Electrical Engineering MS

Electrical Engineering (communications focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-602
Random Signals and Noise
In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-692
Communication Networks
This course covers communication networks in general and the internet in particular. Topics include layers service models, circuit and packet switching, queuing, pipelining, routing, packet loss and more. A five-layer model is assumed and the top four levels are covered in a top-down approach: starting with the application layer, going down through the transport layer to the network layer and finally the data link layer. Emphasis is placed on wireless networks and network security. Students would perform a basic research assignment consisting of a literature survey, performance analysis and dissemination of results in written and oral presentation. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).
3
EEEE-693
Digital Data Communication
Principles and practices of modern digital data communication systems. Topics include pulse code transmission and error probabilities, M-ary signaling and performance, AWGN channels, band-limited and distorting channels, filter design, equalizers, optimal detection for channels with memory, synchronization methods, non-linear modulation, and introduction to multipath fading channels, spread spectrum and OFDM. Students would perform a basic research assignment consisting of a literature survey, performance analysis and dissemination of results in written and oral presentation. (Prerequisites: EEEE-602 or equivalent course.) Lecture 3 (Spring).
3
EEEE-694
Sensor Array Processing for Wireless Communications
This course offers a broad overview of sensor-array processing, with a focus on wireless communications. It aims at providing the students with essential and advanced theoretical and technical knowledge that finds direct application in modern wireless communication systems that employ multi-sensor arrays and/or apply user-multiplexing in the code domain (CDMA). Theory and practices covered in this course can be extended in fields such as radar, sonar, hyperspectral image processing, and biomedical signal processing. Topics covered: uniform linear antenna arrays (inter-element spacing and Nyquist sampling in space); linear beamforming, array beam patterns, array gain, and spatial diversity; interference suppression in the absence of noise (null-steering beamforming); optimal beamforming in AWGN (matched filter); optimal beamforming in the presence of colored interference; estimation of filters from finite measurements and adaptive beamforming (SMI and variants, RLS, LMS and variants, CMA, and AV); BPSK demodulation with antenna arrays (multiple users and AWGN); BPSK demodulation in CDMA (multiple users and AWGN); ML and subspace methods (MUSIC, root MUSIC, Minimum-norm, Linear Predictor, Pisarenko) for Direction-of-arrival estimation; BPSK demodulation with antenna arrays in CDMA systems (space-time processing). (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
EEEE-797
Wireless Communication
The course will cover advanced topics in wireless communications for voice, data and multimedia. Topics covered are: 1) Channel modeling: Overview of current wireless systems, modeling wireless channels, path loss for different environments, log-normal shadowing, flat and frequency-selective multipath fading, LS estimation of channel parameters, and capacity limits of wireless communication channels. 2) Transmission over fading channels, 3) Techniques to improve the speed and performance of wireless inks (adaptive modulation and diversity techniques such as maximum gain combining to compensate for flat-fading). 4) Techniques to combat frequency-selective fading (adaptive equalization, space time coding, multicarrier modulation (OFDM), and spread spectrum). 5) Applications for these systems, including the evolution of cell phones and PDAs, sensor networks will be discussed. (Prerequisites: EEEE-593 or EEEE-693 and EEEE-602 or equivalent course.) Lecture 3 (Fall).
3
 
Graduate Elective
3
Total Semester Credit Hours
30

Electrical Engineering (controls focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-602
Random Signals and Noise
In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-661
Modern Control Theory
This course deals with a complete description of physical systems its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input-output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for desired performance, state feedback, observers and their realizations. (Co-requisites: EEEE-707 or equivalent course.) Lecture 3 (Fall).
3
EEEE-663
Real-Time & Embedded Systems
This first course in a graduate elective sequence will begin by presenting a general road map of real-time and embedded systems. The course will be conducted in a studio class/lab format with lecture material interspersed with laboratory work. This course will introduce a representative family of microcontrollers that will exemplify unique positive features as well as limitations of microcontrollers in embedded and real-time systems. These microcontrollers will then be used as external, independent performance monitors of more complex real-time systems. The majority of the course will present material on a commercial real-time operating system and using it for programming projects on development systems and embedded target systems. Some fundamental material on real-time operating systems and multiprocessor considerations for real-time systems will also be presented. Examples include scheduling algorithms, priority inversion, and hardware-software co-design. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-765
Optimal Control
The course covers different optimization techniques, as applied to feedback control systems. The main emphasis will be on the design of optimal controllers for digital control systems. The major topics are: Different performance indices, formulation of optimization problem with equality constraints, Lagrange multipliers, Hamiltonian and solution of discrete optimization problem. Discrete Linear Quadratic Regulators (LQR), optimal and suboptimal feedback gains, Riccati equation and its solution, linear quadratic tracking problem. Dynamic Programming - Bellman's principle of optimality - Optimal controllers for discrete and continuous systems - Systems with magnitude constraints on inputs and states. (Prerequisites: EEEE-661 or equivalent course.) Lecture 3 (Spring).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Electives
6
Total Semester Credit Hours
30

