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E-raamat: Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation

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This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs.





This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed.





The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressedsensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.
Part I Model-Based Actions: Filtering, Prediction, Smoothing
1 Kalman Filter, Particle Filter and Other Bayesian Filters
3(148)
1.1 Introduction
3(2)
1.2 Preliminaries
5(11)
1.2.1 A Basic Example
7(3)
1.2.2 Prediction with the Gauss-Markov Model
10(2)
1.2.3 Continuation of a Simple Example of Recursive Wiener Filter
12(4)
1.3 Kalman Filter
16(16)
1.3.1 The Algorithm
17(4)
1.3.2 Evolution of Filter Variables
21(6)
1.3.3 Several Perspectives
27(2)
1.3.4 Some Connections
29(1)
1.3.5 Numerical Issues
30(1)
1.3.6 Information Filter
31(1)
1.4 Nonlinear Conditions
32(20)
1.4.1 Propagation and Nonlinearity
32(7)
1.4.2 Jacobian. Hessian. Change of Coordinates
39(2)
1.4.3 Local Linearization
41(2)
1.4.4 Example of a Body Falling Towards Earth
43(9)
1.5 Extended Kalman Filter (EKF)
52(10)
1.5.1 The EKF Algorithm
52(6)
1.5.2 Assessment of the Linearized Approximation
58(4)
1.6 Unscented Kalman Filter (UKF)
62(14)
1.6.1 The Unscented Transform
63(7)
1.6.2 The Unscented Kalman Filter (UKF)
70(6)
1.7 Particle Filter
76(24)
1.7.1 An Implementation of the Particle Filter
77(5)
1.7.2 Resampling Schemes
82(2)
1.7.3 Multinomial Resampling
84(2)
1.7.4 Systematic Resampling
86(1)
1.7.5 Stratified Resampling
87(1)
1.7.6 Residual Resampling
87(1)
1.7.7 Comparison
88(1)
1.7.8 Roughening
88(1)
1.7.9 Basic Theory of the Particle Filter
89(1)
1.7.10 Sequential Monte Carlo (SMC)
90(3)
1.7.11 Proposal Importance Functions
93(4)
1.7.12 Particle Filter Variants
97(1)
1.7.13 Marginalized Particle Filter (Rao-Blackwellized Particle Filter)
98(1)
1.7.14 Regularized Particle Filters
99(1)
1.8 The Perspective of Numerical Integration
100(13)
1.8.1 Geometry
101(1)
1.8.2 Quadrature
102(3)
1.8.3 Other Approximations
105(3)
1.8.4 Gaussian Filters
108(4)
1.8.5 Assumed Density. Expectation Propagation
112(1)
1.9 Other Bayesian Filters
113(6)
1.9.1 Ensemble Kalman Filter (EnKF)
114(1)
1.9.2 Iterative Kalman Filter
115(1)
1.9.3 Gaussian Particle Filter
116(1)
1.9.4 Divide and Conquer
116(2)
1.9.5 Combinations
118(1)
1.9.6 Algorithms with Special Characteristics
118(1)
1.10 Smoothing
119(15)
1.10.1 Optimal Prediction
119(1)
1.10.2 One-Stage Smoothing
120(2)
1.10.3 Three Types of Smoothers
122(10)
1.10.4 Bayesian Smoothing
132(2)
1.11 Applications of Bayesian Filters
134(5)
1.11.1 Navigation
134(1)
1.11.2 Tracking
134(1)
1.11.3 Information Fusion
135(1)
1.11.4 SLAM
135(2)
1.11.5 Speech, Sounds
137(1)
1.11.6 Images
137(1)
1.11.7 Earth Monitoring and Forecasting
137(1)
1.11.8 Energy and Economy
138(1)
1.11.9 Medical Applications
138(1)
1.11.10 Traffic
138(1)
1.11.11 Other Applications
139(1)
1.12 Frequently Cited Examples
139(3)
1.12.1 Bearings-Only Tracking
139(1)
1.12.2 Other Tracking Cases
140(1)
1.12.3 Univariate Non-stationary Growth Model
140(1)
1.12.4 Financial Volatility Model
141(1)
1.12.5 Nonlinear Series
141(1)
1.12.6 The Pendulum
142(1)
1.13 Resources
142(9)
1.13.1 MATLAB
142(1)
1.13.2 Internet
143(1)
References
144(7)
Part II Sparse Representation. Compressed Sensing
2 Sparse Representations
151(112)
2.1 Introduction
151(1)
2.2 Sparse Solutions
152(30)
2.2.1 The Central Problem
152(3)
2.2.2 Norms and Sparsity
155(1)
2.2.3 Solving Sparsity Optimization Problems
156(26)
2.3 Compressed Sensing
182(11)
2.3.1 Statement of the Approach
182(2)
2.3.2 Compression and Recovery. The Matrix A
184(5)
2.3.3 Incoherence and Sensing
189(1)
2.3.4 Stable and Robust Recovery
190(1)
2.3.5 Phase Transitions
191(1)
2.3.6 Some Applications
192(1)
2.4 Image Processing
193(22)
2.4.1 Texture + Cartoon
193(9)
2.4.2 Patches
202(6)
2.4.3 Morphological Components
208(7)
2.5 An Additional Repertory of Applicable Concepts and Tools
215(18)
2.5.1 Sparse Representation in MATLAB
215(4)
2.5.2 Diffusion in 2D
219(9)
2.5.3 Bregman-Related Algorithms
228(5)
2.6 Matrix Completion and Related Problems
233(8)
2.6.1 Matrix Completion
234(4)
2.6.2 Decomposition of Matrices
238(3)
2.7 Experiments
241(9)
2.7.1 Signal Denoising Based on Total Variation (TV)
241(3)
2.7.2 Picture Reconstruction Based on Matrix Completion
244(4)
2.7.3 Text Removal
248(2)
2.8 Resources
250(13)
2.8.1 MATLAB
250(2)
2.8.2 Internet
252(1)
References
253(10)
Appendix A Selected Topics of Mathematical Optimization 263(104)
Appendix B Long Programs 367(62)
Index 429
Prof. Jose M. Giron-Sierra was born in Valladolid, Spain. He receive his Ph.D. in Physics in 1978, Universidad Complutense de Madrid, Spain. Prof. Giron-Sierra wrote more than 160 publications in various international journals. He is IEEE, AIAA, and Eurosim member and belongs to two IFAC Technical Committees.