Electrical Engineering (EENG)

Department of Computer Science and Electrical Engineering

GARY M. CARTER, Chair
JOEL M. MORRIS, Graduate Program Director

Professors
ADALI, TULAY, Ph.D., North Carolina State University: Statistical signal processing, machine learning for signal processing, adaptive signal processing, biomedical data analysis, and communications
CARTER, GARY M., Ph.D., Massachusetts Institute of Technology: Optical communications, non-linear optics, lasers, bio-photonics
CHANG, CHEIN-I, Ph.D., University of Maryland, College Park: Multispectral/hyper-spectral imaging, chemical/biological defense, automatic target recognition (ATR), computer-aided diagnosis for medical imaging, visual information systems and retrieval
CHEN, YUNG JUI (RAY), Ph.D., University of Pennsylvania: Optical networks, integrated optics and opto-electronic integrated circuits, device physics, ultra-fast optics and non-linear optics
CHOA, FOW-SEN, Ph.D., State University of New York, Buffalo: MOCVD growth, quantum cascade lasers, mid-IR and THz photonic devices, chip-scale integrated sensor systems, RF-photonic and optical switching devices
JOHNSON, ANTHONY, Ph.D., City College of the City University of New York: Director of the Center for Advanced Studies in Photonics Research (CASPR); Ultra-fast optics, non-linear optics and ultra-fast photophysics of nano-structured materials
MENYUK, CURTIS R., Ph.D., University of California, Los Angeles: Optical communications, non-linear optics, theoretical electromagnetics
MORRIS, JOEL M., Ph.D., The Johns Hopkins University: Communication theory and statistical signal processing theory with applications in sensing, detection, estimation, and characterization, error correction codes, adaptive importance sampling for statistical performance assessment, joint time-frequency/time-scale analysis and presentations
YAN, LI, Professor, Ph.D., University of Maryland, College Park; Ultra-fast optics, non-linear optics, solid-state and fiber lasers, optical communications

Associate Professor
RUTLEDGE, JANET, Ph.D., Georgia Institute of Technology: Modeling and Compensating for the effects of sensorineural hearing loss and other communication disorders

Professor of Practice
LABERGE, E.F. CHARLES, Ph.D., UMBC: Coding theory, signal processing, communication system design, interface analysis, safety-critical avionics, system engineering

Degrees Offered

M.S. (thesis and non-thesis), Professional M.S., and Ph.D. Check the CSEE Department and ENEE Graduate Program websites www.csee.umbc.edu or www.cs.umbc.edu/programs/graduate/electrical-engineering-ms-phd/, respectively, for current and further details on these degrees, the 5-year B.S./M.S. degree, and program certificates.

Program Description

The CSEE Department offers a graduate program (EENG/ENEE) leading to the Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Electrical Engineering (EE). The diversity of course offerings and research interests within the department, and interactions with the medical and dental schools at the University of Maryland, Baltimore, and other science and engineering departments at UMBC, encompass a broad spectrum of strictly electrical engineering and inter-disciplinary instruction and research topics. The M.S. program has three (3) possible tracks of study: (a) nano/micro/opto-electronics, photonics, and sensor technology (nEPS); (b) communications, sensor systems, and signal processing (CSSP); and (c) systems engineering (SE). The Ph.D. program has only the first two (2) tracks of study (nEPS and CSSP). The faculty's interests and the various topics defining these tracks of study are:

Communications: random processes, detection and estimation theory, information theory, source and channel coding, communication theory, wired/wireless/optical-fiber communications, data compression, adaptive and machine-learning techniques.

Nano/micro/opto-electronics: solid-state electronics, semiconductor devices and processing technology, semiconductor opto-electronics, compound semiconductor electronics, and integrated circuits.

Photonics: electromagnetic theory, quantum electronics, solid-state and fiber lasers, semiconductor and quantum-cascade lasers, fiber-optic communications, optical networking and interconnections, non-linear/integrated optics/ultra-fast/sub-wavelength optics, and bio/nano/silicon-photonics.

Sensor Technology: bio-chemical and opto-electronic-sensors.

Signal Processing: signal and linear system theory; digital signal processing (DSP); statistical signal processing (detection, estimation, machine-learning); adaptive and learning techniques; speech processing; pattern recognition; spectral, time-frequency, and joint-domain analysis; biomedical signal processing; and sensor-based systems and networks.

