Alexander D Poularikas

Understanding Digital Signal Processing with Matlab(r) and Solutions

• Gebundenes Buch

Produktbeschreibung

The book discusses signals that most electrical engineers detect and study. The vast majority of signals could never be detected due to random additive signals, known as noise, that distorts them or completely overshadows them. The text presents the methods for extracting the desired signals from the noise. It includes examples that use MATLAB.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 455
• Erscheinungstermin: 9. November 2017
• Englisch
• Abmessung: 254mm x 178mm x 25mm
• Gewicht: 1030g
• ISBN-13: 9781138081437
• ISBN-10: 1138081434
• Artikelnr.: 50294726

Autorenporträt

Dr. Poularikas previously held the positions of Professor at University of Rhode Island, Kingston, USA, Chairman of the Engineering Department at the University of Denver, Colorado, USA, and Chairman of the Electrical and Computer Engineering Department at the University of Alabama in Huntsville, USA. He has published, coauthored, and edited 14 books and served as an editor-in-chief of numerous book series. A Fulbright scholar, lifelong senior member of the IEEE, and member of Tau Beta Pi, Sigma Nu, and Sigma Pi, he received the IEEE Outstanding Educators Award, Huntsville Section in both 1990 and 1996. Dr. Poularikas holds a Ph.D from the University of Arkansas, Fayetteville, USA.

Inhaltsangabe

Abbreviations
Chapter 1 Continuous and Discrete Signals
Chapter 2 Fourier Analysis of Continuous and Discrete Signals
Chapter 3 The z-Transform, Difference Equations, and Discrete Systems
Chapter 4 Finite Impulse Response (FIR) Digital Filter Design
Chapter 5 Random Variables, Sequences, and Probability Functions
Chapter 6 Linear Systems with Random Inputs, Filtering, and Power Spectral
Density
Chapter 7 Least Squares-Optimum Filtering
Chapter 8 Nonparametric (Classical) Spectra Estimation
Chapter 9 Parametric and Other Methods for Spectra Estimation
Chapter 10 Newton's and Steepest Descent Methods
Chapter 11 The Least Mean Square (LMS) Algorithm
Chapter 12 Variants of Least Mean Square Algorithm
Chapter 13 Nonlinear Filtering
Appendix 1: Suggestions and Explanations for MATLAB Use
Appendix 2: Matrix Analysis
Appendix 3: Mathematical Formulas
Appendix 4: MATLAB Function Bibliography
Index