Electrical and Computer Engineering
Srinivas Tadigadapa, PhD
Professor and Chair
Waleed Meleis, PhD
Associate Professor and Associate Chair
Masoud Salehi, PhD
Associate Professor and Director of Graduate Studies
409 Dana Research Center
The Department of Electrical and Computer Engineering (ECE) graduate program offers a Master of Science in Electrical and Computer Engineering, a Master of Science in Electrical and Computer Engineering Leadership, a Doctor of Philosophy in Electrical Engineering, and a Doctor of Philosophy in Computer Engineering.
ECE’s graduate program is a dynamic and thriving center of world-recognized research in a wide range of areas. The department has strong ties to local industry and the world-famous hospitals and medical centers of Boston and is involved in many joint research projects with them. With four NSF- and DHS-funded research centers and over 20 industrial partners, faculty and students are actively conducting cutting-edge research in areas such as computer vision; pattern recognition and machine learning; brain-computer interface; power systems and power electronics; underwater communication networks and signal processing; robotics; information theory; communications, control, and signal processing; RF, electromagnetics, optics, and magnetic materials; micro/nanomechanical structures and advanced nanomaterials; power-first system/computer architecture; internet-of-things; ultra-low power biomedical and neural circuits and systems.
ECE’s graduate program educates MS and PhD students with deep fundamental and practical knowledge in the various disciplines of electrical and computer engineering by offering a strong curriculum and providing opportunities for research in these disciplines. The department educates the next generation of highly skilled engineers and researchers with necessary skills to address the future needs of industry, government, and humanity.
Mission of the Department
The primary educational missions of the electrical and computer engineering department are to educate undergraduate students so they have the opportunity to obtain successful careers in electrical and computer engineering and related disciplines, and pursue advanced study such as graduate study in engineering or related disciplines, and to educate graduate students so they can make meaningful contributions to research and industry.
Overview of Programs Offered
ECE offers the following graduate degree programs:
- Master of Science in Electrical and Computer Engineering (MSECE)
- Master of Science in Electrical and Computer Engineering Leadership (MSECEL)
- Doctor of Philosophy in Computer Engineering (PhD)
- Doctor of Philosophy in Electrical Engineering (PhD)
All degrees can be pursued on either a full- or part-time basis consistent with residency requirements for the PhD degrees. The master's curriculum includes areas of concentration in the following:
1. Communications, Control, and Signal Processing (CCSP)
2. Computer Networks and Security (CNWS)
3. Computer Systems and Software (CSYS)
4. Computer Vision, Machine Learning, and Algorithms (CVLA)
5. Electromagnetics, Plasma, and Optics (ELPO)
6. Microsystems, Materials, and Devices (MSMD)
7. Power Systems (POWR)
MSECE students pursue their degree by selecting one of the two tracks—MSECE with thesis and course track (MST) or MSECE course-only track (MSC).
Electrical and Computer Engineering PhD Course Requirements
The student and his or her dissertation committee determine the program of study. A typical program comprises 24 semester hours of course work beyond the Master of Science degree. Students who enter the program with a bachelor’s degree complete the curriculum for a Master of Science degree with an area of concentration. After that, as a minimum, the PhD program must include at least 16 semester hours of graduate course work beyond the Master of Science degree, at least 8 semester hours of which must be graduate-level ECE courses. Students who enter the program with a relevant and approved Master of Science degree complete a minimum of 16 semester hours of graduate course work, at least 8 semester hours of which must be graduate-level ECE courses. All students must achieve a minimum cumulative GPA of 3.000.
Master of Science Degree Requirements
Students must complete a minimum of 32 semester hours of approved course work with a minimum GPA of 3.000. MST track students must complete an 8-semester-hour thesis as part of their program of study. Full- and part-time students should follow the same curriculum requirements.
Students who select the MST track must form a thesis committee comprised of at least three members. The thesis committee must include the thesis advisor, and at least two members must be tenured or tenure-track ECE faculty. The student shall present the thesis to this committee and to the ECE department at-large in the form of a seminar before final approval of the thesis.
The ECE department requires the master’s degree students who hold research assistantships to register full-time.
Course Requirements for MSC Students
The program requires 32 semester hours of graduate-level courses. At least five of these courses must be from the list of “depth” courses in the student’s concentration and at least two must be outside this list; these courses are known as “breadth” courses. None of these courses can be from the list of “excluded courses.” For students in the computer-engineering-related concentrations— computer systems and software; computer networks and security; and computer vision, machine learning, and algorithms—at least 20 semester hours of the 32 required semester hours must be graduate-level ECE courses. For other concentrations, at least 24 semester hours of the 32 required semester hours must be graduate-level ECE courses. More details on MSC requirements can be found in the Graduate Program Guide.
Course Requirements for MST Students
The program requires 24 semester hours of graduate-level courses. At least three of these courses must be from the list of “depth” courses in the student’s concentration and at least one must be outside this list; these courses are known as “breadth” courses. None of these courses can be from the list of “excluded courses.” At least 12 semester hours of the required 24 semester hours must be graduate-level ECE courses. In addition, the program requires 8 semester hours of Thesis (EECE 7990). More details on MST requirements can be found in the Graduate Program Guide.
Graduate Certificate Options
Students enrolled in a master's degree have the opportunity to also pursue one of the many engineering graduate certificate options in addition to or in combination with the MS degree. Students should consult their faculty advisor regarding these options.
Gordon Institute of Engineering Leadership Option
Students have the opportunity to pursue the Master of Science in Electrical and Computer Engineering Leadership (MSECEL) along with the Graduate Certificate in Engineering Leadership.
In addition, students have the opportunity to pursue the Gordon Engineering Leadership Program in combination with the Master of Science in Electrical and Computer Engineering. This option results in an increase in total hours beyond that required for the master's degree only.
Doctor of Philosophy (PhD)
- Computer Engineering
- Computer Engineering—Advanced Entry
- Electrical Engineering
- Electrical Engineering—Advanced Entry
Master of Science (MS)
Master of Science in Electrical and Computer Engineering (MSECE)
- Concentration in Communications, Control, and Signal Processing
- Concentration in Computer Systems and Software
- Concentration in Computer Networks and Security
- Concentration in Computer Vision, Machine Learning, and Algorithms
- Concentration in Electromagnetics, Plasma, and Optics
- Concentration in Microsystems, Materials, and Devices
- Concentration in Power Systems
Master of Science in Electrical and Computer Engineering Leadership (MSECEL)
Electrical and Computer Engineering Courses
EECE 5115. Dynamical Systems in Biological Engineering. 4 Hours.
Provides an introduction to the theoretical analysis and modeling of dynamical systems in biology, ranging from molecular to population applications. Topics include difference and differential equation models, with basic theory including nondimensionalization, steady states, linearization, stability, eigenvalues, global behavior, singular perturbations, multistability, hysteresis, cooperativity, periodic solutions, excitable systems, bifurcations, and an introduction to spatial (PDE) models. Develops all concepts in the context of concrete biological applications, such as gene regulation, chemical reaction networks and stoichiometry, drug models and PK/PD, receptor/ligand interactions, synthetic constructs, action potential generation, enzymatic reactions, population interactions, epidemiology, epigenetic phenomena including differentiation, and transport, chemotaxis, and diffusion. BIOE 5115 and EECE 5115 are cross-listed.
