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Researcher
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Yaosuo Xue
- Alexander I Kolesnikov
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- Bekki Mills
- Bryan Maldonado Puente
- Corey Cooke
- Fei Wang
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- Gurneesh Jatana
- John Holliman II
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- Keju An
- Mark Loguillo
- Mark M Root
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- Rafal Wojda
- Ryan Kerekes
- Sally Ghanem
- Shannon M Mahurin
- Sreenivasa Jaldanki
- Suman Debnath
- Sunil Subedi
- Tao Hong
- Tomonori Saito
- Victor Fanelli
- Yonghao Gui

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.

Neutron scattering experiments cover a large temperature range in which experimenters want to test their samples.

Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems.

Neutron beams are used around the world to study materials for various purposes.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Multi-terminal DC (MTdc) systems based on high-voltage DC (HVDC) transmission technology is an upcoming concept. In such systems, either asymmetric monopole or bi-pole systems are generally employed. Such systems are not suitable for easy expansion.