Section
Courses
ELEG 636 Statistical Signal Processing
A first-course on the theory and applications of statistical signal processing. Topic will benefit students interested in the design and analysis of signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.
The course provides numerous examples, which illustrate both theory and applications for problems such as high-resolution spectral analysis, system identification, digital filter design, adaptive beamforming and noise cancellation, and tracking and localization. Prerequisite is a background in probability and random processes and linear and matrix algebra and exposure to basic signal processing.
Topics include
- Stationary Processes and Models
- Spectral analysis Maximum likelihood and robust estimation principles
- Eigenanalysis and steepest descent optimization
- Wiener filters and the LMS algorithm
- Method of Least Squares and the RLS algorithm
- Back-propagation learning
- Applications to beamforming, channel equalization, high resolution spectral analysis
ELEG 833 Nonlinear Signal Processing (Spring)
Nonlinear signal processing methods find numerous applications in such fields as imaging, teletraffic, communications, hydrology, geology, and economics—fields where nonlinear systems and non-Gaussian processes emerge. Within a broad class of nonlinear signal processing methods, this course provides a unified treatment of optimal and adaptive signal tools that mirror those of Wiener and Widrow, extensively presented in the linear filter theory literature. The methods detailed in this course can thus be tailored to effectively exploit nonGaussian signal statistics in a system or it’s inherent nonlinearities to overcome many of the limitations of the traditional practices used in signal processing.
Topics include
- A review of non-Gaussian models, with an emphasis on the class of generalized Gaussian distributions and the class of stable distributions
- The basic principles of order statistics
- Maximum likelihood and robust estimation principles
- Signal processing tools based on weighted medians and stack filters
- Filters based on linear combinations of order statistics and various generalizations
- Signal processing methods tailored for signals described by stable distributions
- Applications to Internet traffic, imaging, communications, time-frequency analysis, microarrays
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