Digital Signal Processing, 4th Edition
©2007 |Pearson | Available
John G. Proakis, Northeastern University
Dimitris K Manolakis, Massachusetts Institute of Technology, Lincoln Laboratory
©2007 |Pearson | Available
A significant revision of a best-selling text for the introductory digital signal processing course.
This book presents the fundamentals of discrete-time signals, systems, and modern digital processing and applications for students in electrical engineering, computer engineering, and computer science.The book is suitable for either a one-semester or a two-semester undergraduate level course in discrete systems and digital signal processing. It is also intended for use in a one-semester first-year graduate-level course in digital signal processing.
NEW - added a new chapter on adaptive filters
NEW - substantially modified and updated the chapter on multirate digital signal processing
NEW - substantially modified and updated the chapter on sampling and reconstruction of signals
NEW - new material added on the Discrete Cosine Transform.
* A balanced coverage is provided of both theory and practical applications.
* Includes many examples throughout the book and approximately 500 homework problems.
* Describes the operations and techniques involved in the analog-to-digital conversion of analog signals.
* Studies the characterization and analysis of linear time-invariant discrete-time systems and discrete- time signals in the time domain.
* Considers both the bilateral and the unilateral z-transform, and describes methods for determining the inverse z-transform.
* Analyzes signals and systems in the frequency domain, and presents Fourier series and Fourier transform in both continuous-time and discrete-time signals.
* Treats the realization of IIR and FIR systems, including direct-form, cascade, parallel, lattice and lattice-ladder realizations.
* Looks at the basics of sampling rate conversion and presents systems for implementing multirate conversion.
* Offers a detailed examination of power spectrum estimation, with discussions on nonparametric and model-based methods, as well as eigen-decomposition- based methods, including MUSIC and ESPRIT.
Extend learning beyond the classroom
Pearson eText is an easy-to-use digital textbook that students can purchase on their own or you can assign for your course. Creating a course allows you to personalize your Pearson eText so students see the connection between their reading and what they learn in class, motivating them to keep reading, and keep learning. Learn more about Pearson eText.
Balanced coverage of digital signal processing theory and practical applications
Benefits of creating a Pearson eText course
1.1 Signals, Systems, and Signal Processing
1.2 Classification of Signals
1.3 The Concept of Frequency in Continuous-Time and Discrete-Time Signals
1.4 Analog-to-Digital and Digital-to-Analog Conversion
1.5 Summary and References
2 Discrete-Time Signals And Systems
2.1 Discrete-Time Signals
2.2 Discrete-Time Systems
2.3 Analysis of Discrete-Time Linear Time-Invariant systems
2.4 Discrete-Time Systems Described by Difference Equations
2.5 Implementation of Discrete-Time Systems
2.6 Correlation of Discrete-Time Signals
2.7 Summary and References
3 The Z-Transform And Its Application To The Analysis Of Lti Systems
3.1 The z-Transform
3.2 Properties of the z-Transform
3.3 Rational z-Transforms
3.4 Inversion of the z-Transform
3.5 Analysis of Linear Time Invariant Systems in the z-Domain
3.6 The One-sided z-Transform
3.7 Summary and References
4 Frequency Analysis Of Signals And Systems
4.1 Frequency Analysis of Continuous-Time Signals
4.2 Frequency Analysis of Discrete-Time Signals
4.3 Frequency-Domain and Time-Domain Signal Properties
4.4 Properties of the Fourier Transform for Discrete-Time Signals
4.5 Summary and References
5 Frequency Domain Analysis Of Lti Systems
5.1 Frequency-Domain Characteristics of Linear Time-Invariant Systems
5.2 Frequency Response of LTI Systems
