Award Data

The event of nuclear explosion generates harsh radiation conditions, evaluation of which is critical in protecting the military and civilian personnel from serious harm.  Due to these harsh conditions only very simple methods of radiation assessment are employed, such as measurement of equivalent dose.  Improvements to current instrumentation are sought that would provide energy resolved dose in ...

Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

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 which will enable wide dynamic range temperature me ...

This program seeks to develop ultrafast, radiation hard, and rugged scintillators for nuclear battlefields based on high  melting-point temperatures oxide crystal. Doping, co-doping, and alloying schemes will be explored to reach the target of < 5ns decay time, light yield >1000 ph/MeV, moderate energy resolution

For effective protection against radiated threats, produced by high altitude electromagnetic pulse (HEMP) caused by nuclear detonations and high-power microwave (HPM) Directed Energy (DE) weapons it is important to understand not only the physics of the threats, but also to quantify the effects on mission-critical electrical systems. EMP/HMP simulators enable threat level testing of MCS and provid ...

Toyon Research Corporation (Toyon) and the University of California, Santa Barbara (UCSB) propose research, development, and demonstration of algorithms and an efficient software prototype for 3D model reconstruction using single images collected via remote sensing. In particular, the effort is focused on processing of high-resolution commercial satellite images for 3D reconstruction of buildings. ...

This STTR Phase II project proposes development of a statistical relational learning framework for identification of the function of laboratory equipment from imagery. The proposed framework uses semantic reasoning to incorporate evidence from multiple classifiers and feature extractors, domain knowledge, and scene context for scene understanding and labeling. The Phase II effort will focus on dev ...

There is significant interest in multi-functional materials enabling gamma-ray spectroscopy, neutron/gamma pulse shape discrimination (PSD), ultra-fast response, and time-of-flight (TOF) neutron detection. These materials would be used in a variety of mission scenarios for the localization and monitoring of special nuclear materials. Commercial inorganic scintillators may offer some of these chara ...

Current state-of-the-art object detection algorithms are almost exclusively based on Deep Convolutional Neural Network (DCNN). These algorithms all require a large number 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 ...

Special Operations Forces (SOF) operators are among the most elite, and highly qualified individuals in the U.S. military. Extraordinary physical and mental demands are placed upon them, to include superior performance standards, high operational tempos, and the pressure to excel in extreme environments for extended periods of time. In the SOF community, serious injuries are the norm rather than t ...