ECE 4110

ECE 4110

Course information provided by the Courses of Study 2021-2022.

Introduction to models for random signals in discrete and continuous time; Markov chains, Poisson process, queuing processes, power spectral densities, Gaussian random process. Response of linear systems to random signals. Elements of estimation and inference as they arise in communications and digital signal processing systems.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: ECE 2200 and ECE 3100, or equivalents.

  • Knowledge of a variety of mathematical models for random phenomena.
  • Ability to classify models with respect to stationarity, Markov property, asymptotics, and more.
  • Ability to make optimal inferences and estimates with respect to such criteria as minimum error probability, and minimum mean square error.
  • Become aware of applications to communications, machine learning, statistical physics and more.
  • Response of linear systems to random process inputs (time permitting).

View Enrollment Information

  •   Regular Academic Session.  Choose one lecture and one discussion. Combined with: ECE 5110

  • 4 Credits Graded

  • 11338 ECE 4110   LEC 001

  • Instruction Mode: In Person

  • 11339 ECE 4110   DIS 201

  • Instruction Mode: In Person