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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. High efficiency VOC biofiltration using microencapsulated organisms

    SBC: Nano Terra, Inc.            Topic: ST16C001

    Effective management of air quality in closed environments is important for the health and performance of military personnel. High concentrations of volatile organic compounds (VOCs) including aromatics, halocarbons, and aldehydes are present in spacecraft and submarines. Traditional approaches to treat VOCs based on catalytic oxidation or photooxidation are costly, bulky, and not effective at low ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  2. Optimizing Human-Automation Team Workload through a Non-Invasive Detection System

    SBC: Cognionics, Inc.            Topic: ST16C003

    This STTR project aims to assess the feasibility of using laryngeal EMG to detect operator workload and strain. Phase I will develop a wearable neckband device positioning an array of laryngeal EMG electrodes plus additional sensors for measuring masseter EMG, heart rate variability, GSR and estimated relative blood pressure. The neckband will be optimized to be both wearable, comfortable and resi ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  3. Nonintrusive Detector of Acute Cognitive Strain (DACS)

    SBC: Quantum Applied Science And Research, Inc.            Topic: ST16C003

    Modern defense systems place high cognitive demands on warfighters, often taxing the limit of human capabilities and causing operators to suffer Acute Cognitive Strain (ACS), wherein performance deteriorates markedly, leading to a loss of situational awareness and control, and decrements in team cooperativity. ACS leads to physiological changes driven by sympathetic system activation, including i ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  4. Physiology-based Operator Confidence Assessment System (PhOCAS)

    SBC: SOAR TECHNOLOGY, INC.            Topic: ST16C003

    Investigates and explores the solution space for physiologically-based emotion detection within the design and development of a Physiology-based Operator Confidence Assessment System (PhOCAS). The PhOCAS project uses UCF ISTs experience as experts on augmented cognition and physiological assessment for determining user state (such as workload) combined with SoarTechs intelligent systems capabiliti ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  5. Multiplexed Biofiltration of Volatile Organic Compounds

    SBC: WARNER BABCOCK INSTITUTE FOR GREEN CHEMISTRY LLC            Topic: ST16C001

    Volatile organic compounds (VOCs) in air, especially in closed environments pose a major health threat. There is a critical need to remove these pollutant, and biofiltration is a promising solution to this need. This is a Phase I STTR proposal to develop...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  6. Optimizing Human-Automation Team Workload through a Non-Invasive Detection System

    SBC: Stottler Henke Associates, Inc.            Topic: ST16C003

    We propose to investigate, in collaboration with the Massachusetts General Hospital Voice Center and Altec, Inc., the application of surface electromyography (sEMG) to assessing cognitive workload, strain, and overload. Specifically, sEMG sensors placed on the face and neck will detect emotional/motor responses to workload strain. The proposed effort will build on the substantial sEMG experience o ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  7. Automation Support using non-Invasive Measures of Operator Vocalization (ASIMOV)

    SBC: Charles River Analytics, Inc.            Topic: ST16C003

    Human-machine teams are increasingly prevalent across the DoD. These teams unite human operators with advanced automated teammates to execute complex, mission-critical tasks. Unfortunately, this collaboration between automated and human teammates can increase operator strain, because operators must complete their own tasks while also monitoring teammates and distributing tasks; one specific strain ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  8. Optimizing Human-Automation Team Workload through a Non-Invasive Detection System

    SBC: Sonalysts, Inc.            Topic: ST16C003

    In this project, Sonalysts will team with the University of North Dakota to establish the feasibility of a deployable, unobtrusive suite of sensors and data processing approaches collectively known as Cognitively-Oriented Physiological Indicators of Load, Operational performance, and Tension (CO-PILOT). CO-PILOT will provide real-time indicators of operator state that can be used to inform adaptiv ...

    STTR Phase I 2017 Department of DefenseDefense Advanced Research Projects Agency
  9. Computational Biology Platform Technology for Cellular Reprogramming

    SBC: IREPROGRAM, LLC            Topic: ST17C001

    Methods for interconversion between cell types (cellular reprogramming) are currently discovered through resource intensive trial and error. Experiments may test a multitude of transcription factors to identify correct combinations that influence cell fate. In addition, reprogramming approaches commonly use stem cell intermediates such as induced pluripotent stem cells (iPSCs), which are generated ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  10. 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
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