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Specifications on digital hardware systems typically contain descriptions and requirements expressed in natural language and diagrams of various types. The objective of the research reported here is the automatic detection of common references ("coreferences") to objects in natural language specification statements in order to permit automatic integration of requirements. This thesis describes a prototype system for detecting coreferences. First, the natural language statements are translated into conceptual graphs (semantic nets). Then, these graphs are scanned by a rule-based system to determine whether each concept that is encountered is the definition of a new concept or a reference to a previously defined concept. Tests performed on the system developed indicate a high percentage rate of correct classifications. / Master of Science

This paper describes the building of a parallel converter for a high-speed serial bus. The high-speed serial port of the Macintosh personal computer is used to implement the bus, while an MC68000 Educational Computer Board is used to perform the serial-to-parallel conversion. The device's performance is evaluated, and possible methods for improving its performance are discussed.

As network processors have advanced in speed and efficiency they have become more and more complex in both hardware and software configurations. Intel's IXP1200 is one of these new network processors that has been given to different universities worldwide to conduct research on. The goal of this thesis is to take the first step in starting that research by providing a stable system that can provide a reliable platform for further research. This thesis introduces the fundamental hardware of Intel's IXP1200 and what it takes to install both hardware and software using both Windows 2000 and Linux 7.2 as the operating system in support for the IXP1200. This thesis will provide information on the installation of hardware and software configuration for the IXP1200 including Intel's Software Development Kit (SDK). Upon completion this platform can then be used to conduct further research in the development of the IXP1200 network processor. It provides a hardware and software installation checklist and documentations of problems encountered and recommendations for their resolution. Along with providing an example of using preexisting code that has been modified to filter packets of TCP or UDP to different ports.

Summary in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong. / Bibliography: leave 116-117.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Engineering. / Includes bibliographical references. / by Antonio C. Gellineau. / M.S.

Tsang, Chi Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 66-69). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Statement of Originality --- p.v / Table of Contents --- p.vii / List of Figures --- p.x / Nomenclature --- p.xii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- A Ubiquitous Digital Writing System --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- MEMS Motion Sensing Technology --- p.6 / Chapter 2.2.1 --- Micro-Electro-Mechanical Systems (MEMS) --- p.6 / Chapter 2.2.2 --- Principle of a MEMS Accelerometer --- p.6 / Chapter 2.2.3 --- Principle of a MEMS Gyroscope --- p.7 / Chapter 2.3 --- Architecture of Ubiquitous Digital Writing System --- p.8 / Chapter 2.3.1 --- Micro Inertial Measurement Unit (μlMU) --- p.8 / Chapter 