Electrical Engineering (digital systems focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-620
Design of Digital Systems
The purpose of this course is to expose students to complete, custom design of a CMOS digital system. It emphasizes equally analytical and CAD based design methodologies, starting at the highest level of abstraction (RTL, front-end)), and down to the physical implementation level (back-end). In the lab students learn how to capture a design using both schematic and hardware description languages, how to synthesize a design, and how to custom layout a design. Testing, debugging, and verification strategies are formally introduced in the lecture, and practically applied in the lab projects. Students are further required to choose a research topic in the area of digital systems, perform bibliographic research, and write a research paper following a prescribed format. (Prerequisites: EEEE-420 and EEEE-480 or equivalent courses or graduate standing in EEEE-MS.) Lab 3, Lecture 3 (Fall, Spring).
3
EEEE-621
Design of Computer Systems
The purpose of this course is to expose students to the design of single and multicore computer systems. The lectures cover the design principles of instructions set architectures, non-pipelined data paths, control unit, pipelined data paths, hierarchical memory (cache), and multicore processors. The design constraints and the interdependencies of computer systems building blocks are being presented. The operation of single core, multicore, vector, VLIW, and EPIC processors is explained. In the first half of the semester, the lab projects enforce the material presented in the lectures through the design and physical emulation of a pipelined, single core processor. This is then being used in the second half of the semester to create a multicore computer system. The importance of hardware/software co-design is emphasized throughout the course. Students are further required to choose a research topic in the area of computer systems, perform bibliographic research, and write a research paper following a prescribed format. (Prerequisites: EEEE-420 or equivalent course or graduate standing in EEEE-MS.) Lab 2, Lecture 3 (Fall).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-720
Advanced Topics in Digital Systems Design
In this course the student is introduced to a multitude of advanced topics in digital systems design. It is expected that the student is already familiar with the design of synchronous digital systems. The lecture introduces the operation and design principles of asynchronous digital systems, synchronous and asynchronous, pipelined and wave pipelined digital systems. Alternative digital processing paradigms are then presented: data flow, systolic arrays, networks-on-chip, cellular automata, neural networks, and fuzzy logic. Finally, digital computer arithmetic algorithms and their hardware implementation are covered. The projects reinforce the lectures material by offering a hands-on development and system level simulation experience. (Prerequisites: EEEE-520 or EEEE-620 or equivalent courses.) Lecture 3 (Spring).
3
EEEE-721
Advanced Topics in Computer Systems Design
In this course the student is introduced to advanced topics in computer systems design. It is expected that the student is already familiar with the design of a non-pipelined, single core processor. The lectures cover instruction level parallelism, limits of the former, thread level parallelism, multicore processors, optimized hierarchical memory design, storage systems, and large-scale multiprocessors for scientific applications. The projects reinforce the lectures material, by offering a hands-on development and system level simulation experience. (Prerequisites: EEEE-521 or EEEE-621 or equivalent courses.) Lecture 3 (Spring).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Electives
6
Total Semester Credit Hours
30

