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Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 220. Thesis director: Barry N. Haack. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Environmental Science and Public Policy. Title from PDF t.p. (viewed July 7, 2008). Includes bibliographical references (p. 194-219). Also issued in print.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 63-66). Also available in electronic version.

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• Abstract Integrated digital imaging systems are widely used in consumer electronics today. Current digital image sensors have a linear respeiiSt. The limited dynamic range of linear digital image sensors results in saturation when the input dynamic range of the scene is larger than that of the camera. This limitation could be overcome using pixels with an output that is proportional to the logarithm of the detected photocurrent. Conventional CMOS pixels with a logarithmic response, using a transistor operating in the sub-threshold region, are capable of capturing wide dynamic range scenes with more than six decades of illumination intensity. But these pixels suffer from fixed pattern noise, slow response and low sensitivity. A five transistor (5T) pixel circuit for a standard 0.35-fLm CMOS process which integrates the photocurrent linearly and capable of a logarithmic response is described in the thesis. A key component of the 5T pixel is a time-dependent reference voltage. This voltage is applied to the gate of one of the transistors inside each pixel in the array for the duration of the exposure to generate a logarithmic response. A model derived to generate the reference voltage is described. Improvements were made to the reference voltage model to take into consideration the non-ideal effects such as charge feedthrough and threshold voltage variations. A potential problem associated with successfully tonemapping low photo currents with the 5T pixel has been described and a method to calculate the optimal value of reference current Iret proposed. This was shown to lead to an optimum photoresponse. Measurement results from fabricated 1-D and 2-D arrays of 5T pixels are presented and analysed. An overall DR of 97-dB (almost 5 decades) has been achieved from 100 mlux to 6.7 Klux. The slope of the logarithmic photoresponse was shown to be adjustable and controlled by the slope parameter S in the reference voltage model. A large output swing of over 1 V due to the large photoresponse slope in the logarithmic region results in greater signal-to-noise ratio compared to the conventional logarithmic pixel based on the subthreshold transistor operation (60 m V/decade). Digital and analogue reference voltage generating techniques are described with circuits implemented in 0.35-fLm CM OS process. Finally, a 5T NMOS pixel that is capable of WDR imaging with superior low-light performance (23 mlux) and greater DR (1l0-dB) than the 5T PMOS pixel is described. [ a

Liu, Renting. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 40-46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work and System Overview --- p.6 / Chapter 2.1 --- Previous Work in Blur Analysis --- p.6 / Chapter 2.1.1 --- Blur detection and estimation --- p.6 / Chapter 2.1.2 --- Image deblurring --- p.8 / Chapter 2.1.3 --- Low DoF image auto-segmentation --- p.14 / Chapter 2.2 --- System Overview --- p.15 / Chapter 3 --- Blur Features and Classification --- p.18 / Chapter 3.1 --- Blur Features --- p.18 / Chapter 3.1.1 --- Local Power Spectrum Slope --- p.19 / Chapter 3.1.2 --- Gradient Histogram Span --- p.21 / Chapter 3.1.3 --- Maximum Saturation --- p.24 / Chapter 3.1.4 --- Local Autocorrelation Congruency --- p.25 / Chapter 3.2 --- Classification --- p.28 / Chapter 4 --- Experiments and Results --- p.29 / Chapter 4.1 --- Blur Patch Detection --- p.29 / Chapter 4.2 --- Blur degree --- p.33 / Chapter 4.3 --- Blur Region Segmentation --- p.34 / Chapter 5 --- Conclusion and Future Work --- p.38 / Bibliography --- p.40 / Chapter A --- Blurred Edge Analysis --- p.47

