EE515 – Advanced Digital Signal Processing

This course examines the properties of signals and systems, sampling, data acquisition, Discrete-Time Fourier Transform (DTFT), Discrete Fourier Transform (DFT), Z-transform theory, spectral analysis, digital filter design and discrete transforms.  Practical applications of digital signal processing will be emphasized with a number of hands on MATLAB Programming exercises.

EE561 - Random Signal Analysis, W, 11:10-1:40PM

Mathematical techniques for analysis and measurement of random signals and processes needed as a foundation for work in radar/sonar, communication theory, or detection, and estimation.  Probability; random variables; correlation functions and power spectra stationarity, ergodicity; linear and nonlinear systems with random inputs.  Each part of the course includes computer exercises using MATLAB performed by the student to demonstrate and reinforce the concepts learned in the class.

EE 671 Statistical Signal Processing

Binary Hypothesis Tests Detection Criteria, Performance, ROC, M hypotheses, Estimation Theory, Random Parameters, Bayes Estimation, Real Parameter Estimation, Multiple Parameter Estimation, Composite Hypotheses, the General Gaussian Problem. Representation of Random Processes, Deterministic Functions, Orthogonal Representations, Random Process Characterization, Homogeneous Integral Equations and Eigenfunctions, Periodic Processes, Vector Random Processes, Detection and Estimation of Signal Parameters, Detection and Estimation in AWGN, Detection and Estimation in Nonwhite Gaussian Noise.  The Course is accompanied with MATLAB Projects.

ENGR 520- Math Analysis for Grad. Students

The course is concerned with the introduction to first-order and higher-order differential equations, Laplace Transform, matrices, systems of linear differential equations, orthogonal functions and Fourier Series and boundary-value problems.

EE 634- Fundamentals of Digital Image Processing

This course deals with the fundamentals of the major topics of digital image processing.  The topics used in the course include the two-dimensional systems and mathematical preliminaries, image sampling and quantization, image transforms, stochastic models, image enhancement, filtering, restoration, reconstruction, and compression.  This course is accompanied with MATLAB projects.

EE 621: Fundamentals of Kalman Filtering and Smoothing (Prereq.: EE 561)

This course covers basic problem of state estimation (prediction, Kalman filtering, smoothing), the steady-state Kalman filtering for linearized state variable model, and state estimation for the "not-so-basic" state estimation.  The state estimation is also discussed for nonlinear models.  Each part of the course includes computer exercises using MATLAB performed by the student to demonstrate and reinforce the concepts learned in the class. These exercises include: implementation of the Kalman filter, and applications of the Kalman filtering and smoothing.

ENGR 652:  Advanced Optical and Image Processing:

(Prereqs: EE 311 Signals and systems and EE 362 Analog and Digital Signal Processing or equivalent).

This course deals with advanced techniques in digital and optical image processing. The topics covered in the course include (a) image acquisition, (b) image enhancement techniques such as filtering using the two-dimensional Fourier domain, edge detection, intensity transformation, and neighborhood processing, (c) image restoration through noise smoothers, geometric transformation, Wiener filters and the inverse problem, (d)  interpolation and resizing, (e) image registration which includes super-resolution,  image mosaicking and image fusion, color demosaicking, stereo imaging for 3D depth map and motion analysis, (f)  color and pseudo color and PCA, (g) Morphological image processing for medical imaging and fingerprint analysis, (h) 3D Imaging using tomography, and (i) 3d imaging using digital holography (if time permits).

ENGR 502: Introduction to Optics:

In recent years, photonics has found increasing applications in areas such as communications, image processing, sensing and displays. The objective of this course is to provide a thorough survey of this rapidly expanding and important area of electrical engineering. This course will cover the primary theories of light including ray optics, diffraction, as well as the interaction of light with matter, polarization, Fourier optics, lasers and coherence. Practical applications of photonics such as imaging systems, holography, fiber optics, laser and detectors will be covered.

ENGR 505: Optical Waveguides:

Light propagation in slab, cylindrical, and rectangular wave guides. Signal dispersion and attenuation in optical fibers. Perturbation and effective index techniques will be also discussed. Coupled mode theory and its applications as well as Beam propagation method (BPM) will be introduced.

ENGR 518 Experimental Techniques for Graduate Students; 3.0 credits

This course introduces students to the different aspects of experimental research in engineering. The course will cover fundamental issues such as: planning and design of an experimental campaign, laboratory safety, data acquisition and signal processing. State-of-the-art experimental techniques in different areas of engineering research will also be presented, with focus on modern, non-intrusive, laser-based measurement methods. The goal is provide students with the knowledge required to plan, design and conduct an experimental campaign, which they can eventually apply to their own research efforts.

ENGR 550: Semiconductor Optoelectronics - Materials and Devices:

This graduate-level course covers the principles and operation of various electro-optic devices such as semiconductor light sources, photo-detectors, image detectors, acousto-optic devices and light switches, and how they are used to design electro-optic systems.

