yu, f. t. s. (2001). introduction to information optics

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Contents
Preface
Chapter 1 Entropy Information and Optics
- 1.1. Information Transmission
- 1.2. Entropy Information
- 1.3. Communication Channel
  - 1.3.1. Memoryless Discrete Channel
  - 1.3.2. Continuous Channel
- 1.4. Band-limited Analysis
  - 1.4.1. Degrees of Freedom
  - 1.4.2. Gabor's Information Cell
- 1.5. Signal Analysis
  - 1.5.1. Signal Detection
  - 1.5.2. Statistical Signal Detection
  - 1.5.3. Signal Recovering
  - 1.5.4. Signal Ambiguity
  - 1.5.5. Wigner Distribution
- 1.6. Trading Information with Entropy
  - 1.6.1. Demon Exorcist
  - 1.6.2. Minimum Cost of Entropy
- 1.7. Accuracy and Reliability Observation
  - 1.7.1. Uncertainty Observation
- 1.8. Quantum Mechanical Channel
  - 1.8.1. Capacity of a Photon Channel
Chapter 2 Signal Processing with Optics
- 2.1. Coherence Theory of Light
- 2.2. Processing under Coherent and Incoherent Illumination
- 2.3. Fresnel-Kirchhoff and Fourier Transformation
  - 2.3.1. Free Space Impulse Response
  - 2.3.2. Fourier Transformation by Lenses
- 2.4. Fourier Transform Processing
  - 2.4.1. Fourier Domain Filter
  - 2.4.2. Spatial Domain Filter
  - 2.4.3. Processing with Fourier Domain Filters
  - 2.4.4. Processing with Joint Transformation
  - 2.4.5. Hybrid Optical Processing
- 2.5. Image Processing with Optics
  - 2.5.1. Correlation Detection
  - 2.5.2. Image Restoration
  - 2.5.3. Image Subtraction
  - 2.5.4. Broadband Signal Processing
- 2.6. Algorithms for Processing
  - 2.6.1. Mellin-Transform Processing
  - 2.6.2. Circular Harmonic Processing
  - 2.6.3. Homomorphic Processing
  - 2.6.4. Synthetic Discriminant Algorithm
  - 2.6.5. Simulated Annealing Algorithm
- 2.7. Processing with Photorefractive Optics
  - 2.7.1. Photorefractive Effect and Materials
  - 2.7.2. Wave Mixing and Multiplexing
  - 2.7.3. Bragg Diffraction Limitation
  - 2.7.4. Angular and Wavelength Selectivities
  - 2.7.5. Shift-Invariant Limited Correlators
- 2.8. Processing with Incoherent Light
  - 2.8.1. Exploitation of Coherence
  - 2.8.2. Signal Processing with White Light
  - 2.8.3. Color Image Preservation and Pseudocoloring
- 2.9. Processing with Neural Networks
  - 2.9.1. Optical Neural Networks
  - 2.9.2. Holpfield Model
  - 2.9.3. Inpattern Association Model
Chapter 3 Communication with Optics
- 3.1. Motivation of Fiber-Optic Communication
- 3.2. Light Propagation in Optical Fibers
  - 3.2.1. Geometric Optics Approach
  - 3.2.2. Wave-Optics Approach
  - 3.2.3. Other Issues Related to Light Propagating in Optical Fiber
- 3.3. Critical Components
  - 3.3.1. Optical Transmitters for Fiber-Optic Communications—Semiconductor Lasers
  - 3.3.2. Optical Receivers for Fiber-Optic Communications
  - 3.3.3. Other Components Used in Fiber-Optic Communications
- 3.4. Fiber-Optic Networks
  - 3.4.1. Types of Fiber-Optic Networks Classified by Physical Size
  - 3.4.2. Physical Topologies and Routing Topologies Relevant to Fiber-Optic Networks
  - 3.4.3. Wavelength Division Multiplexed Optics Networks
  - 3.4.4. Testing Fiber-Optic Networks
Chapter 4 Switching with Optics
- 4.1. Figures of Merits for an Optical Switch
- 4.2. All-Optical Switches
  - 4.2.1. Optical Nonlinearity
  - 4.2.2. Etalon Switching Devices
  - 4.2.3. Nonlinear Directional Coupler
  - 4.2.4. Nonlinear Interferometric Switches
- 4.3. Fast Electro-optic Switches: Modulators
  - 4.3.1. Direct Modulation of Semiconductor Lasers
  - 4.3.2. External Electro-optic Modulators
- 4.4. Optical Switching Based on MEMS
  - 4.4.1. MEMS Fabrications
  - 4.4.2. Electrostatic Actuators
  - 4.4.3. MEMS Optical Switches
- 4.5. Summary
Chapter 5 Transformation with Optics
- 5.1. Huygens–Fresnel Diffraction
- 5.2. Fresnel Transform
  - 5.2.1. Definition
  - 5.2.2. Optical Fresnel Transform
- 5.3. Fourier Transform
- 5.4. Wavelet Transform
  - 5.4.1 Wavelets
  - 5.4.2. Time–frequency Joint Representation
