A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.
Recently, several researchers @cite_35 @cite_18 @cite_34 have developed a blob-tracking algorithm that uses raw fast camera data directly with GPI technique. In @cite_35 @cite_34 , they leverage a contouring method, database techniques and image analysis software to track the blob motion and changes in the structure of blobs. After normalizing each frame by an average frame created from roughly one thousand frames around the target time frame, the resulting images are contoured and the closed contours satisfying certain size constraints are determined as blobs. Then, an ellipse is fitted to the contour midway between the smallest level contours and the peak. All information about blobs are added into a SQL database for more data analysis. This method is close to our approach but it can not be used for real-time blob detection since they compute time-averaged intensity to normalize the local intensity. Additionally, only closed contours are treated as blobs, which may miss blobs at the edges of the regions of interest. Finally, these methods are still post-run-analysis, which cannot provide real-time feedback in fusion experiments.
@cite_35 @cite_18 @cite_34
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Abstract Fast 2-D cameras examine a variety of important aspects of the plasma edge and in-vessel components on the National Spherical Torus Experiment (NSTX). Four Phantom and two Miro visible-light cameras manufactured by Vision Research are used on NSTX for edge studies. Each camera can take several gigabytes (GBs) of data during each plasma pulse. Timely access to this amount of data can itself be a challenge, but analysing all these data using manual frame-by-frame examination is not practical. This paper describes image analysis, database techniques, and visualization methods used to organize the fast camera data and to facilitate physics insights from it. An example is presented of analysing and characterizing the size, movement and dynamics of coherent plasma structures (typically referred to as “blobs”) near the plasma edge. Software tools that generate statistics of blob speed, shape, amplitude, size, and orientation are described. The characteristics of emitted blobs affect plasma confinement and heat loads on plasma facing components, and are thus of particular interest to future machines like ITER. This article is part of Ralph Kube's doctoral thesis, which is available in Munin at http: hdl.handle.net 10037 7088