Elect. Engineering - ECE GRAD (ECE-GY)

The student is required to conduct a theoretical and/or experimental project in a research area in electrical engineering, computer engineering, electrophysics, system engineering, or telecommunication networks. The project is chosen based on the student’s specialized interest and preparation and is guided by a faculty member who is expert in the chosen subject. Oral-thesis defense and formal, bounded thesis are required. Registration of at least 6 credits over continuous semesters is required. A student must secure a thesis adviser before registration. | Prerequisite: Degree status.

The dissertation is an original investigation of an electrical-engineering problem. The work must demonstrate creativity and include features of originality and utility worthy of publication in a recognized journal. Candidates must successfully defend their dissertations orally and submit a bounded thesis. Registration of at least 21 credits over continuous semesters is required. | Prerequisite: Passing PhD qualifying examination.

This course introduces fundamentals of systems engineering process. Topics: Multi-disciplinary systems methodology, design and analysis of complex systems. Brief history of systems engineering. Mathematical models. Objective functions and constraints. Optimization tools. Topics to be covered include identification, problem definition, synthesis, analysis and evaluation activities during conceptual and preliminary system design phases. Decision analysis and utility theory. Information flow analysis in organizations. Elements of systems management, including decision styles, human information processing, organizational decision processes and information system design for planning and decision support. Basic economic modeling and analysis. Requirements development, life-cycle costing, scheduling and risk analysis. Application of computer-aided systems engineering (CASE) tools. | Prerequisite: Graduate status.

The course focuses on in-depth introduction to theory and application of linear operators and matrices in finite-dimensional vector space. Topics: determinants, eigenvalues and eigenvectors. Theory of linear equations. Canonical forms and Jordan canonical form. Matrix analysis of differential and difference equations. Singular value decomposition. Variational principles and perturbation theory. Numerical methods. | Prerequisites: Graduate status

This course covers CMOS processing technology, MOS transistor theory, static/dynamic circuit and logic design techniques, circuit performance estimation, standard cells and gate arrays, clocking strategies, input/output structures, datapath, memory and control logic design. Advanced VLSI CAD tools are used for schematic capture, layout, timing analysis and simulations for functionality and performance. | Prerequisite: Senior or Graduate status, CS-UY 2204 & EE-UY 3114 or equivalent.

This course provides an overview of the unique concepts and techniques needed to design and implement computer systems having real-time response requirements in an embedded environment. It contrasts the concepts and techniques of real time and embedded systems with those of more traditional computer systems. Topics include: Basic concepts of real time and embedded systems, hardware features, programming languages, real time operating systems, synchronization techniques, performance optimization and current trends in real time and embedded systems such as incorporating internet connectivity. | Prerequisite: Knowledge of C, Pascal or other programming language and a basic understanding of computer architecture.

This course shows how a hardware-description language (for example, VHDL) can be used for computer hardware modeling, logic synthesis, register-level synthesis and simulation. The resulting design with hundreds or thousands of gates is then ready to be downloaded to form FPGA chips or silicon cells. Programs used: QuickVHDL, modeling and simulation tools from Mentor Graphics or similar large-scale programs. A design project is required and students make a written and oral presentation. | Prerequisite: Graduate status.

The course covers limits to the ongoing miniaturization (Moore’s Law) of the successful silicon device technology imposed by physical limitations of energy dissipation, quantum tunneling and discrete quantum electron states. Topics: quantum physical concepts and elementary Schrodinger theory. Conductance quantum and magnetic flux quantum. Alternative physical concepts for devices of size scales of 1 to 10 nanometers, emphasizing role of power dissipation. Tunnel diode, resonant tunnel diode, electron wave transistor; spin valve, tunnel valve, magnetic disk and random access memory; single electron transistor, molecular crossbar latch, quantum cellular automata including molecular and magnetic realizations. Josephson junction and rapid single flux quantum’ computation. Photo- and x-ray lithographic patterning, electron beam patterning, scanning probe microscopes for observation and for fabrication; cantilever array as dense memory, use of carbon nanotubes and of DNA and related biological elements as building blocks and in self-assembly strategies. Co-listed as PH-GY 5493. | Prerequisites: PH-UY 2033 or instructor's permission.

