Algorithms for statistical signal processing

Contributor(s): Proakis, John G. : ..[et al.]Material type: TextTextPublication details: Delhi : Pearson Education., 2002Description: xii, 564 p. : illustrationsISBN: 0130622192 ; 9780130622198; 8178085739 ; 9788178085739 Subject(s): Signal processing - Digital techniques - Mathematics | Algorithms | Digital signal processing | Statistics | Statistical news theory | Stochastic signalDDC classification: 005.1
Contents:
Characterization of Signals -- Characterization of Linear Time-Invariant Systems -- Sampling of Signals -- Linear Filtering Methods Based on the DFT -- The Cepstrum -- Algorithms for Convolution and Dft -- Modulo Polynomials -- Circular Convolution as Polynomial Multiplication mod u[superscript N]--1 -- A Continued Fraction of Polynomials -- Chinese Remainder Theorem for Polynomials -- Algorithms for Short Circular Convolutions -- How We Count Multiplications -- Cyclotomic Polynomials -- Elementary Number Theory -- Convolution Length and Dimension -- The DFT as a Circular Convolution -- Winograd's DFT Algorithm -- Number-Theoretic Analogy of DFT -- Number-Theoretic Transform -- Split-Radix FFT -- Autogen Technique -- Linear Prediction and Optimum Linear Filters -- Innovations Representation of a Stationary Random Process -- Forward and Backward Linear Prediction -- Solution of the Normal Equations -- Properties of the Linear Prediction-Error Filters -- AR Lattice and ARMA Lattice-Ladder Filters -- Wiener Filters for Filtering and Prediction -- Least-Squares Methods for System Modeling and Filter Design -- System Modeling and Identification -- Least-Squares Filter Design for Prediction and Deconvolution -- Solution of Least-Squares Estimation Problems -- Adaptive Filters -- Applications of Adaptive Filters -- Adaptive Direct-Form FIR Filters -- Adaptive Lattice-Ladder Filters -- Recursive Least-Squares Algorithms for Array Signal Processing -- QR Decomposition for Least-Squares Estimation.
Summary: Suitable for graduate-level courses in Digital Signal Processing in ECE and applied mathematics departments. This presentation of computational algorithms for statistical signal processing focuses on advanced topics, such as algorithms for adaptive filtering, least squares methods, power spectrum estimation, and high-order spectral estimation.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Reference Books Reference Books Main Library
Reference 		Reference 005.1 ALG (Browse shelf(Opens below)) Available 008556
Total holds: 0

Included Index.

Characterization of Signals --
Characterization of Linear Time-Invariant Systems --
Sampling of Signals --
Linear Filtering Methods Based on the DFT --
The Cepstrum --
Algorithms for Convolution and Dft --
Modulo Polynomials --
Circular Convolution as Polynomial Multiplication mod u[superscript N]--1 --
A Continued Fraction of Polynomials --
Chinese Remainder Theorem for Polynomials --
Algorithms for Short Circular Convolutions --
How We Count Multiplications --
Cyclotomic Polynomials --
Elementary Number Theory --
Convolution Length and Dimension --
The DFT as a Circular Convolution --
Winograd's DFT Algorithm --
Number-Theoretic Analogy of DFT --
Number-Theoretic Transform --
Split-Radix FFT --
Autogen Technique --
Linear Prediction and Optimum Linear Filters --
Innovations Representation of a Stationary Random Process --
Forward and Backward Linear Prediction --
Solution of the Normal Equations --
Properties of the Linear Prediction-Error Filters --
AR Lattice and ARMA Lattice-Ladder Filters --
Wiener Filters for Filtering and Prediction --
Least-Squares Methods for System Modeling and Filter Design --
System Modeling and Identification --
Least-Squares Filter Design for Prediction and Deconvolution --
Solution of Least-Squares Estimation Problems --
Adaptive Filters --
Applications of Adaptive Filters --
Adaptive Direct-Form FIR Filters --
Adaptive Lattice-Ladder Filters --
Recursive Least-Squares Algorithms for Array Signal Processing --
QR Decomposition for Least-Squares Estimation.

Suitable for graduate-level courses in Digital Signal Processing in ECE and applied mathematics departments. This presentation of computational algorithms for statistical signal processing focuses on advanced topics, such as algorithms for adaptive filtering, least squares methods, power spectrum estimation, and high-order spectral estimation.

There are no comments on this title.

to post a comment.

© University of Vavuniya

---