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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. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA18A001

    The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT l ...

    STTR Phase II 2020 Department of DefenseNational Geospatial-Intelligence Agency
  2. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. Laser-based in-situ, real-time imaging of Temperature in Detonation Environments

    SBC: EXMAT RESEARCH INC            Topic: DTRA19B001

    ExMat Research proposes to develop a hardened and cost-scalable laser-based temperature diagnostic system capable of spatially quantifying the localized temporal evolution of temperature in a detonation environment. During Phase I, we will attempt to determine the technical feasibility of the proposed concept. While ExMat Research will be guiding and overseeing the overall project, Washington Stat ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  4. Discrete 3-D Electronics for Mobile Radiation Detection Systems

    SBC: RADIATION DETECTION TECHNOLOGIES, INC.            Topic: DTRA18B002

    Phase II will utilize knowledge gained from phase I work to develop and execute a manufacturing process suitable for producing quantities of 3-D printed discrete circuits for radiation detection systems.  The goal is to support mobile radiation detection system requirements for high voltage, analog amplification, and MCA functionality to produce differential pulse height spectra in time sequence. ...

    STTR Phase II 2020 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. DOEYK: Detecting Objects with Enhanced YOLOv3 and Knowledge Graph

    SBC: Intelligent Automation, Inc.            Topic: DTRA19B002

    Current state-of-the-art object detection algorithms are almost exclusively based on Deep Convolutional Neural Network (DCNN). These algorithms all require a large amount of labeled examples for each of the object categories they can recognize. These algorithms will fail for novel objects that only very few or even no prior examples are available. These algorithms are also far less accurate when c ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  7. 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
  8. 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
  9. Compact Laser Drivers for Photoconductive Semicond

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

    For effective protection against radiated threats, it is important to understand not only the physics of the threats, but also to quantify the effects they have on mission-critical electrical systems. Radiated vulnerability and susceptibility testing requires delivery of high peak power and peak electric fields to distant targets. The most practical solution to simulate such environments on large ...

    STTR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  10. Hierarchical, Layout-Aware, Radiation Effects Tools Vertically Integrated into an EDA Design Flow for Rad-Hard by Design

    SBC: RELIABLE MICROSYSTEMS LLC            Topic: DTRA16A003

    The goal of this workis to establish a radiation-aware capability in a commercial EDA design flow that will enable first-pass success in radiation resiliency for DoD ASIC designs in much the same way that existing EDA design suites ensure first pass functionality and performance success of complex ASICs destined for commercial applications.Such an integrated capability does not presently exist.The ...

    STTR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
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