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: An Exploration of Random Processes for EngineersAn Exploration of Random Processes for Engineers
by - University of Illinois at Urbana-Champaign
These notes were written for a graduate course on random processes. Students are assumed to have had a previous course in probability, some familiarity with real analysis and linear algebra, and some familiarity with complex analysis.
(16764 views)
Book cover: Introduction To Random ProcessesIntroduction To Random Processes
by - McGraw-Hill
A first course on random processes for graduate engineering and science students, particularly those with an interest in the analysis and design of signals and systems. The book includes detailed coverage of minimum-mean-squared-error estimation.
(13872 views)
Book cover: The Fundamentals of Signal AnalysisThe Fundamentals of Signal Analysis
- Agilent Technologies
This text is a primer for those who are unfamiliar with the advantages of analysis in the frequency and modal domains and Dynamic Signal Analyzers. The authors avoid the use of rigorous mathematics and instead depend on heuristic arguments.
(14528 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.
(12317 views)