Image Processing: automatic target recognition, pattern recognition, image coding and compression, multi-/hyper-spectral imaging, biomedical imaging and image analysis, visual information systems and retrieval.

Systems Engineering: life-cycles of complex systems; system architecture and design; system modeling, simulation, and analysis; system implementation, integration, and test; and systems of systems.

ENEE students, except for those in the SE track, may select their course and research plan in one track of study or in an interdisciplinary area approved by their advisor and the Graduate Program Director. A departmental brochure that describes in more detail the department, its graduate programs, degree requirements, and the research interests of the faculty can be obtained from the graduate program specialist or can be viewed at either the CSEE Department or ENEE Graduate Program websites, www.csee.umbc.edu or www.cs.umbc.edu/programs/graduate/electrical-engineering-ms-phd/, respectively.

Degree Requirements

Master of Science (M.S.) Degree

Within five (5) years of admission, the student must earn a minimum of 30 credit hours for the thesis option or 33 credit hours for the non-thesis (w/project) option. All M.S. students MUST choose either the thesis or non-thesis (w/project) option: there is no course-only option. Students must satisfy the grade and course requirements for their field of specialty and option. Unless approved by the Graduate Program Director (GPD), a maximum of six (6) credits of courses outside the EE program will be accepted. These courses must be at the graduate level and must be approved by the student�s advisor and the GPD prior to registration.

Course Requirements

Four-of-eight (4 of 8) core courses required for all students (12 credits):

  • ENEE 601: Linear Systems Theory
  • ENEE 620: Probability and Random Processes
  • ENEE 621: Detection and Estimation Theory
  • ENEE 622: Information Theory
  • ENEE 630: Solid-State Electronics
  • ENEE 631: Semiconductor Devices
  • ENEE 680: Electromagnetic Theory
  • ENEE 683: Lasers

Two (2) or four (4) EE electives (6 or 12 credits): 600-level. Check the ENEE Graduate Program website for the current list of approved ENEE 600-level choices for the chosen track. The 2 electives requirement is for the thesis option, and the 4 electives for the non-thesis option.

Two (2) additional electives (6 credits): 600/700 level, advisor-approved courses (can include a maximum of 3 credits from ENEE 698/699).

EE Graduate Seminar: M.S. students are required to take EE Graduate Seminar (ENEE 608) for one (1) semester, preferably in the first year.

Transfer Credits: No more than six (6) credits may be transferred from another university or from UMBC as a non-degree student. Course credit transfer must be approved by the Graduate Program Director.

Grade Requirements: Students must receive a 'B' or higher in each of the three (3) core courses, and achieve an average grade of 'B' or better (equivalently, a grade point average (GPA) of 3.0 or better) across the three (3) core plus two (2) focused elective courses. Grades with + and - are possible for graduate courses. A minimum grade of 'B' (3.0 GPA) is required for courses taken for the degree.

M.S. Thesis Option (30 credits)

A student may undertake the M.S. thesis option, which would be supervised by a faculty member as the research advisor (or co-advisor). The thesis option requires a minimum of eight (8) graduate-level courses (24 credits), as specified above, and six (6) credits of M.S. thesis research (ENEE 799). The thesis research must contain a tangible research component. Upon completion of the thesis, it must be defended with an oral public exam/presentation and accepted with the approval of the student's thesis committee. The committee must consist of at least three (3) graduate faculty members within the department. Copies of the approved thesis must be submitted both to the CSEE Department and the Graduate School.

Non-Thesis Option (33 credits)

A student may undertake the M.S. non-thesis option, which would be supervised by a faculty member as the research advisor (or co-advisor). The non-thesis option requires a minimum of ten (10) graduate-level courses (30 credits), as specified above, and three (3) credits of graduate project research (ENEE 698) resulting in a scholarly paper that must be approved by the advisor and read by another faculty member. A copy of the approved scholarly paper must be submitted to the department.

For the Systems Engineering (SE) track, the four (4) electives (12 credits) must be

  • ENEE 660: System Engineering Principles
  • ENEE 661: System Architecture and Design
  • ENEE 662: System Modeling, Simulation, and Analysis
  • ENEE 663: System Implementation, Integration, and Test
Doctor of Philosophy (Ph.D.) Degree

Each field of specialty sets its course requirements for doctoral students in that field. The department�s minimum requirement is eleven (11) courses (beyond the bachelor�s degree). The doctoral student must spend the equivalent of at least three years of full-time residency, with at least one year on the UMBC campus. The doctoral dissertation must be an original and substantive contribution to knowledge in the student�s major field. It must demonstrate the student�s ability to carry out a program of research and to report the results in accordance with standards observed in the recognized scientific journals related to that field.

