COURSE LISTING
Electrical
Engineering:
The
following conventions are used for numbering graduate courses in different areas
of electrical engineering (x stands for a digit in the range 0-9):
60x:
61x, 71x, 81x: Signal Processing
62x, 72x, 82x: Communications
63x, 73x, 83x: Microelectronics
64x, 65x, 74x, 75x, 84x, 85x: Computer Engineering
68x, 78x, 88x: Photonics
69x, 79x, 800, 89x: Research and Independent Study
ENEE
601 Signal and Linear Systems Theory
Credits: 3
Course Description: 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.
ENEE
608 Graduate Seminar
Credits:
0
This
course exposes the graduate student in EE to the current research in areas of
interest to the department's faculty and students. The speakers are usually
researchers outside, as well as inside, the department and university. On
occasion, speakers may be faculty members or advanced students. There are no
credits for this course, which meets once a week, but all graduate students are
required to attend (one semester for M.S. students and two semesters for Ph.D.
students).
ENEE 610
Digital Signal Processing
Credits: 3
Course
Description: 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 will also 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) Multirate DSP and filterbanks; (4) Digital filter design methods
and verification; (5) DSP hardware architecture; and (6) DSP
simulation/laboratory experiences.
Prerequisites: ENEE 601, 620, or their equivalent, or permission of
instructor.
ENEE 611 Adaptive Signal Processing
Credits:
3
Fundamentals
of adaptive filters and associated algorithms: Mean-square error and least
squares approaches; steepest-descent algorithm; the least-mean-square adaptive
filters, recursive least-squares adaptive filters, frequency-domain and subband
adaptive filters, and unsupervised adaptive filters; analysis of these adaptive
filters, and discussion of selected applications.
Prerequisites:
ENEE 601 or 610, and 620, or consent of instructor.
ENEE 612 Digital Image Processing
Credits:
3
Principles
of two-dimensional processing of image data: fundamentals of 2D signal
processing, image transforms, image enhancement, image filtering and
restoration, color image processing, image coding and wavelet quantization,
image thresholding and segmentation, image interpretation and recognition,
applications of image processing.
Co-requisite:
ENEE 620; Prerequisites: MATLAB, or consent of instructor.
ENEE
620 Probability and Random Processes
Credits:
3
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 second order); applications to communications and signal processing. Prerequisite: Undergraduate probability or consent of instructor.
Prerequisite:
Undergraduate probability or consent of instructor.
ENEE 621 Detection and Estimation Theory I
Credits:
3
Fundamentals
of detection and estimation 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 posteriori estimators),
linear Bayesian estimators (Wiener filters), and Kalman filters.
Prerequisite:
ENEE 620 or consent of instructor.
ENEE 622 Information Theory
Credits:
3
Shannon’s
information measures: entropy, differential entropy, information divergence,
mutual information, and their basic properties. Entropy rates. Asymptotic
equipartition property. Weak and strong typicality. Joint typicality.
Shannon’s source coding theorem and its converse. Prefix-free and uniquely
decodable source codes. Huffman and Shannon codes. Universal source coding.
Source-coding with a fidelity criterion: the rate-distortion function and its
achievability. Channel capacity and its computation. Shannon’s channel coding
theorem. Strong coding theorem, error exponents. Fano’s inequality and the
converse to the coding theorem. Feedback capacity. Joint source-channel coding.
Discrete-time additive Gaussian channels. The covering lemma. Continuous-time
additive Gaussian channels. Parallel additive Gaussian channels: waterfilling.
Additional topics: Narrowband time-varying channels, fading channels, side
information, wideband channels. Network coding. Information theory in relation
to statistics and geometry.
Prerequisites:
Strong grasp of basic probability theory
ENEE
623 Communication Theory
Credits:
3
A
review of the Shannon capacity of the discrete-time additive Gaussian channel.
Continuous-time additive Gaussian channels. Elementary signal design principles:
baseband and passband pulse amplitude modulation, matched filtering, geometric
representation of signals, optimum receivers. Orthogonal signaling and
performance analysis: Shannon capacity, reliability function, and cut-off rate.
RS and BCH codes. Hard- and soft-decision decoding. Capacity approaching codes.
Signaling in the band-limited region: Shannon capacity, pulse shaping, lattice
codes, trellis codes, multilevel
coding, constellation shaping. Equalization and precoding for linear Gaussian
channels; waterfilling, multicarrier signaling. Additional topics: Signaling in
fading media, multisensor and multiuser communications, synchronization.
Prerequisites:
ENEE 601, ENEE 621, and ENEE 622
ENEE 624 Error Correcting Codes
Credits:
3
Fundamentals
of error correction coding theory: linear block and trellis codes, decoder
structures, random and burst error detection and correction techniques,
encoding/decoding performance and bounds, concatenated codes and interleaving
structures, and turbo and LDPC codes and iterative
Prerequisites:
ENEE 620 and 622 or 623, or consent of instructor.