EECE 5155. Wireless Sensor Networks and the Internet of Things. 4 Hours.
Covers design and modeling of architectures, communication protocols, and algorithms for wireless sensor networks. The first part of the course covers general aspects of wireless sensor networking, including protocol design, modeling, and simulation at all layers of the communication stack. The second part covers standardization efforts, including Bluetooth, IEEE 802.15.4 and Zigbee, RFID, 6LowPan, and Internet of Things, among others. The third part covers applications of sensor networks technology to many challenging problems of our times, including cyber- physical systems, smart cities, smart transportation systems, and underwater sensing systems.
EECE 5161. Thin Film Technologies. 4 Hours.
Covers the fundamentals of vacuum technology, thin film deposition technologies, characterization technologies, their applications in different industries, and the frontiers of research activities on thin film deposition technologies. Thin films are fundamental building blocks for integrated circuits chips, microelectromechanical systems (MEMS) devices, and nanoelectromechanical system devices (NEMS), etc., and play critical roles in determining the performance of IC circuits, MEMS, and NEMS devices. Topics include vacuum technologies; vacuum pumps; vacuum system design and analysis; different thin film deposition technologies, including sputtering, chemical vapor deposition, electrochemical deposition, atomic layer deposition, etc.; and different thin film characterization technologies, in particular the magnetic thin film characterization technologies, including VSM, PPMS, FMR, MOKE, etc. Students who do not meet course prerequisites may seek permission of instructor.
EECE 5170. Introduction to Multiferroics Materials and Systems. 4 Hours.
Offered by the NSF Nanosystems Engineering Research Center for Translational Applications of Nanoscale Multiferroic Systems (TANMS) and co-taught by professors from UCLA, UC Berkeley, Cornell, California State University Northridge, and Northeastern University. Course lectures will be available online for remote students. Covers introduction to multiferroics, atomic structure of multiferroics (chemistry), multiferroic material science, continuum-level analysis of multiferroic materials, and multiferroic devices.
EECE 5550. Mobile Robotics. 4 Hours.
Investigates the science and engineering of mobile robots. Topics may include kinematics, dynamics, numerical methods, state estimation, control, perception, localization and mapping, and motion planning for mobile robots. Emphasizes practical robot applications ranging from disaster response to healthcare to space exploration.
EECE 5552. Assistive Robotics. 4 Hours.
Investigates the what (modeling), how (design), and why (analysis) of assistive robotics through the use of model-based design process. System models are essential to four key aspects of the assistive robot design process: derivation of executable specifications, hardware and software design based on simulations, implementation by code generation, and continuous testing and verification. Topics may include modeling continuous and discrete dynamics, heterogeneous models, hybrid systems, stochastic models, models of computation, analysis and design of embedded control systems with applications in assistive robotics, system simulation, and validation and verification techniques. Course projects emphasize model-based design for control of assistive robots in smart environments.
EECE 5554. Robotics Sensing and Navigation. 4 Hours.
Examines the actual sensors and mathematical techniques for robotic sensing and navigation with a focus on sensors such as cameras, sonars, and laser scanners. These are used in association with techniques and algorithms for dead reckoning and visual inertial odometry in conjunction with GPS and inertial measurement units. Covers Kalman filters and particle filters as applied to the SLAM problem. A large component of the class involves programming in both the ROS and LCM environments with real field robotics sensor data sets. Labs incorporate real field sensors and platforms. Culminates with both an individual design project and a team-based final project of considerable complexity.
EECE 5576. Wireless Communication Systems. 4 Hours.
Examines fundamental principles of wireless system design, focusing on modern techniques used in cellular systems and wireless local area networks. Covers various levels of system design, from modulation/detection to traffic analysis. Introduces basics of radio propagation and studies their effect on communication signals. Special topics include spatial frequency reuse; call blocking and cellular system capacity; power control and hand-off strategies; channel access and sharing; orthogonal frequency division multiplexing (OFDM—a modulation technique used in WLAN and the fourth-generation [4G] cellular systems) and spread spectrum modulation (third-generation WCDMA systems); diversity techniques and multi-input multi-output (MIMO) signal processing. Requires an undergraduate course in communications systems.
EECE 5580. Classical Control Systems. 4 Hours.
Introduces the analysis and design of classical control systems. Examines control system objectives, modeling and mathematical description, transfer function and state-variable representations, feedback control system characteristics, system responses, and stability of feedback systems. Also addresses compensator design based on root-locus and frequency response, and modern control system design using state-variable feedback.
EECE 5581. Lab for EECE 5580. 1 Hour.
Accompanies EECE 5580. Covers the practical aspects of control systems design through lab experiments. Topics vary and include computer simulation, digital computer control, and use of CAD packages such as MATLAB for analysis and design of control systems. Examples emphasize concepts introduced in EECE 5580, such as system response to stimuli, stability, and robustness.
EECE 5606. Micro- and Nanofabrication. 4 Hours.
Provides an overview of integrated circuit fabrication from the viewpoint of a process engineer. Offers students an opportunity to fabricate micro- and nanoscale devices in integrated lab sessions. Focuses on the physics, chemistry, and technology of integrated circuit fabrication in the lecture portion of the course, while students fabricate and test novel devices (an electrohydrodynamic micropump and three-dimensional carbon nanotube interconnects) in integrated lab sessions. Concentrates on silicon IC technology but also includes examples from other materials and device systems including microelectromechanical (MEMS) technologies that are used to build devices such as accelerometers, pressure sensors, and switches for telecommunications and other current examples provided from nanofabrication and nanotechnology. Lab hours are arranged.
EECE 5610. Digital Control Systems. 4 Hours.
Covers sampling and analysis tools for linear discrete-time dynamic systems, including the design of digital control systems using transform techniques by discrete equivalent and direct design methods; root locus, Bode and Nyquist diagrams, and Nichols charts; controller implementation issues, such as digital filter realizations, nonlinear effects due to quantization, round off, dead band, and limit cycles; and selection of the sampling rate.
EECE 5627. Arithmetic and Circuit Design for Inexact Computing with Nanoscaled CMOS. 4 Hours.
Studies the principles of inexact (approximate) computing through arithmetic and circuit design. By reducing circuit complexity, critical path delay, and power dissipation at the expense of introducing processing errors in computation, inexact computing is one of the leading emerging paradigms in nanoscale computing. Topics include basic computer arithmetic, approximation criteria, error analysis, nanoscale CMOS principles (PTMs), case studies, and experimental assessment.
EECE 5639. Computer Vision. 4 Hours.