2.3.2 --- Data Transmission Module --- p.10 / Chapter 2.3.3 --- User Interface Software --- p.10 / Chapter 2.4 --- Summary --- p.12 / Chapter 3 --- Calibration of μ-Inertial Measurement Unit --- p.13 / Chapter 3.1 --- Introduction --- p.13 / Chapter 3.2 --- Sources of Error --- p.13 / Chapter 3.2.1 --- Deterministic Errors --- p.13 / Chapter 3.2.2 --- Stochastic Error --- p.14 / Chapter 3.3 --- Calibration of Accelerometers --- p.14 / Chapter 3.4 --- Coordinate Transformation with Gravity Compensation --- p.15 / Chapter 3.4.1 --- Coordinate Transformation --- p.16 / Chapter 3.4.2 --- Attitude Determination --- p.18 / Chapter 3.4.3 --- Gravity Compensation --- p.19 / Chapter 3.5 --- Summary --- p.20 / Chapter 4 --- Zero Velocity Compensation --- p.21 / Chapter 4.1 --- Introduction --- p.21 / Chapter 4.2 --- Algorithm Description --- p.21 / Chapter 4.2.1 --- Stroke Segmentation --- p.22 / Chapter 4.2.2 --- Zero Velocity Compensation (ZVC) --- p.22 / Chapter 4.3 --- Experimental Results and Discussion --- p.23 / Chapter 4.4 --- Summary --- p.24 / Chapter 5 --- Kalman Filtering --- p.28 / Chapter 5.1 --- Introduction --- p.28 / Chapter 5.2 --- Summary of Kalman filtering algorithm --- p.28 / Chapter 5.2.1 --- System Model --- p.28 / Chapter 5.2.2 --- Initialization --- p.29 / Chapter 5.2.3 --- Time Update --- p.32 / Chapter 5.2.4 --- Measurement Update --- p.33 / Chapter 5.2.5 --- Stroke Segmentation --- p.34 / Chapter 5.3 --- Summary --- p.34 / Chapter 6 --- Error Compensation from Position Feedback --- p.35 / Chapter 6.1 --- Introduction --- p.35 / Chapter 6.2 --- Global Positioning System (GPS) --- p.35 / Chapter 6.3 --- Zero z-axis Kalman Filtering --- p.36 / Chapter 6.3.1 --- Algorithm Implementation --- p.36 / Chapter 6.3.2 --- Experimental Results and Discussion --- p.40 / Chapter 6.4 --- Combined Electromagnetic Resonance (EMR) Position Detection Board and μlMU --- p.43 / Chapter 6.4.1 --- EMR Position Detection System --- p.43 / Chapter 6.4.2 --- A Combined Scheme --- p.44 / Chapter 6.4.3 --- Algorithm Implementation --- p.46 / Chapter 6.4.4 --- Synchronization --- p.50 / Chapter 6.4.5 --- Experimental Results and Discussion --- p.50 / Chapter 6.5 --- Summary --- p.54 / Chapter 7 --- Conclusion --- p.55 / Chapter 7.1 --- Future Work --- p.56 / Chapter 7.1.1 --- Improvement in the μlMU --- p.56 / Chapter 7.1.2 --- Combined Camera Optical Tracking and μlMU --- p.57 / Chapter 7.2 --- Concluding Remarks --- p.58 / Chapter A --- Derivation of Kalman Filtering Algorithm --- p.59 / Chapter A.1 --- Introduction --- p.59 / Chapter A.2 --- Derivation of a Priori State Estimation Equation --- p.60 / Chapter A.3 --- Derivation of a Posteriori State Estimation Equation --- p.60 / Chapter A.4 --- Derivation of a Priori Error Covariance Matrix --- p.61 / Chapter A.5 --- Derivation of the Optimal Kalman Gain --- p.62 / Chapter A.6 --- Derivation of a Posteriori Error Covariance Matrix --- p.63 / Chapter B --- Derivation of Process Noise Covariance Matrix --- p.64 / Bibliography --- p.66 / Publications --- p.69

