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Award Data
The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.
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Bounding generalization risk for Deep Neural Networks
SBC: Euler Scientific Topic: NGA20A001Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels. In an ...
STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency -
Algorithms for Look-down Infrared Target Exploitation
SBC: SIGNATURE RESEARCH, INC. Topic: NGA18A001The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT l ...
STTR Phase II 2020 Department of DefenseNational Geospatial-Intelligence Agency -
Human Performance Optimization
SBC: HVMN Inc. Topic: SOCOM17C001During altitude-induced hypoxia, operator cognitive and physical capacity degrades, compromising individual and team performance. Cognitive degradation is linked to falling brain energy levels, increased reliance on anaerobic energy production and lactate accumulation. Ketones are the evolutionary alternative substrate to glucose for brain metabolic requirements; previous studies demonstrated that ...
STTR Phase II 2019 Department of DefenseSpecial Operations Command -
Human Performance Optimization
SBC: REJUVENATE BIO INC Topic: SOCOM17C001Special OperationsForces (SOF)are an integral aspect of the US military. SOF operators are among the most elite and highly qualified individuals in the U.S.military. As such, extraordinary physical and mental demands are placed upon them to excel in extreme environments for extended periods of time. This unrelenting cycle of combat deployments and intense pre-deployment training shortens the funct ...
STTR Phase II 2019 Department of DefenseSpecial Operations Command -
Metrology of Thin Films on Sapphire Substrate
SBC: Optowares Incorporated Topic: DMEA16B001There is a lack of a non-destructive metrology tool to measure the thickness of thin films on sapphire substrates due to the transparency of the substrate. Leveraging our extensive experience building sensor systems combined with MIT Lincoln Laboratory’s expertise in theoretical modeling, we will design and build an innovative thin film measurement tool using Raman spectroscopy. Our metrology sy ...
STTR Phase II 2019 Department of DefenseDefense Microelectronics Activity -
Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery
SBC: TOYON RESEARCH CORPORATION Topic: 1On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...
STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Algorithms for Look-down Infrared Target Exploitation
SBC: SIGNATURE RESEARCH, INC. Topic: 1Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...
STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
System for Nighttime and Low-Light Face Recognition
SBC: Systems & Technology Research LLC Topic: SOCOM18A001Face recognition performance using deep learning has seen dramatic improvements in recent years. This improvement has been fueled in part by the curation of large labeled training datasets with millions of images of hundreds of thousands of subjects.This results in effective generalization for matching over pose, illumination, expression and age variation, however these datasets have traditionally ...
STTR Phase I 2018 Department of DefenseSpecial Operations Command -
Human Performance Optimization: Ketone Esters for Optimization of Operator Performance in Hypoxia
SBC: HVMN Inc. Topic: SOCOM17C001In the setting of altitude-induced hypoxia, operator cognitive capacity degrades and can compromise both individual and team performance. This degradation is linked to falling brain energy (ATP) levels and an increased reliance on anaerobic energy production from glucose. Ketone bodies are the evolutionary alternative substrate to glucose for brain metabolic requirements; previous studies have sho ...
STTR Phase I 2018 Department of DefenseSpecial Operations Command -
Human Performance Optimization
SBC: REJUVENATE BIO INC Topic: SOCOM17C001Special Operations Forces (SOF) are an integral aspect of the US military. SOF operators are among the most elite and highly qualified individuals in the U.S. military. As such, extraordinary physical and mental demands are placed upon them to excel in extreme environments for extended periods of time. This unrelenting cycle of combat deployments and intense pre-deployment training shortens the fu ...
STTR Phase I 2018 Department of DefenseSpecial Operations Command