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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. Generative Modeling of Multispectral Satellite Imagery

    SBC: Applied Research In Acoustics LLC            Topic: DTRA22D001

    To address the challenge DTRA faces in identifying rare objects of interest to defeat improvised threat networks using multispectral imagery, small business ARiA and research institution Michigan Technological University (MTU) will develop and demonstrate the feasibility of the Generative Augmentation Process (GAP). The Phase I effort will (1) conduct a proof-of-concept study for GAP by developing ...

    STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency
  2. Generative Modeling of Multispectral Satellite Imagery

    SBC: NOVATEUR RESEARCH SOLUTIONS LLC            Topic: DTRA22D001

    This STTR Phase I project proposes novel deep learning models for generating realistic multi-spectral remote sensing imagery, specifically in the infrared (IR) and near-infrared (NIR) bands. The proposed system enables synthesis of semantically realistic imagery and provides parametric control of synthesizing objects-of-interest, type of terrain and land cover, time or season, weather, cloud cover ...

    STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency
  3. CO2mposite: Recycling of CO2, Carbon Fiber Waste, and Biomaterials into Composite Panels for Lower Embodied Carbon Building Materials

    SBC: SKYNANO LLC            Topic: NA

    "SkyNano, Endeavor Composites, and The University of Tennessee Knoxville propose to develop a composite panel that contains CO2-derived carbon nanotubes (CNTs), recycled carbon fiber waste, and bio-derived natural fibers that exhibits excellent mechanical and functional properties, while maintaining a carbon-negative footprint on a cradle-to-gate and cradle-to-grave basis. By the end of the Phase ...

    STTR Phase I 2022 Department of EnergyARPA-E
  4. Wide Area Distributed Algorithms for Cooperative Source Identification, Characterization, and Localization

    SBC: THE PROBITAS PROJECT, INC.            Topic: DTRA21B003

    Current radiation detection algorithms are based on the concept that each detector operates independently. The Probitas Project, Inc. (Probitas) and the Lawrence Berkeley National Laboratory (LBNL) propose to show the benefits of data fusion to improve the identification, localization, and characterization of a radioactive source in a complex scene as compared to a singular detector algorithm. We ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  5. Numerics-Informed Neural Networks (NINNs)

    SBC: KARAGOZIAN & CASE, INC.            Topic: DTRA21B002

    The overall goal is to develop numerics-informed neural networks (NINNs) and DeepOnets for chemical reactions and for PDEs with spatial derivatives improve the computational efficiency of the chemical kinetics models for chemical weapon agents and simulants. Based on the first NINN developed by the Karniadakis’s group in 2018, which blends the multi-step time-stepping with deep neural networks, ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  6. Numerically Inspired Deep Neural Nets for Chemically Reacting Flows

    SBC: APPLIED SIMULATIONS INC            Topic: DTRA21B002

    The project will develop numerically inspired deep neural nets (NINNs) in order to replace the stiff ordinary differential equation (SODE) solvers currently being used to integrate chemical species in high-fidelity computational fluid dynamics simulations. Unlike traditional deep neural nets, the architectures and optimization strategies used to learn the physics of a problem will be based on the ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  7. SAR AI Training dataset generated using Reification

    SBC: Arete Associates            Topic: DTRA21B001

    The Synthetic Aperture Radar (SAR) Image Generation Data Augmentation (SIGDA) system is achieved using SAR simulators and the Arete’s Reification approach. Large, realistic datasets will be generated using the Arete Reification capability. These large Reified datasets are then used to train machine learning or Artificial Intelligence (AI), Automatic Target Recognition (ATR) classification algori ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  8. A Broadband and Compact Dual Comb Spectrometer for Precise Field Detection of Trace Elements and Chemicals

    SBC: OPTICSLAH, LLC            Topic: DTRA20B003

    There is a growing need to accurately characterize nuclear materials not only for nuclear safeguards and nonproliferation treaty verification but also for forensics and provenance. Presently, nuclear material identification requires samples to be collected and sent to laboratories for analysis using large and expensive equipment, such as inductively coupled plasma mass spectrometers (ICP-MS) or se ...

    STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency
  9. Integrated Circuits

    SBC: NU-TREK, INC.            Topic: DTRA20B002

    The Nu-Trek team is proposing to develop µDet, a low Size, Weight, and Power (SWaP) read out integrated circuit (IC) for gamma and neuron detectors. µDet offers pulse shape digitization, which in turn enables gamma-neutron discrimination. This is a game changing capability that brings laboratory-level functionality to the field. In Phase I the Nu-Trek Team will develop a baseline design for the ...

    STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency
  10. Eye-readable Solution-based Dye Displacement Probe for Large-area Detection of Opioids

    SBC: INTELLIGENT OPTICAL SYSTEMS, INC.            Topic: CBD20AT001

    Intelligent Optical Systems, Inc., in collaboration with Bowling Green State University, proposes to develop a field-rugged, eye-readable indicating spray solution that can immediately detect synthetic opioids over a large area of contamination (i.e., military vehicles, individual protective equipment, clandestine labs, etc.). The proposed chemosensor in a spray solution format will detect multipl ...

    STTR Phase I 2021 Department of DefenseOffice for Chemical and Biological Defense
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