NEW TECHNICAL ELECTIVE COURSE

EE 499 Vector Space Methods in Signal Processing

    Prerequisites: MATH260, EE230

     Co-requisites: EE 430

A new technical elective course on signal processing is offered in the Spring semester.

The main goal of this course is to bridge the gap between introductory signal processing classes (EE430) and the mathematics prevalent in signal processing research and practice.

The course provides a unified applied treatment of fundamental mathematics, including linear algebra, probability theory, and optimization, for important signal processing and estimation problems such as denoising, deconvolution, compression, filter design, smoothing and prediction.

  • Fourth-year students from all specialization fields other than power systems area can take this course as a technical elective.
  • Undergraduate and graduate students, who need to access the signal processing research literature or widely used deterministic and stochastic signal processing methods, can benefit from the contents of the course.
  • One third of the course will provide a unified treatment of vector-space concepts in relation to signal processing, one third will focus on the use of these concepts for deterministic problems, and the remaining will cover the probabilistic frameworks for signal processing applications.

Please check ODTUSyllabus program for more details about the course. If you have further questions, feel free to contact Assist. Prof. Figen S. Oktem.

Course Objective 1: Students will gain an understanding of basic vector space methods prevalent in signal processing research and practice.

Student Learning Outcomes:

  • Demonstrate a knowledge of vector-space concepts in relation to signals and systems
  • Gain essential knowledge for solving linear equations using linear algebraic approaches as well as statistical approaches

Course Objective 2: Students will be able to apply the learned mathematical tools and algorithms to real-world signal processing problems.

Student Learning Outcomes:

  • Integrate and use vector space tools for signal processing applications
  • Develop realistic solutions for important signal processing and estimation problems