Jun Liu

US Patent:

20020122828, Sep 5, 2002

Filed:

Mar 2, 2001

Appl. No.:

09/798603

Inventors:

Jun Liu - Murray Hill NJ, US

International Classification:

A61K009/14
A61K009/16
A61K009/50

US Classification:

424/497000

Abstract:

Hybrid porous materials useful for releasing bioactive materials in response to an external stimulus at a constant release rate are formed of a porous inorganic gel having a polymer network integrated within the pores of a porous gel. The hybrid porous material is made by forming a mixture of a polymer precursor, a cross linking agent, an initiator, an alcohol, a ceramic precursor, water and an acid. The mixture is then formed either by first polymerizing the polymer precursor to form a polymer network, and then forming the ceramic precursor into a porous inorganic gel, or by first forming the ceramic precursor into a porous inorganic gel and then polymerizing the polymer precursor to form a polymer network. Either approach will yield a porous inorganic gel having a polymer network integrated within the pores of the porous gel.

US Patent:

20110205359, Aug 25, 2011

Filed:

Feb 19, 2010

Appl. No.:

12/709192

Inventors:

Kuo Chu LEE - Princeton Junction NJ, US
Hasan Timucin OZDEMIR - Plainsboro NJ, US
Xiangjun SHI - Plainsboro NJ, US
Lipin LIU - Belle Mead NJ, US
Namsoo JOO - East Brunswick NJ, US
Juan YU - Cranbury NJ, US
Jun LIU - Ewing NJ, US

Assignee:

PANASONIC CORPORATION - Osaka

International Classification:

H04N 7/18

US Classification:

348143, 348E07085

Abstract:

A video surveillance system is disclosed. The system includes a model database storing a plurality of models and a vector database storing a plurality of vectors of recently observed trajectories. The system includes a model building module that builds a new motion model corresponding to the motion data of the current trajectory data structure. The system generates a current trajectory data structure having motion data and abnormality scores. The system also includes a database purging module configured to determine a subset of vectors that is most similar to the current trajectory data structure based on a measure of similarity between the subset of vectors and the current trajectory data structure. The database purging module is further configured to replace one of the motion models in the model database with the new motion model based on an amount of vectors in the subset vectors the recentness of the subset of vectors.

US Patent:

20130097593, Apr 18, 2013

Filed:

Sep 18, 2012

Appl. No.:

13/622048

Inventors:

Nishkam Ravi - Lawrenceville NJ, US
Jun Liu - Princeton NJ, US
Srimat T. Chakradhar - Manalapan NJ, US

Assignee:

NEC Laboratories America, Inc. - Princeton NJ

International Classification:

G06F 9/45

US Classification:

717158

Abstract:

A method for compiler-guided optimization of MapReduce type applications that includes applying transformations and optimizations to Java bytecode of an original application by an instrumenter which carries out static analysis to determine application properties depending on the optimization being performed and provides an output of optimized Java bytecode, and executing the application and analyzing generated trace and feeds information back into the instrumenter by a trace analyzer, the trace analyzer and instrumenter invoking each other iteratively and exchanging information through files.

US Patent:

20130121554, May 16, 2013

Filed:

Sep 14, 2012

Appl. No.:

13/616484

Inventors:

Jun Liu - Plainsboro NJ, US
Jeremy Rapin - Paris, FR
Alban Lefebvre - Plainsboro NJ, US
Mariappan S. Nadar - Plainsboro NJ, US
Michael Zenge - Nurnberg, DE
Edgar Müller - Heroldsbach, DE

International Classification:

G06T 11/00

US Classification:

382131

Abstract:

A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.

US Patent:

20130289912, Oct 31, 2013

Filed:

Feb 28, 2013

Appl. No.:

13/779999

Inventors:

Jun Liu - Plainsboro NJ, 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 - Ithaca NY, US
Zhili Yang - West Windsor NJ, US
Nirmal Janardhanan - Plainsboro NJ, US
Michael Zenge - Nurnberg, DE

Assignee:

Siemens Aktiengesellschaft - Munchen
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 2D 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

US Patent:

20130320974, Dec 5, 2013

Filed:

Dec 18, 2012

Appl. No.:

13/717842

Inventors:

Jun Liu - Plainsboro NJ, US
Jeremy Rapin - Paris, FR
Alban Lefebvre - Jersey City NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

Assignee:

Siemens Corporation - Iselin NJ

International Classification:

G01R 33/48

US Classification:

324309

Abstract:

A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying WW=I, wherein I is an identity matrix, a regularization parameter λ≧0, and a counter limit k, initializing a variable z=Wv, and intermediate quantities p=q=0, calculating y=arg min1/2∥z−(p+z)∥+λ∥z∥for 0≦i≦k, wherein z denotes values of an MR image sought to be reconstructed, updating p=(p+z)−y, updating z=arg min1/2∥z−(q+z)∥+g(z), whereinand updating q=(q+y)−z, wherein x=Wz is a solution ofthat specifies a reconstruction of the MR image.

US Patent:

20140037228, Feb 6, 2014

Filed:

Jul 25, 2013

Appl. No.:

13/950535

Inventors:

Alban Lefebvre - Jersey City NJ, US
Axel Loewe - Monmouth Junction NJ, US
Mariappan S. Nadar - Plainsboro NJ, US
Jun Liu - Plainsboro NJ, US

Assignee:

SIEMENS CORPORATION - Iselin NJ

International Classification:

G06T 1/20

US Classification:

382304

Abstract:

A computer-implemented method for calculating a multi-dimensional wavelet transform in an image processing system comprising a plurality of computation units includes receiving multi-dimensional image data. An overlap value corresponding to a number of non-zero filter coefficients associated with the multi-dimensional wavelet transform is identified. Then the multi-dimensional image data is divided into a plurality of multi-dimensional arrays, wherein the multi-dimensional arrays overlap in each dimension by a number of pixels equal to the overlap value. A multi-dimensional wavelet transform is calculated for each multi-dimensional array, in parallel, across the plurality of computation units.

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.