Electrical Engineering - EE
Fundamentals of signals and systems, mathematical theory of continuous and discrete systems, linear time invariant systems, linear time varying systems, state space model and approaches, stability, controllability and observability, minimal realizations. Co-requisite: ENEE 620.
This is a first-year graduate course for communication and signal processing majors in electrical engineering (EE) that covers the fundamentals of digital signal processing (DSP). The goal of this course is to provide the first-year EE graduate student with the foundations and tools to understand, design and implement DSP systems, in both hardware and software. MATLAB and SystemView will be the primary vehicles to provide the student with hands-on DSP design and simulation experience. The student also will acquire an understanding of DSP hardware basics and architecture. Topics covered include: (1) A/D-D/A conversion and quantization, number representations and finite wordlength effects; (2) FIR, IIR and lattice filter structures, block diagram and equivalent structures; (3) multi-rate DSP and filterbanks; (4) digital filter design methods and verification; (5) DSP hardware architecture and (6) DSP simulation/laboratory experiences. Prerequisites: ENEE 601, ENEE 620 or their equivalent or permission of instructor.
Fundamentals of probability theory and random processes for electrical engineering applications and research: set and measure theory and probability spaces; discrete and continuous random variables and random vectors; probability density and distribution functions and probability measures; expectation, moments and characteristic functions; conditional expectation and conditional random variables; limit theorems and convergence concepts; random processes (stationary/non-stationary, ergodic, point processes, Gaussian, Markov and secondorder); applications to communications and signal processing. Prerequisite: Undergraduate probability course work or consent of instructor.
Fundamentals of detection and destimation theory for statistical signal processing applications: theory of hypothesis testing (binary, multiple and composite hypotheses and Bayesian, Neyman Pearson and minimax approaches); theory of signal detection (discrete and continuous time signals; deterministic and random signals; white Gaussian noise, general independent noise and special classes of dependent noise, e.g. colored Gaussian noise; signal design and representations); theory of signal parameter estimation: minimum variance unbiased (MVU) estimation, Cramer-Rao lower bound, general MVU estimation, linear models, maximum likelihood estimation, least squares, general Bayesian estimators (minimum mean-square error and maximum a posterior estimators); linear Bayesian estimators (Wiener filters) and Kalman filters. Prerequisite: ENEE 620 or consent of instructor.
Fundamentals of solid-state physics for the micro-electronics field: review of quantum mechanics and statistical mechanics, crystal lattices, reciprocal lattices, dynamics of lattices, classical concepts of electron transport, band theory of electrons, semiconductors and excess carriers in semiconductors. Prerequisite: Consent of instructor.
Principles of semiconductor device operation: review of semiconductor physics, p_n junction diodes, bipolar transistors, metal semiconductor contacts, JFETs and MESFETs, and MIS and MOSFET structures. Prerequisite: ENEE 630 or consent of instructor.
Fundamentals of dynamics in electromagnetic theory: theoretical analysis of Maxwell’s equations, electrodynamics, plane waves, waveguides, dispersion, radiating systems and diffraction. Prerequisite: Consent of instructor.
Introduction to basic theory of lasers: introduction to quantum mechanics and timedependent perturbation theory, interaction of radiation and matter, stimulated and spontaneous emissions, rate equations, laser amplification and oscillation, noise in lasers and laser amplifiers, semi-conductor lasers. Prerequisite: ENEE 680 or consent of instructor.