**Ph.D. Program** (Electrical Engineering)

**ACADEMIC COURSEWORKS** (Jan'00 - Present)

**1. Advanced Digital Signal Processing **(ECE 6250)

An introduction to advanced signal processing methods that are used in a variety of applications areas.

**2. Linear Systems and Controls **(ECE 6550)

Introduction to linear system theory and feedback control. Topics include state space representations, controllability and observability, linear feedback control.

**3. Optimal Estimation **(ECE 6555)

Techniques for signal and state estimation in the presence of measurement and process noise with the emphasis on Wiener and Kalman filtering.

**4. Advanced Digital Communications **(ECE 6603)

The theory and practice of efficient digital communications over linear dispersive channels, including adaptive equalization and synchronization.

**5. Personal and Mobile Communications **(ECE 6604)

To introduce various topics that are fundamental to cellular mobile telephone systems.

**6. Coding Theory and Applications **(ECE 6606)

To introduce the theory and practice of error control coding, with emphasis on linear, cyclic, convolutional, and parallel concatenated codes.

**7. Research Seminar **(ECE 8010)

Seminar presentations describing Related research projects, centers, and other activities at Georgia Tech.

**8. Doctoral Thesis **(ECE 9000)

N/A

**9. Topics in Linear Algebra **(MATH 4305)

Description Finite dimensional vector spaces, inner product spaces, least squares, linear transformations, the spectral theorem for normal transformations. Applications to convex sets, positive matrices, difference equations.

**10. Algebra II **(MATH 6122)

Description Graduate level linear and abstract algebra including rings, fields, modules, some algebraic number theory and Galois theory. (2nd of two courses)

**11.** **Linear Statistical Models **(MATH 6266)

Basic unifying theory underlying techniques of regression, analysis of variance and covariance, from a geometric point of view. Modern computational capabilities are exploited fully. Students apply the theory to real data through canned and coded programs