by Kin-wai Mak. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 52-54). / Chapter 1 --- Introduction to Parallel Computing --- p.7 / Chapter 1.1 --- Parallel Computer Models --- p.8 / Chapter 1.2 --- Forms of Parallelism --- p.12 / Chapter 1.3 --- Performance Evaluation --- p.15 / Chapter 1.3.1 --- Finding Machine Parameters --- p.15 / Chapter 1.3.2 --- Amdahl's Law --- p.19 / Chapter 1.3.3 --- Gustafson's Law --- p.20 / Chapter 1.3.4 --- Scalability Analysis --- p.20 / Chapter 2 --- Introduction to Image Processing --- p.26 / Chapter 2.1 --- Image Restoration Problem --- p.26 / Chapter 2.1.1 --- Toeplitz Least Squares Problems --- p.29 / Chapter 2.1.2 --- The Need For Regularization --- p.31 / Chapter 2.1.3 --- Guide Star Image --- p.32 / Chapter 3 --- Toeplitz Solvers --- p.34 / Chapter 3.1 --- Introduction --- p.34 / Chapter 3.2 --- Parallel Implementation --- p.38 / Chapter 3.2.1 --- Overview of MasPar --- p.38 / Chapter 3.2.2 --- Design Methodology --- p.39 / Chapter 3.2.3 --- Implementation Details --- p.42 / Chapter 3.2.4 --- Application to Ground Based Astronomy --- p.44 / Chapter 3.2.5 --- Performance Analysis --- p.46 / Chapter 3.2.6 --- The Graphical Interface --- p.48 / Bibliography

by Chen Zhi-Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references. / List of Symbols S --- p.1 / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Active Vision Paradigm and Calibration of Active Vision System --- p.1.1 / Chapter 1.1.1 --- Active Vision Paradigm --- p.1.1 / Chapter 1.1.2 --- A Review of the Existing Active Vision Systems --- p.1.1 / Chapter 1.1.3 --- A Brief Introduction to Our Active Vision System --- p.1.2 / Chapter 1.1.4 --- The Stages of Calibrating an Active Vision System --- p.1.3 / Chapter 1.2 --- Projective Invariants and Their Applications to Feature Tracking --- p.1.4 / Chapter 1.3 --- Thesis Overview --- p.1.4 / References --- p.1.5 / Chapter Chapter 2 --- Calibration for an Active Vision System: Camera Calibration / Chapter 2.1 --- An Overview of Camera Calibration --- p.2.1 / Chapter 2.2 --- Tsai's RAC Based Camera Calibration Method --- p.2.5 / Chapter 2.2.1 --- The Pinhole Camera Model with Radial Distortion --- p.2.7 / Chapter 2.2.2 --- Calibrating a Camera Using Mono view Noncoplanar Points --- p.2.10 / Chapter 2.3 --- Reg Willson's Implementation of R. Y. Tsai's RAC Based Camera Calibration Algorithm --- p.2.15 / Chapter 2.4 --- Experimental Setup and Procedures --- p.2.20 / Chapter 2.5 --- Experimental Results --- p.2.23 / Chapter 2.6 --- Conclusion --- p.2.28 / References --- p.2.29 / Chapter Chapter 3 --- Calibration for an Active Vision System: Head-Eye Calibration / Chapter 3.1 --- Why Head-Eye Calibration --- p.3.1 / Chapter 3.2 --- Review of the Existing Head-Eye Calibration Algorithms --- p.3.1 / Chapter 3.2.1 --- Category I Classic Approaches --- p.3.1 / Chapter 3.2.2 --- Category II Self-Calibration Techniques --- p.3.2 / Chapter 3.3 --- R.Tsai's Approach for Hand-Eye (Head-Eye) Calibration --- p.3.3 / Chapter 3.3.1 --- Introduction --- p.3.3 / Chapter 3.3.2 --- Definitions of Coordinate Frames and Homogeoeous Transformation Matrices --- p.3.3 / Chapter 3.3.3 --- Formulation of the Head-Eye Calibration Problem --- p.3.6 / Chapter 3.3.4 --- Using Principal Vector to Represent Rotation Transformation Matrix --- p.3.7 / Chapter 3.3.5 --- Calculating R cg and Tcg --- p.3.9 / Chapter 3.4 --- Our Local Implementation of Tsai's Head Eye Calibration Algorithm --- p.3.14 / Chapter 3.4.1 --- Using Denavit - Hartternberg's Approach to Establish a Body-Attached Coordinate Frame for Each Link of the Manipulator --- p.3.16 / Chapter 3.5 --- Function of Procedures and Formats of Data Files --- p.3.23 / Chapter 3.6 --- Experimental Results --- p.3.26 / Chapter 3.7 --- Discussion --- p.3.45 / Chapter 3.8 --- Conclusion --- p.3.46 / References --- p.3.47 / Appendix I Procedures --- p.3.48 / Chapter Chapter 4 --- A New Tracking Method for Shape from Motion Using an Active Vision System / Chapter 4.1 --- Introduction --- p.4.1 / Chapter 4.2 --- A New Tracking Method --- p.4.1 / Chapter 4.2.1 --- Our approach --- p.4.1 / Chapter 4.2.2 --- Using an Active Vision System to Track the Projective Basis Across Image Sequence --- p.4.2 / Chapter 4.2.3 --- Using Projective Invariants to Track the Remaining Feature Points --- p.4.2 / Chapter 4.3 --- Using Factorisation Method to Recover Shape from Motion --- p.4.11 / Chapter 4.4 --- Discussion and Future Research --- p.4.31 / References --- p.4.32 / Chapter Chapter 5 --- Experiments on Feature Tracking with 3D Projective Invariants / Chapter 5.1 --- 8-point Projective Invariant --- p.5.1 / Chapter 5.2 --- Projective Invariant Based Tranfer between Distinct Views of a 3-D Scene --- p.5.4 / Chapter 5.3 --- Transfer Experiments on the Image Sequence of an Calibration Block --- p.5.6 / Chapter 5.3.1 --- Experiment 1. Real Image Sequence 1 of a Camera Calibration Block --- p.5.6 / Chapter 5.3.2 --- Experiment 2. Real Image Sequence 2 of a Camera Calibration Block --- p.5.15 / Chapter 5.3.3 --- Experiment 3. Real Image Sequence 3 of a Camera Calibration Block --- p.5.22 / Chapter 5.3.4 --- Experiment 4. Synthetic Image Sequence of a Camera Calibration Block --- p.5.27 / Chapter 5.3.5 --- Discussions on the Experimental Results --- p.5.32 / Chapter 5.4 --- Transfer Experiments on the Image Sequence of a Human Face Model --- p.5.33 / References --- p.5.44 / Chapter Chapter 6 --- Conclusions and Future Researches / Chapter 6.1 --- Contributions and Conclusions --- p.6.1 / Chapter 6.2 --- Future Researches --- p.6.1 / Bibliography --- p.B.1

by Chan Kwong Wing Raymond. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 115-123). / Acknowledgments --- p.i / Table of Contents --- p.ii-v / List of Tables --- p.vi / List of Figures --- p.vii / Abstract --- p.1 / Chapter Chapter 1 : --- Introduction / Chapter 1.1 --- An Introduction to Video Compression --- p.3 / Chapter 1.2 --- Overview of Problems --- p.4 / Chapter 1.2.1 --- Analog Video and Digital Problems --- p.4 / Chapter 1.2.2 --- Low Bit Rate Application Problems --- p.4 / Chapter 1.2.3 --- Real Time Video Compression Problems --- p.5 / Chapter 1.2.4 --- Source Coding and Channel Coding Problems --- p.6 / Chapter 1.2.5 --- Bit-rate and Quality Problems --- p.7 / Chapter 1.3 --- Organization of the Thesis --- p.7 / Chapter Chapter 2 : --- Background and Related Work / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- Analog Video --- p.9 / Chapter 2.1.2 --- Digital Video --- p.10 / Chapter 2.1.3 --- Color Theory --- p.10 / Chapter 2.2 --- Video Coding --- p.12 / Chapter 2.2.1 --- Predictive Coding --- p.12 / Chapter 2.2.2 --- Vector Quantization --- p.12 / Chapter 2.2.3 --- Subband Coding --- p.13 / Chapter 2.2.4 --- Transform Coding --- p.14 / Chapter 2.2.5 --- Hybrid Coding --- p.14 / Chapter 2.3 --- Transform Coding --- p.15 / Chapter 2.3.1 --- Discrete Cosine Transform --- p.16 / Chapter 2.3.1.1 --- 1-D