2019-2020 Graduate Catalog [ARCHIVED CATALOG]
Course Descriptions
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Course descriptions are arranged alphabetically by the course prefix code letters, as listed here. For the purpose of brevity, course descriptions may consist of sentence fragments. Unless otherwise specified, graduate courses carry three credits. |
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Computer Science |
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CSCI 6682 - Wireless Networks
Prerequisite: CSCI 6642 . The fundamentals of wireless network technologies will be studied, including various wireless spectra, wireless communication, signal propagation, antenna technologies, and physical and MAC layer protocols. Commonly-used Wireless Personal Area Network (WPAN) technologies such as Bluetooth and ZigBee, and Wireless Local Area Netowrk (WLAN) technologies such as WiFi and HIPERLAN will also be examined. The last part of the course will focus on wireless security.
3 credits.
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CSCI 6683 - Long Range Wireless Network Technologies
Prerequisite: CSCI 6682 . A continuation of CSCI 6682 , students will work on software and hardware used in industry, and the course prepares graduate students for careers working with different vendors, enterprises, and service providers that work on wireless technologies. It provides in-depth knowledge of cellular and mobile wireless systems. Labs will provide hands-on experience on advanced technologies in communication systems, and will bridge theory with applications and help students receive training required to meet the desired needs of industry. Topics include cellular systems starting from the first generation (1G) to the fourth generation (4G), including GSM and LTE, WiMAX (the IEEE 802.16 standard), and wireless ad-hoc networks including wireless sensor networks. 3 credits.
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CSCI 6690 - Master's Project
Prerequisites: 15 credit hours, a cumulative grade point average (GPA) of at least 3.3, and completion of all core courses. Petition to register must be approved by a supervising faculty member, the program coordinator, and the department chair. Completion of a significant project in the student's concentration area under the guidance on an advisor, such study terminating in a technical report of academic merit. For example, the project may be a survey of a technical area in computer science or may involve the solution of an actual or hypothetical technical problem. 3 credits.
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CSCI 6692 - Internship I
Prerequisites: CSCI 6620 ,18 graduate credit hours, cumulative GPA of 3.0 or better, and consent of the graduate coordinator and advisor. An on-the-job learning experience with a selected organization, taken for academic credit under the supervision of a faculty internship advisor. 1 credits.
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CSCI 6693 - Internship II
A continuation of Internship I. 1 credits.
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CSCI 6694 - Internship III
A continuation of Internship II. 1 credits.
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CSCI 6695 - Independent Study I
Prerequisite: petition to register must be approved by a supervising faculty member, the program coordinator, and the department chair. Independent study under the guidance of an advisor in an area disignated by the program coordinator in consultation with the student. 3 credits.
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CSCI 6696 - Independent Study II
A continuation of Independent study I. 3 credits.
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CSCI 6698 - Thesis I
Prerequisites: 15 credit hours and completion of all core courses. Petition to register must be approved by a supervising faculty member, the program coordinator, and the department chair. Periodic meeting and discussion of the individual student's progress in the preparation of a thesis 3 credits.
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CSCI 6699 - Thesis II
A continuation of Thesis I 3 credits.
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Data Science |
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DSCI 6001 - Math for Data Scientists
Prerequisites: no formal prerequisites; familiarity with linear algebra, calculus, and some object-oriented programming language is recommended. Provides a review of core skills in linear algebra, analysis, statistics, and differential calculus with a focus on hands-on applications for data science use cases. 3 credits.
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DSCI 6002 - Data Exploration
Prerequisites: no formal prerequisites; familiarity with linear algebra, calculus, and some object-oriented programming language is recommended. Introduction to the infrastructure and architecture of data warehousing systems, with a focus on querying, exploring, understanding, and transforming data features for statistical and machine learning applications. 3 credits.
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DSCI 6003 - Machine Learning & Data Analysis I
Prerequisites: DSCI 6001 , DSCI 6002 . Students may receive exemption from prerequisites at the instructor's discretion. Essential elements of machine learning, with a focused introduction to core supervised and unsupervised learning algorithms, statistical modeling, and key best practice techniques for building well-trained models. Designed with coding lab practice to develop implementation skills. 3 credits.
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DSCI 6004 - Unstructured Data/Natural Lang Proc
Prerequisites: DSCI 6001 , DSCI 6002 . Students may receive exemption from prerequisites at the instructor's discretion. Essential data science skills involved in working with unstructured data: transforming it into structured data types able to be analyzed, processed, and used for machine learning and information retrieval algorithms. Material focuses on natural language processing and classification techniques used in text mining. 3 credits.