  - 5.4.3. Properties of Wavelets
- 5.5. Physical Wavelet Transform
  - 5.5.1. Electromagnetic Wavelet
  - 5.5.2. Electromagnetic Wavelet Transform
  - 5.5.3. Electromagnetic Wavelet Transform and Huygens Diffraction
- 5.6. Wigner Distribution Function
  - 5.6.1. Definition
  - 5.6.2. Inverse Transform
  - 5.6.3. Geometrical Optics Interpretation
  - 5.6.4. Wigner Distribution Optics
- 5.7. Fractional Fourier Transform
  - 5.7.1. Definition
  - 5.7.2. Fractional Fourier Transform and Fresnel Diffraction
- 5.8. Hankel Transform
  - 5.8.1. Fourier Transform in Polar Coordinate System
  - 5.8.2. Hankel Transform
- 5.9. Radon Transform
  - 5.9.1. Definition
  - 5.9.2. Image Reconstruction
- 5.10. Geometric Transform
  - 5.10.1. Basic Geometric Transformations
  - 5.10.2. Generalized Geometric Transformation
  - 5.10.3. Optical Implementation
- 5.11. Hough Transform
  - 5.11.1. Definition
  - 5.11.2. Optical Hough Transform
Chapter 6 Interconnection with Optics
- 6.1. Introduction
- 6.2. Polymer Waveguides
  - 6.2.1. Polymeric Materials for Waveguide Fabrication
  - 6.2.2. Fabrication of Low-Loss Polymeric Waveguides
  - 6.2.3. Waveguide Loss Measurement
- 6.3. Thin-Film Waveguide Couplers
  - 6.3.1. Surface-Normal Grating Coupler Design and Fabrication
  - 6.3.2. 45° Surface-Normal Micromirror Couplers
- 6.4. Integration of Thin-Film Photodetectors
- 6.5. Integration of Vertical Cavity Surface-Emitting Lasers (VCSELs)
- 6.6. Optical Clock Signal Distribution
- 6.7. Polymer Waveguide-Based Optical Bus Structure
  - 6.7.1. Optical Equivalent for Electronic Bus Logic Design
- 6.8. Summary
Chapter 7 Pattern Recognition with Optics
- 7.1. Basic Architectures
  - 7.1.1. Correlators
  - 7.1.2. Neural Networks
  - 7.1.3. Hybrid Optical Architectures
  - 7.1.4. Robustness of JTC
- 7.2. Recognition by Correlation Detections
  - 7.2.1. Nonconventional Joint-Transform Detection
  - 7.2.2. Nonzero-order Joint-Transform Detection
  - 7.2.3. Position-Encoding Joint-Transform Detection
  - 7.2.4. Phase-Representation Joint-Transform Detection
  - 7.2.5. Iterative Joint-Transform Detection
- 7.3. Polychromatic Pattern Recognition
  - 7.3.1. Detection with Temporal Fourier-Domain Filters
  - 7.3.2. Detection with Spatial-Domain Filters
- 7.4. Target Tracking
  - 7.4.1. Autonomous Tracking
  - 7.4.2. Data Association Tracking
- 7.5. Pattern Recognition Using Composite Filtering
  - 7.5.1. Performance Capacity
  - 7.5.2. Quantization Performance
- 7.6. Pattern Classification
  - 7.6.1. Nearest Neighbor Classifiers
  - 7.6.2. Optical Implementation
- 7.7. Pattern Recognition with Photorefractive Optics
  - 7.7.1. Detection by Phase Conjugation
  - 7.7.2. Wavelength-Multiplexed Matched Filtering
  - 7.7.3. Wavelet Matched Filtering
- 7.8. Neural Pattern Recognition
  - 7.8.1. Recognition by Supervised Learning
  - 7.8.2. Recognition by Unsupervised Learning
  - 7.8.3. Polychromatic Neural Networks
Chapter 8 Information Storage with Optics
- 8.1. Digital Information Storage
- 8.2. Upper Limit of Optical Storage Density
- 8.3 Optical Storage Media
  - 8.3.1. Photographic Film
  - 8.3.2. Dichromated Gelatin
  - 8.3.3. Photopolymers
  - 8.3.4. Photoresists
  - 8.3.5. Thermoplastic Film
  - 8.3.6. Photorefractive Materials
  - 8.3.7. Photochromic Materials
  - 8.3.8. Electron-Trapping Materials
  - 8.3.9. Two Photon–Absorption Materials
  - 8.3.10. Bacteriorhodospin
  - 8.3.11. Photochemical Hole Burning
  - 8.3.12. Magneto-optic Materials
  - 8.3.13. Phase-Change Materials
- 8.4. Bit-Pattern Optical Storage
  - 8.4.1. Optical Tape
  - 8.4.2. Optical Disk
  - 8.4.3. Multilayer Optical Disk
  - 8.4.4. Photon-Gating 3-D Optical Storage
  - 8.4.5. Stacked-Layer 3-D Optical Storage
  - 8.4.6. Photochemical Hole-Burning 3-D Storage
- 8.5. Holographic Optical Storage
  - 8.5.1. Principle of Holography
  - 8.5.2. Plane Holographic Storage
  - 8.5.3. Stacked Holograms for 3-D Optical Storage
