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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. Unified Logging Architecture for Performance and Cybersecurity Monitoring

    SBC: INNOVATIVE DEFENSE TECHNOLOGIES, LLC            Topic: N19AT012

    In order to achieve real-time monitoring, analysis, and alerting for complex systems, a unified logging architecture must exist that can support the collection and analysis of big data. Our technical objective is to develop a unified logging architecture that supports collection, aggregation, storage, and analysis of system performance and cybersecurity logs, events, and alerts produced by Naval C ...

    STTR Phase I 2019 Department of DefenseNavy
  2. Process to Mitigate Catastrophic Optical Damage to Quantum Cascade Lasers

    SBC: IRGLARE LLC            Topic: N19AT004

    The development of a catastrophic optical damage model for quantum cascade lasers describing instantaneous laser damage at high optical power levels is proposed. The model will be validated by comparison to experimental data. Based on obtained results, changes to laser design and laser fabrication resulting in an increased damage threshold will be implemented. The work will ultimately result into ...

    STTR Phase I 2019 Department of DefenseNavy
  3. Predictive Graph Convolutional Networks- 19-008

    SBC: METRON INCORPORATED            Topic: N19AT017

    Metron and Northeastern University propose to design, develop, and validate a proof-of-concept predictive Graph Convolutional Network (GCN) capability using open source Reddit and GDELT data. We propose: (1) to extract and preprocess open-source Reddit and GDELT data, (2) to design a predictive graph convolutional neural network model, (3) to implement and train that model, and (4) to validate the ...

    STTR Phase I 2019 Department of DefenseNavy
  4. Magnetoelectric Modules for Scavenging UAV Power from Electric Utility Lines

    SBC: NANOSONIC INC.            Topic: N19AT019

    NanoSonic will work with Penn State to develop, demonstrate and manufacture materials and systems to allow unmanned aerial vehicles (UAVs) to scavenge magnetic field energy from electric power lines and operate continuously in the field. NanoSonic will work with energy harvesting researcher Dr. Shashank Priya and a major US aerospace company to design, fabricate and demonstrate a prototype system ...

    STTR Phase I 2019 Department of DefenseNavy
  5. Seamless Wireless Charging of Micro and Small Unmanned Aerial System Through Local Power Transmission Infrastructure

    SBC: EH GROUP INC            Topic: N19AT019

    Wireless charging of unmanned aerial system (UAS) platforms from the environment has the potential to greatly increase flight and mission times. A promising option is to use electromagnetic fields from the power transmission infrastructure as an energy source. EH Group and the University of Alabama propose a design for UAS wireless charging in the near-field environment of the commercial power tra ...

    STTR Phase I 2019 Department of DefenseNavy
  6. Energy Scavenging to Power Fielded Unmanned Aerial Systems

    SBC: LUNA INNOVATIONS INCORPORATED            Topic: N19AT019

    Unmanned aerial systems (UAS) provide strategic advantage for our nation’s warfighters, and the use of micro- and small-scale platforms on the battlefield is expected to increase significantly in coming years. This presents a logistical challenge in managing how system batteries are recharged throughout the UAS lifespan. The desired goal is to develop power systems that enable persistent deploym ...

    STTR Phase I 2019 Department of DefenseNavy
  7. Data Analytics and Machine Learning Toolkit to Accelerate Materials Design and Processing Development

    SBC: CFD RESEARCH CORPORATION            Topic: N19AT020

    Navy has identified refractory high entropy alloy (RHEA) and metal additive manufacturing as two potential areas of interest. This includes designing new RHEA and optimizing metal additive manufacturing with specific material property requirements. Developing materials and processes via applying traditional experimentation and process optimization techniques is painfully slow due to the large numb ...

    STTR Phase I 2019 Department of DefenseNavy
  8. Local Stochastic Prediction for UUV/USV Environmental Awareness

    SBC: APPLIED OCEAN SCIENCES, LLC            Topic: N19AT022

    This project delivers a system to assess local uncertainties and track the evolution of the maritime environment around unmanned platforms at sea. The system uses Navy ocean forecasts for initial environmental guesses and outlooks and implements a Reduced Order Model (ROM) derived from Dynamically Orthogonal (DO) solutions to deliver a local uncertainty picture (for the next 24-48 hours). The ROM- ...

    STTR Phase I 2019 Department of DefenseNavy
  9. Reduced Order Modeling (ROM) for UUV/USV Environmental Awareness-- 19-013

    SBC: METRON INCORPORATED            Topic: N19AT022

    In Phase I, Metron and the University of Miami (UM) propose to develop a theoretic reduction of dynamics framework applicable to the prediction of oceanographic fields in geophysical fluid dynamic models for use onboard unmanned platforms. Our approach leverages, extends and combines modern advances in the renormalization group and Bayesian probability combined with fluid dynamics modeling and for ...

    STTR Phase I 2019 Department of DefenseNavy
  10. Integrated photonic Raman sensor on a chip

    SBC: PARTOW TECHNOLOGIES LLC            Topic: N19AT023

    A photonic integrated spectrometer based on high-index contrast thin film platform is proposed for Raman signal processing. Raman signal generation on the chip via waveguide collection integrated with a spectrometer is proposed to increase the efficiency and signal to noise ratio and significantly reduce cost and the size of Raman sensor systems. All components of the proposed Raman detection syst ...

    STTR Phase I 2019 Department of DefenseNavy
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