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DSCI 6005 - Machine Learning & Data Analysis II
Prerequisites: DSCI 6003 , DSCI 6004 . Students may receive exemption from prerequisites at the instructor's discretion. Advanced topics in machine learning with focus on optimization, probability theory, multi-model ensemble techniques, time series analysis, instrumental variable analysis, and reinforcement learning. 3 credits.
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DSCI 6006 - Leadership and Entrepreneurism
Prerequisites: DSCI 6003 , DSCI 6004 . Students may receive exemption from prerequisites at the instructor's discretion. Core skills necessary for professional data scientists to succeed in an industry setting. Students learn the skills of data visualization in parallel with the soft skills of communicating with a non-technical audience, interviewing skills, and core data science leadership skills. Emphasis is placed on enabling students to listen to articulated business needs or problem cases and learn how to propose as well as execute data science solutions to effectively meet these needs. 3 credits.
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DSCI 6007 - Distributed & Scalable Data Engineering
Prerequisites: DSCI 6005 , DSCI 6006 . Students may receive exemption from prerequisites at the instructor's discretion. Advanced topics in big data infrastructure and architectures focusing on computing resources and programming environments to support the development of efficiently scalable, high-volume distributed machine learning algorithms. 3 credits.
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DSCI 6008 - Special Topics
Students may receive exemption from prerequisites at the instructor's discretion. Advanced topics in big data infrastructure and architectures focusing on computing resources and programming environments to support the development of efficiently scalable, high-volume distributed machine learning algorithms. 3 credits.
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DSCI 6009 - Data Science Focus Elective
The focus elective is an independent study of a specific application area for data science. Some examples include: health, sciences, stock market, and astronomy. Students may work as a group on an application area project. 3 credits.
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DSCI 6010 - Data Science Internship
Prerequisites: DSCI 6005 , DSCI 6006 . Students may receive exemption from prerequisites at the instructor's discretion. This internship course has students join an industry organization. In Session 2, students begin interviewing and arranging for internships with Galvanize industry partners that have data science or data engineering needs. Students are encouraged to begin integration work for the internship in Session 2 as soon as placement is achieved, and will focus on it in Session 3. Students will also be placed with a faculty or industry mentor/advisor to facilitate and supervise the internship. At least 300 hours of work preceding and during the internship are required. 3 credits.
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DSCI 6051 - Data Science Capstone Project
Prerequisites: DSCI 6005 , DSCI 6006 . Students may receive exemption from prerequisites at the instructor's discretion. The final capstone project comprises design and development of the strategic vision and the tactical operation plan for a data science challenge in a specific focus area. Students will participate in the organization, formulation, implementation, and presentation of the work done in the focus area. This course will provide the student with an opportunity to apply the data science skills developed in the program to a protracted real world project. Students will work with an industry partner or government sponsor to leverage a data set provided by the sponsor. Each student will develop his or her project by constructing a problem statement, proposing a solution, developing an experimental process for achieving a solution, and identifying success criteria for completion to be approved by the supervising faculty member and project sponsor. 3 credits.
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DSCI 6601 - Introduction to Applied Mathematics for Computing
Prerequisite: calculus. Fundamentals and application of mathematical tools needed for graduate study in data science. Key concepts from calculus, probability, statistics, and linear algebra are reviewed with an emphasis on application to real problems using software. This course is intended for students with computer science or non-technical backgrounds or who have otherwise taken mathematics courses but not courses requiring application to problem solving. This course does not count toward the 30-credit minimum for the M.S. in Data Science program.
3 credits.
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DSCI 6602 - Introduction to Programming for Data Science
A first course in computer programming using one or more scripting languages popular in data science, for those with little or no experience in programming. Problem solving methods, program planning, development, testing, and debugging. Sound programming practices and good style. Functions, libraries, basic types, and data structures. Loading, manipulating, and storing data from files. Extensive programming will be required. 3 credits.
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English |
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ENGL 6600 - English Language Workshop
Enrollment in this course is limited to and required of students who are not native speakers of English and who lack adequate background in English instruction. Students whose TOEFL scores are less than 560 (220 on the computer-based test) and/or students who enter the Graduate School following completion of an intensive English language program are required to take and pass this training course in the first term of enrollment at the Graduate School. The course emphasizes development of conversation, pronunciation, and composition skills and includes orientation to the Peterson Library and instruction in writing a research paper. 0 credits.
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ENGL 6634 - Applied Linguistics
This course is designed for teachers of writing at all levels. It helps students develop insights into sentence structure and development which, in turn, will be beneficial for transmitting systematic editing techniques at various school levels. The course will focus on sentence structure and touch upon phonetics and language history. Cross-listed with EDUC 6634 . 3 credits.
