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Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-P-1186
Agency Tracking Number: N13A-016-0331
Amount: $79,862.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N13A-T016
Solicitation Number: 2013.A
Solicitation Year: 2013
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-07-01
Award End Date (Contract End Date): 2014-04-30
Small Business Information
555 Quince Orchard Road Suite 300
Gaithersburg, MD -
United States
DUNS: 121257443
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Alberto Lacaze
 (240) 631-0008
Business Contact
 Alberto Lacaze
Title: President
Phone: (240) 631-0008
Research Institution
 Southwest Research Institute
 Michael Ladika
6220 Culebra Rd.
San Antonio, TX 78238-5166
United States

 (210) 684-5111
 Domestic Nonprofit Research Organization

Autonomous systems continue to be outfitted with larger amounts of sensors that are capable of collecting extremely large amounts of data over the course of a mission. Even autonomous systems with high storage capacities can run into storage limitations when burdened with large amounts of sensor data over long mission durations. This proposal will develop a Learning-based Approach for Relevant Data Abstraction (LARDA) from a set of sensors that produce a large volume of raw data on-board an autonomous system. LARDA will generate a data abstraction and handling framework that is generic enough to be useful for a variety of current and future autonomous systems, but specific enough to directly support missions fielded with autonomous systems in the near-term. The core algorithms of this framework will comprise both supervised and unsupervised machine learning techniques to extract, cluster, and label relevant features from sensor data that can support planning and decision-making for future autonomous missions.

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

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