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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. Medium Voltage Direct Current (MVDC) Fault Detection, Localization, and Isolation

    SBC: ISSAC Corp            Topic: N16AT009

    The ISSAC Team leverages existing knowledge and expertise in power system monitoring, fault identification, localization and isolation in conjunction with rich, deep data analytics for pattern matching to devise a system for Medium Voltage Direct Current (MVDC) power system fault management. Because of the differences between AC and DC power grids there are a significant number of problems in deal ...

    STTR Phase I 2016 Department of DefenseNavy
  2. Marburg Virus Prophylactic Medical Countermeasure

    SBC: Flow Pharma, Inc.            Topic: CBD18A002

    Through this STTR contract, we propose to evaluate the efficacy of our vaccine, FlowVax Marburg, in nonhuman primates (NHPs). This will be achieved through four Tasks. In Task 1, we will manufacture the vaccine in a quantity sufficient for the animal studies. In Task 2, we will perform MHC genotyping on a representative population of NHPs and, based on results, select a set of MHC-matched NHPs for ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  3. 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
  4. Magnesium Alloys for Additive Manufacturing by Artificial Intelligence (MAGAMAI)

    SBC: Intelligent Automation, Inc.            Topic: N20BT026

    NAVY seeks high strength, low density, and high corrosion resistant alloys for structural components which can be processed by additive manufacturing (AM). Magnesium (Mg) alloys are candidates for fuel-efficiency applications, especially the aircrafts. They satisfy density, strength, and stiffness for many designs. However, their low corrosion resistance cannot ensure design lifetimes.  This limi ...

    STTR Phase I 2021 Department of DefenseNavy
  5. 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
  6. 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
  7. LEARNING-BASED APPROACH FOR RELEVANT DATA EXTRACTION (LARDE)

    SBC: ROBOTIC RESEARCH OPCO LLC            Topic: N13AT016

    Robotic Research, LLC (RR) and Southwest Research Institute (SwRI) are creating a prototype Learning-based Approach for Relevant Data Extraction (LARDE). The LARDE framework is a data extraction and handling framework that can intelligently reduce the volume of raw data from on-board sensors, and organize and persistently store the reduced relevant dataset.

    STTR Phase II 2015 Department of DefenseNavy
  8. LCS Radar Modeling for Training (LRMT)

    SBC: Intelligent Automation, Inc.            Topic: N14AT012

    We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eli

    STTR Phase I 2015 Department of DefenseNavy
  9. LCS Radar Modeling for Training (LRMT)

    SBC: Intelligent Automation, Inc.            Topic: N14AT012

    We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eliminate the need for live training by faithfully capturing the scenarios encountered by a radar operator. The primary target radars for the propose ...

    STTR Phase II 2016 Department of DefenseNavy
  10. Large-scale Entity Linking and Disambiguation with DeepDive

    SBC: CLEARCUT ANALYTICS, INC            Topic: N16AT016

    DeepDive is a system for extracting relational databases from dark data: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data --- scientific papers, Web classified ads, customer service notes, and so on --- were instead in a relational database, it would give analysts access to a massiv ...

    STTR Phase I 2016 Department of DefenseNavy
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