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ENGL 6659 - Writing and Speaking for Professionals
A practical, tool-oriented approach for professionals who need to perfect writing and speaking skills for career advancement or presentations in graduate courses. Students generate work-related writing/speaking assignments and negotiate learning contracts based on editing, writing, and speaking methods related to individual needs and objectives. Cross-listed with HUMN 6659 . 3 credits.
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ENGL 6670 - Selected Topics
A study of relevant topics of particular interest to students and instructor. Course may be taken more than once. 3 credits.
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Economics & Business Analytics |
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BANL 6100 - Business Analytics
This course reviews statistical concepts and methods with emphasis on data analytics and visualizations. Topics to be covered include descriptive statistics, plots and graphs for discrete and continuous data, statistical inference, regression, and selected non-parametrics including chi-square. In addition, power pivot and other Excel analytical tools will be covered. Students will obtain a solid introduction to R as a functional programming language and will be able to use Excel and R to effectively compute statistical and graphical procedures. 3 credits.
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BANL 6310 - Data Visualization and Communication
Prerequisite: BANL 6100 . This course focuses on the art of communicating ideas imbedded in data through visual means to include spatial representations. Students are introduced to industry-standard graphic and data design techniques used to create understandable visualizations in order to communicate effectively with a particular audience. Techniques in organizing and articulating data are developed using real world examples. The course materials, assignments and project will all be prepared using the R programming language. 3 credits.
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BANL 6320 - Supervised Machine Learning
Prerequisite: BANL 6100 . The course consists of applied training in foundational topics for supervised learning such as Linear Regression, Nearest Neighbors, and Neural Networks. It first builds a sound understanding of data preparation, exploration, and reduction methods. It covers both prediction as well as classification processes. The emphasis is on understanding the application of a wide range of modern machine learning techniques to specific decision-making situations across business domains, rather than on mastering the mathematical and computational foundations of the techniques. The R programming language will be used for instruction. 3 credits.
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BANL 6420 - Unsupervised Machine Learning
Prerequisite: BANL 6100 . The course consists of applied training in foundational topics for unsupervised learning such as Association Rules, Cluster Analysis, and Text Mining. It first builds a sound understanding of data preparation, exploration, and reduction methods. It covers both prediction as well as classification processes. The emphasis is on understanding the application of a wide range of modern machine learning techniques to specific decision-making situations across business domains. The R programming language will be used for instruction. 3 credits.
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BANL 6430 - Database Management for Business Analytics
Prerequisite: BANL 6100 . This course will introduce students to foundations of relational database design and management with a focus on business domains and business analytics applications. Topics include database design principles (to include connecting and updating), entity-relationship diagrams, constructing queries in SQL, and analyzing databases -- critical skills for data analysts. The course materials, assignments, and project will all be prepared using the R programming language. 3 credits.
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BANL 6500 - Global Supply Chain Management
This course discusses the managerial activities required to provide the right product or service in the right quantity ,with the right quality from the right source at the right time for the right price, through the use of global supply chains. The course focuses on contemporary strategic issues that affect both large and small corporations. Topics include key supply chain metrics, basic tools for supply chain management, procurement and outsourcing decisions, supplier selection and relationship management, logistics, and supply chain integration and coordination for the highest customer service. 3 credits.
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BANL 6550 - Managing Quality in the Supply Chain
This course introduces concepts and principles of business process improvement, and quality assurance in organizations. It examines the primary tools and methods used to monitor, measure, improve, and control business processes, and quality from a holistic supply chain perspective. Topics include statistical process control, Lean Six Sigma principles, and continuous improvement. 3 credits.
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BANL 6600 - Power BI and Dashboarding
This course introduces key concepts, skills, and methods in Business Analytics for data-driven decision making in organizations. Using Microsoft Power BI, the course trains the student in dashboarding, and Power BI's quantitative, and computational capabilities across various business domains. The mix of topics includes data access, data wrangling, visualization, and machine learning via R programming language in Power BI. 3 credits.
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ECON 6601 - Macroeconomics and Microeconomics
A basic theoretical foundation for students who lack adequate background in economics. An introduction to and review of basic economic principles. 3 credits.
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ECON 6603 - Microeconomic Analysis
Survey of the behavior and decision choices of individual economic agents (e.g., consumers, firms, and resource owners) under alternative market conditions, time horizons, and uncertainty. 3 credits.