5.3 Correlation Functions and Spectra at the Output of LTI Systems
5.4 Linear Time-Invariant Systems as Frequency-Selective Filters
5.5 Inverse Systems and Deconvolution
5.6 Summary and References
6 Sampling And Reconstruction Of Signals
6.1 Ideal Sampling and Reconstruction of Continuous-Time Signals
6.2 Discrete-Time Processing of Continuous-Time Signals
6.3 Analog-to-Digital and Digital-to-Analog Converters
6.4 Sampling and Reconstruction of Continuous-Time Bandpass Signals
6.5 Sampling of Discrete-Time Signals
6.6 Oversampling A/D and D/A Converters
6.7 Summary and References
7 The Discrete Fourier Transform: Its Properties And Applications
7.1 Frequency Domain Sampling:The Discrete Fourier Transform
7.2 Properties of the DFT
7.3 Linear Filtering Methods Based on the DFT
7.4 Frequency Analysis of Signals Using the DFT
7.5 The Discrete Cosine Transform
7.6 Summary and References
8 Efficient Computaiton Of The Dft: Fast Fourier Transform Algorithms
8.1 Efficient Computation of the DFT: FFT Algorithms
8.2 Applications of FFT Algorithms
8.3 A Linear Filtering Approach to Computation of the DFT
8.4 Quantization Effects in the Computation of the DFT
8.5 Summary and References
9 Implementation Of Discrete-Time Systems
9.1 Structures for the Realization of Discrete-Time Systems
9.2 Structures for FIR Systems
9.3 Structures for IIR Systems
9.4 Representation of Numbers
9.5 Quantization of Filter Coefficients
9.6 Round-Off Effects in Digital Filters
9.7 Summary and References
10 Design Of Digital Filers
10.1 General Considerations
10.2 Design of FIR Filters
10.3 Design of IIR Filters From Analog Filters
10.4 Frequency Transformations
10.5 Summary and References
11 Multirate Digital Signal Processing
11.2 Decimation by a Factor D
11.3 Interpolation by a Factor I
11.4 Sampling Rate Conversion by a Rational Factor I/D
11.5 Implementation of Sampling Rate Conversion
11.6 Multistage Implementation of Sampling Rate Conversion
11.7 Sampling Rate Conversion of Bandpass Signals
11.8 Sampling Rate conversion by an Arbitrary Factor
11.9 Applications of Sampling Rate Conversion
11.10 Digital Filter Banks
11.11 Two-Channel Quadrature Mirror Filter Bank
11.12 M-Channel QMF Bank
11.13 Summary and References
12 Linear Prediction And Optimum Linear Filters
12.1 Random Signals, Correlation Functions and Power Spectra
12.2 Innovations Representation of a Stationary Random Process
12.3 Forward and Backward Linear Prediction
12.4 Solution of the Normal Equations
12.5 Properties of the Linear Prediction-Error Filters
12.6 AR Lattice and ARMA Lattice-Ladder Filters
12.7 Wiener Filters for Filtering and Prediction
12.8 Summary and References
13 Adaptive Filters
13.1 Applications of Adaptive Filters
13.2 Adaptive Direct-Form FIR Filters-The LMS Algorithm
13.3 Adaptive Direct-Form FIR Filters-RLS Algorithms
13.4 Adaptive Lattice-Ladder Filters
13.5 Summary and References
14 Power Spectrum Estimation
14.1 Estimation of Spectra from Finite-Duration Observations of Signals
14.2 Nonparametric Methods for Power Spectrum Estimation
14.3 Parametric Methods for Power Spectrum Estimation
14.4 Filter Bank Methods
14.5 Eigenanalysis Algorithms for Spectrum Estimation
14.6 Summary and References
Appendix A Random Number Generators
Appendix B Tables of Transition Coefficients for the Design of Linear-Phase Filters
References and Bibliography
Instructor's Solutions Manual (catalog download), 4th Edition
Proakis & Manolakis
PowerPoints, 4th Edition
Download Chapter 1 (0.5MB)
Download Chapter 2 (4.0MB)
Download Chapter 3 (1.3MB)
Download Chapter 4 (1.9MB)
Download Chapter 5 (2.1MB)
Download Chapter 6 (2.9MB)
Download Chapter 7 (1.4MB)
Download Chapter 8 (2.0MB)
Download Chapter 9 (2.8MB)
Download Chapter 10 (3.4MB)
Download Chapter 11 (2.8MB)
Download Chapter 12 (0.6MB)
Download Chapter 13 (1.8MB)
Download Chapter 14 (2.2MB)
Dsgital Signal Processing Errata
Pearson offers affordable and accessible purchase options to meet the needs of your students. Connect with us to learn more.
