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 ...

Webbing is strong, woven material that is used to secure cargo as well as for safety equipment such as seat belts, harnesses, and parachute rigging. Due to its extensive use in Military applications, the strength of the webbing is a key component of equipment design, especially in the case of safety gear that protects soldiers, as lives may be dependent on the strength and proper performance of th ...

Modern and next generation military aircraft face increasing challenges as thermal demands grow while available heat sinks reduce. Legacy platforms upgraded with advanced electrical systems are also encountering similar thermal constraints. Modeling and simulation (M&S) tools provide a cost-effective solution to the design, analysis, and optimization of growing thermal management challenges, but t ...

Intelligent Optical Systems, Inc., in collaboration with Bowling Green State University, proposes to develop a field-rugged, eye-readable indicating spray solution that can immediately detect synthetic opioids over a large area of contamination (i.e., military vehicles, individual protective equipment, clandestine labs, etc.). The proposed chemosensor in a spray solution format will detect multipl ...

We propose a suite of video processing algorithms utilizing the machine learning (ML) techniques of artificial intelligence (AI) reinforcement learning, deep learning, and transfer learning to process submarine imagery obtained by means of periscope cameras. Machine learning (ML) can help in addressing the challenge of human failure of assessing the data of periscope imagery. Though pre-tuned blac ...

Magnetrons are compact, inexpensive, and highly efficient sources of RF power used in many industrial and commercial applications. For most of these applications, the requirement is for RF power without regard to precise frequency or phase control, and noise riding on the RF signal is not important. For many accelerator, defense, and communications applications, however, these characteristics prev ...

Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought t ...

Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in training simulators for periscope operators .  We refer to this solution as RealSynth360.  This novel capability will be created by combining the latest advances ...

Current design tools for gas turbine engines invoke a variety of simplifying assumptions to estimate heat transfer to solid/metal engine components (e.g., isothermal boundary conditions). These approximations are often not valid, result in inaccurate predictions of heat transfer, and ultimately compromise the thermal integrity of propulsion and power systems. Wall-modeled large eddy simulation (WM ...

The overall goal of this STTR Phase I project is to develop a concept to mitigate the effects of motion/vibration for a shipboard material extrusion additive manufacturing (AM) system.  NAVSEA has been installing advanced manufacturing equipment, including 3D printers, onboard ships in support of shipboard operations and to evaluate performance of the equipment in shipboard environments and in re ...