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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

  1. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep 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
  2. Human Performance Optimization

    SBC: HVMN Inc.            Topic: SOCOM17C001

    During 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
  3. Human Performance Optimization

    SBC: REJUVENATE BIO INC            Topic: SOCOM17C001

    Special 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
  4. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On 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
  5. Human Performance Optimization: Ketone Esters for Optimization of Operator Performance in Hypoxia

    SBC: HVMN Inc.            Topic: SOCOM17C001

    In 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
  6. Development of Advanced Military Prosthetic Shoulder System

    SBC: Sarcos Group LC            Topic: A05161

    A new dual pump hydraulic supply designed to enable energetically autonomous exoskeleton robots will be developed, tested and demonstrated. This new hydraulic supply will be integrated with a high performance hydraulically actuated full body exoskeleton robot and used to test and demonstrate the overall performances of such systems. New control policies that include: (i) an assist mode, where the ...

    STTR Phase II 2017 Department of DefenseSpecial Operations Command
  7. Data Driven Intent Recognition Framework

    SBC: Other Lab Inc.            Topic: NSF13599

    A critical aspect of exoskeleton control that has to date introduced a performance limitation is the ability of the exoskeleton to recognize the intent of the operator so it can apply assistance to their desired motion. This intent recognition effort is typically solved using ad-hoc methods where subject matter experts make design decisions and tune transitions to identify intended maneuvers as re ...

    STTR Phase II 2016 Department of DefenseSpecial Operations Command
  8. A High Power Density Rotary Engine for Unmanned Aerial Vehicles

    SBC: ATS-MER, LLC            Topic: N10AT001

    In Phase I MER demonstrated the superior performance and power-to-weight ratios of the VS-Rotary engine. In Phase II MER will team with the University of Arizona who will provide engine modeling, simulation, and analysis along with advanced precision machining and fabrication. MER will also team with NCE of Tucson to provide test and systems integration development of the engine to the applicatio ...

    STTR Phase II 2014 Department of DefenseSpecial Operations Command
  9. Innovative 2D/3D Building, Asset, and Resource Tracking Visualization Tool

    SBC: KUTTA TECHNOLOGIES, INC.            Topic: N/A

    In this proposal, Kutta capitalizes on existing DOD investments and its own 2D and 3D visualization tools, and leverages the world-renowned computer graphics department at Arizona State University (ASU). This prior work experience and knowledge allows the team to build a resource and asset tracking tool for incident commanders with powerful 2D and 3D visualization capabilities. Kutta and its prest ...

    STTR Phase I 2006 Department of Homeland Security
  10. 3D Building Visualization Tool for Incident Commanders

    SBC: KUTTA TECHNOLOGIES, INC.            Topic: HSB062005

    In this proposal, Kutta capitalizes on existing DOD investments and its own 2D and 3D visualization tools, and leverages the world-renowned computer graphics department at Arizona State University (ASU). This prior work experience and knowledge allows the team to build a resource and asset tracking tool for incident commanders with powerful 2D and 3D visualization capabilities. Kutta and its prest ...

    STTR Phase II 2008 Department of Homeland Security
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