<p>This dissertation presents a method capable of smoothing the silhouette of a 3D model using interpolation to find smooth edges. The method has as goal to be used with normal mapping to improve the performance and give a better result with a low polygonal count. To do this the lines located on the silhouette of a model is interpolated to find a curve that is used as clipping frame in the stencil buffer. This method is able to modify the silhouette for the better. The amount of interpolation is rather limited.</p>

abstract: Stereolithography files (STL) are widely used in diverse fields as a means of describing complex geometries through surface triangulations. The resulting stereolithography output is a result of either experimental measurements, or computer-aided design. Often times stereolithography outputs from experimental means are prone to noise, surface irregularities and holes in an otherwise closed surface. A general method for denoising and adaptively smoothing these dirty stereolithography files is proposed. Unlike existing means, this approach aims to smoothen the dirty surface representation by utilizing the well established levelset method. The level of smoothing and denoising can be set depending on a per-requirement basis by means of input parameters. Once the surface representation is smoothened as desired, it can be extracted as a standard levelset scalar isosurface. The approach presented in this thesis is also coupled to a fully unstructured Cartesian mesh generation library with built-in localized adaptive mesh refinement (AMR) capabilities, thereby ensuring lower computational cost while also providing sufficient resolution. Future work will focus on implementing tetrahedral cuts to the base hexahedral mesh structure in order to extract a fully unstructured hexahedra-dominant mesh describing the STL geometry, which can be used for fluid flow simulations. / Dissertation/Thesis / Masters Thesis Aerospace Engineering 2014

This dissertation presents a method capable of smoothing the silhouette of a 3D model using interpolation to find smooth edges. The method has as goal to be used with normal mapping to improve the performance and give a better result with a low polygonal count. To do this the lines located on the silhouette of a model is interpolated to find a curve that is used as clipping frame in the stencil buffer. This method is able to modify the silhouette for the better. The amount of interpolation is rather limited.

No description available.

Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below. We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a so-called simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship. Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined. The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications. We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM. We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended Rauch-Tung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity. / LINK-SIC

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Quantile function modeling is a more robust, comprehensive, and flexible method of statistical analysis than the commonly used mean-based methods. More and more data are collected in the form of multivariate, functional, and multivariate functional data, for which many aspects of quantile analysis remain unexplored and challenging. This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions. Firstly, it proposes bivariate quantile analysis methods that can predict the joint distribution of bivariate response and improve on conventional univariate quantile regression. The proposed robust statistical techniques are applied to examine barley plants grown in saltwater and freshwater conditions providing interesting insights into barley’s responses, informing future crop decisions. Secondly, it proposes modeling and visualization of bivariate functional data to characterize the distribution and detect outliers. The proposed methods provide an informative visualization tool for bivariate functional data and can characterize non-Gaussian, skewed, and heavy-tailed distributions using directional quantile envelopes. The radiosonde wind data application illustrates our proposed quantile analysis methods for visualization, outlier detection, and prediction. However, the directional quantile envelopes are convex by definition. This feature is shared by most existing methods, which is not desirable in nonconvex and multimodal distributions. Thirdly, this challenge is addressed by modeling multivariate functional data for flexible quantile contour estimation and prediction. The estimated contours are flexible in the sense that they can characterize non-Gaussian and nonconvex marginal distributions. The proposed multivariate quantile function enjoys the theoretical properties of monotonicity, uniqueness, and the consistency of its contours. The proposed methods are applied to air pollution data. Finally, we perform quantile spatial prediction for non-Gaussian spatial data, which often emerges in environmental applications. We introduce a copula-based multiple indicator kriging model, which makes no distributional assumptions on the marginal distribution, thus offers more flexibility. The method performs better than the commonly used variogram approach and Gaussian kriging for spatial prediction in simulations and application to precipitation data.

No description available.

Hydrologic Engineering Center's River Analysis System (HEC-RAS) software is commonly used to perform hydraulic analysis for floodplain delineation studies. In addition to floodplains, the hydraulic analysis also includes modeling a floodway in detailed floodplain study areas. Floodway modeling is an iterative process in which the 1% annual chance flood discharge is restricted within a floodway without exceeding a designated increase, called the surcharge (usually 1 foot), in water surface elevation. An engineer models flows along a reach to meet Federal Emergency Management Agency's (FEMA) surcharge requirements. We present a tightly coupled system comprising of a commercial GIS (ArcGIS) and HECRAS that automates HECRAS's floodway encroachments modeling. The coupled system takes an automated approach, in which an initial floodway is developed by running HEC-RAS in an iterative fashion with minimal user intervention. A customized ArcGIS visual environment has been developed to edit, remodel, spatially analyze and map floodway boundaries. Four different encroachments fine-tuning options are provided which eliminates the need for a modeler to switch between HECRAS and GIS in the floodway modeling process. Thus, the tool increases the productivity of a modeler by cutting down on manual modeling time during floodway iterations and transition between HECRAS and ArcGIS. The transfer of HECRAS model output into the ArcGIS environment facilitates quick and efficient spatial analysis. The final step in the floodway modeling process is to develop a smooth floodway boundary that can be mapped on a DFIRM. We have developed automated mapping algorithms that accomplish this task. Some manual fine-tuning is required to finalize the floodway to be printed on FEMA's Flood Insurance Rate Maps (FIRMs). / Ph. D.

<p>Multi-Target Tracking (MTT), where the number of targets as well as their states are time-varying, concerns with the estimation of both the number of targets and the individual states from noisy sensor measurements, whose origins are unknown. Filtering typically produces the best estimates of the target state based on all measurements up to current estimation time. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimation of target states. This thesis proposes smoothing methods for various estimation methods that produce delayer, but better, estimates of the target states.</p> <p>First, we propose a novel smoothing method for the Probability Hypothesis Density (PHD) estimator. The PHD filer, which propagates the first order statistical moment of the multitarget state density, a computationally efficient MTT algorithm. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent Sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. The proposed PHD smoothing method involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula.</p> <p>Second, we propose a Multiple Model PH (MMPHD) smoothing method for tracking of maneuvering targets. Multiple model approaches have been shown to be effective for tracking maneuvering targets. MMPHD filter propagates mode-conditioned PHD recursively. The proposed backward MMPHD smoothing algorithm involves the estimation of a continuous state for target dynamic as well as a discrete state vector for the mode of target dynamics.</p> <p>Third, we present a smoothing method for the Gaussian Mixture PHD (GMPHD) state estimator using multiple sensors. Under linear Gaussian assumptions, the PHD filter can be implemented using a closed-form recursion, where the PHD is represented by a mixture of Gaussian functions. This can be extended to nonlinear systems by using the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF). In the case of multisenor systems, a sequential update of the PHD has been suggested in literature. However, this sequential update is susceptible to imperfections in the last sensor. In this thesis, a parallel update for GMPHD filter is proposed. The resulting filter outputs are further improved using a novel closed-form backward smoothing recursion.</p> <p>Finally, we propose a novel smoothing method for Kalman based Interacting Multiple Model (IMM) estimator for tracking agile targets. The new method involves forwarding filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion.</p> / Thesis / Doctor of Philosophy (PhD)