Optical Signal Processing

Optical Signal Processing: Fundamentals presents the background material necessary for an understanding of modern optical methods of signal processing. Intended for graduate students in electrical engineering, physics, and optical engineering, this text covers fundamentals of optics; the electro-optic, acousto-optic, and magneto-optic effects; stochastic processes; and filters.

1. Introduction.- 1.1 Why Optical Signal Processing?.- 1.2 Signal Processing: Tools and Applications.- 1.3 Arrangement of the Book.- 2. Optics Fundamentals.- 2.1 Maxwell's Equations.- 2.2 Boundary Conditions.- 2.3 Snell's Laws.- 2.4 Total Internal Reflection and Optical Tunneling.- 2.5 Transmission Lines.- 2.6 Reflection and Transmission Coefficients for Electromagnetic Waves.- 2.7 Group and Phase Velocity.- 2.8 Gaussian Beam Propagation.- 2.9 Geometrical Optics.- 2.10 Gradient Optical Fiber.- 2.11 Integrated Optics and Step-Index Optical Fibers.- 2.12 Propagation in Anisotropic Media.- 2.13 Electro-optic Effect.- 2.14 The Acousto-optic or Elasto-optic Effect.- 2.15 Magneto-optics.- 2.16 Wave Equation with Source and Boundary.- 2.17 Fourier Optics.- 3. Signal Processing Fundamentals.- 3.1 Analog Signals and Systems.- 3.2 Discrete Systems.- 3.3 Noise and Stochastic Processes.- 3.4 Filters.- 3.5 Adaptive Filters.- 3.6 Power Spectra Estimation.- 3.7 Kalman Filtering.- 3.8 Two-Dimensional Signal Processing.- 3.9 Stochastic Processes: Multidimensional.- 3.10 The Ambiguity Function, Wigner Distribution Function and Triple Correlation.- 4. Introduction to SAW and CCD Technology.- 4.1 History of CCD and SAW Devices.- 4.2 Why SAWs Became Popular and Useful in the 1960s.- 4.3 Charge Coupled Devices.- 4.4 Magneto-Static Waves.- 4.5 ACT Devices.- 4.6 Comparison of Technologies.- Appendices.- A. Matrices.- A.1 The Hamilton-Cayley Theorem.- A.2 Some Definitions.- A.3 Matrix Inversion.- A.4 Gaussian Elimination Method.- A.5 Successive Orthogonalization of a Matrix.- A.6 Circulant Matrices and Fourier Matrices.- A.7 Pseudo-Inverse, Singular-Value Decomposition, Overdetermination and Principle of Least Squares: Kalman Filtering.- A.8 Coordinate Transformation.- B. OrthogonalFunctions and Polynomials.- B.1 Sturm-Liouville Equation.- B.2 Fourier Series.- B.3 Hypergeometric Series.- B.4 Legendre Polynomials.- B.5 Hermite Polynomials.- B.6 Laguerre Polynomials.- B.7 Generalized Laguerre Polynomials.- B.8 Chebyshev Polynomials.- B.9 Bessel Functions.- C. Principle of Stationary Phase.- D. Vectors.- D.1 Important Results.- D.2 Green's Theorem: Scalar.- D.3 Green's Theorem: Vector.- E. Symmetry Properties of Different Coefficients in Crystal Classes.- References.