Doctoral students must: (a) submit their PhD Comprehensive Portfolio and receive a pass grade (P) within four (4) semesters of entrance to the program (six (6) semesters for part-time students); (b) develop and defend a doctoral dissertation proposal and be admitted to doctoral candidacy within four (4) years of entrance to the program; and (c) complete all Ph.D. requirements for their field of specialty within four (4) years of admission to doctoral candidacy.

PhD Comprehensive Portfolio

The ENEE PhD Comprehensive Portfolio consists of three (3) components: (a) GPA performance on the four (4) core plus ENEE 610 courses, (b) the Research Activity Report (RAR), and (c) two (2) faculty support letters. Students will be dismissed from the doctoral program if they fail to pass the comprehensive portfolio evaluation within the specified time frame. See the ENEE Graduate Program website at www.csee.umbc.edu or www.cs.umbc.edu/programs/graduate/electrical-engineering-ms-phd/ for details on the ENEE PhD Comprehensive Portfolio policy.

Course Requirements

Students must satisfy the minimum course requirements for their field of specialty (typically 11 courses totaling 33 credits) excluding the EE graduate seminar (ENEE 608), graduate research credits prior to Ph.D. candidacy, and doctoral dissertation research credits. Four (4) of the eleven (11) courses must be chosen from the eight (8) core courses and the fifth (5th) course is Digital Signal Processing (ENEE 610). Students may take ENEE 898 for research credits before being admitted to Ph.D. candidacy and must take twelve (12) credits of ENEE 899 for doctoral dissertation research after admittance to Ph.D. candidacy. Specifically:

Four-of-eight (4 of 8) core courses required for all students (12 credits):

  • ENEE 601: Linear Systems Theory
  • ENEE 620: Probability and Random Processes
  • ENEE 621: Detection and Estimation Theory
  • ENEE 622: Information Theory
  • ENEE 630: Solid-State Electronics
  • ENEE 631: Semiconductor Devices
  • ENEE 680: Electromagnetic Theory
  • ENEE 683: Lasers

Required core course: ENEE 610: Digital Signal Processing (3 credits)

Four (4) EE electives (12 credits): two (2) or more at 700-level. Check the ENEE Graduate Program website for the current list of approved ENEE 700-level choices for the chosen track.

Three (3) additional electives (9 credits): advisor-approved 600/700 level, which can include a maximum of three (3) credits of ENEE 698/699

EE Dissertation Research: ENEE 899 (12 credits, over at least two (2) semesters).

EE Graduate Seminar: Ph.D. students are required to take EE Graduate Seminar (ENEE 608) for two (2) semesters, preferably in the first year.

Grade Requirements

A grade of 'B' (3.0 GPA) or better is required in all courses and a 3.33 GPA minimum overall (including the transfer courses). Grades with + and - are possible for graduate courses.

PhD Comprehensive Portfolio

The ENEE PhD Comprehensive Portfolio consists of three (3) components: (a) GPA performance on the four (4) core plus ENEE 610 courses, (b) the Research Activity Report (RAR), and (c) two (2) faculty support letters. Students will be dismissed from the doctoral program if they fail to pass the comprehensive portfolio evaluation within the specified time frame. See the ENEE Graduate Program website at www.csee.umbc.edu or www.cs.umbc.edu/programs/graduate/electrical-engineering-ms-phd/ for details on the ENEE PhD Comprehensive Portfolio policy.

Preliminary Examination (Prelim)

Students must select a dissertation advisor and a dissertation preliminary examination committee, and they must pass a two-part preliminary examination. Students will present and defend their dissertation proposal to the preliminary committee. The committee examines the students orally on their proposal and research area(s) to assess their ability to complete the proposed research. Each full-time student must pass the preliminary exam within one-and-a-half (1.5) years after passing the comps to remain in the Ph.D. program, and part-time students will be given two-and-a-half (2.5) years to pass the preliminary examination. Check the ENEE Graduate Program website for updates to this requirement.

Ph.D. Candidacy

After passing the preliminary exam and completing the course requirements, the Graduate Program Committee recommends to the Graduate School that the student be admitted to Ph.D. candidacy.

