Hybrid Function Representation for Heterogeneous Objects

Heterogeneous object modelling is an emerging area where geometric shapes are considered in concert with their internal physically-based attributes. This paper describes a novel theoretical and practical framework for modelling volumetric heterogeneous objects on the basis of a novel unifying functionally-based hybrid representation called HFRep. This new representation allows for obtaining a continuous smooth distance field in Euclidean space and preserves the advantages of the conventional representations based on scalar fields of different kinds without their drawbacks. We systematically describe the mathematical and algorithmic basics of HFRep. The steps of the basic algorithm are presented in detail for both geometry and attributes. To solve some problematic issues, we have suggested several practical solutions, including a new algorithm for solving the eikonal equation on hierarchical grids. Finally, we show the practicality of the approach by modelling several representative heterogeneous objects, including those of a time-variant nature

Hybrid modelling of time-variant heterogeneous objects

The physical world consists of a wide range of objects of a diverse constitution. Past research was mainly focussed on the modelling of simple homogeneous objects of a uniform constitution. Such research resulted in the development of a number of advanced theoretical concepts and practical techniques for describing such physical objects. As a result, the process of modelling and animating certain types of homogeneous objects became feasible. In fact most physical objects are not homogeneous but heterogeneous in their constitution and it is thus important that one is able to deal with such heterogeneous objects that are composed of diverse materials and may have complex internal structures. Heterogeneous object modelling is still a very new and evolving research area, which is likely to prove useful in a wide range of application areas. Despite its great promise, heterogeneous object modelling is still at an embryonic state of development and there is a dearth of extant tools that would allow one to work with static and dynamic heterogeneous objects. In addition, the heterogeneous nature of the modelled objects makes it appealing to employ a combination of different representations resulting in the creation of hybrid models. In this thesis we present a new dynamic Implicit Complexes (IC) framework incorporating a number of existing representations and animation techniques. This framework can be used for the modelling of dynamic multidimensional heterogeneous objects. We then introduce an Implicit Complexes Application Programming Interface (IC API). This IC API is designed to provide various applications with a unified set of tools allowing these to model time-variant heterogeneous objects. We also present a new Function Representation (FRep) API, which is used for the integration of FReps into complex time-variant hybrid models. This approach allows us to create a practical multilevel modelling system suited for complex multidimensional hybrid modelling of dynamic heterogeneous objects. We demonstrate the advantages of our approach through the introduction of a novel set of tools tailored to problems encountered in simulation applications, computer animation and computer games. These new tools empower users and amplify their creativity by allowing them to overcome a large number of extant modelling and animation problems, which were previously considered difficult or even impossible to solve.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Three-dimensional modeling of natural heterogeneous objects

En la medicina y otros campos relacionados cuando se va a estudiar un objeto natural, se toman imágenes de tomografía computarizada a través de varios cortes paralelos. Estos cortes se apilan en datos de volumen y se reconstruyen en modelos computacionales con el fin de estudiar la estructura de dicho objeto. Para construir con éxito modelos tridimensionales es importante la identificación y extracción precisa de todas las regiones que comprenden el objeto heterogéneo natural. Sin embargo, la construcción de modelos tridimensionales por medio del computador a partir de imágenes médicas sigue siendo un problema difícil y plantea dos problemas relacionados con las inexactitudes que surgen de, y son inherentes al proceso de adquisición de datos. El primer problema es la aparición de artefactos que distorsionan el límite entre las regiones. Este es un problema común en la generación de mallas a partir de imágenes médicas, también conocido como efecto de escalón. El segundo problema es la extracción de mallas suaves 3D que se ajustan a los límites de las región que conforman los objetos heterogéneos naturales descritos en las imágenes médicas. Para resolver estos problemas, se propone el método CAREM y el método RAM. El énfasis de esta investigación está puesto en la exactitud y fidelidad a la forma de las regiones necesaria en las aplicaciones biomédicas. Todas las regiones representadas de forma implícita que componen el objeto heterogéneo natural se utilizan para generar mallas adaptadas a los requisitos de los métodos de elementos finitos a través de un enfoque de modelado de ingeniería reversa, por lo tanto, estas regiones se consideran como un todo en lugar de piezas aisladas ensambladas.In medicine and other related fields when a natural object is going to be studied, computed tomography images are taken through several parallel slices. These slices are then stacked in volume data and reconstructed into 3D computer models. In order to successfully build 3D computer models of natural heterogeneous objects, accurate identification and extraction of all regions comprising the natural heterogeneous object is important. However, building 3D computer models of natural heterogeneous objects from medical images is still a challenging problem, and poses two issues related to the inaccuracies which arise from and are inherent to the data acquisition process. The first issue is the appearance of aliasing artifacts in the boundary between regions, a common issue in mesh generation from medical images, also known as stair-stepped artifacts. The second issue is the extraction of smooth 3D multi-region meshes that conform to the region boundaries of natural heterogeneous objects described in the medical images. To solve these issues, the CAREM method and the RAM method are proposed. The emphasis of this research is placed on accuracy and shape fidelity needed for biomedical applications. All implicitly represented regions composing the natural heterogeneous object are used to generate meshes adapted to the requirements of finite element methods through a reverse engineering modeling approach, thus these regions are considered as whole rather than loosely assembled parts.Doctor en IngenieríaDoctorad