K12 Educators: Contact your Savvas Learning Company Account General Manager for purchase options. Instant Access ISBNs are for individuals purchasing with credit cards or PayPal.
Savvas Learning Company is a trademark of Savvas Learning Company LLC.
Known as a digital communications expert, inspiring educator, and prolific writer, John G. Proakis has helped shape electrical engineering and digital communications programs and composed textbooks that have influenced graduate students worldwide. Dr. Proakis developed an outstanding reputation of providing inspired teaching and supervision of students with an academic career that began in 1969 with the Electrical Engineering Department at Northeastern University, MA, USA. As the chair of Northeastern's Department of Electrical and Computer Engineering, Dr. Proakis helped transform the department from a teaching environment to a dynamic research-active department. Dr. Proakis also served as associate dean and director of Northeastern's Graduate School of Engineering. Of his ten textbooks on digital communication and signal processing, Digital Communications (McGraw Hill) is perhaps the best known. Considered the most influential resource on the topic and now in its fifth edition, the textbook has educated generations of students and engineers about the fundamentals associated with the digital information age. His other influential textbooks include Introduction to Digital Signal Processing (Prentice Hall), Communication Systems Engineering (Prentice Hall), and Fundamentals of Communication Systems (Prentice Hall). Dr. Proakis has also expanded engineering education beyond theory to laboratory experiments and simulation techniques using computers and software. His textbooks in this area include Digital Signal Processing Using MATLAB (CL-Engineering) and Contemporary Communication Systems Using MATLAB and Simulink (Cengage Learning). Through these approachable books, Dr. Proakis has helped expose students early on to the MATLAB development and simulation tool that they will likely need to use throughout their professional careers. Dr. Proakis also served as editor of the five-volume Wiley Encyclopedia of Telecommunications. An IEEE Life Fellow and recipient of the IEEE Signal Processing Society Education Award (2004), Dr. Proakis is a Professor Emeritus with Northeastern University and an Adjunct Professor at the University of California in San Diego, CA, USA.
Dr. Dimitris G. Manolakis, a senior staff member in the Applied Space Systems Group, joined Lincoln Laboratory at the Massachusetts Institute of Technology in 1999 and has combined an extensive research career with a commitment to education. Dr. Manolakis' work has included the exploration and development of techniques in digital signal processing, adaptive filtering, array processing, pattern recognition, and remote sensing. His recent research has focused on algorithms for hyperspectral target detection and modeling of spatio-temporal count data from down-looking sensors. Throughout his career, Dr. Manolakis has been involved in educating future engineers. He has taught undergraduate and graduate courses at the University of Athens, at which he earned a bachelor's degree in physics and a doctorate in electrical engineering; Northeastern University, at which he is an adjunct professor; Boston College; and Worcester Polytechnic Institute. In addition, through an in-house technical education program, he conducts courses in digital and statistical signal processing and adaptive filtering to explain fundamental principles and concepts to Lincoln Laboratory staff members embarking on research in these areas. In 2013, Dr. Manolakis was recognized with an IEEE Signal Processing Society Education Award for his dedication to advancing education through the development of curriculum materials, publication of scholarly texts, and teaching.
Dr. Manolakis is a prolific writer. He has authored or coauthored more than 135 articles on topics ranging from digital signal processing to hyperspectral remote sensing of chemical plumes to hyperspectral image processing for automatic target detection; these articles have been cited in almost 5000 scientific publications. In addition, he has coauthored three textbooks that are widely used in academia: Digital Signal Processing: Principles, Algorithms, and Applications (Prentice Hall, 2006, 4th ed.), which has been translated into six languages and cited 41,000 times; Statistical and Adaptive Signal Processing (Artech House, 2005); and Applied Digital Signal Processing (Cambridge University Press, 2011).
We're sorry! We don't recognize your username or password. Please try again.
The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning.
You have successfully signed out and will be required to sign back in should you need to download more resources.