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Optical Signal Processing is a collection of synopses of the works of many experts in the different fields of optical signal processing. The book also includes systems or algorithms that have been successfully tried and used. The monograph is divided into seven parts. Part I discusses color image processing and white-light Fourier transformations, while Part II covers topics related to pattern recognition such as optical feature extraction and unconventional correlators. Part III deals with temporal signal processing and its related optical architectures, acoustooptic synthetic aperture radar processors, and acoustooptic signal processors. Part IV tackles nonlinear optical processors and waveguide devices. Part V discusses optical and tomographic transformation. Part VI deals with optical numeric processing, optical linear algebra processors, and related algorithm and software. Part VII talks about devices and components and their applications such as fiber-optic delay-line signal processors and spatial light modulators. The text is recommended for engineers and scientists in the field of optical signal processing, especially those who would like to know more of its advancements.
Language
Place of publication
Publishing group
Elsevier Science & Techn.
ISBN-13
978-0-323-14522-0 (9780323145220)
Schweitzer Classification
List of ContributorsPrefaceI. White-Light Processors 1.1 Color Image Processing I. Introduction II. White-Light Optical Processing III. Source Encoding and Image Sampling IV. Color Image Processing V. Concluding Remarks References 1.2 White-Light Fourier Transformations I. Introduction II. Linear System Formalism III. Achromatic Fourier Transform Systems IV. Design of an Achromatic Fourier Processor V. Summary ReferencesII. Pattern Recognition 2.1 Optical Feature Extraction I. Introduction II. Optical Feature Extraction III. Fourier-Transform Feature Space and Unique Sampling IV. Hough-Transform Feature Space V. Space-Variant Feature Space and Optical Feature Extraction VI. Chord-Distribution Feature Space and Parameter Estimation VII. Moment Feature Space and Unique Properties VIII. Summary and Conclusion References 2.2 Unconventional Correlators I. Introduction II. Definitions and Theorems III. Optical Correlation Systems IV. Summary and Outlook References 2.3 Optical Implementation of Associative Memory Based on Models of Neural Networks I. Introduction II. Linear Systems as Association Memories III. Optoelectronic Implementation of One-Dimensional Neural Nets IV. Optoelectronic Implementation of Two-Dimensional Neural Nets V. Holographic Associative Memories VI. Conclusion ReferencesIII. Temporal Signal Processing 3.1 Optical Architectures for Temporal Signal Processing I. Introduction II. Basic Operations III. Spectrum Analysis of Continuous Functions IV. Time-Frequency Analysis V. Discrete Operations VI. Summary References 3.2 Acoustooptic Synthetic Aperture Radar Processors I. Introduction II. Synthetic Aperture Radar III. Acoustooptic Processor IV. Programmable Architecture V. Conclusions References 3.3 Acoustooptic Signal Processors I. Introduction II. Acoustooptic Processor Components III. Power Spectrum Analyzers IV. Interferometric Spectrum Analyzer V. Correlators VI. Summary ReferencesIV. Nonlinear Optical Processors 4.1 Nonlinear Optical Waveguide Devices I. Introduction II. Scalar Wave Equation for Mode Analysis III. Perturbational Formula for Change of Dielectric Tensor IV. Coherent Coupling between Modes in a Nonlinear Optical Waveguide V. All-Optical Interferometer VI. Nonlinear Waveguide Coupler VII. Nonlinear Waveguide Materials, Devices, and Design Considerations ReferencesV. Transformations 5.1 Optical Transformations I. Introduction II. Geometric Transformations III. Transdimensional Transformations IV. Linear Shift-Variant Transformations V. Nonlinear Transformations VI. Conclusions References 5.2 Tomographic Transformations in Optical Signal Processing I. Introduction and Definitions II. Applications III. Summary and Conclusions ReferencesVI. Optical Numerical Processing 6.1 Optical Linear Algebra Processors II. Generic OLAP Architecture and Its Component Evaluation III. Basic OLAP Architectures IV. Iterative Algorithm for Solving Linear Algebraic Equations V. Optical Matrix Decomposition Techniques VI. Error Sources and Modeling VII. Number Representation VIII. Two System Case Studies IX. Applications X. Summary and Discussion References 6.2 Algorithms and Software I. Perspectives II.