Le nuage de point intelligent

Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robot’s navigation, as a layer for floor-plan’s creation and building’s construction, as a digital asset for environment modelling and incident prediction... Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process suffering from error-prone human interpretation. This highlights a strong need for domain-related data analysis to create a coherent and structured information. The thesis clearly tries to solve automation problematics in point cloud processing to create intelligent environments, i.e. virtual copies that can be used/integrated in fully autonomous reasoning services. We tackle point cloud questions associated with knowledge extraction – particularly segmentation and classification – structuration, visualisation and interaction with cognitive decision systems. We propose to connect both point cloud properties and formalized knowledge to rapidly extract pertinent information using domain-centered graphs. The dissertation delivers the concept of a Smart Point Cloud (SPC) Infrastructure which serves as an interoperable and modular architecture for a unified processing. It permits an easy integration to existing workflows and a multi-domain specialization through device knowledge, analytic knowledge or domain knowledge. Concepts, algorithms, code and materials are given to replicate findings and extend current applications.Les ensembles discrets de données spatiales, appelés nuages de points, forment souvent le support principal pour des scénarios d’aide à la décision. Par exemple, nous pouvons utiliser ces données comme référence pour les voitures autonomes et la navigation des robots, comme couche pour la création de plans et la construction de bâtiments, comme actif numérique pour la modélisation de l'environnement et la prédiction d’incidents... Les applications sont nombreuses et potentiellement croissantes si l'on considère les nuages de points comme des actifs de réalité numérique. Cependant, cette expansion se heurte à des limites techniques dues principalement au manque d'information sémantique au sein des ensembles de points. La création de liens avec des sources de connaissances est encore un processus très manuel, chronophage et lié à une interprétation humaine sujette à l'erreur. Cela met en évidence la nécessité d'une analyse automatisée des données relatives au domaine étudié afin de créer une information cohérente et structurée. La thèse tente clairement de résoudre les problèmes d'automatisation dans le traitement des nuages de points pour créer des environnements intelligents, c'est-àdire des copies virtuelles qui peuvent être utilisées/intégrées dans des services de raisonnement totalement autonomes. Nous abordons plusieurs problématiques liées aux nuages de points et associées à l'extraction des connaissances - en particulier la segmentation et la classification - la structuration, la visualisation et l'interaction avec les systèmes cognitifs de décision. Nous proposons de relier à la fois les propriétés des nuages de points et les connaissances formalisées pour extraire rapidement les informations pertinentes à l'aide de graphes centrés sur le domaine. La dissertation propose le concept d'une infrastructure SPC (Smart Point Cloud) qui sert d'architecture interopérable et modulaire pour un traitement unifié. Elle permet une intégration facile aux flux de travail existants et une spécialisation multidomaine grâce aux connaissances liée aux capteurs, aux connaissances analytiques ou aux connaissances de domaine. Plusieurs concepts, algorithmes, codes et supports sont fournis pour reproduire les résultats et étendre les applications actuelles.Diskrete räumliche Datensätze, so genannte Punktwolken, bilden oft die Grundlage für Entscheidungsanwendungen. Beispielsweise können wir solche Daten als Referenz für autonome Autos und Roboternavigation, als Ebene für die Erstellung von Grundrissen und Gebäudekonstruktionen, als digitales Gut für die Umgebungsmodellierung und Ereignisprognose verwenden... Die Anwendungen sind zahlreich und nehmen potenziell zu, wenn wir Punktwolken als Digital Reality Assets betrachten. Allerdings stößt diese Erweiterung vor allem durch den Mangel an semantischen Informationen innerhalb von Punkt-Ensembles auf technische Grenzen. Die Verbindung von Wissensquellen ist immer noch ein sehr manueller und zeitaufwendiger Prozess, der unter fehleranfälliger menschlicher Interpretation leidet. Dies verdeutlicht den starken Bedarf an domänenbezogenen Datenanalysen, um eine kohärente und strukturierte Information zu schaffen. Die Arbeit versucht eindeutig, Automatisierungsprobleme in der Punktwolkenverarbeitung zu lösen, um intelligente Umgebungen zu schaffen, d.h. virtuelle Kopien, die in vollständig autonome Argumentationsdienste verwendet/integriert werden können. Wir befassen uns mit Punktwolkenfragen im Zusammenhang mit der Wissensextraktion - insbesondere Segmentierung und Klassifizierung - Strukturierung, Visualisierung und Interaktion mit kognitiven Entscheidungssystemen. Wir schlagen vor, sowohl Punktwolkeneigenschaften als auch formalisiertes Wissen zu verbinden, um schnell relevante Informationen mithilfe von domänenzentrierten Grafiken zu extrahieren. Die Dissertation liefert das Konzept einer Smart Point Cloud (SPC) Infrastruktur, die als interoperable und modulare Architektur für eine einheitliche Verarbeitung dient. Es ermöglicht eine einfache Integration in bestehende Workflows und eine multidimensionale Spezialisierung durch Gerätewissen, analytisches Wissen oder Domänenwissen. Konzepte, Algorithmen, Code und Materialien werden zur Verfügung gestellt, um Erkenntnisse zu replizieren und aktuelle Anwendungen zu erweitern

