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Space-Based Computational Hyperspectral Machine Vision using Compressed Sensing Neural Networks

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9453-19-P-0690
Agency Tracking Number: F19A-015-0209
Amount: $149,993.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF19A-T015
Solicitation Number: 19.A
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-06-25
Award End Date (Contract End Date): 2020-06-25
Small Business Information
40 Corporate Park Drive
Hopewell Junction, NY 12533
United States
DUNS: 129457037
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Le Li
 (845) 897-0138
Business Contact
 Le Li
Phone: (845) 897-0138
Research Institution
 William Marsh Rice University
 Kevin Kelly Kevin Kelly
6100 Main St,
Houston,, TX 77005
United States

 (713) 348-3565
 Nonprofit College or University

In this STTR Phase I proposal, Kent Optronics (KOI) together with its partner, Rice University, propose to develop novel deep learning algorithms to perform machine vision tasks such as target recognition and tracking utilizing the direct measurements from a compressive hyperspectral imaging system. By skipping the hypercube reconstruction, this combination of hardware and software will allow real-time, actionable reaction to the incoming datastream. The proposed space-qualified computational sensor is a hyperspectral imager based on the principle leveraging on a structured illuminator. In combination with the sparse recovery algorithms the sensor can efficiently recover the volume density of a participating medium which is described by volume densities rather than boundary surfaces, e.g. translucent objects, smoke, clouds, mixing fluids, and biological tissues. In Phase I, a thorough trade analysis and model validation test on both a manifold secant learning algorithm will be compared with a novel dynamic multi-rate compressive neural network approach in simulation. In Phase II, both of these approaches will be incorporated, tested and qualified in real-world compressive hyperspectral imaging hardware

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

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