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Heterogeneous Data Discovery Using Deep Neural Networks

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-16-C-0232
Agency Tracking Number: F16A-T12-0150
Amount: $149,960.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF16-AT12
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-07-19
Award End Date (Contract End Date): 2017-04-19
Small Business Information
1715 Iron Horse Drive
Longmont, CO 80501
United States
DUNS: 079575167
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 David Ohm, PhD
 (303) 523-6810
Business Contact
 David Ohm, PhD
Phone: (303) 523-6810
Research Institution
 Utah State University
 Jake Gunther, PhD
Division of Sponsored Programs - VP for Research and Graduate Studies 1415 Old Main Hill- Room 64
Logan, UT 84322
United States

 (435) 374-9178
 Nonprofit College or University

Improving feature extraction, event detection, and target classification in multi-sensor systems requires new mathematical methods and processing techniques. In addition, previous research and experience suggests that leveraging sensor data that has not experienced significant dimensionality reduction can preserve subtle features when processed jointly with other relevant data. However, traditional ISR solutions typically process and analyze sensor outputs independently, often without a priori, or jointly-coupled, information between other sensor types - leaving the capabilities of these systems limited and predictable. Therefore, we propose research to discover new multi-sensor methods, and overcome traditional single-sensor limitations, by utilizing and developing state-of-the-art nonlinear processing methods based on the fusion of Deep Convolutional Neural Networks (DCNN) and non-parametric signal processing algorithms. We will combine new mathematical methods and robust processing techniques in order to discover new observables from multiple inputs, even from sensors that return disparate forms of data. The proposed methods are will suited to exploit jointly-coupled information from multi-sensor data that is analyzed up-stream (i.e., in a raw or weakly-processed form). This research will open up a new area for continued research along with the opportunity to discover new features in multi-sensor configurations that are not available in traditional stove-pipe solutions.

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

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