Signal Processing and Data Analysis

Table of Content:
Chapter 1 Concepts and theoretical background in signals and systems
1.1 Introduction
1.2 Key concepts
1.3 Linear time invariant system and convolution
1.4 Characteristics of linear time invariant system
Chapter 2 Fourier transform and frequency domain analysis
2.1 Introduction
2.2 Fourier series for continuous -time system
2.3 Fourier series for discrete -time system
2.4 Fourier transform for continuous -time system
2.5 Fourier transform for discrete -time system
2.6 Frequency domain analysis for signals and systems
Chapter 3 Laplace transform and complex frequency domain analysis
3.1 Introduction
3.2 Laplace transform
3.3 Continuous -time signal and complex frequency domain analysis
3.4 Z transform
3.5 Discrete -time signal and complex frequency domain analysis
Chapter 4 Discretization of continuous -time signal and serialization of discrete-time signal
4.1 Introduction
4.2 Sampling continuous -time signal
4.3 Interpolation and fitting for discrete-time signal
Chapter 5 Discrete Fourier transform and fast Fourier transform
5.1 Introduction
5.2 Discrete Fourier transform
5.3 Problems in discrete Fourier transform
5.4 Two dimensional Fourier transform
5.5 Fast Fourier transform
5.6 Application of Fast Fourier transform
Chapter 6 Digital filter and design
6.1 Introduction
6.2 Digital filter structure
6.3 Infinite impulse response filter
6.4 Finite impulse response filter
6.5 Lattice structure for digital filter
6.6 Infinite impulse response filter design
6.7 Finite impulse response filter design
Chapter 7 Finite-length effect in digital signal processing
7.1 Introduction
7.2 Analog to digital converter
7.3 Quantification of digital filter
7.4 Finite-length effect in digital filter calculation
7.5 Finite-length effect in discrete Fourier transform
Chapter 8 Error analysis and signal pre-treatment
8.1 Introduction
8.2 Concept and classification of error
8.3 uncertainty of measurement
8.4 Least square method in data analysis
8.5 Regression analysis
8.6 Trends and outliers
8.7 Case study on temperature measurement
Chapter 9 Random signal processing
9.1 Introduction
9.2 Concepts and characteristics of random signal
9.3 Stochastic process and stochastic signal
9.4 Frequently0used stochastic signal and noise
9.5 Stochastic signal for linear system
9.6 Classical analysis for stochastic signal
9.7 Parameter analysis for stochastic signal
Chapter 10 Correlation function estimation for stochastic signal and power spectral density estimation
10.1 Introduction
10.2 Correlation function and power spectral density
10.3 Self-correlation
10.4 Classical methods for spectral estimation
10.5 Methods for spectral estimation after 1960s
10.6 Cepstrum analysis
10.7 Application of spectral estimation in signal processing
Chapter 11 Statistical optimal filter for stochastic signals
11.1 Introduction
11.2 Theoretical background of Wiener filter
11.3 Wiener predictor
11.4 Kalman filter
Chapter 12 Adaptive filtering
12.1 Introduction
12.2 Adaptive transversal filter and stochastic gradient method
12.3 Least mean square algorithm for adaptive filter
12.4 Least square algorithm for adaptive filter
12.5 Application of adaptive filter
Chapter 13 Statistic analysis of higher-order and Fractional lower-order signals
13.1 Higher-order cumulant
13.2 Higher-order spectrum and higher-order estimation
13.3 ?-stable process and lower-order statistics
13.4 Application of lower-order signals
Chapter 14 Modern signal processing
14.1 Time-frequency analysis
14.2 Wavelet analysis
14.3 Hibert-Huang transformation