Ohio State University Columbus, OH Jun 2011 to Jun 2012 Undergraduate Research AssistantState Grid Corporation of China Yixing, CN Jun 2010 to Sep 2010 Technical Assistant (internship)Yixing Yushun Electronic Technology Yixing, CN Jun 2009 to Sep 2009 Assistant Hardware Engineer (internship)
Education:
University of California, Los Angeles Los Angeles, CA 2014 MS in Electrical EngineeringThe Ohio State University Columbus, OH 2012 BS in Electrical and Computer Engineering
Skills:
Linear Programming, Convex Optimization, Adaptive Filtering, Linear Optimal Estimation, Stochastic Processes, System Control, Power systems, Power electronics, Feedback Control, Linear Dynamic Systems
University of Southern California Los Angeles, CA May 2012 to Aug 2012 Research project: Hands-on wafer processing fabricationYuyao Xindongyuan Plastic & Chemical Co., Ltd. Yuyao, Zhejiang, China May 2006 to Aug 2011 Intern as a quality inpectorEast China University of Science and Technology
Jan 2010 to Jun 2010 Research project: Study of the Modification of Bismaleimide (BMI) ResinEast China University of Science and Technology
Apr 2009 to May 2009 Research project: Research on Electric Conductivity of Three Kinds of Polyaniline/Inorganic Acid Hybrids
Education:
University of Southern California Los Angeles, CA 2011 to 2013 Master of Science in Materials EngineeringEast China University of Science and Technology 2006 to 2010 Bachelor of Engineering in Polymer Materials and Engineering
Tucson Pathology Associates PCTucson Pathology Associates 350 N Wilmot Rd, Tucson, AZ 85711 5208733929 (phone), 5208733742 (fax)
Education:
Medical School Wenzhou Med Coll, Wenzhou, Zhejiang, China Graduated: 1987
Languages:
English
Description:
Dr. Zhang graduated from the Wenzhou Med Coll, Wenzhou, Zhejiang, China in 1987. She works in Tucson, AZ and specializes in Hematology and Cytopathology.
- St. Michael, BB Yu Zhang - Thousand Oaks CA, US Shuimei Zhang - Oak Park CA, US Xin Zhang - Agoura Hills CA, US
International Classification:
H04B 7/0456 H04B 17/11 H04B 17/21
Abstract:
This document describes techniques and systems for independent transmit and receive channel calibration for multiple-input multiple-output (MIMO) systems. Antenna responses are collected from each virtual channel of a MIMO system at an angle respective to an object. The transmit components and the receive components of the virtual channels are separated and organized into vectors (one for the transmit components and one for the receive components). Calibration values for elements of the vectors are computed and maintained in a transmit calibration matrix and a receive calibration matrix, respectively. Together, the transmit calibration matrix and the receive calibration matrix may include fewer elements than a calibration matrix for the virtual channels and, therefore, may require less memory and fewer computations to calibrate a MIMO system than using other calibration techniques. As such, described is a less expensive and less complex way to calibrate MIMO system by accurately approximating an ideal antenna array.
A transparent display device that can be mounted to existing computer cases with a window. Once the device is connected, software guides users to align the inside edges of their case to create a 3D effect inside the case. The software allows users to add their custom components so that the 3D effects can interact with those components. An example would be animated waves will appear to splash against a graphics card instead of moving through it. The software will also have a store allowing users to find and download additional animation, 3D effects, music visualizers, and customize their machine by combining all these elements.
Linear Prediction-Based Bistatic Detector For Automotive Radar
- St. Michael, BB Zhengzheng Li - Agoura Hills CA, US Yu Zhang - Thousand Oaks CA, US
International Classification:
G01S 7/02 G01S 13/58 G01S 13/931
Abstract:
The disclosure provides systems, apparatuses, and techniques for operating automotive MIMO radars in crowded multi-path environments to obtain reliable detections by linearly predicting whether a bistatic condition occurred. To avoid saturating computing resources processing bistatic detections, the described techniques enable a radar system to quickly identify and discard from the field-of-view radar detections that are likely a result of bistatic conditions. By ignoring unusable radar returns that are likely a result of bistatic conditions, an example radar system can focus on processing radar returns from static conditions, for example, in providing radar-based detections as output to an automotive system that is driving a vehicle in an autonomous or a semi-autonomous mode. In so doing, the example radar system provides a highly accurate static object detector that is sufficiently quick in detecting bistatic conditions for use in vehicle-safety systems as well as autonomous and semi-autonomous control.
