Signal Processing for Intelligent Sensor Systems with MATLAB 2nd Edition by David C Swanson ISBN 1420043048 9781420043044

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ISBN 10: 1420043048 
ISBN 13: 9781420043044
Author: David C Swanson

Signal Processing for Intelligent Sensor Systems with MATLAB 2nd Table of contents:

Part I Fundamentals of Digital Signal Processing
Chapter 1 Sampled Data Systems
1.1 A/D Conversion
1.2 Sampling Theory
1.3 Complex Bandpass Sampling
1.4 Delta–Sigma Analog Conversion
1.5 MATLAB® Examples
1.6 Summary, Problems, and References
Problems
References
Chapter 2 z-Transform
2.1 Comparison of Laplace and z-Transforms
2.2 System Theory
2.3 Mapping of s-Plane Systems to the Digital Domain
2.4 MATLAB® Examples
2.5 Summary
Problems
References
Chapter 3 Digital Filtering
3.1 FIR Digital Filter Design
3.2 IIR Filter Design and Stability
3.3 Whitening Filters, Invertibility, and Minimum Phase
3.4 Filter Basis Polynomials
3.4.1 Butterworth Filters
3.4.2 Chebyshev Type I Filters
3.4.3 Chebyshev Type II Filters
3.4.4 Elliptical Filters
3.4.5 Bessel Filters
3.4.6 High-Pass, Band-Pass, and Band-Stop Filter Transformations
3.4.7 MA Digital Integration Filter
3.5 MATLAB® Examples
3.6 Summary
Problems
References
Chapter 4 Digital Audio Processing
4.1 Basic Room Acoustics
4.2 Artificial Reverberation and Echo Generators
4.3 Flanging and Chorus Effects
4.4 Bass, Treble, and Parametric Filters
4.5 Amplifier and Compression/Expansion Processors
4.6 Digital-to-Analog Reconstruction Filters
4.7 Audio File Compression Techniques
4.8 MATLAB® Examples
4.9 Summary
Problems
References
Chapter 5 Linear Filter Applications
5.1 State Variable Theory
5.1.1 Continuous State Variable Formulation
5.1.2 Discrete State Variable Formulation
5.2 Fixed-Gain Tracking Filters
5.3 2D FIR Filters
5.4 Image Upsampling Reconstruction Filters
5.5 MATLAB® Examples
5.6 Summary
Problems
References
Part II Frequency Domain Processing
Chapter 6 Fourier Transform
6.1 Mathematical Basis for the Fourier Transform
6.2 Spectral Resolution
6.3 Fast Fourier Transform
6.4 Data Windowing
6.5 Circular Convolution Issues
6.6 Uneven-Sampled Fourier Transforms
6.7 Wavelet and Chirplet Transforms
6.8 MATLAB® Examples
6.9 Summary
Problems
References
Chapter 7 Spectral Density
7.1 Spectral Density Derivation
7.2 Statistical Metrics of Spectral Bins
7.2.1 Probability Distributions and PDFs
7.2.2 Statistics of the NPSD Bin
7.2.3 SNR Enhancement and the Zoom FFT
7.2.4 Conversion of Random Variables
7.2.5 Confidence Intervals for Averaged NPSD Bins
7.2.6 Synchronous Time Averaging
7.2.7 Higher-Order Moments
7.2.8 Characteristic Function
7.2.9 Cumulants and Polyspectra
7.3 Transfer Functions and Spectral Coherence
7.4 Intensity Field Theory
7.4.1 Point Sources and Plane Waves
7.4.2 Acoustic Field Theory
7.4.3 Acoustic Intensity
7.4.4 Structural Intensity
7.4.5 Electromagnetic Intensity
7.5 Intensity Display and Measurement Techniques
7.5.1 Graphical Display of the Acoustic Dipole
7.5.2 Calculation of Acoustic Intensity from Normalized Spectral Density
7.5.3 Calculation of Structural Intensity for Compressional and Bending Waves
7.5.4 Calculation of the Poynting Vector
7.6 MATLAB® Examples
7.7 Summary
Problems
References
Chapter 8 Wavenumber Transforms
8.1 Spatial Transforms
8.2 Spatial Filtering and Beamforming
8.3 Image Enhancement Techniques
8.4 JPEG and MPEG Compression Techniques
8.5 Computer-Aided Tomography
8.6 Magnetic Resonance Imaging
8.7 MATLAB® Examples
8.8 Summary
Problems
References
Part III Adaptive System Identification and Filtering
Chapter 9 Linear Least-Squared Error Modeling
9.1 Block Least Squares
9.2 Projection-Based Least Squares
9.3 General Basis System Identification
