Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (23)
- Computing and Computational Sciences Directorate (35)
- Energy Science and Technology Directorate
(217)
- Fusion and Fission Energy and Science Directorate (21)
- Information Technology Services Directorate (2)
- Isotope Science and Enrichment Directorate (6)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities (27)
- (-) National Security Sciences Directorate (17)
Researcher
- Ali Passian
- Joseph Chapman
- Nicholas Peters
- Sam Hollifield
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Chad Steed
- Diana E Hun
- Hsuan-Hao Lu
- Joseph Lukens
- Junghoon Chae
- Mingyan Li
- Muneer Alshowkan
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Travis Humble
- Vincent Paquit
- Aaron Myers
- Aaron Werth
- Alexander I Wiechert
- Anees Alnajjar
- Benjamin Manard
- Brian Weber
- Brian Williams
- Bryan Maldonado Puente
- Charles F Weber
- Charlie Cook
- Christopher Hershey
- Claire Marvinney
- Corey Cooke
- Costas Tsouris
- Craig Blue
- Daniel Rasmussen
- Derek Dwyer
- Emilio Piesciorovsky
- Eve Tsybina
- Gary Hahn
- Gina Accawi
- Gurneesh Jatana
- Harper Jordan
- Isaac Sikkema
- James Klett
- Jason Jarnagin
- Joanna Mcfarlane
- Joel Asiamah
- Joel Dawson
- John Lindahl
- Jonathan Willocks
- Joseph Olatt
- Justin Cazares
- Kevin Spakes
- Kunal Mondal
- Lilian V Swann
- Louise G Evans
- Luke Koch
- Mahim Mathur
- Mariam Kiran
- Mark M Root
- Mark Provo II
- Mary A Adkisson
- Matt Larson
- Matt Vick
- Mengdawn Cheng
- Michael Kirka
- Nance Ericson
- Nolan Hayes
- Obaid Rahman
- Oscar Martinez
- Paula Cable-Dunlap
- Peter Wang
- Raymond Borges Hink
- Richard L. Reed
- Rob Root
- Ryan Kerekes
- Sally Ghanem
- Samudra Dasgupta
- Srikanth Yoginath
- T Oesch
- Tony Beard
- Vandana Rallabandi
- Varisara Tansakul
- Viswadeep Lebakula
- Yarom Polsky

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

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

High-gradient magnetic filtration (HGMF) is a non-destructive separation technique that captures magnetic constituents from a matrix containing other non-magnetic species. One characteristic that actinide metals share across much of the group is that they are magnetic.

Technologies directed to polarization agnostic continuous variable quantum key distribution are described.
Contact:
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

The development of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions.

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

Polarization drift in quantum networks is a major issue. Fiber transforms a transmitted signal’s polarization differently depending on its environment.

This invention addresses a key challenge in quantum communication networks by developing a controlled-NOT (CNOT) gate that operates between two degrees of freedom (DoFs) within a single photon: polarization and frequency.