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SBIR-STTR Award
7
Machine learning for standoff detection of Special Nuclear Material (SNM)
Award last edited on: 9/14/2018
Sponsored Program
SBIR
Awarding Agency
DOD : DTRA
Total Award Amount
$149,965
Award Phase
1
Solicitation Topic Code
DTRA162-001
Principal Investigator
Stanislav Shalunov
Company Information
Clostra Inc
55 Taylor Street
San Fransisco, CA 94102
(415) 275-3415
contact@clostra.com
www.clostra.com
Location:
Single
Congr. District:
12
County:
San Francisco
Phase I
Contract Number:
HDTRA1-17-P-0021
Start Date:
3/23/2017
Completed:
10/29/2017
Phase I year
2017
Phase I Amount
$149,965
Deep Learning for standoff detection of Special Nuclear Material (DLeN) applies the same deep learning techniques that allow computers to beat human performance in image recognition and the game of Go to detecting Special Nuclear Material. Spectral analysis and signal processing can in some cases be augmented by the use of much larger neural nets that conduct much deeper analysis of features of the sensor data. This may enable the extraction of information indicating presence of SNM from a standoff distance and with a shorter amount of time. Training a deep neural net is very computationally intensive and requires specialized hardware. Execution is very computationally inexpensive and can easily happen in JVM even with very modest CPU and memory. Phase 1 of the project determines feasibility by training a deep neural net to analyze sensor data. The success metrics are the false negative and false positive rates.
Phase II
Contract Number:
----------
Start Date:
00/00/00
Completed:
00/00/00
Phase II year
----
Phase II Amount
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