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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. (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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. Radioactive Anomaly Detection and Identification Algorithm Suite (RADIAS) for Enhanced Radiological Search

    SBC: PHYSICAL SCIENCES INC.            Topic: NGA192002

    Physical Sciences Inc. (PSI) proposes to develop an advanced machine learning (ML) algorithm to detect threat-based anomalies in gamma-ray spectra in real-time. If a network of distributed R/N sensors is employed, the algorithms will also be capable of tracking such anomalies through the network. The Radiation Anomaly Detector (RAD) will be packaged with PSI’s award winning Poisson Clutter Spli ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  8. High Efficiency Semiconductors for Nuclear Detection

    SBC: RADIATION MONITORING DEVICES, INC.            Topic: NGA192003

    There is a need for low cost, high performance gamma-ray detectors for national and homeland security applications for detection, identification and localization of special nuclear materials. Common detectors used in this application include scintillators coupled to photomultiplier tubes or silicon photodiodes, and semiconductor detectors like cadmium zinc telluride. Semiconductor detector offer ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  9. Perovskite-based Multi-modal Detector and Imaging System

    SBC: CAPESYM INC            Topic: NGA192003

    Perovskites are rapidly emerging as attractive radiation detectors. The goal of this program is to develop a multi-modal detection sensor based on perovskite materials. The developed detector will be integrated with a high resolution active pixel array and the pixel signals will be processed by high speed electronics to create scene images of different radiation types.

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  10. Vehicle Reidentification-Aided Network Topology Inference (VRANTI)

    SBC: Systems & Technology Research LLC            Topic: NGA201005

    Systems & Technology Research (STR) proposes to develop Vehicle Reidentification-Aided Network Topology Inference (VRANTI), a novel system for estimating proximity network graphs of traffic cameras to facilitate intelligence applications such as tracking and monitoring of traffic systems. Network inference will be performed using statistical analyses of features extracted from camera video feeds, ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
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