7857681
A 3D User Interface for Visualizing Neuron Location in Invertebrate Ganglia
Invertebrate nervous systems serve as important models for neuroscience research because they are comprised of relatively small numbers of individually identified neurons. There is no universal means of documenting locations of individual neurons that allows variability between specimens and can be generalized to different species. We present a new technique for visualizing and documenting neuron location. First, we describe a 3D user interface that allows neuroscientists to directly mark neuron locations on a 3D deformable model. A new mapping scheme is proposed that specifies the location of a neuron in a common coordinate system that accommodates the individual variability in size and shape of ganglia.
15780176
Understanding the design space of referencing in collaborative augmented reality environments
For collaborative environments to be successful, it is critical that participants have the ability to generate effective references. Given the heterogeneity of the objects and the myriad of possible scenarios for collaborative augmented reality environments, generating meaningful references within them can be difficult. Participants in co-located physical spaces benefit from non-verbal communication, such as eye gaze, pointing and body movement; however, when geographically separated, this form of communication must be synthesized using computer-mediated techniques. We have conducted an exploratory study using a collaborative building task of constructing both physical and virtual models to better understand inter-referential awareness -- or the ability for one participant to refer to a set of objects, and for that reference to be understood. Our contributions are not necessarily in presenting novel techniques, but in narrowing the design space for referencing in collaborative augmented reality. This study suggests collaborative reference preferences are heavily dependent on the context of the workspace.
35564489
Accelerated 2D Image Processing on GPUs
Graphics processing units (GPUs) in recent years have evolved to become powerful, programmable vector processing units. Furthermore, the maximum processing power of current generation GPUs is roughly four times that of current generation CPUs (central processing units), and that power is doubling approximately every nine months, about twice the rate of Moore's law. This research examines the GPU's advantage at performing convolutionbased image processing tasks compared to the CPU. Straight-forward 2D convolutions show up to a 130:1 speedup on the GPU over the CPU, with an average speedup in our tests of 59:1. Over convolutions performed with the highly optimized FFTW routines on the CPU, the GPU showed an average speedup of 18:1 for filter kernel sizes from 3x3 to 29x29.
2014328
A 3D reconstruction algorithm based on 3D deformable atlas
Reconstructing 3D models from 2D images is an important problem in visualization, particularly in medical and biological visualization. We present a new algorithm for 3D model reconstruction from 2D-images. In our approach, a 3D atlas is deformed to fit the feature data set in the target images. Compared with existing 3D reconstruction techniques, our approach is more efficient and easier to control. The resultant model has good inherent connectivity and smoothness. The resolution of the final model is determined by the resolution of reference model rather than the dataset. The model generates highly detailed view with a limited number of images. This algorithm has been successfully applied to medical simulation.
6725306
Design and Implementation of a Workflow Rendering Engine
Business visualization is an effective tool for business decision making and problem solving. Despite its obvious benefits, 3D visualization is far less common in practice than it should be due to the lack of software support. To address this problem, we have developed a workflow rendering engine that facilitates the 3D visualization of business process information for organizational process analysis and efficiency studies. The workflow information is represented in XML format to be compatible with the new Web graphics standard X3D. The rendering engine is implemented using C++ and OpenGL in order to take full advantage of modern 3D graphics hardware. The object-oriented design makes this engine highly extensible.
16266615
NeuroQL: A Domain-Specific Query Language for Neuroscience Data
In this paper, we propose a domain-specific query language called NeuroQL for the neuroscience domain. NeuroQL is designed primarily for neuroinformatics database users and aims to enable users to directly interact with neuroscience databases in their professional concepts and terms with the help of a conceptual data model. NeuroQL is DBMS independent and can be translated into traditional query language such as SQL, OQL and XQuery. It integrates some object-oriented features, and supports neuron domain-specific data types and query operators, which can dynamically evolve when the underlying database schema evolves.
8902892
Dynamic Load Balancing for I/O- and Memory-Intensive Workload in clusters Using a Feedback Control Mechanism
One common assumption of the existing models of load balancing is that the weights of resources and I/O buffer size are statically configured. Though the static configuration of these parameters performs well in a cluster where the workload can be predicted, its performance is poor in dynamic systems where the workload is unknown. In this paper, a new feedback control mechanism is proposed to improve the overall performance of a cluster with I/O-intensive and memory-intensive workload. The mechanism dynamically adjusts the resource weights as well as the I/O buffer size. Results from a trace-driven simulation show that this mechanism is effective in enhancing the performance of a number of existing load-balancing schemes.
43360105
Factorizations of regular graphs
Let G be a k-regular graph of order 2n such that k≥n. Hilton (J. Graph Theory, 9 (1985), 193–196) proved that G contains at least ⌊k3⌋ edge-disjoint 1-factors. Hilton's theorem is improved in this paper that G contains at least ⌊k2⌋ edge-disjoint 1-factors. The following result is also proved in this paper: Let G be a 2-connected, k-regular, non-bipartite graph of order at most 3k − 3 and x, y be a pair of distinct vertices. If Gβ {x, y} is connected, then G contains an (x, y)-Hamilton path.
26735928
I/O Response Time in a Fault-Tolerant Parallel Virtual File System
A fault tolerant parallel virtual file system is designed and implemented to provide high I/O performance and high reliability. A queuing model is used to analyze in detail the average response time when multiple clients access the system. The results show that I/O response time is with a function of several operational parameters. It decreases with the increase in I/O buffer hit rate for read requests, write buffer size for write requests and number of server nodes in the parallel file system, while higher I/O requests arrival rate increases I/O response time.
5775560
Hierarchical Bloom filter arrays (HBA): a novel, scalable metadata management system for large cluster-based storage
An efficient and distributed scheme for file mapping or file lookup scheme is critical in decentralizing metadata management within a group of metadata servers. This work presents a technique called HBA (hierarchical Bloom filter arrays) to map file names to the servers holding their metadata. Two levels of probabilistic arrays, i.e., Bloom filter arrays, with different accuracies are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, while the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns. Extensive trace-driven simulations have shown our HBA design to be highly effective and efficient in improving performance and scalability of file systems in clusters with 1,000 to 10,000 nodes (or superclusters).
2274256
Overlay Networks with Linear Capacity Constraints
Overlay networks are virtual networks residing over the IP network; consequently, overlay links may share hidden lower level bottlenecks. Previous work has assumed an independent overlay model: a graph with independent link capacities. We introduce a model of overlays that incorporates correlated link capacities and linear capacity constraints (LCC) to formulate hidden shared bottlenecks; we refer to these as LCC-overlays. We define metrics to qualitatively measure overlay quality in terms of its accuracy (in representing the true network topology) and efficiency (that is, performance). Through analysis and simulations, we show that LCC-overlay is perfectly accurate and, hence, enjoys much higher efficiency than the inaccurate independent overlay. We discover that even a highly restricted LCC class-node-based LCC-yields near-optimal accuracy and significantly higher efficiency. We study two network flow problems in the context of LCC-graphs: widest path and maximum flow. We prove that Widest Path with LCC is NP-complete. We formulate Maximum Flow with LCC as a linear program and propose an efficient distributed algorithm to solve it. Based on the LCC model, we further study the problem of optimizing delay while still maintaining optimal or near-optimal bandwidth. We also outline a distributed algorithm to efficiently construct an overlay with node-based LCC.