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. Online Music-based Game for Children with Speech- Language Delays/Disorders

    SBC: Iqsonics LLC            Topic: 91990019R0011

    The game, which is used by children alone or in tandem with a special education practitioner or caregiver, supports children in understanding and expressing musical patterns, and in learning to sing songs before speaking. This project will extend the existing version by developing a prototype that includes additional songs for increased speech-language learning outcomes, an animated onscreen singe ...

    SBIR Phase I 2019 Department of EducationInstitute of Education Sciences
  2. LabAR: An Augmented Reality Learning System for STEM

    SBC: LightUp, Inc.            Topic: 91990019R0011

    The team will develop a new component of the AR app, a prototype with computer vision and recognition software to present real-world phenomena that are ordinarily not observable in nature. In Phase I, the research team will create a magnetism module would show students the invisible magnetic field all around them and how it interacts with magnetic objects. At the end of Phase I, in a pilot study w ...

    SBIR Phase I 2019 Department of EducationInstitute of Education Sciences
  3. VR-enhanced Immersive Science Investigations into Biology and Genetics

    SBC: LIGHTHAUS INC            Topic: 91990019R0011

    The team will develop a prototype of an immersive virtual reality experience for high school students to learn about the scientific processes and the biology of plants. The prototype will be designed to be multi-player where groups of students collaboratively conduct open-ended cross-breeding experiments, record images to export to reports, track and analyze data, and draw conclusions. At the end ...

    SBIR Phase I 2019 Department of EducationInstitute of Education Sciences
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Generalized Change Detection to Cue Regions of Interest

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181006

    Toyon Research Corporation proposes to research and develop algorithms for generalized change detection, by leveraging and exploringexisting and proven effective traditional and deep learning methods, with a unique 3D reconstruction component. The vast majority of themassive amounts of imagery data will have small pixel level differences due to a multitude of unimportant changes: minor misregistra ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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