  - 8.5.4. Volume Holographic 3-D Optical Storage
- 8.6. Near Field Optical Storage
- 8.7. Concluding Remarks
Chapter 9 Computing with Optics
- 9.1. Introduction
- 9.2. Parallel Optical Logic and Architectures
  - 9.2.1. Optical Logic
  - 9.2.2. Space-Variant Optical Logic
  - 9.2.3. Programmable Logic Array
  - 9.2.4. Parallel Array Logic
  - 9.2.5. Symbolic Substitution
  - 9.2.6. Content-Addressable Memory
- 9.3. Number Systems and Basic Operations
  - 9.3.1. Operations with Binary Number Systems
  - 9.3.2. Operations with Nonbinary Number Systems
- 9.4. Parallel Signed-Digit Arithmetic
  - 9.4.1. Generalized Signed-Digit Number Systems
  - 9.4.2. MSD Arithmetic
  - 9.4.3. TSD Arithmetic
  - 9.4.4. QSD Arithmetic
- 9.5. Conversion between Different Number Systems
  - 9.5.1. Conversion between Signed-Digit and Complement Number Systems
  - 9.5.2. Conversion between NSD and Negabinary Number Systems
- 9.6. Optical Implementation
  - 9.6.1. Symbolic Substitution Implemented by Matrix–Vector Operation
  - 9.6.2. SCAM-Based Incoherent Correlator for QSD Addition
  - 9.6.3. Optical Logic Array Processor for Parallel NSD Arithmetic
- 9.7. Summary
Chapter 10 Sensing with Optics
- 10.1. Introduction
- 10.2. A Brief Review of Types of Fiber-Optic Sensors
  - 10.2.1. Intensity-Based Fiber-Optic Sensors
  - 10.2.2. Polarization-Based Fiber-Optic Sensors
  - 10.2.3. Phase-Based Fiber Optic Sensors
  - 10.2.4. Frequency (or Wavelength)–Based Fiber-Optic Sensors
- 10.3. Distributed Fiber-Optic Sensors
  - 10.3.1. Intrinsic Distributed Fiber-optic Sensors
  - 10.3.2. Quasi-distributed Fiber-optic Sensors
- 10.4. Summary
Chapter 11 Information Display with Optics
- 11.1. Introduction
- 11.2. Information Display Using Acousto-optic Spatial Light Modulators
  - 11.2.1. The Acousto-optic Effect
  - 11.2.2. Intensity Modulation of Laser
  - 11.2.3. Deflection of Laser
  - 11.2.4. Laser TV Display Using Acousto-optic Devices
- 11.3. 3-D Holographic Display
  - 11.3.1. Principles of Holography
  - 11.3.2. Optical Scanning Holography
  - 11.3.3. Synthetic Aperture Holography
- 11.4. Information Display Using Electro-optic Spatial Light Modulators
  - 11.4.1. The Electro-optic Effect
  - 11.4.2. Electrically Addressed Spatial Light Modulators
  - 11.4.3. Optically Addressed Spatial Light Modulators
- 11.5. Concluding Remarks
Chapter 12 Networking with Optics
- 12.1. Background
- 12.2. Optical Network Elements
  - 12.2.1. Optical Fibers
  - 12.2.2. Optical Amplifiers
  - 12.2.3. Wavelength Division Multiplexer/Demultiplexer
  - 12.2.4. Transponder
  - 12.2.5. Optical Add/Drop Multiplexer
  - 12.2.6. Optical Cross-Connect
  - 12.2.7. Optical Monitoring
- 12.3. Design of Optical Transport Network
  - 12.3.1. Optical Fiber Dispersion Limit
  - 12.3.2. Optical Fiber Nonlinearity Limit
  - 12.3.3. System Design Examples
- 12.4. Applications and Future Development of Optical Networks
  - 12.4.1. Long-haul Backbone Networks
  - 12.4.2. Metropolitan and Access Networks
  - 12.4.3. Future Development
Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
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Nội dung

[...]... 3-D Optical Storage 8.4.6 Photochemical Hole-Burning 3-D Storage 8.5 Holographic Optical Storage 8.5.1 Principle of Holography 8.5.2 Plane Holographic Storage 8.5.3 Stacked Holograms for 3-D Optical Storage 8.5.4 Volume Holographic 3-D Optical Storage 8.6 Near Field Optical Storage 8.7 Concluding Remarks References Exercises Chapter 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 Computing with Optics Introduction Parallel... application of which can range from very abstract artistic interpretations to very efficient scientific usages This chapter discusses the relationship between entropy information and optics Our intention is not to provide a detailed discussion, however, but to cover the basic fundamentals that are easily applied to optics We note that entropy information was not originated by