The human body possesses a highly developed range of senses that help orienting oneself in everyday life. Especially when it comes to navigating, perceiving and reacting to the world around us, people tend to rely mostly on their vision and hearing. Suffering from an impairment of either one, or both of the predominating senses means having to counterbalance this constraint. People suffering from blindness and deaf-blindness compensate their impairment mainly by relying on their haptic perception. In this case, information is usually communicated by braille or vibrotactile means. To offer another non-visual and non-audial communication concept this thesis work introduces, the thermal textile pixels. A thermal textile pixel consists of an external thermal device, able to generate hot and cold thermal impulses, and a textile interface to transmit the signal. In order to design such thermal textile pixel it was crucial to be aware of the thermal transfer occurring through and within an textile. Numerous research studies have examined the thermal properties of textiles, especially in the context of clothing comfort, thermal comfort. Nevertheless, it should be considered that as a thermal textile pixel, the textile forms part of a system, governed by many parameters. Therefore, for designing such a device it is important to be aware of the temporal and spatial resolution of the thermal transmitted signal. These characteristics are influenced by multiple textile parameters. For this purpose, a thermal study has been performed investigating in- and out-of-plane signal transmission by textiles in combination with an external thermal device. Using an external thermal device such as a Peltier element allowed to expose the specimens to heating as well as to active cooling. Different knitted structures and material combinations have been examined to gain a first impression on the behaviour of thermal pixels. It was found that thickness and density were the most influential factors for out-of-plane heat transfer. In-plane was found influenced mainly by fibre conductivity. An anisotropic behaviour was noted in-plane, as well as between in- and out-of-plane for heat transfer. Investigating active cooling signals, it was found that a significant decline of performance was noted for all specimens. Plain PA was found to be most suitable for the transmission of heat signals. But did not perform equally well during active cooling phases. Plain Shieldex was observed to perform most steady during heating and active cooling.

[pt] A crescente disponibilidade de dados de sensoriamento remoto vem criando novas oportunidades e desafios em aplicações de monitoramento de processos naturais e antropogénicos em escala global. Nos últimos anos, as técnicas de aprendizado profundo tornaram-se o estado da arte na análise de dados de sensoriamento remoto devido sobretudo à sua capacidade de aprender automaticamente atributos discriminativos a partir de grandes volumes de dados. Um dos problemas chave em análise de imagens é a segmentação semântica, também conhecida como rotulação de pixels. Trata-se de atribuir uma classe a cada sítio de imagem. As chamadas redes totalmente convolucionais de prestam a esta função. Os anos recentes têm testemunhado inúmeras propostas de arquiteturas de redes totalmente convolucionais que têm sido adaptadas para a segmentação de dados de observação da Terra. O presente trabalho avalias cinco arquiteturas de redes totalmente convolucionais que representam o estado da arte em segmentação semântica de imagens de sensoriamento remoto. A avaliação considera dados provenientes de diferentes plataformas: veículos aéreos não tripulados, aeronaves e satélites. Cada um destes dados refere-se a aplicações diferentes: segmentação de espécie arbórea, segmentação de telhados e desmatamento. O desempenho das redes é avaliado experimentalmente em termos de acurácia e da carga computacional associada. O estudo também avalia os benefícios da utilização do Campos Aleatórios Condicionais (CRF) como etapa de pósprocessamento para melhorar a acurácia dos mapas de segmentação. / [en] The increasing availability of remote sensing data has created new opportunities and challenges for monitoring natural and anthropogenic processes on a global scale. In recent years, deep learning techniques have become state of the art in remote sensing data analysis, mainly due to their ability to learn discriminative attributes from large volumes of data automatically. One of the critical problems in image analysis is the semantic segmentation, also known as pixel labeling. It involves assigning a class to each image site. The so-called fully convolutional networks are specifically designed for this task. Recent years have witnessed numerous proposals for fully convolutional network architectures that have been adapted for the segmentation of Earth observation data. The present work evaluates five fully convolutional network architectures that represent the state of the art in semantic segmentation of remote sensing images. The assessment considers data from different platforms: unmanned aerial vehicles, airplanes, and satellites. Three applications are addressed: segmentation of tree species, segmentation of roofs, and deforestation. The performance of the networks is evaluated experimentally in terms of accuracy and the associated computational load. The study also assesses the benefits of using Conditional Random Fields (CRF) as a post-processing step to improve the accuracy of segmentation maps.