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ECON 6604 - Macroeconomic Analysis
Study of the performance and fluctuations of the economy, focusing on economic policies that affect performance. Topics include consumption and investment, the determinants of changes in wages and prices, monetary and fiscal policies, money, interest rates, the federal budget, the national debt, and interdependence and policy between countries. 3 credits.
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ECON 6625 - Industrial Relations
Survey of problems, strategies, and policies of management interactions with formal and informal labor organizations. Labor legislation, collective bargaining, productivity analysis, and arbitration are stressed, with emphasis on negotiating strategies and techniques. 3 credits.
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ECON 6627 - Economics of Labor Relations
Survey of labor economics using the tools of economic and institutional analysis. Emphasis on human resources and demographics pertaining to labor markets. 3 credits.
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ECON 6629 - Business and Society
Prerequisite: ECON 6633 . Topics include forces shaping business institutions through emerging social, legal, ethical, and political issues such as pollution control, workplace issues, equal employment opportunity, product safety, and relations with external stakeholders. Also addressed, using lectures and cases, will be laws and regulations that govern and restrict business activities. 3 credits.
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ECON 6633 - Economics for Managers
This course addresses how scarcity forces individuals, firms, and societies to choose among alternative uses of its limited resources. At the same time, the various choices made by different economic agents must be mutually consistent. Markets are a mechanism to achieve such reconciliation. The course seeks to make the students understand how economists model the choice process of individual consumers and firms, and how markets work to coordinate these choices. It also examines how well markets perform this function using the economist's criterion of market efficiency. 1.5 credits.
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ECON 6635 - Business Forecasting
Prerequisite: BANL 6100 . The focus of this course is on statistical and data analytical methods for the preparation of business forecasts. A variety of empirical techniques are covered: smoothing methods, moving averages, regression analysis, classical time-series decomposition methods, and ARIMA (Box-Jenkins) models. Emphasis is placed upon building forecasting models and evaluating their reliability. The focus is on time-series data. R is the preferred statistical package. 3 credits.
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ECON 6644 - Managing in a Global Economy
This course provides the basic theoretical foundation of both macroeconomics and international economics. The course will help the students to make decisions in today's global economy. Topics may include comparative advantage, gains from trade, measuring national output, inflation, unemployment, productivity, growth, the role of economic policy and institutions in the performance of firms and nations, currency exchange rates, capital markets, open economy, trade liberalization, and economic integration. 3 credits.
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ECON 6665 - Urban and Regional Economic Development
Prerequisite: ECON 6633 . Techniques, methods of analysis, and models utilized in the development process. Emphasis on job creation, manufacturing assistance, free enterprise zones, and regional planning. 3 credits.
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ECON 6670 - Selected Topics
A study of selected issues of particular interest to students and instructor. May be taken more than once. 3 credits.
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ECON 6687 - Collective Bargaining
Recommended prerequisite: ECON 6625 . Emphasis on contract negotiation, whether in a formal or informal bargaining scenario. Contract development covers wages, benefits, job security, management's rights, equal opportunity, and grievance procedures. Additional time devoted to third-party settlements - the arbitration process. 3 credits.
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ECON 6690 - Research Project
Prerequisite: consent of the instructor. A major independent research study/project carried out under faculty supervision. 3 credits.
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ECON 6693 - Internship
Prerequisites: 15 graduate hours and consent of the program coordinator. A supervised work experience in a selected organization, arranged for course credit and directed by a faculty advisor. 3 credits.
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ECON 6695 - Independent Study I
A planned program of individual study under the supervision of a member of the faculty. 3 credits.
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ECON 6696 - Independent Study II
A continuation of Independent Study I. 3 credits.
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ECON 6698 - Thesis I
Prerequisite: completion of 15 credits of graduate work. Periodic meeting and discussions of the individual student's progress in the preparation of a thesis. 3 credits.
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ECON 6699 - Thesis II
A continuation of Thesis I 3 credits.
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Electrical and Computer Engineering |
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ELEC 5501 - Digital Systems
Prerequisite: consent of the program coordinator. Course focuses on sequential logic design. Both synchronous and asynchronous techniques are covered with an emphasis on controller-based modular design. Design with a hardware description language. Advanced topics will be covered as time permits. Course includes laboratory activity. This course is intended for those students whose undergraduate background did not emphasize this content. 3 credits.
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ELEC 5520 - Probability Theory and Applications
Axioms of probability, joint and conditional probability, random variables, probability density, mass, and cumulative distribution functions, Bernoulli trials, Binomial, Poisson and Gaussian random variables, pair of random variables, functions of one and two random variables, characteristic functions, sequences of independent random variables, central limit theorem, and laws of large numbers. Introduction to random processes. Autocorrelation and spectral density functions. Noise in electronic systems. 3 credits.
