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1st Student's Major

Computer Information Science

1st Student's College

Science, Engineering and Technology

Students' Professional Biography

Shauna Smith is a senior majoring in Computer Science with a minor in Mathematics at Minnesota State University, Mankato. During her enrollment at MSU, Mankato, she has been involved with the IMPACT Concert Company, the Minnesota State University Ski and Snowboard club, intramural softball teams, and the College of Science, Engineering, and Technology Student Advisory Board. She has also been employed with the Computer Science and Information Systems and Technology departments as both a lab instructor and tutor for various undergraduate level classes since she was a sophomore. During the fall 2008 semester, she was employed by Walter’s Publishing Company in Waseca, MN to develop graphical user interface tests for the EZBook yearbook software. She has also had two previous internships at IBM in Rochester, MN. The first internship included IBM i and web-based programming for manufacturing software support. During the second internship, she was part of a highly selective and innovative Extreme Blue Speed Team that developed new server technologies and tools to improve the administration of VoIP products. After graduation in December, she hopes to get an innovative and challenging job in the area of software development.

Mentor's Name

Rebecca Bates

Mentor's Email Address

rebecca.bates@mnsu.edu

Mentor's Department

Integrated Engineering

Mentor's College

Science, Engineering and Technology

Other Mentors

Deborah Nykanen

Abstract

A previous research study conducted at Michigan Technological University by Dr. Deborah Nykanen and her colleague Dr. Daniel Harris analyzed storm data in order to develop algorithms that will allow coarse resolution rainfall forecasted by weather models to be optimally used in high resolution hydrology models with the goal of improving stream flow predictions and early detection algorithms that can be used to warn communities about potential flash floods. This research was performed by analyzing a series of independent radar images derived from Weather Surveillance Radar-1988 Doppler (WSR-88D) data obtained from Dr. James A. Smith at Princeton University using a series of computer programs written by the original researcher and her colleagues. The program was run using a sequential algorithm that can take up to 17 hours to execute. Because of the structure of the problem, there was an opportunity for applying concurrent computing techniques to the program code. In order to speed up the program execution time, several different concurrent computing approaches have been applied to the code. Speedup analysis has been conducted for each different concurrent approach improving the code execution time by up to a factor of 93. The analysis results show how different concurrent approaches affect the speedup of code. The faster code will aid in analyzing future storm data, allowing more data to be analyzed in a shorter amount of time, and will eventually be used in improving lead time on high resolution stream flow predictions and flash flood warnings. The speedup provided by the concurrent computing approaches has been verified on previously analyzed data.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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