This thesis contains a study of the robustness of the artificial neural network used in the ATLAS track reconstruction algorithm as a tool to recover tracks in dense environments. Different variations, motivated by potential discrepancies between data and simulation, are performed to the neural network’s input while monitoring the corresponding change in the output. Within reasonable variation magnitudes, the neural networks prove to be robust to most variations. In addition, a measurement of charged particle distributions is summarised. This is one of the first such measurements carried out for proton-proton colli- sions at √s = 13 TeV, limited to a phase space defined by transverse momentum pT &gt; 100 MeV and absolute pseudorapidity |η| &lt; 2.5. Tracks are corrected for de- tector inefficiencies and unfolded to particle-level. The result is compared to the prediction of different models. Overall, the EPOS and Pythia 8 A2 models show the best agreement with the data. / Spår från elektriskt laddade partiklar rekonstrueras i ATLAS genom att kombinera mätningar från de innersta subdetektorerna. I de extrema miljöer som skapas i proton-proton-kollisionerna i Large Hadron Collider vid CERN är det av yttersta vikt att algoritmen för att rekonstruera spår är högpresterande. Uppgiften är särskilt svår i partikelrika miljöer där flera partiklar färdas nära varandra, åtskilda av avstånd jämförbara med storleken på detektorns utläsningselement. Ett artificiellt neuralt nätverk används i algoritmen för att klassificera mätdata från pixeldetektorn, belägen närmast interaktionspunkten, för att lyckas identifiera spår i partikelrika miljöer som annars hade gått förlorade. I denna avhandling utreds det neurala nätverkets stabilitet. Dess känslighet studeras genom att manuellt manipulera dess indata och därefter utvärdera dess resultat. Nätverket tränas med simulerad data. Variationerna i indata är utformade för att undersöka skillnader mellan data och simulering, orsakade av osäkerheter i simuleringsmodellen eller osäkerheter i pixeldetektorns kalibrering. Av de undersökta variationerna har en osäkerhet i skalan eller utläsningströskeln för pixeldetektorns kalibrering den största effekten på nätverkets resultat. Andra variationer har en betydligt mindre påverkan. Avhandlingen presenterar också en studie av distributioner av elektriskt laddade partiklar producerade i proton-proton-kollisioner. Det är en av de första studierna av partikeldistributioner för Large Hadron Colliders andra körning med mass-centrum-energi √s = 13 TeV. Mätningen är begränsad till fasrymden definierad av en transversell rörelsemängd pT &gt; 100 MeV, och absolut rapiditet |η| &lt; 2.5. Spår av partiklar rekonstrueras och korrigeras för detektorns ineffektiviteter för att presenteras på partikelnivå. Dessa jämförs sedan med förutsägelser från olika modeller. Modellerna EPOS och Pythia 8 A2 är generellt de som bäst överensstämmer med data. Författaren har undersökt partiklar som migrerar in och ut ur fasrymden. Andelen spår associerade till partiklar som migrerat utifrån uppskattas med simulerad data, till som mest 10% nära fasrymdens gränser. Osäkerheten på denna andel uppskattas till att vara som mest 4.5%, huvudsakligen orsakad av osäkerheten på mängden material i de innersta subdetektorerna. / <p>QC 20160817</p>

Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.

Theoretical part of this project is focused on rendering of volumetric data. It compares and appraise individual methods and thus readers get a good basic knowledge of commonnest causes of problems. Texture Mapped Volume Rendering and Volume Ray-casting methods are described in detail and the latter method is used in implementation of graphic system designed in this thesis. Secondary goals of this work are usage of less powerful hardware for volume-rendering, methods of optimization and dynamic change of output quality.

No description available.

This licentiate thesis concerns the development and characterisation of X-ray imaging detector systems. “Colour” X-ray imaging opens up new perspectives within the fields of medical X-ray diagnosis and also in industrial X-ray quality control. The difference in absorption for different “colours” can be used to discern materials in the object. For instance, this information might be used to identify diseases such as brittle-bone disease. The “colour” of the X-rays can be identified if the detector system can process each X-ray photon individually. Such a detector system is called a “single photon processing” system or, less precise, a “photon counting system”. With modern technology it is possible to construct photon counting detector systems that can resolve details to a level of approximately 50 µm. However with such small pixels a problem will occur. In a semiconductor detector each absorbed X-ray photon creates a cloud of charge which contributes to the picture achieved. For high photon energies the size of the charge cloud is comparable to 50 µm and might be distributed between several pixels in the picture. Charge sharing is a key problem since, not only is the resolution degenerated, but it also destroys the “colour” information in the picture. The problem involving charge sharing which limits “colour” X-ray imaging is discussed in this thesis. Image quality, detector effectiveness and “colour correctness” are studied on pixellated detectors from the MEDIPIX collaboration. Characterisation measurements and simulations are compared to be able to understand the physical processes that take place in the detector. Simulations can show pointers for the future development of photon counting X-ray systems. Charge sharing can be suppressed by introducing 3D-detector structures or by developing readout systems which can correct the crosstalk between pixels.

