832921
Hardware-assisted feature analysis and visualization of procedurally encoded multifield volumetric data
We take a new approach to interactive visualization and feature detection of large scalar, vector, and multifield computational fluid dynamics data sets that is also well suited for meshless CFD methods. Radial basis functions (RBFs) are used to procedurally encode both scattered and irregular gridded scalar data sets. The RBF encoding creates a complete, unified, functional representation of the scalar field throughout 3D space, independent of the underlying data topology, and eliminates the need for the original data grid during visualization. The capability of commodity PC graphics hardware to accelerate the reconstruction and rendering and to perform feature detection from this functional representation is a powerful tool for visualizing procedurally encoded volumes. Our RBF encoding and GPU-accelerated reconstruction, feature detection, and visualization tool provides a flexible system for visually exploring and analyzing large, structured, scattered, and unstructured scalar, vector, and multifield data sets at interactive rates on desktop PCs.
9961789
Interactively visualizing procedurally encoded scalar fields
While interactive visualization of rectilinear gridded volume data sets can now be accomplished using texture mapping hardware on commodity PCs, interactive rendering and exploration of large scattered or unstructured data sets is still a challenging problem. We have developed a new approach that allows the interactive rendering and navigation of procedurally-encoded 3D scalar fields by reconstructing these fields on PC class graphics processing units. Since the radial basis functions (RBFs) we use for encoding can provide a compact representation of volumetric scalar fields, the large grid/mesh traditionally needed for rendering is no longer required and ceases to be a data transfer and computational bottleneck during rendering. Our new approach will interactively render RBF encoded data obtained from arbitrary volume data sets, including both structured volume models and unstructured scattered volume models. This procedural reconstruction of large data sets is flexible, extensible, and can take advantage of the Moore's Law cubed increase in performance of graphics hardware.
6888787
Distributed data management for large volume visualization
We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "enhanced time-space partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying user-specified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by logistical networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.
14351696
A sufficient condition for a semicomplete multipartite digraph to be Hamiltonian
Abstract A multipartite tournament is an orientation of a complete k -partite graph for some k ⩾ 2. A factor of a digraph D is a collection of vertex disjoint cycles covering all the vertices of D . We show that there is no degree of strong connectivity which together with the existence of a factor will guarantee that a multipartite tournament is Hamiltonian. Our main result is a sufficient condition for a multipartite tournament to be Hamiltonian. We show that this condition is general enough to provide easy proofs of many existing results on paths and cycles in multipartite tournaments. Using this condition, we obtain a best possible lower bound on the length of a longest cycle in any strongly connected multipartite tournament.
2129354
Visualization of neuronal fiber connections from DT-MRI with global optimization
Diffusion Tensor MRI (DT-MRI) provides valuable 3D data describing diffusion characteristics of water molecules in the human brain. From DT-MRI, it is hoped that neuronal fiber connections among cortical regions can be reliably extracted and adequately interpreted. To achieve this goal, several significant challenges persist. In this paper, by means of dynamic programming we have developed a global fiber reconstruction algorithm enabling efficient visualization of neuronal connections queried by both the start and end points on the fly. Besides an inherent ability to handle noisy datasets, our algorithm also naturally addresses situations where neuronal fibers branch or cross each other. We demonstrate the efficacy of our approach with visualization of neuronal connections among activated brain cortical regions detected by functional MRI (fMRI).
2348760
Adaptive live video streaming by priority drop
In this paper we explore the use of priority progress streaming (PPS) for video surveillance applications. PPS is an adaptive streaming technique for the delivery of continuous media over variable bit-rate channels. It is based on the simple idea of reordering media components within a time window into priority order before transmission. The main concern when using PPS for live video streaming is the time delay introduced by reordering. In this paper we describe how PPS can be extended to support live streaming and show that the delay inherent in the approach can be tuned to satisfy a wide range of latency constraints while supporting fine-grain adaptation.
3242205
OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services
This paper describes the current state of the OmniSeer system. OmniSeer supports intelligence analysts in the handling of massive amounts of data, the construction of scenarios, and the management of hypotheses. OmniSeer models analysts with dynamic user models that capture an analyst's context, interests, and preferences, thus enabling more efficient and effective information retrieval. OmniSeer explicitly represents the prior and tacit knowledge of analysts, thus enabling transfer and reuse of such knowledge. Both the user and cognitive models employ a Bayesian network fragment representation, which supports principled probabilistic reasoning and analysis. An independent evaluation of OmniSeer was carried out at NIST and will be used to guide further development.