Introduces topics such as image formation, segmentation, feature extraction, matching, shape recovery, dynamic scene analysis, and object recognition. Computer vision brings together imaging devices, computers, and sophisticated algorithms to solve problems in industrial inspection, autonomous navigation, human-computer interfaces, medicine, image retrieval from databases, realistic computer graphics rendering, document analysis, and remote sensing. The goal of computer vision is to make useful decisions about real physical objects and scenes based on sensed images. Computer vision is an exciting but disorganized field that builds on very diverse disciplines such as image processing, statistics, pattern recognition, control theory, system identification, physics, geometry, computer graphics, and learning theory. Requires good programming experience in Matlab or C++.
EECE 5640. High-Performance Computing. 4 Hours.
Covers accelerating scientific and other applications on computer clusters, many-core processors, and graphical processing units (GPUs). Modern computers take advantage of multiple threads and multiple cores to accelerate scientific and engineering applications. Topics covered include parallel computer architecture, parallel programming models, and theories of computation, as well as models for many-core processing. Highlights implementation of computer arithmetic and how it varies on different computer architectures. Includes an individual project where each student is expected to implement an application, port that application to several different styles of parallelism, and compare the results. Programming is done in variants of the C programming language.
EECE 5642. Data Visualization. 4 Hours.
Introduces relevant topics and concepts in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concept, animation techniques, pattern analysis, and computational methods. Topics include tools and techniques for practical visualization and elements of related fields, including computer graphics, human perception, computer vision, imaging science, multimedia, human-computer interaction, computational science, and information theory. Covers examples from a variety of scientific, medical, interactive multimedia, and artistic applications. Includes hands-on exercises and projects. Emphasizes modern engineering applications of computer vision, graphics, and pattern classification methodologies for data visualization.
EECE 5643. Simulation and Performance Evaluation. 4 Hours.
Studies simulation and performance evaluation in computer systems. Primarily covers both classic and timely techniques in the area of performance evaluation, including capacity planning to predict system performance, scheduling, and resource allocation in computer systems. Introduces basic computational and mathematical techniques for modeling, simulating, and analyzing the performance by using simulation, including models, random-number generation, statistics, and discrete event-driven simulation.
EECE 5644. Introduction to Machine Learning and Pattern Recognition. 4 Hours.
Studies machine learning, the study and design of algorithms that enable computers/machines to learn from experience/data. Covers a range of algorithms, focusing on the underlying models between each approach. Emphasizes the foundations to prepare students for research in machine learning. Topics include Bayes decision theory, maximum likelihood parameter estimation, model selection, mixture density estimation, support vector machines, neural networks, probabilistic graphics models, and ensemble methods (boosting and bagging). Offers students an opportunity to learn where and how to apply machine learning algorithms and why they work.
EECE 5647. Nanophotonics. 4 Hours.
Introduces basic concepts and recent developments in nanophotonic materials and devices. Nanophotonics is one very important research area in nanotechnology. Discusses the fundamentals of electromagnetics (Maxwell’s equations, polarization, wave propagations, etc.); quantum mechanics; and typical nanofabrication and characterization techniques. Focuses on specific topics in nanophotonics, including silicon photonics; photonic crystals; plasmonics and optical metamaterials, with their diverse applications in optical circuits; imaging; optical trapping; biomedical sensing; and energy harvesting. Offers students an opportunity to obtain a fundamental understanding of the property and manipulation of light at the nanoscale.
EECE 5648. Biomedical Optics. 4 Hours.
Covers biomedical optics and discusses the theory and practice of biological and medical applications of lasers. Topics covered include fundamentals of light propagation in biological tissues, light-matter interactions such as elastic and inelastic scattering; fluorescence and phosphorescence; diagnostic imaging techniques such as confocal fluorescence microscopy, diffuse optical tomography, and optical coherence tomography; and therapeutic interventional techniques, including photodynamic therapy, laser thermal therapies, and fluorescence-guided surgeries.
EECE 5649. Design of Analog Integrated Circuits with Complementary Metal-Oxide-Semiconductor Technology. 4 Hours.
Covers theoretical analysis, practical design, and simulation of analog integrated circuits implemented in complementary metal-oxide-semiconductor (CMOS) fabrication process technologies. Introduces cadence tools for circuit simulations, physical layout, and layout verification. Begins with basic concepts such as CMOS device models, DC and small-signal analysis techniques for single- and multistage amplifiers, biasing configurations, and reference generation circuits. Explores differential signal processing, operational amplifiers, operational transconductance amplifiers, and common-mode feedback circuits. Analysis methods include the evaluation of linearity, noise, stability, and device mismatches from process variations. Addresses some advanced design techniques, such as linearity improvement methods, frequency compensation, and digitally assisted performance tuning.
EECE 5652. Microwave Circuits and Networks. 4 Hours.
Addresses novel applications of analytical and engineering techniques for RF/Microwave Circuits. Covers transmission lines, impedance matching, S-parameters, high-frequency circuit analysis, power dividers, resonators, and filters. Emphasizes presenting fundamental concepts, essential mathematical formulas and theorems, and engineering applications. Provides ample examples to ensure participants are given an opportunity to fully appreciate the power of the techniques described and to gain extensive experience in the area of high-frequency circuits, from theory formulation to novel engineering designs.
EECE 5664. Biomedical Signal Processing. 4 Hours.
Introduces biomedical signal processing and biomedical imaging and image processing. Specific topics covered depend on instructor and/or student’s areas of interest and are drawn from a variety of application areas. They include the nature and processing of intrinsic signals such as cardiac and neurological bioelectric signals, natural processing of external signals such as auditory and visual processing, and topics related to a variety of medical and biological imaging modalities.
EECE 5666. Digital Signal Processing. 4 Hours.
Presents the theory and practice of modern signal processing techniques. Topics include the characteristics of discrete signals and systems, sampling, and A/D conversion; the Z-transform, the Fourier transform, and the discrete Fourier transform; fast Fourier transform algorithms; design techniques for IIR and FIR digital filters; and quantization effects in digital signal processing. Graduate students may register for this course only if they did not complete an undergraduate course in digital signal processing; such graduate registration requires approval of instructor and an internal departmental petition.
EECE 5680. Electric Drives. 4 Hours.
Examines all subsystems that comprise an electric drive including electric machines, power electronic converters, mechanical system requirements, feedback controller design, and interactions with utility systems. Based on an integrative approach that requires minimal prerequisites: a junior-level course in signals and systems and some knowledge of electromagnetic field theory (possibly from physics classes), and does not require separate courses in electric machines, controls, or power electronics.
EECE 5681. Lab for EECE 5680. 0 Hours.
Accompanies EECE 5680. Covers topics from the course through various experiments.
EECE 5682. Power Systems Analysis 1. 4 Hours.
Covers fundamentals including phasors, single-phase and balanced three-phase circuits, complex power, and network equations; symmetrical components and sequence networks; power transformers, their equivalent circuits, per unit notation, and the sequence models; transmission line parameters including resistance, inductance, and capacitance for various configurations; steady-state operation of transmission lines including line loadability and reactive compensation techniques; power flow studies including Gauss-Speidel and Newton Raphson interactive schemes; symmetrical faults including formation of the bus impedance matrix; and unsymmetrical faults including line-to-ground, line-to-line, and double line-to-ground faults.