Fast Algorithms --- p.16 / Chapter 2.3.1.2 --- 2-D Fast Algorithms --- p.17 / Chapter 2.3.1.3 --- Multidimensional DCT Algorithms --- p.17 / Chapter 2.3.2 --- Quantization --- p.18 / Chapter 2.3.3 --- Entropy Coding --- p.18 / Chapter 2.3.3.1 --- Huffman Coding --- p.19 / Chapter 2.3.3.2 --- Arithmetic Coding --- p.19 / Chapter Chapter 3 : --- Existing Compression Scheme / Chapter 3.1 --- Introduction --- p.20 / Chapter 3.2 --- Motion JPEG --- p.20 / Chapter 3.3 --- MPEG --- p.20 / Chapter 3.4 --- H.261 --- p.22 / Chapter 3.5 --- Other Techniques --- p.23 / Chapter 3.5.1 --- Fractals --- p.23 / Chapter 3.5.2 --- Wavelets --- p.23 / Chapter 3.6 --- Proposed Solution --- p.24 / Chapter 3.7 --- Summary --- p.25 / Chapter Chapter 4 : --- Fast 3D-DCT Algorithms / Chapter 4.1 --- Introduction --- p.27 / Chapter 4.1.1 --- Motivation --- p.27 / Chapter 4.1.2 --- Potentials of 3D DCT --- p.28 / Chapter 4.2 --- Three Dimensional Discrete Cosine Transform (3D-DCT) --- p.29 / Chapter 4.2.1 --- Inverse 3D-DCT --- p.29 / Chapter 4.2.2 --- Forward 3D-DCT --- p.30 / Chapter 4.3 --- 3-D FCT (3-D Fast Cosine Transform Algorithm --- p.30 / Chapter 4.3.1 --- Partitioning and Rearrangement of Data Cube --- p.30 / Chapter 4.3.1.1 --- Spatio-temporal Data Cube --- p.30 / Chapter 4.3.1.2 --- Spatio-temporal Transform Domain Cube --- p.31 / Chapter 4.3.1.3 --- Coefficient Matrices --- p.31 / Chapter 4.3.2 --- 3-D Inverse Fast Cosine Transform (3-D IFCT) --- p.32 / Chapter 4.3.2.1 --- Matrix Representations --- p.32 / Chapter 4.3.2.2 --- Simplification of the calculation steps --- p.33 / Chapter 4.3.3 --- 3-D Forward Fast Cosine Transform (3-D FCT) --- p.35 / Chapter 4.3.3.1 --- Decomposition --- p.35 / Chapter 4.3.3.2 --- Reconstruction --- p.36 / Chapter 4.4 --- The Fast Algorithm --- p.36 / Chapter 4.5 --- Example using 4x4x4 IFCT --- p.38 / Chapter 4.6 --- Complexity Comparison --- p.43 / Chapter 4.6.1 --- Complexity of Multiplications --- p.43 / Chapter 4.6.2 --- Complexity of Additions --- p.43 / Chapter 4.7 --- Implementation Issues --- p.44 / Chapter 4.8 --- Summary --- p.46 / Chapter Chapter 5 : --- Quantization / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- Dynamic Ranges of 3D-DCT Coefficients --- p.49 / Chapter 5.3 --- Distribution of 3D-DCT AC Coefficients --- p.54 / Chapter 5.4 --- Quantization Volume --- p.55 / Chapter 5.4.1 --- Shifted Complement Hyperboloid --- p.55 / Chapter 5.4.2 --- Quantization Volume --- p.58 / Chapter 5.5 --- Scan Order for Quantized 3D-DCT Coefficients --- p.59 / Chapter 5.6 --- Finding Parameter Values --- p.60 / Chapter 5.7 --- Experimental Results from Using the Proposed Quantization Values --- p.65 / Chapter 5.8 --- Summary --- p.66 / Chapter Chapter 6 : --- Entropy Coding / Chapter 6.1 --- Introduction --- p.69 / Chapter 6.1.1 --- Huffman Coding --- p.69 / Chapter 6.1.2 --- Arithmetic Coding --- p.71 / Chapter 6.2 --- Zero Run-Length Encoding --- p.73 / Chapter 6.2.1 --- Variable Length Coding in JPEG --- p.74 / Chapter 6.2.1.1 --- Coding of the DC Coefficients --- p.74 / Chapter 6.2.1.2 --- Coding of the DC Coefficients --- p.75 / Chapter 6.2.2 --- Run-Level Encoding of the Quantized 3D-DCT Coefficients --- p.76 / Chapter 6.3 --- Frequency Analysis of the Run-Length Patterns --- p.76 / Chapter 6.3.1 --- The Frequency Distributions of the DC Coefficients --- p.77 / Chapter 6.3.2 --- The Frequency Distributions of the DC Coefficients --- p.77 / Chapter 6.4 --- Huffman Table Design --- p.84 / Chapter 6.4.1 --- DC Huffman Table --- p.84 / Chapter 6.4.2 --- AC Huffman Table --- p.85 / Chapter 6.5 --- Implementation Issue --- p.85 / Chapter 6.5.1 --- Get Category --- p.85 / Chapter 6.5.2 --- Huffman Encode --- p.86 / Chapter 6.5.3 --- Huffman Decode --- p.86 / Chapter 6.5.4 --- PutBits --- p.88 / Chapter 6.5.5 --- GetBits --- p.90 / Chapter Chapter 7 : --- "Contributions, Concluding Remarks and Future Work" / Chapter 7.1 --- Contributions --- p.92 / Chapter 7.2 --- Concluding Remarks --- p.93 / Chapter 7.2.1 --- The Advantages of 3D DCT codec --- p.94 / Chapter 7.2.2 --- Experimental Results --- p.95 / Chapter 7.1 --- Future Work --- p.95 / Chapter 7.2.1 --- Integer Discrete Cosine Transform Algorithms --- p.95 / Chapter 7.2.2 --- Adaptive Quantization Volume --- p.96 / Chapter 7.2.3 --- Adaptive Huffman Tables --- p.96 / Appendices: / Appendix A : The detailed steps in the simplification of Equation 4.29 --- p.98 / Appendix B : The program Listing of the Fast DCT Algorithms --- p.101 / Appendix C : Tables to Illustrate the Reording of the Quantized Coefficients --- p.110 / Appendix D : Sample Values of the Quantization Volume --- p.111 / Appendix E : A 16-bit VLC table for AC Run-Level Pairs --- p.113 / References --- p.115

by Ho, Hau Lai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 97-103). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview of Attractor Image Coding --- p.2 / Chapter 1.2 --- Scope of Thesis --- p.3 / Chapter 2 --- Fundamentals of Attractor Coding --- p.6 / Chapter 2.1 --- Notations --- p.6 / Chapter 2.2 --- Mathematical Preliminaries --- p.7 / Chapter 2.3 --- Partitioned Iterated Function Systems --- p.10 / Chapter 2.3.1 --- Mathematical Formulation of the PIFS --- p.12 / Chapter 2.4 --- Attractor Coding using the PIFS --- p.16 / Chapter 2.4.1 --- Quadtree Partitioning --- p.18 / Chapter 2.4.2 --- Inclusion of an Orthogonalization Operator --- p.19 / Chapter 2.5 --- Coding Examples --- p.21 / Chapter 2.5.1 --- Evaluation Criterion --- p.22 / Chapter 2.5.2 --- Experimental Settings --- p.22 / Chapter 2.5.3 --- Results and Discussions --- p.23 / Chapter 2.6 --- Summary --- p.25 / Chapter 3 --- Attractor Coding with Adjacent Block Parameter Estimations --- p.27 / Chapter 3.1 --- δ-Minimum Edge Difference --- p.29 / Chapter 3.1.1 --- Definition --- p.29 / Chapter 3.1.2 --- Theoretical Analysis --- p.31 / Chapter 3.2 --- Adjacent Block Parameter Estimation Scheme --- p.33 / Chapter 3.2.1 --- Joint Optimization --- p.34 / Chapter 3.2.2 --- Predictive Coding --- p.36 / Chapter 3.3 --- Algorithmic Descriptions of the Proposed Scheme --- p.39 / Chapter 3.4 --- Experimental Results --- p.40 / Chapter 3.5 --- Summary --- p.50 / Chapter 4 --- Attractor Coding using Lapped Partitioned Iterated Function Sys- tems --- p.51 / Chapter 4.1 --- Lapped Partitioned Iterated Function Systems --- p.53 / Chapter 4.1.1 --- Weighting Operator --- p.54 / Chapter 4.1.2 --- Mathematical Formulation of the LPIFS --- p.57 / Chapter 4.2 --- Attractor Coding using the LPIFS --- p.62 / Chapter 4.2.1 --- Choice of Weighting Operator --- p.64 / Chapter 4.2.2 --- Range Block Preprocessing --- p.69 / Chapter 4.2.3 --- Decoder Convergence Analysis --- p.73 / Chapter 4.3 --- Local Domain Block Searching --- p.74 / Chapter 4.3.1 --- Theoretical Foundation --- p.75 / Chapter 4.3.2 --- Local Block Searching Algorithm --- p.77 / Chapter 4.4 --- Experimental Results --- p.79 / Chapter 4.5 --- Summary --- p.90 / Chapter 5 --- Conclusion --- p.91 / Chapter 5.1 --- Original Contributions --- p.91 / Chapter 5.2 --- Subjects for Future Research --- p.92 / Chapter A --- Fundamental Definitions --- p.94 / Chapter B --- Appendix B --- p.96 / Bibliography --- p.97