ENEE 625 Data Compression
Credits:
3
Principles and techniques of data
compression: review of source coding theory; lossless data compression
techniques such as Huffman coding, bit-plane coding, predictive coding,
arithmetic coding, and LZW coding; and lossy data compression techniques such as
transform coding, wavelet transform coding, scalar quantization, vector
quantization, and sub-band coding.
Prerequisites:
ENEE 620 and 622, or consent of instructor.
ENEE 630 Solid-state Electronics
Credits:
3
Fundamentals
of solid-state physics for the microelectronics 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.
ENEE 631 Semiconductor Devices
Credits:
3
Principles
of semiconductor device operation: review of semiconductor physics, p‑n
junction diodes, bipolar transistors, metal semiconductor contacts, JFETs and
Prerequisite:
ENEE 630, or consent of instructor.
ENEE 632 Integrated Circuits
Credits:
3
Fundamentals
of bipolar and MOS analog and digital integrated circuit techniques: basic IC
structure and fabrication, passive components, bipolar transistors and diode,
characterictics matching, temperature compensation, output stages, frequency
analysis, OpAmps., voltage regulators, multiplers, PLLs, MOS digital and analog
circuits, memories, A/D converters, CMOS logic circuits.
Prerequisite:
ENEE 630, 631 or consent of instructor.
ENEE 634 Microwave Device and Circuit Design
Credits:
3
Basic
concept and knowledge of microwave devices and integrated circuits for wireless
communications, transmission lines and lumped elements, impedance matching
networks, hybrids, couplers, filters, multiplexers, oscillators, amplifiers,
detectors, and mixers, microwave tubes or frequency multiplers, MMIC, and
laboratory.
Prerequisites:
ENEE 681 or consent of instructor.
ENEE 635 Introduction to Optical
Communications
Credits:
3
Introduction
to basic principles of optical communications: Optical fibers, transmitters,
receivers, optical system design and performance, optical amplifiers, and
multi-channel communication systems.
Prerequisite:
ENEE 630, or consent of instructor.
ENEE 636 Introduction to Wireless Communications
Credits:
3
Introduction
to wireless communication systems, the cellular concept, mobil radio
propagation: large-scale path loss and small-scale fading and multipath,
modulation techniques, equalization, diversity, compression, multi-access
techniques, wireless networking and wireless systems and standards
Prerequisite:
consent of instructor.
ENEE 660 Systems Engineering Principles
Credits: 3
This is a first semester, required, graduate course for systems engineering (EE) majors that covers the introduction to systems engineering. The course will address: (1) systems engineering principles (2) systems engineering methodologies (3) integration of technical disciplines and (4) systems engineering management. The goal of this course is to provide the beginning graduate student with the foundational framework to understand requirements and capabilities based design and how the traditional systems engineering process may need to adjust to accommodate these philosophies. The content of the course will result from the decomposition of system life cycle phases to illustrate the many engineering specialties and disciplines that are required to systematically engineer, deploy and sustain complex systems for missions to be performed in aerospace and electronics domains. The intent is to achieve a balance between understanding the system engineering process and its execution under differing design or acquisition philosophies.
Prerequisite: B.S. degree in EE or related field; Co-requisite: none
ENEE 661 System Architecture and Design
Credits 3
This is a required graduate course for the systems engineering (SE) track within the MSEE program. The course content includes both theoretical and practical considerations for the development of a system architecture and hardware and software system design within the overall systems engineering process. Major topics include development of an operational concept, functional decomposition, top-down vs. bottom up techniques, requirements allocation and partitioning, interface definition, inclusion of integrity, reliability, and maintainability within the design concept, validation and verification. The use of technical performance budgeting, quality function deployment techniques, and statistical and linear models in the design process, will be discussed. Detailed examples of these techniques will be used to illustrate the various techniques.
Prerequisite: B.S. degree in EE or related field. ENEE 660 (SE Principles) may be taken concurrently.
ENEE 662 Modeling, Simulation, and Analysis
Credits 3
This is a required course for the Systems Engineering (SE) track in the MSEE program. It is intended for those who wish to understand the art of building and using models and simulations for analysis. It covers the major types of models and simulations, their key features, and the process of developing those simulations. Topics addressed include simulation architectures; cost and risk analysis; experimental design; simulation control and interfaces; languages and hardware platforms; requirements and architecture definition; simulation design and implementation; verification, validation, and accreditation; estimating, planning, and controlling simulation efforts; and the current state of the art for simulation.
Prerequisites: B.S. degree in EE or related field and a working knowledge of C/C++ or a similar programming language. ENEE 660 and 661 or consent of instructor.
