Signal Processing for Intelligent Sensor Systems with MATLAB, 2nd Edition

Book description

Signal Processing for Intelligent Sensors with MATLAB, Second Edition once again presents the key topics and salient information required for sensor design and application. Organized to make it accessible to engineers in school as well as those practicing in the field, this reference explores a broad array of subjects and is divided into sections:

Table of contents

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

Product information

  • Title: Signal Processing for Intelligent Sensor Systems with MATLAB, 2nd Edition
  • Author(s): David C. Swanson
  • Release date: July 2011
  • Publisher(s): CRC Press
  • ISBN: 9781439896280