18332258
Simulation and visualization of knee joint contact using deformable model
We introduce a new method for simulating and visualizing femur-menisci contact in human knee joint using computer generated 3D models. Knee joint contact analysis is important for understanding or solving clinical problems. Traditional knee biomechanical analysis is mostly conducted on a cadaver or based on mathematical descriptions. The lack of 3D graphical visualization makes it difficult to understand the analysis and integrate it into virtual medical simulation systems. Our method addresses this problem by using patient specific knee models and motion data to obtain the contact area information. Our method employs a new physics-based algorithm for menisci deformation and can calculate and visualize knee-menisci contact areas during gait cycles.
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.
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.
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.
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.
17203024
A channel-aware adaptive control to the MAC protocol in rate adaptive wireless LANs
Due to the low efficiency of legacy IEEE 802.11 MAC protocol, it has been the main bottleneck of wireless local area networks (WLANs). In this paper, we focus on multi-rate WLANs and propose an adaptive transmission control scheme, which adapts the transmission probability according to the number of active stations, transmission rates, and channel conditions. At first, an analytical model is built in terms of the proposed scheme to explore the throughput of the network. Then, by means of the characteristics of practical system, a heuristic algorithm is developed to approach the maximum throughput on-line. Extensive numerical calculations and simulations based on NS-2 are implemented to evaluate the performance of the proposed algorithm and the impact of inaccurate estimation of parameters. The results show that our algorithm outperforms the existing adaptive algorithms in imperfect channel, and insensitive to inaccurate estimation of parameters. Copyright © 2005 John Wiley & Sons, Ltd.
6280246
A dynamic load balancing scheme for I/O-intensive applications in distributed systems
In this paper, a new I/O-aware load-balancing scheme is presented to improve overall performance of a distributed system with a general and practical workload including I/O activities. The proposed scheme dynamically detects I/O load imbalance on nodes of a distributed system and determines whether to migrate the I/O requests of some jobs from overloaded nodes to other less- or under-loaded nodes, depending on data migration cost and remote I/O access overhead. Besides balancing I/O load, the scheme judiciously takes into account both CPU and memory load sharing in distributed systems, thereby maintaining the same level of performance as the existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation study show that, compared with the existing schemes that only consider CPU and memory, the proposed scheme reduces the mean slowdown by up to 54.5% (with an average of 39.9%). On the other hand, when compared to the existing approaches that only consider I/O, the proposed scheme reduces the mean slowdown by up to 57.2% (with an average of 31.6%). More importantly, the new scheme improves over a very recent algorithm found in the literature that considers all the three resources by up to 49.6% (with an average of up to 41.9%).
179733
Exploiting redundancy to boost performance in a RAID-10 style cluster-based file system
While aggregating the throughput of existing disks on cluster nodes is a cost-effective approach to alleviate the I/O bottleneck in cluster computing, this approach suffers from potential performance degradations due to contentions for shared resources on the same node between storage data processing and user task computation. This paper proposes to judiciously utilize the storage redundancy in the form of mirroring existed in a RAID-10 style file system to alleviate this performance degradation. More specifically, a heuristic scheduling algorithm is developed, motivated from the observations of a simple cluster configuration, to spatially schedule write operations on the nodes with less load among each mirroring pair. The duplication of modified data to the mirroring nodes is performed asynchronously in the background. The read performance is improved by two techniques: doubling the degree of parallelism and hot-spot skipping. A synthetic benchmark is used to evaluate these algorithms in a real cluster environment and the proposed algorithms are shown to be very effective in performance enhancement.
32403517
Multi-resolution LOD Volume Rendering in Medicine
This paper presents a level of detail (LOD) selection algorithm for multi-resolution volume rendering using 3D texture mapping. It uses an adaptive scheme that renders the volume in a region-of-interest at a high resolution and the volume away from this region at progressively lower resolutions. The algorithm is based on several important criteria, rendering is done adaptively by selecting high-resolution cells close to a center of attention and low-resolution cells away from this area. In addition, our hierarchical level-of-detail representation guarantees consistent interpolation between different resolution levels. Experiments have been applied to a number of large medical data and produced high quality images at interactive frame rates using standard PC hardware.
8105623
Computerized tumor boundary detection using a Hopfield neural network
The authors present a new approach for detection of brain tumor boundaries in medical images using a Hopfield neural network. The boundary detection problem is formulated as an optimization process that seeks the boundary points to minimize an energy functional based on an active contour model. A modified Hopfield network is constructed to solve the optimization problem. Taking advantage of the collective computational ability and energy convergence capability of the Hopfield network, the authors' method produces the results comparable to those of standard "snakes"-based algorithms, but it requires less computing time. With the parallel processing potential of the Hopfield network, the proposed boundary detection can be implemented for real time processing. Experiments on different magnetic resonance imaging (MRI) data sets show the effectiveness of the authors' approach.