermin: 20. September 2007
  • Englisch
  • ISBN-13: 9781402064104
  • Artikelnr.: 37339880
Autorenporträt
Dr. E.S. Gopi: 7 Years of Professional experience in academia.

Lecturer since April 2005 - National Institute of Technology, Tiruchi-15 (Formerly called as Regional Engineering College, Government of India).

Senior Lecturer: 2001 -April 2005 Sri Venkateswara College of Engineering, Chennai

1999-2001- Sri Muthukumuran Institute of Technology, Chennai

1998-1999 - Raja Rajeswari Engineering College

Inhaltsangabe
Preface. Acknowledgments.
Chapter 1 ARTIFICIAL INTELLIGENCE.
1 Particle Swarm Algorithm. 1-1 How are the values for the variables 'x' and 'y' are updated in every Iteration? 1-2 PSO Algorithm to maximize the function F(X,Y,Z). 1-3 m-Program for PSO Algorithm. 1-4 Program Illustration.
2 Genetic Algorithm. 2-1 Roulette Wheel Selection Rule. 2-2 Example. 2-2-1 m-Program for Genetic Algorithm. 2-2-2 Program Illustration. 2-3 Classification of Genetic Operators. 2-3-1 Simple Crossover. 2-3-2 Heuristic Crossover. 2-3-3 Arith crossover.
3 Simulated Annealing. 3-1 Simulated Annealing algorithm. 3-2 Example. 3-3 m-program for simulated Annealing.
4 Back propagation Neural Network. 4-1 Single Neuron architecture. 4-2 Algorithm. 4-3 Example. 4-4 m-program for training the Artificial Neural Network for the problem proposed in the previous section.
5 Fuzzy Logic Systems. 5-1 Union and Intersection of two fuzzy sets. 5-2 Fuzzy logic systems. 5-2-1 Algorithm. 5-3 Why Fuzzy logic systems? 5-4 Example. 5-5 m-program for the realization of fuzzy logic system for the Specifications given in section 5-4.
6 Ant Colony Optimization. 6-1 Algorithm. 6-2 Example. 6-3 m-program for finding the optimal order using Ant colony technique for the specifications given in the section 6-2.
Chapter 2 PROBABILITY AND RANDOM PROCESS.
1 Independent Component Analysis. 1-1 ICA for tow mixed signals. 1-1-1 ICA Algorithm. 1-2 m-program for Independent Component Analysis.
2 Gaussian Mixture Model. 2-1 Expectation-Maximization Algorithm. 2-1-1 Expectation stage. 2-1-2 Maximization stage. 2-2 Example. 2-3 m-program for Gaussian Mixture model.
3 K-means Algorithm for Pattern recognition. 3-1 K-means Algorithm. 3-2 Example. 3-3 m-program for the k-means Algorithm applied for the example given in section 3-2.
4 Fuzzy K-means Algorithm for Pattern recognition. 4-1 Fuzzy k-means Algorithm. 4-2 Example. 4-3 m-program for the Fuzzy k-means algorithm applied for the example given in section 4-2.
5 Mean and Variance Normalization. 5-1 Algorithm. 5-2 Example. 5-3 m-program for Mean and Variance Normalization.
Chapter 3 NUMERICAL LINEAR ALGEBRA.
1 Hotelling Transformation. 1-1 Diagonalization of the matrix 'CM'. 1-2 Example. 1-3 m-program for Hotelling Transformation.
2 Eigen Basis. 2-1 Example.
3 Singular Value Decomposition. 3-1 Example.
4 Projection Matrix. 4-1 Projection of the vector 'a' on the vector 'b'. 4-2 Projection of the vector on the plane described by the two columns of the matrix 'X'. 4-2-1 Example 1. 4-2-2 Example 2.
5 Orthonormal Vectors. 5-1 Gram-Schmidt Orthogonalization procedure. 5-2 Example. 5-3 Need for orthonormal basis. 5-4 m-program for Gram-Schmidt Orthogonalization procedure.
6 Computation of the powers of the matrix 'A'.
7 Determination of Kth element in the sequence.
8 Computation of Exponential of the matrix 'A'.
9 Solving Differential equation using Eigen decomposition.
10 Computation of Pseudo Inverse of the matrix 'A'.
11 Computation of Transformation matrices. 11-1 Transformation matrix for Fourier transformation. 11-2 Transformation matrix for Basis co-efficient transformation. 11-3 Transformation matrix for obtaining co-efficient of Eigen basis. 11-4 Transformation matrix for obtaining co-efficient of Wavelet Basis.
12 System stability test using Eigen values.
13 Positive definite matrix test for minimal location of the function f(x1, x2, x3, x4...xn)
14 Wavelet transformation using matrix method. 14-1 Haar Transformation. 14-1-1 Example. 14-1-2 m-program for Haar forward and inverse transformation. 14-2 Daubechies-4 Transformation. 14-2-1 Example. 14-2-2 m-program for Daubechies-4 forward and inverse transformation.
Chapter 4 SELECTED APPLICATIONS.
1 Ear Pattern
Rezensionen
From the reviews:
"The presented book is devoted to the realization of the Digital Signal Processing (DSP) algorithms, using Matlab. ... The book is written in such a way that it is suitable for non-mathematical readers and is very much suitable for the beginners who are doing research in Digital Signal Processing." (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1189, 2010)