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Award Data

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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. Adaptive Markov Inference Game Optimization (AMIGO) for Rapid Discovery of Evasive Satellite Behaviors

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: AF17CT02

    Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The focus of this project is to develop a stochastic approach for rapid discovery of evasive satellite behaviors. Designing the innovative decision support tool has numerous challenges: (i) partial observable actions; (ii) eva ...

    STTR Phase I 2018 Department of DefenseAir Force
  2. Adaptive multi-sensor wide area situational awareness system- MP 85-12

    SBC: METRON, INCORPORATED            Topic: AF12BT14

    ABSTRACT: Existing machine learning algorithms have difficulty using all available data about a problem. This STTR will develop a new algorithm that can make full use of all available data, whether that data is labeled or not, and even when some data types or data resolutions are not available during operation. BENEFIT: This STTR will develop a novel machine learning algorithm for reasoning abo ...

    STTR Phase I 2013 Department of DefenseAir Force
  3. Adaptive Quantum-Dot Photodetectors with Bias-Tunable Barriers

    SBC: ESENSORS INC.            Topic: AF08BT02

    The proposed research program focuses on design, fabrication, and characterization of quantum-dot infrared photodetectors (QDIPs) which features bias-tunable parameters, including the spectral response, optical gain, and operating time. Wide variations of detector parameters can be realized through the bias-tunable potential barriers surrounding quantum dots. Changes in bias will transform the ba ...

    STTR Phase I 2010 Department of DefenseAir Force
  4. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry se ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  5. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: X-Wave Innovations, Inc.            Topic: DLA18A001

    Additive Manufacturing (AM) is a modern and increasingly popular manufacturing process for metallic components, but suffers from well known problems of inconsistent quality of the finished product. Process monitoring and feedback control are therefore crucial research areas with a goal of solving this problem. To address this concern, X-wave Innovations, Inc. (XII) and the University of Dayton Res ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  6. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: SENVOL LLC            Topic: DLA18A001

    The Department of Defense (DoD) has a demand for out-of-production parts to maintain mission readiness of various weapons platforms. Additive manufacturing (AM) is an exciting and promising manufacturing technique that can make out-of-production parts and holds the potential to solve supply chain issues, such as high costs (i.e. for low-volume parts) and sole sourcing risks. The ability of AM to s ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  7. A High Performance and Cost Effective Ultra High Performance Concrete

    SBC: i2C Solutions, LLC            Topic: AF12BT04

    ABSTRACT: Adversarial installations, such as those housing the means for nuclear weapons production, are increasingly being constructed in heavily fortified locations and often using ultra high performance concrete (UHPC) as the construction material. As such, the U.S. Air Force has considerable interest in further developments of ultra high performance concrete (UHPC) to maintain an advantage o ...

    STTR Phase I 2013 Department of DefenseAir Force
  8. A Multi-Modal State and Measurement Filter for RSO Tracking

    SBC: DECISIVE ANALYTICS CORPORATION            Topic: AF09BT11

    Joint Space Operations Center under the United States Strategic Command employs a worldwide network of 29 sensors, known as the Space Surveillance Network (SSN), to track more than 17,000 man-made objects in Earth orbit with sizes 10 centimeters or larger. Decisive Analytics Corporation and the University of Texas Austin Center for Space Research propose an innovate framework for solving stochast ...

    STTR Phase I 2010 Department of DefenseAir Force
  9. Anisotropic Property Manipulation of Selective Laser Melted GRCop-84

    SBC: Special Aerospace Services            Topic: AF18AT009

    In partnership with the Colorado School of Mines Alliance for the Development of Additive Processing Technologies and with support from the Johns Hopkins University Energetic Research Group, Special Aerospace Services will provide the Air Force with characterization of fully dense Selective Laser Melted GRCop-84 subjected to a variety of manipulations that affect key performance metrics for regene ...

    STTR Phase I 2018 Department of DefenseAir Force
  10. Application of Hierarchical Memory Models to Automatic Target Recognition Modeling and Simulation

    SBC: Novateur Research Solutions, LLC            Topic: AF18AT014

    This SBIR Phase I project proposes a visual processing system for automated target recognition. Inspired by biological vision systems and hierarchical memory models, the proposed system is capable of learning hierarchical invariant features from unlabeled data that are independent of object labels. The model exploits these learned features to create hierarchical representations of target memories ...

    STTR Phase I 2018 Department of DefenseAir Force
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