Dec 22, 2024  
2019-2020 Undergraduate Academic Catalog 
    
Catalog Navigation
  Catalog Home
  Academic Calendar
  University Overview
  Enrollment Management Department
  Academic Regulations and Policies
  Student Financial Services
  Academic Departments, Undergraduate Degree Programs, Minors and Certificates
  Degree Programs, Minors and Certificates
  University Scholars Honors Program
  Reserve Officers’ Training Corps. (ROTC)
  English as a Second Language
  Course Descriptions
  Administration, Board of Regents, Faculty and Advisory Committees
  Campus Map
  Student Resources
  My Portfolio
HELP
2019-2020 Undergraduate Academic Catalog [ARCHIVED CATALOG]

Facebook this Page (opens a new window)
Tweet this Page (opens a new window)
Add to Portfolio (opens a new window)

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)
Coordinator
Dr. Jay Wierer


Facebook this Page (opens a new window)
Tweet this Page (opens a new window)
Add to Portfolio (opens a new window)