November 2007
Software Enabling Technologies for Petascale Science
Arie Shoshani, Lawrence Berkeley National Laboratory
Ilkay Altintas, San Diego Supercomputer Center
Alok Choudhary, Northwestern University
Terence Critchlow, Pacific Northwest National Laboratory
Chandrika Kamath, Lawrence Livermore National Laboratory
Bertram Ludäscher, University of California, Davis
Jarek Nieplocha, Pacific Northwest National Laboratory
Steve Parker, University of Utah
Rob Ross, Argonne National Laboratory
Nagiza Samatova, Oak Ridge National Laboratory
Mladen Vouk, North Carolina State University

Feature Extraction and Tracking

As part of the Data Mining and Analysis (DMA) layer, the SDM center is developing scalable algorithms for the interactive exploration of large, complex, multi-dimensional scientific data. By applying and extending ideas from data mining, image and video processing, statistics, and pattern recognition, we are developing a new generation of computational tools and techniques that are being used to improve the way in which scientists extract useful information from data.4 These tools were applied to problems in a variety of application areas, including separation of signals in climate data from simulations, the identification of key features in sensor data from the D-III-D Tokamak, and the classification and characterization of orbits in Poincaré plots in Fusion data.

Figure 4

Figure 4. A schematic of the NSTX.

A specific example of the effectiveness of such techniques is the identification of the movement of “blobs” in images from fusion experiments, using data from the National Spherical Torus Experiment (NSTX),5 shown in Figure 4. A blob is a coherent structure in the image that carries heat and energy from the center of the torus to the wall. Figure 5 shows bright blobs extracted from experimental images from the NSTX. The blobs are high energy regions. If they hit the torus wall that confines the plasma, it can vaporize. The figure shows movement of the blobs over time. A key challenge to the analysis is the lack of a precise definition for these structures. Figure 5 shows three consecutive images from an NSTX sequence. The original images are somewhat noisy and must first be processed to remove the noise. We have applied our background subtraction software to remove the quiescent background intensity in the sequences. Next, ambient background intensity, which is approximated by the median of the sequence, is removed, thus highlighting the blob regions, as shown in the second row of the figure. We then use image processing techniques to identify and track the blobs over time, as shown in the third row. The goal is to validate and refine the theory of plasma turbulence.

Figure 5

Figure 5. Tracking of “blobs” in Fusion images.

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Reference this article
Shoshani, A., Altintas, I., Choudhary, A., Critchlow, T., Kamath, C., Ludäscher, B., Nieplocha, J., Parker, S., Ross, R., Samatova, N., Vouk, M. "Scientific Data Management: Essential Technology for Accelerating Scientific Discoveries," CTWatch Quarterly, Volume 3, Number 4, November 2007. http://www.ctwatch.org/quarterly/articles/2007/11/scientific-data-management-essential-technology-for-accelerating-scientific-discoveries/

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