Seeking a challenging position as a tester, which allows me to both further utilize my previous experience and acquire new abilities.
- 4 year+ experienced on Matlab & Simulink
- 2 years+ JAVA
- 6 months on Python teaching
- 2 years+ C, SQL 2008/2012, VBA
- Experience with Qualcomm tools like QXDM, QPST, QCAT
- OS: Windows, Linux(Ubuntu),iOS, Android
- Knowledge of Wireless communication, TCP/IP, UDP/IP, CDMA, 802.11a/b/g/n/ac, OFDM/MIMO and Call processing
- Good skill on writing test scenario
- Proficient in office computer application
Confidential, Kansas City, KS
- Android phone function testing, performance testing, pressure testing and FOTA testing.
- Set up and implement project testing plans and testing strategies
- Testing of 3rd party companies to complete required independent testing.
- Analyze carrier’s requirements and compliances to improve testing cases data bases, drug ADB logs for analysis, learn projects lesson and manage project history bugs
- Work with R&D team, HQ testing Team and Sale Team to make projects TA on time
- Use QXDM, QPST, QCAT to pull logs, debug and trouble - shoot
- Write bug scenario include: WIFI and Bluetooth.
- Be responsible for Chameleon testing on OMA platform
- Driving Test on ZTE 810,9510,9520
- Assist professor with lab work and research, mainly focus on implementing image processing algorithm and collecting/ preparing experiment data.
- Conduct scientific literature survey and top conference paper presentation.
Python and Math Tutor Student Success Center
- Teach 3 students with Algebra & teach 3 students with Python
- Help student with trouble-shooting, analysis bugs and debugging.
Face Recognition via Sparse Representation
- Mainly using the well-known l1-norm constrained least-square reconstruction minimization technique to realize face recognition. The training set is constructed as follows: each subject is presented in the training with many images representing various lighting conditions, so that a probe image (testing image) of certain illumination conditions can be represented by a sparse linear combination of the training samples.
Audio Graphic Equalizer:
- The system decomposes the input into high frequency and low frequency components by using linear phase high-pass and low-pass filters. Different gains are introduced to the filter outputs to emphasize or attenuate the corresponding frequency band components. The resultant signals of the two channels are added together to form the final output.