University of Dayton
Computer Science Department
Computer
Science Colloquium
2005/2006 Academic Year
Fall Semester
Dates: Tuesday,
Colloquium Coordinator: Dr. Jennifer Seitzer
Tuesday,
Reception: Computer Science Conference
Room
Autonomous Mobile Robot using Fuzzy
Logic
Priti K. Gaonkar
Department of Computer Science
ABSTRACT
An autonomous mobile robot
implies that the robot reaches a set destination on its own, without human intervention,
in an environment not specially designed for it. In this research a fuzzy
navigational algorithm using a mapped environment is used to develop the
intelligent system. The navigational algorithm uses a layered motion
controller. The motion controller is made up of three layers. The first layer,
which is the Orientation Layer, assists the robot in keeping it pointed in the
general direction of the goal frame to achieve the final destination. The next
layer, which is the PD (Proportional-plus-Derivative) Control Layer, directs
the robot through passageways efficiently. Depending on the type of passageways
there are different modes the robot may use: Wall-Hugging, Centering and Blind
modes. The third layer is the Obstacle
Avoidance Layer. Ultrasonic sensors are used for obstacle avoidance and to
counter unexpected changes in the environment. A basic platform has been
developed for this robot that uses a modular approach. It consists of three
separate modules which are the supervisor module, the motor driver module, and
the sensor module.
Ms. Priti
K. Gaonkar
Priti K. Gaonkar obtained her BS
in electronics engineering from the
Thursday,
Reception: Computer Science Conference
Room
Data Mining in the Real
World
Nick Payne, Ph.D.
Private Consultant
ABSTRACT
It is said that Data Mining and Statistical Data Analysis use different tools. What I hope to show is that one can and should use all the “statistical” tools when mining, just with a different mindset. Also, a major data analytic issue overlooked in both Statistical analysis and Data Mining is differences that can exist between variables of interest and recorded data. They often have different characteristics. We will examine these as well as view an application that is currently being used by the Cincinnati Police Department.
Dr. Nick Payne
Nick Payne has his
PhD in Chemical Engineering from the
Friday,
Reception:
Evolving
Pattern Recognition Systems
Dr.
Mateen Rizki
Department of Computer Science
ABSTRACT
The availability of inexpensive
sensors coupled with the rise of the internet has led to a rapid expansion in
the amount of data available for analysis. Although there are a myriad of uses
for this data, one of the most common applications is pattern recognition. The
traditional approach to creating pattern recognition systems is human intensive
requiring experts with training in pattern recognition to collaborate with
experts who have knowledge of the problem domain to develop a custom
recognition system for a specific problem. In contrast, our work has focused on
the design and implementation of software tools and techniques that use
evolutionary computation to synthesize recognition systems from raw data. Thereby
reducing the personnel requirements and time needed to deploy new recognition
systems. In this talk, I will give an overview of evolutionary computation,
describe our approach for evolving recognition systems, and present results
obtained using our techniques for a variety of problems in image processing,
radar target identification and gene marker selection.
Dr. Matt Rizki
Dr. Mateen Rizki received his B.S. degree in computer science from the
Past colloquia:
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