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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

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. Physical Sub-Model Development for Turbulence Combustion Closure

    SBC: COMBUSTION RESEARCH & FLOW TECHNOLOGY INC            Topic: AF13AT12

    ABSTRACT: The innovation proposed is a computationally-tractable, physics-based, portable turbulent combustion modeling strategy for application to a wide range of Air Force aero-propulsive systems, including augmentors, liquid rockets and scramjets. This modeling strategy will be implemented within an Application Programming Interface (API) library suitable for easy integration within Air Force ...

    STTR Phase I 2014 Department of DefenseAir Force
  2. Decision Making under Uncertainty for Dynamic Spectrum Access

    SBC: INFOBEYOND TECHNOLOGY LLC            Topic: AF13AT02

    ABSTRACT: Due to scarcity of spectrum, Dynamic Spectrum Access (DSA) becomes a needed technology to improve the utilization of electromagnetic spectrum for DoD satellite communication. However, current DSA approaches are developed for terrestrial communications without addressing the unique challenges for SATCOM environments such as error-prone spectrum sensing, high mobility, and large coverage. ...

    STTR Phase I 2014 Department of DefenseAir Force
  3. Pantograph: Secure, Cross-domain Object Models

    SBC: ATC-NY INC            Topic: AF13AT08

    ABSTRACT: Most cross-domain information flows require some human intervention to ensure that the requirements for releasability are met. Such intervention is expensive and slow, and can form a bottleneck in operations. Unfortunately, fully automated sharing of information across security domain boundaries is also fraught with difficulties due to problems with identifying releasable information, a ...

    STTR Phase I 2014 Department of DefenseAir Force
  4. Secure Efficient Cross-domain Protocols

    SBC: INFOBEYOND TECHNOLOGY LLC            Topic: AF13AT08

    ABSTRACT: Coordinating and sharing information across multi-level security (MLS) networks are of great interest in many military applications. However, it is very challenging to accomplish those goals due to the heterogeneous security classifications of different network domains. The recent proposed cross-domain solutions (CDS) provide initial steps to make such applications possible. However, th ...

    STTR Phase I 2014 Department of DefenseAir Force
  5. Broad-Spectrum PV Devices Based on Charged Quantum Dots

    SBC: Optoelectronic Nanodevices LLC            Topic: AF13AT13

    ABSTRACT: This project addresses the need for high-efficiency broad-spectrum PV cells. The proposed original PV design is based on quantum dots with built-in charge (Q-BIC), where the dot charging is realized by selective doping of dot medium. The preliminary data demonstrate that the charged dots placed in a single p-n-junction strongly enhance harvesting and conversion of sub-bandgap photons an ...

    STTR Phase I 2014 Department of DefenseAir Force
  6. High Speed High Accuracy Artificial Neural Networks for UAV based Target Identification

    SBC: UHV TECHNOLOGIES, INC.            Topic: AF18BT007

    The machine learning and artificial intelligence community has recently garnered much attention for ground breaking performance of novel neural network architectures for self-driving cars. One of the machine learning methods used in self-driving cars is semantic segmentation. In this fashion each pixel in an image is label with a class, allowing for contour-based image segmentation which is differ ...

    STTR Phase I 2019 Department of DefenseAir Force
  7. Contour Based Image Segmentation

    SBC: VY CORP            Topic: AF18BT007

    We propose to detect and identify moving objects in airborne imagery and full-motion video in unconstrained environments. Conventional techniques result in too many false positives; a new topology is needed to automatically detect and identify moving targets from a moving platform in airborne imagery. Our software is designed to analyze video imagery and deliver curve metadata that can be organize ...

    STTR Phase I 2019 Department of DefenseAir Force
  8. Multiphysics Modeling of Dynamic Combustion Processes

    SBC: COMBUSTION RESEARCH & FLOW TECHNOLOGY INC            Topic: AF18BT010

    The objective of this effort is to develop a zonal, multi-physics modeling framework for dynamic combustion processes that can capture relevant local physics and simulate system behavior. The primary focus will be on liquid rocket combustors with secondary application to gas turbines. The goal is to obtain an order of magnitude reduction in current simulation time within acceptable error limits. T ...

    STTR Phase I 2019 Department of DefenseAir Force
  9. Advanced Diagnostic for Performance and Combustion Characterization in Rotational Detonation Rocket Engine (RDRE)

    SBC: Exo-Atmospheric Technologies LLC            Topic: AF19AT011

    Rotating Detonation Rocket Engines (RDRE)are being developed to take advantage of the near instantaneous heat release potential of detonation waves versus conventional deflagration-based chemical reactions in combustion applications. However, the detonation product environment is extreme and current instrumentation to measure wall / surface conditions within the detonation chamber are lacking. The ...

    STTR Phase I 2019 Department of DefenseAir Force
  10. Space-Based Computational Hyperspectral Machine Vision using Compressed Sensing Neural Networks

    SBC: Kent Optronics, Inc.            Topic: AF19AT015

    In this STTR Phase I proposal, Kent Optronics (KOI) together with its partner, Rice University, propose to develop novel deep learning algorithms to perform machine vision tasks such as target recognition and tracking utilizing the direct measurements from a compressive hyperspectral imaging system. By skipping the hypercube reconstruction, this combination of hardware and software will allow real ...

    STTR Phase I 2019 Department of DefenseAir Force
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