Electric & Computer Engineering

This course introduces microprocessor architecture and microcomputer systems including memory and input/output interfacing. Topics include low-level language programming, bus architecture, I/O systems, memory systems, interrupts, and other related topics.

This is an introductory course which covers basic theories and techniques of digital VLSI design in CMOS technology. In this course, we will study the fundamental concepts and structures of designing digital VLSI systems include CMOS devices and circuits, standard CMOS fabrication processes, CMOS design rules, static and dynamic logic structures, interconnect analysis, CMOS chip layout, simulation and testing, low power techniques, design tools and methodologies, VLSI architecture.

Presents the physical layer of wireless communication systems, implementation of speech coding, error control, modulation/demodulation and filtering schemes for wireless links using digital signal processors for base band functions.Prerequisite: EECE-453.Course Offering: Spring Semester

Consists of telecommunications systems design for point-to-point and mass data distribution, modulation techniques, propagation modes and control methods.Prerequisite: EECE-453. Course Offering: Spring Semester

Presentation of current engineering topics by faculty, students, and individual guest speakers

Ordinary differential equations, finite differences and their applications to engineering problems. Fourier series and integrals, Laplace transform, partial differential equations, Bessel & Legendre polynomials.

Vector calculus, vector fields, dyadics, tensors, boundary value problems, solution to linear homogeneous boundary value problems, separation of variables and Green's functions, two-dimensional potential problem and informational mapping, introduction to non-linear differential equations and variations and perturbation methods.

Elementary constrained optimization, optimum operating strategies, control system structure, megawatt frequency control, voltage control, optimum systems control, power-pool control, contingency analyses and power systems state estimation.

Computer application to operation, control and analysis of power systems. Load flow, load forecasting, unit commitment, load scheduling, network modeling, fault study, transient stability analysis, reliability, future expansion of systems, security and contingency analysis, on-line dispatch techniques and state estimation in power systems.

Overview of artificial intelligence, representation of knowledge, rule based expert systems, introduction to expert systems languages such as LISP, OPS, and PROLOG, basic concepts of fuzzy theory, relations, regression models, mathematical programming, neural networks, learning architectures, application of neural networks and expert systems, fuzzy systems to control, communications and power systems.

System equations, system representation, control system characteristics, root locus, frequency response, closed loop performance, root locus compensation and cascade and feedback compensation.

System equations, system representation, control system characteristics, root locus, frequency response, closed loop performance, root locus compensation and cascade and feedback compensation

Axioms of probability measure, random variables, functions of random variables, stochastic processes, stationary and ergodic processes, correlation and power spectrum, linear mean-square estimation, and applications.

The course covers theory and methods for digital signal processing including basic principles governing the analysis and design of discrete-time systems as signal processing devices. DSP is a logical extension of Signals and Systems in which we take a comprehensive view of discrete-time systems.

The purpose of this course is for the design and performance of research leading to a Masters. See note on page 577 related to thesis hours.

State variable description of dynamic systems, solutions of differential and difference equations by transition matrix, controllability and observability of linear systems, perturbation of nonlinear systems, stability of nonlinear systems, Liapunov's direct method, realization of transfer matrices by state equations, state and output feedback, pole assignment using state and output feedback reconstruction of state from output.

Theorems of extremum, applications of the theorem, illustrative problem, theorems on necessary conditions for extremum of functions and functionals, theorems on sufficient conditions for extremum of functions and functionals, simplex method for solving linear programming problems, dynamic programming and decomposition theorem, and non-linear optimization.

Statistical detection theory, hypothesis testing, optimum decision rule, Bayes criterion, Nyaman- Pearson criterion, minmax testing, multiple observation, composite hypothesis testing, sequential detection.

Gaussian and Markov processes, stochastic differential equations, single and multiple observation decision theory, Bayesian estimation theory, maximum likelihood estimation, optimum linear filtering, smoothing and prediction, nonlinear estimation.

This course is designed to introduce emerging topics related to cybersecurity challenges and practical cyber-defense/countermeasures in networked Cyber-Physical Systems (CPS) and Internet-of-Things (IoT). The course will cover fundamental concepts, technologies, theoretical understanding and practical basis for cybersecurity of networked CPS/IoT. Graduate students will complete an independent research project which involves a written and oral presentation not required at the undergraduate level.

This is an intermediate-level research methods course. The goals of this course is to help students fully understand basic concepts and techniques of quantitative empirical research and to stimulate their interests to learn more about quantitative research. At the end of the course, students will be not only equipped with basic analytical techniques, but also able to to plan their own empirical research.

This course will be structured around recent (and a few not-so-recent) research papers related to resource allocation in data networks. It is loosely organized into three topics: access pricing, combining resources from heterogeneous network technologies, and the role of content providers. See page 577 for additional information related to Special Topics courses.

This course will be structured around recent (and a few not-so-recent) research papers related to resource allocation in data networks. It is loosely organized into three topics: access pricing, combining resources from heterogeneous network technologies, and the role of content providers. See page 577 for additional information related to Special Topics courses.

Deregulated electrical power systems, system security, investments in generation and transmission, ancillary services, and nodal pricing.

This course is designed to introduce computational methods used for power grid operation and planning. The course will help students understand the various computational methods that form the basis of major commercial software packages used by grid analysts and operators. Students are expected to have some basic understanding of principles of power system analysis including power system models, power flow calculation, economic dispatch, reliable and stability analysis. See page 577 for additional information related to Special Topics courses.

The purpose of this course is to take students from a point at which they have general ideas about their dissertation topic through the development of a solid structure, research strategy and drafting of framing chapters. See pg. 577 for additional information.

In this course, classical and modern optimization techniques are covered in sufficient detail to allow students to use them in almost any engineering and non-engineering areas. The focus of the course is on electrical power systems applications. Electrical power industry is going through a major transformation and relies on optimal planning and operations to increase energy efficiency, lower energy cost and address environmental concerns.