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ELEC 6600 - Electromagnetic Waves
Prerequisite: consent of the program coordinator. Basic electromagnetic theory including static fields of electric charges and the magnetic fields of steady electric currents. Fundamental field laws including Coulomb's Law, Gauss's Law, Biot Savart's Law and Ampere's Law. Maxwell's Equations, scalar and vector potentials, Laplace's equation and boundary conditions. Magnetization, polarization. This course is intended for those students whose undergraduate background did not emphasize this content. 3 credits.
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ELEC 6602 - Embedded Systems
Prerequisite: consent of the program coordinator. Introduction to the architecture of digital computers, stored program concept, instruction processing, memory organization, instruction formats, addressing modes, instruction sets, assembler and machine language programming, direct memory access, bus structure and control signals. Course includes laboratory activities, and is intended for those students whose undergraduate background did not emphasize this content. 3 credits.
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ELEC 6603 - Discrete and Continuous Systems I
Prerequisite: linear system analysis. This course exposes the students to the tools and mathematical techniques used in the analysis of continuous-time and discrete-time signals and systems. Topics include a thorough coverage of Fourier series, Fourier Transform, Hilbert transform, Laplace transform, Z transform, discrete-time Fourier transform (DTFT), discrete Fourier transform (DFT), fast Fourier transform (FFT), and state-space analysis. 3 credits.
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ELEC 6604 - Discrete and Continuous Systems II
Prerequisites: ELEC 6603 , MATH 5511 or consent of the instructor. Proficiency in Mathematica, or MATLAB is desirable. Mathematical review: Quadratic forms, convergence, matrix calculus, solutions to systems of linear equations. Nonlinear state equation representation of physical systems: linearization of nonlinear state equations about trajectories, time-varying state equation solutions, Peano-Baker series, existence, uniqueness, complete solution, time-varying state transition matrix properties, time-invariant case. Stability: uniform stability, uniform exponential stability, Lyapunov stability criteria. 3 credits.
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ELEC 6605 - Computer Controlled Systems
Prerequisites: ELEC 6604 , ELEC 6650 . Disturbance models, design, analog design, state space design methods, pole placement design based on input-output models, optimal design methods (state space approach), optimal design methods (input-output approach), identification, adaptive control, implementation of digital controllers, reduction of the effects of disturbances, stochastic models of distrubances, continuous time stochastic differential equaltion. 3 credits.
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ELEC 6606 - Robot Control
Prerequisite: ELEC 6605 . Orientation coordinate transformations, configuration coordinate transformations, Denavit-Hartenberg coordinate transformation, D-H matrix composition, inverse configuration kinematics, motion kinematics, force and torque relationships, force and moment translation, trajectories, coordinated motion, inverse dynamics, position control, feedback systems, performance measures, PID control, inverse dynamic feedforward control, nonlinear control. 3 credits.
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ELEC 6607 - Adaptive Control
Prerequisites: ELEC 6605 , ELEC 6650 or consent of the instructor. An introduction to adaptive control methods and their application. The identification and control of linear deterministic time-invariant dynamical systems with parametric uncertainty are emphasized. Topics such as real time parameter estimation, model reference adaptive systems, robust adaptive control, and implementation issues are covered. 3 credits.
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ELEC 6610 - Networking I
Discussion of TCP/IP and OSI reference models, LANS and WANS, different topologies, the internet structure, Data and signals, sampling, bandwidth, transmission, impairment, digital and analog transmission, multiplexing and spreading, guided and unguided media. Switching and virtual circuit networks, telephone networks, DSL, Cable moderm. Error detection and correction, hamming codes, CRC, checksums, lab experiments. 3 credits.
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ELEC 6611 - Networking II
Prerequisite: ELEC 6610 . Network layer design, routing algorithms, congestion control algorithms, transport layer issues, application layer, network security, lab experiments. 3 credits.
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ELEC 6615 - Introduction to Computer Logic
Prerequisite: CSCI 6604 or CSCI 6610 or equivalent. Introduction to logic elements and to their application in digital networks for processing numerical data. The course deals with analysis and design techniques of combinational and sequential networks and includes a discussion of logic variables, switching functions, optimal realizations, multivariable systems. Design examples will include logic circuits for addition, multiplication, counting, parity generation, and detection. 3 credits.
