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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.
Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.
The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
Through the use of splicing methods, joining two different fiber types in the tow stage of the process enables great benefits to the strength of the material change.
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).