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Award Data

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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. Comprehensive Surf Zone Modeling Tool

    SBC: Arete Associates            Topic: N19AT010

    Areté Associates, along with STTR partner Rochester Institute of Technology (RIT), are proposing a comprehensive software capability for scene generation, object insertion, and performance modeling for passive and active EO COBRA sensors over the surf zone. The Surf Zone Modeling Tool (SZT) will incorporate several technologies, including: open-source and Areté-designed SZ ocean physics models, ...

    STTR Phase I 2019 Department of DefenseNavy
  2. Quantum Cascade Laser Array with Integral Wavelength Beam Combining

    SBC: MIRIOS, INC.            Topic: N19AT005

    Mirios Inc., in collaboration with UCSB and NRL, proposes to construct a high-brightness MWIR (~4.5-4.9 µm) light source, consisting of a single silicon chip with an array of Quantum Cascade Lasers which are fully-integrated with wavelength beam combiners.

    STTR Phase I 2019 Department of DefenseNavy
  3. Unified Logging Architecture for Performance and Cybersecurity Monitoring

    SBC: REAL-TIME INNOVATIONS, INC.            Topic: N19AT012

    We propose to develop an open, highly scalable, extensible and secure unified logging architecture for performance and cybersecurity monitoring for Naval Control Systems (NCSs). The primary goal of this architecture is to realize a centralized logging infrastructure for monitoring the status of the entire NCS through collecting and aggregating logs from all subsystems. It will be built upon widely ...

    STTR Phase I 2019 Department of DefenseNavy
  4. 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
  5. 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
  6. GECCO: Gecko-gripper for EOD with Cavitation Cleaning Operation

    SBC: VALOR ROBOTICS, LLC            Topic: N19AT011

    The objective of the Phase I proposal is to investigate the application of controlled cavitation cleaning technology in conjunction with gecko-inspired mechanical adhesion and soft elastomeric applicators for use in non-intrusive EOD operations. This investigation requires the proof-of-concept testing and validation of a controlled cavitation cleaning mechanism, and a soft robotic gecko-inspired m ...

    STTR Phase I 2019 Department of DefenseNavy
  7. Power Dense Turbo-Compression Cooling Driven by Waste Heat

    SBC: MANTEL TECHNOLOGIES, INC.            Topic: N19AT013

    The U.S. Navy seeks methods to improve the fuel economy of marine diesel engines through utilization of waste heat. Low temperature engine jacket water, lubrication oil, and aftercooler air are largely untapped streams of thermal energy on these ships, but their utilization circumvents many operation challenges associated with exhaust gases. For example, variable and high exhaust gas temperatures ...

    STTR Phase I 2019 Department of DefenseNavy
  8. High Speed Spinning Scroll Expander (HiSSSE)- Organic Rankine Cycle for Increased Naval Ship Power Density and Fuel Efficiency

    SBC: Air Squared, Inc.            Topic: N19AT013

    Waste heat from Naval diesel generators provides significant opportunity to introduce organic Rankine cycles (ORC) to increase their fuel efficiency. The objective of the proposed effort is to design and demonstrate a high-speed, spinning scroll expander (HiSSSE) ORC as a power dense waste heat recovery system for diesel generators on ships. The system will leverage Air Squared’s spinning scroll ...

    STTR Phase I 2019 Department of DefenseNavy
  9. Predictive Graph Convolutional Networks

    SBC: Arete Associates            Topic: N19AT017

    The US Navy’s mission to maintain, train and equip combat-ready Naval forces requires that decision makers have situational awareness of the capabilities, limitations, vulnerabilities/opportunities for adversarial and allied forces. An incomplete or inaccurate understanding of the current landscape and associated trends could lead to suboptimal mission readiness and outcomes. Analysts need tools ...

    STTR Phase I 2019 Department of DefenseNavy
  10. 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
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