Radar System With Paired One-Dimensional And Two-Dimensional Antenna Arrays
This document describes techniques and systems of a radar system with paired one-dimensional (1D) and two-dimensional (2D) antenna arrays. Even with fewer antenna elements than a traditional radar system, the paired arrays enable an example radar system to have a comparable angular resolution at a lower cost. For example, the 1D array includes antenna elements positioned in a first direction (e.g., azimuth direction) and spaced by a first distance and a second distance. The 2D array includes at least four other antenna elements positioned in the first direction and a second direction (e.g., elevation direction). The other antenna elements are spaced by a third distance in the second direction and by the sum of the first direction and the second direction in the first direction. A processor can associate, using shared angle estimates, angles in the first direction and the second direction for respective objects.
- St. Michael, BB Yu Zhang - Thousand Oaks CA, US Edgar Ortiz - Newbury Park CA, US Clinton Colby - Santa Rosa Valley CA, US
International Classification:
G01S 13/90 G06T 5/00 G06T 5/20 B60W 50/06
Abstract:
This document describes techniques for enabling de-aliased imaging for a synthetic aperture radar. Radar signals processed by a synthetic aperture radar (SAR) system may include false detections in the form of aliasing induced by grating lobes. The techniques described herein can reduce the adverse effects of grating lobes by obtaining an initial SAR image using a back-projection algorithm. Aliasing effects (e.g., false detections) in this initial image may be common due to the limitations of an SAR system moving at non-uniform speeds. A refined image is produced from the initial image by applying a de-aliasing filter to the initial image. The refined image may have reduced or eliminated false detections that attribute to aliasing effects, resulting in a better representation of the environment of the vehicle.
Two-Step Code Generator For Phase Coded Frequency Modulated Continuous Wave (Fmcw) Multi Input Multi Output (Mimo) Radar
- St. Michael, BB Yu Zhang - Thousand Oaks CA, US Xin Zhang - Calabasas CA, US
International Classification:
G01S 13/32 G01S 7/35
Abstract:
A two-step optimization method for scheduling transmissions in an MIMO (multi-input multi-output) includes determining a first phase code for each transmission according to a first equation, placing each first phase code in a set of first phase codes, and determining a cost function of the set of first phase codes, determining a second phase code for each transmission according to a second equation, determining an updated cost function corresponding to replacing each of the first phase codes with a corresponding one of the second phase codes, and determining which set of phase codes has a smaller cost function.
INTERNATIONAL BUSINESS MACHINES CORPORATION - , US Deepak S. Turaga - Hawthorne NY, US Yu Zhang - Los Angeles CA, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - ARMONK NY
International Classification:
G06N 99/00
US Classification:
706 12
Abstract:
A method of meta-learning includes receiving a prediction objective, extracting a plurality of subsets of data from a distributed dataset, generating a plurality of local predictions, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective, combining the plurality of local predictions, and generating a final prediction based on the combined local predictions.
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Zhang Yu (2000-2001), Roger Busser (1980-1985), Hunter Ledbetter (1977-1981), Carrie Brokaw (1998-1999), Dennis Zamec (1960-1965), Kelly Dzendzel (1996-2000)
Googleplus
Yu Zhang
Work:
IBM (2009) Hewlett-Packard (2007-2009) Huawei (2005-2007)
Education:
Changchun university
Yu Zhang
Work:
Lenovo Group - Staff Researcher China Academy of Space Technology - System Designer (2011-2012)
Education:
Beijing University of Aeronautics and Astronautics - Human Factor
Yu Zhang
Work:
MicroStrategy - SE (2011)
Education:
Zhejiang University - SC
About:
码农
Bragging Rights:
工作了!!
Yu Zhang
Education:
Beijing University of Posts and Telecommunications - Optics, Nankai University - Optical Information Science and Technology, Nankai University - Material Chemistry