Tong, Fung Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 94-96). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.x / Chapter Chapter 1 - --- Introduction --- p.1 / Chapter Chapter 2 - --- State of the art in Vision-based Localization --- p.6 / Chapter 2.1 --- Extended Kalman Filter-based Localization --- p.6 / Chapter 2.1.1 --- Overview of the EKF algorithm --- p.6 / Chapter 2.1.2 --- Process of the EKF-based localization algorithm --- p.8 / Chapter 2.1.3 --- Recent EKF-based vision-based localization algorithms --- p.10 / Chapter 2.1.4 --- Advantages of the EKF-based localization algorithms --- p.11 / Chapter 2.1.5 --- Disadvantages of the EKF-based localization algorithm --- p.11 / Chapter 2.2 --- Monte Carlo Localization --- p.12 / Chapter 2.2.1 --- Overview of MCL --- p.12 / Chapter 2.2.2 --- Recent MCL-based localization algorithms --- p.14 / Chapter 2.2.3 --- Advantages of the MCL-based algorithm --- p.15 / Chapter 2.2.4 --- Disadvantages of the MCL-based algorithm --- p.16 / Chapter 2.3 --- Summary --- p.16 / Chapter Chapter 3 - --- Vision-based Localization as an Optimization Problem --- p.18 / Chapter 3.1 --- "Relationship between the World, Camera and Robot Body Coordinate System" --- p.18 / Chapter 3.2 --- Formulation of the Vision-based Localization as an Optimization Problem --- p.21 / Chapter 3.3 --- Summary --- p.26 / Chapter Chapter 4 - --- Existing Search Algorithms --- p.27 / Chapter 4.1 --- Overview of the Existing Search Algorithms --- p.27 / Chapter 4.2 --- Search Algorithm for the Proposed Objective Function --- p.28 / Chapter 4.3 --- Summary --- p.30 / Chapter Chapter 5 - --- Proposed Vision-based Localization using Genetic Algorithm --- p.32 / Chapter 5.1 --- Mechanism of Genetic Algorithm --- p.32 / Chapter 5.2 --- Formation of Chromosome --- p.35 / Chapter 5.3 --- Fitness Function --- p.39 / Chapter 5.4 --- Mutation and Crossover --- p.40 / Chapter 5.5 --- Selection and Stopping Criteria --- p.42 / Chapter 5.6 --- Adaptive Search Space --- p.44 / Chapter 5.7 --- Overall Flow of the Proposed Algorithm --- p.46 / Chapter 5.8 --- Summary --- p.47 / Chapter Chapter 6 - --- Experimental Results --- p.48 / Chapter 6.1 --- Test Robot --- p.48 / Chapter 6.2 --- Simulator --- p.49 / Chapter 6.2.1 --- Camera states simulation --- p.49 / Chapter 6.2.2 --- Oscillated walking motion simulation --- p.50 / Chapter 6.2.3 --- Input images simulation --- p.50 / Chapter 6.3 --- Computer for simulations --- p.51 / Chapter 6.4 --- Position and Orientation errors --- p.51 / Chapter 6.5 --- Experiment 1 一 Feature points with quantized noise --- p.53 / Chapter 6.5.1 --- Setup --- p.53 / Chapter 6.5.2 --- Results --- p.56 / Chapter 6.6 --- Experiment 2 一 Feature points added with Gaussian noise --- p.62 / Chapter 6.6.1 --- Setup --- p.62 / Chapter 6.6.2 --- Results --- p.62 / Chapter 6.7 --- Experiment 3 一 Noise reduction performance of the adaptive search space strategy --- p.77 / Chapter 6.7.1 --- Setup --- p.77 / Chapter 6.7.2 --- Results --- p.79 / Chapter 6.8 --- Experiment 4 一 Comparison with benchmark algorithms --- p.83 / Chapter 6.8.1 --- Setup --- p.83 / Chapter 6.8.2 --- Results --- p.85 / Chapter 6.9 --- Discussions --- p.88 / Chapter 6.10 --- Summary --- p.90 / Chapter Chapter 7- --- Conclusion --- p.91 / References --- p.94

Random Telegraph Signal (RTS) noise is a random noise source defined by discrete and metastable changes in the magnitude of a signal. Though observed in a variety of physical processes, RTS is of particular interest to image sensor fabrication where progress in the suppression of other noise sources has elevated its noise contribution to the point of approaching the limiting noise source in scientific applications. There have been two basic physical sources of RTS noise reported in image sensors. The first involves a charge trap in the oxide layer of the source follower in a CMOS image sensor. The capture and emission of a charge changes the conductivity across the source follower, altering the signal level. The second RTS source in image sensors has been reported in CCD and CMOS architectures and involves some metastability in the structure of the device within the light collection area. A methodology is presented for the analysis of RTS noise. Utilizing wavelets, a time-based signal has white noise removed, while RTS transitions are preserved. This allows for the simple extraction of RTS parameters, which provide valuable insight into defects in semiconductor devices. The scheme is used to extract RTS transition amplitudes and time constants from radiation damaged CMOS image sensor pixels. Finally, the generation of ionizing radiation induced RTS centers is investigated and discussed. Surprisingly, the number of RTS centers does not scale linearly with absorbed dose, but instead follows a quadratic dependence. The implications and possible mechanisms behind the generation of these RTS centers are discussed.

Dissertation (Ph.D.)--Auburn University, / Abstract. Vita. Includes bibliographic references (p.102-108).