Diode-Pumped High-Energy Laser Amplifiers for Ultrashort Laser Pulses The PENELOPE Laser System

The ultrashort chirped pulse amplification (CPA) laser technology opens the path to high intensities of 10^21 W/cm² and above in the laser focus. Such intensities allow laser-matter interaction in the relativistic intensity regime. Direct diode-pumped ultrashort solid-state lasers combine high-energy, high-power and efficient amplification together, which are the main advantages compared to flashlamp-pumped high-energy laser systems based on titanium-doped sapphire. Development within recent years in the field of laser diodes makes them more and more attractive in terms of total costs, compactness and lifetime. This work is dedicated to the Petawatt, ENergy-Efficient Laser for Optical Plasma Experiments (PENELOPE) project, a fully and directly diode-pumped laser system under development at the Helmholtz–Zentrum Dresden – Rossendorf (HZDR), aiming at 150 fs long pulses with energies of up to 150 J at repetition rates of up to 1 Hz. The focus of this thesis lies on the spectral and width manipulation of the front-end amplifiers, trivalent ytterbium-doped calcium fluoride (Yb3+:CaF2) as gain material as well as the pump source for the final two main amplifiers of the PENELOPE laser system. Here, all crucial design parameters were investigated and a further successful scaling of the laser system to its target values was shown. Gain narrowing is the dominant process for spectral bandwidth reduction during the amplification at the high-gain front-end amplifiers. Active or passive spectral gain control filter can be used to counteract this effect. A pulse duration of 121 fs was achieved by using a passive spectral attenuation inside a regenerative amplifier, which corresponds to an improvement by a factor of almost 2 compared to the start of this work. A proof-of-concept experiment showed the capability of the pre-shaping approach. A spectral bandwidth of 20nm was transferred through the first multipass amplifier at a total gain of 300. Finally, the predicted output spectrum calculated by a numerical model of the final amplifier stages was in a good agreement with the experimental results. The spectroscopic properties of Yb3+:CaF2 matches the constraints for ultrashort laser pulse amplification and direct diode pumping. Pumping close to the zero phonon line at 976nm is preferable compared to 940nm as the pump intensity saturation is significantly lower. A broad gain cross section of up to 50nm is achievable for typical inversion levels. Furthermore, moderate cryogenic temperatures (above 200K) can be used to improve the amplification performance of Yb3+:CaF2. The optical quality of the doped crystals currently available on the market is sufficient to build amplifiers in the hundred joule range. The designed pump source for the last two amplifiers is based on two side pumping in a double pass configuration. However, this concept requires the necessity of brightness conservation for the installed laser diodes. Therefore, a fully relay imaging setup (4f optical system) along the optical path from the stacks to the gain material including the global beam homogenization was developed in a novel approach. Beside these major parts the amplifier architecture and relay imaging telescopes as well as temporal intensity contrast (TIC) was investigated. An all reflective concept for the relay imaging amplifiers and telescopes was selected, which results in several advantages especially an achromatic behavior and low B-Integral. The TIC of the front-end was improved, as the pre- and postpulses due to the plane-parallel active-mirror was eliminated by wedging the gain medium