The course focuses on limits to the performance of binary computers, traveling salesman and factorization problems, security of encryption. Topics: the concept of the quantum computer based on linear superposition of basis states. The information content of the qubit. Algorithmic improvements enabled in the hypothetical quantum computer. Isolated two-level quantum systems, the principle of linear superposition as well established. Coherence as a limit on quantum computer realization. Introduction of concepts underlying present approaches to realizing qubits (singly and in interaction) based on physical systems. The systems under consideration are based on light photons in fiber optic systems; electron charges in double well potentials, analogous to the hydrogen molecular ion; nuclear spins manipulated via the electron nuclear spin interaction and systems of ions such as Be and Cd which are trapped in linear arrays using methods of ultra-high vacuum, radiofrequency trapping and laser-based cooling and manipulation of atomic states. Included: summary and comparison of the several approaches. Co-listed as PH-GY 5553. | Prerequisites: PH-GY 2004 Introductory Physics II.

The course focuses on basic concepts in electric power systems. Topics include: three-phase circuits; component modeling (generators, transmission lines, transformers, etc.); per-unit system; symmetrical components; power flow; short circuit; transient stability; introduction to advanced topics: contingency analysis, optimum power flow, electromagnetic transients, geomagnetically induced currents, and harmonics. The course is complemented by laboratory experiments on synchronous and induction (wind) generators. | Prerequisite: Advisor consent (knowledge of electric circuits and electromagnetic energy conversion)

This course introduces the finite elements method for solving electrical engineering problems. Topics: a refresher of basic concepts of electromagnetism. Introduction to the solution methods of partial differential equations. Comparative summary of the solution methods for Maxwell equations. Finite elements, Garlekin and least squares approaches. Description of some commercial software packages. In this hands-on course, students do assignments and final projects using the finite elements software COMSOL Multiphysics. | Pre-Requisite: Graduate Status or EE-UY 3604 and EE-UY 3824.

Guided studies on various topics in Electrical Engineering.

The course covers the following topics: Principles of M-ary communication: signal space methods, optimum detection. Fundamental parameters of digital communication systems, various modulation techniques and their performance in terms of bandwidth efficiency and error probability. Efficient signaling with coded waveforms. Block coding and convolutional coding. Joint modulation and coding. Equalization for communication over bandlimited channels. Brief overview of digital communications over fading multipath channels. | Prerequisites: EE-UY 3404, EL-GY 6303. *Online version available.

Guided studies on various topics in Electrical Engineering.

Guided studies on various topics in Electrical Engineering.

Mathematical information measures: entropy, relative entropy and mutual information. Asymptotic equipartition property, entropy rates of stochastic processes. Lossless source encoding theorems and source coding techniques. Channel capacity, differential entropy and the Gaussian channel. Lossy source coding rate distortion theory. Brief overview of network information theory. | Prerequisite: ECE-GY 6303 and Graduate status.

Discrete and continuous-time linear systems. Z-transform. Fourier transforms. Sampling. Discrete Fourier transform (DFT). Fast Fourier transform (FFT). Digital filtering. Design of FIR and IIR filters. Windowing. Least squares in signal processing. Minimum-phase and all-pass systems. Digital filter realizations. Matlab programming exercises. Co-listed as BE-GY 6403 | Prerequisites: Graduate status. *Online version available.

