38631229
A Scalable Framework for Distributed Virtual Reality Using Heterogeneous Processors
We propose a scalable framework for virtual reality systems in a distributed environment. As the application scope of and member participation in a virtual environment increase, information sharing among geographically distributed users becomes critical and challenging. In the proposed framework, we partition the virtual environment into a group of cells and upload them to a number of heterogeneous Internet nodes. When a user sends a request to explore the distant virtual environment, visible cells will be identified and processed in parallel to produce a minimal amount of imagery results for remote transmission. To ensure scalability, we extend our scalable occlusion culling scheme using Plenoptic Opacity Function to speed up the identification process of visible cells in a virtual environment. We perform effective occlusion culling in two passes based on a non-binary opacity definition. Our experimental results justify both the efficiency and scalability of our framework in exploring large-scale virtual environments.
623541
A multiresolution volume rendering framework for large-scale time-varying data visualization
We present a new parallel multiresolution volume rendering framework for large-scale time-varying data visualization using the wavelet-based time-space partitioning (WTSP) tree. Utilizing the wavelet transform, a large-scale time-varying data set is converted into a space-time multiresolution data hierarchy, and is stored in a time-space partitioning (TSP) tree. To eliminate the parent-child data dependency for reconstruction and achieve load-balanced rendering, we design an algorithm to partition the WTSP tree and distribute the wavelet-compressed data along hierarchical space-filling curves with error-guided bucketization. At run time, the WTSP tree is traversed according to the user-specified time step and tolerances of both spatial and temporal errors. Data blocks of different spatio-temporal resolutions are reconstructed and rendered to compose the final image in parallel. We demonstrate that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.
10392277
Visibility Culling Using Plenoptic Opacity Functions for Large Data Visualization
13640860
Parallel Multiresolution Volume Rendering of Large Data Sets with Error-Guided Load Balancing
We present a new parallel multiresolution volume rendering algorithm for visualizing large data sets. Using the wavelet transform, the raw data is first converted into a multiresolution wavelet tree. To eliminate the parent-child data dependency for reconstruction and achieve load-balanced rendering, we design a novel algorithm to partition the tree and distribute the data along a hierarchical space-filling curve with error-guided bucketization. At run time, the wavelet tree is traversed according to the user-specified error tolerance, data blocks of different resolutions are decompressed and rendered to compose the final image in parallel. Experimental results showed that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of data with arbitrary error tolerances.
2569639
Visibility culling for time-varying volume rendering using temporal occlusion coherence
Typically there is a high coherence in data values between neighboring time steps in an iterative scientific software simulation; this characteristic similarly contributes to a corresponding coherence in the visibility of volume blocks when these consecutive time steps are rendered. Yet traditional visibility culling algorithms were mainly designed for static data, without consideration of such potential temporal coherency. We explore the use of temporal occlusion coherence (TOC) to accelerate visibility culling for time-varying volume rendering. In our algorithm, the opacity of volume blocks is encoded by means of plenoptic opacity functions (POFs). A coherence-based block fusion technique is employed to coalesce time-coherent data blocks over a span of time steps into a single, representative block. Then POFs need only be computed for these representative blocks. To quickly determine the subvolumes that do not require updates in their visibility status for each subsequent time step, a hierarchical "TOC tree" data structure is constructed to store the spans of coherent time steps. To achieve maximal culling potential, while remaining conservative, we have extended our previous POP into an optimized POP (OPOP) encoding scheme for this specific scenario. To test our general TOC and OPOF approach, we have designed a parallel time-varying volume rendering algorithm accelerated by visibility culling. Results from experimental runs on a 32-processor cluster confirm both the effectiveness and scalability of our approach.
3057656
Scalable Data Servers for Large Multivariate Volume Visualization
Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets
10628567
Shape Segmentation and Applications in Sensor Networks
Many sensor network protocols in the literature implicitly assume that sensor nodes are deployed uniformly inside a simple geometric region. When the real deployment deviates from that, we often observe degraded performance. It is desirable to have a generic approach to handle a sensor field with complex shape. In this paper, we propose a segmentation algorithm that partitions an irregular sensor field into nicely shaped pieces such that algorithms and protocols that assume a nice sensor field can be applied inside each piece. Across the segments, problem dependent structures specify how the segments and data collected in these segments are integrated. This unified topology-adaptive spatial partitioning would benefit many settings that currently assume a nicely shaped sensor field. Our segmentation algorithm does not require sensor locations and only uses network connectivity information. Each node is given a 'flow direction' that directs away from the network boundary. A node with no flow direction becomes a sink, and attracts other nodes in the same segment. We evaluate the performance improvements by integrating shape segmentation with applications such as distributed indices and random sampling.