Dissertation Research

The student conducts and reports (dissertation) on a significant original research topic under the guidance of their dissertation advisor. The doctoral dissertation must be an original and substantive contribution to knowledge in the student's major field. It must demonstrate the student's ability to: (a) conduct a program of research and (b) report the results in accordance with standards observed in the recognized scientific journals related to that field. This research must be completed and defended within four (4) years of admission to candidacy. Students must be admitted to candidacy at least two full sequential semesters before the date on which the doctoral degree is to be conferred. Doctoral candidates take at least sixteen (16) dissertation credits (ENEE 899). The Ph.D. dissertation committee must include four (4) graduate faculty members from the CSEE department and one external member.

Residency Requirements

A minimum of three (3) years of full-time graduate study or its equivalent is required. At least one year of full-time study must be completed at UMBC.

Program Admission Requirements

When seeking admission to the graduate program in Electrical Engineering, applicants must satisfy all entrance requirements of the Graduate School at UMBC. These include the submission of official transcripts, three letters of recommendation, statement of purpose, Graduate Record Examination (GRE General Test) scores and, for international students, scores for the TOEFL. All original application materials must be sent directly to the Graduate School, not the graduate program. Application deadlines for international and domestic students are January 1/June 1 for the fall semester, and June 1/November 1 for the spring semester. The application review process will begin by January 1 for admission in the fall semester, and by October 1 for admission in the following spring semester. Early application is recommended.

In addition to the requirements of the graduate school, minimum requirements for admission to the graduate program in Electrical Engineering are a B.S. degree from an ABET-accredited undergraduate program in Electrical Engineering with a GPA equivalent to 'B+' or higher. Individuals whose records indicate strong potential for successful pursuit of the master�s or doctoral degree objectives and who have similar undergraduate preparation with strong academic records in computer science, mathematics, physics or other areas of engineering or science are encouraged to apply (B.S. degrees in engineering technology are not considered equivalent to the B.S. degree in engineering or the B.A. degree in the sciences). Students whose degrees are not in electrical engineering generally will be required to take courses to make up deficiencies in their backgrounds. Students who plan to pursue the Ph.D. degree but who do not already have an M.S. degree are advised to apply for admission to the M.S. program. Applicants are judged competitively by the program�s admissions committee, and those who appear suitably qualified to complete the requirements of the intended degree program successfully are selected for admission, subject to available resources. Applications are not processed until all documents and fees are received.

Facilities and Special Resources

Faculty and students in the electrical engineering program at UMBC have access to extensive computational resources. The research and instructional activities of the department are supported by a number of new modern laboratories. Laser-based laboratories support research in ultra-fast non-linear optics and optical spectroscopy, solid state, diode and fiber lasers. Device fabrication laboratories support research in optical and electronic properties of compound semiconductors and organic polymers and in exploring and developing new materials, micro/nano device structures and processing technologies via CAIBE. Compound semiconductor growth research, such as quantum cascade lasers, is being pursued using MOCVD techniques. The optical communication and optical networking laboratories contains high-performance, fiber-optics communication equipment to perform experiments in digital transmission using multi-channels over long distances and optical networking. The communications and signal processing laboratory supports research in the areas of communication theory and statistical signal processing theory with their applications. The remote sensing signal and image processing laboratory supports research in multi-spectral and hyper-spectral imagery, pattern recognition, target tracking and detection, image coding and progressive image transmission, computer vision, and medical imaging. The machine learning laboratory supports research in theory and algorithms in adaptive and/or non-linear signal processing for communications and biomedical image analysis. Collaborations with nearby federal facilities include ARL, LTS, LPS, NASA, NIH, NIST, and NRL, and with the Kennedy Krieger Center at Johns Hopkins University and the Department of Radiology at the University of Maryland Medical School.

Financial Assistance

Financial aid is available on a competitive basis to a limited number of qualified graduate students in the form of graduate teaching assistantships (TAs), graduate research assistantships (RAs), work-study positions, and hourly employment as graders. Graduate RAs are often available to students actively engaged in a master�s thesis or doctoral dissertation research, and are awarded and renewed subject to availability of funds and satisfactory research progress. Students are encouraged to apply directly to nationally awarded fellowship programs.

COURSE LISTING

For ENEE (also CMPE and CMSC) course descriptions, and current year special topic course listings and descriptions, see the CSEE Graduate Program(s) website www.cs.umbc.edu/programs/graduate/ or CSEE Department website www.csee.umbc.edu. The set of ENEE 691 courses address specialized electrical engineering topics representing the research focus of the faculty, and are scheduled according to student and faculty interests.

