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. Rechargeable carbon-oxygen battery: A new class of ultra low-cost, lightweight energy storage technology

    SBC: Noon Energy Inc.            Topic: G

    Noon Energy has developed a new class of battery technology that will cost-effectively turn intermittent solar and wind electricity into on-demand power. It uses ultra-low-cost storage media and can match the energy efficiency of lithium-ion technology. It stores energy by splitting CO2 into solid carbon and oxygen in a flow battery configuration, utilizing abundant materials and simple reaction c ...

    SBIR Phase I 2020 Department of EnergyARPA-E
  2. Space Division Multiplexing with Multi-Core Fiber for Energy Efficient Integrated Photonic Networking Technologies

    SBC: ULTRA-LOW LOSS TECHNOLOGIES LLC            Topic: G

    The trend for short-reach optical fiber communications, those used for data centers and HPC, has been to use simple and power-efficient approaches to scale bandwidth. Approaches include parallel single mode (ex. more fibers), multi-level modulation coupled with direct detection (ex. four-level pulse amplitude modulation or PAM-4), and wide channel spacing (i.e. coarse WDM or CWDM). Bandwidth and c ...

    SBIR Phase I 2020 Department of EnergyARPA-E
  3. Co-Generation of Low-Energy, CO2-Free Hydrogen and Ordinary Portland Cement from Ca-Rich Rocks

    SBC: Brimstone Energy            Topic: G

    We are building a technology that produces low cost hydrogen while releasing low or no carbon dioxide emissions. Key to our affordable hydrogen is the co-generation of valuable co-products like: Sulfuric Acid or Cement. Our processes are lower emission, lower energy and lower cost than conventional production method. We validated a proof-of-concept in our lab in May 2019 and our objective is to de ...

    SBIR Phase I 2020 Department of EnergyARPA-E
  4. SeaSTAR: Selective Thalassic Ambulatory Retriever (STAR)

    SBC: Other Lab Inc.            Topic: G

    Growth, electrification, and increased installation of renewable energy is driving significant increases in worldwide demand for metals like manganese, nickel, copper, and cobalt. This increase in commodity prices and projected demand has driven interest in exploring new sources of minerals such as deep sea mining. The abyssal plain contains locations of concentrated deposits of polymetallic nodul ...

    SBIR Phase I 2020 Department of EnergyARPA-E
  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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