Class Time
Fall Semester
Course Description
This course emphasizes the application of theories in probability, random variables and stochastic processes in the analysis and modelling of engineering systems. Topics covered include a brief review of probability and random variables; random processes, autocorrelation and power spectral density; first order systems with stochastic inputs: AR and ARMA models, random walk; point processes: Poisson and renewal processes, counting process; discrete random processes: birthdeath processes, markov chains, elementary queuing theory; introduction to estimation theory: parameter estimation, linear estimation, spectrum estimation, Kalman filtering. Areas of application include digital communications, speech and image processing, control, radar, and Monte Carlo simulations.