optical scientists, but rather by... entropy information, interest in its application has never totally been absent from the optical standpoint As a result of the recent development of optical communication, signal processing, and computing, among other discoveries, the relationship between optics and entropy information has grown more profound than ever 2 1 Entropy Information and Optics 1.1 INFORMATION TRANSMISSION Although we seem to know... (1.28) where H(A/B) represents the amount of information loss (e.g., due to noise) or the equivocation of the channel, which is the average amount of information needed to specify the noise disturbance in the channel And H(B/A) is referred to as the noise entropy of the channel To conclude this section, we note that the entropy information can be easily extended to continuous product space, such as p(a)log2p(a)da,... Sensors 10.4 Summary References Exercises 589 589 600 612 613 615 Chapter 11 617 Information Display with Optics 11.1 I ntrod action 11.2 Information Display Using Acousto-optic Spatial Light Modulators 11.2.1 The Acousto-optic Effect 11.2.2 Intensity Modulation of Laser 11.2.3 Deflection of Laser 11.2.4 Laser TV Display Using Acousto-optic Devices 11.3 3-D Holographic Display 11.3.1 Principles of Holography... not only the source of energy necessary to live — plants grow up by drawing energy from sunlight; light is also the source of energy for information - our vision is based on light detected by our eyes (but we do not grow up by drawing energy from light to our body through our eyes) Furthermore, applications of optics to information technology are not limited to vision and can be found almost everywhere... interests in optical applications to information technology In view of the great number of contributions in this area, we have not been able to include all of them in this book Chapter 1 Entropy Information and Optics Francis T.S Yu THE PENNSYLVANIA STATE UNIVERSITY Light is not only the mainstream of energy that supports life; it also provides us with an important source of information One can easily imagine... therefore able to advance themselves above the rest of the animals on this planet Earth It is undoubtedly true that if humans did not have eyes, they would not have evolved into their present form In the presence of light, humans are able to search for the food they need and the art they enjoy, and to explore the unknown Thus light, or rather optics, provide us with a very valuable source of information, ... TRANSMISSION Although we seem to know the meaning of the word information, fundamentally that may not be the case In reality, information may be defined as related to usage From the viewpoint of mathematic formalism, entropy information is basically a probabilistic concept In other words, without probability theory there would be no entropy information An information transmission system can be represented by... temporal coded language is sent to the user Viewing a television show, for example, represents a one-way spatial-temporal transmission It is interesting to note that temporal and spatial information can be traded for information transmission For instance, television signal transmission is a typical example of exploiting the temporal information transmission for spatial information transmission On the . class="bi x0 y0 w0 h0" alt="" Introduction to Information Optics This page intentionally left blank Introduction to Information Optics Edited by FRANCIS T. S. YU The Pennsylvania State. artistic interpretations to very efficient scientific usages. This chapter discusses the relationship between entropy information and optics. Our intention is not to provide a detailed. represents a one-way spatial-temporal transmission. It is interesting to note that temporal and spatial information can be traded for information transmission. For instance, television

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