Machine Learning Can Detect Hazardous Materials
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עברית (Hebrew)
Integrated machine learning platforms can significantly reduce time, redundancy, cost, and improve the accuracy in detecting threats such as explosives, chemical agents, and narcotics.
The Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program recently awarded funding to two small businesses to develop non-contact, inexpensive machine learning training and classification technologies.
Physical Sciences and Alakai Defense Systems each received approximately $1 million in SBIR Phase II funding to develop technologies that can rapidly and accurately identify unknown spectrometer signals as safe or threatening.
Participation in the DHS SBIR Program is subject to demonstration of feasibility in Phase I, for each companies’ compact, accurate and rapid classification Machine Learning Module for Detection Technologies solutions.
Under Phase II, PSI will continue to develop its deep-learning algorithm for detection and classification of trace explosives, opioids, and narcotics on surfaces, for optical spectroscopic systems. PSI will extend the algorithm’s capabilities from infrared reflectance spectroscopy to include Raman spectroscopy, as well as a proposed operational module prototype, which will have a classification accuracy of greater than 90 percent.
During their Phase II efforts, Alakai, will continue the development of the Agnostic Machine Learning Platform for Spectroscopy (AMPS) that rapidly and accurately detects trace quantities of hazardous and related chemicals from a variety of spectroscopic instruments.
At the completion of the 24-month Phase II contract, SBIR awardees will have developed a prototype to demonstrate the advancement of the technology, spearheading the potential for Phase III funding, according to hstoday.us.
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