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Machine Learning Methods to Catalog Sources from Diverse, Widely Distributed Sensors

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9453-19-P-0685
Agency Tracking Number: F19A-012-0202
Amount: $149,811.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF19A-T012
Solicitation Number: 2019.1
Timeline
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-07-17
Award End Date (Contract End Date): 2020-07-17
Small Business Information
148 Middle St Suite 1D, Portland, ME, 04101
DUNS: 962583956
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr. Caryl Johnson
 (207) 699-4017
 caryl.johnson@introspectivesystems.com
Business Contact
 Kay Aikin
Phone: (207) 699-4051
Email: kay.aikin@introspectivesystems.com
Research Institution
 Southern Research
 Dr. Seth Cohen
 757 Tom Martin Drive
Birmingham, AL, 35211
 (205) 581-2649
 Domestic nonprofit research organization
Abstract
The efficient detection and understanding of clandestine nuclear device testing is critical to the security and credibility of the organizations currently tasked with this important work. At present, the Air Force Tactical Applications Center (AFTAC) and likewise, the Comprehensive Test Ban Treaty Organization (CTBTO) are running methods and algorithms that are out of date. These approaches use a complex Export-Transform-Load (ETL) that is focused on a central database, constituting a single point of failure as well as being a restriction to increasing the quantity and quality of the analytical effort. th Detecting and understanding of clandestine nuclear device testing is a problem that has been around for a very long time. Most of the work in this area has been based on physical attributes as revealed in the structure of seismic waves propagating through the earth. What advancement can be made in a system that has been established for more than half a century The opportunity is to apply machine learning (ML) to improve the analysis of seismic waves. This proposal will build off a proven seismic processing system that provides adaptable streaming analytics and eventually into a Distributed HPC Seismic System fully integrated with todays and future machine learning.

* Information listed above is at the time of submission. *

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