京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18654号 / 工博第3963号 / 新制||工||1610(附属図書館) / 31568 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 准教授 横松 宗太 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Non-invasive techniques to explore intracranial compliance and pressure have been extensively explored in recent years. Previous techniques have used expensive technologies to make these measurements, often with difficulty. We present a novel, inexpensive provocative test to observe trends in intracranial compliance measurement targeted towards the treatment and management of hydrocephalus. Two techniques are proposed which derive data from the digital and supraorbital arteries as well as tympanic membrane displacement. This requires the use of two photo-plethysmographic sensors and a TMD sensor. A common tilt table apparatus is used to methodically and artificially increase intracranial pressure to stress the cranial system during the test. The results from this test are computed using a digital signal processing algorithm to determine phase difference between the waveforms. Further research is also proposed.

Discrete Fourier Transforms (DFTs) are used in a wide variety of dif-ferent scientific areas. In addition, there is an ever increasing demand on fast and effective ways of computing DFT problems with large data sets. The FFTW library is one of the most common used libraries when computing DFTs. It adapts to the system architecture and predicts the most effective way of solving the input problem. Previous studies have proved the FFTW library to be superior to other DFT solving libraries. However, not many have specifically examined the cache memory performance, which is a key factor for overall performance. In this study, we examined the cache memory utilization when computing 1-D complex DFTs using the FFTW library. Testing was done using bench FFT, Linux Perf and testing scripts. The results from this study show that cache miss ratio increases with problem size when the input size is smaller than the theoretical input size matching the cache capacity. This is also verified by the results from the L2 prefetcher miss ratio. However, the study show that cache miss ratio stabilizes when exceeding the cache capacity. In conclusion, it is possible to use bench FFT and Linux Perf to measure cache memory utilization. Also, the analysis shows that cache memory performance is good when computing 1-D complex DFTS using the FFTW library, since the miss ratios stabilizes at low values. However, we suggest further examination ofthe memory behaviour for DFT computations using FFTW with larger input sizes and a more in-depth testing method. / Diskret Fouriertransform (DFT) används inom många olika vetenskapliga områden. Det finns en ökande efterfrågan på snabba och effektiva sätt att beräkna DFT-problem med stora mängder data. FFTW-biblioteket är ett av de mest använda biblioteken vid beräkning av DFT-problem. FFTW-biblioteket anpassar sig till systemarkitekturen och försöker generera det mest effektiva sättet att lösa ett givet DFT-problem. Tidigare studier har visat att FFTW-biblioteket är effektivare än andra bibliotek som kan användas för att lösa DFT-problem. Däremot har studierna inte fokuserat på minneshanteringen, vilket är en nyckelfaktor för den generella prestandan. I den här studien undersökte vi FFTW-bibliotekets cache-minneshanteringen vid beräkning av 1-D komplexa DFT-problem. Tester utfördes med hjälp av bench FFT, Linux Perf och testskript. Resultaten från denna studie visar att cache-missförhållandet ökar med problemstorleken när problemstorleken ärmindre än den teoretiska problemstorleken som matchar cachekapaciteten. Detta bekräftas av resultat från L2-prefetcher-missförhållandet. Studien visar samtidigt att cache-missförhållandet stabiliseras när problemstorleken överskrider cachekapaciteten. Sammanfattningsvis går det att argumentera för att det är möjligt att använda bench FFT och Linux Perf för att mäta cache-minneshanteringen. Analysen visar också att cache-minneshanteringen är bra vid beräkning av 1-D komplexa DFTs med hjälp av FFTW-biblioteket eftersom missförhållandena stabiliseras vid låga värden. Vi föreslår dock ytterligare undersökning av minnesbeteendet för DFT-beräkningar med hjälp av FFTW där problemstorlekarna är större och en mer genomgående testmetod används.