Chan Kwok-po. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references. / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Characterization of Nonlinearity in Power Amplifier --- p.6 / Chapter 2.1. --- Ideal Amplifier Representation --- p.6 / Chapter 2.2. --- Nonlinear Amplifier Representation --- p.7 / Chapter 2.2.1 --- Series Representation --- p.7 / Chapter 2.2.2 --- AM-AM and AM-PM Distortion --- p.7 / Chapter 2.2.3 --- Two-tone Intermodulation --- p.9 / Chapter 2.2.4 --- Nonlinearity on Digital Modulation Formats --- p.11 / Chapter Chapter 3 --- Linearization Techniques --- p.13 / Chapter 3.1. --- Power Back-off --- p.14 / Chapter 3.2. --- Feedback Technique --- p.15 / Chapter 3.3. --- Pre-distortion Technique --- p.16 / Chapter 3.4. --- Feed-forward Technique --- p.18 / Chapter 3.5. --- Linearization Systems with Signal Processing --- p.19 / Chapter 3.5.1 --- Envelope Elimination and Restoration (EER) --- p.19 / Chapter 3.5.2 --- Linear Amplification Using Nonlinear Components (LINC) --- p.20 / Chapter 3.5.3 --- Combined Analogue-locked Loop Universal Modulator (CALLUM) --- p.21 / Chapter 3.5.4 --- Linear Amplification Employing Sampling Techniques (LIST) --- p.21 / Chapter 3.6. --- Other Linearization Techniques --- p.22 / Chapter Chapter 4 --- Feed-forward Power Amplifier System --- p.23 / Chapter 4.1. --- General Description --- p.23 / Chapter 4.2. --- Adaptive Feed-forward Power Amplifier System --- p.25 / Chapter 4.2.1 --- Power Minimization --- p.28 / Chapter 4.2.2 --- Pilot Injection Technique --- p.29 / Chapter 4.2.3 --- Look-up-table Technique (Temperature Compensation) --- p.31 / Chapter 4.2.4 --- Correlation Based Feedback Control (Dual-loop) --- p.32 / Chapter 4.2.5 --- Correlation Based Feedback Control (Triple-loop) --- p.34 / Chapter 4.2.6 --- Digital Implementation on Adaptive FFPA --- p.35 / Chapter Chapter 5 --- DSP-based Adaptive FFPA Analysis --- p.37 / Chapter 5.1. --- System Architecture --- p.37 / Chapter 5.2. --- System Modeling --- p.39 / Chapter 5.3. --- Principle of Adaptation --- p.40 / Chapter 5.3.1 --- Adaptation in Error Extraction Loop --- p.40 / Chapter 5.3.2 --- Adaptation in Main-tone Suppression Loop --- p.43 / Chapter 5.3.3 --- Adaptation in Distortion Cancellation Loop --- p.44 / Chapter 5.3.4 --- Complex Adaptation --- p.46 / Chapter 5.4. --- Adaptation Performance Analysis --- p.47 / Chapter 5.4.1 --- Condition for Convergence --- p.47 / Chapter 5.4.2 --- Rate of Convergence --- p.48 / Chapter 5.4.3 --- Misadjustment --- p.49 / Chapter 5.4.4 --- Summary of the System Performance --- p.51 / Chapter 5.5. --- System Design Consideration --- p.51 / Chapter 5.5.1 --- Quadrature Sampling --- p.51 / Chapter 5.5.2 --- Data Processing --- p.52 / Chapter 5.6. --- Sensitivity Analysis --- p.55 / Chapter 5.6.1 --- Vector Representation --- p.55 / Chapter 5.6.2 --- Amplitude and Phase Matching --- p.56 / Chapter 5.6.3 --- Time-delay Matching --- p.58 / Chapter 5.7. --- Analog-to-digital Interface: Design Consideration --- p.60 / Chapter 5.7.1 --- Sampling Rate Consideration --- p.60 / Chapter 5.7.2 --- Finite Word-length --- p.61 / Chapter 5.8. --- Digital-to-analog Interface: Design Consideration --- p.63 / Chapter Chapter 6 --- New DSP