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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. 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 MGH Voice Center and Altec, Inc., 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 of our partner, MGH (including resea ...

    STTR Phase II 2018 Department of DefenseDefense Advanced Research Projects Agency
  2. Group Dynamics Modeling, Forecasting, and Validation

    SBC: Systems & Technology Research LLC            Topic: ST12B002

    Building on our prior research in group discovery and tracking, we propose to develop new algorithms for predicting future behaviors of social media groups. These algorithms will be based on emerging social interaction theories and new computational models of group dynamics at multiple levels, from individual roles and interactions to aggregate trends within and across groups. Our approach takes a ...

    STTR Phase II 2017 Department of DefenseDefense Advanced Research Projects Agency
  3. 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 II STTR proposal is to develop a novel, modular and scalable approach to biofiltration for effective management of air pollutants in closed environments. We propose a novel ...

    STTR Phase II 2018 Department of DefenseDefense Advanced Research Projects Agency
  4. Memristor-CMOS Analog Co-Processor for Efficient Computation of PDEs

    SBC: SPERO DEVICES, INC.            Topic: ST15C002

    Spero Devices is proposing the development of an analog co-processor to accelerate computation of partial differential equations (PDEs) prevalent in scientific simulation. Examples include Navier-Stokes equations for computational fluid dynamics (CFD) an...

    STTR Phase II 2017 Department of DefenseDefense Advanced Research Projects Agency
  5. Cognitive Bias in Online Communication Activities (C-BOCA)

    SBC: SOAR TECHNOLOGY, INC.            Topic: N13AT024

    This effort will analyze publicly available online communication data, uniquely applying empirical results from the cognitive bias literature, to quantify the impact of cognitive biases on exposure to, use of, and propagation of online information. SoarTech, with its partners, will develop and evaluate methods to identify and measure cognitive biases in online information environments. The team wi ...

    STTR Phase II 2018 Department of DefenseDefense Advanced Research Projects Agency
  6. REsilience & Stability In DENse Terrains (RESIDENT)

    SBC: Boston Fusion Corp.            Topic: ST17C003

    Boston Fusion Corp. and Arizona State University will research and develop REsilience & Stability in DENse Terrains (RESIDENT), a multi-model, multi-scale framework for assessing indicators of stability and resilience in dense urban environments. Our team consists of subject matter experts in the Social and Computer Sciences providing the bedrock on which to build accurate mathematical models of u ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  7. STability and Resilience Analysis Technology for Urban Systems analysis (STRATUS)

    SBC: Systems & Technology Research LLC            Topic: ST17C003

    The unique scale, population density, complexity, and connectedness of megacities requires new tools for detecting and assessing risks related to civil unrest, rule of law, terrorism, and other sources of instability, and for understanding the underlying dynamics. In addition, gray zone operations pose a new and strategically important class of threats to the stability of nation states and cities ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  8. Complex Networks for Computational Urban Resilience (CONCUR)

    SBC: Perceptronics Solutions, Inc.            Topic: ST17C003

    CONCUR develops a computational framework for assessing and characterizing urban environments stability or fragility in response to volatility and stress, identifying specific weaknesses as well as key tipping points which could lead to rapid systemic failure. CONCUR explicitly models urban environments as emergent complex systems, focusing attention on the critical triggers that could lead to rap ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  9. 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
  10. Algorithms for Look-down Infrared Target Exploitation

    SBC: Signature Research, Inc.            Topic: 1

    Signature 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
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