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ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
Real-time tracking and monitoring of radioactive/nuclear materials during transportation is a critical need to ensure safety and security. Current technologies rely on simple tagging, using sensors attached to transport containers, but they have limitations.
Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).
This technology provides a device, platform and method of fabrication of new atomically tailored materials. This “synthescope” is a scanning transmission electron microscope (STEM) transformed into an atomic-scale material manipulation platform.