Award Data

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The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until June, 2020.

  1. Single-Mast Transmit-Receive Antenna for Long-Range 4- 5.5 MHz Coastal HF Radars

    SBC: CODAR OCEAN SENSORS, LTD            Topic: 824

    TECHNICAL ABSTRACT: The U.S. High Frequency Radar (HFR) network contains more than 140 coastal stations that provide hourly two-dimensional coastal surface currents. Approximately one-third of these are Long Range (LR) systems that transmit in the 4–5.5 MHz frequency band with offshore ranges and resolutions of 160-220 km and 6 km, respectively. Currents provided by this network have numerous ap ...

    SBIR Phase II 2018 Department of CommerceNational Oceanic and Atmospheric Administration
  2. Low Cost, High Precision Water Monitoring System

    SBC: Swift Engineering, Inc.            Topic: 833

    TECHNICAL ABSTRACT: This paper proposes development of a low-cost water monitoring system wirelesslynetworked to upload data to FieldKit for cloud based data visualization and validation. This will enable citizen scientists to collect a variety of parameters including conductivity, temperature, depth, and ocean noise measurements on an adhoc basis while contributing to a large data repository whic ...

    SBIR Phase II 2018 Department of CommerceNational Oceanic and Atmospheric Administration
  3. Adaptive mask flow photometer

    SBC: Actinix            Topic: 822

    TECHNICAL ABSTRACT: A flow micro-photometer is proposed that can measure absorption and backscatter from single aquatic particles including phytoplankton, detritus and minerals. This instrument will make use of a novel adaptive diaphragm to define an analysis region of interest that exactly matches the size, shape and orientation of each particle being analyzed. A micro-fluidic chip will be used t ...

    SBIR Phase II 2018 Department of CommerceNational Oceanic and Atmospheric Administration
  4. Space weather-based position error maps for TEC- OnLine (SpoT-On)

    SBC: SPACE ENVIRONMENT TECHNOLOGIES, LLC            Topic: 831

    TECHNICAL ABSTRACT: The Space weather-based Position error maps for TEC - On-line (SpoT-On) project will use GPS-GNSS based TEC data, integrated into the Global Assimilation of Ionospheric Measurements (GAIM) operational system at the Utah State University Space Weather Center to produce an order of magnitude improved TEC position correction maps. These will be publicly and globally accessible. A ...

    SBIR Phase II 2018 Department of CommerceNational Oceanic and Atmospheric Administration
  5. 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
  6. Algorithms for Look-down Infrared Target Exploitation

    SBC: Signature Research, Inc.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. Variational Object Recognition and Grouping Network

    SBC: Intellisense Systems, Inc.            Topic: NGA181005

    To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. Bayesian Urban Degradation Assessment

    SBC: Intellisense Systems, Inc.            Topic: NGA181004

    To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181010

    The National Geospatial-Intelligence Agency (NGA) ingests and analyzes raw imagery from multiple sources to form actionable intelligenceproducts that can be disseminated across the intelligence community (IC). To effectively meet these demands NGA must continue to improveits automated and semi-automated methods for target detection and classification. Of particular concern is furthering NGA's abil ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Automated Assessment of Urban Environment Degradation for Disaster Relief andReconstruction

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181004

    Toyon Research Corp. proposes development of a system that automates disaster assessment based on fusion of overhead and ground-basedimages, video, and other data. In Phase I, we will investigate various possible data sources and the benefits of fusing the data in automatedanalysis. We will select and curate data for processing in a Phase I feasibility study. Damage assessment will be performed in ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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