This course introduces fundamentals of image and video processing, including color image capture and representation; color coordinate conversion; contrast enhancement; spatial domain filtering (linear convolution, median and morphological filtering); two-dimensional (2D) Fourier transform and frequency domain interpretation of linear convolution; 2D Discrete Fourier Transform (DFT) and DFT domain filtering; image sampling and resizing; geometric transformation and image registration; video motion characterization and estimation; video stabilization and panoramic view generation; basic compression techniques (entropy coding, vector quantization, predictive coding, transform coding); JPEG image compression standard; wavelet transform and JPEG2000 standard; video compression using adaptive spatial and temporal prediction; video coding standards (MPEGx/H26x); Stereo and multi-view image and video processing (depth from disparity, disparity estimation, video synthesis, compression). Students will learn to implement selected algorithms in MATLAB or C-language. | Prerequisites: Graduate Standing or Undergraduate Standing having completed EE-UY 3054 and EE-UY 2233. Suggested Corequisites: EL-GY 6113 and EL-GY 6303 (not required)

This course is an introduction to the field of machine learning, covering fundamental techniques for classification, regression, dimensionality reduction, clustering, and model selection. A broad range of algorithms will be covered, such as linear and logistic regression, neural networks, deep learning, support vector machines, tree-based methods, expectation maximization, and principal components analysis. The course will include hands-on exercises with real data from different application areas (e.g. text, audio, images). Students will learn to train and validate machine learning models and analyze their performance. May not take if student has already completed ECE-UY 4563. | Prerequisite: Graduate status with undergraduate level probability theory

Real-time implementation of algorithms for digital signal processing (DSP) with an emphasis on audio processing. Audio input-output and buffering. Filtering (recursive and non-recursive filters, structures). Fast Fourier transform and windowed spectral analysis. Digital audio effects (delay line, amplitude modulation, reverberation, distortion, phase vocoder). Time-varying and adaptive filters. Students with learn to implement these algorithms for real-time audio processing in software (e.g., Matlab and Python). | Prerequisites: EE-UY 3054 or equivalent (for undergraduate students) or Graduate Standing.

Design of single-input-output and mul-tivariable systems in frequency domain. Stability of interconnected systems from component transfer functions. Parameterization of stabilizing controllers. Introduction to optimization (Wiener-Hopf design). | Prerequisites: Graduate status and EE-UY 3064.

Basic system concepts. Equations describing continuous and discrete-time linear systems. Time domain analysis, state variables, transition matrix and impulsive response. Transform methods. Time-variable systems. Controllability, observability and stability. SISO pole placement, observer design. Sampled data systems. | Prerequisites: Graduate status and EE-UY 3054 or EL-GY 5253.

The goal of this class is to provide a broad and rigorous introduction to game-theoretic methods and algorithms for complex systems. The material spans disciplines as diverse as engineering (including control theory and signal processing), computer science (including artificial intelligence, algorithms and distributed systems), micro-economic theory, operations research, public policies, psychology and belief systems. A primary focus of the course is on the application of cooperative and non-cooperative game theory for both static and dynamic models, with deterministic as well as stochastic descriptions. The coverage will encompass both theoretical and algorithmic developments, with multi-disciplinary applications. | Prerequisites: Graduate Standing

Continuous and discrete random variables and their joint probability distribution and density functions; Functions of one random variable and their distributions; Independent random variables and conditional distributions; One function of one and two random variables; Two functions of two random variables and their joint density functions; Jointly distributed discrete random variables and their functions; Characteristic functions and higher order moments; Covariance, correlation, orthogonality; Jointly Gaussian random variables; Linear functions of Gaussian random variables and their joint density functions. Stochastic processes and the concept of Stationarity; Strict sense stationary (SSS) and wide sense stationary (WSS) processes; Auto correlation function and its properties; Poisson processes and Wiener processes; Stochastic inputs to linear time-invariant (LTI) systems and their input-output autocorrelations; Input-output power spectrum for linear systems with stochastic inputs; Minimum mean square error estimation (MMSE) and orthogonality principle; Auto regressive moving average (ARMA) processes and their power spectra. Co-listed as BE-GY 6453. | Prerequisite: Graduate status. *Online version available.

