This thesis is focused on application development for simulation lenses’ optical distortions and also for creation own patterns. In the first part are discussed common problems of optical distortion and concept of software analysis. Further is described realization and implementation of particular modules in the application. In the end is show up graphical user interface and its functionality.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20908号 / 理博第4360号 / 新制||理||1626(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 樽家 篤史, 教授 佐々木 節, 教授 川合 光 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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Digital video is becoming extremely important nowadays and its importance has greatly increased in the last two decades. Due to the rapid development of information and communication technologies, the demand for Ultra-High Definition (UHD) video applications is becoming stronger. However, the most prevalent video compression standard H.264/AVC released in 2003 is inefficient when it comes to UHD videos. The increasing desire for superior compression efficiency to H.264/AVC leads to the standardization of High Efficiency Video Coding (HEVC). Compared with the H.264/AVC standard, HEVC offers a double compression ratio at the same level of video quality or substantial improvement of video quality at the same video bitrate. Yet, HE-VC/H.265 possesses superior compression efficiency, its complexity is several times more than H.264/AVC, impeding its high throughput implementation. Currently, most of the researchers have focused merely on algorithm level adaptations of HEVC/H.265 standard to reduce computational intensity without considering the hardware feasibility. What’s more, the exploration of efficient hardware architecture design is not exhaustive. Only a few research works have been conducted to explore efficient hardware architectures of HEVC/H.265 standard. In this dissertation, we investigate efficient algorithm adaptations and hardware architecture design of HEVC intra encoders. We also explore the deep learning approach in mode prediction. From the algorithm point of view, we propose three efficient hardware-oriented algorithm adaptations, including mode reduction, fast coding unit (CU) cost estimation, and group-based CABAC (context-adaptive binary arithmetic coding) rate estimation. Mode reduction aims to reduce mode candidates of each prediction unit (PU) in the rate-distortion optimization (RDO) process, which is both computation-intensive and time-consuming. Fast CU cost estimation is applied to reduce the complexity in rate-distortion (RD) calculation of each CU. Group-based CABAC rate estimation is proposed to parallelize syntax elements processing to greatly improve rate estimation throughput. From the hardware design perspective, a fully parallel hardware architecture of HEVC intra encoder is developed to sustain UHD video compression at 4K@30fps. The fully parallel architecture introduces four prediction engines (PE) and each PE performs the full cycle of mode prediction, transform, quantization, inverse quantization, inverse transform, reconstruction, rate-distortion estimation independently. PU blocks with different PU sizes will be processed by the different prediction engines (PE) simultaneously. Also, an efficient hardware implementation of a group-based CABAC rate estimator is incorporated into the proposed HEVC intra encoder for accurate and high-throughput rate estimation. To take advantage of the deep learning approach, we also propose a fully connected layer based neural network (FCLNN) mode preselection scheme to reduce the number of RDO modes of luma prediction blocks. All angular prediction modes are classified into 7 prediction groups. Each group contains 3-5 prediction modes that exhibit a similar prediction angle. A rough angle detection algorithm is designed to determine the prediction direction of the current block, then a small scale FCLNN is exploited to refine the mode prediction.

The goal of this thesis is to use fringe-pattern phase analysis to calibrate the distortion of a camera lens. The benefit of using this method is that the distortion can be calculated using data from each individual pixel and the methodology does not need any model. The phase used to calibrate the images is calculated in two different ways, either utilizing the monogenic signal or through fringe-pattern phase analysis. The calibration approaches were also validated through different methods. Primarily by utilizing the Hough transform and calibrating simulated distortion. The thesis also introduces a validation approach utilizing the phase orientation calculated through the monogenic signal. The thesis also implements different approaches such as flat field correction to limit the impact of the image sensor noise to mitigate the phase noise. It is also investigated which fringe-pattern frequencies are best suited for calibration through comparative analysis. The comparative analysis identified problems with too high and low frequencies of the fringe-patterns when calibrating using fringe-pattern phase analysis.

An effective approach to determine optimum welding process parameters is implementation of advanced computer aided engineering (CAE) tool that integrates efficient optimization techniques and numerical welding simulation. In this thesis, an automated computational methodology to determine optimum arc welding process parameters is proposed. It is a coupled Genetic Algorithms (GA) and Finite Element (FE) based optimization method where GA directly utilizes output responses of FE based welding simulations for iterative optimization. Effectiveness of the method has been demonstrated by predicting optimum parameters of a lap joint specimen of two thin steel plates and automotive structure of nonlinear welding path for minimum distortion. Three dimensional FE models have been developed to simulate the arc welding process and subsequently, the models have been used by GA as the evaluation model for optimization. The optimization results show that such a CAE based methodology can contribute to facilitate the product design and development.

Concepts of tree coding and of rate-distortion theory are applied to the problem of the transmission of analog signals over digital channels. Coding schemes are developed which yield improvements of up to six dB in signal-to-noise ratio over conventional techniques for the reproduction of speech waveforms. / Thesis / Master of Engineering (MEngr)

Anorexia Nervosa (AN) is a severe and complex psychiatric disorder characterized by significantly low body weight, an intense fear of gaining weight, and a distorted body image. Despite otherwise successful treatments, body image distortions are often persistent and are considered a critical factor in the persistence, development, and relapse of AN. The present review outlines the current literature on tactile distance processing in AN to investigate and discuss its proposed role in the persistence and development of a distorted body image. The discussion of literature further discusses its implications for cognitive neuroscience since AN patients display similar symptoms as patients diagnosed with body image-related neuropsychiatric disorders with damage to the parietal lobe. The evidence suggests that individuals with AN have poorer tactile distance estimation than controls but do not differ regarding tactile detection. This review outlines the current literature, identifies gaps within the methodology, and proposes novel directions for future research.