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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.
SBC: Intelligent Automation, Inc. Topic: DTRA19B002
Current state-of-the-art object detection algorithms are almost exclusively based on Deep Convolutional Neural Network (DCNN). These algorithms all require a large amount of labeled examples for each of the object categories they can recognize. These algorithms will fail for novel objects that only very few or even no prior examples are available. These algorithms are also far less accurate when c ...STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
Fully Metallic Self-Fragmenting Structural Reactive Materials Using Composites and Alloys Comprised of Aluminum, Lithium, and MagnesiumSBC: Adranos Energetics LLC Topic: DTRA16A002
While aluminum casing materials provide some enhanced performance and thermal loading to explosive ordinance, their overall effectiveness is highly limited by incomplete combustion and long residence times. In order to reduce these problems, the casing material must be designed to facilitate rapid fragmentation through either specialized casing geometries or greatly refined initial particle sizes. ...STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency
SBC: Reliable MicroSystems, LLC Topic: DTRA16A003
Establish a radiation-aware analysis capability in a commercial EDA design flow that will enable first-pass success in radiation-hardened by design (RHBD) for DoD ASICs in much the same way that existing EDA design suites ensure first pass functionality and performance success of complex ASICs destined for commercial applications. Layout-aware, calibrated single-event radiation models that captur ...STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency