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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

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.

  1. Further Experimental Validation of a Fluid-Structure-Material Interaction (FSMI) Modeling and Simulation Toolset

    SBC: ATA Engineering, Inc.            Topic: AF17AT025

    ATA Engineering, Inc. (ATA), partnered with a nonprofit research institution, CUBRC, Inc., (CUBRC), proposes a Phase II STTR project to further develop and continue validating a novel multiphysics simulation technology. The project team will implement the technology in a fluid-structure-material interaction (FSMI) software toolset that incorporates mutual interactions between aerodynamics and stru ...

    STTR Phase II 2020 Department of DefenseAir Force
  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. Novel Circulating RNA-based Markers as Diagnostic Biomarkers of Infectious Diseases

    SBC: CFD Research Corporation            Topic: CBD18A001

    In resource limited settings, rapid and accurate diagnosis of infections is critical for managing potential exposures to highly virulent pathogens, whether occurring from an act of bioterrorism or a natural event. This is especially important for hard to detect intracellular bacterial and alphavirus infections, that overlap symptomatically and often treated empirically due to a lack of reliable an ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  5. Fully Adaptive Radar Resource Allocation

    SBC: Information Systems Laboratories, Inc.            Topic: AF19BT003

    In this proposal, ISL and MTRI will develop an advanced cognitive fully adaptive radar (CoFAR) and electronic warfare (EW) resource optimizer and scheduler. While the basic architecture for CoFAR has been developed previously, there is a need for a real-time scheduler that can handle the many complex operations and demands on sensor tasking that will arise when operating in a highly contested envi ...

    STTR Phase I 2020 Department of DefenseAir Force
  6. Passive Image Processing Algorithms for Automated Target Attitude Estimation

    SBC: TOYON RESEARCH CORPORATION            Topic: AF19BT005

    Weapons systems testing and analysis is crucial to engineering, understanding, qualifying, and deploying advanced weapons systems. With the boom in camera technology over the past decade image based methods to support weapons testing and analysis have become the state of practice for military test ranges. At the same time, there has been an explosion of research and development in computer vision ...

    STTR Phase I 2020 Department of DefenseAir Force
  7. Information Extraction for New Emerging Noisy User-generated Micro-Text

    SBC: INFERLINK CORPORATION            Topic: AF19BT006

    Neural networks have proved highly effective at extracting information from text. However, noisy microtext has proved to be particularly difficult because low-level syntactic cues much less useful. In this project, we propose to explore ways of incorporating strong semantic, expectation-based models into a neural net architecture to improve performance on microtext extraction. In phase I, we will ...

    STTR Phase I 2020 Department of DefenseAir Force
  8. Test Rig for Effective Reproduction of Inlet Distorted Supersonic Flows

    SBC: CFD Research Corporation            Topic: AF19BT012

    The subsonic diffuser duct of a modern tactical aircraft is the most difficult component to verify performance at off-design conditions, because of the large cost of testing/modeling complete installations at large scale. It is desirable to effectively reproduce the flow at the end of a supersonic inlet for interfacing with a direct-connect subsonic duct rig. The CFDRC team proposes to deliver a r ...

    STTR Phase I 2020 Department of DefenseAir Force
  9. Adaptable Cyber Defense for Autonomous Air Operations

    SBC: INFERLINK CORPORATION            Topic: AF19CT003

    Cyber defense is difficult, but presents a particularly thorny problem for legacy systems, including legacy embedded systems, where in many such cases source code may not even be available. In this project, we proposed to investigate and extend a pattern-based approach recently developed by USC-ISI for analyzing and retrofitting binary code to protect against potential attacks. In this project, we ...

    STTR Phase I 2020 Department of DefenseAir Force
  10. Transfer Learning and Deep Transfer Learning for Military Applications

    SBC: Arete Associates            Topic: AF19CT004

    Aided Target Recognition (AiTR) algorithms augment human decision-making, but require a large database of labeled targets for training. Applied to new domains, these algorithms fail to transfer knowledge and require substantial retraining. Areté and University of Alabama Huntsville (UAH) propose the development of the Domain Extracted Feature Transfer (DEFT) Network to transfer knowledge from a ...

    STTR Phase I 2020 Department of DefenseAir Force
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