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E-raamat: Digital Signal Processing with Examples in MATLAB 2nd edition [Taylor & Francis e-raamat]

(Sandia National Laboratories, Albuquerque, New Mexico, USA), (Los Alamos National Laboratory, New Mexico, USA)
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Digital Signal Processing with Examples in MATLAB 2nd editionSuurem pilt
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"The Preface to the First Edition, which has been altered slightly to make its contents current, follows below. It remains the main preface to this text. The purpose of this second edition, its intended audience, and our reasons for writing it are still the same as those described in the first preface. In this second edition we have attempted to correct and improve the original text in response to comments from colleagues, students, and other friends who have suggested ways to clarify and improve the book. We have also added topics that have become the basis for current DSP applications of which we are aware, as well as several topics which, we have been informed repeatedly, "should have been included" in the first edition. These include a chapter on modeling analog systems and a chapter on pattern recognition (as used in discrimination, detection, and decision making) using support vector machines, as well as sections on the chirp-z transform, resampling and waveform reconstruction, the discrete sine transform, logarithmic and nonuniform sampling, and other items, including an appendix containing a table of transforms more comprehensive than the table in the first edition"--

"This book explains the fundamentals of digital signal processing and how to apply DSP to the design of signal processing systems. Using the book perations, including spectral analysis and modeling. This second edition features new sections on simulation of continuous systems, the Chirp-z transform, linear and logarithmic sampling and resampling, the discrete sine transform, spread-spectrum techniques, digital differentiation, and integration. It also includes more information on the power density spectrum, implementation of the FFT, and the equation error method in adaptive signal processing"--

