1 - 10 of 14 Results
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.
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.
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.
The lattice collimator places a grid of shielding material in front of a radiation detector to reduce the effect of background from surrounding materials and to enhance the RPM sensitivity to point sources rather than distributed sources that are commonly associated with Natur
Pyrolysis evolved gas analysis – mass spectrometry (EGA-MS) and pyrolysis gas chromatography – MS (GC-MS) – are powerful analytical tools for polymer characterization.
We have developed an aerosol sampling technique to enable collection of trace materials such as actinides in the atmosphere.
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).