IBM and Stanford University team up for a new perspective on SETI signal analytics

Co-authors: Frank Fan, Kenny Smith, Jason Wang, Austin Hou, Rafael Setra, Qi Yang IBM and students from the Stanford University have teamed up to use IBM Spark services to analyze astronomical radio signal data for their projects in Mining Massive Data Sets. Using IBM’s Spark@SETI environment in the IBM Cloud, two different teams tackled the complex problem of signal feature extraction and classification using several terabytes of […]

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Signal Classification: Powerful Patterns from Simple Features

This is the third in the Series of SETI project postings.  SETI sparks Machine Learning to sift Big Data and Types of BigData from the Allen Telescope Array are available on the IBM Emerging Technologies Blog. IBM jStart team has partnered with the SETI Institute to develop a Spark application to analyze the millions of radio events that have been […]

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SETI sparks Machine Learning to sift Big Data

The SETI Institute’s mission is to explore, understand and explain the origin and nature of life in the universe. A central element of the Institute’s operations is the Allen Telescope Array (ATA) located in the Hat Creek Radio Observatory in California. This phased array observatory combines over 40 radio dishes to look for faint signals which may betray the presence […]

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