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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
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- Daniel Rasmussen
- Debraj De
- Gautam Malviya Thakur
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- Gurneesh Jatana
- Hsin Wang
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- Liangyu Qian
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- Nedim Cinbiz
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- Obaid Rahman
- Paul Abraham
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Todd Thomas
- Tony Beard
- Vilmos Kertesz
- Xiaohan Yang
- Xiuling Nie
- Yang Liu

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.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

Enzymes for synthesis of sequenced oligoamide triads and tetrads that can be polymerized into sequenced copolyamides.
Contact
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

Detection of gene expression in plants is critical for understanding the molecular basis of plant physiology and plant responses to drought, stress, climate change, microbes, insects and other factors.

The technologies provide a system and method of needling of veiled AS4 fabric tape.

ORNL will develop an advanced high-performing RTG using a novel radioisotope heat source.

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

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).