E-Book, Englisch, 471 Seiten
Poularikas Understanding Digital Signal Processing with MATLAB® and Solutions
Erscheinungsjahr 2017
ISBN: 978-1-351-62327-8
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 471 Seiten
ISBN: 978-1-351-62327-8
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
The book discusses receiving signals that most electrical engineers detect and study. The vast majority of signals could never be detected due to random additive signals, known as noise, that distorts them or completely overshadows them. Such examples include an audio signal of the pilot communicating with the ground over the engine noise or a bioengineer listening for a fetus’ heartbeat over the mother’s. The text presents the methods for extracting the desired signals from the noise. Each new development includes examples and exercises that use MATLAB to provide the answer in graphic forms for the reader's comprehension and understanding.
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Weitere Infos & Material
CHAPTER 1: CONTINUOUS AND DETERMINISTIC SIGNALS
1.1 Continuous Deterministic Signals
1.2 Sampling of Continuous Signals-Discrete Signals
1.3 Signal conditioning and manipulation
1.4 Convolution of analog and discrete signals
1.5 MATLAB use for vectors and arrays (matrices)
CHAPTER 2: FOURIER ANALYSIS OF CONTINUOUS AND DISCRETE SIGNALS
2.1 Introduction
2.2 Fourier transform (FT) of deterministic signals
2.3 Sampling of signals
2.4 Discrete time Fourier transform (DTFT)
2.5 DTFT of finite-time sequences
2.6 The discrete Fourier transform (DFT)
Appendix 2.1 Fourier transform properties
Appendix 2.2 Fourier transform pairs
Appendix 2.3 DTFT properties
Appendix 2.4 DFT properties
CHAPTER 3: THE Z-TRANSFORM, DIFFERENCE EQUATIONS AND DISCRETE SYSTEMS
3.1 The z-transform
3.2 Properties of the z-transform
3.3 Inverse z-transform
3.4 Transfer function
3.5 Frequency response of discrete systems
3.6 Z-transform solution of difference equations
CHAPTER 4: DIGITAL FILTER DESIGN
4.1 Introduction
4.2 Finite impulse response (FIR) filter
Appendix 4.1: Window characteristics and performance
CHAPTER 5: RANDOM VARIABLES, SEQUENCIES AND PROBABILITY FUNCTIONS
5.1 Random signals and distributions
5.2 Averages
5.3 Stationary processes
5.4 Probability density functions
5.5 Transformations of PDF’s
CHAPTER 6: LINEAR SYSTEMS WITH RANDOM INPUTS, FILTERING POWER SPECTRAL DENSITY
6.1 Spectral representation
6.2 Linear systems with random inputs
6.3 Autoregressive moving average processes
6.4 Autoregressive (AR) process
6.5 Parametric representations of stochastic processes: ARMA and ARMAX models
CHAPTER 7: LEAST SQUARES-OPTIMUM FILTERING
7.1 Introduction
7.2 The least squares approach
7.3 linear least squares
7.3.1 Matrix formulation of linear least squares
7.4 Point estimation
7.5 Mean square error (MSE)
7.6 Finite impulse response (FIR) Wiener filter
7.7 Wiener solution----Orthogonal principle
7.8 Wiener filtering examples
CHAPTER 8: NONPARAMETRIC (CLASSICAL) SPECTRA ESTIMATION
8.1 Periodogram and correlogram spectra estimation
8.2 Book proposed method for better resolution using transformation of the random variables
8.3 Daniel periodogram
8.4 Bartlett periodogram
8.5 Blackman-Tukey (BT) method
8.6 Welch method
Appendix 8.1: Important window and their spectra
CHAPTER 9: PARAMETRIC AND OTHER METHODS FOR SPECTRA ESTIMATION
9.1 Introduction
9.2 AR, MA and ARMA models
9.3 Yule-Walker (YW) equations
9.4 Least-squares (LS) method and linear prediction
9.5 Minimum variance
9.6 Model order
9.7 Levinson-Durbin algorithm
9.8 Maximum entropy method
9.9 spectrums of segmented signals
9.10 Eigenvalues and eigenvectors of matrices (see also Appendix 2)
CHAPTER 10: NEWTON’S AND STEEPEST DESCENT METHODS
10.1 Geometric properties of the error surface
10.2 One-dimensional gradient search method
10.3 Steepest descent algorithm
10.4 Newton’s method
10.5 Solution of the vector difference equation
CHAPTER 11: THE LEAST MEAN-SQUARE (LMS) ALGORITHM
11.1 Introduction
11.2 The LMS algorithm
11.3 Examples using the LMS algorithm
11.4 *Performance analysis of the LMS algorithm
11.5 *Complex representation of the LMS algorithm
CHAPTER 12: VARIANTS OF LEST MEAN-SQUARE ALGORITHM
12.1 The Normalized Least Mean-Square Algorithm
12.2 Power Normalized LMS
12.3 Self-Correction LMS Filter
12.4 The Sign-Error LMS Algorithm
12.5 The NLMS Sign-Error Algorithm
12.6 The Sign-Regressor LMS Algorithm
12.7 Self-Correcting Sign-Regressor LMS Algorithm
12.8 The Normalized Sign-Regressor LMS Algorithm
12.9 The Sign-Sign LMS Algorithm
12.10 The normalized Sign-Sign LMS Algorithm
12.11 Variable Step-Size LMS Algorithm
12.12 The Leaky LMS Algorithm
12.13 The Linearly Constrained LMS Algorithm
12.14 The Least Mean Fourth Algorithm
12.15 The Least Mean Mixed Normal (LMMN) LMS Algorithm
12.16 Short-Length Signal of the LMS Algorithm
12.17 The Transform Domain LMS Algorithm
12.18 The Error Normalized Step-Size LMS Algorithm
12.19 The Robust Variable Step-Size LMS Algorithm
12.20 The Modified LMS Algorithm
12.21 Momentum LMS Algorithm
12.22 The Block LMS Algorithm
12.23 The Complex LMS Algorithm
12.24 The Affine LMS Algorithm
12.25 The Complex Affine LMS Algorithm
CHAPTER 13: NONLINEAR FILTERING
13.1 Introduction
13.2 Statistical Preliminaries
13.3 Mean Filter
13.4 Median Filter
13.5 Trimmed-Type Mean Filter
13.6 L-Filters
13.7 Ranked-Order Statistic Filter
13.8 Edge-Enhancement Filters
13.9 R-Filters
APPENDICES
Appendix 1: Suggestions and explanations for MATLAB use
Appendix 2: MATLAB functions
Appendix 3: Mathematical formulas
Appendix 4: Langrange multiplier method
Appendix 5: Matrix analysis
