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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. Handheld ANmaly Recognition Tool (HART)

    SBC: SPECTRAL LABS INCORPORATED            Topic: DHS201006

    Check point screening is key to mitigating threats in aviation transport as well as in missions protecting critical infrastructure, high-value cultural institutions, and persons, in a variety of missions from civilian security to law enforcement and corrections to military.These missions are made more challenging by the range of threats presents, from metallic weapons to liquid-based explosives.It ...

    SBIR Phase I 2020 Department of Homeland Security
  2. Handheld Advanced Detection/Imaging TechNlogy System

    SBC: LUNA INNOVATIONS INCORPORATED            Topic: DHS201006

    TeraMetrix is a wholly owned subsidiary of Luna InNvations Incorporated.Both TeraMetrix and Luna InNvations meet the SBIR eligibility requirements in size and Phase II conversion.We have attached Luna InNvations' registration SBC_000671230 because it includes ALL Luna employees and eliminates the question of does TeraMetrix as a subsidiary of Luna meet the size requirements. Luna as a whole, inclu ...

    SBIR Phase I 2020 Department of Homeland Security
  3. Targeted Surface Interrogation Scanning System

    SBC: INTELLISENSE SYSTEMS INC            Topic: DHS201007

    To address the DHS's need for a quick and efficient targeted surface interrogation technique to locate and detect trace residues of interest, including explosives and illicit drugs, on carry-on baggage and items, Intellisense Systems, Inc. proposes to develop a new rapid Targeted Surface Interrogation Scanning (TASIS) system, based on ultraviolet Raman spectroscopy and fast data processing/renderi ...

    SBIR Phase I 2020 Department of Homeland Security
  4. Automating tilt and roll in ground-based photos and video frames

    SBC: INTERNATIONAL ASSOCIATION OF VIRTUAL ORGANIZATIONS, INCORPORATED            Topic: NGA201006

    NGA seeks an innovation to fully automate processes that recover camera orientation parameters, specifically for ground-based “photo” (aka image) and video frame use cases. The ability to use these ground-based systems represents an enhanced aspect to traditional photogrammetry, and in many regards, folding in hand-held systems, and considering the nuances associated with these collects, is ye ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  5. Learning traffic camera locations using vehicle re-identification

    SBC: Arete Associates            Topic: NGA201005

    In its effort to provide necessary intelligence and analysis, the National Geospatial-Intelligence Agency (NGA) utilizes extensive traffic camera systems. However, the large amount of data overwhelms both analysts and existing processing methods. In order to provide a better understanding and reduce the search space for common problems such as target tracking, it is necessary to extract the camera ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  6. SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNING

    SBC: GEOMETRIC DATA ANALYTICS INC.            Topic: NGA20A001

    We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  7. 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
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