Algorithms for High Performance Adaptive FFPA --- p.67 / Chapter 6.1. --- Variable Loop-gain Algorithm --- p.67 / Chapter 6.2. --- Variable Step-size Algorithm --- p.71 / Chapter 6.3. --- Least-mean-fourth Algorithm --- p.74 / Chapter Chapter 7 --- Implementation of DSP-based Adaptive FFPA --- p.79 / Chapter 7.1. --- Hardware Construction --- p.79 / Chapter 7.2. --- Experimental Results: LMS Algorithm --- p.82 / Chapter 7.3. --- Experimental Results: Variable Loop-gain Algorithm --- p.86 / Chapter 7.4. --- Experimental Results: Variable Step-size Algorithm --- p.88 / Chapter 7.5. --- Experimental Results: Lesat-mean-fourth Algorithm --- p.90 / Chapter Chapter 8 --- Conclusion --- p.92 / Appendix I Matlab Program for Computer Simulation of Adaptive FFPA --- p.A-l / Appendix II DSP Program for Experimental Adaptive FFPA --- p.A-5 / References --- p.R-1 / Author's Publications --- p.AP-1

Photocopy of typescript. / Digitized by Kansas Correctional Industries

Continuous-time (CT) data conversion and continuous-time digital signal processing (DSP) are an interesting alternative to conventional methods of signal conversion and processing. This alternative proposes time-based encoding that may not suffer from aliasing; shows superior spectral properties (e.g. no quantization noise floor); and enables time-based, event-driven, flexible signal processing using digital circuits, thus scaling well with technology. Despite these interesting features, this approach has so far been limited by the CT encoder, due to both its relatively poor energy efficiency and the constraints it imposes on the subsequent CT DSP. In this thesis, we present three principles that address these limitations and help improve the CT ADC/DSP system. First, an adaptive-resolution encoding scheme that achieves first-order reconstruction with simple circuitry is proposed. It is shown that for certain signals, the scheme can significantly reduce the number of samples generated per unit of time for a given accuracy compared to schemes based on zero-order-hold reconstruction, thus promising to lead to low dynamic power dissipation at the system level. Presented next is a novel time-based CT ADC architecture, and associated encoding scheme, that allows a compact, energy-efficient circuit implementation, and achieves first-order quantization error spectral shaping. The design of a test chip, implemented in a 0.65-V 28-nm FDSOI process, that includes this CT ADC and a 10-tap programmable FIR CT DSP to process its output is described. The system achieves 32 dB – 42 dB SNDR over a 10 MHz – 50 MHz bandwidth, occupies 0.093 mm2, and dissipates 15 µW–163 µW as the input amplitude goes from zero to full scale. Finally, an investigation into the possibility of CT encoding using voltage-controlled oscillators is undertaken, and it leads to a CT ADC/DSP system architecture composed primarily of asynchronous digital delays. The latter makes the system highly digital and technology-scaling-friendly and, hence, is particularly attractive from the point of view of technology migration. The design of a test chip, where this delay-based CT ADC/DSP system architecture is used to implement a 16-tap programmable FIR filter, in a 1.2-V 28-nm FDSOI process, is described. Simulations show that the system will achieve a 33 dB – 40 dB SNDR over a 600 MHz bandwidth, while dissipating 4 mW.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 185-191. / M.S.

No description available.

