ESE 500 Introduction to Engineering Education (Non-Regular Course)
This graduate course provides an in-depth examination of engineering knowledge and
practices in the context of secondary science content and instruction. The focus is
on engineering design principles and how they may be applied to biology, chemistry,
and physics disciplinary domains. Key concepts of effective engineering education
will be introduced: design-based approaches, optimization, STEM integration, assessment,
and transfer of science principles to technology solutions. Students will participate
in engineering education opportunities through project design, research, and/or curriculum
opportunities at the secondary and post-secondary levels. Fall, Spring, Summer, 3
credits, grading ABCF.
ESE 501 System Specification and Modeling
A comprehensive introduction to the field of System-on-Chip design. Introduces basic
concepts of digital system modeling and simulation methodologies. Various types of
hardware description language (HDL) will be studied, including Verilog, VHDL and System
C. Topics include top-down and bottom-up design methodology, specification language
syntax and semantics, RTL, behavioral and system-level modeling, and IP core development.
Included are three projects on hardware modeling and simulation. Fall, 3 credits,
ESE 502 Linear Systems - CORE COURSE
Development of transfer matrices and state-space equations from the concepts of linearity,
timeinvariance, causlity and lumpedness. Op-amp circuit implementations. Solutions
and equivalent state equations. companion and modal forms. Stability and Lyapunov
equations. controllability, observability, and their applications in minimal realization,
state feedback and state estimators. Coprime fraction of transfer functions and their
designs in pole-placement and model matching. Both the continuous-time and discrete-time
systems will be studied. Fall, 3 credits, grading ABCF.
ESE 503 Stochastic Systems – CORE COURSE
Basic probability concepts and application. Probabilistic bounds, characteristic functions
and multivariate distributions. Central limit theorem, normal random variables. Stochastic
processes in communications, control and other signal processing systems. Stationarity,
ergodicity, correlation functions, spectral densities and transmission properties.
Optimum linear filtering, estimation and prediction. Fall, 3 credits, grading ABCF.
ESE 504 Performance Evaluation of Communication and Computer Systems
Advanced queueing models and algorithms for communication and computer systems. Mean
value analysis and convolution algorithm. Transient analysis and M/G/1 queue. Models
for traffic characterization in broadband integrated networks. Buffer sizing calculations.
Bursty and selfsimilar traffic. Prerequisite: ESE 503 or permission of instructor.
Spring, 3 credits, grading ABCF.
ESE 505 Wireless Communications
This course covers first year graduate level material in the area of wireless communications:
wireless channels, overview of digital communications and signal processing for wireless
communications, voice and data applications, design basics for wireless modems, analysis
of system issues like resource management and handoff, cellular and wireless LAN systems.
Fall and Spring, 3 credits, grading ABCF.
ESE 506 Wireless Network
This course will examine the area of wireless and mobile computing, looking at the
unique network protocol challenges and opportunities presented by wireless communications
and host or router mobility. The course will give a brief overview of fundamental
concepts in mobile wireless systems and mobile computing, it will then cover system
and standards issues including second generation circuit switches and third generation
packet switched networks, wireless LANs, mobile IP, ad-hoc networks, sensor networks,
as well as issues associated with small handheld portable devices and new applications
that can exploit mobility and location information. This is followed by several topical
studies around recent research publications in mobile computing and wireless networking
field. This course will make the system architecture and applications accessible to
the electrical engineer. Prerequisite: ESE 505 and ESE 546 or ESE 548 or permission
of instructor. Fall, 3 credits, grading ABCF.
ESE 507 Advanced Digital System Design and Generation
This course focuses on languages, tools, and abstractions for design and implementation
of digital systems. Course material is divided roughly into three categories: Limitations
and constraints on modern digital sys- tems; Hardware design abstractions, languages,
and tools (including the SystemVerilog hardware description language); and new architectures
and paradigms for digital design. Coursework will be primarily project and assignment
based; there will also be reading and discussion of published papers in these areas.
Students should have experience with hardware description languages (VHDL, Verilog,
or SystemVerilog,) and soft- ware (C, C++ or Java). Fall, 3 credits, grading ABCF.
