508 research outputs found
Low-latency big data visualisation
Diese Arbeit hat sich zum Ziel gesetzt, Methoden aufzuzeigen, âBig-Dataâ-Archive zu organisieren und zentrale Elemente der enthaltenen Informationen zu visualisieren. Anhand von drei wissenschaftlichen Experimenten werde ich zwei âBig-Dataâ- Herausforderungen, Datenvolumen (Volume) und HeterogenitĂ€t (Variety), untersuchen und eine Visualisierung im Browser prĂ€sentieren, die trotz reduzierter Datenrate die wesentliche Information in den DatensĂ€tzen enthĂ€lt
Geometric data understanding : deriving case specific features
There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation.
This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned.
In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics.
The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa kÀytetÀÀn perinteisesti tarkkoja malleja, jolloin datassa esiintyvÀt epÀtarkkuudet edustavat melua. Toisessa perinteessÀ nojataan suuren datamÀÀrÀn tilastolliseen luonteeseen, jolloin geometrinen sÀÀnnönmukaisuus on datan sisÀsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epÀsuorasti. TÀmÀ työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. NÀmÀ ovat todellisia ongelmia, joissa geometrinen sisÀltö on tavoittamattomissa raakadatan tasolla.
TÀmÀ sisÀltö voitaisiin jossain mÀÀrin löytÀÀ ja mallintaa koneoppimisen keinoin, esim. syvÀoppimisen avulla, mutta joko geometria pitÀÀ kattaa suoraan nÀytteistÀmÀllÀ tai tarvitaan neuronien lisÀkerros geometrisia invariansseja varten. Geometrinen sisÀltö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenÀiseen dataesitykseen, jossa poikkeavat nÀytteet tai melu voidaan helposti erottaa.
TÀssÀ työssÀ tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. EnsimmÀinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiÀ piirteitÀ. Toinen esimerkki koskee projektiivisia menetelmiÀ kÀytettÀessÀ stereovideosignaalia uinnin analytiikkaan.
Tavoite on löytÀÀ merkityksellisiÀ paikallisen geometrian esityksiÀ, jotka samalla mahdollistavat muun geometrian ymmÀrrykseen perustuvan analyysin. Piirteet liittyvÀt suoraan johonkin geometriseen suureeseen, ja tÀmÀ helpottaa luonnollisella tavalla geometristen rajoitteiden kÀsittelyÀ, kuten vÀitöstyössÀ osoitetaan. Myös visualisointi ja lisÀpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lÀhestymistapa suo selkeÀn vertailumenetelmÀn perinteisemmille koneoppimisen lÀhestymistavoille, esim. hermoverkkomenetelmille. NeljÀnneksi, useimmat koneoppimismenetelmÀt voivat hyödyntÀÀ tÀssÀ työssÀ esitettyjÀ geometrisia piirteitÀ lisÀÀmÀllÀ ne muiden piirteiden joukkoon
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPRâSAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
Remote Sensing
This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
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Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications
The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data
New Global Perspectives on Archaeological Prospection
This volume is a product of the 13th International Conference on Archaeological Prospection 2019, which was hosted by the Department of Environmental Science in the Faculty of Science at the Institute of Technology Sligo. The conference is held every two years under the banner of the International Society for Archaeological Prospection and this was the first time that the conference was held in Ireland. New Global Perspectives on Archaeological Prospection draws together over 90 papers addressing archaeological prospection techniques, methodologies and case studies from 33 countries across Africa, Asia, Australasia, Europe and North America, reflecting current and global trends in archaeological prospection. At this particular ICAP meeting, specific consideration was given to the development and use of archaeological prospection in Ireland, archaeological feedback for the prospector, applications of prospection technology in the urban environment and the use of legacy data. Papers include novel research areas such as magnetometry near the equator, drone-mounted radar, microgravity assessment of tombs, marine electrical resistivity tomography, convolutional neural networks, data processing, automated interpretive workflows and modelling as well as recent improvements in remote sensing, multispectral imaging and visualisation
Arquitetura multi-cùmara e multi-algoritmo para perceção visual a bordo do ATLASCAR2
Road detection is a crucial concern in Autonomous Navigation and Driving Assistance. Despite the multiple existing algorithms to detect the road, the literature does not offer a single effective algorithm for all situations. A global more robust set-up would count on multiple distinct algorithms running in parallel, or even from multiple cameras. Then, all these algorithmsâ outputs should be merged or combined to produce a more robust and informed detection of the road lane, so that it works in more situations than each algorithm by itself. This dissertation integrated in the ATLAS-CAR2 project, developed at the University of Aveiro, proposes a ROS-based architecture to manage and combine multiple sources of lane detection algorithms ranging from the algorithms that return the spatial localization of the road lane lines and those whose results are the navigable zone represented as a polygon. The architecture is fully scalable and has proved to be a valuable tool to test and parametrise individual algorithms. The combination of the algorithmsâ results used in this work uses a confidence based merging of individual detections.A deteçaÌo de estradas eÌ uma questaÌo crucial na NavegaçaÌo AutoÌnoma e na AssisteÌncia aÌ ConduçaÌo. Apesar de os muÌltiplos algoritmos existentes para detetar a estrada, a literatura naÌo oferece um uÌnico algoritmo eficaz para todas as situaçoÌes. Uma configuraçaÌo global mais robusta incorporaria vaÌrios algoritmos distintos e executados em paralelo, ou mesmo baseado em muÌltiplas caÌmaras. EntaÌo, todos os resultados destes algoritmos devem ser fundidos ou combinados para produzir uma deteçaÌo mais robusta e informada da via da estrada, para que funcione em mais situaçoÌes do que cada algoritmo funcionando individualmente. Esta dissertaçaÌo integrada no projeto ATLASCAR2, desenvolvido na Universidade de Aveiro, propoÌe uma arquitetura baseada em ROS para gerir e combinar muÌltiplas fontes de algoritmos de deteçaÌo de vias da estrada, desde algoritmos que devolvem a localizaçaÌo espacial da faixa de rodagem ateÌ aÌqueles cujos resultados saÌo a zona navegaÌvel representada como um polıÌgono. A arquitetura eÌ totalmente escalaÌvel e provou ser uma ferramenta valiosa para testar e parametrizar algoritmos individuais. A combinaçaÌo dos resultados dos algoritmos utilizados neste trabalho utiliza uma combinaçaÌo de deteçoÌes individuais baseada na confiança.Mestrado em Engenharia MecĂąnic
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks
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