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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

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. Stable High Bandwidth AO Control with physical DM constraints

    SBC: Guidestar Optical Systems, Inc.            Topic: AF18AT008

    Adaptive optics (AO) can compensate for the aberrating effects of atmospheric turbulence which degrade the performance of high energy laser (HEL) weapon systems and, as such, is an enabling technology for effective deployment of HEL weapon systems. A key component in an HEL AO system is the deformable mirror (DM). However, mechanical constraints in currently available DMs limits AO system performa ...

    STTR Phase II 2020 Department of DefenseAir Force
  3. 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
  4. 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
  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. TeraNet – A Wideband Terahertz Communication Network

    SBC: Intelligent Automation, Inc.            Topic: AF19BT008

    Terahertz communication is anticipated to provide ultra-high capacity links up to 1Tbps. Thus, Terahertz communication is promising to support wide varieties of emerging high throughput and low latency applications, including military applications. However, the Terahertz channel suffers from severe path loss and is highly sensitive to obstacles. Hence, novel solutions should be designed to account ...

    STTR Phase I 2020 Department of DefenseAir Force
  9. Multiband Equipment for Spectrum Agility (MESA)

    SBC: First RF Corporation            Topic: AF19BT009

    In this Phase I STTR program, FIRST RF and GMU will investigate the interrelated trades of directional antenna hardware capabilities and the operational advantages of spectrum agility for airborne networking. GMU’s modeling and simulation (M&S) capabilities combined with the FIRST RF expertise in advanced antenna hardware will provide Air Force with a powerful team to understand the advanta ...

    STTR Phase I 2020 Department of DefenseAir Force
  10. Machine Learning based Domain Adaptation (MLB-DA) for Multiple Source Classification and Fusion

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: AF19CT002

    Generalizing models learned on one domain to novel domains has been a major obstacle in the quest for universe object recognition. The performance of the learned models degrades significantly when testing on novel domains due to the presence of domain shift. In this proposal, we aim to develop a deep learning-based multi-source self-correcting approach to fuse data with different modalities at the ...

    STTR Phase I 2020 Department of DefenseAir Force
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