ESE 509 Modern Energy Technologies
This course will cover a broad array of technologies that are essential to the modern
energy industry, specifically focusing on the most contemporary topics and “hot” areas
of research, development, and deployment. Students will gain a quantitative understanding
of selected energy generation technologies, energy storage technologies, and pollution
control technologies. For each of these topics, we will cover the physical principle
of operation, as well as the economics and environmental impact. Fall, 3 credits,
ESE 510 Electronic Circuits
This course is only for students in the Optoelectromechanical Systems Eng. program
and cannot be used to fulfill any ESE degree requirement. This is a course in the
design and analysis of analog circuits, both discrete and integrated. The first part
of the course presents basic topics related to circuit analysis: laws, theorems, circuit
elements, and transforms. Fundamental semiconductor devices are introduced next. A
number of aspects of circuit design beginning with basic device operation through
the design of large analog functional blocks including amplifiers, oscillators, and
filters are discussed. Fall, 3 credits, grading ABCF.
ESE 511 Solid-State Electronics – CORE COURSE
A study of the electron and hole processes in solids leading to the analysis and design
of solid-state electronic devices. Solutions to the Schrodinger representation of
quantum effects, perturbation techniques. Simple band structure, effective mass theorem.
Derivation and application of the Boltzman transport theory. Electrical and thermal
conductivities of metals and of semiconductors, Hall effect, thermal effects, and
their application to electronic devices. Properties of semiconductors and the theories
underlying the characteristics of semiconductor devices. Fall, 3 credits, grading
ESE 512 Introduction to Quantum Systems Engineering
A first introduction to engineering quantum systems, including quantum computers.
Includes introduction to quantum principles, quantum information, quantum computation,
quantum gates and quantum algorithms. Discusses quantum encryption, Shor’s algorithm
and Grover’s algorithm. Principles of quantum technology will be addressed. A-F
grading, 3 credits.
ESE513 Introduction to Photovoltaics
Introduction to the basic concepts of photovoltaic solar energy conversion, including:
1. The solar resource in the context of global energy demand; 2. The operating principles
and theoretical limits of photovoltaic devices; 3. Device fabrication, architecture,
and primary challenges and practical limitations for the major technologies and materials
used for photovoltaic devices. Students will gain knowledge of: the device physics
of solar cells, the operating principles of the major commercial photovoltaics, and
a basic understanding of the role of photovoltaics in the context of the global energy
system. Pre/co –requisites ESE 231. Spring, 3 credits, grading ABCF.
ESE 514 MOS Transistor Modeling
An overview of the metal-oxide semiconductor (MOS) transistor and its models for circuit
analysis. The course is modular in structure. In a common first part, CMOS fabrication,
device structure and operation are introduced. Starting from basic concepts of electrostatics,
MOS field-effect transistor operation is presented in an intuitive fashion, and no
advanced background in solid-state theory is required. Analytical models of increasing
complexity and their SPICE implementations are discussed. The second part of the course
allows students to focus on their field of preference: Device physics; Digital circuits;
Analog circuits. The course includes a project in one of these subtopics. Fall, 3
credits, grading ABCF.
ESE 515 Quantum Electronics I
Physics of microwave and optical lasers. Topics include introduction to laser concepts;
quantum theory; classical radiation theory; resonance phenomena in two-level systems:
Block equations - Kramers Kronig relation, density matrix; rate equation and amplification;
CO2 lasers; discharge lasers; semiconductor lasers. Fall, 3 credits, grading ABCF.
ESE 516, 517 Integrated Electronic Devices and Circuits I and II
Theory and applications: elements of semiconductor electronics, methods of fabrication,
bipolar junction transistors, FET, MOS transistors, diodes, capacitors and resistors.
Design techniques for linear digital integrated electronic components and circuits.