EECE 5683. Power Systems Lab. 1 Hour.
Accompanies EECE 5682. Addresses topics such as transmission line constants, load flow and short-circuit studies, and transient stability. Includes upgrading the design of a small power system. Requires concurrent registration in EECE 5682.
EECE 5684. Power Electronics. 4 Hours.
Provide tools and techniques needed to analyze and design power conversion circuits that contain switches. The first part of the course emphasizes understanding and modeling of such circuits, and provides a background for engineering evaluation of power converters. The second part covers dynamics and control of this class of systems, enabling students to design controllers for a variety of power converters and motion control systems. Addresses a set of analytical and practical problems, with emphasis on a rigorous theoretical treatment of relevant questions. Designed for students with primary interests in power conditioning, control applications, and electronic circuits, but it could prove useful for designers of high-performance computers, robots, and other electronic and electromechanical (mechatronic) systems in which the dynamical properties of power supplies become important.
EECE 5685. Lab for EECE 5684. 0 Hours.
Accompanies EECE 5684. Covers topics from the course through various experiments.
EECE 5686. Electrical Machines. 4 Hours.
Reviews phasor diagrams and three-phase circuits; the magnetic aspects including magnetic circuits and permanent magnets; transformers, their equivalent circuits, and performance; principles of electromechanical energy conversion; elementary concepts of rotating machines including rotating magnetic fields; and steady-state theory and performance of induction machines, synchronous machines, and direct current machines.
EECE 5688. Analysis of Unbalanced Power Grids. 4 Hours.
Examines common types of power system faults. Starts with a detailed description of three-phase modeling of basic power system elements such as transmission lines, transformers, and generators. Then presents fundamentals of three-phase circuit analysis in the steady state, both for balanced and unbalanced operating conditions. Uses symmetrical component transformation and positive, negative, and zero sequence networks to analyze unbalanced systems. Presents methods to calculate fault currents and postfault bus voltages. Reviews basic protective relaying and relay settings using typical distribution system examples.
EECE 5697. Acoustics and Sensing. 4 Hours.
Introduces the fundamental concepts of acoustics and sensing with waves. Offers a unified theoretical approach to the physics of image formation through scattering and wave propagation in sensing. Topics include the linear and nonlinear acoustic wave equation; sources of sound; reflection, refraction, transmission, and absorption; bearing and range estimation by sensor array processing, beam forming, matched filtering, and focusing; diffraction, bandwidth, ambient noise, and reverberation limitations; scattering from objects, surfaces, and volumes by Green’s theorem; forward scatter, shadows, Babinet’s principle, extinction, and attenuation; ray tracing and waveguides in remote sensing; and applications to acoustic, radar, seismic, thermal, and optical sensing and exploration.
EECE 5698. Special Topics in Electrical and Computer Engineering. 4 Hours.
Covers special topics in electrical and computer engineering. Topics are selected by the instructor and vary from semester to semester. May be repeated up to four times.
EECE 6000. Introduction to Cooperative Education. 1 Hour.
Designed to introduce graduate engineering students to the cooperative education program and focuses on skills that provide a basis for successful co-op engagement. Affords students the opportunity to develop job-search, job-survival, and career-management skills. Seeks to help students understand the co-op program, policies, and expectations; understand how to use the Northeastern Web site to access online information used in the job-search process; identify and describe their skills and work values and how they relate to their career choices; learn how to write and critique a resume; learn and practice proper interviewing skills and techniques; and communicate their interests, skills, needs, and future plans to their co-op coordinator and future employers.
EECE 6962. Elective. 1-4 Hours.
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
EECE 6964. Co-op Work Experience. 0 Hours.
Provides eligible students with an opportunity for work experience. May be repeated without limit.
EECE 6965. Co-op Work Experience Abroad. 0 Hours.
Provides eligible students with an opportunity for work experience abroad. May be repeated without limit.
EECE 7105. Optics for Engineers. 4 Hours.
Provides an introductory graduate course in optics, presenting the engineering concepts necessary to understand and evaluate electro-optical systems. Begins with a brief but rigorous treatment of geometric optics, including matrix methods, aberrations, and pupils and windows, with practical examples of optical instruments and electro-optical systems. Topics include polarization, interference, diffraction, and optical properties of crystals, thin films, optical resonators, guided waves, modulators, and detectors. Presents concepts with examples from modern optical systems such as LIDAR, fiber-optical sensors, range finders, infrared systems, and optical communication systems. Requires a Bachelor of science in engineering or physics.
EECE 7150. Autonomous Field Robotics. 4 Hours.
Examines the role of software and hardware in the design and use of real autonomous systems, including autonomous cars, autonomous underwater vehicles, and unmanned aerial systems. Focuses on using real large-scale robotics systems in real-world settings.
EECE 7200. Linear Systems Analysis. 4 Hours.
Covers fundamental algebraic concepts and algebraic structures. Topics include linear operators and their representations; matrices, algebraic equations, equivalence, and similarity transformations; introduction to the state-variable theory of continuous and discrete linear systems; standard canonical representations, the concept of state, and the representation of interconnected systems, linear spaces, the state equations, and their solution; stability; and introduction to the general control problem in terms of controllability and observability.
EECE 7201. Solid State Devices. 4 Hours.
Covers the fundamental elements of solid-state device physics and the application of these principles. Seeks to provide students with the opportunity to develop an understanding of pn junctions, bipolar junction transistors, and MOSFETs.
EECE 7202. Electromagnetic Theory 1. 4 Hours.
Examines the fundamental equations, their physical meaning, principal mathematical techniques, and important engineering applications. Topics include sources of the electromagnetic field, Lorentz force equation, integral form of Maxwell’s equations and point relations (differential equations and boundary conditions), electromagnetic energy and power, propagation of uniform and nonuniform plane waves in homogeneous media, reflection and refraction, scalar and vector potentials, solutions in the absence of boundaries for static and dynamic problems, solutions to boundary value problems, duality, uniqueness, images, physical theory of diffraction, and general theory of metal and dielectric wave-guides and resonators for Cartesian and cylindrical systems.
EECE 7203. Complex Variable Theory and Differential Equations. 4 Hours.
Comprises the theory of functions of a complex variable. Covers Cauchy’s integral and related theorems, Taylor and Laurent series, analytic continuation, and multivalued functions. Considers special functions of mathematical physics using generating functions, Taylor and Laurent expansions, and various integral representations. Reviews applications of complex variable theory drawn from optics and electromagnetic theory and from digital signal processing and digital communications. Focuses on the theory of ordinary and partial differential equations of mathematical physics. Develops series solutions of ordinary differential equations of second order using the tools of complex variable theory. Covers Sturm-Liouville theory and uses it to develop eigen function and Green function solutions of homogeneous and inhomogeneous partial differential equations. Requires knowledge of undergraduate advanced calculus.
