While current approaches to digital image processing within the context of machine learning and deep learning are motivated by biological processes within the human brain, they are, however, also limited due to the current state of the art of input and output devices as well as the algorithms that are concerned with the processing of their data. In order to generate digital images from real-world scenes, the utilized digital images' underlying lattice formats are predominantly based on rectangular or square structures. Yet, the human visual perception system suggests an alternative approach that manifests itself within the sensory cells of the human eye in the form of hexagonal arrangements. As previous research demonstrates that hexagonal arrangements can provide different benefits to image processing systems in general, this contribution is concerned with the synthesis of both worlds in the form of the biologically inspired hexagonal deep learning for hexagonal image processing. This contribution is therefore concerned with the design, the implementation, and the evaluation of hexagonal solutions to currently developed approaches in the form of hexagonal deep neural networks. For this purpose, the respectively realized hexagonal functionality had to be built from the ground up as hexagonal counterparts to otherwise conventional square lattice format based image processing and deep learning based systems. Furthermore, hexagonal equivalents for artificial neural network based operations, layers, as well as models and architectures had to be realized. This also encompasses the related evaluation metrics for hexagonal lattice format based representations of digital images and their conventional counterparts in comparison. Therefore, the developed hexagonal image processing and deep learning framework Hexnet functions as a first general application-oriented open science framework for hexagonal image processing within the context of machine learning. To enable the evaluation of hexagonal approaches, a set of different application areas and use cases within conventional and hexagonal image processing – astronomical, medical, and industrial image processing – are provided that allow an assessment of hexagonal deep neural networks in terms of their classification capabilities as well as their general performance. The obtained and presented results demonstrate the possible benefits of hexagonal deep neural networks and their hexagonal representations for image processing systems. It is shown that hexagonal deep neural networks can result in increased classification capabilities given different basic geometric shapes and contours, which in turn partially translate into their real-world applications. This is indicated by a relative improvement in F1-score for the proposed hexagonal and square models, ranging from 1.00 (industrial image processing) to 1.03 (geometric primitives) with single classes even reaching a relative improvement of over 1.05. However, possible disadvantages are also given by the increased complexity of hexagonal algorithms. This is evident by the present potential in regard to runtime optimizations that have yet to be realized for certain hexagonal operations in comparison to their currently deployed square equivalents.:1 Introduction and Motivation 2 Fundamentals and Methods 3 Implementation 4 Test Results, Evaluation, and Discussion 5 Conclusion and Outlook

Sowohl in der Forschung als auch in der industriellen Anwendung erfahren drahtlose Sensornetze (engl. Wireless Sensor Networks, WSN) eine immer höhere Popularität. Dies liegt nicht zuletzt an den Forschungsgebieten Car2X-Kommunikation, Internet of Things sowie Umweltüberwachung als Teil des Katastrophenschutzes. Typischer Schwerpunkt ist das energieeffiziente und latenzarme Übertragen von Informationen in einem drahtlos verknüpften Netz aus stark ressourcenbeschränkten Hardwareplattformen. Die Dissertation beschäftigt sich mit der Forschungsfrage, inwieweit Methoden und konkrete Algorithmen aus dem Big-Data-Computing vorteilhaft in drahtlosen Sensornetzen eingesetzt werden können. Während es im ersten Moment nicht intuitiv erscheinen mag, dass Ansätze aus dem Big-Data-Bereich, die bereits konventionelle Rechensysteme an ihre Grenzen bringen, für Sensornetze relevant sein sollen, zeigt die Arbeit Parallelen auf, die zwischen klassischem Big Data und einer Teilgruppe von Sensornetzen existieren. Im Rahmen der Arbeit wird dazu der Begriff drahtloses Big-Data-Sensornetz (Big Data WSN) definiert und darauf aufbauend werden Anforderungen an ein Datenaggregationssystem für ebensolche Sensornetze erörtert sowie konkrete Big-Data-Algorithmen konzeptionell übertragen und adaptiert. Es wird gezeigt, dass sich sogenannte datenbank-orientierte Aggregationssysteme zur Umsetzung dieser Anforderungen eignen und dass ein solches existierendes System für Big Data WSN erweitert werden kann. Die Hauptbeiträge der Dissertation sind die Vorstellung eines Routingverfahrens für Big Data WSN, das anwendungsrelevante Knoten energieeffizient ermittelt, die Übertragung von Big Data Algorithmen auf WSN zur Reduzierung der zu übertragenden beziehungsweise zu speichernden Datenmenge sowie ein Ereignissystem zur Steigerung der Energieeffizienz und Verringerung der Latenz von Ereignisreaktionen.:1. Einleitung 2. Grundlagen 3. Stand der Forschung 4. Konzeptionierung eines Aggregationsverfahrens für WSN im Big-Data-Kontext 5. Big-Data-WSN-Aggregationssystem Planetary 6. Auswertung und Ergebnisse 7. Zusammenfassung Anhang

