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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. Advanced Electromagnetic Modeling with High Geometric Fidelity Using High-Order Curved Elements

    SBC: VIRTUAL EM INC.            Topic: N20BT028

    Virtual EM is proposing a method to achieve orders of magnitude improvement in computational efficiency in full-wave CEM codes by using high-order curved elements. Virtual EM’s own commercial product VirAntenn™ will provide the CEM setting for both developing and implementing the new capability in Phase I and Phase II, respectively. Using multi-wavelength long cells with high-order basis forms ...

    STTR Phase I 2020 Department of DefenseNavy
  2. Durable Elastomeric Low Adhesion Icephobic Surfaces

    SBC: HygraTek LLC            Topic: N14AT013

    In this Phase I effort, HygraTek will explore a novel anti-ice coating formulation to develop icephobic surfaces for naval ship superstructures, decks, equipment and vehicles on board naval ships, e.g. fighter jets, helicopters etc. HygraTek has recently developed a series of different environmentally safe, non-fluorinated, transparent, icephobic coatings. These coatings display the lowest ice-adh ...

    STTR Phase I 2014 Department of DefenseNavy
  3. Object Cueing Using Biomimetic Approaches to Visual Information Processing

    SBC: SOAR TECHNOLOGY, INC.            Topic: N14AT008

    Understanding imagery from unmanned automated systems in a timely fashion requires support systems for end users that can filter and preprocess massive data via complex vision and understanding. A new computer system that mimics both the bottom-up biological and top-down cognitive processes of the human visual system will provide breakthrough decision support for immediate imagery analysis. We pro ...

    STTR Phase I 2014 Department of DefenseNavy
  4. Reduction of Predictable Spurs in the ADC outputs using AI

    SBC: VIRTUAL EM INC.            Topic: N20AT025

    An AI-based algorithm is being proposed to increase ADC linearity by 10dB. Neural Nets will be investigated in conjunction with models of spurs to accomplish the task.

    STTR Phase I 2020 Department of DefenseNavy
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