Award Data

Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

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 ...

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 ...

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 ...

For effective protection against EMP and HPM threats, it is important to understand the physics of the threats, and also to quantify the effects they have on electrical systems. EMP and HPM vulnerability testing requires delivery of high peak power and electric fields to distant targets. The most practical solution to simulate such environments is to develop a modular, optically-isolated MV-antenn ...

This program will demonstrate how additive manufacturing technologies can be used with reactive and high energy materials to create rapid and flexible fabrication of payload and munitions. Our primary approach to this problem will be to use powder bed binder printing techniques to print reactive structures. The anticipated feedstock will consist of composite particles containing all reactant spe ...

Improving the effectiveness of counter-WMD operations requires improved understanding of weapon-target interaction. Specifically, time-resolved measurements of temperature and composition are required to allow temporal evolution of a detonation fireball. To address this need, SA Photonics will develop MONITOR, a laser-based temperature diagnostic that will enable wide dynamic range temperature mea ...

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 ...

The discovery and commercial development of licensed vaccines often take many years of research followed by years of pre-clinical and clinical development. We propose to assess the feasibility of using a novel irradiation-inactivation technology to develop vaccines more rapidly. The technology utilizes a manganese-peptide complex to protect antigenic proteins from ionizing radiation while allowing ...

Nalas Engineering and Johns Hopkins University collaborated in a Phase I STTR program to study reactive mixtures of HI3O8 and nanocomposite fuels previously developed by the Weihs Group. These fuel/oxidizer mixtures are uniquely able to simultaneously produce heat and biocidal iodine gas, a combination designed to destroy biological weapons. The team at Nalas focused on evaluating conditions for p ...