EE 527: Fundamentals of Neural Networks:

Introduces basic concepts of neural networks using the general framework of parallel distributed processing. Deals with architecture, principles of operation, training algorithms and applications of a number of neural networks. Each part of the course includes computer exercises using MATLAB performed by the student to demonstrate and reinforce the concepts learned in the class.

ENGR 520: Math Analysis for Grad. Students:

This course covers the needed analytical skills in applied mathematics for graduate studies. It discusses both theorems and applied examples of six topics: ODEs, Laplace & Fourier Transforms, PDEs, Vector Calculus and Coordinates, Matrix Theory, and Complex Analysis

ENGR 516 Computational Methods for Graduate Students:

Discretization methods (finite differences, finite volumes, finite elements), stability and convergence; parabolic, hyperbolic, and elliptic PDEs: model equations and numerical solutions method. Numerous programming exercises will be assigned.

EE 540: Introduction to Antenna Systems

A review of electromagnetics is given with an emphasis on concepts needed for antenna theory (i.e., Vector Potentials, Free Space Green's functions, etc.). Basic concepts such as directivity, gain, bandwidth, beam width, polarization, and aperture size are defined and discussed. Antennas are presented from a circuit theory perspective. Case studies of dipole and loop antennas are developed to illustrate the analytical techniques. Radiation from apertures is also presented. Antenna arrays are treated and basic concepts such as scanning, amplitude distributions, grating lobes, and beam squint are introduced. The uniform and binomial distributions are treated in depth. The course concludes with a discussion of measurement techniques.

EE 541: Electromagnetic Theory

Theory of electromagnetic field equations and their application to wave propagation in waveguides and resonant structures. Includes partially filled waveguides, corrugated guide, and other structures.

EE 542: Antennas and Propagation for Wireless Communications

This course addresses issues related to wireless communications from a perspective of antennas and propagation. The electromagnetic theory and communications components of wireless communication systems are linked together for analyzing and designing such systems. The important role of antennas in setting up cellular communication systems is studied and critical propagation issues in the design of such systems are presented. Topics that will be discussed in the course include cellular communications history and principles, basic concepts in electromagnetic wave theory, reflection, transmission and polarization, antennas and radiation, Fresnel Theory, line-of-sight, models for radio propagation, flat earth, terrain roughness, diffraction theory, propagation in presence of buildings, fading, diversity, link budgets, system design issues.

EE 543: Intro to Remote Sensing and Imaging Applications

This course addresses the theory and principles of passive and active remote sensing at different frequencies. The course emphasis is on electromagnetic phenomena rather than image processing techniques for the remotely captured data. Topics include wave propagation and scattering from targets and natural surfaces, basic antenna systems, radiometry and the radar equation. Effects of different media and boundaries such as rough surfaces on wave characteristics (e.g. dispersion, reflection, refraction, attenuation) are discussed.

EE 544: RF and Microwave Circuits

This course is an introduction to the analysis and design of RF and microwave circuits from the perspective of distributed circuit theory. Fundamental concepts of impedance matching, network theory, S-parameters, coupled line theory, and system noise are discussed in relation to the design of passive and active components used in wireless communication circuits and mixed-signal circuits. Topics covered in the course include transmission lines, impedance matching, filters, power dividers, couplers, resonators, oscillators, mixers, active microwave transistor amplifiers, and design techniques for optimizing system gain, bandwidth, noise figure, and input/output impedance. Industry-standard microwave CAD tools will be used to analyze and design circuits. Students will fabricate several circuits in planar microstrip and learn how to characterize circuit performance on network/spectrum analyzer instruments to complete the design learning cycle.

EE 545: Basics of Computational Electromagnetics

This course is designed to provide undergraduate/graduate students in electrical engineering, with special interest in subject areas related to antennas, radar, and electromagnetic compatibility studies, necessary computational skills and tools useful in their professional career.

EE 551: Method of Moments in Electromagnetics

This course is designed to provide graduate students in electrical engineering (with specialization in Electromagnetic Theory) with numerical and computational skills and concepts to tackle electromagnetic wave phenomenon, radiation and scattering in the frequency domain.

EE 552: Mathematical Analysis for Electromagnetics

This course covers various mathematical methods useful for solving electromagnetic field problems. The topics covered are: a) Vector calculus: Green's theorem, line, surface and volume integrals, Divergence and curl of vectors, Divergence and Stokes theorem. b) Partial Differential Equations: Separation of variables, Fourier series and integrals. Separation in Cylindrical and Spherical coordinates. c) Greens functions for non-homogenous partial differential equations: Construction of Green's functions for Poisson and Wave equations.

EE 660: Electromagnetic Radiation and Scattering

Formerly EE 746 Introduces advanced electromagnetic theory with emphasis on radiation and scattering theory. Field equations derived for radiation and scattering problems and applied to simple antennas and bodies. Geometrical optics and geometrical theory of diffraction are presented for antenna problems, edge diffraction, and scattering from simple conducting bodies. Prereq.: EE 541 or equivalent.