Sep 27, 2022  
2019-2020 Undergraduate Academic Catalog 
2019-2020 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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EE 4250 - Advanced Signal Processing

3 lecture hours 0 lab hours 3 credits
Course Description
This course introduces students to advanced topics in signal processing. The course will focus on two main areas of signal processing: statistical signal processing and digital image processing. Adaptive filtering will be the primary focus of the statistical signal processing segment with applications such as gradient descent, LMS, and RLS algorithms. Techniques for image enhancement, restoration, and compression will be covered as applications of digital image processing. MATLAB will be used extensively as a simulation tool. (prereq: (EE 3220 or EE 3221 ) and (MA 262  or MA 3620), or consent of the instructor)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Determine the optimal filter that produces the minimum mean squared error at its output
  • Apply adaptive filtering algorithms, such as gradient search, LMS, or RLS, to various signal and noise filtering situations
  • Determine the maximum likelihood estimator for a set of randomly distributed data
  • Apply and compare two-dimensional filters to images in the spatial- and frequency-domains
  • Use nearest-neighbor or bilinear interpolation to determine the values of pixels in a resized or transformed image
  • Identify types (such as smoothing or sharpening) of image filters
  • Complete a project on a topic related to statistical and/or image processing not covered in class

Prerequisites by Topic
  • Fourier series/transform methods
  • Sampling theorem
  • Random processes and expectations
  • Linear algebra
  • Some previous use of MATLAB is desired

Course Topics
  • Prerequisite review: random variables and statistics, DSP (3 classes)
  • Statistical image processing (12 classes) - topics may include: autocorrelation functions, Wiener filter, gradient search/steepest descent, LMS algorithm, RLS algorithm, maximum likelihood estimation
  • Digital image processing (12 classes) - topics may include: 2D signals and systems, sampling, filtering, edge detection, digital image enhancement: spatial and frequency domains, digital image restoration, digital image compression
  • Final project (3 classes)

Dr. Jay Wierer

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