Logo

Introduction to Signal Processing

Large book cover: Introduction to Signal Processing

Introduction to Signal Processing
by

Publisher: Prentice Hall
ISBN/ASIN: 0132091720
ISBN-13: 9780132091725
Number of pages: 398

Description:
Provides an applications-oriented introduction to digital signal processing. Orfandis covers all the basic DSP concepts and methods, such as sampling, discrete-time systems, DFT/FFT algorithms, and filter design. The book emphasizes the algorithmic, computational, and programming aspects of DSP, and includes a large number of worked examples, applications, and computer examples.

Home page url

Download or read it online for free here:
Download link
(6.8MB, PDF)

Similar books

Book cover: Elements of Signal Detection and EstimationElements of Signal Detection and Estimation
by - Prentice Hall
This is an introduction to signal-detection theory, a subject fundamental to the design of detectors of weak signals in the presence of random noise, and to the design of optimal receivers of communication, radar, sonar and optical signals.
(7610 views)
Book cover: Digital Signal Processing and AnalysisDigital Signal Processing and Analysis
by - University of Michigan
Course objectives: 1. to teach students the concepts of discrete-time signals, including mathematical representations; 2. to teach students the concepts of linear time-invariant discrete-time systems; 3. to introduce the concepts of filter design.
(12306 views)
Book cover: The Scientist and Engineer's Guide to Digital Signal ProcessingThe Scientist and Engineer's Guide to Digital Signal Processing
by - California technical Publishing
Digital Signal Processing is one of the most powerful technologies that will shape science and engineering in the twenty-first century. The book presents the fundamentals of DSP using examples from common science and engineering problems.
(54505 views)
Book cover: R. R. Bahadur's Lectures on the Theory of EstimationR. R. Bahadur's Lectures on the Theory of Estimation
by - IMS
In this volume the author covered what should be standard topics in a course of parametric estimation: Bayes estimates, unbiased estimation, Fisher information, Cramer-Rao bounds, and the theory of maximum likelihood estimation.
(5845 views)