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SBC: MAYACHITRA, INC. Topic: N20AT007
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 ...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: MAPP BIOPHARMACEUTICAL, INC. Topic: CBD18A002
There are currently no vaccines or therapeutics available for Marburg Virus Disease (MVD). Given the specter of weaponization and the terrible morbidity and high mortality rate of MVD, this represents a critical threat to the operational readiness of the Warfighter. While traditional vaccines have contributed greatly to public health, they have some limitations especially in the context of operati ...STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
SBC: Calabazas Creek Research, Inc. Topic: N20AT015
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 ...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
SBC: DYMENSO LLC Topic: N20AT013
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 ...STTR Phase I 2020 Department of DefenseNavy
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: PHYSICAL SCIENCES INC. Topic: N20AT015
The Navy desires a compact and highly efficient S-band magnetron source with stabilized output capable of modulation over a narrow bandwidth. In this Phase I STTR proposal, Physical Sciences Inc (PSI) outlines the development of an injection locked “cooker” magnetron which can be used for frequency shift keying (FSK) or phase shift keying (PSK) in a portable high power transmission device. In ...STTR Phase I 2020 Department of DefenseNavy
SBC: Pendar Technologies, LLC Topic: N20AT003
Pendar Technologies proposes to develop a QCL simulation tools that leverage machine learning to dramatically improve the speed of QCL device design. The innovative QCL design suite proposed will benefit from recent advances made by Pendar in bandstructure engineering, laser cavity design and thermal management at the chip and the package level.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