An introductory, systems-level approach to wireless networks covering basic physical-layer aspects of wireless communications as well as implications in the medium access control (MAC), networking and application layers. Topics include channel and rate modeling, interference and spatial reuse, auto repeat request (ARQ), quality of service, random access, scheduling, security, mobility and intermittent communication. Overviews and examples from state-of-the-art cellular and wireless local area networks (LAN) standards as well as modern multimedia applications will be provided. There will be a lab component to complement the lecture material. The course is designed as a first course in wireless networks for students both intending to specialize in wireless communications as well as students who are interested in the consequences of wireless communications in other areas including multimedia delivery, networking and mobile applications." | Prerequisite: An undergraduate course on networks equivalent to ECE-GY 3613, or ECE-GY 6353 or its equivalent.

This course introduces basic local area networking technologies and protocols in a set of lectures and laboratory experiments. Topics: link level protocols. Local area networks: CSMA/CD, Token Ring, IEEE standards and protocols. The Internet protocol suite: IP, ARP, RARP, ICMP, UDP and TCP. LAN Interconnection: bridges, routers and gateways. Application protocols: SNMP, FTP, SMTP and NFS. | Prerequisite: ECE-UY 3613

Data center and cloud computing are key technologies in building large-scale Internet services. Many service providers rely on data center and cloud computing platforms to provide applications, storage, computation, etc. This course covers the fundamental knowledge of data center and cloud computing and offers hands-on experience. Topics to be discussed include data center and cloud platform architecture, data center network designs, software-defined networks, data center security, traffic engineering, resource management, green data centers, and multi-access edge computing. The course provides a series of labs for students to learn various tools used in data centers and cloud computing. | Prerequisites: ECE-GY 6353 or equivalent course.

This course covers the basics, architectures, protocols and technologies for high-speed networks. Topics: synchronous optical network (SONET), asynchronous transfer mode (ATM), ATM adaptation layer (AAL), 10/100/1000/10G Ethernet, Ethernet over SONET (EOS), quality of service control, packet scheduling, network processor, buffer management, flow and congestion control, TCP, high-speed TCP and XCP, Routing and IP fast rerouting, WDM networks, MPLS and GMPLS. Each student is required to complete a project that can be reading, software design or hardware design. | Prerequisites: EE-UY 136 or EL-GY 5373 or equivalent

The course will begin by providing a device-oriented overview of integrated circuits and silicon fabrication processes and their ramifications on the transistor models. Subsequently, we will discuss various amplifier topologies in ICs using these devices, and also examine in detail topics such as frequency response, linearity, biasing, feedback, operational amplifiers, compensation, and noise. The blocks and circuit architectures discussed in this course are the core components of most integrated systems and essential in applications such as communications, multimedia, imaging, sensors, and biomedical. | Prerequisites: Graduate Standing or EE-UY 3124 and cumulative GPA of 3.0 or higher (Undergraduate students)

This course continues from EL-GY 6473 and covers top-down VLSI design using VHDL including structural design, modeling, algorithmic and register level design, synthesis, prototyping and implementation using FPGAs and methods to design for test (DFT). This course provides a solid background and hands-on experiences with the CMOS VLSI design process in which custom design techniques (covered in EL-GY 6473) are married with HDL synthesis to produce complex systems. Students complete a project covering design partitioning, placement and routing, automated synthesis and standard cell design and use. The course explores how these techniques are used in designing ASICs, System-on-Chips (SoC) and advanced microprocessors. | Prerequisite: EL-GY 6473.

This course shows how a hardware-description language (for example, VHDL) can be used for computer hardware modeling, logic synthesis, register-level synthesis and simulation. The resulting design with hundreds or thousands of gates is then ready to be downloaded to form FPGA chips or silicon cells. Programs used: QuickVHDL, modeling and simulation tools from Mentor Graphics or similar large-scale programs. A design project is required and students make a written and oral presentation. | Prerequisite: Graduate status.

This course covers CMOS processing technology, MOS transistor theory, static/dynamic circuit and logic design techniques, circuit performance estimation, standard cells and gate arrays, clocking strategies, input/output structures, datapath, memory and control logic design. Advanced VLSI CAD tools are used for schematic capture, layout, timing analysis and simulations for functionality and performance. | Prerequisite: Senior or Graduate status, CS-UY 2204 & EE-UY 3114 or equivalent.