EECE 7204. Applied Probability and Stochastic Processes. 4 Hours.
Covers fundamentals of probability and stochastic processes with applications to estimation and queuing theory. Includes basic laws of probability, conditioning, and Bayes rule. Topics include random variables and their functions; PDF, PMF, and CDF notions; statistical averages; moments and characteristic functions; multiple random variables; joint and conditional PDF and PMF; multiple functions of random variables; correlation and covariance; mean squared estimation of random variables; Markov, Chebychev, and Chernov inequalities; various notions of convergence of random variable sequences; laws of large numbers; central limit theorem; and large deviation theory. As time permits, discusses basic notions of estimation and properties of estimators, unbiased and minimum variance estimation, CRLB, sufficient statistics, consistency of estimators, basic notions of discrete and continuous-time random processes, mean and autocorrelation function, WSS and cyclo-stationary processes, ergodicity of random processes, and other topics. Requires a strong understanding of linear systems, transform techniques, and linear algebra.
EECE 7205. Fundamentals of Computer Engineering. 4 Hours.
Introduces fundamental techniques in computer engineering used throughout the graduate curriculum. Covers basic programming and analysis methods and the formulation and solution of a wide range of computer engineering problems. Also discusses the applications of algorithm analysis and complexity theory to analyzing and solving problems. Emphasizes those fundamental computational problems and related algorithms whose solution can be obtained in polynomial time. For basic computational problems such as sorting, searching, elementary graph algorithms, shortest-paths problems, as well as flow problems in networks, many different algorithms and data structures are described and analyzed, implemented, and compared both from a theoretical and from an experimental point of view.
EECE 7211. Nonlinear Control. 4 Hours.
Discusses phase plane analysis for nonlinear systems. Topics include fundamentals of Lyapunov theory; absolute stability, passivity, averaging, singular perturbation, input-output stability, and other advanced stability topics; describing functions; nonlinear control methods based on linearization, feedback linearization, sliding control, Lyapunov, and passivity and center manifold theory and bifurcations.
EECE 7213. System Identification and Adaptive Control. 4 Hours.
Discusses fundamental issues of adaptive identification and control, such as stability of adaptive systems, convergence, persistent excitation, and robustness. Identification is the process of mathematically modeling a system based on measurement data that may be limited or uncertain. Adaptive control, then, is the means by which a system that is poorly modeled is controlled adequately. Enhances the underlying basic ideas that are essential for adaptive control. Emphasizes recursive approaches, such as recursive least squares algorithm, where parameter estimates are updated in real time. Covers simple adaptive systems, adaptive observers, and adaptive control. Discusses in detail two major adaptive schemes, model reference adaptive control (MRAC) and self-tuning regulators (STR).
EECE 7214. Optimal and Robust Control. 4 Hours.
Explores state-space, time-domain techniques for analyzing and designing optimal and robust linear control systems. Introduces basic concepts of dynamic optimization and applies them to problems of short-term and long-term optimal control, path planning and stabilization, state estimation, and filtering. Emphasizes linear quadratic optimization, H2 control, H-infinity control, and mu-synthesis. Reviews pertinent linear systems concepts and discusses connections with a geometric intuition relating quadratic optimization to projections.
EECE 7224. Power Systems State Estimation. 4 Hours.
Offers an up-to-date account of the strategies utilized in state estimation of electric power systems. Provides a broad overview of power system operation and the role of state estimation in overall energy management. Presents an abundance of examples, models, tables, and guidelines to clearly examine new aspects of state estimation, the testing of network observability, and methods to assure computational efficiency.
EECE 7226. Modeling and Simulation of Power System Transients. 4 Hours.
Presents computer modeling of linear and nonlinear power system components to be used in transient studies. Covers methods of digital simulation of power systems operating in the steady-state and transient conditions. Discusses use of transient simulation programs for design and analysis of power systems. Students are asked to carry out a term project and deliver a presentation about its outcome.
EECE 7228. Advanced Power Electronics. 4 Hours.
Designed to familiarize students with advanced power electronic circuits. Covers single-phase and three-phase rectifiers and inverters, including their principles of the operation, design, analysis, and applications. Diode rectifiers, phase-controlled rectifiers, and switch mode rectifiers and inverters are among the topics. Introduces different modulation techniques. If time permits, covers three-phase ac-ac converters and soft switching techniques, as well.
EECE 7237. Special Topics in Power Electronics. 4 Hours.
Covers aspects of power electronics not studied in other courses. Topics may vary from year to year. May be repeated without limit.
EECE 7240. Analog Integrated Circuit Design. 4 Hours.
Treats the analysis and design of analog ICs, their functional performance, and applications. Focuses on the various building blocks of analog circuits, their operation, and the underlying principles and techniques, with analysis supplemented by CAD simulation. Topics include modeling and layout of CMOS, bipolar, BiCMOS devices, and passive components; DC building blocks, including precision current and voltage references; performance analysis of signal gain, impedances, and frequency response and speed of basic/compound amplifier structures; architectures of operational amplifiers, including low-voltage, OTAs, and three-stage designs; feedback and performance merits, topologies, instability, and frequency compensation of feedback amplifiers; nonlinear and analog computation IC functions; noise in ICs, physical origins and device modeling , noise circuit analysis, SNR and NF, and techniques for the enhancement of system noise performance.
EECE 7242. Integrated Circuits for Mixed Signals and Data Communication. 4 Hours.
Covers analysis and design of ICs for high-speed communications and mixed-signal processing. Focuses on performance of CMOS and BiCMOS implementations of building blocks for these systems. Covers passive R, L, C, and active devices for ICs; broadband amplifiers, TIAs, limiters, buffers/drivers, muxes, and demuxes; circuit noise modeling and analysis and methods for optimization of SNR and BER, with applications to optical communication; baseband and HF filters; design methods of L-C, OTA-C, MOSFET-C, and switched-C filters; data conversion and D-A and A-D characteristics, popular DAC architectures, serial and parallel ADCs, and high-resolution techniques; clock generators and oscillators, L-C resonator-based designs, VCOs, PLLs and frequency synthesis, and CDR circuits. Requires a verification review of a selected publication relevant to the course. Students who do not meet course prerequisites may seek permission of instructor.
EECE 7244. Introduction to Microelectromechanical Systems (MEMS). 4 Hours.
Introduces microelectromechanical systems, including principles of sensing and actuation, microfabrication technology for MEMS, noise concepts, and packaging techniques. Covers a wide range of disciplines, from electronics to mechanics, material properties, microfabrication technology, electromagnetics, and optics. Studies several classes of devices including inertial measurement devices, pressure sensors, rf components, and optical MEMS. Devotes the last third of the semester largely to projects involving design of MEMS devices to specifications in a realistic fabrication process.
EECE 7245. Microwave Circuit Design for Wireless Communication. 4 Hours.
