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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

Download all 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. Ship-based Operations for UAS Swarms with Autonomous Pinniped Homing Over Nasty Environments (SOUSAPHONE)

    SBC: Charles River Analytics, Inc.            Topic: 9501

    Small Unmanned aircraft systems (UAS) play a critical and growing role in government, military, commercial, and scientific operations across a range of missions such as weather monitoring, natural disaster assessment, surveillance, and infrastructure inspection. Their versatility, maneuverability, and dependability, coupled with their ability to keep operators out of harm’s way, make them critic ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  2. (Anno) Tator Online: A Web Application for Exploration and Curation of Underwater Video and Imagery

    SBC: CVISION AI INC            Topic: 9302

    Using modern web technologies, we will build an application that can be used to explore, enrich, and evaluate the wealth of underwater video and imagery being collected by NOAA and its partners. Existing data portals are primarily for text based or keyword search, and for viewing of existing data, lacking the ability for rich interaction, visualization of metadata, and adding new types of annotati ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  3. Underwater Adhesive for Coral Restoration

    SBC: TIAX LLC            Topic: 9201

    TIAX proposes to demonstrate the feasibility of a quick tack, long pot life, high strength underwater adhesive formulated to be delivered through a caulk gun like application device. This two part adhesive will pass through a static mixing nozzle during application, removing the need for hand-mixing the adhesive prior to use. Our adhesive will adhere strongly to commonly used artificial and natura ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  4. Benthic Mapping of Geological, Biogeochemical and Biodiversity Parameters through an Autonomous Vehicle and Deep Learning Software Workflow

    SBC: CoastalOceanVision, Inc            Topic: 9603

    The time has come to integrate the capabilities we have developed for real-time habitat processing on shipboard with the HabCam towed vehicle, into an autonomous vehicle with 3D reconstruction of seafloor topology, substrate classification, single target identification, hyperspectral imaging for physiological information, and plankton classification as an index of ecosystem health. Integrated toge ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  5. MWM Array Bending Stress and Crack Detection In-Line Inspection Module

    SBC: Jentek Sensors, Inc.            Topic: 20PH1

    In-line inspection (ILI) is a cornerstone for pipeline integrity. Advances to ILI tools are essential to maintaining pipeline safety. Gaps exist for the detection of circumferential cracking (at and away from welds and dents) and bending stresses. Few technologies exist that can make reputable claims for stress measurement that are non-destructive, reliable, and appropriate for integration onto an ...

    SBIR Phase I 2020 Department of Transportation
  6. Bayesian Assessments and Real-Time Rider Alerting and Cueing for Upcoming Danger Avoidance (BARRACUDA)

    SBC: Charles River Analytics, Inc.            Topic: 151FH3

    Riding a motorcycle can be a risky activity due to hazards that riders encounter. Some hazards, such as uneven terrain, sand/gravel, and potholes, do not pose a risk to automobiles, but can be dangerous for motorcyclists. However, no motorcycle-specific hazard tracking or alerting systems are currently available for riders. To address this need, Charles River Analytics has designed and demonstrate ...

    SBIR Phase II 2020 Department of Transportation
  7. Automated Fillet Identification

    SBC: CVISION AI INC            Topic: 9205

    We propose to develop a fillet identification methodology using visible imagery that can be collected and processed on commodity hardware such as modern smartphones, dramatically increasing the availability of fillet identification technology. In order to accomplish this, we will gather an annotated data set of fillet pictures, traceable to verifiable whole fish images. Using these pictures, we wi ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  8. Semi-autonomous Capabilities for the Operation of Unmanned Teams (SCOUT)

    SBC: Charles River Analytics, Inc.            Topic: 9401

    Natural disasters and other severe weather events have the potential to create loss of life and damage property on a large scale. Preparing for and responding to these incidents is a complex, multi-phase process. NOAA’s 2019-2022 Strategic Plan is to achieve the vision of a Weather Ready Nation to reduce the impacts of weather, water, and climate events and harness cutting-edge science, technolo ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  9. Automated Monitoring of VOCs with a Compact Gas Chromatography-Proton Transfer Reaction Mass Spectrometer (GC-mVocus)

    SBC: Aerodyne Research, Inc.            Topic: 9502

    Volatile organic compounds (VOCs) are emitted from a wide variety of biogenic and anthropogenic sources. VOCs transform in the atmosphere, forming ozone and oxygenated VOCs (OVOCs), which in turn can form fine particulates or condense onto preexisting particulate matter (PM). Both ozone and fine PM are deleterious to human health and alter the Earth’s climate. Measurements of (O)VOCs are necessa ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  10. Hybrid Machine Learning Approaches for Radiation Signature Identification

    SBC: Radiation Monitoring Devices, Inc.            Topic: NGA192002

    To improve the identification and detection of radio-logical materials, we propose a hybrid supervised learning and unsupervised machine learning approach to reduce the false positive rate, increase the accuracy and throughput, and augment the capabilities of the human operators. At the end of the Phase I, we will have a machine learning algorithm that is trained to recognize a variety of nuclear ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
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