This course provides an overview of the unique concepts and techniques needed to design and implement computer systems having real-time response requirements in an embedded environment. It contrasts the concepts and techniques of real time and embedded systems with those of more traditional computer systems. Topics include: Basic concepts of real time and embedded systems, hardware features, programming languages, real time operating systems, synchronization techniques, performance optimization and current trends in real time and embedded systems such as incorporating internet connectivity. | Prerequisite: Knowledge of C, Pascal or other programming language and a basic understanding of computer architecture.

Introduction to semiconductor materials, energy band structures, and carrier transport; p-n junctions and Schottky barriers; heterostructures; bipolar and field-effect transistors; and introduction/survey of some electronic/optoelectronic devices that utilizes above device concepts. | Prerequisites: Graduate Standing or Undergraduate Standing with 3.0 GPA or higher and completion of MA-UY 2034 and PH-UY 2023.

Introduction to T&D systems. Choice of voltage and frequency. Radial and meshed networks. Aerial lines: construction, parameters and thermal rating. Cables: installations, impedance and thermal ring. Transformers and reactors: types, connections and parallel operation. Capacitors: construction and application to transmission, distribution and industrial systems. Grounding systems. Characteristics of loads: customer classes, voltage sensitivity, duty cycle, and load growth. Loss minimization by system reconfiguration and capacitor switching. Modern grids: nano-, micro-, mini-, smart-, and super-grid. | Prerequisite: Knowledge of Electric Power System"

This course teaches multi-disciplinary fundamentals of power engineering, economics, optimization, and policy analysis that constitute modern power system economics and planning. These fundamentals make it possible to understand and study the concept of smart grids as a particular case of large-scale, network-constrained infrastructure that can be simulated by using various optimization techniques. The course also provides knowledge to pursue advanced work on transmission- and distribution-level smart grid technologies, e.g. renewable generation, demand response, energy storage. | Prerequisites: Graduate status and EL-GY 5613 or equivalent.

Protective relay functions and classification. Electromechanical relay types, operating principles and basic characteristics. Communication channels for relaying. Current and voltage transformers, transducers. Protection of busses, transformers, generators, motors and other station equipment by the zone protection method. Distribution and transmission line relaying systems. Relay setting calculations. Primary and backup protection, application and philosophy with applied relay engineering examples. | Prerequisites: Graduate status and EL-GY 5613 or equivalent.

This course focuses on optimal control problem for deterministic and stochastic systems with various constraints. Topics: solution for both continuous and discrete-time systems using the maximum principle and dynamic programming. Fuel and time optimal control problems. Optimal filtering and estimation. Stochastic and hybrid optimal control problems. Computational methods. Multidisciplinary applications of optimal control. | Prerequisites: Graduate status, ECE-GY 6623 or permission of the instructor.

Class D and E rectifiers. Class D inverters. Class E inverters. Phase-controlled resonant inverters. Class DE inverters. Resonant dc-dc converters. Soft switching. Quasiresonant and multiresonant converters. Control and modeling of resonant converters. | Prerequisite(s): EE-UY 3824 or an approved equivalent

Reduction of load performance characteristics to the motor shaft. Electromechanical energy conversion. Acceleration and deceleration time. Construction of load diagram. Choice of motor type and size for different duty cycles. Four quadrant operation. Basics of Direct-Current and Induction motor drives. Permanent magnet and synchronous drives. Electrical braking. Conventional and modern speed control of DC and AC drives. Also included are many worked examples taken from practical electric drive systems. | Prerequisites: Graduate status and EE-UY 3824 or an approved equivalent.

This course introduces Maxwell’s equations, wave equation, vector potentials, boundary conditions and Poynting vector. Time-harmonic fields and phasor approach are introduced. The properties of freely propagating plane waves in uniform and layered media are derived, as well as waves guided by structures, including various transmission lines, hollow waveguides and dielectric waveguides. A unified treatment of wave propagation is given with general theorems and examples drawn from microwaves, integrated circuits and optics. | Prerequisites: Graduate status and EE-UY 3604.

