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 ...

The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT l ...

Phase II will utilize knowledge gained from phase I work to develop and execute a manufacturing process suitable for producing quantities of 3-D printed discrete circuits for radiation detection systems.  The goal is to support mobile radiation detection system requirements for high voltage, analog amplification, and MCA functionality to produce differential pulse height spectra in time sequence. ...

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 ...

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 ...

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 ...

The goal of this workis to establish a radiation-aware capability in a commercial EDA design flow that will enable first-pass success in radiation resiliency for DoD ASIC designs in much the same way that existing EDA design suites ensure first pass functionality and performance success of complex ASICs destined for commercial applications.Such an integrated capability does not presently exist.The ...

For effective protection against radiated threats, it is important to understand not only the physics of the threats, but also to quantify the effects they have on mission-critical electrical systems. Radiated vulnerability and susceptibility testing requires delivery of high peak power and peak electric fields to distant targets. The most practical solution to simulate such environments on large ...

At the completion of the research and development effort a Li-foil multi-wire proportional counter (MWPC) neutron detector with more than 625-cm2 of active area will be included in a portable radiation detection system. The system is expected to outperform the standards set by the ANSI N42.53 standards, and gamma-ray rejection ratio (GRR) of 1.0x10-8 or better. A gamma-ray spectrometer for isotope ...