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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 April, 2020.

  1. Integrated Multi-mode Handheld RIID

    SBC: Radiation Monitoring Devices, Inc.            Topic: DTRA152007

    The goal of the project is to develop a handheld instrument that detects, identifies, and categorizes radioactive sources based on gamma, thermal-, and fast-neutron signatures. Existing radioisotope identification devices (RIIDs) are typically biased toward gamma spectroscopy; some instruments may use an additional inefficient neutron counter in form of a He-3 tube. The recent development of mat ...

    SBIR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  2. Memory Instrumentation and Performance Simulation (MIPS)

    SBC: ATC-NY, Inc.            Topic: DTRA172003

    Next-generation high-performance computing (HPC) hardware, such as the Intel Xeon Phi Knights Landing Many-Integrated-Core processor, provide new deep memory architectures that offer the promise of increased performance. The challenge in taking full advantage of this architecture is selecting which data structures will be placed in the high-bandwidth memory. Optimizing data structure placement in ...

    SBIR Phase I 2018 Department of DefenseDefense Threat Reduction Agency
  3. Tools for Memory Hierarchy Optimization on Pre-Exascale HPC Architectures

    SBC: Ep Analytics, Inc            Topic: DTRA172003

    DTRA uses High Fidelity Computer Codes (HFCC) to investigate weapon effects and techniques for countering Weapons of Mass Destruction (WMD). End-to-end HFCC simulations in support of the DTRA Agent Defeat Warfighter Capability will require calculations including multiple phenomena that occur in vastly different time scales (-sec to hours). As DTRA becomes increasingly reliant on computational mode ...

    SBIR Phase I 2018 Department of DefenseDefense Threat Reduction Agency
  4. Hardware-in-the-Loop Scintillation Simulator for MILSATCOM links in a Nuclear Disturbed Communication Environment

    SBC: WELKIN SCIENCES, LLC            Topic: DTRA172006

    In response to SBIR Topic DTRA172-006, Welkin Sciences proposes to design and build a new all-digital fading channel simulator to be called CoLTSAD. This new simulator design will be the latest addition to our family of Configurable Link Test Sets (CoLTS). It will be capable of emulating the full range of MIL-STD-3053 scintillating channel conditions for all SATCOM frequency bands up to Ka-band. C ...

    SBIR Phase I 2018 Department of DefenseDefense Threat Reduction Agency
  5. Hardware-in-the-Loop Scintillation Simulator for MILSATCOM links in a Nuclear Disturbed Communication Environment

    SBC: Echo Ridge, LLC            Topic: DTRA172006

    Echo Ridge proposes to deliver a Hardware-in-the-Loop RF environment emulator with high fidelity scintillation simulation within which propagation models will in real-time accurately simulate a nuclear-disturbed environment for testing MILSATCOM communications links. The emulator will accommodate the attachment of physical devices (MILSATCOM communications link equipment or simulators), and/or mod ...

    SBIR Phase I 2018 Department of DefenseDefense Threat Reduction Agency
  6. Low Voltage Radiation Hardened Optoelectronics for Optical Interconnects

    SBC: QUANTTERA LLC            Topic: DTRA152001

    The Defense Threat Reduction Agency (DTRA) recognizes the need for low-power high-bandwidth radiation-hard optical interconnects to process more data more quickly and to replace electronic data.Our companys core development in optical communications with semiconductor materials is a unique fit for DTRAs low-power consumption high-bandwidth radiation-hard intra-chip communication components for sat ...

    SBIR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  7. Real-Time Frequency-Selective Fading Channel Realization Generator

    SBC: WELKIN SCIENCES, LLC            Topic: DTRA122020

    During Phase I and a first Phase II, Welkin Sciences developed the Channel Realization Generator (CReG) algorithm, an enhanced functional replacement for the ACIRF code intended to be embedded into software link simulations and HWIL fading channel simulators. The proposed second Phase II effort will refine the CReG documentation and its software and firmware implementations. Many in the strategic ...

    SBIR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  8. Innovative Mitigation of Radiation Effects in Advanced Technology Nodes

    SBC: Microelectronics Research Development Corporation            Topic: DTRA16A003

    Micro-RDC has developed portable radiation effects test structures that scale to new process nodes.These structures will enable the investigation of the effects of radiation on the new technology from the material processing level as well as the circuit level.Fabricating the chosen structures and the refinement of software to extract the model parameters will be completed in this effort.A suite of ...

    STTR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  9. 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
  10. 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

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