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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.
How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
Distortion in scanning tunneling microscope (STM) images is an unavoidable problem. This technology is an algorithm to identify and correct distorted wavefronts in atomic resolution STM images.
The QVis Quantum Device Circuit Optimization Module gives users the ability to map a circuit to a specific quantum devices based on the device specifications.
QVis is a visual analytics tool that helps uncover temporal and multivariate variations in noise properties of quantum devices.
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.