Shan, Qi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 124-132). / Abstracts in English and Chinese. / Chapter 1 --- Overview --- p.1 / Chapter 1.1 --- Image Blur Overview --- p.1 / Chapter 1.2 --- Blur Identification in a Transparency's Perspective --- p.3 / Chapter 1.3 --- From Transparencies to Natural Image Priors --- p.7 / Chapter 1.4 --- Discussion of the Linear Motion Model --- p.9 / Chapter 1.5 --- Binary Texture Restoration and High-Order MRF Optimization --- p.9 / Chapter 2 --- A Review on Previous Work --- p.13 / Chapter 2.1 --- Spatially-Invariant Blur Recovery --- p.13 / Chapter 2.2 --- Spatially-Variant Blur Recovery --- p.16 / Chapter 2.3 --- Markov Random Field Inference --- p.18 / Chapter 3 --- Motion Blur in a Transparency's Perspective --- p.20 / Chapter 3.1 --- Analysis of Object Motion Blur --- p.20 / Chapter 3.1.1 --- 1D Object Motion Blur --- p.20 / Chapter 3.1.2 --- 2D Object Motion Blur --- p.23 / Chapter 3.2 --- Modeling 2D Object Motion Blur --- p.26 / Chapter 3.3 --- Optimization Procedure --- p.27 / Chapter 3.3.1 --- Blur Kernel Estimation --- p.29 / Chapter 3.3.2 --- Latent Binary Matte Estimation --- p.30 / Chapter 3.4 --- Generalized Transparency in Motion Blur --- p.33 / Chapter 3.4.1 --- Camera Motion Blur Estimation --- p.35 / Chapter 3.4.2 --- Implementation --- p.37 / Chapter 3.5 --- Analysis and Results --- p.38 / Chapter 3.5.1 --- Evaluation of the Kernel Initialization --- p.40 / Chapter 3.5.2 --- Evaluation of Binary Alpha Initialization --- p.40 / Chapter 3.5.3 --- Robustness to Noise --- p.41 / Chapter 3.5.4 --- Natural Image Deblurring Results --- p.41 / Chapter 3.6 --- Proofs --- p.50 / Chapter 4 --- Rotational Motion Deblurring --- p.55 / Chapter 4.1 --- Motion blur descriptor --- p.55 / Chapter 4.1.1 --- Descriptor analysis --- p.56 / Chapter 4.2 --- Optimization --- p.59 / Chapter 4.2.1 --- Parameter initialization --- p.59 / Chapter 4.2.2 --- Iterative optimization --- p.62 / Chapter 4.2.3 --- Recover the color image --- p.65 / Chapter 4.3 --- Result and analysis --- p.65 / Chapter 5 --- Image Deblurring using Natural Image Priors --- p.70 / Chapter 5.1 --- Problem Definition --- p.70 / Chapter 5.2 --- Analysis of Ringing Artifacts --- p.71 / Chapter 5.3 --- Our model --- p.74 / Chapter 5.3.1 --- Definition of the probability terms --- p.75 / Chapter 5.4 --- Optimization --- p.81 / Chapter 5.4.1 --- Optimizing L --- p.83 / Chapter 5.4.2 --- Optimizing f --- p.86 / Chapter 5.4.3 --- Optimization Details and Parameters --- p.87 / Chapter 5.5 --- Experimental Results --- p.90 / Chapter 6 --- High Order MRF and its Optimization --- p.94 / Chapter 6.1 --- The Approach --- p.95 / Chapter 6.1.1 --- Polynomial Standardization --- p.95 / Chapter 6.1.2 --- Polynomial Graph Construction --- p.97 / Chapter 6.1.3 --- Polynomial Graph Partition --- p.103 / Chapter 6.1.4 --- Multi-Label Expansion --- p.105 / Chapter 6.1.5 --- Analysis --- p.106 / Chapter 6.2 --- Experimental Results --- p.108 / Chapter 6.3 --- Summary --- p.112 / Chapter 6.4 --- Proofs --- p.112 / Chapter 7 --- Conclusion --- p.117 / Chapter 7.1 --- Solving Linear Motion Blur in a Transparency's Perspective --- p.117 / Chapter 7.2 --- Rotational Motion Deblurring --- p.119 / Chapter 7.3 --- Image Deblurring using Natural Image Priors --- p.119 / Chapter 7.4 --- Contribution --- p.121 / Chapter 7.5 --- Discussion and Open Questions --- p.121 / Bibliography --- p.124