Seattle Nephrology & Endocrinology 1130 N 185 St STE 201, Seattle, WA 98133 206 542-1000 (phone), 206 542-5353 (fax)
Education:
Medical School Yonsei Univ, Coll of Med, Sudai Moon Ku, Seoul, So Korea Graduated: 1991
Conditions:
Diabetes Mellitus (DM) Disorders of Lipoid Metabolism Hypothyroidism Non-Toxic Goiter Overweight and Obesity
Languages:
English Korean
Description:
Dr. Kim graduated from the Yonsei Univ, Coll of Med, Sudai Moon Ku, Seoul, So Korea in 1991. She works in Shoreline, WA and specializes in Endocrinology, Diabetes & Metabolism and Diabetes. Dr. Kim is affiliated with Kindred Hospital Seattle Northgate, Northwest Hospital & Medical Center and Swedish Edmonds Hospital.
Academic Services and Assessment at Lincoln Memorial University
Location:
Harrogate, Tennessee
Industry:
Higher Education
Work:
Lincoln Memorial University since Oct 2005
Academic Services and Assessment
Oklahoma State University College of Osteopathic Medicine 2004 - 2005
Administrative Director, OMECO
Education:
University of Oklahoma Health Sciences Center 1996 - 1999
MPH, Health Administration and Policy
Teach For China since Jun 2013
Teaching Fellow
Korea Institute for International Economic Policy - Seoul, South Korea Jun 2012 - Jul 2012
Research Assistant
US Embassy Seoul - Seoul, South Korea Dec 2011 - May 2012
Economic Affairs Intern
POPBAR - Greater New York City Area May 2011 - Jul 2011
Business Intern
US Committee for Human Rights in North Korea - Washington D.C. Metro Area Sep 2010 - Apr 2011
Research Analyst
Education:
Georgetown University | Edmund A. Walsh School of Foreign Service 2009 - 2013
Bachelor of Science, International Economics, Concentration: Development
The Chinese University of Hong Kong 2011 - 2012
International Asian Studies Program, International Economics
Shanghai International Studies University 2010 - 2010
Tenafly High School 2005 - 2009
High School Diploma
Georgetown University
Interests:
Program Evaluation, Development Policy, Econometric Modeling, Chinese Economy, Asian Financial Infrastructure, Emerging Markets
Joohee Kim - Atlanta GA, US Russell M. Mersereau - Atlanta GA, US Yucel Altunbasak - Norcross GA, US
Assignee:
Georgia Tech Research Corp. - Atlanta GA
International Classification:
H03M 13/35 H04L 1/02
US Classification:
714774, 370436, 370437, 714820, 714821
Abstract:
Methods and systems for streaming data in a network. Whether the network is experiencing high packet loss may be determined by a rate control module. If high packet loss is experienced, data is encoded into multiple streams by a coder using temporal domain partitioning. If high packet loss is not experienced, then data may is encoded by using frequency domain partitioning. Unequal error protection is applied to each of the streams so more important bit planes in a bit of a stream are provided with more error protection than less important bit planes. The streams are transmitted along, respectively, independent paths to a decoder. The streams are decoded, and errors in the decoded streams are corrected by using information from one or more of the other decoded streams. The decoded corrected streams are reconstructed into the data.
System For Detecting Pedestrians By Fusing Color And Depth Information
Maziar LOGHMAN - Chicago IL, US Joohee KIM - Oak Brook IL, US
Assignee:
ILLINOIS INSTITUTE OF TECHNOLOGY - Chicago IL
International Classification:
G08G 1/16 G06T 7/00 H04N 13/02
Abstract:
A region of interest (ROI) generation method for stereo-based pedestrian detection systems. A vertical gradient of a clustered depth map is used to find ground plane and variable-sized bounding boxes are extracted on a boundary of the ground plane as ROIs. The ROIs are then classified into pedestrian and non-pedestrian classes. Simulation results show the algorithm outperforms the existing monocular and stereo-based methods.
Multi-Resolution Depth Estimation Using Modified Census Transform For Advanced Driver Assistance Systems
Maziar Loghman - Chicago IL, US Joohee Kim - Oak Brook IL, US
International Classification:
G06T 7/00 H04N 13/02 G06T 5/00
Abstract:
A computer-implemented depth estimation method based on non-parametric Census transform with adaptive window patterns and semi-global optimization. A modified cross-based cost aggregation technique adaptively creates the shape of the cross for each pixel distinctly. In addition, a depth refinement algorithm fills holes within the estimated depth map using the surrounding background depth pixels and sharpens the object boundaries by exerting a trilateral filter to the generated depth map. The trilateral filter uses the curvature of pixels as well as texture and depth information to sharpen the edges.
Low-Complexity Depth Map Encoder With Quad-Tree Partitioned Compressed Sensing
Ying Liu - North Tonawanda NY, US Joohee Kim - Oak Brook IL, US
International Classification:
H04N 19/597 H04N 13/00 H04N 19/129
Abstract:
A variable block size compressed sensing (CS) method for high efficiency depth map coding. Quad-tree decomposition is performed on a depth image to differentiate irregular uniform and edge areas prior to CS acquisition. To exploit temporal correlation and enhance coding efficiency, the quad-tree based CS acquisition is further extended to inter-frame encoding, where block partitioning is performed independently on the I frame and each of the subsequent residual frames. At the decoder, pixel domain total-variation minimization is performed for high quality depth map reconstruction.