
Discrete Signals and Inverse Problems
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Content
- Intro
- Contents
- Preface
- Brief Comments on Notation
- 1 Introduction
- 1.1 Signals, Systems, and Problems
- 1.2 Signals and Signal Processing - Application Examples
- 1.3 Inverse Problems - Application Examples
- 1.4 History - Discrete Mathematical Representation
- 1.5 Summary
- Solved Problems
- Additional Problems
- 2 Mathematical Concepts
- 2.1 Complex Numbers and Exponential Functions
- 2.2 Matrix Algebra
- 2.3 Derivatives - Constrained Optimization
- 2.4 Summary
- Further Reading
- Solved Problems
- Additional Problems
- 3 Signals and Systems
- 3.1 Signals: Types and Characteristics
- 3.2 Implications of Digitization - Aliasing
- 3.3 Elemental Signals and Other Important Signals
- 3.4 Signal Analysis with Elemental Signals
- 3.5 Systems: Characteristics and Properties
- 3.6 Combination of Systems
- 3.7 Summary
- Further Reading
- Solved Problems
- Additional Problems
- 4 Time Domain Analyses of Signals and Systems
- 4.1 Signals and Noise
- 4.2 Cross- and Autocorrelation: Identifying Similarities
- 4.3 The Impulse Response - System Identification
- 4.4 Convolution: Computing the Output Signal
- 4.5 Time Domain Operations in Matrix Form
- 4.6 Summary
- Further Reading
- Solved Problems
- Additional Problems
- 5 Frequency Domain Analysis of Signals (Discrete Fourier Transform)
- 5.1 Orthogonal Functions - Fourier Series
- 5.2 Discrete Fourier Analysis and Synthesis
- 5.3 Characteristics of the Discrete Fourier Transform
- 5.4 Computation in Matrix Form
- 5.5 Truncation, Leakage, and Windows
- 5.6 Padding
- 5.7 Plots
- 5.8 The Two-Dimensional Discrete Fourier Transform
- 5.9 Procedure for Signal Recording
- 5.10 Summary
- Further Reading and References
- Solved Problems
- Additional Problems
- 6 Frequency Domain Analysis of Systems
- 6.1 Sinusoids and Systems - Eigenfunctions
- 6.2 Frequency Response
- 6.3 Convolution
- 6.4 Cross-Spectral and Autospectral Densities
- 6.5 Filters in the Frequency Domain - Noise Control
- 6.6 Determining H with Noiseless Signals (Phase Unwrapping)
- 6.7 Determining H with Noisy Signals (Coherence)
- 6.8 Summary
- Further Reading and References
- Solved Problems
- Additional Problems
- 7 Time Variation and Nonlinearity
- 7.1 Nonstationary Signals: Implications
- 7.2 Nonstationary Signals: Instantaneous Parameters
- 7.3 Nonstationary Signals: Time Windows
- 7.4 Nonstationary Signals: Frequency Windows
- 7.5 Nonstationary Signals: Wavelet Analysis
- 7.6 Nonlinear Systems: Detecting Nonlinearity
- 7.7 Nonlinear Systems: Response to Different Excitations
- 7.8 Time-Varying Systems
- 7.9 Summary
- Further Reading and References
- Solved Problems
- Additional Problems
- 8 Concepts in Discrete Inverse Problems
- 8.1 Inverse Problems - Discrete Formulation
- 8.2 Linearization of Nonlinear Problems
- 8.3 Data-Driven Solution - Error Norms
- 8.4 Model Selection - Ockham's Razor
- 8.5 Information
- 8.6 Data and Model Errors
- 8.7 Nonconvex Error Surfaces
- 8.8 Discussion on Inverse Problems
- 8.9 Summary
- Further Reading and References
- Solved Problems
- Additional Problems
- 9 Solution by Matrix Inversion
- 9.1 Pseudoinverse
- 9.2 Classification of Inverse Problems
- 9.3 Least Squares Solution (LSS)
- 9.4 Regularized Least Squares Solution (RLSS)
- 9.5 Incorporating Additional Information
- 9.6 Solution Based on Singular Value Decomposition
- 9.7 Nonlinearity
- 9.8 Statistical Concepts - Error Propagation
- 9.9 Experimental Design for Inverse Problems
- 9.10 Methodology for the Solution of Inverse Problems
- 9.11 Summary
- Further Reading
- Solved Problems
- Additional Problems
- 10 Other Inversion Methods
- 10.1 Transformed Problem Representation
- 10.2 Iterative Solution of System of Equations
- 10.3 Solution by Successive Forward Simulations
- 10.4 Techniques from the Field of Artificial Intelligence
- 10.5 Summary
- Further Reading
- Solved Problems
- Additional Problems
- 11 Strategy for Inverse Problem Solving
- 11.1 Step 1: Analyze the Problem
- 11.2 Step 2: Pay Close Attention to Experimental Design
- 11.3 Step 3: Gather High-quality Data
- 11.4 Step 4: Preprocess the Data
- 11.5 Step 5: Select an Adequate Physical Model
- 11.6 Step 6: Explore Different Inversion Methods
- 11.7 Step 7: Analyze the Final Solution
- 11.8 Summary
- Solved Problems
- Additional Problems
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- R
- S
- T
- U
- V
- W
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