Covers planar microwave circuits and integrated circuits (MMICs) for wireless communication systems. Employs microwave CAD tools in design projects as well as in-class case-study examples. Reviews communication system basics, modulation and demodulation, architectures of receivers and transmitters, and system performance. Covers planar transmission lines and coupled lines and their application to important devices and microwave circuit functions and multiport networks using S-parameters, flow graphs, and Smith charts. Studies microwave filters, narrowband and broadband amplifiers, their gain and stability, impedance matching, and noise performance, as well as mixers and frequency-conversion techniques. Finishes with design and performance of microwave oscillators. Covers wireless standards, multiple-access techniques, and recent advances if time permits.
EECE 7250. Power Management Integrated Circuits. 4 Hours.
Presents power management circuits with a focus on modern system on a chip (SoC). Introduces linear regulators, switching converters, switched-capacitor converters, voltage references, energy harvesters, and battery chargers. Studies various control methods, design trade-offs, and performance metrics in the context of an SoC. Introduces emerging energy-harvesting techniques for IC design. After completing this course, the successful student should be able to design, characterize, choose, or specify power-management circuits or ICs for a system.
EECE 7258. Human Sensing and Recognition. 4 Hours.
Covers the state-of-the-art human-centered recognition technologies, including face/human detection, face/body tracking, face recognition, head/body pose estimation, expression recognition, body language recognition, gait analysis, hand/body/eye gesture, action/activity analysis, and so forth. Human-centered computing is an emerging technology that utilizes the intrinsic physiological or behavioral traits of individuals for machine-based automatic and reliable identification. It attracts much attention due the increasing demand for the security, privacy, and health-care-related human-centered applications.
EECE 7263. Humanoid Robotics. 4 Hours.
Investigates the emerging field of humanoid robotics. Topics may include humanoid designs, software and hardware architectures, sensing and perception, motion planning and control, high-level task planning and control, grasping and manipulation, benchmarking, and experimental methods. Course projects emphasize model-based control of humanoids for completing practical tasks from space exploration to disaster response.
EECE 7270. Electromagnetic Theory 2. 4 Hours.
Continues EECE 7202. Examines important electrodynamic applications by the use of advanced mathematical techniques. Topics include general theory of wave-guides and resonators with application to the cylindrical geometry; dielectric rod wave-guide; optical fibers; radiation; linear antennas; loop antenna; linear arrays; ray optics; scattering and diffraction of waves for planar, cylindrical, and spherical geometries; and effects of random media.
EECE 7271. Computational Methods in Electromagnetics. 4 Hours.
Presents solutions to problems in electromagnetics using a wide variety of numerical and computational methods. Discusses in detail the finite difference approximations of partial differential equations and the finite difference time-domain method of simulating electromagnetic wave propagation and scattering. Uses moment methods to solve the integral equations related to currents and charges on wire structures. Uses finite element and higher-order finite difference methods to solve problems in electrostatics and wave propagation. Discusses efficient matrix methods, relaxation methods, the conjugate gradient technique, and multidimensional Newton’s method in the context of electromagnetic field simulation.
EECE 7275. Antennas and Radiation. 4 Hours.
Presents the fundamental theory and properties of antennas. Topics include equivalence, reciprocity, uniqueness, Huygen’s principle, antenna impedance, and diffraction; linear, loop, array, and aperture antennas including horns, reflectors, lenses, and microstrip; transmitting and receiving antennas and transmission formulas; and numerical antenna analysis methods.
EECE 7293. Modern Imaging. 4 Hours.
Covers basic and advanced topics in imaging engineering. Starts with the formulation of typical forward problems in electromagnetic and acoustic wave field propagation and scattering, emphasizing biomedical and nondestructive testing applications, and continues with a survey of imaging methodologies including the so-called qualitative imaging methods. Topics covered are: obstacle scattering, inhomogeneous medium scattering, uniqueness and stability in inverse scattering, imaging with finite data, point-source method and its applications, singular sources and shape reconstruction, linear sampling methods, signal-subspace-based methods, noniterative approaches for the inverse medium problem, intensity-only imaging, estimation theory in imaging and the question of superresolution, and selected topics in compressive sensing and quantum imaging.
EECE 7296. Electronic Materials. 4 Hours.
Offers a basic treatment of electronic materials from atomic, molecular, and application viewpoints. Topics include atomic structure and bonding in materials, structure of materials, and crystal defects. These topics lay a foundation for thermal and electronic conduction, which is the underlying physics of electronic devices. Examines the electronic properties of semiconductors, dielectric, magnetic, superconducting, and optical materials. The latter half of the course deals with an introduction to state-of-the-art electronic materials, including semiconductor nanoelectronics, magnetic semiconductors and Spintronics, molecular electronics, carbon nanotubes, conducting polymers, graphene and graphane, and other topics representing recent technological breakthroughs in the area of electronic materials.
EECE 7297. Advanced Magnetic Materials—Magnetic Devices. 4 Hours.
Covers magnetism and magnetic materials, their applications in different industries, magnetic devices, and the frontiers of research activities on magnetism and magnetic materials. Topics include magnetics units, magnetic materials classification, origin of ferromagnetism and ferrimagnetism, magnetic anisotropies, magnetostriction, magnetic domain theory, ferromagnetic/ferrimagnetic resonance, soft magnetic materials, hard magnetic materials, applications of magnetic materials, information storage, and leading-edge research. Includes lectures on different magnetic sensors—including AMR, GMR, TMR, fluxgate, magnetoelectric sensors, etc.—and on microwave magnetic devices—including tunable filters, phase shifters, isolators, circulators, etc.
EECE 7298. Magnetic Materials—Fundamentals and Measurements. 4 Hours.
Covers the fundamentals of magnetism and magnetic materials, their applications in different industries, and the frontiers of research activities on magnetism and magnetic materials. Includes magnetic units, magnetic materials classification, origin of ferromagnetism and ferrimagnetism, magnetic anisotropies, magnetostriction, magnetic domain theory, and information storage. Also covers different magnetic material characterization methods, including B-H looper, VSM, MOKE, field-sweep FMR, frequency-sweep FMR, permeameters, etc.
EECE 7310. Modern Signal Processing. 4 Hours.
Covers theory and practice of modern signal processing techniques with emphasis on optimal filtering and multirate signal processing. Includes the principle of orthogonality, Wiener and Kalman filters, linear prediction, spectral factorization, the Yule-Walker equations, decimation and interpolation, Noble identities and polyphase representation, and maximally decimated filter banks.
EECE 7311. Two Dimensional Signal and Image Processing. 4 Hours.
Examines the fundamentals of two-dimensional signal processing, with emphasis on image processing. Topics include signals, systems, and transforms in two dimensions; design and analysis of FIR and IIR filters; DFT and FFT algorithms; generation of digital image from the source; image digitizers and display devices; image transforms; techniques for point-wise, local, and global image enhancement; statistical image restoration techniques including recursive estimation; image coding techniques in spatial and transform domain including coding for facsimile transmission; and feature analysis. Requires a good understanding of linear systems, transform techniques, linear algebra, and random processes.
