2404821
Project Grant
Overview
Grant Description
Sbir phase I: synthesizing large and diverse data-sets for training machine learning algorithms using physical modeling and simulation -the broader/commercial impact of this small business innovation research (SBIR) phase I project, would be to diminish the hurdles in building a synthetic data generator for robust automated detection technologies.
Passenger and personal property screening is an essential component of Department of Homeland Security's (DHS) strategy to combat terrorism and targeted violence. The synthetic data generated by the proposed technology can be used to train as well as characterize the advanced screening solutions deployed. This will boost understanding of expected field performance and hence confidence in systems used to protect people and critical infrastructure.
At airports, fewer false alarms from people and baggage screening equipment would translate to shorter lines, smaller wait times and decreased stress levels. Better threat detection rates would boost confidence in the screening solutions and truly help in reducing anxiety surrounding air travel, large gatherings, and outdoor events. The apparatus for generating synthetic data can also benefit education and training of the budding STEM workforce in advanced technologies.
This small business innovation research (SBIR) phase I project aims to demonstrate that a novel radiation physics solver based on first principles can generate synthetic data that matches the realism of data obtained through manual acquisition on physical radiation-based scanners. In addition, the solver can grow/widen the sample probability distribution to more closely match the population probability distribution than manually acquired data and do so in a hitherto unrealized linear computational time.
In an emerging world of machine learning based automated threat or anomaly detection, this cost-effective data synthesis fulfills an immediate need to address the problem of data paucity to both train and test such algorithms. The research and development effort in this phase-I project will be focused on developing computational methods to estimate the residual energy post photon-matter interaction in a cost-efficient manner. Representative object assemblies will be constructed to virtually scan and generate realistic and precisely annotated imaging data.
Appropriate metrics will also be developed to measure the quality of the created synthetic images. The challenge will be to match the resolving power of the relevant modality as it applies to specific application areas. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria. - Subawards are not planned for this award.
Passenger and personal property screening is an essential component of Department of Homeland Security's (DHS) strategy to combat terrorism and targeted violence. The synthetic data generated by the proposed technology can be used to train as well as characterize the advanced screening solutions deployed. This will boost understanding of expected field performance and hence confidence in systems used to protect people and critical infrastructure.
At airports, fewer false alarms from people and baggage screening equipment would translate to shorter lines, smaller wait times and decreased stress levels. Better threat detection rates would boost confidence in the screening solutions and truly help in reducing anxiety surrounding air travel, large gatherings, and outdoor events. The apparatus for generating synthetic data can also benefit education and training of the budding STEM workforce in advanced technologies.
This small business innovation research (SBIR) phase I project aims to demonstrate that a novel radiation physics solver based on first principles can generate synthetic data that matches the realism of data obtained through manual acquisition on physical radiation-based scanners. In addition, the solver can grow/widen the sample probability distribution to more closely match the population probability distribution than manually acquired data and do so in a hitherto unrealized linear computational time.
In an emerging world of machine learning based automated threat or anomaly detection, this cost-effective data synthesis fulfills an immediate need to address the problem of data paucity to both train and test such algorithms. The research and development effort in this phase-I project will be focused on developing computational methods to estimate the residual energy post photon-matter interaction in a cost-efficient manner. Representative object assemblies will be constructed to virtually scan and generate realistic and precisely annotated imaging data.
Appropriate metrics will also be developed to measure the quality of the created synthetic images. The challenge will be to match the resolving power of the relevant modality as it applies to specific application areas. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria. - Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Santa Clara,
California
95051-1418
United States
Geographic Scope
Single Zip Code
Quantireal was awarded
Project Grant 2404821
worth $275,000
from National Science Foundation in July 2024 with work to be completed primarily in Santa Clara California United States.
The grant
has a duration of 8 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Synthesizing large and diverse data-sets for training machine learning algorithms using physical modeling and simulation
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project, would be to diminish the hurdles in building a synthetic data generator for robust automated detection technologies. Passenger and personal property screening is an essential component of Department of Homeland Security’s (DHS) strategy to combat terrorism and targeted violence. The synthetic data generated by the proposed technology can be used to train as well as characterize the advanced screening solutions deployed. This will boost understanding of expected field performance and hence confidence in systems used to protect people and critical infrastructure. At airports, fewer false alarms from people and baggage screening equipment would translate to shorter lines, smaller wait times and decreased stress levels. Better threat detection rates would boost confidence in the screening solutions and truly help in reducing anxiety surrounding air travel, large gatherings, and outdoor events. The apparatus for generating synthetic data can also benefit education and training of the budding STEM workforce in advanced technologies.
This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate that a novel radiation physics solver based on first principles can generate synthetic data that matches the realism of data obtained through manual acquisition on physical radiation-based scanners. In addition, the solver can grow/widen the sample probability distribution to more closely match the population probability distribution than manually acquired data and do so in a hitherto unrealized linear computational time. In an emerging world of machine learning based automated threat or anomaly detection, this cost-effective data synthesis fulfills an immediate need to address the problem of data paucity to both train and test such algorithms. The research and development effort in this Phase-I project will be focused on developing computational methods to estimate the residual energy post photon-matter interaction in a cost-efficient manner. Representative object assemblies will be constructed to virtually scan and generate realistic and precisely annotated imaging data. Appropriate metrics will also be developed to measure the quality of the created synthetic images. The challenge will be to match the resolving power of the relevant modality as it applies to specific application areas.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 7/8/24
Period of Performance
7/1/24
Start Date
3/31/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2404821
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
For-Profit Organization (Other Than Small Business)
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
UVDPPWN7CVM1
Awardee CAGE
9R9U2
Performance District
CA-17
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 7/8/24