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Development of an AgNstic Machine Learning Platform for Spectroscopy (AMPS)

Award Information
Agency: Department of Homeland Security
Branch: N/A
Contract: 70RSAT20C00000034
Agency Tracking Number: 20.1-DHS201-009-0014-I
Amount: $149,855.40
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DHS201-009
Solicitation Number: 20.1
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-05-18
Award End Date (Contract End Date): 2020-11-17
Small Business Information
8285 Bryan Dairy Rd, #125 Suite 125
largo, FL 33777-5028
United States
DUNS: 783099851
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Robert Waterbury
 Dir of Engineering
 (727) 492-4248
Business Contact
 Kenneth Pohl
Title: Director of Programs
Phone: (727) 541-1600
Research Institution

Rapid and accurate detection of trace quantities of hazardous and related chemicals can greatly enhance safety and save lives in a variety of fields from military theaters, to law enforcement and public safety. A wide range of sophisticated spectroscopy systems are being continually developed and improved to meet this goal, but they all share a similar challenge: how to rapidly detect trace levels of threat chemicals within a spectrum that may be compromised by Nise, background, chemical interferents, or a number of other effects that vary depending on the instrument and application. Each hardware developer may spend time and resources developing an algorithm to solve this problem, and in the worst cases this process can slow the development of the instrument or compromise the measured performance. Therefore, Alakai is proposing the development of the AgNstic Machine Learning Platform for Spectroscopy (AMPS) that can be trained and deployed to rapidly process and accurately identify threat chemicals in spectra obtained from a wide variety of spectroscopic instruments.

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

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