Associate Professor, University of Utah
In this paper, we investigate a point cloud-based approach to direct slicing of NURBS-based models. It uses the original NURBS model and converts the model into a point cloud based on layer thickness and accuracy requirements for direct slicing.
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We propose a new data scalable design for representing and exploring data relationships in PCPs. The approach exploits the point/line duality property of PCPs and a local linear assumption of data to extract and represent relationship summarizations. This approach simultaneously shows relationships in the data and the consistency of those relationships.
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In this paper, we propose a novel technique for analyzing multiway dependencies through an overview+detail visualization. In this approach, the overview represents all pairwise, 3-, and 4-way dependencies in the data using glyphs that provide a global visual exploration interface for selecting candidate relationships.
Continue readingPoint cloud-based datasets have been critical to a number of research communities, including graphics, robotics, and CAD because in many ways, points better represent many data sources than triangulations. Visualizing points on remote devices remains somewhat problematic to this day. Very large datasets have the problem of needing to be transmitted, have any data structures built locally, and rendered on the local machine. This paper addresses the question of what’s the point in rendering billions of points, when the display only has 2 million pixels?
Continue readingPoint cloud computation for object slicing in 3-d printing William Oropallo, Les A. Piegl, Paul Rosen, and Khairan Rajab In CAD Conference
Continue readingIn this paper we describe the Myocardial Uncertainty Viewer (muView) system for exploring data stemming from the simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multi-valued and volumetric, and thus, for every data point, we have a collection of samples describing cardiac electrical properties. View combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables change the propagation of their effects.
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In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability.
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We have created the Neurostimulation Uncertainty Viewer (nuView) tool for exploring data arising from deep brain stimulation (DBS). nuView provides a collection of visual methods to explore the activated tissue to enhance understanding of electrode usage for improved therapy with DBS.
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Investigators Collaborators About ALMA-TDA is a persistent homology based engine for noise removal in ALMA data cubes. ALMA-TDA uses a
Continue readingThis work introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains nontrivial separation structure, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregions of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise.
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