Experimental laws, network theorems, and computer analysis techniques of electrical circuit analysis. Network responses to various forcing functions using time-domain and phasor-domain methods. This is for students enrolled in this course on the PSU campus.
Survey of modern approaches to estimating the state of linear and nonlinear dynamic systems. Topics include linear systems theory, the Kalman filter, the extended Kalman filter, unscented Kalman filter, and the particle filter. Lectures and assignments will include theory, implementation, and applications of these methods. Designed to give a solid introduction and fundamental understanding of the advantages, limitations, and tradeoffs for each of these methods.
Introduction to continuous time and discrete time systems. Thorough exposure to the Laplace transform for circuit and system analysis, transfer functions, bode plots, analog filters, and two-port networks.
Prerequisite: ECE 221, ECE 201; Mth 256 or concurrent. Section for on-campus students. Section for IIIST students. Continuous-time and discrete-time Fourier series, continuous-time Fourier transform, discrete-time Fourier transform, fast Fourier transform, sampling, aliasing, communications, modulation, discrete-time filters.
Prerequisite: ECE 222. This is the IIIST section for this course.
Unified introduction to the theory, implementation, and applications of statistical signal processing methods. Focus on estimation theory, random signal modeling, characterization of stochastic signals and systems, and nonparametric estimation. Designed to give a solid foundation in the underlying theory balanced with a discussion of the practical advantages and limitations of nonparametric estimation methods. Introduction to electric circuits and the basics of circuit analysis; for students in electrical, computer, mechanical and civil engineering.
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