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Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Andrzej Nycz
- Chris Masuo
- Diana E Hun
- Hongbin Sun
- Luke Meyer
- Peter Wang
- Philip Bingham
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- Gurneesh Jatana
- Ilias Belharouak
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- Mark M Root
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- Nolan Hayes
- Obaid Rahman
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Ruhul Amin
- Ryan Kerekes
- Sally Ghanem
- Vishaldeep Sharma

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

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.

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

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).

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.