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Digital Signal Processing Using MATLAB®: A Problem Solving Companion, International Edition 4th edition [Pehme köide]

(Northeastern University), (Northeastern University)
  • Formaat: Paperback / softback, 672 pages, kõrgus x laius x paksus: 235x187x33 mm, kaal: 982 g
  • Ilmumisaeg: 01-Jan-2016
  • Kirjastus: CL Engineering
  • ISBN-10: 1305637534
  • ISBN-13: 9781305637535
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Digital Signal Processing Using MATLAB®: A Problem Solving Companion, International Edition 4th editionSuurem pilt
  • Formaat: Paperback / softback, 672 pages, kõrgus x laius x paksus: 235x187x33 mm, kaal: 982 g
  • Ilmumisaeg: 01-Jan-2016
  • Kirjastus: CL Engineering
  • ISBN-10: 1305637534
  • ISBN-13: 9781305637535
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781305637535.html
Learn to use MATLAB® as a useful computing tool for exploring traditional Digital Signal Processing (DSP) topics and solving problems to gain insight with this supplementary text. DIGITAL SIGNAL PROCESSING USING MATLAB®: A PROBLEM SOLVING COMPANION, 4E greatly expands the range and complexity of problems that you can effectively study. Since DSP applications are primarily algorithms implemented on a DSP processor or software, they require a significant amount of programming. Using interactive software, such as MATLAB®, enables you to focus on mastering new and challenging concepts rather than concentrating on programming algorithms. This edition discusses interesting, practical examples and explores useful problems. New online chapters introduce advanced topics, such as optimal filters, linear prediction, and adaptive filters, which are essential in furthering your academic studies at the graduate level.
Preface xi
1 Introduction 1(21)
1.1 Overview of Digital Signal Processing
2(3)
1.2 A Brief Introduction to MATLAB
5(13)
1.3 Applications of Digital Signal Processing
18(2)
1.4 Brief Overview of the Book
20(2)
2 Discrete-Time Signals And Systems 22(37)
2.1 Discrete-Time Signals
22(14)
2.2 Discrete Systems
36(4)
2.3 Convolution
40(7)
2.4 Difference Equations
47(6)
2.5 Problems
53(6)
3 The Discrete-Time Fourier Analysis 59(44)
3.1 The Discrete-Time Fourier Transform (DTFT)
59(8)
3.2 The Properties of the DTFT
67(7)
3.3 The Frequency Domain Representation of LTI Systems
74(6)
3.4 Sampling and Reconstruction of Analog Signals
80(17)
3.5 Problems
97(6)
4 The z-Transform 103(38)
4.1 The Bilateral z-Transform
103(4)
4.2 Important Properties of the z-Transform
107(5)
4.3 Inversion of the z-Transform
112(6)
4.4 System Representation in the z-Domain
118(10)
4.5 Solutions of the Difference Equations
128(6)
4.6 Problems
134(7)
5 The Discrete Fourier Transform 141(71)
5.1 The Discrete Fourier Series
142(7)
5.2 Sampling and Reconstruction in the z-Domain
149(5)
5.3 The Discrete Fourier Transform
154(11)
5.4 Properties of the Discrete Fourier Transform
165(15)
5.5 Linear Convolution Using the DFT
180(7)
5.6 The Fast Fourier Transform
187(13)
5.7 Problems
200(12)
6 Implementation Of Discrete-Time Filters 212(79)
6.1 Basic Elements
213(1)
6.2 IIR Filter Structures
214(14)
6.3 FIR Filter Structures
228(11)
6.4 Overview of Finite-Precision Numerical Effects
239(1)
6.5 Representation of Numbers
240(15)
6.6 The Process of Quantization and Error Characterizations
255(7)
6.7 Quantization of Filter Coefficients
262(15)
6.8 Problems
277(14)
7 FIR Filter Design 291(79)
7.1 Preliminaries
292(3)
7.2 Properties of Linear-Phase FIR Filters
295(14)
7.3 Window Design Technique
309(21)
7.4 Frequency-Sampling Design Technique
330(14)
7.5 Optimal Equiripple Design Technique
344(16)
7.6 Problems
360(10)
8 IIR Filter Design 370(88)
8.1 Some Preliminaries
371(3)
8.2 Some Special Filter Types
374(11)
8.3 Characteristics of Prototype Analog Filters
385(22)
8.4 Analog-to-Digital Filter Transformations
407(20)
8.5 Lowpass Filter Design Using MATLAB
427(5)
8.6 Frequency-Band Transformations
432(13)
8.7 Problems
445(13)
