EE210: Engineering Analysis I
This is a sophomore-level undergraduate course dealing with some basic theoretical concepts in engineering math and using Matlab to apply these concepts in practice. The theoretical topics to be covered are linear algebra (matrix and vector mathematics) and probability theory (including random variable and random processes). Student will learn the Matlab programming/analysis as part of this class, using it to apply the theoretical concepts learned to practical applications.
EE312: Signals & Systems I
The basic properties of signals and systems are covered as well as common transformations including the continuous and discrete Fourier series and transform representations. The Laplace transform is also covered along with a basic introduction to Nyquist sampling. Matlab is used to introduce students to signal processing and communications applications of the material.
EE395: Introduction to Digital Signal Processing
This is an undergraduate course in digital signal processing. Topics to be covered here include sampling/reconstruction, LTI discrete-time systems, discrete-time Fourier and z-transforms, digital filter design and realization, and DSP applications.
EE545: Digital Signal Processing II
This is a first year graduate course in digital signal processing. Topics to be covered here include advanced sampling, Fourier and z-transforms, digital filter design and realization, implementation issues, and an introduction to multirate signal processing, 2D signal processing, and adaptive filtering. An undergraduate course in DSP is required.
EE555: Advanced Linear Systems
This is an advanced level class that studies linear systems and the associated mathematical theory. The coverage of this class includes linear equations, spectral theory, normal matrices, projections, quadratic forms, and dynamical systems (both discrete and continuous time).
EE565: Pattern Recognition
This is an advanced, graduate level class in pattern recognition/classification. The major focus is statistically-based techniques, although other techniques like k-nearest neighbor and neural networks are also covered in detail. Matlab-based programming problems will be assigned.
EE573: Signal Compression
This is an introductory graduate course in signal and data compression (i.e., source coding). We will cover the basics of lossless compression techniques (e.g., entropy coding) and various lossy quantization techniques. We will also study rate-distortion analysis of lossy compression systems. Finally, we will
study how these basic techniques are combined to create standardized image, video, and audio compression algorithms like JPEG, JPEG 2000, and MPEG.
EE585: Telemetering Systems
Integration of components into a command and telemetry system. Topics include analog and digital modulation formats, synchronization, link effects, and applicable standards.
EE 594: Adaptive Filtering
Graduate level treatment of time-adaptive filtering. Covers LMS, RLS, and Kalman filtering both from a theoretical and an application viewpoint.
EE595: Multirate & Wavelets
Advanced course in digital signal processing dealing with multirate systems, filter banks, and wavelets. Applications will also be covered.
EE596: Image Processing
This is a first year graduate course in digital image processing. Topics to be covered here include image enhancement, image restoration, and image compression (Chapters 2-6 are covered along with parts of Chapters 7 and 8). Ability to program in Matlab is expected coming into the class.