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SBC: Arete Associates Topic: N20AT014
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 ...STTR Phase I 2020 Department of DefenseNavy
SBC: FASTER LOGIC, LLC Topic: N20AT016
Whereas quantum computers stand to drastically transform computation for a number of existing and future problems, its realization in the near term produces certain challenges. Simulation and Emulation techniques make it possible to consider the advantages of quantum computation in real-world applications in cryptography, machine learning, signal processing, and cybersecurity. They also open t ...STTR Phase I 2020 Department of DefenseNavy
SBC: SEATREC, INC. Topic: N20AT023
Seatrec will collaborate with a team from the Woods Hole Oceanographic Institution to demonstrate the technical feasibility and commercial applicability of a novel energy harvesting system that converts thermal energy from high-latitude air-sea temperature differences into electricity. This capability will extend the endurance and capability of observing system elements, reduce battery waste, a ...STTR Phase I 2020 Department of DefenseNavy
SBC: TOYON RESEARCH CORPORATION Topic: N20AT024
To support Navy Live, Virtual, and Constructive (LVC) training for surface fleets during periods of long transit, the Navy would like to consider alternative communication paths that can link shore based trainers and simulation capabilities with trainers and training systems afloat. To support full spectrum training during the training events, there is a desire to selectively turn off communicatio ...STTR Phase I 2020 Department of DefenseNavy
SBC: TRITON SYSTEMS, INC. Topic: N20AT018
Although metal AM technologies have continued to progress, there are still many different challenging factors to a build that impact part quality and the amount of time it takes to successfully process a first-run component without defects. Triton Systems proposes to develop a machine learning algorithm that adjusts print parameters during the build in reaction to in-situ sensor data in order to ...STTR Phase I 2020 Department of DefenseNavy
SBC: FREEDOM PHOTONICS LLC Topic: N20BT030
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.STTR Phase I 2020 Department of DefenseNavy
SBC: Photonic Systems, Inc. Topic: N20AT012
Photonic Systems, Inc. (PSI) and Harvard University propose to collaborate in Phases I and II of this STTR program towards the goal of demonstrating a broadband RF/photonic signal link with a specific combination of performance parameters and other features not available from present state-of-the-art links. The solicitation’s goal – specifically, an electromagnetic attack-resilient electro-op ...STTR Phase I 2020 Department of DefenseNavy
SBC: FREEDOM PHOTONICS LLC Topic: N20AT012
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.STTR Phase I 2020 Department of DefenseNavy
SBC: Arete Associates Topic: N20AT007
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 ...STTR Phase I 2020 Department of DefenseNavy
SBC: Objectsecurity LLC Topic: N20AT011
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 ...STTR Phase I 2020 Department of DefenseNavy