京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第21649号 / 医博第4455号 / 新制||医||1034(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 宮本 享, 教授 森田 智視, 教授 鈴木 実 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Context: On airport runways, monitoring is done by Precision Runway Monitor (PRM) method with the help of radar. Most of the airports are built near the forests so there is a greater chance of mam-mal intrusion onto the runways leading to massive accidents. At many airports, there are applied old traditional, mostly manual methods in detecting mammals on the runway. Accidents caused by wildlife strikes between aircraft and mammals are increasing day to day, and this is approximately 3%-10% of all reported collisions [1]. We propose a system that monitors the airport runway by detecting mammals. Objectives: The main objective of this project is to investigate and evaluate the possibility of using thermal vision methods to detect the obstacles encountered on the runways. The system should work in real time. Methods: Mammals detection can be done by using a thermal camera with a thermal sensitivity of less than 50mK and a resolution of 640 x 480 pixels. The thermal camera uses an uncooled microbolometer sensor which is lighter, consumes less power and can see through almost all weather conditions like mist, fog, snow etc. Machine Learning based algorithms like background subtraction are used in detecting the mammal, and contours are used to estimate the size and distance. Results: As a result, the mammals moving on the runway can be detected at a distance of up to 400 m. The system estimates a distance of a moving animal and its size with an accuracy of around 90%. Conclusions: A runway monitoring system is needed to prevent wildlife strikes in airports. The proposed system prevents accidents to some extent. However, further tests are required before its commercialisation. There is a need for further quantitative and qualitative validation of the models in full-scale industry trials.

Identification of genes specific to fiber development would improve the efforts in developing cotton plants with superior fiber quality. Through genetic introgression, 17 interspecific chromosome substitution lines (CS-B lines) of upland cotton in G. hirsutum (TM-1) have been developed and released recently. These substitution lines have TM1 as background and contain either whole chromosome or chromosome arms of G. barbadense (line 3-79) chromosomes. CS-B25 has chromosome 25 from G. barbadense substituted into TM-1 G. hirsutum was reported to show superior fiber properties. In this study, suppression subtraction hybridization (SSH) combined with Affymetrix cotton genome microarray arrays were used to identify differentially expressed genes in CS-B25. An SSH fiber cDNA library was constructed with differentially expressed genes identified in CS-B25. Microarray analysis showed that 23 genes were up-regulated and 9 down-regulated. Majority of these genes were involved in Ethylene signal pathway, Ubiquitin-proteasome pathway and cell wall synthesis.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23567号 / 医博第4781号 / 新制||医||1054(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 花川 隆, 教授 永井 純正, 教授 渡邊 直樹 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