EECE 7312. Statistical and Adaptive Signal Processing. 4 Hours.
Uses linear mean square estimation concepts to explore some important areas of statistical and adaptive signal processing. Offers students an opportunity to gain a thorough understanding and working knowledge of FIR Wiener filtering, linear prediction, and autoregressive model matching; autocorrelation estimation and the deterministic least squares method; LMS and RLS adaptive filters; order recursive (triangular and lattice) architectures; and beamforming in antenna arrays. Emphasizes performance analysis of adaptive filters under nonstationary conditions; triangular covariance factorization; geometric derivation of RLS adaptive algorithms; a factual knowledge of some basic concepts concerning fundamentals of regularized least squares and the Kalman filter interpretation of the RLS algorithm; IIR (Laguerre-based) lattice configuration; and nonlinear adaptive filtering.
EECE 7323. Numerical Optimization Methods. 4 Hours.
Introduces fundamental theoretical and algorithmic concepts behind numerical optimization theory for objective functions with finite numbers of parameters. Optimization problems arise ubiquitously in all areas of engineering and science. Presents established numerical methods for iterative unconstrained and constrained optimization. Topics covered include line-search and trust-region strategies, gradient descent and Newton methods and their variations, linear and quadratic programming, penalty-augmented Lagrangian methods, sequential quadratic programming, and interior point methods. The course relies on the use of Matlab in projects. Requires a basic knowledge of calculus and linear algebra.
EECE 7329. Special Topics in Signal Processing 3. 4 Hours.
Covers aspects of signal processing not studied in other courses. Topics may vary from year to year. Topics may include physics-based image restoration methods for subsurface sensing problems, fundamentals of linear and nonlinear inverse problems, wave-field signal processing, and tomographic imaging. May be repeated without limit.
EECE 7336. Digital Communications. 4 Hours.
Covers fundamentals of digital communications and coding and the basic structure of a communication system. Topics include modeling of information sources; entropy; rate distortion function; lossless and lossy source coding theorems; Huffman coding; Lempel-Ziv algorithm; scalar and vector quantization; digital modulation schemes and their spectral characterization including PAM, MPSK, QAM, OQPSK, MSK, pi/4-QPSK, CPFSK, CPM, and GMSK; and orthogonal, biortogonal, and simplex signaling. Explores optimal receiver design and probability of error derivation for various systems. Covers noncoherent detection and DPSK systems and their performance. Discusses synchronization systems, analysis of PLL in the presence of noise, methods of timing recovery, channel capacity, and Shannon’s noisy channel coding theorem. Studies cutoff rate and its communication system design. Other topics include coding systems, linear block codes, soft and hard decision decoding, performance of linear block codes, cyclic codes, convolutional codes, Viterbi decoding, error probability bounds, concatenated codes, MAP decoding, Trellis code modulation, communication over band-limited channels, ISI, Nyquist conditions, raised cosine signaling, partial response signaling, equalization techniques, linear adaptive equalization, decision feedback equalizers, maximum likelihood sequence detection, and communication over fading channels.
EECE 7337. Information Theory. 4 Hours.
Discusses basic properties of entropy and mutual information, Shannon’s fundamental theorems on data compression and data transmission in the single-user case, binning, and covering lemmas. Topics include rate distortion theory, feedback in one-way channels, Slepian-Wolf coding of correlated information sources, source coding with side information at the receiver, multiple access channel and its capacity region, and the capacity region of the Gaussian multiple access channel. Also covers broadcast channels, superposition coding, and the capacity region of the degraded broadcast channel; performance and comparison of TDMA, FDMA, and CDMA systems from a theoretical point of view; capacity issues for time-varying channels and channels with memory; relation between information theory and statistics; Stein’s lemma; and large deviation theory.
EECE 7345. Big Data and Sparsity in Control, Machine Learning, and Optimization. 4 Hours.
Covers the issue of handling large data sets and sparsity priors, presenting very recently developed techniques that exploit a deep connection to semi-algebraic geometry, rank minimization, and matrix completion. Focuses on applications, including control and filter design subject to information flow constraints, subspace clustering and classification on Riemannian manifolds, and activity recognition and classification and anomaly detection from video sequences. The goal of this course is to introduce the subject to people in the systems, machine-learning, and computer vision communities faced with “big data” and scaling problems and serve as a quick reference guide, summarizing the state of the art as of today and providing a comprehensive set of references.
EECE 7346. Probabilistic System Modeling and Analysis. 4 Hours.
Covers fundamentals of probabilistic system modeling, building toward techniques that allow analyzing complex stochastic systems in a tractable fashion. Modeling large and complex systems requires reasoning about probabilistic behavior at a large scale. Reviews classic topics like Markov chains, convergence to a steady state, renewal processes, renewal reward processes, the strong law of large numbers, and the elementary renewal theorem. Additional topics include the asymptotic behavior of probabilistic systems, including stochastic approximation/Robbins-Monro type algorithms, and ODE/fluid limits. Illustrates how these modeling techniques can be applied in modeling real systems and adaptive algorithms, including queueing systems, distributed systems, and online learning algorithms like stochastic gradient descent.
EECE 7352. Computer Architecture. 4 Hours.
Presents many of the issues involved in the design and analysis of new and evolving computer architectures. Topics include all aspects of the system including the microprocessor, memory, I/O, and networking. Emphasizes the connection between architecture and the underlying software that drives it. Topics include pipelining, superscalar, out-of-order execution and completion, data flow, caching, prefetching, virtual memory, RAID, and ATM switching. Performance analysis is another fundamental theme of this course. A project is assigned that involves the creation of a trace-driven simulation model to study the performance of various hardware or software architectural features. Also provides a survey of the current state of the art in processor architectures and provides additional readings from recent research in the field. Requires a working knowledge of C programming language.
EECE 7353. VLSI Design. 4 Hours.
Covers all aspects of VLSI design and engineering including VLSI design methodology; MOS transistors and circuits; CAD tools to create, extract, simulate, and evaluate physical layouts; CMOS fabrication process; evaluation and optimization of circuit area, power consumption, and propagation delay; CAD tools to design CMOS systems with standard cells; system clocking design and evaluation; the characteristics and limitations of CAD tools, such as simulation, placement, and routing; VLSI testing, fault models, test vector generation, and design for testability; design projects going through a complete VLSI design cycle; and a research project targeting a specific area of VLSI engineering. Requires a knowledge of electronics and digital systems design.
EECE 7360. Combinatorial Optimization. 4 Hours.
Introduces combinatorial optimization, an emerging field that combines techniques from applied mathematics, operations research, and computer science to solve optimization problems over discrete structures. Emphasizes problems that arise in the areas of electrical and computer engineering including VLSI, computer-aided design, parallel computing, computer architecture, and high-performance compiling. Covers the foundations of algorithm analysis including asymptotic notation and complexity theory, and a range of optimization techniques including divide and conquer, local optimization, dynamic programming, branch and bound, simulated annealing, genetic algorithms, approximation algorithms, integer and linear programming, matroid theory, and greedy algorithms. Considers the efficient generation of optimal solutions, the development and evaluation of heuristics, and the computation of tight upper and lower bounds.