Electrical Engineering (electromagnetics focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-602
Random Signals and Noise
In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-617
Microwave Circuit Design
The primary objective is to study the fundamentals of microwave engineering with emphasis on microwave network analysis and circuit design. Topics include microwave transmission lines such as wave-guides, coax, microstrip and stripline, microwave circuit theory such as S- matrix, ABCD matrices, and even odd mode analysis, analysis and design of passive circuits and components, matching networks, microwave resonators and filters. Microwave circuit design projects will be performed using Ansoft's Designer software. (Prerequisites: EEEE-374 or equivalent course or graduate standing in EEEE-MS.) Lecture 3 (Spring).
3
EEEE-629
Antenna Theory
The primary objective is to study the fundamental principles of antenna theory applied to the analysis and design of antenna elements and arrays including synthesis techniques and matching techniques. Topics include antenna parameters, linear antennas, array theory, wire antennas, microstrip antennas, antenna synthesis, aperture antennas and reflector antennas. A significant portion of the course involves design projects using some commercial EM software such as Ansoft Designer, Ansoft HFSS and SONNET and developing Matlab codes from theory for antenna synthesis and antenna array design. The measurement of antenna input and radiation characteristics will be demonstrated with the use of network analyzers, and spectrum analyzers in an anechoic chamber. (Prerequisites: EEEE-374 or equivalent course or graduate standing in EEEE-MS.) Lecture 3 (Fall).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-710
Advanced Electromagnetic Theory
The primary objective is to provide the mathematical and physical fundamentals necessary for a systematic analysis of electromagnetic field problems. Topics included: electromagnetic theorems and principles, scattering and radiation integrals, TE and TM in rectangular and circular waveguides, hybrid LSE and LSM modes in partially filled guides, dielectric waveguides, the Green's function. The course will also include projects using advanced EM modeling software tools. (Prerequisites: EEEE-617 and EEEE-629 or equivalent course.) Lecture 3 (Spring).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-718
Design and Characterization of Microwave Systems
There are two primary course objectives. Design of experiments to characterize or measure specific quantities, working with the constraints of measurable quantities using the vector network analyzer, and in conjunction with the development of closed form analytical expressions. Design, construction and characterization of microstrip circuitry and antennas for specified design criteria obtaining analytical models, using software tools and developing measurements techniques. Microwave measurement will involve the use of network analyzers, and spectrum analyzers in conjunction with the probe station. Simulated results will be obtained using some popular commercial EM software for the design of microwave circuits and antennas. (Prerequisites: EEEE-617 and EEEE-629 or equivalent courses. Co-requisite: EEEE-790 or EEEE-792 or equivalent course.) Lecture 3 (Fall).
3
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Elective
3
Total Semester Credit Hours
30

Electrical Engineering (integrated electronics focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-610
Analog Electronics Design
This is a foundation course in analog integrated electronic circuit design and is a perquisite for the graduate courses in analog integrated circuit design EEEE-726 and EEEE-730. The course covers the following topics: (1)CMOS Technology (2) CMOS active and passive element models (3) Noise mechanisms and circuit noise analysis (4) Current mirrors (5) Differential amplifiers, cascade amplifiers (6) Multistage amps and common mode feedback (7) Stability analysis of feedback amplifiers; (8) Advanced current mirrors, amplifiers, and comparators (9) Band gap and translinear cells (10) Matching. (Prerequisites: EEEE-480 or equivalent course or graduate standing in EEEE-MS.) Lecture 3 (Fall).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-711
Advanced Carrier Injection Devices
A graduate course in the fundamental principles and operating characteristics of carrier-injection-based semiconductor devices. Advanced treatments of pn junction diodes, metal-semiconductor contacts, and bipolar junction transistors form the basis for subsequent examination of more complex carrier-injection devices, including tunnel devices, transferred-electron devices, thyristors and power devices, light-emitting diodes (LEDs), and photodetectors. Topics include heterojunction physics and heterojunction bipolar transistors (HBT). (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).
3
EEEE-712
Advanced Field Effect Devices
An advanced-level course on MOSFETs and submicron MOS devices. Topics include MOS capacitors, gated diodes, long-channel MOSFETs, subthreshold conduction and off-state leakage, short-channel effects, hot-carrier effects, MOS scaling and advanced MOS technologies. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).
3
EEEE-726
Mixed-Signal IC Design
This is the first course in the graduate course sequence in analog integrated circuit design EEEE-726 and EEEE-730. This course covers the following topics: (1)Fundamentals of data conversion (2) Nyquist rate digital-to-analog converters (3) Quantization noise and analysis (4) Nyquist rate analog-to-digital converters (5) Sample and hold circuits (6) Voltage references (7) Static and dynamic testing of digital-to-analog converters (8) Cell based design strategies for integrated circuits (9)Advanced topics in data conversion. (Prerequisites: EEEE-510 or EEEE-610 or equivalent course.) Lecture 3 (Spring).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Elective
6
Total Semester Credit Hours
30

Electrical engineering (MEMS focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-661
Modern Control Theory
This course deals with a complete description of physical systems its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input-output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for desired performance, state feedback, observers and their realizations. (Co-requisites: EEEE-707 or equivalent course.) Lecture 3 (Fall).
3
EEEE-689
Fundamentals of MEMS
Microelectromechanical systems (MEMS) are widely used in aerospace, automotive, biotechnology, instrumentation, robotics, manufacturing, and other applications. There is a critical need to synthesize and design high performance MEMS which satisfy the requirements and specifications imposed. Integrated approaches must be applied to design and optimized MEMS, which integrate microelectromechanical motion devices, ICs, and microsensors. This course covers synthesis, design, modeling, simulation, analysis, control and fabrication of MEMS. Synthesis, design and analysis of MEMS will be covered including CAD. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-787
MEMS Evaluation
This course focuses on evaluation of MEMS, microsystems and microelectromechanical motion devices utilizing MEMS testing and characterization. Evaluations are performed using performance evaluation matrices, comprehensive performance analysis and functionality. Applications of advanced software and hardware in MEMS evaluation will be covered. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Electives
9
Total Semester Credit Hours
30

