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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. General Purpose Radiation Detector Front End and Digital Processor

    SBC: H3D INC            Topic: DTRA20B002

    This project aims to create a general-purpose readout architecture that will allow the rapid deployment of next generation detection systems. The system will be based on the development of a programmable general-purpose integrated circuit (GPIC) that has the front-end electronics required to read out signals from a variety of radiation detectors, especially next generation scintillators and semico ...

    STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency
  2. 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
  3. 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
  4. 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
  5. Hardened, Optically-Based Temperature Characterization of Detonation Environments

    SBC: SA PHOTONICS, LLC            Topic: DTRA19B001

    Improving the effectiveness of counter-WMD operations requires improved understanding of weapon-target interaction. Specifically, time-resolved measurements of temperature and composition are required to allow temporal evolution of a detonation fireball. To address this need, SA Photonics will develop MONITOR, a laser-based temperature diagnostic that will enable wide dynamic range temperature mea ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  6. Development of powder bed printing (3DP) for rapid and flexible fabrication of energetic material payloads and munitions

    SBC: MAKEL ENGINEERING, INC.            Topic: DTRA16A001

    This program will demonstrate how additive manufacturing technologies can be used with reactive and high energy materials to create rapid and flexible fabrication of payload and munitions. Our primary approach to this problem will be to use powder bed binder printing techniques to print reactive structures. The anticipated feedstock will consist of composite particles containing all reactant spe ...

    STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency
  7. Modular Pulse Charger and Laser Triggering System for Large-Scale EMP and HPM Applications

    SBC: SCIENTIFIC APPLICATIONS & RESEARCH ASSOCIATES, INC.            Topic: DTRA16A004

    For effective protection against EMP and HPM threats, it is important to understand the physics of the threats, and also to quantify the effects they have on electrical systems. EMP and HPM vulnerability testing requires delivery of high peak power and electric fields to distant targets. The most practical solution to simulate such environments is to develop a modular, optically-isolated MV-antenn ...

    STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency
  8. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Algorithm Performance Evaluation with Low Sample Size

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA20C001

    The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend t ...

    STTR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
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