9.3.1 Mechanics of the Human Ear
9.3.2 Least-Squares Curve Fitting
9.3.3 Pole–Zero Filter Models
9.4 MATLAB® Examples
9.5 Summary
Problems
References
Chapter 10 Recursive Least-Squares Techniques
10.1 RLS Algorithm and Matrix Inversion Lemma
10.1.1 Matrix Inversion Lemma
10.1.2 Approximations to RLS
10.2 LMS Convergence Properties
10.2.1 System Modeling Using Adaptive System Identification
10.2.2 Signal Modeling Using Adaptive Signal-Whitening Filters
10.3 Lattice and Schur Techniques
10.4 Adaptive Least-Squares Lattice Algorithm
10.4.1 Wiener Lattice
10.4.2 Double/Direct Weiner Lattice
10.5 MATLAB® Examples
10.6 Summary
Problems
References
Chapter 11 Recursive Adaptive Filtering
11.1 Adaptive Kalman Filtering
11.2 IIR Forms for LMS and Lattice Filters
11.3 Frequency Domain Adaptive Filters
11.4 MATLAB® Examples
11.5 Summary
Problems
References
Part IV Wavenumber Sensor Systems
Chapter 12 Signal Detection Techniques
12.1 Rician PDF
12.1.1 Time-Synchronous Averaging
12.1.2 Envelope Detection of a Signal in Gaussian Noise
12.2 RMS, CFAR Detection, and ROC Curves
12.3 Statistical Modeling of Multipath
12.3.1 Multisource Multipath
12.3.2 Coherent Multipath
12.3.3 Statistical Representation of Multipath
12.3.4 Random Variations in Refractive Index
12.4 MATLAB® Examples
12.5 Summary
Problems
References
Chapter 13 Wavenumber and Bearing Estimation
13.1 Cramer–Rao Lower Bound
13.2 Bearing Estimation and Beam Steering
13.2.1 Bearings from Phase Array Differences
13.2.2 Multiple Angles of Arrival
13.2.3 Wavenumber Filters
13.3 Field Reconstruction Techniques
13.4 Wave Propagation Modeling
13.5 MATLAB® Examples
13.6 Summary
Problems
References
Chapter 14 Adaptive Beamforming and Localization
14.1 Array “Null-Forming”
14.2 Eigenvector Methods of MUSIC and MVDR
14.3 Coherent Multipath Resolution Techniques
14.3.1 Maximal Length Sequences
14.4 FMCW and Synthetic Aperture Processing
14.5 MATLAB® Examples
14.6 Summary
Problems
References
Part V Signal Processing Applications
Chapter 15 Noise Reduction Techniques
15.1 Electronic Noise
15.2 Noise Cancellation Techniques
15.3 Active Noise Attenuation
15.4 MATLAB® Examples
15.5 Summary
Problems
References
Chapter 16 Sensors and Transducers
16.1 Simple Transducer Signals
16.2 Acoustic and Vibration Sensors
16.2.1 Electromagnetic Mechanical Transducer
16.2.2 Electrostatic Transducer
16.2.3 Condenser Microphone
16.2.4 Micro-Electromechanical Systems
16.2.5 Charge Amplifier
16.2.6 Reciprocity Calibration Technique
16.3 Chemical and Biological Sensors
16.3.1 Detection of Small Chemical Molecules
16.3.2 Optical Absorption Chemical Spectroscopy
16.3.3 Raman Spectroscopy
16.3.4 Ion Mobility Spectroscopy
16.3.5 Detecting Large Biological Molecules
16.4 Nuclear Radiation Sensors
16.5 MATLAB® Examples
16.6 Summary
Problems
References
Chapter 17 Intelligent Sensor Systems
17.1 Automatic Target Recognition Algorithms
17.1.1 Statistical Pattern Recognition
17.1.2 Adaptive Neural Networks
17.1.3 Syntactic Pattern Recognition
17.2 Signal and Image Features
17.2.1 Basic Signal Metrics
17.2.2 Pulse-Train Signal Models
17.2.3 Spectral Features
17.2.4 Monitoring Signal Distortion
17.2.5 Amplitude Modulation
17.2.6 Frequency Modulation
17.2.7 Demodulation via Inverse Hilbert Transform
17.3 Dynamic Feature Tracking and Prediction
17.4 Intelligent Sensor Agents
17.4.1 Internet Basics
17.4.2 IP Masquerading/Port Forwarding
17.4.3 Security versus Convenience
17.4.4 Role of the DNS Server
17.4.5 Intelligent Sensors on the Internet
17.4.6 XML Documents and Schemas for Sensors
17.4.7 Architectures for Net-Centric Intelligent Sensors
17.5 MATLAB® Examples
17.6 Summary

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