Digital Signal Processing

Reinforced with appropriate software, this introduction to modern methods in the developing field of Digital Signal Processing (DSP) delivers a course text for primarily post-graduates reading areas in electrical engineering, control engineering, communication systems engineering, engineering mathematics and computer science. Its emphasis on current programming practices is an attractive feature to engineers and industrial researchers for whom DSP has important applications. The focus of the book is on the design of digital algorithms and the processing of digital signals in different areas of communications and control and provides the reader with a comprehensive introduction to the underlying principles and mathematical models used to analyse and process different types of digital signals. Readers will also gain an appreciation of the software engineering methodologies used in the design and construction of DSP packages. The book gives a number of case studies that have been designed to enhance the material and provides some interesting digressions which are based on some of the authors research interests. These case studies range from theoretical studies in modelling the propagation and scattering of waves from inhomogeneous materials through to novel methods of cryptography and digital watermarking for IT security and multi-fractal financial analysis for example.

Contents

Part I Mathematical Methods for Signal Analysis.
Introduction to complex analysis; the delta function and the Green's function; Fourier series; the Fourier transform, convolution and correlation, the sampling theorem; the Laplace transform, the Hilbert transform and quadrature detection, modulation and demodulation, the Gabor transform, the z-transform, the Wigner transform, the wavelet transform.

Part II Computational Techniques in Linear Algebra.
Basic linear algebra, types of linear systems; formal methods of solution; direct methods of solution; vector and matrix norms, conditioning and the condition number, iterative improvement, the least squares method; iterative methods of solution, the conjugate gradient method; the computation of eigenvalues and eigenvectors.

Part III Programming and Software Engineering.
Number systems and numerical error, programming languages, software design methods, structured and modular programming; software engineering for DSP in C.

Part IV Digital Signal Processing: Methods, Algorithms and Building a Library.
Digital frequency filtering, the DFT and FFT, computing with the FFT, spectral leakage and windowing; inverse filters, the Wiener filter, the matched filter, constrained deconvolution, homomorphic filtering, noise and chaos; Bayesain estimation methods, the maximum entropy method, spectral extrapolation; FIR and IIR filters, non-stationary signal processing; random fractal and multi-random-fractal signals.

Readership.
Post-graduate students in applied mathematics, physics, electrical and control engineering and applied computing. Research scientists in areas that include telecommunications, medical instrumentation, control engineering, defense and IT security for example.

Contents

• Introduction
• Mathematical Methods for Signal Analysis
  • Complex Analysis
  • The Delta Function
  • The Fourier Series
  • The Fourier Transform
  • Other Integral Transforms
• Computational Techniques in Linear Algebra
  • Matrices and Matrix Algebra
  • Direct Methods of Solution
  • Vector and Matrix Norms
  • Iterative Methods of Solution
  • Eigenvalues and Eigenvectors
• Programming and Software Engineering
  • Principles of Software Engineering
  • Modular Programming in C
• DSP: Methods, Algorithms and Building a Library
  • Digital Filters and the FFT
  • Frequency Domain Filtering with Noise
  • Statistics, Entropy and Extrapolation
  • Digital filtering in the Time Domain
  • Random Fractal Signals
  • Summary
  • Appendixes