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ECE Course Information

Internet-of-Things: Principals

In Internet of Things (IoT), trillion of products and systems are expected to be supported by the Internet—sensing and collecting data from the environment, and then pushing data into the cloud data-center for processing before extracting knowledge or performing a specific action. A reliable architecture is critical to ensure ease of connectivity, control and communications between things, servers, and even with other IoT systems. This course covers the following topics: IoT technologies; Incentives for crowd sensing; Quality of information and management in crowd sensing, and Context awareness in IoT. The course will include software development using TCOM designed sensor and TI Tag CC2560 platforms

Deep learning

Deep learning is a study of machine learning models composed of multiple layers of representation (hence deep). Topics will include a review of of artificial neural networks and the backpropagation algorithm, regularization methods, optimization methods, weight initialization techniques, convolutional neural networks, recurrent neural networks, and autoencoders. Applications for computer vision, natural language processing, and communications would be discussed. Prerequisite: linear algebra, Python.

Information theory and statistical learning

The course will provide an overview of information theory and statistical learning. Core information theory ideas including information measures, AEP, source and channel coding theory will be covered. And their connections to powerful statistical learning techniques such as Bayesian learning, decision forests, and belief propagation on graphical models will be discussed. Prerequisite: probability, calculus.

Fundamentals of Network Design

This is an introductory course covering the foundation of computer networks, which includes the OSI model, the Internet model, routing algorithms, congestion controls, queueing theory, notions of fairness, and an introduction of software defined networking. Prerequisite: probability, calculus

Big Data Analytics

This is an introductory course of big data analytics. We will look into examples of big data and identify what are and what are not big data problems. Students will learn to recast big data problems as data science questions.The course will cover discuss the 5V's of big data and cover its four important aspects: collection, storage, organization, and processing. We will also look into processing platforms such as Hadoop and Spark. Other open source tools such as MLlib, Pig, and Hive will also be discussed. Prerequisite: Linux, linear algebra, some programming experience

Predictive Network Analytics

This course focuses on fundamentals and applications of data analysis techniques in context of digital network performance monitoring and optimization. Data Analytics uses techniques from statistics and machine learning to analyze data and create models that can be used to categorize data, identify root cause of faults, or predict future values in the observed data such as traffic, parameter drifts, user locations etc. In emerging networks, such predictions are playing more and more important role in ensure that QoS for subscribers and key performance indicators (KPIs) for operators are maintained across the entire system, from edge node to core network. The primary aims of the module are to :- 1) Analyze the types of data that are or can be collected for network and service monitoring and prediction. 2) provide a foundation in the underlying technologies which support predictive as well as descriptive Network Analytics 3) Through case studies and lab sessions, equip students with the necessary skills to allow them to design and implement solutions to Network Analytic problems.

Optical Communications (Optical Fiber and Free Space Optical Communications)

The increasing demand for data and video-intensive content and the adoption of cloud services are driving needs for ever increasing capacity in service provider’s backbone and metro networks while also creating needs for high speed interconnect of hyper-scale data centers. The first part of this course gives an introduction to modern optical fiber communication technologies. Topics covered include the modulation formats, multiplexing techniques, impairments in optical fibers, and methods to compensate for the impairments. The course also covers optical receiver design for both direct-detection and digital coherent (intradyne) receivers, including discussions of the underlying digital electronic signal processing (DSP) at both the receiver and the transmitter. The second part focuses on free space optical communication with topics covering the fundamentals, design issues, and applications in contemporary telecommunications practice.

ECE 5403 Linear Systems Analysis

This course provides the background in the methods of linear analysis that are needed for students in all areas of systems engineering (e.g. communication, signal processing, control and power). It’s appropriate for graduate and undergraduate students that have solid background in Linear Algebra. Topics include orthonormal vectors and QR factorization; least-squares methods (including regularized least-squares and minimum norm methods); symmetric matrices, quadratic forms and matrix norm; SVD and applications; autonomous and non autonomous linear dynamical systems and solution via Laplace transform and matrix exponential; eigenvectors, diagonalization and Jordan canonical form; controllability and state transfer; observability and state reconstruction.

