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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. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

    STTR Phase II 2022 Department of DefenseNational Geospatial-Intelligence Agency
  2. 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
  3. 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
  4. 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
  5. Metrology of Thin Films on Sapphire Substrate

    SBC: OPTOWARES INC            Topic: DMEA16B001

    There is a lack of a non-destructive metrology tool to measure the thickness of thin films on sapphire substrates due to the transparency of the substrate. Leveraging our extensive experience building sensor systems combined with MIT Lincoln Laboratory’s expertise in theoretical modeling, we will design and build an innovative thin film measurement tool using Raman spectroscopy. Our metrology sy ...

    STTR Phase II 2019 Department of DefenseDefense Microelectronics Activity
  6. 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
  7. 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
  8. Metrology of thin films on sapphire substrate

    SBC: OPTOWARES INC            Topic: DMEA16B001

    There is a lack of non-destructive metrology tool to measure the thickness of thin films on sapphire substrate due to the transparency of the

    STTR Phase I 2017 Department of DefenseDefense Microelectronics Activity
  9. Botnet Analytics Appliance (BNA)

    SBC: MILCORD LLC            Topic: HSB061008

    Recent reports indicate the activity of more than 6,000 botnet C and C servers. 70 million zombies are responsible for 80 percent of SPAM. Given the exponential growth of the botnet threat, the security of our nation s cyber infrastructure demand automated botnet activity monitoring solutions. In Phase I, Milcord developed a feasibility prototype of a Bayesian Activity Monitor for Botnet Defense. ...

    STTR Phase II 2007 Department of Homeland Security
  10. Botnet Analytics Appliance (BNA)

    SBC: MILCORD LLC            Topic: N/A

    As reported by Internet security threat reports, Bot networks are becoming the focal point for cybercriminals. Milcord and the University of Wisconsin, responds to this challenge with our proposal ¿ a ¿Bayesian Activity Monitor for Botnet Defense¿ (BAM-BD). In this proposal, we will research, design, and develop a botnet detection and mitigation tool that automatically classifies botnet behavio ...

    STTR Phase I 2006 Department of Homeland Security
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