This course introduces the physics, instrumentation and signal processing methods used in X-ray imaging (projection radiography), X-ray computed tomography, nuclear medicine (SPECT/PET), ultrasound imaging, magnetic resonance imaging and optical imaging. Co-listed with BE-GY 6203 Prerequisites: Undergraduate level courses in multivariable calculus (MA-UY 2112 & MA-UY 2122 or MA-UY 2114), physics (PH-UY 2033), probability (MA-UY 3012), signals and systems (EE-UY 3054). Students who do not have prior courses in signals ans systems must take EL-GY 6113 / BE-GY 6403 - Digital Signal Processing I as a prerequisite or must obtain instructor's approval; EL-GY 6123 - Image and Video Processing is also recommended but not required.

This course provides students with an understanding of computer systems architectures and fundamental computer- performance and capacity-improvement techniques. An assembly language and an instruction set are presented and a uniprocessor computer is built to implement the instruction set. Processor implementation with a data path and hardwired and microprogrammed control is introduced, and pipelining is described as a strategy to improve throughput. Memory-hierarchy alternatives are introduced to improve the capacity of the computing system. The concept of virtual memory and its hardware implementation is introduced. Out-of-order processors, and associated instruction scheduling algorithms and techniques are described and evaluated. Branch prediction is introduced. The main memory system is described and pre-fetching is discussed as a technique to improve main memory access latency. The course concludes with an introduction to single chip multi-core computing technology. Hands-on programming exercises to illustrate the concepts are inter-woven throughout the course. | Prerequisites: Undergraduate degree in EE/CE/CS

This course provides an overview of deep neural network learning (covering mathematical foundations as well as example applications in NLP, computer vision, and reinforcement learning). Upon successful completion, students will be able to grasp the mathematical basics of deep learning, solve practical machine learning problems in applications, and implement software prototypes of deep learning solutions to these problems. | Prerequisites: ECE-GY 6143 or CS-GY 6923 or equivalent graduate course

This course presents the main concepts, techniques, algorithms, and state-of-the-art approaches in modern machine learning from both theoretical and practical perspective. Students will be exposed to new mathematical proof techniques and up-to-date machine learning coding environments and benchmark datasets. The program of the course includes empirical risk minimization, support vector machines, kernels, optimization techniques for machine learning, clustering, principal component analysis, Expectation-Maximization, online learning algorithms, boosting, decision trees, graphical models, and deep learning. The course contains tutorials on selected most popular machine learning software environments. The course finally emphasizes interesting and important open problems in the field. Mathematical maturity (https://en.wikipedia.org/wiki/Mathematical_maturity) is required from students registering for the course. | Prerequisites: A grade of A- or better in (ECE-GY 6143 or CS-GY 6923) and a grade of B+ or better in ECE-GY 6303.

This course presents the concepts, techniques, algorithms, and state-of-the-art approaches for robot perception, localization, and mapping. The course will show the theoretical foundations and will also have a substantial experimental component based on Matlab/ROS. The course will start from basic concepts in probability and then introduce probabilistic approaches for data fusion such as Bayes Filters, Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter. Then, the course will introduce the SLAM problem showing how this has recently been solved using batch optimization and graph methods. Finally, mapping algorithms will also be briefly discussed. | Prerequisites: ECE-GY 6253 or instructor's approval

Topics covered in this course include canonical forms; control system design objectives; feedback system design by MIMO pole placement; MIMO linear observers; the separation principle; linear quadratic optimum control; random processes; Kalman filters as optimum observers; the separation theorem; LQG; Sampled-data systems; microprocessor-based digital control; robust control and the servocompensator problem. | Prerequisites: Graduate status and EL-GY 6253.

