This paper investigates requirements for remote data access when used in an interactive visualization environment. A conceptually simple interface to deal with persistent and transient data that are organized on structured grids is specified. Although some parts of the interface are stated only very generally, or as requirements, a first prototype confirms that efficient utilization of network resources should be achievable for a wide range of applications. 1.

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Abstract — Many emerging high performance applications require distributed infrastructure that is significantly more powerful and flexible than traditional Grids. Such applications require the optimization, close integration, and control of all Grid resources, including networks. The EnLIGHTened (ENL) Computing Project has designed an architectural framework that allows Grid applications to dynamically request (inadvance or on-demand) any type of Grid resource: computers, storage, instruments, and deterministic, high-bandwidth network paths, including lightpaths. Based on application requirements, the ENL middleware communicates with Grid resource managers and, when availability is verified, co-allocates all the necessary resources. ENL’s Domain Network Manager controls all network resource allocations to dynamically setup and delete dedicated circuits using Generalized Multiprotocol Label Switching (GMPLS) control plane signaling. In order to make optimal brokering decisions, the ENL middleware uses near-real-time performance information about Grid resources. A prototype of this architectural framework on a national-scale testbed implementation has been used to demonstrate a small number of applications. Based on this, a set of changes for the middleware have been laid out and are being implemented. I.

With the advent of high-bandwidth wireless networks and pervasive computing, the space and platform barriers for visualization are being broken. Mobile visualization, or ubiquitous visualization, allows users to visualize data anywhere, anytime, on various mobile devices connected by wireless networks. In this paper, we investigate how to achieve volume visualization on mobile devices. We present a client-oblivious data model for various visualization tasks on mobile devices ranging from powerful workstations to PDAs and cell phones. Our model integrates volumes and some pre-computed features, such as iso-surfaces, into one hierarchical structure, which can be compressed and progressively transmitted over networks. Two novel algorithms are introduced. One is for mesh reconstruction from point-based models and the other is for volume reconstruction from surfacebased models. The construction, compression, and transmission of our data model are presented. 1.

Figure 1: Three interpolated time steps of an AMR simulation of a black hole collision. The shaded isosurfaces depict the event horizons of the black holes, and the volume rendered scalar field shows the gravitational waves that are emitted during the merger. Analysis of phenomena that simultaneously occur on different spatial and temporal scales requires adaptive, hierarchical schemes to reduce computational and storage demands. Adaptive Mesh Refinement (AMR) schemes support both refinement in space that results in a time-dependent grid topology, as well as refinement in time that results in updates at higher rates for refined levels. Visualization of AMR data requires generating data for absent refinement levels at specific time steps. We describe a solution starting from a given set of “key frames ” with potentially different grid topologies. The presented work was developed in a project involving several research institutes that collaborate in the field of cosmology and numerical relativity. AMR data results from simulations that are run on dedicated compute machines and is thus stored centrally, whereas the analysis of the data is performed on the local computers of the scientists. We built a distributed solution using remote procedure calls (RPC). To keep the application responsive, we split the bulk data transfer from the RPC response and deliver it asynchronously as a binary stream. The number of network round-trips is minimized by using high level operations. In summary, we provide an application for exploratory visualization of remotely stored AMR data.

Loss of bone mass and structure is one of the most prominent risk factors associated with human spaceflight. The main objective of this multifaceted MAP project was therefore to establish a precise diagnostic method for quantifying alterations in the structural composition of human trabecular bone. Tools based on evaluation of 2-D Computed Tomography (CT) and 3-D micro-CT (µCT) images by measures of complexity were developed. These tools quantify different aspects of spatial bone architecture. The measures, methods and results were validated by comparison with conventional histomorphometry and biomechanical testing. In order to visualise the obtained µCT data, special tools had to be developed able to handle huge amounts of data. Additionally, bone models were designed to simulate and predict bone loss, provide test objects for the new structural measures, and gain insight into the effects of mechanical loading on bone structural alterations. A novel ultrasonic methodology for bone structure assessment is in development using the results of experiments into skeletal discordance and new insights in ultrasound technology. The outcome of this comprehensive project will have an impact on health care for space crews as well as for patients with bone diseases on Earth. 1.

Charting out the complete brain microstructure of a mammalian species is a grand challenge. Recent advances in serial sectioning microscopy such as the Knife-Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical sectioning technique, have the potential to finally address this challenge. Nevertheless, there still are several obstacles remaining to be overcome. First, many of these serial sectioning microscopy methods are still experimental and are not fully automated. Second, even when the full raw data have been obtained, morphological reconstruction, visualization/editing, statistics gathering, connectivity inference, and network analysis remain tough problems due to the unprecedented amounts of data. In this thesis, I describe construction of a general data acquisition and analysis framework to overcome these challenges, with a focus on data from the C57BL/6 mouse brain. Since there has been no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. In sum, I construct a general framework for automated data acquisition and

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processing applied to brain