ENEE 601
Signal and Linear Systems Theory [3]
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 [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 master's students and two semesters for doctoral students).

ENEE 610
Digital Signal Processing [3]

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. Prerequisite: ENEE 601, ENEE 620 or their equivalent or consent of instructor.

ENEE 611
Adaptive Signal Processing [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 sub-band adaptive filters and unsupervised adaptive filters; analysis of these adaptive filters and discussion of selected applications. Prerequisites: ENEE 601 or ENEE 610 and ENEE 620 or consent of instructor.

ENEE 612
Digital Image Processing [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, Prerequisite: MATLAB or consent of instructor.

ENEE 620
Probability and Random Processes [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 course work or consent of instructor.

ENEE 621
Detection and Estimation Theory I [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 posterior estimators); linear Bayesian estimators (Wiener filters) and Kalman filters. Prerequisite: ENEE 620 or consent of instructor.

ENEE 622
Information Theory [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 and waterfilling. Additional topics: narrow-band time-varying channels, fading channels, side information, wideband channels, network coding, information theory in relation to statistics and geometry. Prerequisite: Strong grasp of basic probability theory.

ENEE 623
Communication Theory I [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 and 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, multi-level coding and constellation shaping. Equalization and precoding for linear Gaussian channels, waterfilling, multi-carrier signaling. Additional topics: signaling in fading media, multi-sensor and multi-user communications, synchronization. Prerequisites: ENEE 601, ENEE 621 and ENEE 622.

ENEE 624
Error-Correcting Codes [3]

Focusing on the fundamentals of art theory, criticism, analysis and evaluation, this course will examine contemporary art, theory and the historical and philosophical issues that shape and define art and culture. Note: Required course for the M.F.A. degree.

ENEE 625
Data Compression [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 quantitation, vector quantitation, predictive coding and sub-band coding. Prerequisites: ENEE 620 and ENEE 622 or consent of instructor.

ENEE 630
Solid-State Electronics [3]

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, semi-conductors and excess carriers in semi-conductors. Prerequisite: Consent of instructor.

ENEE 631
Semiconductor Devices [3]

Principles of semi-conductor device operation: review of semi-conductor physics, p-n junction diodes, bipolar transistors, metal semi-conductor contacts, JFETs and MESFETs and MIS and MOSFET structures. Prerequisite: ENEE 630 or consent of instructor.

ENEE 632
Integrated Circuits [3]

Fundamentals of bipolar and MOS analog and digital integrated circuit techniques: basic IC structure and fabrication, passive components, bipolar transistors and diode, characteristics 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, ENEE 631 or consent of instructor.

ENEE 634
Microwave Device and Circuit Design [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. Prerequisite: ENEE 681 or consent of instructor.

ENEE 635
Introduction to Optical Communications [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 [3]

Introduction to wireless communication systems, the cellular concept, mobile radio propagation, large-scale path loss and small-scale fading, multi-path modulation techniques, equalization, diversity, compression, multi-access techniques, wireless networking and wireless systems and standards. Prerequisite: Consent of instructor.

ENEE 660
Systems Engineering Principles [3]

This is a first-semester, required graduate course for Systems Engineering (SE) 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.

ENEE 661
System Architecture and Design [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 developing 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 [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: BS degree in EE or related field and a working knowledge of C/C++ or similar programming language. In addition, students are required to pass a Mathematics and MATLAB fundamentals test OR pass ENEE 669: Mathematics and MATLAB Fundamentals for Engineers.

ENEE 663
System Implementation, Integration and Test [3]

This is a second-semester, required graduate course for the Systems Engineering (SE) track within the MSEE program, which 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 671
Service Oriented Architecture [3]

This course examines the design consequences in following SOA architectural principles including: Encapsulation, Loose Coupling (Independence), Service Contract (for Communication), Service Abstraction (hiding logic), Reusability, Composability (coordination of composite services), Autonomy (control over encapsulated logic), Statelessness (retention of data from an activity) and Discoverability (finding and accessing services based upon intuitive identification). The course emphasizes the practical implementation of useful enterprise-wide systems using SOA. Working in teams, students will architect, design and implement a system project via simulation of performance and behavior. As result, students will gain fundamental knowledge and hands-on experience to permit them to function as individual contributors and integration leads in the context of an industrial environment.

ENEE 680
Electromagnetic Theory I [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 [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 andsemi-conductor lasers. Prerequisite: ENEE 680 or consent of instructor.

