Author: brucej

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Spring-Term CSCI ‘bots invade Leyburn Library

This year’s W&L Spring-Term Festival took place in Leyburn Library, and the Computer Science Department was there in force: the twelve students enrolled in CSCI 250: Introduction to Robotics demonstrated robots that flew over obstacles, followed their creators around like a pet, played a game of Tron, and obeyed commands issued from an XBox Kinect sensor.  There was even some cross-project interference, as the Kinect-driven bot tried to steal the Rovipet’s beloved green ball.

CSCI 250 also featured a field trip to the Areva Nuclear Power facility in Lynchburg, where we got to see some bigger robots in action.

               

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CSCI Flies High at the Spring Term Fair

W&L Computer Science presented results from two new spring-term courses at the Spring Term Fair on 21 May 2010. Professor Sara Sprenkle’s CSCI 335: Software Engineering through Web Applications featured a much-needed revision to W&L’s daily Campus Notices, and Prof Simon Levy’s CSCI 251: iPhone Application Development debuted some student-authored apps.  This video, courtesy of Professor Ken Lambert, shows senior Sam Reed ’10 flying a prototype AR.Drone quad-rotor helicopter from an iPad.  Sam’s work enabled flying the drone to from this new tablet device, and projecting the image from the drone’s camera to a video screen via the iPad.

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Honor’s Thesis Abstract — Pasko Paskov

A Parallel Algorithm for Derivation of Regression Coefficients on the Graphics Processing Unit

Pasko Shterev Paskov

Regression analysis is one of the most common methods of statistical inference, finding its roots into scientific research from all areas for more than two centuries. It is used widely due to its intuitive way to establish a relationship between observations of different variables, and therefore provide empirical proof for a hypothetical connection, or dependence, between them.  Regression is an invaluable tool for both research and commerce alike, and has understandably received much attention from software companies in the past two decades, as they realized the immense potential of computers to improve and facilitate the use of the method. Although the contribution of such software to the use of regression should not be understated, the massive amounts of information that have become available with the rise of the digital age has made it increasingly more time consuming, and at instances near impossible, for machines to derive the estimated coefficients of regression. This is a very computationally intensive problem, and improving the efficiency of the algorithm is crucial to time-sensitive applications of regression. The series of graphics cards introduced in the past two years has found wide recognition as providing an accessible alternative to parallel computer clusters for many applications. The architecture and parallel capabilities of the GPU entail a great potential for an improvement of regression analysis calculations. This thesis introduces a new parallel regression algorithm in CUDA for use on the GPU, and demonstrates that this algorithm is between four times faster for smaller datasets and six hundred times faster for larger, depending also on the GPU architecture.

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Honor’s Thesis Abstract — Alexander Jackson

A Parallel Algorithm for Fast Edge Detection on the Graphics Processing Unit

Alexander Jackson

Often, it is a race against time to make a proper diagnosis of a disease. In areas of the world where qualified medical personnel are scarce, work is being done on the automated diagnosis of illnesses. Automated diagnosis involves several stages of image processing on lab samples in search of abnormalities that may indicate the presence of such things as tuberculosis. These imageprocessing tasks are good candidates for migration to parallelism which would significantly speed up the process. However, a traditional parallel computer is not a very accessible piece of hardware to many. The graphics processing unit (GPU) has evolved into a highly parallel component that recently has gained the ability to be utilized by developers for non-graphical computations.

This paper demonstrates the parallel computing power of the GPU in the area of medical image processing. We present a new algorithm for performing edge detection on images using NVIDIA’s CUDA programming model in order to program the GPU in C. We evaluated our algorithm on a number of sample images and compared it to two other implementations; one sequential and one parallel. This new algorithm produces impressive speedup in the edge detection process.

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Anne Van Devender, a senior women’s soccer standout and Computer Science major, awarded 2009 Marjorie Berkley Scholar-Athlete Award as top female student-athlete in ODAC conference

wlu-van-devender-headshotFollow the links to the official ODAC article: http://odac.bridgewater.edu/scholar_ath.htm and the WLU article: http://www.wlu.edu/x32874.xml

Congratulations Anne!

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Honors Thesis Presentations: Friday, May 22

Friday, May 22
2:30 P.M.

A Parallel Algorithm for Fast Edge Detection on the Graphics Processing Unit

by Alex Jackson
Parmly Hall P405

Reception @ 2:15

AND

Friday, May 22
3:30 P.M.

A Parallel Algorithm for Derivation of Regression Coefficients on the Graphics Processing Unit

by Pasko Paskov
Parmly Hall P405

Reception @ 2:15

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CS Students to Present @ SSA

Science, Society, and the Arts (http://ssa.wlu.edu) conference has been scheduled for FEBRUARY 27, 2009. Computer Science students will be presenting as follows:

8:30 a.m. – 10:30 a.m., Poster Session I
Location: Science Center Great Hall
“Duo: An Integrated Development Environment Designed for Pair Programming”
Anne Van Devender

“Web-based Logic Tutorial”
Nicole Carter and Josiah Davis

8:30 a.m. – 10:00 a.m., Studies in Math, Computer Science and Science
Location: Reid Hall 111
“Parallel Computing in the Python Programming Language”
Alexander Jackson

“SLogo Drawing Software”
Eric Gehman, John Ivy, William Richardson, Bena Tshishiku

2:30 p.m. – 4:30 p.m., Poster Session II
Location: Science Center Great Hall
“An Empirical Study of Statistical Data Models for Effective Automated Testing of Web Applications”
Lucy Simko

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Anne Van Devender Teaches Middle School Girls

841300191Senior Anne Van Devender teaches girls from Maury River Middle School Computer Science.  As a member of WITS (Women in Technology and Science),  Anne introduced the girls to the basics of HTML through creating their own web pages.