EECE 7364. Mobile and Wireless Networking. 4 Hours.
Introduces the fundamental techniques and protocols in first- and second-generation, and emerging third-generation, wireless systems. Examines how mobility affects networks, systems, and applications. Mobility of devices and end-users has behavioral implications at all layers of the Internet protocol stack, from the MAC layer up through the application layer. Handling mobility efficiently requires more information sharing between network layers than is typically considered. Topics include cellular system, medium access control protocols for wireless systems, mobility management and signaling within mobile networks, common air interfaces (AMPS, IS-136, IS-95, or GSM), wireless data networking (CDPD), ad hoc networks, Bluetooth, Mobile IP, and PCS systems. Also introduces students to the problems and current research in the provision of quality of service (QoS) in wireless networks. Methodology includes lectures, textbooks, and emphasis on readings from relevant literature.
EECE 7368. High-Level Design of Hardware-Software Systems. 4 Hours.
Presents state-of-the-art methods, tools, and techniques for system-level design and modeling of complete multiprocessor systems from specification down to implementation across hardware-software boundaries. Recognizes that system complexities are growing exponentially, driven by ever-increasing application demands and technological advances that allow one to put complete multiprocessor systems on a chip (MPSoCs). System-level design that jointly covers hardware and software is one approach to address the associated complexities in the design process and the market pressures. Using system-level design languages (e.g., SpecC, SystemC), offers students an opportunity to specify, simulate, analyze, model, and design hardware-software systems based on examples of typical embedded applications. Requires working knowledge of C/C++, algorithms, and data structures.
EECE 7370. Advanced Computer Vision. 4 Hours.
Offers students an opportunity to obtain practical knowledge in computer vision and to develop skills for being a successful researcher in this field. The goal of the field of computer vision is to make useful decisions about real physical objects and scenes based on sensed images. Achieving this goal requires obtaining and using descriptions (models) of the sensors and the world. Computer vision is an exciting field that builds on very diverse disciplines such as image processing, statistics, pattern recognition, control theory and system identification, physics, geometry, computer graphics, and machine learning. Course material includes state-of-the-art in the field, current research trends, and algorithms and their applications, with an emphasis on the mathematical methods used.
EECE 7374. Fundamentals of Computer Networks. 4 Hours.
Focuses on fundamental concepts of computer networks with a particular focus on the Internet. Covers the language and practices of computer networking at all levels of various network protocol stacks. Basic concepts include general definitions and network organization. Delves into the protocol stack following a top-down approach, covering the application layer (with Internet applications); the transport layer, with its functions and services (e.g., the TCP protocol); the network layer, with a discussion on forwarding and routing and the IP protocol; and the data link layer, with an emphasis on multiaccess. Concludes with current topics including networks analysis/modeling, physical layer/cross-layer design, emerging technologies, and mobility.
EECE 7376. Operating Systems: Interface and Implementation. 4 Hours.
Covers fundamentals of operating systems (OS) design, including theoretical, OS-generic design considerations as well as the practical, implementation-specific challenges in the development of a real OS. Requires proficiency in the C programming language, the GNU tool set for C programming, and debugging in Unix operating systems.
EECE 7377. Scalable and Sustainable System Design. 4 Hours.
Focuses on data center scale system design issues. Covers advanced issues in designing high-performance computing and data storage systems. Through a mix of lectures and paper discussions, offers students an opportunity to learn how parallel computing systems work and review recent research related to scalability, energy efficiency, sustainability, resilience, and big data management. Topics include high-performance scalable parallelization strategies for emerging computational applications from different science and engineering domains. Successful students should be able to understand the design trade-offs in designing, engineering, and operating large-scale parallel computing systems. Features a research-oriented project that serves as the experiential learning component of the course for gaining hands-on experience in solving real-world problems in parallel computing. Students are expected to present their results and findings and submit a written report.
EECE 7390. Computer Hardware Security. 4 Hours.
Presents the foundations for understanding the new and evolving area of hardware security and trust, which have become major concerns for national security over the past decade. Coverage includes security and trust issues in all types of electronic devices and systems, such as ASICs, COTS, FPGAs, microprocessors/DSPs, and embedded systems. Topics encompass the state-of-the-art research fronts such as hardware support for system security, hardware implementations of security primitives, physical attacks and tamper resistance, analysis and practices of side-channel attacks and countermeasures, security for RFID tags, physically unclonable functions, design for hardware trust, hardware Trojan detection and localization, etc. Requires solid knowledge of digital system design, integrated circuits synthesis flow, and embedded systems recommended.
EECE 7397. Advanced Machine Learning. 4 Hours.
Covers topics in advanced machine learning. Presents materials in the current machine learning literature. Focuses on graphical models, latent variable models, Bayesian inference, and nonparametric Bayesian methods. Seeks to prepare students to do research in machine learning. Expects students to read conference and journal articles, present these articles, and write an individual research paper. CS 7140 and EECE 7397 are cross-listed.
EECE 7398. Special Topics. 4 Hours.
Covers topics of interest to the faculty member conducting this class for advanced study. May be repeated without limit.
EECE 7399. Preparing High-Stakes Written and Oral Materials. 4 Hours.
Focuses on how to think through and develop critical materials that have high-stakes impact. These could include writing a compelling technical paper or a winning proposal for external funding, making a compelling oral presentation for a job interview or thesis defense, or presenting arguments to a CEO about strategic directions for a complex project. Includes hands-on exercises and class exercises around challenges defined by the instructor or by guest lecturers.
EECE 7400. Special Problems in Electrical Engineering. 1-4 Hours.
Offers theoretical or experimental work under individual faculty supervision. May be repeated without limit.
EECE 7674. Master’s Project. 4 Hours.
Offers analytical and/or experimental work leading to a written report and a final short presentation by the end of the semester.
EECE 7962. Elective. 1-4 Hours.
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
EECE 7990. Thesis. 4-8 Hours.
Offers analytical and/or experimental work conducted under the auspices of the department. May be repeated once.
EECE 7996. Thesis Continuation. 0 Hours.
Offers analytical and/or experimental work conducted under the auspices of the department.
EECE 8986. Research. 0 Hours.
Offers students an opportunity to conduct full-time research under faculty supervision. May be repeated without limit.
EECE 9986. Research. 0 Hours.
Offers students an opportunity to conduct full-time research under faculty supervision. May be repeated without limit.
EECE 9990. Dissertation. 0 Hours.
Offers theoretical and/or experimental work conducted under the auspices of the department. Required to be taken in two consecutive semesters. Includes attendance at Distinguished Lecture Series (DLS). May be repeated once.
EECE 9996. Dissertation Continuation. 0 Hours.
Offers continued dissertation work conducted under the supervision of a departmental faculty member. Includes attendance at Distinguished Lecture Series (DLS). Requires prior completion of EECE 9990 twice. May be repeated without limit.