ECE 5223 Stochastic Systems

This courses provides students with the tools for modeling, identification, estimation and optimal control of discrete time stochastic systems. It is appropriate for students that have good background in probability theory and stochastic processes as well as linear systems analysis. Topics include review of state space models for linear dynamic systems; properties of linear stochastic systems; controlled Markov chains, introduction to dynamic programming; optimal estimation and control of linear systems (including, LQR, Kalman filter and LQG); nonparametric identification methods; parametric identification methods.

TCOM 5183/ECE 5183 Quantum Information Theory

This is an introductory course of quantum information theory. The goal is to introduce students to the concepts and tools of quantum information and to demonstrate how these tools can be used to analyze quantum information protocols. Topics covered include quantum state, quantum measurement, quantum channels, No-Cloning Theorem, Bell's inequalities, entanglement, quantum dense coding, quantum teleportation, distance measures, quantum entropy, quantum mutual information, information of quantum channels.Prerequisite: linear algebra.

TCOM 5553/ECE 5553 Telecommunication Technologies

This course focuses on the modern telecommunication systems concepts, design, architecture, planning and optimization. The course covers both wireless cellular systems and optical fiber networks. Emphasis is placed on tools and concepts that can be leveraged to enhance performance and efficiency of telecommunication systems. The course is roughly divided into four parts: Part I: [Lecture No. 1-4]: Course starts with review of fundamental concepts of telecommunication networks including notions of grade of service, soft and hard blocking, quality of service. Key state of the art telecommunication technologies including satellite and optical fiber based technologies are also discussed and compared. Part I then delves into basics of cellular systems planning and dimensioning. This is followed by introduction to the standard specific air interface and key operational principles of 2G and 3G systems. Part II: [Lecture No. 6-8]: Second part of the course starts with basics of Medium Access Techniques, including CDMA and OFDMA and then delves into OFDM based communication system design. Part III: [Lecture No. 9-12]: This part covers the architecture, planning and operation of LTE. Part IV: [Lecture No. 14-15]: This part covers advance concepts in LTE and LTE-A including Mobility management, SON and MIMO.

TCOM 5970/ECE 5973 Emerging Topics in 5G and 6G

This course provides an overview of selected topics that are currently being researched globally for developing 5G and 6G cellular network technologies. These topics include: self-organizing networks, applications of big data analytics for optimizing cellular networks, network densification, split of control and data plane, network function virtualization, heavy and localized cache, infrastructure sharing, concurrent operation at multiple frequency bands, simultaneous use of different MAC and PHY layers, and flexible spectrum allocation. To achieve its objectives, the course is divided into three parts: 1) Overview of 5G and 6G Landscape 2) Tools and techniques for designing Self Organizing Networks (SON) 3) Big Data Empowered SON for enabling 5G and beyond. 

ECE5973 980/ECE 5973 Local Wireless Networks and Standards

The class Provides system approach to building wireless networks. It articulates underlying principles, commonalities, differences, and specific implementation issues associated with most wireless systems: IEEE802.11b,g,a,n, and ac; IEEE802.15.1 (Bluetooth); IEEE802.19.1 (TV white space); IEEE802.22 (Cognitive Wireless Network); IEEE802.15.2 (Wireless Coexistence Protocols). Particular attention is paid to the principles of air-interface design, the principles of wireless network operation, wireless WANs, local broadband and ad hoc networks.

TCOM 5123/ECE 5123 Wireless Communication

The class will cover wireless communications principles: 1) System design fundamentals: frequency reuse, channel assignment, handoff strategies, and interference and system capacity; 2) Mobile radio propagation path loss: reflection, diffraction, scattering, outdoor/indoor propagation models; 3) Mobile radio propagation fading and multipath: multipath measurements, mobile multipath channel parameters, fading, Rayleigh and Ricean distributions, and statistical models; 4) Modulation techniques: FM, AM, BPSK, DPSK, QPSK, MSK, GMSK, DS-SS, FH-SS and their performance in fading and multipath channels; 5) Channel coding, Equalization, and Diversity.

TCOM 5533 Telecommunications Industry Overview

This course focuses on the management of telecommunication and information technologies. It is an overview course and provides the student interested in telecommunications an introduction to technology, market and regulatory issues facing managers in a competitive, global economy. The course will explore telecommunications and current status of public and private networks (voice, data, video, wireless). Emerging technologies are explored as well as applications of these technologies and their implication for organizations. Regulation of and competition in the marketplace are discussed.

