Xiao L Chen

6463053, Oct 8, 2002

Dec 1, 1998

09/201997

Xiao L. Chen - Plano TX

Nortel Networks Limited - St. Laurent

H04L 1266

370352, 370401

A method for a user of telecommunication services to dynamically interchange routing paths between a carrier network circuit and the Internet for connecting to a destination. A user code is allocated which marks a connection request as requesting switching/routing via an IP network. When the code is detected by a carrier switch, the call connections are routed through the IP network. The user is provided with the option of terminating the IP network connection and selecting carrier network connection without call interruption.

20170371017, Dec 28, 2017

Jun 22, 2017

15/629779

- Erlangen, DE
Hasan Ertan Cetingul - Fulton MD, US
Boris Mailhe - Plainsboro NJ, US
Mariappan S. Nadar - Plainsboro NJ, US
Xiao Chen - Princeton NJ, US

G01R 33/56

A system and method including receiving magnetic resonance (MR) imaging data from a first MR scanner device, the MR imaging data including data for a plurality of MR scans of different structural or anatomical regions; generating, based on the MR imaging data, normalized reference data including statistical information for each MR scan; learning a transformation, based on the normalized reference data, to correlate a set of input MR imaging data to the normalized reference data; and storing a record of the transformed imaging data.

20170372193, Dec 28, 2017

May 16, 2017

15/596124

- Erlangen, DE
Hasan Ertan Cetingul - Fulton MD, US
Benjamin L. Odry - West New York NJ, US
Xiao Chen - Princeton NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

G06N 3/02
G06T 5/00
G06T 7/00

For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.