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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

  1. 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
  2. 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
  3. Novel Structure-Preserving Algorithms for Accurate Rocket Trajectory Propagation

    SBC: OPTIMAL SYNTHESIS INC.            Topic: MDA17T002

    The Department of Defense uses large-scale high-resolution federated simulations to propagate rocket vehicle trajectories. Runge-Kutta methods have served as a de-facto standard while conducting such simulations. However, there are several challenges while using Runge-Kutta methods for this task. Firstly, there should be exact time-step matching between federates, otherwise the states have to be i ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  4. SmallSat Stirling Cryocooler for Missile Defense (SSC-X)

    SBC: Wecoso, LLC            Topic: MDA17T003

    West Coast Solutions (WCS), in collaboration with the Georgia Institute of Technology and Creare LLC, proposes an adaptation of our SmallSat Stirling Cryocooler (SSC) technology in response to STTR Topic MDA17-T003: High-Efficiency, Low-Volume, Space-Qualified Cryogenic-Coolers. In Phase 1 we will scale up a design currently in development for NASA to meet the Missile Defense Agency (MDA) topic re ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  5. Lightweight Structural Components of a Missile Body

    SBC: Alpha Star Corporation            Topic: MDA17T004

    The Ground-Based Interceptor (GBI) missile is the weapon component of the Ground-Based Midcourse Defense (GMD) system that consists of a rocket booster and kinetic kill vehicle. Recently, MDA has sought technologies to improve the performance of the booster vehicle (BV). To date, studies have shown that reductions in weight have a direct impact on overall effectiveness. The current proposal aims t ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  6. Smallsat Cryocooler System

    SBC: Iris Technology Corporation            Topic: MDA17T003

    The Iris Technology team which also include Northrop Grumman Aerospace Systems (NGAS) and the University of Wisconsin, is attacking the problem of high-efficiency, low-volume, space-qualified cryocooler systems.The team has a firm starting point by leveraging the Northrop Grumman Microcryocooler and the Iris Technology mLCCE (Miniature Low Cost Control Electronics).TMU enhancement will start with ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
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