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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. Full Mueller Matrix Characterization of Imaged Samples using Digital Holography

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: AF18AT007

    The Phase II effort will be to clearly demonstrate the feasibility and build a prototype of a noncontact, high-quality holographic polarimetry system with pixel level depth and Mueller matrix information with a user-friendly interface to image and display this data. The measurements of each data product will be validated with trusted truth samples. The system will be reproducible and will have a d ...

    STTR Phase II 2019 Department of DefenseAir Force
  2. Autonomous Decision Making via Hierarchical Brain Emulation-- 19-009

    SBC: METRON INCORPORATED            Topic: AF19AT009

    The objective of this project is to develop human intelligence-inspired algorithms that exploit multi-modal sources of low and high quality data to achieve a series of objectives such as detection, localization, tracking, and classification. A Bayesian model-based hierarchical adaptive decision making (HADM) algorithm will be developed which includes multiple levels of decision making organized in ...

    STTR Phase I 2019 Department of DefenseAir Force
  3. Data Science Techniques for Various Mission Planning Processes and Performance Validation

    SBC: PERCEPTRONICS SOLUTIONS, INC            Topic: N19BT029

    Mission and planning is a difficult and time-consuming process that places a heavy burden on manpower and critical thinking and is performed under significant pressure. Existing and emerging artificial intelligence (AI) and machine learning (ML) techniques are well-suited to assisting humans with these challenges. While the promise of AI/ML is great, there are significant obstacles to operationali ...

    STTR Phase I 2019 Department of DefenseNavy
  4. AI-Driven, Secure Navy Mission Planning via Deep Reinforcement Learning and Attribute-Based Multi-Level Security

    SBC: EH GROUP INC            Topic: N19BT029

    Current mission planning systems allow strike planners and operations centers to perform time-sensitive strike planning, execution monitoring, and validate mission effects using XML-based tools that visualize time critical attack plan and track plan status vs. execution. In this proposed STTR Phase I design for the Next Generation Navy Mission Planning (NGNMPS) system, we will identify expanded op ...

    STTR Phase I 2019 Department of DefenseNavy
  5. Ocean Surface Vector Winds (OSVW)

    SBC: ATMOSPHERIC & SPACE TECHNOLOGY RESEARCH ASSOCIATES LLC            Topic: N16BT026

    Ocean surface winds are critically important in naval operations. They may aid, hinder, or negate maneuvers and operations, and are a primary consideration in routing ships. Continuous and reliable information on favorable and unfavorable sea state is critical for a broad range of naval missions, including strategic ship movement and positioning, aircraft carrier operations, aircraft deployment, e ...

    STTR Phase II 2019 Department of DefenseNavy
  6. Rapid Discovery of Evasive Satellite Behaviors

    SBC: DATA FUSION & NEURAL NETWORKS, LLC            Topic: AF17CT02

    The problem addressed in this effort is to automatically learn historical ephemeris space catalog time, position, and velocity entity track update error uncertainties (i.e., without track error covariances) and to automatically (e.g., without expert event labeling) produce: – unmodeled non-gravitational space catalog update flags – abnormal unmodeled catalog update flags with abnorma ...

    STTR Phase II 2019 Department of DefenseAir Force
  7. Conjugate heat transfer for LES of gas turbine engines

    SBC: CASCADE TECHNOLOGIES INC            Topic: N19BT027

    Current design tools for gas turbine engines invoke a variety of simplifying assumptions to estimate heat transfer to solid/metal engine components (e.g., isothermal boundary conditions). These approximations are often not valid, result in inaccurate predictions of heat transfer, and ultimately compromise the thermal integrity of propulsion and power systems. Wall-modeled large eddy simulation (WM ...

    STTR Phase I 2019 Department of DefenseNavy
  8. Large Eddy Simulation (LES) Flow Solver Suitable for Modeling Conjugate Heat Transfer

    SBC: Kord Technologies, Inc.            Topic: N19BT027

    Accurate prediction heat transfer in gas turbine components subject to cooling requires high fidelity modeling of heat transfer in the presence of high Reynolds number turbulent flow. The cooling internal to the blades results in sustained temperature gradients within the structural parts, from low temperature in the interior of the structure to increasingly higher temperature closer to the surfac ...

    STTR Phase I 2019 Department of DefenseNavy
  9. Wavelength-Agile Real Time Tabletop X-ray Nanoscope based on High Harmonic Beams

    SBC: KAPTEYN-MURNANE LABORATORIES, INC            Topic: ST15C001

    Nanoscale, material sensitive, imaging techniques are critical for progress in many disciplines as we learn to master science and technology at the smallest dimensions — on the nanometer to atomic-scale. However, progress in both science and technology is becoming increasingly limited by the constraints of current imaging techniques and metrologies. Fortunately, by combining coherent extreme UV ...

    STTR Phase II 2019 Department of DefenseDefense Advanced Research Projects Agency
  10. Smart Baseplate for Additive Manufacturing

    SBC: LUNA INNOVATIONS INCORPORATED            Topic: DLA18A001

    Additive manufacturing (AM) has rapidly evolved into a valuable technique for making parts which, at times, cannot be fabricated through conventional machining methods, or for fabrication of small quantities of complex parts. One challenge in the area of AM is the lack of real-time feedback on the fabrication process and the quality of the part being made. This is especially critical given the rel ...

    STTR Phase II 2019 Department of DefenseDefense Logistics Agency
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