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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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 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
  2. Emergency Digital Paging over Public Television (eDPPT)

    SBC: DEVICE SOLUTIONS INC            Topic: DHS201003

    Device Solutions (DS) and the Wireless Research Center (WRC) are teaming to create a development plan for inNvative digital paging for emergency responders using public television ATSC 3.0.Leveraging open standards, broadcast and public safety infrastructure, modern network devices, and new wireless electronics, our approach will provide responders and incident commanders with improved pager cover ...

    SBIR Phase I 2020 Department of Homeland Security
  3. Generalized Change Detection to Cue Regions of Interest

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181006

    Toyon Research Corporation proposes to research and develop algorithms for generalized change detection, by leveraging and exploringexisting and proven effective traditional and deep learning methods, with a unique 3D reconstruction component. The vast majority of themassive amounts of imagery data will have small pixel level differences due to a multitude of unimportant changes: minor misregistra ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  4. 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
  5. 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
  6. Hardened Encryption Routing to Mitigate External Susceptibility

    SBC: Physical Optics Corporation            Topic: DHS201001

    To address the DHS's need for security of multimedia messages from the public to the Next Generation 9-1-1 (NG9-1-1) Public Safety Answering Point (PSAP) Emergency Communications Cybersecurity Center (EC3) within NG9-1-1 Emergency Service Internet Protocol Networks (ESINets), Physical Optics Corporation (POC) proposes to develop a new Hardened Encryption Routing to Mitigate External Susceptibility ...

    SBIR Phase I 2020 Department of Homeland Security
  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. 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
  9. 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
  10. 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
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