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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. 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
  2. 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
  3. 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
  4. 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
  5. Ultrahigh-Bandwidth Robust Performance Diagnostics for Rotating Detonation Engines

    SBC: SPECTRAL ENERGIES LLC            Topic: AF19AT011

    Spectral Energies proposes to design a multisensory diagnostic suite for measurements within elevated-pressure RDEs. This sensor will utilize tunable-laser absorption spectroscopy to measure temperature, pressure and H2O concentrations in the annulus of a rocket-RDE and background-oriented schlieren imaging system for flow density gradient imaging to provide time resolved information about the sho ...

    STTR Phase I 2019 Department of DefenseAir Force
  6. 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
  7. Volume Digital Holographic Wavefront Sensor Phase 2

    SBC: NUTRONICS, INC.            Topic: AF18AT006

    Through the execution of our Phase 1 effort, Nutronics, Inc. and Montana State University developed an improved means to optimize the Pellizzarri cost functional for coherent imaging using digital holography. Our algorithm developed during the Phase 1 effort accelerates convergence times by a factor of 20-40 for the majority of scenarios evaluated. Our proposed Phase 2 effort has a two-fold focus: ...

    STTR Phase II 2019 Department of DefenseAir Force
  8. Biological Microdosimetry System

    SBC: QUINC.TECH INC.            Topic: AF18AT001

    The Biomagnetics Micro Dosimetry System (BMDS) program will design, model, and simulate a microdosimetry system that can measure and create a three dimensional map of weak radiofrequency signals in biological organisms. The heart of the BMDS project is the front end that delivers very sensitive, broad band measurements with high spatial resolution. The front end is a valuable tool in the investiga ...

    STTR Phase II 2019 Department of DefenseAir Force
  9. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    This Phase II project aims to assemble the key set of sensor modalities that are needed to reliably view the key process anomalies and properties of laser powder bed fusion. The research team will down-select from the Phase I sensors investigated and integrate the sensors into a sensor fusion software package that facilitates data collection and synchronization, and eventually feedback control of ...

    STTR Phase II 2019 Department of DefenseDefense Logistics Agency
  10. Virtual Reality for Multi-INT Deep Learning (VR-MDL)

    SBC: INFORMATION SYSTEMS LABORATORIES INC            Topic: AF19AT010

    Recent advances and successes of deep learning neural networks (DLNN) techniques and architectures have been well publicized over the last several years. Voluminous, high-quality and annotated training data, or trial and error in a realistic environment, is required to achieve the promised performance potential of DLNNs. Unfortunately for DoD and/or Intelligence Community (IC) applications of mult ...

    STTR Phase I 2019 Department of DefenseAir Force
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