Award Data

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 ...

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 ...

In this program, Freedom Photonics and its research partner institution will demonstrate an analog optical link using novel record performance laser, modulator and photodiode technology. Preliminary designs for a miniature, deployable implementation will be conducted as well in Phase I.

High power generation at millimeter wave (mm-wave) frequencies is expensive and the concurrent need for wide bandwidths at these frequencies creates an extremely challenging problem. Currently the most stringent requirements for mm-wave power and bandwidth can only be practically met by vacuum electronics (VE) technology. At present, vacuum amplifiers with the required performance are prohibitivel ...

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 ...