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 September, 2020.

  1. Reverse Engineering and Manufacturing of Alternative Display Equipment

    SBC: Intellisense Systems, Inc.            Topic: DLA182004

    To address the Defense Logistics Agency (DLA)s need for KWD-ML-4-115AL alternative supply sources, Intellisense Systems, Inc. (ISI) proposes to develop a new Reverse Engineering and Manufacturing of Alternative Display Equipment (RE-MADE) process specifically to reverse engineer (RE) the KWD-ML-4-115AL display to improve product availability and increase competition. The proposed RE process is bas ...

    SBIR Phase I 2018 Department of DefenseDefense Logistics Agency
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. Improving Uncertainty Estimation with Neural Graphical Models

    SBC: MAYACHITRA, INC.            Topic: NGA181005

    Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. Blending Ground View and Overhead Models

    SBC: Arete Associates            Topic: NGA181008

    We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Logistics Augmented Reality Real-time Efficiency Enhancement System

    SBC: Intellisense Systems, Inc.            Topic: DLA181001

    To address the DLAs need for the implementation of augmented reality (AR) in DLAs procurement, logistics, and distribution processes, Intellisense Systems, Inc. (ISS) proposes to develop a new Logistics Augmented Reality Real-Time Efficiency Enhancement (LARREE) system. LARREE is based on novel ultra-low-cost infrastructure enhancements for real-time tracking, using interoperable software database ...

    SBIR Phase I 2018 Department of DefenseDefense Logistics Agency
  10. Additive Manufacturing Process Monitoring and Control Technologies

    SBC: FLIGHTWARE INC            Topic: DLA181002

    The In Process Control for L-PBF (IPCL) program leverages and in process inspection method recently developed and successfully demonstrated for NASA. This Layer Topographic Map or LTM method measures the surface if every melt layer using a commercial laser profilometer. LTM software detects, locates and identifies flawed regions within a layer for every layer in the part. It does so for several co ...

    SBIR Phase I 2018 Department of DefenseDefense Logistics Agency
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