9 Sampling Rate Conversion 458(60)
9.1 Introduction
459(2)
9.2 Decimation by a Factor D
461(9)
9.3 Interpolation by a Factor I
470(7)
9.4 Sampling Rate Conversion by a Rational Factor I/D
477(5)
9.5 FIR Filter Designs for Sampling Rate Conversion
482(18)
9.6 FIR Filter Structures for Sampling Rate Conversion
500(10)
9.7 Problems
510(8)
10 Round-Off Effects In Digital Filters 518(55)
10.1 Analysis of A/D Quantization Noise
518(12)
10.2 Round-Off Effects in IIR Digital Filters
530(27)
10.3 Round-Off Effects in FIR Digital Filters
557(12)
10.4 Problems
569(4)
11 Applications In Adaptive Filtering 573(13)
11.1 LMS Algorithm for Coefficient Adjustment
575(3)
11.2 System Identification or System Modeling
578(1)
11.3 Suppression of Narrowband Interference in a Wideband Signal
579(3)
11.4 Adaptive Line Enhancement
582(1)
11.5 Adaptive Channel Equalization
582(4)
12 Applications In Communications 586(28)
12.1 Pulse-Code Modulation
586(4)
12.2 Differential PCM (DPCM)
590(3)
12.3 Adaptive PCM and DPCM (ADPCM)
593(4)
12.4 Delta Modulation (DM)
597(4)
12.5 Linear Predictive Coding (LPC) of Speech
601(4)
12.6 Dual-Tone Multifrequency (DTMF) Signals
605(4)
12.7 Binary Digital Communications
609(2)
12.8 Spread-Spectrum Communications
611(3)
13 Random Processes 614(72)
13.1 Random Variable
615(13)
13.2 A Pair of Random Variables
628(14)
13.3 Random Signals
642(8)
13.4 Power Spectral Density
650(8)
13.5 Stationary Random Processes through LTI Systems
658(10)
13.6 Useful Random Processes
668(16)
13.7 Summary and References
684(2)
14 Linear Prediction And Optimum Linear Filters 686(83)
14.1 Innovations Representation of a Stationary Random Process
687(14)
14.2 Forward and Backward Linear Prediction
701(16)
14.3 Solution of the Normal Equations
717(13)
14.4 Properties of the Linear Prediction-Error Filters
730(4)
14.5 AR Lattice and ARMA Lattice-Ladder Filters
734(9)
14.6 Wiener Filters for Filtering and Prediction
743(23)
14.7 Summary and References
766(3)
15 Adaptive Filters 769
15.1 Applications of Adaptive Filters
769(46)
15.2 Adaptive Direct-Form FIR Filters
815(34)
15.3 Summary and References
849
Bibliography B-1
Index I-1
Affiliation: University of California, San Diego and Northeastern University Bio: Dr. John Proakis is an Adjunct Professor at the University of California at San Diego and a Professor Emeritus at Northeastern University. He was a faculty member at Northeastern University from 1969 through 1998 and held several academic positions including Professor of Electrical Engineering, Associate Dean of the College of Engineering and Director of the Graduate School of Engineering, and Chairman of the Department of Electrical and Computer Engineering. His professional experience and interests focus in areas of digital communications and digital signal processing. He is co-author of several successful books, including DIGITAL COMMUNICATIONS, 5E (2008), INTRODUCTION TO DIGITAL SIGNAL PROCESSING, 4E (2007); DIGITAL SIGNAL PROCESSING LABORATORY (1991); ADVANCED DIGITAL SIGNAL PROCESSING (1992); DIGITAL PROCESSING OF SPEECH SIGNALS (2000); COMMUNICATION SYSTEMS ENGINEERING, 2E (2002); DIGITAL SIGNAL PROCESSING USING MATLAB V.4, 3E (2010); CONTEMPORARY COMMUNICATION SYSTEMS USING MATLAB, 2E (2004); ALGORITHMS FOR STATISTICAL SIGNAL PROCESSING (2002); FUNDAMENTALS OF COMMUNICATION SYSTEMS (2005). Dr. Vinay K. Ingle is an Associate Professor of Electrical and Computer Engineering at Northeastern University. He received his Ph.D. in electrical and computer engineering from Rensselaer Polytechnic Institute in 1981. He has broad research experience and has taught courses on topics including signal and image processing, stochastic processes, and estimation theory. Dr. Ingle has co-authored numerous higher level books including DSP LABORATORY USING THE ADSP-2181 MICROPROCESSOR (Prentice Hall, 1991), DISCRETE SYSTEMS LABORATORY (Brooks-Cole, 2000), STATISTICAL AND ADAPTIVE SIGNAL PROCESSING (Artech House, 2005), and APPLIED DIGITAL SIGNAL PROCESSING (Cambridge University Press, 2011).