Provided by publisher.
Foreword to the Second Edition xv
Foreword to the First Edition in Memory of Richard W. Hamming (1915--1998) xvii
Preface to the Second Edition xix
Preface to the First Edition xxi
Authors xxv
1 Introduction
1(18)
1.1 Digital Signal Processing
1(2)
1.2 How to Read This Text
3(1)
1.3 Introduction to MATLAB®
3(1)
1.4 Signals, Vectors, and Arrays
4(2)
1.5 Review of Vector and Matrix Algebra Using MATLAB® Notation
6(7)
1.6 Geometric Series and Other Formulas
13(3)
1.7 MATLAB® Functions in DSP
16(1)
1.8 The
Chapters Ahead
17(2)
References
17(1)
Further Reading
18(1)
2 Least Squares, Orthogonality, and the Fourier Series
19(20)
2.1 Introduction
19(1)
2.2 Least Squares
19(5)
2.3 Orthogonality
24(2)
2.4 The Discrete Fourier Series
26(13)
Exercises
33(4)
References
37(2)
3 Correlation, Fourier Spectra, and the Sampling Theorem
39(50)
3.1 Introduction
39(1)
3.2 Correlation
40(2)
3.3 The Discrete Fourier Transform (DFT)
42(1)
3.4 Redundancy in the DFT
43(2)
3.5 The FFT Algorithm
45(2)
3.6 Amplitude and Phase Spectra
47(4)
3.7 The Inverse DFT
51(1)
3.8 Properties of the DFT
52(5)
3.9 Continuous Transforms, Linear Systems, and Convolution
57(5)
3.10 The Sampling Theorem
62(2)
3.11 Waveform Reconstruction and Aliasing
64(8)
3.12 Resampling
72(4)
3.13 Nonuniform and Log-Spaced Sampling
76(13)
Exercises
84(3)
References
87(1)
Further Reading
88(1)
4 Linear Systems and Transfer Functions
89(48)
4.1 Continuous and Discrete Linear Systems
89(1)
4.2 Properties of Discrete Linear Systems
89(3)
4.3 Discrete Convolution
92(1)
4.4 The z-Transform and Linear Transfer Functions
93(3)
4.5 The Complex z-Plane and the Chirp z-Transform
96(5)
4.6 Poles and Zeros
101(4)
4.7 Transient Response and Stability
105(2)
4.8 System Response via the Inverse z-Transform
107(2)
4.9 Cascade, Parallel, and Feedback Structures
109(2)
4.10 Direct Algorithms
111(3)
4.11 State-Space Algorithms
114(2)
4.12 Lattice Algorithms and Structures
116(8)
4.13 FFT Algorithms
124(5)
4.14 Discrete Linear Systems and Digital Filters
129(1)
4.15 Functions Used in This
Chapter
130(7)
Exercises
131(4)
References
135(1)
Further Reading
136(1)
5 FIR Filter Design
137(24)
5.1 Introduction
137(1)
5.2 An Ideal Lowpass Filter
138(1)
5.3 The Realizable Version
139(3)
5.4 Improving an FIR Filter with Window Functions
142(6)
5.5 Highpass, Bandpass, and Bandstop Filters
148(2)
5.6 A Complete FIR Filtering Example
150(2)
5.7 Other Types of FIR Filters
152(1)
5.8 Digital Differentiation
152(2)
5.9 A Hilbert Transformer
154(7)
Exercises
155(4)
References
159(1)
Further Reading
160(1)
6 IIR Filter Design
161(38)
6.1 Introduction
161(1)
6.2 Linear Phase
162(1)
6.3 Butterworth Filters
163(4)
6.4 Chebyshev Filters
167(6)
6.5 Frequency Translations
173(4)
6.6 The Bilinear Transformation
177(3)
6.7 IIR Digital Filters
180(5)
6.8 Digital Resonators and the Spectrogram
185(4)
6.9 The All-Pass Filter
189(1)
6.10 Digital Integration and Averaging
189(10)
Exercises
193(3)
References
196(1)
Further Reading
197(2)
7 Random Signals and Spectral Estimation
199(32)
7.1 Introduction
199(1)
7.2 Amplitude Distributions
200(4)
7.3 Uniform, Gaussian, and Other Distributions
204(5)
7.4 Power and Power Density Spectra
209(4)
7.5 Properties of the Power Spectrum
213(3)
7.6 Power Spectral Estimation
216(5)
7.7 Data Windows in Spectral Estimation
221(2)
7.8 The Cross-Power Spectrum
223(3)
7.9 Algorithms
226(5)
Exercises
226(3)
References
229(1)
Further Reading
230(1)
8 Least-Squares System Design
231(42)
8.1 Introduction
231(1)
8.2 Applications of Least-Squares Design
232(3)
8.3 System Design via the Mean-Squared Error
235(4)
8.4 A Design Example
239(3)
8.5 Least-Squares Design with Finite Signal Vectors
242(2)
8.6 Correlation and Covariance Computation
244(3)
8.7 Channel Equalization
247(3)
8.8 System Identification
250(3)
8.9 Interference Canceling
253(4)
8.10 Linear Prediction and Recovery
257(4)
8.11 Effects of Independent Broadband Noise
261(12)
Exercises
263(7)
References
270(1)
Further Reading
271(2)
9 Adaptive Signal Processing
273(36)
9.1 Introduction
273(2)
9.2 The Mean-Squared Error Performance Surface
275(1)
9.3 Searching the Performance Surface
276(5)
9.4 Steepest Descent and the LMS Algorithm
281(7)
9.5 LMS Examples
288(3)
9.6 Direct Descent and the RLS Algorithm
291(5)
9.7 Measures of Adaptive System Performance
296(4)
9.8 Other Adaptive Structures and Algorithms
300(9)
Exercises
301(5)
References
306(1)
Further Reading
307(2)
10 Signal Information, Coding, and Compression
309(54)
10.1 Introduction
309(1)
10.2 Measuring Information
310(2)
10.3 Two Ways to Compress Signals
312(2)
10.4 Adaptive Predictive Coding
314(5)
10.5 Entropy Coding
319(9)
10.6 Transform Coding and the Discrete Cosine Transform
328(7)
10.7 The Discrete Sine Transform
335(7)
10.8 Multirate Signal Decomposition and Subband Coding
342(10)
10.9 Time-Frequency Analysis and Wavelet Transforms
352(11)
Exercises
356(5)
References
361(2)
11 Models of Analog Systems
363(40)
11.1 Introduction
363(1)
11.2 Impulse-Invariant Approximation
364(4)
11.3 Final Value Theorem
368(2)
11.4 Pole-Zero Comparisons
370(2)
11.5 Approaches to Modeling
372(2)
11.6 Input-Invariant Models
374(8)
11.7 Other Linear Models
382(4)
11.8 Comparison of Linear Models
386(3)
11.9 Models of Multiple and Nonlinear Systems
389(8)
11.10 Concluding Remarks
397(6)
Exercises
397(4)
References
401(1)
Further Reading
402(1)
12 Pattern Recognition with Support Vector Machines
403(50)
12.1 Introduction
403(3)
12.2 Pattern Recognition Principles
406(5)
12.3 Learning
411(6)
12.3.1 The Independent and Identically Distributed Sample Plan
412(1)
12.3.2 Learning Methods
413(4)
12.4 Support Vector Machines
417(19)
12.4.1 The Support Vector Machine Function Class
417(3)
12.4.2 The Support Vector Machine Learning Strategy
420(3)
12.4.3 The Core Support Vector Machine Algorithm
423(1)
12.4.3.1 Constructing the Primal, Dual, and Dual-to-Primal Map
424(6)
12.4.3.2 Margin, Support Vectors, and the Sparsity of Exact Solutions
430(3)
12.4.3.3 Decomposition Algorithms for the Dual Quadratic Programming Problem
433(2)
12.4.3.4 Rate Certifying Decomposition Algorithms
435(1)
12.5 Multi-Class Classification
436(1)
12.6 MATLAB® Examples
436(17)
Exercises
445(5)
References
450(3)
Appendix: Table of Laplace and z Transforms 453(8)
Index 461
Samuel D. Stearns is a professor emeritus at the University of New Mexico, where has been involved in adjunct teaching and research since 1960. An IEEE fellow, Dr. Stearns was also a distinguished member of the technical staff at Sandia National Laboratories for 27 years. His principal technical areas are DSP and adaptive signal processing.

Don R. Hush is a technical staff member at the Los Alamos National Laboratory. An IEEE senior member, Dr. Hush was previously a technical staff member at Sandia National Laboratories and a professor at the University of New Mexico. He was also an associate editor for IEEE Transactions on Neural Networks and IEEE Signal Processing Magazine.
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