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In this work, we present a workflow for semi-automatic extraction of geo-references and motion events from the book 'The Travels of Marco Polo'. These are then used to create 3D renderings of the space and movement which allows readers to visually trace Marco Polo's route themselves to provide the exprience of the entirety of the journey

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Es wird eine Methodik zur Testprogrammverifikation und deren automatisierte Ausführung für ein namhaftes, industriell eingesetztes Testsystem vorgestellt. Der Ansatz basiert auf der Spezifikation von Bauteilvarianten. Die Bauteilantworten einer Variante werden während der Ausführung emuliert und ersetzen Messwerte physischer Bauteile durch das Testsystem. Die Generierung und Emulation der Bauteilvarianten erfolgen automatisiert. Es wird ausschließlich die Softwareumgebung des Testsystems benötigt. Weder physische Bauteile noch Online-Testzeit auf dem Testsystem sind notwendig.

Adaptive anisotropic refinement of finite element meshes allows to reduce the computational effort required to achieve a specified accuracy of the solution of a PDE problem. We present a new approach to adaptive refinement and demonstrate that this allows to construct algorithms which generate very flexible and efficient anisotropically refined meshes, even improving the convergence order compared to adaptive isotropic refinement if the problem permits.

Point-based rendering methods have proven to be effective for the display of large point cloud surface models. For a realistic visualization of the models, transparency and shadows are essential features. We propose a method for point cloud rendering with transparency and shadows at interactive rates. Our approach does not require any global or local surface reconstruction method, but operates directly on the point cloud. All passes are executed in image space and no pre-computation steps are required. The underlying technique for our approach is a depth peeling method for point cloud surface representations. Having detected a sorted sequence of surface layers, they can be blended front to back with given opacity values to obtain renderings with transparency. These computation steps achieve interactive frame rates. For renderings with shadows, we determine a point cloud shadow texture that stores for each point of a point cloud whether it is lit by a given light source. The extraction of the layer of lit points is obtained using the depth peeling technique, again. For the shadow texture computation, we also apply a Monte-Carlo integration method to approximate light from an area light source, leading to soft shadows. Shadow computations for point light sources are executed at interactive frame rates. Shadow computations for area light sources are performed at interactive or near-interactive frame rates depending on the approximation quality.

Level-set methods have become a valuable and well-established field of visualization over the last decades. Different implementations addressing different design goals and different data types exist. In particular, level sets can be used to extract isosurfaces from scalar volume data that fulfill certain smoothness criteria. Recently, such an approach has been generalized to operate on unstructured point-based volume data, where data points are not arranged on a regular grid nor are they connected in form of a mesh. Utilizing this new development, one can avoid an interpolation to a regular grid which inevitably introduces interpolation errors. However, the global processing of the level-set function can be slow when dealing with unstructured point-based volume data sets containing several million data points. We propose an improved level-set approach that performs the process of the level-set function locally. As for isosurface extraction we are only interested in the zero level set, values are only updated in regions close to the zero level set. In each iteration of the level-set process, the zero level set is extracted using direct isosurface extraction from unstructured point-based volume data and a narrow band around the zero level set is constructed. The band consists of two parts: an inner and an outer band. The inner band contains all data points within a small area around the zero level set. These points are updated when executing the level set step. The outer band encloses the inner band providing all those neighbors of the points of the inner band that are necessary to approximate gradients and mean curvature. Neighborhood information is obtained using an efficient kd-tree scheme, gradients and mean curvature are estimated using a four-dimensional least-squares fitting approach. Comparing ourselves to the global approach, we demonstrate that this local level-set approach for unstructured point-based volume data achieves a significant speed-up of one order of magnitude for data sets in the range of several million data points with equivalent quality and robustness.