ENEE 684
Introduction to Photonics [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 non-linear susceptibilities, second harmonic generation, sum-frequency generation, parametric down-conversion and oscillation, self-focusing, self- and cross-phase modulation, optical solutions, four-wave mixing, Raman scattering, Brillouin scattering, phase conjugation, photo-refractive optics, photo detectors and noise characteristics. Prerequisite: ENEE 680.

ENEE 685/CMPE 485
Introduction to Communication Networks [3]

The fundamentals of communication and computer networking, seven-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 and quality of services (QoS). Prerequisite: Consent of instructor.

ENEE 691
Topics in Electrical Engineering [3]

ENEE 698
Research Project in Electrical Engineering (Systems Engineering Project) [1-3]

Individual project on a 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 699
Independent Study [1-3]

Independent study of topics in electrical engineering. Prerequisite: Consent of instructor.

ENEE 710
Digital Speech Processing [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 ENEE 611 or consent of instructor.

ENEE 711
Neural Networks in Signal Processing [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, multi-layer 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. Prerequisite: ENEE 620 or consent of instructor.

ENEE 712
Pattern Recognition [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, ENEE 620 and ENEE 621 or consent of instructor.

ENEE 718
Advanced Topics in Signal Processing [3]

ENEE 718 comprises advanced topic courses in signal processing that reflect the research interests of the faculty and their doctoral students. A specific offering under this title is designated by a letter appended to this course number and is generally not offered every year. Prerequisite: Depends on offering; consent of instructor.

ENEE 721
Statistical Signal Processing [3]

Statistical inference. Point and interval estimation. State-space estimation. Elements of large- and small-sample theory. Array processing. Multi-channel signal processing. Reduced rank methods. Optimal and suboptimal multi-user detection. Low-complexity maximum likelihood detection. Iterative detection and its theoretical foundations. The relationship between statistical inference, statistical mechanics and information theory. Prerequisites: ENEE 620 and ENEE 621 or consent of instructor.

ENEE 723
Multi-user Communication [3]

This is an advanced course in wireless communication theory that focuses on several aspects of multi-user communication including current progress in multi-user Shannon theory, signaling schemes for wireless multi-access, broadcast and interference channels, receivers for fading multi-user wireless channels, interference and power management, multi-antenna signaling, ultra-wideband signaling and the capacity and control of very large wireless networks. Prerequisites: ENEE 622 and ENEE 623 or consent of instructor.

ENEE 728
Advanced Topics in Communications [3]

ENEE 728 comprises advanced topic courses in communications that reflect the research interests of the faculty and their doctoral students. A specific offering under this title is designated by a letter appended to this course number and is generally not offered every year. Prerequisite: Depends on offering; consent of instructor.

ENEE 737
Semi-conductor Device Processing Techniques [3]

Introduction to basic semi-conductor device processing techniques: etching, photo-lithography, metalization and device characterization. Laboratory exercises will complement the lectures and demonstrate the principles. Prerequisites: ENEE 630 and ENEE 631 or consent of instructor.

ENEE 738
Characteristics of Semi-conductor Opto-electronics [3]

Introduction to current semi-conductor opto-electronic devices and survey of new research results: review of semi-conductor physics and device characteristics; optical receiver concepts, such as photo-conductors, metal semi-conductor 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, 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, mode-locking, line width and noise. Prerequisites: ENEE 630, ENEE 631, ENE 680 and ENEE 683 or consent of instructor.

ENEE 785
Advanced Topics in Optical Networks [3]

This is an inter-disciplinary 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
Advanced Topics in Electrophysics and Photonics [3]

ENEE 788 comprises advanced topic courses in photonics that reflect the research interests of the faculty and their doctoral 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 [1-6]

This course is for MSEE students engaged in master's thesis research; may be taken for repeated credits, but a maximum of six credit hours can be applied toward master's thesis-option requirements. Must be taken over at least two semesters. Prerequisite: Open only to MSEE thesis-option students.

ENEE 898
Pre-Candidacy Doctoral Research [1-6]

Research on doctoral dissertation conducted under the direction of a faculty advisor before candidacy.

ENEE 899
Doctoral Dissertation Research [6]

Doctoral students must take this course over at least two semesters. Only a maximum of 12 credit hours can be applied toward the doctoral. requirements, and only six credit hours can be taken before admission to Ph.D. candidacy. Prerequisite: Open only to EE students who have passed the Ph.D. qualifying exam.