ENEE 663 System Implementation, Integration, and Test
Credits 3
This is a second semester, required, graduate course for the Systems Engineering (SE) track within the MSEE program that covers the conversion of a design into product elements, integration of these elements into a system, and verification that the resulting system performs properly in its operational environment. The course will address: (1) the systems engineer's role in the product development organization, (2) processes used to manage product teams, technical budgets, baselines, and schedules during product development, (3) integration methodologies and techniques for avoiding or resolving interface issues, and (4) types and methods of product testing. The goal of this course is to acquaint the EE graduate student with an understanding of the processes by which complex aerospace, information, or other industry systems are built and tested by integrating the efforts of a large product team encompassing many engineering specialties, and the methods used for technical management of this team and the resulting product. Specific processes depend on the development environment and the product customer. This course emphasizes aerospace and information systems.
Prerequisites: ENEE 660 and ENEE 661, or consent of instructor.
ENEE 664 Systems of Systems (elective)
Credits 3
Incorporation of legacy components and systems into a larger system;Management of the process of achieving system interoperability; Standards for system interoperability; Horizontal integration of data elements; Rationalization of Data Elements, IER's; Simulation and verification of end-to-end performance of the entire system; Baselining and configuration management; Product level specs and specification trees.
ENEE 680 Electromagnetic Theory I
Credits:
3
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.
ENEE 683 Lasers
Credits:
3
Introduction to basic theory of lasers: Introduction to quantum mechanics and time dependent perturbation theory, interaction of radiation and matter, stimulated and spontaneous emissions, rate equations, laser amplification and oscillation, noise in lasers and laser amplifiers, semiconductor lasers.
Prerequisites:
ENEE 680, or consent of instructor.
ENEE 684 Introduction to Photonics
Credits
3
This course covers the fundamentals of photonics and their applications. Subjects include crystal and polarization optics, Jones calculus and Stokes parameters, polarization mode dispersion, fiber optics, planar waveguide optics, electro-optics, acousto-optics, second and third order nonlinear susceptibilities, second harmonic generation, sum-frequency generation, parametric down-conversion and oscillation, self-focusing, self- and cross-phase modulation, optical solitions, four-wave mixing, Raman scattering, Brillouin scattering, phase conjugation, photorefractive optics, photo detectors and noise characteristics.
Prerequisite: ENEE 680.
ENEE 685/CMPE 485 Introduction to Communication Networks
Credits:
3
The
fundamentals of communication and computer networking, 7-layer OSI model, review
of queuing models, transmissions, WDM, circuit and packet switching, data link
and medium access technologies, X.25, Frame Relays, ISDN, xDSL, cable modem,
SONET, the network layer, ATM, TCP/IP, routing techniques, the transport and
application layers, quality of Services (QoS).
Prerequisite: consent of instructor.
ENEE
698 Research Project in Electrical Engineering
Credits:
1‑3
Individual
project on topic in electrical engineering. The project will result in a
scholarly paper, which must be approved by the student’s advisor and read by
another faculty member. Required of
non‑thesis option M.S. students. NOTE:
May be taken for repeated credit up to a maximum of three credits.
Prerequisite: Completion of core courses, or consent of instructor.
ENEE 698 Research Project in Electrical Engineering (Systems Engineering Project)
Credits 1-3
This is an individual industry-based Systems Engineering project. The project will result in a technical-report/scholarly paper, which must be approved by the student's advisor and an industry/government mentor approved by the department.
ENEE
699 Independent Study
Credits:
1‑3
Independent
study of topics in electrical engineering.
Prerequisite:
Consent of instructor.
ENEE
710 Digital Speech Processing
Credits:
3
Fundamentals
and techniques for the digital processing of speech: digital signal processing
concepts review, speech production models, characteristics of the speech signal,
time domain speech analysis, linear predictive coding (LPC), homomorphic speech
processing, speech enhancement, speech recognition, speech coding, and speech
synthesis.
Prerequisites:
ENEE 610 and 611, or consent of instructor.
ENEE 711 Neural Networks in Signal Processing
Credits:
3
Fundamentals
and characteristics of artificial neural network paradigms and their properties
in association, learning,
generalization, and self organization: introduction and survey of various neural
network models and paradigms, multilayer perceptron and the radial basis
function networks, sum-of-squares and information-theoretic cost functions,
different learning procedures (gradient optimization, conjugate gradients,
Newton, etc.), learning and generalization properties, applications in
communications and biomedical signal processing, and comparisons with linear
adaptive signal processing theory and techniques.
Prerequisites:
ENEE 620 or consent of instructor.
