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Abstraction and Model Simplification to Identify Interesting Data (RAMS)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC21C0109
Agency Tracking Number: 211188
Amount: $124,984.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: S5
Solicitation Number: SBIR_21_P1
Timeline
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-05-14
Award End Date (Contract End Date): 2021-11-19
Small Business Information
P.O. Box 422
Trumansburg, NY 14886-0422
United States
DUNS: 101321479
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Paul Nicotera
 (607) 257-1975
 pnicotera@atcorp.com
Business Contact
 Richard Smith
Phone: (607) 257-1975
Email: rsmith@atcorp.com
Research Institution
N/A
Abstract

Remote sensing platforms are often able to transfer only a small portion of all collected data to end-users, requiring significant manual effort to select the most relevant information for analysis.nbsp; To address this challenge, the ATC-NY team will develop Response Abstraction and Model Simplification (RAMS), a decision-support tool that assists scientists and automates remote and deep-space data collection for known events.nbsp; RAMS operates efficiently on remote sensing platforms by quantizing samples of telemetry data to enable highly parallel processing of Quantized Neural Network (QNN) operations.nbsp; RAMS also applies transfer learning and active learning techniques to train effective event detection models that reproduce human data-selection processes using a limited number of examples.nbsp; Using RAMS, scientists supporting the Magnetospheric Multiscale (MMS) mission identify several examples of target signals for magnetic reconnection events near the Earthrsquo;s magnetopause and magnetotail, which RAMS uses to automatically select such events in future data.

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

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