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Systems biology aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins and metabolites. Comprehensive visualization tools for exploring multivariate data are needed to gain insight into the physiological processes reflected in these molecular profiles. Data fusion methods are required to integratively study high-throughput transcriptomics, metabolomics and proteomics data combined before systems biology can live up to its potential. In this work I explored mathematical and statistical methods and visualization tools to resolve the prominent issues in the nature of systems biology data fusion and to gain insight into these comprehensive data. In order to choose and apply multivariate methods, it is important to know the distribution of the experimental data. Chi square Q-Q plot and violin plot were applied to all M. truncatula data and V. vinifera data, and found most distributions are right-skewed (Chapter 2). The biplot display provides an effective tool for reducing the dimensionality of the systems biological data and displaying the molecules and time points jointly on the same plot. Biplot of M. truncatula data revealed the overall system behavior, including unidentified compounds of interest and the dynamics of the highly responsive molecules (Chapter 3). The phase spectrum computed from the Fast Fourier transform of the time course data has been found to play more important roles than amplitude in the signal reconstruction. Phase spectrum analyses on in silico data created with two artificial biochemical networks, the Claytor model and the AB2 model proved that phase spectrum is indeed an effective tool in system biological data fusion despite the data heterogeneity (Chapter 4). The difference between data integration and data fusion are further discussed. Biplot analysis of scaled data were applied to integrate transcriptome, metabolome and proteome data from the V. vinifera project. Phase spectrum combined with k-means clustering was used in integrative analyses of transcriptome and metabolome of the M. truncatula yeast elicitation data and of transcriptome, metabolome and proteome of V. vinifera salinity stress data. The phase spectrum analysis was compared with the biplot display as effective tools in data fusion (Chapter 5). The results suggest that phase spectrum may perform better than the biplot. This work was funded by the National Science Foundation Plant Genome Program, grant DBI-0109732, and by the Virginia Bioinformatics Institute. / Ph. D.

Jump-diffusion models can under certain assumptions be expressed as partial integro-differential equations (PIDE). Such a PIDE typically involves a convection term and a nonlocal integral like for the here considered models of Merton and Kou. We transform the PIDE to eliminate the convection term, discretize it implicitly using finite differences and the second order backward difference formula (BDF2) on a uniform grid. The arising dense linear system is solved by an iterative method, either a splitting technique or a circulant preconditioned conjugate gradient method. Exploiting the Fast Fourier Transform (FFT) yields the solution in only $O(n\log n)$ operations and just some vectors need to be stored. Second order accuracy is obtained on the whole computational domain for Merton's model whereas for Kou's model first order is obtained on the whole computational domain and second order locally around the strike price. The solution for the PIDE with convection term can oscillate in a neighborhood of the strike price depending on the choice of parameters, whereas the solution obtained from the transformed problem is stabilized. / Master of Science

Yes / Background and Objective Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. Methods First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). Results The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p < 0.0001) and a Bland–Altman plot shows that 95% of the data are between the 2SD agreement lines. Conclusions We demonstrate the effectiveness and robustness of the CEAS system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image.

The rise of machine learning has seen an increase in digital methods for use within health care. Arrythmia detection is one of the areas where this increase is obvious. However, many machine learning methods for arrythmia detection utilise models that are computationally expensive, such as convolutional neural networks, CNNs. This thesis examines whether it is viable to use the Fast Fourier Transform to transform an Electrocardiogram, ECG, signal into its frequency components before training a neural network, NN, on the data. This could allow for a lower computational cost and wider availability of arrythmia detection technology. The results from the model were compared to that of a CNN trained on time domain data. The results show that the CCN model outperforms the NN trained on FFT transformed data but the performance of the model still indicates that valuable information about heart arrythmias does exist within the frequency space. This suggests a potential for future work on the subject.

We describe an implementation of a multi-threaded NFFT (nonequispaced fast Fourier transform) software library and present the used parallelization approaches. Besides the NFFT kernel, the NFFT on the two-sphere and the fast summation based on NFFT are also parallelized. Thereby, the parallelization is based on OpenMP and the multi-threaded FFTW library. Furthermore, benchmarks for various cases are performed. The results show that an efficiency higher than 0.50 and up to 0.79 can still be achieved at 12 threads.