GRADUATE PROGRAM IN COMMUNICATIONS AND

SIGNAL PROCESSING

Dr Joseph Noonan

Program OutlineWe have developed within the Electrical & Computer Engineering Department a graduate program in Communications and Signal Processing, offering a concentration in this area for both the M.S. and Ph.D. degrees. Central to the program are four (4) Communications courses:

EE 107 and EE 108: Communications Systems I and II EE 227: Information Theory EE 229: Detection and Estimation Theory.

These four courses form the core for a curriculum in Communications at the graduate level. Surrounding the core curriculum are varied supplemental courses drawn from the Electrical Engineering, Computer Science, Mathematics, and Physics Departments, depending on the particular interests of the individual student.

Research Summary

• This program is directed by Professor Joseph P. Noonan. Dr. Noonan has thirty years experience in the areas of digital signal processing, probabilistic and statistical modeling and statistical communications theory. His primary research activities concern application of statistical communication theory and digital signal processing to the problems of optimal detection and estimation of signals in noise. Specific application areas include Channel Modeling (Blind Equalization), Spectral Estimation, Image Enhancement, Deconvolution, Time Varying System Modeling, Radar Signal Processing, and Biomedical Signal Processing.

Research

• One major area of research concerns the application of concepts from Information Theory to the important problems of deconvolution and system modeling.This work has applications ranging from communications and image processing to biomedical engineering applications such as epilepsy modeling and prediction.

• Other research includes Blind Equalization for Digital Communication Channels and new wavelet based estimation techniques for transient signals in noise and radar signal detection.

Research

• We are currently continuing research on these areas with the objective of establishing as general a framework as possible for optimal estimation and detection under the umbrella of Information Theory.

Research Areas

• Optimal Detection and Estimation

• High Resolution Estimation • Wavelets for Communications, Signal

Processing, and Stegenographics• Image Processing• Chaos Theory for Communications• Signal Modeling for Medical Diagnostics• Channel Equalization

Wavelet Application

Image Enhancement

Blind Equalization

Stegenographics

EPILEPTIC DETECTION

This study attempts to answer whether wavelet transform analysis improves the ability of subdural, interictal EEG recordings to define seizure foci. Invasive, subdural recordings taken from six patients with focal seizures were studied. All EEG segments examined were interictal and free of epileptiform transients. The EEG data were examined using the continuous wavelet transform (CWT). Leads over seizure foci wee compared to leads uninvolved with seizure activity via a correlation coefficient. From the correlation coefficients, t-values were calculated. Comparisons of seizure (S) versus non-seizure (NS) channels yielded an average absolute t-value of 13.6748, indicating a statistically Significant different between such leads. Conversely, comparisons between S and S channels resulted in an average absolute t-value of only 1.5572. These findings indicate a large, statistically significant difference between leads over seizure foci with those uninvolved in the seizure onset. WT detected such differences despite that fact that none could be distinguished on visual inspection of the EEG waveforms. These results indicate that an inherent difference exists between interictal waveforms taken from the region of the seizure focus versus those distant from the seizure onset, and that WT may be an appropriate tool to help detect such differences.