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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

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.

  1. Miniature Intelligent Spectral Analyzer

    SBC: Physical Optics Corporation            Topic: HSB0181003

    To address the DHS need to rapidly detect radio interference of critical radio frequency (RF) communications channels utilized by first responders, Physical Optics Corporation (POC) proposes to develop a new Miniature Intelligent Spectral Analyzer (MISCAN) device based on a combination of commercial off-the-shelf (COTS) electronic components in a custom software-defined configuration along with in ...

    SBIR Phase I 2018 Department of Homeland Security
  2. Augmented Commercial Radio for Navigation (ACORN)

    SBC: Setter Research, Inc.            Topic: HSB0181004

    The Global Positioning System (GPS) and other global navigation satellite systems (GNSS) have become critical elements of diverse activities including safe and efficient ground and air transportation, manufacturing, power generation, financial transactions, farming, cellular communications, first responder operations, law enforcement, consumer activities, all in addition to military operations for ...

    SBIR Phase I 2018 Department of Homeland Security
  3. Advanced Receiver for Distressed Emitter Localization (ARDEL)

    SBC: TOYON RESEARCH CORPORATION            Topic: HSB0181002

    A majority of U.S. adults own a cell phone and are inclined to use it in emergency situations to call for assistance. Unfortunately, in areas where the density of cell towers is low, such as in rural and off-shore environments, the ability of the wireless network to geolocate the origin of the wireless signal is poor to non-existent. Under the proposed effort, Toyon Research Corporation will devel ...

    SBIR Phase I 2018 Department of Homeland Security
  4. Remote Phone Locator for Improved Emergency Rescue

    SBC: Physical Optics Corporation            Topic: HSB0181002

    To address the Department of Homeland Security (DHS) need for a cell phone location finder for maritime and remote search and rescue (SAR), Physical Optics Corporation (POC) proposes to develop a new REmote Phone Locator for Improved Emergency Rescue (REPLIER). REPLIER leverages novel techniques recently developed at POC to extend the range of cellular communications and integrate commercial cellu ...

    SBIR Phase I 2018 Department of Homeland Security
  5. Soft Targets and Crowded Places Security

    SBC: KARAGOZIAN & CASE, INC.            Topic: DHS201004

    To address the Department of Homeland Security (DHS) Cybersecurity and Infrastructure Security Agency (CISA) requirements and strategic intent, Karagozian and Case, Inc. (K&C) proposes to develop a SECURITY MITIGATION ASSESMENT OF RISKS AND THREATS (SMART) software application for SOFT TARGETS AND CROWDED PLACES (ST-CP) which leverages advanced Geographical Information Systems (GIS) mapping softwa ...

    SBIR Phase I 2020 Department of Homeland Security
  6. Automated and Scalable Analysis of Mobile and IoT Device Firmware

    SBC: RAM LABORATORIES            Topic: HSB0181008

    As Internet of Things (IoT) and mobile devices become increasingly popular and widely used, the security of the firmware running on these devices is paramount.However, due to the lack of an efficient and scalable analysis framework, combined with the increasing pressure to get products to market as quickly as possible, the software running on these devices is never properly checked for security vu ...

    SBIR Phase I 2018 Department of Homeland Security
  7. 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
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