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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. Low Cost W-Band Imaging Array

    SBC: MILLIMETER WAVE SYSTEMS LLC            Topic: CBD22BT001

    Low-cost systems operating at video rates within W-band have remained elusive – especially for stand-off and remote applications. Real time video rate imaging requires parallel detection modalities that traditionally led to high costs and calibration challenges. Substantial advances in low-cost packaging and chip-level integration driven by commercial millimeter-wave applications can now be appl ...

    STTR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense
  2. Low Cost Imaging In The mm Wave Region Using Plasma Waves in High Mobility Transistor

    SBC: BRIMROSE TECHNOLOGY CORP            Topic: CBD22BT001

    In this work, we propose to develop low-cost, high sensitivity high electron mobility transistor-based W-band millimeter wave focal plane array/camera based on mature ternary III-V epitaxial materials of InAlAs on top of InP substrate. The plasma-wave detector uses well established mature technology of high electron mobility transistors which allows future integration and reduces cost. The detecto ...

    STTR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense
  3. 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
  4. 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
  5. Large Multi-Modal Scintillators

    SBC: CAPESYM INC            Topic: DTRA22D002

    This work is focused on the development of fabrication technology for large form-factor scintillation crystals for multi-modal detection of radioactive sources, and development of mobile imaging and mapping instruments based on these large format scintillators.

    STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency
  6. Self-Supervised Training in Geospatial Applications with a Robust Hierarchical Vision Transformer (STAR)

    SBC: UNIVERSITY TECHNICAL SERVICES, INC.            Topic: OSD22A001

    Satellite Imagery in Geospatial Intelligence (GEOINT), in conjunction with imagery intelligence (IMINT), geospatial information, and other means of gaining intelligence, has greatly improved the potential of the warfighter and decision makers enabling them to gain a more comprehensive perspective, an in-depth understanding, and a cross-functional awareness of the operational environment. The Artif ...

    STTR Phase I 2022 Department of DefenseNational Geospatial-Intelligence 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. 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
  9. 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
  10. 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
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