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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

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.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. Machine Learning for Transfer Learning for Periscopes

    SBC: ARETE ASSOCIATES            Topic: N20AT007

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought t ...

    STTR Phase I 2020 Department of DefenseNavy
  2. Machine Learning for Simulation Environment

    SBC: ARETE ASSOCIATES            Topic: N20AT014

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in training simulators for periscope operators .  We refer to this solution as RealSynth360.  This novel capability will be created by combining the latest advances ...

    STTR Phase I 2020 Department of DefenseNavy
  3. Comprehensive Surf Zone Modeling Tool

    SBC: ARETE ASSOCIATES            Topic: N19AT010

    The objective of this project is to advance the capabilities of the Coastal Battlefield Reconnaissance and Analysis (COBRA) system by creating a Surf Zone Modelling Tool (SZT) that can create realistic synthetic imagery of the surf zone (SZ). Through the use of this synthetic imagery the COBRA Program will be enabled to inform concept of operations (CONOPS) in unfamiliar environments as well as mo ...

    STTR Phase II 2020 Department of DefenseNavy
  4. 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
  5. High Hesitivity Magnetic Materials for Magnetic Toroid and Flat Dipole Antennas

    SBC: JEM ENGINEERING, LLC            Topic: N16AT001

    In our Phase I program, JEM and ASU will demonstrate a low cost manufacturing process to achieve the full theoretical hesitivity of a magnetic film that would enable greater effective hesitivity laminate. In Phase II we will develop a viable continuous process for two such materials to achieve light weight, low cost, and improved radiation efficiency; and we will seek to productize this process to ...

    STTR Phase I 2016 Department of DefenseNavy
  6. Process diagnostics to quantify mechanical performance of AM parts

    SBC: POLARONYX INC            Topic: N16AT004

    This Navy STTR Phase I proposal presents an unprecedented NDI tool to quantify mechanical properties of metal parts made with laser additive manufacturing with material characteristics and process parameters. A fiber laser SAW and heterodyne detection is used with LIBS to study both in-process and post-process for both flat and shaped parts. It is the enabling technology for characterize the AM pa ...

    STTR Phase I 2016 Department of DefenseNavy
  7. Air Cycle Machine Low Friction, Medium Temperature, Foil Bearing Coating

    SBC: ACREE TECHNOLOGIES INCORPORATED            Topic: N16AT005

    The purpose of this project is to demonstrate the feasibility of using an innovative, durable, low friction, and non-toxic solid lubricant coating for foil air bearings for air cycle machines (ACM). Acrees coating provides superior wear characteristics at all temperatures and provides a substantial improvement over polyimide type coatings that are currently used on ACMs. The coating consists of tw ...

    STTR Phase I 2016 Department of DefenseNavy
  8. Air Cycle Machine Low Friction, Medium Temperature, Foil Bearing Coating

    SBC: IBC Materials & Technologies, LLC            Topic: N16AT005

    In this proposed SBIR program, IBC Materials & Technologies, in conjunction with our industry partner Mechanical Solutions, Inc. (MSI) and Texas A&M University, will leverage our knowledge and experience in the domain of industrial metallic coatings to develop a metallurgical coating solution for the Air Foil Bearing. IBC has deep expertise in a variety of industrial coating processes including mu ...

    STTR Phase I 2016 Department of DefenseNavy
  9. Nanoporous block polymer separators for high performance and safe Li-ion batteries

    SBC: ADA TECHNOLOGIES, INC.            Topic: N16AT008

    To meet Navy needs for high performance and safe lithium ion (Li-ion) batteries for naval aircraft, ADA Technologies Inc. (ADA) and its university collaborator propose to develop and optimize tailor designed nanoporous separators derived from functionalized block copolymers (polyolefins) with low cost precursors. The innovative strategy provides a powerful tool to allow exquisite tuning of perform ...

    STTR Phase I 2016 Department of DefenseNavy
  10. Novel Separator Materials for Achieving High Energy/Power Density, Safe, Long-Lasting Lithium-ion Batteries for Navy Aircraft Applications.

    SBC: OCEANIT LABORATORIES INC            Topic: N16AT008

    Oceanit proposes to develop and demonstrate novel, tailored, designer separator materials with optimized properties to maximize lithium-ion cell/battery performance, life, safety and reliability.

    STTR Phase I 2016 Department of DefenseNavy
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