Discussion of computer-aided design. MSI and LSI. Fall, Spring, 3 credits each semester,
ESE 518 Advanced Design of low-noise and low-power analog circuits
Design of advanced low-noise and low-power analog and mixed-signal integrated circuits
for radiation sensors. Students will learn state-of-the-art circuit techniques for
low-noise and low-power amplification and processing of signals from sensors. Examples
of circuits are low-noise amplifiers, filters, stabilizers, discriminators, peak detectors,
and pile-up rejectors. Applications range from medical, to security, safety, industrial
measurements and physics research. As a course project, students would develop part
of a front-end circuit from transistor level to physical layout using industry-standard
CAD tools, and/or would participate in the experimental characterization of those
or similar circuits. At the end of the course the student will own a solid background
and the basic instruments to design low-noise and low-power amplifiers and processing
ESE 519 Semiconductor Lasers and Photodectors
The course provides an introduction to performance, testing and fabrication techniques
semiconductor lasers and photodetectors. The topics include fundamentals of laser
and detector operation, devices band diagram, device characteristics, and testing
techniques for analog and digital edge emitting and surface emitting lasers, avalanche
and PIN photodetectors. Special attention is given to the design and working characteristics
of transmitters and pumping lasers for telecommunication networks. Prerequisite: BS
in Physical sciences or Electrical or Computer Engineering. Fall, 3 credits, grading
ESE 520 Applied Electromagnetics – CORE COURSE
Wave phenomena and their importance in electromagnetic engineering. Harmonic waves.
Phase and group velocities. Dispersive and nondispersive propagation. Transmission
lines. Maxwell Equations. Uniform plane waves. Poynting theorems, waveguides, resonators.
Scattering matrix theory. Introduction to antenna theory. Electrostatics and magnetostatics
as special cases of Maxwell equations. Prerequisite: Bachelor’s degree in Physical
Sciences. Spring, 3 credits, grading ABCF.
ESE 522 Fiber Optic Systems
This course covers the essential components of a modern optical fiber communication
system. Following a brief review of optical sources and characterization of optical
fiber waveguides the remainder of the course examines the design of digital fiber
optic links, single wavelength fiber-optic networks and wavelength division multiplexing.
Fall, 3 credits, grading ABCF.
ESE 523 Quantum Computing and Applications
This course is an introduction to and survey of the Quantum Computing, an emerging
interdisciplinary field of science which has the potential to revolutionize computation
over the next ten years, to transform chemistry, medicine, engineering and communications,
as well as to change our understanding of physical world. The course will build intuitive
approach to quantum computation and algorithms, but also will advance relevant vocabulary
and skills for faculties and graduate students in engineering, computing, applied
mathematics, chemistry, physics, and related sciences. The key questions of the quantum
computing will be introduced. How to describe quantum systems and quantum operations?
What is a quantum computer and what are the limits of quantum power? What is the difference
between classical and quantum computation? Quantum teleportation? Quantum entanglement
and superposition? How to mitigate errors and decoherence and transmit information
through noisy channels? What are business applications and engineering challenges
of the quantum computers? What are the gains in running quantum vs. classical algorithms?
What are the physical principles of the current quantum computers hardware and what
are technology requirements for realistic quantum computers? Fall, 4 credits, grading
ESE 524 Microwave Acoustics
Continuum acoustic field equations. Wave equation, boundary conditions and Poynting
vector. Waves in isotropic elastic media: Plane-wave modes, reflection and refraction
phenomena, bulk-acoustic-wave (BAW) waveguides, surface acoustic waves (SAW's). Plane
and guided waves in piezoelectric media. BAW transduction and applications: delay-line
and resonator structures, the Mason equivalent circuit, monolithic crystal filters,
IM CON dispersive delay lines, acoustic microscopes, SAW transduction and applications:
the interdigital transducer, band-pass filters, dispersive filters, convolvers, tapped
delaylines, resonators. Prerequisite: ESE 319. Fall, 3 credits, grading ABCF.
ESE 525 Modern Sensors in AI Applications
The course focuses on the underlying physics principles, design, and practical implementations
of sensors and transducers including piezoelectric, acoustic, inertial, pressure,
position, flow, capacitive, magnetic, optical, radiation, chemical and bioelectric
sensors. Design of interfacing various sensors with electronics and implementation
of intelligence at the sensor level are discussed. Fall, 3 credits, grading ABCF.
ESE 526 Silicon Technology for VLSI
This course introduces the basic technologies employed to fabricate advanced integrated
circuits. These include epitaxy, diffusion, oxidation, chemical vapor deposition,
ion implantation lithography and etching. The significance of the variation of these
steps is discussed with respect to its effect on device performance. The electrical
and the geometric design rules are examined together with the integration of these
fabrication techniques to reveal the relationship between circuit design and the fabrication
process. Fall, 3 credits, grading ABCF.