[pt] A eficiência de culturas pode ser aprimorada monitorando-se suas condições de forma contínua e tomando-se decisões baseadas em suas análises. Os dados para análise podem ser obtidos através de sensores de imagens e o processo de monitoramento pode ser automatizado utilizando-se algoritmos de reconhecimento de imagem com diferentes níveis de complexidade. Alguns dos algoritmos de maior êxito estão relacionados a abordagens supervisionadas de aprendizagem profunda (Deep Learning) as quais utilizam formas de Redes Neurais de Convolucionais (CNNs). Nesta dissertação de mestrado, empregaram-se modelos de aprendizagem profunda supervisionados para classificação, regressão, detecção de objetos e segmentação semântica em tarefas de monitoramento de culturas, utilizando-se amostras de imagens obtidas através de três níveis distintos: Satélites, Veículos Aéreos Não Tripulados (UAVs) e Robôs Terrestres Móveis (MLRs). Ambos satélites e UAVs envolvem o uso de imagens multiespectrais. Para o primeiro nível, implementou-se um modelo CNN baseado em Transfer Learning para a classificação de espécies vegetativas. Aprimorou-se o desempenho de aprendizagem do transfer learning através de um método de análise estatística recentemente proposto. Na sequência, para o segundo nível, implementou-se um algoritmo segmentação semântica multitarefa para a detecção de lavouras de cana-de-açúcar e identificação de seus estados (por exemplo, saúde e idade da cultura). O algoritmo também detecta a vegetação ao redor das lavouras, sendo relevante na busca por ervas daninhas. No terceiro nível, implementou-se um algoritmo Single Shot Multibox Detector para detecção de cachos de tomate. De forma a avaliar o estado dos cachos, utilizaram-se duas abordagens diferentes: uma implementação baseada em segmentação de imagens e uma CNN supervisionada adaptada para cálculos de regressão capaz de estimar a maturação dos cachos de tomate. De forma a quantificar cachos de tomate em vídeos para diferentes estágios de maturação, empregou-se uma implementação de Região de Interesse e propôs-se um sistema de rastreamento o qual utiliza informações temporais. Para todos os três níveis, apresentaram-se soluções e resultados os quais superam as linhas de base do estado da arte. / [en] Crop efficiency can be improved by continually monitoring their state and making decisions based on their analysis. The data for analysis can be obtained through images sensors and the monitoring process can be automated by using image recognition algorithms with different levels of complexity. Some of the most successful algorithms are related to supervised Deep Learning approaches which use a form of Convolutional Neural Networks (CNNs). In this master s dissertation, we employ supervised deep learning models for classification, regression, object detection, and semantic segmentation in crop monitoring tasks, using image samples obtained through three different levels: Satellites, Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Both satellites and UAVs levels involve the use of multispectral images. For the first level, we implement a CNN model based on transfer learning to classify vegetative species. We also improve the transfer learning performance by a newly proposed statistical analysis method. Next, for the second level, we implement a multi-task semantic segmentation algorithm to detect sugarcane crops and infer their state (e.g. crop health and age). The algorithm also detects the surrounding vegetation, being relevant in the search for weeds. In the third level, we implement a Single Shot Multibox detector algorithm to detect tomato clusters. To evaluate the cluster s state, we use two different approaches: an implementation based on image segmentation and a supervised CNN regressor capable of estimating their maturity. In order to quantify the tomato clusters in videos at different maturation stages, we employ a Region of Interest implementation and also a proposed tracking system which uses temporal information. For all the three levels, we present solutions and results that outperform state-of-the art baselines.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第25284号 / 工博第5243号 / 京都大学大学院工学研究科原子核工学専攻 / (主査)教授 田中 浩基, 教授 堀 順一, 准教授 土田 秀次 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

In this thesis, the hugely popular online art experiment r/Place is analyzed as an interesting example of digital art making and collaborative creation on a massive scale. This is done in an effort to contribute to the exploration of how art making and the artist’s role is shaped by a digital component as well as its accompanying opportunities, in this case collaboration. In the analysis the creations and participation of ten Reddit-communities taking part in the experiment are studied through the lens of semiotics and performativity. Through the analysis it was concluded that the art-making and resulting creations were not only shaped by the digital and shared nature of the format but also by the limited resources available, resulting in a strongly competitive atmosphere. The competitiveness highlights how two concepts often seen as opposites, creation and destruction, can be two sides of the same process depending on your perspective.