Jun Liu

US Patent:

20190223070, Jul 18, 2019

Filed:

Jul 27, 2018

Appl. No.:

16/047131

Inventors:

- Bellevue WA, US
Terri Brooks - Prosper TX, US
Egil Gronstad - Encinitas CA, US
John Humbert - Olathe KS, US
Alan Denis MacDonald - Bellevue WA, US
Salvador Mendoza - Issaquah WA, US
Scott Francis Migaldi - Cary IL, US
Gary Jones - Montgomery TX, US
Christopher H. Joul - Bellevue WA, US
Jun Liu - Issaquah WA, US
Ming Shan Kwok - Seattle WA, US
Karunakalage Viraj Rakitha Silva - Renton WA, US

International Classification:

H04W 36/14
H04W 72/08
H04W 72/10
H04W 36/30

Abstract:

An access network can allocate a bearer for a network service associated with a quality-of-service (QoS) value (QV) and a retention-priority value (RPV), and determine a bearer ID for the service based on the QV, the RPV, and a supplemental priority value (SPV) different from the QV and from the RPV. Upon handover of a terminal, session(s) carried by a bearer allocated by the terminal can be terminated. That bearer can be selected using IDs of the bearers and a comparison function that, given two bearer IDs, determines which respective bearer should be terminated before the other. Upon handover of a terminal to an access network supporting fewer bearers per terminal than the terminal has allocated, a network node can select a bearer based on respective QVs and RPVs of a set of allocated bearers. The network node can deallocate the selected bearer.

US Patent:

20160247089, Aug 25, 2016

Filed:

Aug 24, 2015

Appl. No.:

14/834365

Inventors:

- Cary NC, US
Jun Liu - Cary NC, US
James Allen Cox - Cary NC, US

Assignee:

SAS Institute, Inc. - Cary NC

International Classification:

G06N 99/00

Abstract:

A system for machine training can comprise one or more data processors and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: accessing a dataset comprising data tracking a plurality of features; determining a series of values for a regularization parameter of a sparse support vector machine model, the series including an initial regularization value and a next regularization value; computing an initial solution to the sparse support vector machine model for the initial regularization value; identifying, using the initial solution, inactive features of the sparse support vector machine model for the next regularization value; and computing a next solution to the sparse support vector machine model for the next regularization value, wherein computing the next solution includes excluding the inactive features.

US Patent:

20140133724, May 15, 2014

Filed:

Oct 15, 2013

Appl. No.:

14/054003

Inventors:

Jun Liu - Cary NC, US
Zhili Yang - West Windsor NJ, US
Mariappan S. Nadar - Plainsboro NJ, US
Nirmal Janardhanan - Plainsboro NJ, US
Michael Zenge - Nuernberg, DE
Edgar Mueller - Heroldsbach, DE
Qiu Wang - Princeton NJ, US
Axel Loewe - Karlsruhe, DE

Assignee:

Siemens Aktiengesellschaft - Munich
Siemens Corporation - Iselin NJ

International Classification:

G06T 11/00

US Classification:

382131

Abstract:

A computer-implemented method for reconstruction of a magnetic resonance image includes acquiring a first incomplete k-space data set comprising a plurality of first k-space lines spaced according to an acceleration factor and one or more calibration lines. A parallel imaging reconstruction technique is applied to the first incomplete k-space data to determine a plurality of second k-space lines not included in the first incomplete k-space data set, thereby yielding a second incomplete k-space data set. Then, the parallel imaging reconstruction technique is applied to the second incomplete k-space data to determine a plurality of third k-space lines not included in the second incomplete k-space data, thereby yielding a complete k-space data set.

US Patent:

20140088899, Mar 27, 2014

Filed:

Sep 27, 2013

Appl. No.:

14/038958

Inventors:

Jun Liu - Cary NC, US
Hui Xue - Franklin Park NJ, US
Marcel Dominik Nickel - Erlangen, DE
Mariappan S. Nadar - Plainsboro NJ, US
Alban Lefebvre - Jersey City NJ, US
Edgar Mueller - Heroldsbach, DE
Qiu Wang - Princeton NJ, US
Zhili Yang - West Windsor NJ, US
Nirmal Janardhanan - Plainsboro NJ, US
Michael Zenge - Nürnberg, DE

Assignee:

SIEMENS AKTIENGESELLSCHAFT - Munich
SIEMENS CORPORATION - Iselin NJ

International Classification:

G01R 33/24

US Classification:

702 65

Abstract:

A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V from a singular value decomposition of A corresponding to τ leading singular values, calculating P=VV, calculating a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving Mc=(S)cfor c, where cis a vector of coil sensitivity maps for all coils at spatial location r, and