Dong, Zhuxin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 77-80). / Abstracts in English and Chinese. / Abstract --- p.ii / 摘要 --- p.iii / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Figures --- p.vii / List of Tables --- p.ix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Architecture of UDWI --- p.3 / Chapter 1.2 --- Background of IMU Sensor Calibration --- p.5 / Chapter 1.3 --- Organization --- p.7 / Chapter Chapter 2 --- 2D Motion Calibration --- p.10 / Chapter 2.1 --- Experimental Platform --- p.10 / Chapter 2.1.1 --- Transparent Table --- p.10 / Chapter 2.2 --- Matching Algorithm --- p.13 / Chapter 2.2.1 --- Motion Analysis --- p.13 / Chapter 2.2.2 --- Core Algorithm and Matching Criterion --- p.14 / Chapter 2.3 --- Usage of High Speed Camera --- p.17 / Chapter 2.4 --- Functions Realized --- p.17 / Chapter Chapter 3 --- Usage of Camera Calibration --- p.21 / Chapter 3.1 --- Introduction to Camera Calibration --- p.21 / Chapter 3.1.1 --- Related Coordinate Frames --- p.21 / Chapter 3.1.2 --- Pin-Hole Model --- p.24 / Chapter 3.2 --- Calibration for Nonlinear Model --- p.27 / Chapter 3.3 --- Implementation of Process to Calibrate Camera --- p.28 / Chapter 3.3.1 --- Image Capture --- p.28 / Chapter 3.3.2 --- Define World Frame and Extract Corners --- p.28 / Chapter 3.3.3 --- Main Calibration --- p.30 / Chapter 3.4 --- Calibration Results of High Speed Camera --- p.33 / Chapter 3.4.1 --- Lens Selection --- p.33 / Chapter 3.4.2 --- Property of High Speed Camera --- p.34 / Chapter Chapter 4 --- 3D Attitude Calibration --- p.36 / Chapter 4.1 --- The Necessity of Attitude Calibration --- p.36 / Chapter 4.2 --- Stereo Vision and 3D Reconstruction --- p.37 / Chapter 4.2.1 --- Physical Meaning and Mathematical Model Proof --- p.37 / Chapter 4.2.2 --- 3D Point Reconstruction --- p.38 / Chapter 4.3 --- Example of 3D Point Reconstruction --- p.40 / Chapter 4.4 --- Idea of Attitude Calibration --- p.42 / Chapter Chapter 5 --- Experimental Results --- p.45 / Chapter 5.1 --- Calculation of Proportional Parameter --- p.45 / Chapter 5.2 --- Accuracy Test of Stroke Reconstruction --- p.46 / Chapter 5.3 --- Writing Experiments of 26 Letters --- p.47 / Chapter 5.3.1 --- Experimental Results of Letter b --- p.48 / Chapter 5.3.2 --- Experimental Results of Letter n with ZVC --- p.51 / Chapter 5.3.3 --- Experimental Results of Letter u --- p.54 / Chapter 5.4 --- Writing of Single Letter s - Multiple Tests --- p.56 / Chapter 5.5 --- Analysis on Resolution Property of Current Vision Algorithm --- p.58 / Chapter 5.5.1 --- Resolution of Current Algorithm --- p.58 / Chapter 5.5.2 --- Tests with Various Filters --- p.59 / Chapter 5.6 --- Calculation of Static Attitude --- p.61 / Chapter Chapter 6 --- Future Work --- p.64 / Chapter 6.1 --- Another Multiple Tests of Letter k --- p.64 / Chapter 6.2 --- Letter Recognition Based on Neural Networks Classification --- p.66 / Chapter Chapter 7 --- Conclusion --- p.69 / Chapter 7.1 --- Calibration ofMAG-μlMU Sensors --- p.69 / Chapter 7.2 --- Calibration of Accelerometers --- p.70 / Chapter 7.3 --- Calibration of Attitude --- p.70 / Chapter 7.4 --- Future Work --- p.71 / Appendix A The Experimental Results of Writing English Letters --- p.72

Ko, Hoi Fung. / "November 2011." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (p. 121-125). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.1 / Chapter 1.2 --- Contribution --- p.9 / Chapter 1.3 --- Organization of the Thesis --- p.11 / Chapter 2 --- Background Study --- p.12 / Chapter 2.1 --- Review on 3D Tracking Techniques --- p.13 / Chapter 2.1.1 --- Mechanical base --- p.13 / Chapter 2.1.2 --- Acoustic base --- p.14 / Chapter 2.1.3 --- Magnetic base --- p.15 / Chapter 2.1.4 --- Inertial base --- p.17 / Chapter 2.1.5 --- Optical base --- p.18 / Chapter 2.2 --- Summary --- p.19 / Chapter 3 --- Theory and Methodology --- p.21 / Chapter 3.1 --- Design Framework --- p.21 / Chapter 3.1.1 --- Problem Definition --- p.22 / Chapter 3.1.2 --- Concept --- p.22 / Chapter 3.2 --- Finding the orientation --- p.23 / Chapter 3.2.1 --- Measuring the rotation --- p.23 / Chapter 3.2.2 --- Sensor fusion --- p.26 / Chapter 3.3 --- Finding the translational motion --- p.32 / Chapter 3.3.1 --- Translational motion --- p.32 / Chapter 3.3.2 --- Laser speckle pattern --- p.35 / Chapter 4 --- Implementation --- p.38 / Chapter 4.1 --- Hardware Configuration --- p.38 / Chapter 4.1.1 --- Accelerometer --- p.39 / Chapter 4.1.2 --- Gyroscope --- p.40 / Chapter 4.1.3 --- Digital Compass --- p.41 / Chapter 4.1.4 --- Optical flow chip --- p.42 / Chapter 4.1.5 --- Microcontroller --- p.43 / Chapter 4.2 --- Software Implementation --- p.45 / Chapter 4.2.1 --- On the Microcontroller --- p.45 / Chapter 4.2.2 --- On the PC --- p.47 / Chapter 5 --- Experimental Results --- p.50 / Chapter 5.1 --- Experiments on orientation estimation --- p.50 / Chapter 5.1.1 --- Overall Experimental Setup --- p.51 / Chapter 5.1.2 --- Experiment 1: The improvement of static accuracy by utilising two-axis measurement method --- p.52 / Chapter 5.1.3 --- Experiment 2: The improvement of the dynamic response with Kalman filter and gyroscope --- p.55 / Chapter 5.1.4 --- Experiments 3: The static accuracy of the compass module --- p.59 / Chapter 5.1.5 --- Experiment 4: The dynamic accuracy of the compass module with Kalman filter and gyroscope --- p.63 / Chapter 5.1.6 --- Experiment 5: Kalman filter tuning --- p.64 / Chapter 5.2 --- Experiment on Translational accuracy --- p.67 / Chapter 5.2.1 --- Experiment 6: The relation between the output of the chip and the actual displacement --- p.68 / Chapter 5.2.2 --- Experiment 7: Tracking ability with different materials --- p.70 / Chapter 6 --- The Haptic module --- p.73 / Chapter 6.1 --- Introduction --- p.73 / Chapter 6.2 --- Theory of operation --- p.75 / Chapter 6.3 --- Implementation --- p.77 / Chapter 6.4 --- Experiment and evaluation --- p.80 / Chapter 6.4.1 --- Experiment 1: Calibration of the spring . --- p.80 / Chapter 6.4.2 --- Experiment 2: Latency on force output . . --- p.82 / Chapter 6.5 --- Possible applications --- p.85 / Chapter 7 --- 3D input for immersive display --- p.87 / Chapter 7.1 --- Methodology --- p.88 / Chapter 7.1.1 --- Tracking method --- p.89 / Chapter 7.2 --- Implementation --- p.96 / Chapter 7.2.1 --- Hardware setup --- p.96 / Chapter 7.2.2 --- Software implementation --- p.97 / Chapter 7.2.3 --- Setup Calibration --- p.98 / Chapter 7.2.4 --- Laser Spot Detection --- p.99 / Chapter 7.2.5 --- Pose Estimation --- p.100 / Chapter 7.2.6 --- State Tracking --- p.102 / Chapter 7.3 --- Experiment --- p.105 / Chapter 7.3.1 --- Experiment on translational motion --- p.105 / Chapter 7.3.2 --- Experiment on rotational motion --- p.106 / Chapter 7.3.3 --- Experiment on tracking ability --- p.108 / Chapter 7.4 --- Application --- p.109 / Chapter 8 --- Limitations and Discussions --- p.110 / Chapter 8.1 --- The limitation of the orientation tracking module --- p.110 / Chapter 8.2 --- The limitation of the translational motion tracking module --- p.111 / Chapter 8.3 --- The limitation of the haptic module --- p.112 / Chapter 8.4 --- The limitation of the tracking cube setup --- p.113 / Chapter 8.5 --- Comparison of the result of utilizing simple moving average filter and Kalman filter --- p.114 / Chapter 8.6 --- Comparison with other devices on the market --- p.115 / Chapter 8.7 --- Future work --- p.115 / Chapter 9 --- Conclusion --- p.117 / Bibliography --- p.121