Electrical Engineering (robotics focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-636
Biorobotics/Cybernetics
Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lab 2, Lecture 3 (Spring).
3
EEEE-647
Artificial Intelligence Explorations
The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem-solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall).
3
EEEE-685
Principle of Robotics
An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project, in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lab 3, Lecture 3 (Fall).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-784
 Advanced Robotics
This course explores advance topics in mobile robots and manipulators. Mobile robot navigation, path planning, room mapping, autonomous navigation are the main mobile robot topics. In addition, dynamic analysis of manipulators, forces and trajectory planning of manipulators, and novel methods for inverse kinematics and control of manipulators will also be explored. The pre-requisite for this course is Principles of Robotics. However, students would have better understanding of the topics if they had Control Systems and Mechatronics courses as well. The course will be a project based course requiring exploration of a novel area in Robotics and writing an IEEE conference level paper. (Prerequisites: EEEE-585 or EEEE-685 or equivalent course.) Lab 2, Lecture 3 (Spring).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-602
Random Signals and Noise
In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Elective
3
Total Semester Credit Hours
30

Electrical Engineering (signal and image processing focus area), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
EEEE-602
Random Signals and Noise
In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-670
Pattern Recognition
This course provides a rigorous introduction to the principles and applications of pattern recognition. The topics covered include maximum likelihood, maximum a posteriori probability, Bayesian decision theory, nearest-neighbor techniques, linear discriminant functions, and clustering. Parameter estimation and supervised learning as well as principles of feature selection, generation and extraction techniques, and utilization of neural nets are included. Applications to face recognition, classification, segmentation, etc. are discussed throughout the course. (Prerequisites: EEEE-602 and EEEE-707 and EEEE-709 or equivalent courses.) Lecture 3 (Spring).
3
EEEE-678
Digital Signal Processing
In this course, the student is introduced to the concept of multi rate signal processing, Poly phase Decomposition, Transform Analysis, Filter Design with emphasis on Linear Phase Response, and Discrete Fourier Transforms. Topics covered are: Z- Transforms, Sampling, Transform Analysis of Linear Time Invariant Systems, Filter Design Techniques, Discrete Fourier Transforms (DFT), Fast Algorithms for implementing the DFT including Radix 2, Radix 4 and Mixed Radix Algorithms, Quantization Effects in Discrete Systems and Fourier Analysis of Signals. (Prerequisites: EEEE-707 or equivalent course.) Lecture 3 (Fall, Summer).
3
EEEE-707
Engineering Analysis
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall, Spring).
3
EEEE-709
Advanced Engineering Mathematics
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
3
EEEE-779
Digital Image Processing
This is an introductory course in digital image processing. The course begins with a study of two dimensional (2D) signal processing and transform methods with applications to images. Image sampling is discussed extensively followed by gray level description of images and methods of contrast manipulation including linear/nonlinear transformations, histogram equalization and specification. Image smoothing techniques are considered including spatial and frequency domain low pass filtering, AD-HOC methods of noise removal and median filtering. Following this, methods of image sharpening are studied including derivatives and high pass filtering. Edge and line detection algorithms are discussed using masks and Hough transforms. Finally, methods of image segmentation, restoration, compression and reconstruction are also discussed. Several extensive computer lab assignments are required. (Co-requisites: EEEE-678 equivalent course.) Lecture 3 (Fall).
3
EEEE-795
Graduate Seminar
The objective of this course is to introduce full time Electrical Engineering BS/MS and incoming graduate students to the graduate programs, campus resources to support research. Presentations from faculty, upper division MS/PhD students, staff, and off campus speakers will provide a basis for student selection of research topics, comprehensive literature review, and modeling effective conduct and presentation of research. All first year graduate students enrolled full time are required to successfully complete two semesters of this seminar. Seminar 3 (Fall, Spring).
0
Second Year
EEEE-794
Information Theory
This course introduces the student to the fundamental concepts and results of information theory. This is a very important course for students who want to specialize in signal processing, image processing, or digital communication. Topics include definition of information, mutual information, average information or entropy, entropy as a measure of average uncertainty, information sources and source coding, Huffman codes, run-length constraints, discrete memoryless channels, channel coding theorem, channel capacity andShannon's theorem, noisy channels, continuous sources and channels, coding in the presence of noise, performance bounds for data transmission, rate distortion theory. (Prerequisites: EEEE-602 or equivalent course.) Lecture 3 (Spring).
3
EEEE-790
Thesis
An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits. Thesis (Fall, Spring, Summer).
6
 
Graduate Elective
3
Total Semester Credit Hours
30