Courses

Courses instructed by Dr. Willis are provided in the list below. For some courses, detailed information regarding the lecture material has also been provided.

Subcategories

Offered Spring 2012: Signals and Systems

ECGR 2103 Computer Utilization in C++ (3 credit hours)

An introduction to the use of computers and computing methods to solve engineering problems.  Structures and object-oriented programming design using C++.

Offered Spring 2012: Signals and Systems

ECGR 3111 (3 credit hours)

Prerequisite: ECGR 2112 with a C or better. Analysis of continuous-time signal and systems. Input-output relationships of linear time-invariant systems. Transient and steady state analysis. Frequency domain descriptions and Fourier analysis. Analysis and characterization of LTI systems using Laplace transforms.

Offered Fall 2005-2012: Digital Signal Processing

ECGR 4124/5124 (3 credit hours)

Prerequisite: ECGR3111 Signals or Systems with a grade of C or better. Sampling and signal recovery in linear systems; analysis of sampled systems; discrete and fast Fourier transforms; z-transform; discrete convolution; design of FIR and IIR filters.

Offered Spring 2005,2008: Applied Computer Graphics

ECGR 4103/5103 (3 credit hours)

Prerequisite: Permission from department. Interactive graphics; raster, character, vector, graphics, display technologies; rotation, scaling, translating of graphics image; image processing / enhancement; feature extraction; 3-D graphics; hidden lines. Credit will not be given for ECGR5103 where credit has been given for ECGR4103.


Applied Artificial Intelligence

 

ECGR 6119/8119 (3 credit hours)

The theory of machine intelligence. Computational methods for modeling machine intelligence including machine vision and automatic decision making from sensor measurements. Applications of this theory to autonomous robotic decision making such as navigation and industrial quality control.

Medical Signal Processing

ECGR 6127/8127 (3 credit hours)

Prerequisites:  ECGR 3122 or equivalent. Fourier methods of medical signal processing. Physics of image formation for different medical imaging modalities including: planar x-ray, computerized tomography (CT), magnetic resonance imaging, and ultrasound. Signal processing techniques for image reconstruction, enhancement, and multimodal fusion. Credit will not be given ECGR 6127 where credit has been given for ECGR 8127. (Spring)

Spring 2008 Special Topics Course : Pattern Recognition

 

ECGR 4090/6090/8090 (3 credit hours)

This course instructs students in the use probablistic and statistical mathematics to develop computational models for the purpose of recognizing patterns in low-dimensional and high-dimensional spaces. Course topics include probability theory, introductory information theory, linear regression models, linear classifiers. Advanced topics in pattern recognition are also discussed and will vary each year.

Spring 2008 Special Topics Course : Pattern Recognition II

 

ECGR 6090/8090 (3 credit hours)

This course instructs students in the use probablistic and statistical mathematics to develop computational models for the purpose of recognizing patterns in low-dimensional and high-dimensional spaces. Course topics include probability theory, introductory information theory, linear regression models, linear classifiers. Advanced topics in pattern recognition are also discussed and will vary each year.

Fall 2008 Special Topics Course : Recognition from 2D and 3D Images

 

ECGR 4090/6090/8090 (3 credit hours)

This course is intended to teach students how to process images for the purpose of recognizing objects within images. The course will deal primarily with 2D images of natural scenes, i.e., images taken from a conventional digital camera, and with 3D images, i.e., images that represent the geometry of a scene as a collection of (x,y,z) coordinates.

The primary textbook for the course will be Trucco and Verri's Introductory Techniques for 3-D Computer Vision (publisher: Prentice Hall, 1998,ISBN-10: 0132611082, ISBN-13: ISBN-13: 978013261108).

To see what other students have thought of this text look here.

Fall 2007 Special Topics Course : Computer Vision

 

ECGR 4090/6090/8090 (3 credit hours)

This course builds upon image and signal processing concepts and introduces the field of computer vision, modeling human perception with computers. Topics covered include image formation, radiometry, photometry, shading, 3D coordinate systems, homogeneous coordinates, stereoscopic 3D reconstruction, elementary differential geometry, and algorithms for processing 3D range and mesh surface data.

Spring 2006 Special Topics Course : Design of Intelligent Spacecraft

 

ECGR 3090/6090/8090 (3 credit hours)

This is an inter-disciplinary course that integrates concepts from mathematics, physics, engineering and computer science to educate students on the design of intelligent spacecraft. Course instruction takes a new tact best summarized by the expression: All science was new at some point. This approach augments class topics with historic context and, in some cases, facsimiles of original works such as Galileo's theory on planetary motion. Course topics include mathematical models of planetary motion and heat transfer and how these models are used in designing intelligent spacecraft, i.e., robotic systems which can autonomously perform complex space-mission tasks.

Fall 2006 Special Topics Course : Computer Vision / Pattern Recognition

 

ECGR 6090/8090 (3 credit hours)

This course builds upon image and signal processing concepts and introduces the field of computer vision, modeling human perception with computers. Topics covered include image formation, radiometry, photometry, shading, 3D coordinate systems, homogeneous coordinates, stereoscopic 3D reconstruction, elementary differential geometry, and algorithms for processing 3D range and mesh surface data.

Multidimensional Stochastic Signal Processing

ECGR6090/8090 (3 credit hours)

Prerequisites: ECGR 3111 or permission of Department. Review of probability, univariate and multivariate distribution functions, noise modeling, least-squares estimation, non-linear optimization, Markov chains, Bayes theorem; applications. (On demand)