ECE 4110

Course information provided by the Courses of Study 2017-2018.

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 equivalent.

Outcomes
  • Knowledge of a variety of mathematical models for random phenomena.
  • Ability to classify such models as to issues of stationarity, Markovianness, kinds of asymptotic behavior, and sample function continuity and differentiability.
  • Ability to make optimal inferences and estimates with respect to such criteria as minimum error probability, and least mean square error (e.g., Wiener and Kalman filtering). Elements of optimal design are introduced.
  • Response of linear systems to random process inputs.
  • Be aware of common applications of such models to communication systems, sources of noise such as thermal noise, behavior of queues and particle emission systems.

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Enrollment Information
Syllabi: 1 available
  •   Choose one lecture and one discussion.

  • 4 Credits Graded

  • 11294ECE 4110  LEC 001

  • 12135ECE 4110  DIS 201