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ELEC 6620 - Fuzzy Logic and Control
Prerequisites: basic linear algebra, probability, systems theory. Introduction to fuzzy logic and fuzzy control systems. Basic fuzzy logic concepts will be covered, followed by a selection of fuzzy applications from the literature. Topics include fuzzy sets, fuzzy numbers, fuzzy relations, fuzzy logic and appropriate reasoning, fuzzy rule-based systems, fuzzy control, fuzzy classification, fuzzy pattern recognition. Homework will consist of computer exercises and simulations; a final project is required. 3 credits.
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ELEC 6634 - Digital Signal Processing I
Prerequisite: ELEC 6603 . A study of the theories of digital signal processing and their applications. Topics include discrete time signals, the Z-transform, the discrete Fourier transform, the FFT, homomorphic signal processing, and applications of digital signal processing. 3 credits.
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ELEC 6635 - Digital Signal Processing II
Prerequisites: ELEC 6634 and knowledge of programming in MAT-LAB or other high-level language. Wiener filter theory, linear prediction, adaptive linear filters using gradient estimation, Least Mean Squares (LMS) algorithm, least squares formulation and the Recursive Least Squares (RLS) algorithm, fast implementations, recursive adaptive filters, lattice structures, eigenstructure methods for spectral estimation elements of adaptive nonlinear filtering, and applications. 3 credits.
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ELEC 6637 - Power Systems Engineering
Prerequisite: consent of the instructor. Changing power systems landscape, electric energy sources including renewable and various distributed generation (DG), environmental consequences of the electrical energy, AC transmission lines and cables, power flow in transmission networks, loadability of transmission lines, transformers, High Voltage DC (HVDC) transmission lines, power electronics devices and their applications, power quality and power factor, synchronous generators, voltage regulation and stability, peak load issues, ways to prevent voltage collapses, dynamic stability, automatic generation control (AGC). To reinforce the concepts, the course will utilize a number of tools such as PSCAD, POWER WORLD, EMTDC, MATLAB. 3 credits.
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ELEC 6638 - Power Systems Engineering II
Prerequisite: ELEC 6637 . A continuation of Power Systems Engineering I. 3 credits.
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ELEC 6639 - Electric Power Distribution
Prerequisite: ELEC 6637 or equivalent. Structure of electric power distribution, distribution transformers, subtransmission lines, substations, bus schemes, primary and secondary systems, radial and loop feeder designs, voltage drop and regulation, capacitors, power factor correction and voltage regulation, protection, buses, automatic reclosures, and coordination. 3 credits.
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ELEC 6640 - Power Electronics
Switch-mode power electronics, switch-mode DC power supplies, switch-mode converters for DC and AC motor drives, wind/photovoltaic inverters, interfacing power electronics equipment with utility system, power semiconductor devices, magnetic design, electro-magnetic interference (EMI). 3 credits.
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ELEC 6641 - Electric Drives
AC/DC electric-machine drives for speed/;position control, integrated discussion of electric machines, power electronics and control systems. Applications in electric transportation, robotics, process control, and energy conservation, computer simulations. 3 credits.
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ELEC 6642 - Power Electronics Laboratory
Co-requisite: ELEC 6640 . Laboratory to accompany ELEC 6640 . PSpice/Simulink-based simulations of converters, topologies, and control in switch-mode dc power supplies, motor drives for motion control, and inverters for interfacing renewable energy sources to utility grid. 1 credits.
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ELEC 6643 - Electric Drives Laboratory
Co-requisite: ELEC 6641 . To reinforce various concepts from Electric Drives course (ELEC 6641 ) through hands-on experiments. The Electric Drives laboratory is build around DSP-based electric-drives systems. 1 credits.
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ELEC 6645 - Introduction to Communication Systems
The anlysis and design of communication systems. Includes analog and digital signals, sampling, quantization, signal representation. Analog and digital modulation, pulse code modulation, delta modulation, time and frequency muliplexing. Noise in communication systems. 3 credits.
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ELEC 6646 - Digital Communications I
Prerequisites: ELEC 6603 , ELEC 6645 , ELEC 6650 . Digital representation of analog information; nonlinear quantization, baseband digital modulation; line coding techniques, detection of binary signals; matched filter and correlation detectors, signal space representation of M-ary signals and optimal receiver structures; nearest neighbor bound, band-pass modulation techniques including PSK, QAM and FSK; differential and non-coherent demodulation, error rate-bandwidth efficiency comparisons, inter-symbol interference and Nyquist pulse shaping, partial response signaling, linear and nonlinear equalizers, sequence detection and the Viterbi algorithm. 3 credits.
