Award Data

In this program, we propose to adapt a new, high-performance integration platform for RF photonics to operation at 1um, and to realize integrated optical transmitters that meet the requirements of the program.

Hemp fibers have been used in textile applications for thousands of years, but have been limited to textiles for home furnishing, rope, sacks, and other non-clothing textile applications. However, new treatment methods for hemp fibers to improve their hand, as well hemp’s general softening properties with time and wear, make these fibers attractive for use in performance clothing due to hemp’s ...

Decompression sickness (DCS) can occur during rapid pressure changes such as when a diver ascends rapidly or during high altitude flights. Rapid decompression can causes gases dissolved in body fluids and tissues to come out of solution and form bubbles. The symptoms of DCS include painful joints, neurological dysfunction, dermal effects, and cardiopulmonary collapse. Currently, divers rely on dec ...

In order to facilitate collaborative decision-making during modern surface warfare situations, locally learned knowledge among sailors and warfighters must be shared effectively in a timely manner. Current Naval approaches for collecting and sharing knowledge are inefficient and inflexible, as new contents are examined over extended timelines with no ability to dynamically update the knowledge bas ...

Advances in both software and computer hardware have made the finite element method the preeminent choice for analyzing highly complex systems that are of great value to the Department of Defense.   The US Defense industry, however, continues to spend enormous time and resources in mesh generation, a key step in finite element analysis, despite progress that has been made in automated mesh gener ...

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 ...

Modern US Navy ships and submarines are configured with an ever-increasing level of automation, including state-of-the-art embedded wireless sensors that monitor vital system functions. However, sensor nodes have the potential to serve as targets for cybersecurity attacks or be susceptible to corruption through accidental or malicious events. To address these shortfalls and minimize vulnerabilitie ...

Condition-based maintenance plus (CBM+), and cyber-physical systems (CPS) in general, depend on correct sensor data for analysis, decision making and control loops. If the sensor data that arrives at the point of processing is not correct, or more accurate, is outside its accepted error range, then any further processing will be incorrect as well. This could result in, in the case of CBM+, not det ...

Navy is developing the concepts and methods to leverage Machine Learning (ML) techniques for the maintenance decision-making on condition-based maintenance plus (CBM+) platform. Effective health monitoring for condition-based and predictive maintenance requires intelligent sensor selection and placement, and context-aware interpretation of sensor data to detect the many possible fault modes. Moreo ...

NAVY seeks high strength, low density, and high corrosion resistant alloys for structural components which can be processed by additive manufacturing (AM). Magnesium (Mg) alloys are candidates for fuel-efficiency applications, especially the aircrafts. They satisfy density, strength, and stiffness for many designs. However, their low corrosion resistance cannot ensure design lifetimes.  This limi ...