Overview of Ablation Research at Sandia National Laboratories.
Abstract not provided.
Engineering topics
Publications and source records attributed to Anderson, Nicholas.
Abstract not provided.
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Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.
This report summarizes the analysis of data collected by the University of North Dakota Citation II measurement platform during three TRMM Field measurement campaigns. The Citation II made cloud measurements during TEFLUN B in Florida, the LBA program in Brazil, and KWAJEX in Kwajalein. The work performed can be divided into two parts. The first part consisted of reformatting the Citation data into a form more easily used to compare to the satellite information. The second part consisted of examination of the cloud data in order to characterize the properties of the tropical clouds. The reformatting of the Citation data was quite labor intensive and, due to the fact that the aircraft was involved in three of the field campaigns, it required a substantial number of person-hours to complete. Much of the analysis done on the second part was done in conjunction with the thesis work of Nicholas Anderson, then a graduate student at the University of North Dakota.