You are here

Award Data

For best search results, use the search terms first and then apply the filters
Reset

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. 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
  2. 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
  3. Holographic Projection Laser Marking System (HoloMark)

    SBC: Luminit LLC            Topic: DMEA172002

    To address the DMEA need for Through-Lens Fiducial Marking System, Luminit, LLC proposes to develop a new Holographic Projection Laser Marking System (HoloMark). This proposed device is based on a new design that utilizes Luminit-developed mature components like computer-generated hologram technology. The innovation will enable precise, fast and lower cost marks on any substrate, consistent with t ...

    SBIR Phase I 2018 Department of DefenseDefense Microelectronics Activity
  4. Laser Alignment and Infrared Inspection

    SBC: Physical Optics Corporation            Topic: DMEA172002

    To address the DMEAs need to perform failure analysis on microelectronic components using infrared microscope systems to locate areas of interest and then navigate accurately to these same locations in a focused ion beam tool, Physical Optics Corporation (POC) proposes to develop a new Laser Alignment and Infrared Inspection (LAIRI) system. It is based upon the integration of a state-of-the-art co ...

    SBIR Phase I 2018 Department of DefenseDefense Microelectronics Activity
  5. Through-Lens Fiducial Marking System

    SBC: Checkpoint Technologies LLC            Topic: DMEA172002

    The objective of this proposal is to demonstrate the feasibility of the innovative development of a tool that can be integrated into an IR microscope that is able to create fiducial marks on the surface of the backside of silicon.The current state-of-art in semiconductor device analysis involves procedures that begin with the identification of areas of interest under an IR microscope and then thos ...

    SBIR Phase I 2018 Department of DefenseDefense Microelectronics Activity
  6. In-situ Fiducial Marker

    SBC: HEDGEFOG RESEARCH INC.            Topic: DMEA172002

    To address the DMEA's need for a through-lens fiducial marker technology in semiconductor device inspection, Hedgefog Research Inc. (HFR) proposes to develop a new In-situ Fiducial Marker (IFM), based on laser-activated, electrostatically assisted marking at designated spots. Novel system features in IFM will enable non-destructive fiducial marking (diameter < 5 micron) in real time while operatin ...

    SBIR Phase I 2018 Department of DefenseDefense Microelectronics Activity
  7. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA172002

    Toyon Research Corporation proposes to research and develop algorithms for low-shot object detection, adapting popular techniques to address the complexities inherent in ATR for remote sensing. Traditional object detection algorithms rely on large corpora of data which may not be available for more exotic targets (such as foreign military assets), and therefore, traditional Convolutional Neural Ne ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. Variational Object Recognition and Grouping Network

    SBC: INTELLISENSE SYSTEMS INC            Topic: NGA181005

    To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Blending Ground View and Overhead Models

    SBC: Arete Associates            Topic: NGA181008

    We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Improving Uncertainty Estimation with Neural Graphical Models

    SBC: MAYACHITRA, INC.            Topic: NGA181005

    Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
US Flag An Official Website of the United States Government