ENEE 712 Pattern Recognition
Credits:
3
Principles
of statistical pattern recognition; hypothesis testing and decision theory;
parametric estimation (Bayesian estimation, maximum likelihood estimation,
Gaussian mixture analysis); non-parametric estimation (nearest neighbor rule and
Pazen’s window method); density approximation; linear discriminant functions;
feature extraction and selection; feature optimization; neural networks
(single-layer perceptrons, multi-layer neural networks); and applications in
pattern classification.
Prerequisites: ENEE 612, 620, and 621, or consent of instructor.
ENEE
718 Topics in Signal Processing
Credits:
3
ENEE
718 comprises advanced topic courses in signal processing that reflect the
research interests of the faculty and their Ph.D. students. A specific offering
under this title, designated by a letter appended to this course number, is
generally not offered every year.
Prerequisites:
(Depends on offering) Consent of instructor.
ENEE
721 Detection and Estimation Theory II
Credits:
3
Advanced
concepts of signal detection and estimation theory with applications: sequential
detection; non parametric and robust detection concepts; small signal and small
sample size concepts and performance; estimation techniques for smoothing,
filtering, and prediction; recursive, interactive, and extended Kalman filter
and other state estimation techniques and their performance; robust estimation
concepts; general nonlinear filtering and approximately optimal simplified
filters; and discussion of current applications in communications and
statistical signal processing.
Prerequisites:
ENEE 620 and 621, or consent of instructor.
ENEE
723 Communication Theory II
Credits:
3
Digital
signaling over bandwidth constrained channels and channels with distortion:
digital communications over fading multipath channels, inter-symbol interference
and its effects, adaptive equalization, combined coding and modulation
techniques (e.g., trellis coded modulation), and spread spectrum techniques.
Discussion of selective applications.
Prerequisites:
ENEE 620, 621, and 623, or consent of instructor.
ENEE
728 Topics in Communications
Credits:
3
ENEE
728 comprises advanced topic courses in communications that reflect the research
interests of the faculty and their Ph.D. students. A specific offering under
this title, designated by a letter appended to this course number, is generally
not offered every year.
Prerequisite:
(Depends on offering) Consent of instructor.
ENEE 737 Semiconductor Device Processing Techniques
Credits:
3
Introduction
to basic semiconductor device processing techniques: etching, photolithography,
metalization, and device characterization. Laboratory exercises will complement
the lectures and demonstrate the principles.
ENEE
738 Characteristics of Semiconductor Optoelectronics
Credits:
3
Introduction
to current semiconductor optoelectronic devices and survey of new research
results: review of semi-conductor physics and device characteristics; optical
receiver concepts such as photoconductors, metal‑semiconductor concepts,
MSM, pin, receiver design, and APD; waveguide concepts such as waveguide
devices, waveguide modes, waveguide couplers, EO effects and modulation,
periodic waveguides, polarization devices, waveguide filters, and BPM; and LED
amplifier and laser concepts such as edge/surface emitting, optical gain,
traveling wave amplifiers, FP, DFB, DBR, QW lasers, active filters, small signal
modulation, modelocking, line width, and noise.
Prerequisites:
ENEE 630, 631, 681, 682, and 683, or consent of instructor.
ENEE
785 Topics in Optical Networks
Credits:
3
This
is an interdisciplinary course to address the issues of importance in
constructing high‑speed optical networks. It covers the current networks
for both telecoms and datacoms. Network layers, circuit switching and
packet‑switching principle and technologies are described. Depending on
the instructor, technologies related to the physical layer of the system,
protocols and traffic and network control will be covered in more detail.
Projects are required for all students.
Prerequisite:
(Depends on offering) Consent of instructor.
ENEE 788 Topics in
Photonics
Credits: 3
ENEE
788 comprises advanced topic courses in photonics that reflect the research
interests of the faculty and their Ph.D. students. A specific offering under
this title, designated by a letter appended to this course number, is generally
not offered every year.
Prerequisite:
(Depends on offering) Consent of instructor.
ENEE 799 Master's
Thesis Research
Credits: 1-5
This
course is for MSEE students engaged in master's thesis research; may be taken
for repeated credits, but only a maximum of 6 credit hours applied toward M.S.
thesis option requirements. Must be taken over at least two (2)
semesters.
Prerequisite:
Open only to MSEE thesis option students.
ENEE
800 Graduate Research
Credits:
1-6
This
course is for Ph.D. students not yet admitted to Ph.D. Candidacy, and can be
taken for repeat credit.
Prerequisite:
Open only to EE students who have passed the Ph.D. qualifying exam.
ENEE
899 Doctoral Dissertation Research
Credits:
1-6
Ph.D.
students must take this course over at least two semesters. Only a maximum of
twelve (12) credit hours can be applied towards the Ph.D. requirements, and only
six (6) credit hours can be taken before admission to Ph.D. candidacy.