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Correct insertion of computer graphics into live-action broadcasts of outdoor sports requires precise knowledge of the foreground, i.e. players present in the scene. This thesis proposes a foreground detection and segmentation- framework with focus on real-time performance for 1080p resolution. A dataset consisting of four scenes; single-, multi-segment-, transcending-foreground and a light-witch scene all with dynamic backgrounds was constructed together with 26 ground-truths. Results show that the framework should run internally at 288p using GPU acceleration with geometrical nearest-neighbour-interpolation to attain real-time-capability. To maximize accuracy of the results, the framework uses two instances of OpenCV MOG2 in parallel on differently downsampled frames that are bitwise-joined to increase robustness. A set of morphological operations provides post-processing to get spatial coherence and a specific turf- consideration gives accurate contours. Thanks to additional camera- operator input, a crude distance-estimate lets foreground segments fade into background at a predetermined depth. The framework suffers from inaccurate segmentation during rapid light-switches, but recovers in a matter of seconds like the 'vanilla' MOG algorithm. For the specific scenes the framework provides excellent performance, especially considering the light-switch scene by comparison to the MOG-algorithm. For non-specific scenes of the 'BMC 2012' performance does not exceed the current state-of-the-art. / Korrekt placering av datorgrafik i video för tv-produktion kräver god känndedom om aktuell förgrund. Denna avhandling föreslår ett förgrundsdetektions- och segmenterings- ramverk med fokus på realtidsbearbetning av full-HD upplöst sport i utomhusmiljö. För utvärdering skapades ett dataset bestående av fyra scener; singel-, multisegment-, avlägsnande-förgrund och en ljusomväxlingsscen tillsammans med 26 referensförgrunder. För att erhålla realtidsbearbetning skall ramverket internt nyttja 288p upplösning med GPU acceleration och geometrisk närmaste-granne-interpolation. Resultaten visade att maximal noggranhet och ökad robusthet erhölls med två instanser av OpenCV MOG2 arbetandes parallellt på olikt nerskalade bilder för att därefter pixelvis förenas. För att erhålla sammanhängande förgrundssegment nyttjades morfologiska operationer på den binära sammansatta förgrunden vilket tillsammans med en specifik gräskantskorrektion ger precisa konturer. Tack vare givna kameraparametrar kan djupet till förgrundselementen uppskattas därmed låts de övergå till bakgrund för ett visst djupt. Ramverket lider av oprecis segmententering vid snabba ljusomväxlingar, men återhämtar sig när bakgrundsmodellen uppdaterats till de nya ljusförutsättningarna. För ovan nämnda specifika scener presterar ramverket utmärkt, speciellt med avseende på ljusomväxlingen, där prestandan är flerfaldigt bättre än den enskilda 'MOG'-metoden. För generella scener ur 'BMC 2012' datasetet presterar vår metod dock inte bättre än state-of-the-art.

The idea for my Thesis project sprung from a trip I made last June around the Northernmost of the great Japanese islands, Hokkaido. Due to unexpectedly rough weather conditions we had to cancel many of the hikes and climbs that we had planned and instead immerse our bodies and senses in the wonderful onsen culture of Japan. These bathing institutions really lured my architectural interest and especially everything surrounding the actual bathing. The connectivity between architecture and nature, the almost profanely directed rituals, and the special relationship between water, space and materiality was just some of the aspects that originated this project. To create a contemporary bathhouse in Stockholm using the teachings around the onsen and traditional Japanese architecture.

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Raman spectroscopy is an effective optical analysis of the biochemically specific characterization of tissues without contrast agents or exogenous dyes. Applications of Raman spectroscopy include analysis and biomarker investigation, disease diagnosis and surgical guidance. One major challenge in Raman spectroscopy is removing inherent fluorescence background present in samples to acquire Raman signatures. In some tissues, like liver, kidney and darkly pigment skin, the auto-fluorescence background is strong enough to overwhelm the Raman peaks in conventional Near-Infrared (NIR) Raman systems. Recent publications have shown that using Raman systems with excitation sources with wavelengths beyond 830 nm and short-wave infrared (SWIR) InGaAs Array detectors resulted in dramatically reduced auto-fluorescence. The unique characteristics of Raman signals collected from SWIR systems versus NIR Raman systems requires inspection of the suitability of spectral pre-processing techniques. This thesis focused on the development of spectral processing techniques at three different steps; 1) detector background & noise reduction; 2) Auto-fluorescence background subtraction; 3) detection of outlier measurements to assist statistical classification. Detector background and noise reduction was compared between two different techniques, and a direct subtraction method resulted in better performance to reduce fixed pattern noise unique to InGaAs arrays. For the aim 2, three different algorithms for fluorescence background removal were developed, and a modified polynomial fitting method was found to be most appropriate for the low signal-to-noise (SNR) spectra. Finally, local outlier factor(LOF), a multivariate statistical outlier metric, was implemented in a two-stage fashion, and shown to be effective at identifying raw measurement errors and Raman spectra outliers. The overall outcome of this thesis was the evaluation of spectral processing techniques for SWIR Raman spectroscopy systems, and the development of specific techniques to optimize data quality and best prepare spectra for statistical analysis. / Bioengineering