7510303
An adaptive protocol for efficient support of range queries in DHT-based systems
In recent years, distributed hash tables (DHTs) have been proposed as a fundamental building block for large scale distributed applications. Important functionalities such as searching have been added to the DHTs basic lookup capability. However, supporting range queries efficiently remains a difficult problem. We describe an adaptive mechanism that relies on a logical tree data structure, the range search tree (RST), to support range queries efficiently. Nodes in the RST automatically group registrations based on their values. Queries are decomposed into a small number of sub-queries for efficient resolution. The system dynamically optimizes itself to minimize the registration and query cost based on observed load. The system is fully distributed and avoids bottleneck problems encountered in traditional tree-based systems. Extensive simulation results validate the effectiveness of the system.
17381912
Hydrogen-bonding interactions in the active site of a low molecular weight protein-tyrosine phosphatase
Molecular dynamics simulation of the Michaelis complex, phospho-enzyme intermediate, and the wild-type and C12S mutant have been carried out to examine hydrogen-bonding interactions in the active site of the bovine low molecular weight protein-tyrosine phosphatase (BPTP). It was found that the Sγ atom of the nucleophilic residue Cys-12 is ideally located at a position opposite from the phenylphosphate dianion for an inline nucleophilic substitution reaction. In addition, electrostatic and hydrogen-bonding interactions from the backbone amide groups of the phosphate-binding loop strongly stabilize the thiolate anion, making Cys-12 ionized in the active site. In the phospho-enzyme intermediate, three water molecules are found to form strong hydrogen bonds with the phosphate group. In addition, another water molecule can be identified to form bridging hydrogen bonds between the phosphate group and Asp-129, which may act as the nucleophile in the subsequent phosphate hydrolysis reaction, with Asp-129 serving as a general base. The structural difference at the active site between the wild-type and C12S mutant has been examined. It was found that the alkoxide anion is significantly shifted toward one side of the phosphate binding loop, away from the optimal position enjoyed by the thiolate anion of the wild-type enzyme in an SN2 process. This, coupled with the high pKa value of an alcoholic residue, makes the C12S mutant catalytically inactive. These molecular dynamics simulations provided details of hydrogen bonding interactions in the active site of BPTP, and a structural basis for further studies using combined quantum mechanical and molecular mechanical potential to model the entire dephosphorylation reaction by BPTP. c © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 1192–1203, 2000
11678491
X-ray computed tomography methods for in vivo evaluation of local drug release systems
Recent advances in drug delivery techniques have necessitated the development of tools for in vivo monitoring of drug distributions. Gamma emission imaging and magnetic resonance imaging suffer from problems of resolution and sensitivity, respectively. We propose that the combination of X-ray CT imaging and image analysis techniques provides an excellent method for the evaluation of the transport of platinum-containing drugs from a localized, controlled release source. We correlated local carboplatin concentration with CT intensity, producing a linear relationship with a sensitivity of 62.6 /spl mu/g/mL per Hounsfield unit. As an example application, we evaluated the differences in drug transport properties between normal and ablated rabbit liver from implanted polymer millirods. The use of three-dimensional visualization provided a method of evaluating the placement of the drug delivery device in relation to the surrounding anatomy, and registration and reformatting allowed the accurate comparison of the sequence of temporal CT volumes acquired over a period of 24 h. Taking averages over radial lines extending away from the center of the implanted millirods and integrating over clinically appropriate regions, yielded information about drug release from the millirod and transport in biological tissues. Comparing implants in normal and ablated tissues, we found that ablation prior to millirod implantation greatly decreased the loss of drug from the immediate area, resulting in a higher average dose to the surrounding tissue. This work shows that X-ray CT imaging is a useful technique for the in vivo evaluation of the pharmacokinetics of platinated agents.
6460806
Distributed Proximity Maintenance in Ad Hoc Mobile Networks
We present an efficient distributed data structure, called the d-Spanner, for maintaining proximity information among communicating mobile nodes. The d-Spanner is a kinetic sparse graph spanner on the nodes that allows each node to quickly determine which other nodes are within a given distance of itself, to estimate an approximate nearest neighbor, and to perform a variety of other proximity related tasks. A lightweight and fully distributed implementation is possible, in that maintenance of the proximity information only requires each node to exchange a modest number of messages with a small number of mostly neighboring nodes. The structure is based on distance information between communicating nodes that can be derived using ranging or localization methods and requires no additional shared infrastructure other than an underlying communication network. Its modest requirements make it scalable to networks with large numbers of nodes.