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ELEC 6647 - Digital Communications II
Prerequisite: ELEC 6646 . Spread spectrum communications, orthogonal frequency-division multiplexing communications, carrier, symbol, and frame synchronization, information measures, characterization of information sources, Shannon's source coding theorem; Huffman codes; LZW compression algorithm, image and video compression algorithms, discrete channel characterization, channel capacity, noisy-channel coding theorems, capacity of AWGN channel, linear block codes; cyclic codes; BCH codes and Reed-Solomon codes; hard- and soft-decision decoding techniques, convolutional codes; Viterbi algorithm for decoding, trellis-coded modulation, capacity-achieving codes; turbo codes; low-density parity-check codes. 3 credits.
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ELEC 6648 - Microwave Engineering
Prerequisites: undergraduate course in electromagnetics; programming experience, preferably in MATLAB; graduate standing or consent of the instructor. This course is designed to familiarize the students with microwave components and their operating principles. This course covers transmission line, including microstrip and coplanar waveguides, impedance matching, S parameters, Smith chart, couplers/dividers, waveguides, EM simulators, and antennas. Some homework assignments may require use of computer-aided design software. 3 credits.
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ELEC 6649 - Wireless Communications
Prerequisites: ELEC 6646 , ELEC 6650 . Transmission characteristics of mobile radio channels; large- and small-scale effects; path loss and shadowing; Rayleigh and Rician fading models; the Doppler effect; coherence bandwidth and coherence time, digital communication over fading channels; diversity techniques, interleaving, forward error correction; spread spectrum and OFDM systems, multiple access schemes: FDMA, TDMA, CDMA, OFDMA, cellular architecture; capacity calculations, MIMO systems; capacity limits; space-time coding, architecture of modern cellular systems, IEEE 802.11 WLAN technologies. 3 credits.
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ELEC 6650 - Random Processes in Communications and DSP
Prerequisite: ELEC 5520 . Review of random variables, random vectors; introduction to continuous- and discrete-time random processes; stationarity and ergodicity; correlation functions and power spectral density of wide-sense stationary processes, Gaussian and Poisson processes, Markov chains; recurrence, absorption, limiting and steady-state distributions, analysis of linear systems with random inputs, narrowband random processes, stochastic signal representations; orthogonal expansions; the Karhunen-Loeve series, linear mean-square estimation; the orthogonality principle, optimum Wiener and Kalman filtering, simulation of random processes. 3 credits.
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ELEC 6652 - Design of Digital Filters
Techniques in the analysis and design of digital filters. Digital filter terminology and frequency responses, FIR filter design, IIR digital filter design including Butterworth and Chebyshev lowpass, highpass, bandpass, and bandstop filters. The DFT and IDFT; FFT algorithms 3 credits.
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ELEC 6653 - Digital Image Processing
Prerequisites: working knowledge of signal analysis and linear algebra; programming experience (languages such as MATLAB, C.net, java, C++); or consent of the instructor. Fundamental concepts and applications of image processing and analysis. Topics include image formation, imaging geometrics, image transform theory and restoration, encoding and compression. 3 credits.
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ELEC 6656 - Hardware Description Language
General structure of VHSIC (Very High Speed Integrated Circuit) Hardware Description Language (VHDL) code; entities and architecture in VHDL; signals, variables, data types; concurrent signal assignment statements; processes; if, case, and loop statements; components; package; functions and procedures; slices; attributes; generate statements; blocks; projects on design of combinational and sequential circuits using VHDL. 3 credits.
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ELEC 6657 - VLSI Design
Complex logic gates, flip-flop, cascade voltage switch logic, differential split level logic, Schmitt trigger, dynamic logic gates, clocked CMOS logic, Domino logic, SRAM and DRAM, VCO, Voltage generator, lab activities. 3 credits.
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ELEC 6658 - Embedded Applications
Design of advanced embedded microcontroller applications. Interface and control of several devices and buses. Classwork will focus on laboratory exercises and projects. 3 credits.
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ELEC 6659 - System on Chip
Prerequisites: CSCI 6610 , basic knowledge of hardware description language or consent of the instructor. Design of system-on-chip embedded systems using reconfigurable devices; embedded programming principles for real-time execution; exploring the use of Linux in embedded systems; interfacing custom HDL designs with software; multi-core programming and interaction (if time permits). 3 credits.
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ELEC 6660 - Introduction to Smart Grid
Prerequisite: ELEC 6637 . Review of power systems and power electronics, renewable energy sources (RES), wind power, wind turbine, solar power, photovoltaic panels, integration of RES with power grid, energy storage technologies, electric vehicles and their charging infrastructures, microgrid, demand response. Simulation and programming in professional power system software tools, Matlab/Simulink, PowerWorld, Matpower and PSCAD will be required. 3 credits.
