Capstone Final Exam Schedule

 

Final Examination Schedule

PLEASE JOIN US AS THE FOLLOWING CANDIDATES PRESENT THEIR CULMINATING WORK.

Winter 2017

 

Tuesday, February 21

Xiaolin Ma
Chair: Dr. Hazeline Asuncion
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; DISC 464
Optimizing the Performance of LDA-GA Funnel Using a Parallel Computing Technique

Data provenance refer to the process of tracing and recording the origins of data and its movement between databases. LDA-GA program serves as a funnel in the data provenance reconstruction multi-level funneling model. The model has a very good precision and recall, but it is extremely time consuming. In this project, the single-threaded program is optimized with parallel computing techniques. The optimized program can work simultaneously on different machines. On each machine, the computation is carried out multi-threadedly.

By making use of multiple cores on multiple machines, the performance of LDA-GA program is improved greatly. Comparing with the single-threaded version, the multi-threaded one has a multiple times better performance.
 

Wednesday, March 8

Abirami Ramanathan
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
10:00 A.M.; DISC 464
Spatiotemporal Vehicle Tracking, Counting and Classification

Detecting and tracking vehicles from transportation surveillance videos is essential for applications ranging from traffic queue detection, volume calculation to incident and vehicle identification. However, challenges including object occlusion and shadowy condition often lead to poor detection and tracking performance. In this project, we propose a new approach to tackle these challenges. It is done in four stages: background subtraction, vehicle segmentation, shadow detection and removal, and vehicle tracking. Further, we apply the results of the above approach to perform vehicle classification and vehicle counting. Experimental results show that our method is simple, robust and effective to work on videos with occlusion issues and under various illumination/weather conditions..        

 

Questions? Please email Megan Jewell, CSS Graduate Advisor at mjewell@uw.edu

Masters Candidates

Abirami Ramanathan
Xiaolin Ma


 

Final Examination Archive

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Winter 2016