Table of Contents
Introduction
Typical Measurement Systems
Sources of Variability: Noise
Analog Filters: Filter Basics
Analog-to-Digital Conversion: Basic Concepts
Time Sampling: Basics
Data Banks
Problems
Basic Concepts
Noise
Data Functions and Transforms
Convolution, Correlation, and Covariance
Sampling Theory and Finite Data Considerations
Problems
Spectral Analysis: Classical Methods
Introduction
The Fourier Transform: Fourier Series Analysis
Aperiodic Functions
MATLAB Implementation: Direct FFT
Truncated Fourier Analysis: Data Windowing
MATLAB Implementation: Window Functions
Power Spectrum
MATLAB Implementation: The Welch Method for
Power Spectral Density Determination
Problems
Digital Filters
Introduction
The Z-Transform
Finite Impulse Response (FIR) Filters
Infinite Impulse Response (IIR) Filters
Problems
Spectral Analysis: Modern Techniques
Parametric Methods
Nonparametric Analysis: Eigenanalysis Frequency Estimation
Problems
Time–Frequency Analysis
Basic Approaches
Short-Term Fourier Transform: The Spectrogram
The Wigner-Ville Distribution: A Special Case of Cohen’s Class
The Choi-Williams and Other Distributions
MATLAB Implementation
Problems
Wavelet Analysis
Introduction
The Continuous Wavelet Transform
The Discrete Wavelet Transform
Feature Detection: Wavelet Packets
Problems
Advanced Signal Processing Techniques: Optimal and Adaptive Filters
Optimal Signal Processing: Wiener Filters
Adaptive Signal Processing
Phase-Sensitive Detection
Problems
Multivariate Analyses: Principal Component Analysis and Independent Component Analysis
Introduction: Linear Transformations
Principal Component Analysis
Independent Component Analysis
Problems
Fundamentals of Imaging Processing: MATLAB Image Processing Toolbox
Image Processing Basics: MATLAB Image Formats
Image Display
Image Storage and Retrieval
Basic Arithmetic Operations
Advanced Protocols: Block Processing
Problems
Spectral Analysis: The Fourier Transform
The Two-Dimensional Fourier Transform
Linear Filtering
Spatial Transformations
Image Registration
Problems
Image Segmentation
Introduction
Pixel-Based Methods
Continuity-Based Methods
Multithresholding
Morphological Operations
Edge-Based Segmentation
Problems
Image Reconstruction
Introduction
Magnetic Resonance Imaging
Functional MRI
Problems
Classification I: Linear Discriminant Analysis and Support Vector Machines
Introduction
Linear Discriminators
Evaluating Classifier Performance
Higher Dimensions: Kernel Machines
Support Vector Machines
Machine Capacity: Overfitting or "Less Is More"
Cluster Analysis
Problems
Adaptive Neural Nets
Introduction
McCullough-Pitts Neural Nets
The Gradient Descent Method or Delta Rule
Two-Layer Nets: Back-Projection
Three-Layer Nets
Training Strategies
Multiple Classifications
Multiple Input Variables
Problems
Annotated Bibliography
: AM
