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Lufeng Shi Phones & Addresses

  • Leawood, KS
  • Olathe, KS
  • Warren, MI
  • 14402 Bellfort St, Sugar Land, TX 77478 (832) 532-7822
  • 14402 W Bellfort St APT 1222, Sugar Land, TX 77498 (832) 532-7822
  • Houghton, MI

Work

Company: Garmin international Jan 2012 Address: Olathe, KS Position: Software engineer 2

Education

Degree: Doctorates, Doctor of Philosophy School / High School: Michigan Technological University 2006 to 2011 Specialities: Computer Engineering, Philosophy

Skills

Embedded C • Bluetooth • Realtime Programming • Realview • Lauterbach • Embedded Software • Java • Bluetooth Low Energy • Sniffer

Interests

Swimming • Electronic Personal Gadgets • Cooking • Golfing

Industries

Electrical/Electronic Manufacturing

Resumes

Resumes

Lufeng Shi Photo 1

Firmware Engineer

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Location:
1445 Spruce St, Oxford, MI 48370
Industry:
Electrical/Electronic Manufacturing
Work:
Garmin International - Olathe, KS since Jan 2012
Software Engineer 2

General Motors - Warren, MI Nov 2010 - Dec 2011
Contract Research Engineer
Education:
Michigan Technological University 2006 - 2011
Doctorates, Doctor of Philosophy, Computer Engineering, Philosophy
Michigan Technological University 2006 - 2009
Masters, Computer Engineering
Michigan Technological University 2002 - 2006
Bachelors, Electronics Engineering
Skills:
Embedded C
Bluetooth
Realtime Programming
Realview
Lauterbach
Embedded Software
Java
Bluetooth Low Energy
Sniffer
Interests:
Swimming
Electronic Personal Gadgets
Cooking
Golfing

Publications

Us Patents

Enhanced Data Association Of Fusion Using Weighted Bayesian Filtering

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US Patent:
20130236047, Sep 12, 2013
Filed:
Mar 7, 2012
Appl. No.:
13/413861
Inventors:
Shuqing Zeng - Sterling Heights MI, US
Lufeng Shi - Warren MI, US
Daniel Gandhi - Auburndale MA, US
James N. Nickolaou - Clarkston MI, US
Assignee:
GM GLOBAL TECHNOLOGY OPERATIONS LLC - DETROIT MI
International Classification:
G06K 9/00
US Classification:
382103
Abstract:
A method of associating targets from at least two object detection systems. An initial prior correspondence matrix is generated based on prior target data from a first object detection system and a second object detection system. Targets are identified in a first field-of-view of the first object detection system based on a current time step. Targets are identified in a second field-of-view of the second object detection system based on the current time step. The prior correspondence matrix is adjusted based on respective targets entering and leaving the respective fields-of-view. A posterior correspondence matrix is generated as a function of the adjusted prior correspondence matrix. A correspondence is identified in the posterior correspondence matrix between a respective target of the first object detection system and a respective target of the second object detection system.
Lufeng S Shi from Leawood, KS, age ~40 Get Report