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The use of Silicon Carbide Field Effect Transistor (SiC-FET) sensors in cyclic operation is a proven way to quantify different gases. The standard workflow involves extracting shape-defining features such as averages and slopes of the sensor signal. This work’s main goal is to verify if frequency modulation can be used to simultaneously quantify Nitric Oxide (NO), Nitrogen Dioxide (NO2) and Ammonia (NH3). Linear models were chosen, namely: Ordinary Least Squares (OLS), Principal Components Regression (PCR), Partial Least Squares Regression (PLSR) and Ridge regression. Results indicate that these models fail to predict concentrations completely for every gas. Analysis indicates that the features are not linear in terms of concentrations. This work is concluded by recommending a few other alternatives before discarding frequency cycling completely: non-parametric models of regression and different frequency regime, namely the use of triangular waves in future experiments.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23813号 / 医博第4859号 / 新制||医||1059(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 中本 裕士, 教授 花川 隆, 教授 大森 孝一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Nowadays inspections of civil engineering structures are performed manually at close range to be able to assess damages. This requires specialized equipment that tends to be expensive and to produce closure of the bridge. Furthermore, manual inspections are time-consuming and can often be a source or risk for the inspectors. Moreover, manual inspections are subjective and highly dependent on the state of mind of the inspector which reduces the accuracy of this kind of inspections. Image-based inspections using cameras or unmanned aerial vehicles (UAV) combined with image processing have been used to overcome the challenges of traditional manual inspections. This type of inspection has also been studied with the use of machine learning algorithms to improve the detection of damages, in particular cracks. This master’s thesis presents an approach that combines different aspects of the inspection, from the data acquisition, through the crack detection to the quantification of essential parameters. To do this, both digital cameras and a UAV have been used for data acquisition. A convolutional neural network (CNN) for the identification of cracks is used and subsequently, different quantification methods are explored to determine the width and length of the cracks. The results are compared with control measures to determine the accuracy of the method. The results present low to no false negatives when using the CNN to identify cracks. The quantification of the identified cracks is performed obtaining the highest accuracy estimation for 0.2mm cracks.

<p> Magnetic Resonance Imaging (MRI) velocity quantification is addressed in part I of this thesis. In simple MR imaging, data is collected and tissue densities are displayed as images. Moving tissue creates signals which appear as artifacts in the images. In velocity imaging, more data is collected and phase differences are used to quantify the velocity of tissue components. The problem is described and a novel formulation of a regularized, nonlinear inverse problem is proposed. Both Tikhonov and Total Variation Regularization are discussed. Results of numerical simulations show that significant noise reduction is possible.</p> <p> The method is firstly verified with MATLAB. A number of experiments are carried out with different regularization parameters, different magnetic fields and different noise levels. The experiments show that the stronger the complex noise is, the stronger the magnetic field requires for estimating the velocity. The regularization parameter also plays an important role in the experiments. Given the noise level and with an appropriate value of regularization parameter, the estimated velocity converges to ideal velocity very quickly. A proof-of-concept implementation on the Cell BE processor is described, quantifying the performance potential of this platform.</p> <p> The second part of this thesis concerns the evaluation of an elementary function library. Since CBE SPU is designed for compute intensive applications, the well developed Math functions can help developer program and save time to take care other details. Dr. Anand's research group in McMaster developed 28 math functions for CBE SPU. The test tools for accuracy and performance were developed on CBE. The functions were tuned while testing. The functions are either competitive or an addition to the existing SDK1.1 SPU math functions.</p> / Thesis / Master of Applied Science (MASc)