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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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. Machine Learning for Transfer Learning for Periscopes

    SBC: Arete Associates            Topic: N20AT007

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought t ...

    STTR Phase I 2020 Department of DefenseNavy
  2. Marburg Virus Prophylactic Medical Countermeasure

    SBC: MAPP BIOPHARMACEUTICAL, INC.            Topic: CBD18A002

    There are currently no vaccines or therapeutics available for Marburg Virus Disease (MVD). Given the specter of weaponization and the terrible morbidity and high mortality rate of MVD, this represents a critical threat to the operational readiness of the Warfighter. While traditional vaccines have contributed greatly to public health, they have some limitations especially in the context of operati ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  3. CRISIS: Knowledge Graph Based Cyber Resilience Integrated Security Inspection System

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: N20AT011

    Modern US Navy ships and submarines are configured with an ever-increasing level of automation, including state-of-the-art embedded wireless sensors that monitor vital system functions. However, sensor nodes have the potential to serve as targets for cybersecurity attacks or be susceptible to corruption through accidental or malicious events. To address these shortfalls and minimize vulnerabilitie ...

    STTR Phase I 2020 Department of DefenseNavy
  4. TIS: Trusted Sensor Integration

    SBC: Objectsecurity LLC            Topic: N20AT011

    Condition-based maintenance plus (CBM+), and cyber-physical systems (CPS) in general, depend on correct sensor data for analysis, decision making and control loops. If the sensor data that arrives at the point of processing is not correct, or more accurate, is outside its accepted error range, then any further processing will be incorrect as well. This could result in, in the case of CBM+, not det ...

    STTR Phase I 2020 Department of DefenseNavy
  5. CYANDECA: Cyber Anomaly Detection, Classification, and Analysis for Condition Based Monitoring

    SBC: Intelligent Automation, Inc.            Topic: N20AT011

    Navy is developing the concepts and methods to leverage Machine Learning (ML) techniques for the maintenance decision-making on condition-based maintenance plus (CBM+) platform. Effective health monitoring for condition-based and predictive maintenance requires intelligent sensor selection and placement, and context-aware interpretation of sensor data to detect the many possible fault modes. Moreo ...

    STTR Phase I 2020 Department of DefenseNavy
  6. Development of Precision Alignment Techniques for Millimeter Wave Sources

    SBC: DYMENSO LLC            Topic: N20AT013

    High power generation at millimeter wave (mm-wave) frequencies is expensive and the concurrent need for wide bandwidths at these frequencies creates an extremely challenging problem. Currently the most stringent requirements for mm-wave power and bandwidth can only be practically met by vacuum electronics (VE) technology. At present, vacuum amplifiers with the required performance are prohibitivel ...

    STTR Phase I 2020 Department of DefenseNavy
  7. Machine Learning for Simulation Environment

    SBC: Arete Associates            Topic: N20AT014

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in training simulators for periscope operators .  We refer to this solution as RealSynth360.  This novel capability will be created by combining the latest advances ...

    STTR Phase I 2020 Department of DefenseNavy
  8. Frequency and Phase Locking of Magnetrons Using Varactor Diodes

    SBC: CALABAZAS CREEK RESEARCH, INC.            Topic: N20AT015

    Magnetrons are compact, inexpensive, and highly efficient sources of RF power used in many industrial and commercial applications. For most of these applications, the requirement is for RF power without regard to precise frequency or phase control, and noise riding on the RF signal is not important. For many accelerator, defense, and communications applications, however, these characteristics prev ...

    STTR Phase I 2020 Department of DefenseNavy
  9. Ship Vibration Mitigation for Additive Manufactruring Equipment

    SBC: ADVANCED TECHNOLOGY AND RESEARCH CORPORATION            Topic: N20AT010

    The overall goal of this STTR Phase I project is to develop a concept to mitigate the effects of motion/vibration for a shipboard material extrusion additive manufacturing (AM) system.  NAVSEA has been installing advanced manufacturing equipment, including 3D printers, onboard ships in support of shipboard operations and to evaluate performance of the equipment in shipboard environments and in re ...

    STTR Phase I 2020 Department of DefenseNavy
  10. Quantum Emulation Co-processor Circuit Card

    SBC: FASTER LOGIC, LLC            Topic: N20AT016

    Whereas quantum computers stand to drastically transform computation for a number of existing and future problems, its realization in the near term produces certain challenges.  Simulation and Emulation techniques make it possible to consider the advantages of quantum computation in real-world applications in cryptography, machine learning, signal processing, and cybersecurity.  They also open t ...

    STTR Phase I 2020 Department of DefenseNavy
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