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.

"Timely and fundamental subject, sparking interest for students and engineers alike. Starts from the basics and builds up the complexity in a logic and very understandable way, so that both beginners and experienced professionals will be able to profit from the book.The book is very useful as a reference, with an extensive set of digital processing operations and clear MATLAB examples and proposed exercises for all of them. The reader can easily find everything related to one specific topic (eg. Fourier transform)."— Alexandre Giulietti de Barros, Teledyne Anafocus, Spain"The book deals with methods for processing noisy signals. The contents is fairly complete and covers all important topics ranging from discrete and continuous Fourier processing, digital filtering to random signal processing and nonlinear filtering."-Hans-Georg Stark (Aschaffenburg)

• 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 DensityChapter 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 UseAppendix 2: Matrix Analysis Appendix 3: Mathematical Formulas Appendix 4: MATLAB Function BibliographyIndex