TCOM 5543 Network Design and Management

Fundamentals of the systems analysis and design of voice and data communications networks. Covers technical as well as managerial aspects of developing an integrated communications network.

TCOM 5573 Optical Systems and Networks

Comprehensive study of new developments and how optical technology is used in optical systems and networks; covers optical fiber applications as the best transmission medium for high capacity traffic in communications networking; also how advanced photonic technology has enable networks to transport broadband exceeding terabits/second/fiber.

TCOM 5563 Computer and Communications Security

Introduction to security problems in computing and communications, basic encryption and decryption techniques, secure encryption systems, cryptographic protocols and practices, security in networks and distributed systems, legal and ethical issues in computer security.

TCOM 5272 Telecommunication Laboratory

This laboratory course is designed to enhance the understanding of concepts and principles discussed in the computer networking text through a variety of networking exercises. It also emphasizes network performance, simulation, and the Internet protocols. There will be approximately eight laboratory modules. For each module, the students will write a short report after completing the laboratory exercises.

TCOM 5671 Professional Project Proposal

During this enrollment the student will propose a project that will demonstrate the student's comprehensive grasp of his/her field of study. A Project Committee, appointed by the director and consisting of at least three graduate faculty members, will review the proposal and approve the scope of it.

TCOM 5682 Professional Project

During this enrollment the student must satisfactorily complete the Professional Project that was proposed and approved during enrollment in TCOM 5671. At the conclusion of the Project, the Comprehensive Project Committee will evaluate the written report of the Project and determine whether the Project has satisfactorily met the standards of the assessment plan established during the enrollment in TCOM 5671. In addition to the written report of the Project, the Committee will require an oral defense of the Comprehensive Project.

TCOM 5960 Readings in Telecommunications

1-3 hours. May be repeated with change of subject matter; maximum credit nine hours. Devoted to special topics in telecommunications not covered in the regular curriculum or to supervised individual study.

ECE 5353 Fiber Optics

Principles of optical fiber wave-guiding and losses; sources and detectors; receivers; transmission system design; fiber-based broadband networks.

ECE 5513 Communication Theory

Probability theory, stochastic processes, detection, extraction and predictions of signals in noise.

ECE 5973 Applied Electromagnetics

Selected topics of current research interest not covered by regularly scheduled coursework.

ECE 5973 Information Theory

Review of probability theory; asymptotic equipartition property, typicality, information measures, Shannon source and channel coding theorems, error correcting codes, Gaussian channels, methods of type, large deviation theory.

ECE 5980 Res. Masters Thesis

Credit required for MS in ECE, six hours.

ECE 5990 Special Studies

Devoted to special topics in electrical engineering not covered in the regular curriculum or to supervised individual study.

ECE 6973 Advanced Topics in Electrical Engineering - Sensor Engineering and Networking

Basic design of sensor systems, noise, and uncertainty analysis, techniques of multi-sensor fusion, application and design of wireless sensor networks, and current research topics are discussed in this course.

ENGR 6990 Independent Study

1-3 Credit Hours

MATH 4163 Partial Differential Equations

Physical models, classification of equations, Fourier series and boundary value problems, integral transforms, the method of characteristics.

MATH 5383 Applied Modern Algebra

Topics from the theory of error correcting codes, including Shannon's theorem, finite fields, families of linear codes such as Hamming, Golay, BCH, and Reed-Solomon codes. Other topics such as Goppa codes, group codes, and cryptography as time permits. No student may earn credit for both 4383 and 5383. Duplicates one hour of 4323.

MATH 5763 Stochastic Processes

Stochastic processes in discrete time including random walks, recurrent events, Markov chains and branching processes. Processes in continuous time including linear and nonlinear birth-death processes and diffusions. Applications taken from economics, engineering, operations research.

MATH 5093 – Applied Numerical Methods

This course is an introduction to some of the most important numerical methods for treating linear algebra problems (solving systems of linear equations, finding eigenvalues, etc.), for solving initial and boundary value-problems for ordinary differential equations as well as some important for practice partial differential equations (Poisson, heat, and wave equations). Many problems discussed in class and given as a homework will be motivated by physical and engineering applications.