Stability and stabilization for nonlinear systems; Lyapunov stability and functions, input-output stability and control Lyapunov functions. Differential geometric approaches for analysis and control of nonlinear systems: controllability, observability, feedback linearization, normal form, inverse dynamics, stabilization, tracking and disturbance attenuation. Analytical approaches: recursive back stepping, input-to-state stability, nonlinear small-gain methods and passivity. Output feedback designs. Various application examples for nonlinear systems including robotic and communication systems. | Prerequisites: Graduate status and EL-GY 6253 or EL-GY 7253.

This course covers selected topics of current interest in wireless communications. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course centers on selected topics of current interest in signals and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course centers on selected topics of current interest in signals and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course centers on selected topics of current interest in signals and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course centers on selected topics of current interest in signals and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course centers on selected topics of current interest in signals and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This is a 1.5-credit special topics course for graduate students in the general area of control engineering in ECE department.

The course discusses topics of current interest to feedback and control-system engineers. (See department mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course discusses topics of current interest to feedback and control-system engineers. (See department mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course covers selected topics of current interest in telecommunications and networking. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course covers selected topics of current interest in telecommunications and networking. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

The course covers selected topics of current interest in telecommunications and networking. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This course examines special topics of current interest in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This course examines special topics of current interest in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This course examines special topics of current interest in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This course examines special topics of current interest in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This course examines special topics of current interest in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

This course examines special topics of current interest in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) | Prerequisite: Specified when offered.

Special topics of current interest to staff in the field of electronic devices, circuits and systems. (See departmental mailing for detailed description of each particular offering.) Prerequisite: Specified when offered.

The course looks at topics of current interest in electric power engineering. (See departmental mailing for detailed description of each particular offering. | Prerequisite: Specified when offered.

The course looks at topics of current interest in electric power engineering. (See departmental mailing for detailed description of each particular offering. | Prerequisite: Specified when offered.

This course consists of seminar presentations on recent developments in electrical and computer engineering by speakers from industry, research and education institutions. To receive a satisfactory grade, a student must attend at least two thirds of the seminars during the semester registered. A PhD student must register and obtain satisfactory grade for at least four semesters. | Prerequisite: none.

This course requires a student to read advanced literature in a research field relevant to electrical and computer engineering, under guidance of a faculty member who is expert in the field. Oral presentation and a written report is required. Not more than 3 credits may be taken toward the master’s degree. A student must secure a project adviser before registration. | Prerequisite: Degree status.

Theoretical and/or experimental projects in various research areas in electrical and computer engineering. Projects assigned on basis of specialized interest and preparation of the student and conducted under guidance of a faculty member who is expert in the chosen subject. Oral presentation and/or a written report is required at the discretion of the adviser. | Prerequisite: Graduate Standing and at least one semester of coursework

This course requires a student to conduct a theoretical and/or experimental project in a research area in electrical and computer engineering. The project is chosen based on the student’s specialized interest and preparation and is guided by a faculty member who is expert in the chosen subject. Oral presentation or a written report is required at the adviser’s discretion. A student must secure a project adviser before registration. | Prerequisite: Degree status.

This course requires a student to conduct a theoretical and/or experimental project in a research area in electrical and computer engineering. The project is chosen based on the student’s specialized interest and preparation and is guided by a faculty member who is expert in the chosen subject. Oral presentation or a written report is required at the adviser’s discretion. A student must secure a project adviser before registration. | Prerequisite: Degree status.

In the area exam, the student reviews the prior research in the student’s chosen dissertation topic and presents preliminary research results and additional research plan. The area exam is conducted by the Guidance Committee, but may be open to other interested faculty and students. The Guidance Committee attends and evaluates the student's performance and determines whether the student demonstrates the depth of knowledge and understanding necessary to carry out research in the chosen area. Results of the exam will be recorded in the student’s transcript as ECE-GY 9980. The student must submit a written report that summarizes prior research and the future plan at least one week before the scheduled exam time. The report should follow the Ph.D. dissertation template and be at least 25 pages long. The student must take and pass the area exam within 2 years after passing the Ph.D. qualifying exam. Students who fail to pass the exam by the deadline might be disqualified from the program.