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ELEC 6662 - Nanoelectronics
Prerequisite: ELEC 6600 or ELEC 6657 . Nanoelectronics presents the basic principles of nanoscience and nanotechnology (extremely small scale devices) and how they are used to develop and design instrumentation and devices for the future. In this course, the fabrication and other design challenges being faced by the microelectronic technology in keeping up with Moore's Law will be discussed along with their plausible solutions. The physics behind the working of the semiconductor diodes and transistor (BJT, FET, & CMOS) based devices as the size of the device gets smaller will be discussed in detail. The limits of these new techniques will be discussed in detail, especially in light of transport properties. Properties and fabrication methods of carbon nanotubes, graphene, semiconductor nanowires and quantum dots as electronic devices will also be discussed. Application of nanoelectronic devices such as nanosensors and biosensors, micro-fluidics (MEMS/NEMS), and optoelectronic (LASER and LEDs) devices will be also discussed. 3 credits.
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ELEC 6667 - Power Systems Laboratory
Prerequisite or co-requisite: ELEC 6637 . This lab is designed to reinforce the concepts learned in the associated course. Concepts include AC transmission lines and cables, power flow in transmission networks, loadability of transmission lines, transformers, High Voltage DC (HVDC) transmission lines, power quality and power factor, synchronous generators, voltage regulation and stability, peak load issues, ways to prevent voltage collapses, and dynamic stability. Experiments are designed to show the usage of the following software tools in power systems: PSCAD-EMTDC, PowerWorld, and MATLAB. 1 credits.
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ELEC 6670 - Selected Topics
Prerequisite: consent of the instructor. A study of selected topics of particular interest to students and instructor. Course may be taken more than once. 3 credits.
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ELEC 6680 - Optical Fiber Communications
Prerequisite: undergraduate course in electromagnetics. The fundamentals of lightwave technology, optical fibers, light sources, signal degradation, photodetectors, power launching and coupling, design of digital fiber optic links, optical amplifiers, WDM techniques. This course includes selected laboratory experiments. 3 credits.
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ELEC 6681 - Lightwave Technology
Prerequisite: ELEC 6680 . Advanced topics in lightwave technology. Optical fiber waveguides, transmission characteristics of optical fibers, ray theory, and electromagnetic mode theories are considered. Forms of communication systems and distribution networks. Optical sources, detectors, and receivers are discussed in conjunction with modulation formats and system design. 3 credits.
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ELEC 6682 - Computer Architecture
Review of design of large systems, arithmetic and logical operations, design of ALU, design of control unit, microprogramming, RISC architecture, memory organization, design of cache memory, system organization, design of a processor using bit-slice ALU. 3 credits.
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ELEC 6685 - Optimization of Engineering Systems
Prerequisite: ELEC 6604 . The calculus of variations, functionals, linearity of functionals, closeness of functions, the increment of a functional, maxima and minima of functionals, the fundamental theorem of the calculus of variations, the variational problem, Euler-Lagrange equations, boundary conditions, the transversality conditions, piece-wise-smooth extremals, the first and second carrier conditions, Lagrange multiples, the Hamiltonian canonical equations, the control problem, the problems of Lagrange and Mayer, Strong's variation, Legendre conditions, Weierstrass excess function, Pontryagin's minimal principle. 3 credits.
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ELEC 6690 - Research Project
Prerequisites: 15 graduate hours and written consent of the program coordinator. Independent study under the guidance of a faculty advisor, such study terminating in a technical report of academic merit. Research may constitute a survey of a technical area in electrical engineering or involve the solution of an actual or hypothetical technical problem. 3 credits.
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ELEC 6691 - Internship
Prerequisites: graduate standing, 15 graduate credits, and consent of the coordinator. This course provides an opportunity for students to engage in real world practice of what they learn in classes. A student who is accepted in an internship position must work on a project or engage in activities related to his or her field of study in industry or other establishment where he or she works as an employee for at least 200 hours. 1 credits.
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ELEC 6695 - Independent Study I
Prerequisite: consent of the instructor. A planned program of individual study or research under supervision of a faculty member. 1-3 credits.
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ELEC 6696 - Independent Study II
Prerequisite: ELEC 6695 . A continuation of Independent Study I. 3 credits.
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ELEC 6697 - Thesis I
Prerequisites: completion of 15 credits of graduate work; student must have submitted a thesis proposal and performed a literature search in the preceding term. Periodic meetings and discussions of the individual student's progress in the preparation of a thesis. 3 credits.
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