ESE 528 Communication Systems – CORE COURSE
This course provides a general overview of communication theory and addresses fundamental
concepts in this field. After a review of signals and systems representations, various
continuous and digital modulation schemes are analyzed. Spread spectrum systems and
their application to multiuser communications are also addressed. Advanced communication
systems are described and general concepts of wide and local area networks are introduced.
Fall, 3 credits, grading ABCF.
ESE 530 Computer-Aided Design
The course presents techniques for analyzing linear and nonlinear dynamic electronic
circuits using the computer. Some of the topics covered include network graph theory,
generalized tableau and hybrid analysis, companion modeling, Newton's method in n-dimensions,
numerical integration, sensitivity analysis, and optimization. Prerequisite: B.S.
in electrical engineering. Spring, 3 credits, grading ABCF.
ESE 531 Statistical Learning and Inference
Minimum variance unbiased estimation, Cramer- Rao lower bounds, learning and inference
with linear models, maximum likelihood estimation, least squares estimation, Bayesian
inference, statistical decision theory, hypothesis testing with deterministic and
random signals, composite hypothesis testing, model selection. Prerequisite: ESE 503
or permission of instructor. Spring, 3 credits, grading ABCF.
ESE 532 Theory of Digital Communication
Optimum receivers, efficient signaling, comparison classes of signal schemes. Channel
capacity theorem, bounds on optimum system performance, encoding for error reduction,
and the fading channel. Source coding and some coding algorithms. Prerequisite: ESE
503 or permission of instructor. Fall, 3 credits, grading ABCF.
ESE 533 Convex Optimization and Engineering Applications
Introduction to convex optimization and its applications; Convex sets, function, and
basics of convex analysis; Linear and quadratic programs, second-order cone and semidefinite
programming, geometric programming. Duality theory and optimality conditions; Unconstrained
minimization methods; Interior-point methods; Non-differentiable problems; Decomposition
methods. Applications in engineering fields including statistical signal processing,
communications, networking, energy systems, circuit design, and machine learning.
Spring, 3 credits, grading ABCF
ESE 534 Cyber Physical Systems
This course covers important areas from the research literature on cyber-physical
systems. Three application domains are emphasized: medical devices for health care,
smart transportation systems, and smart buildings. Several key cross-cutting principles,
independent of the application domain are also covered, including formal modeling,
embedded systems, real time systems, feedback control, and sensor networks. Prerequisite:
Background in embedded systems and computer networking is necessary. Fall, 3 credits,
ESE 536/CSE 626 Switching and Routing in Parallel and Distributed Systems (cross listed)
This course covers various switching and routing issues in parallel and distributed
systems. Topics include message switching techniques, design of interconnection networks,
permutation, multicast and all-to-all routing in various networks, non-blocking and
re-arrangeable capability analysis and performance modeling. Prerequisites: ESE 503
and 545 or CSE 502 and 547, or permission of the instructor. Fall, 3 credits, grading
ESE 537 Mobile Sensing Systems & Applications
This is a graduate course focusing on recent advances and developments in mobile sensing
systems and their applications, especially those leveraging modern mobile devices
and embedded sensors. Topics include: conventional mote-class sensor networks, participatory
sensing leveraging mobile devices, intelligent hardware and Internet-of-Things, location
sensing, future information centric networking, and applications in smart homes, buildings,
transportation, environment and health/fitness. Students need to read latest literature
and write reviews, work on research problems and develop solutions, present their
work and write formal reports. The practice of the basic research skills are major
components. The course intends to be self-sufficient and prior experiences in programming,
mobile devices and embedded systems is a plus. Fall, 3 credits, grading ABCF.
ESE 538 Nanoelectronics
The major goals and objectives are to provide graduate students with knowledge and
understanding of physical background and applications of nanoelectronics. The course
will cover electrical and optical properties of materials and nanostructures, fabrication
of nanostructures, nanoelectronic devices including resonant-tunneling devices, transistors,
and single-electron transfer devices , as well as applications of nanotechnologies
in molecular biology and medicine. Spring, 3 credits, grading ABCF.
ESE 540 Reliability Theory
Theory of reliability engineering. Mathematical and statistical means of evaluating
the reliability of systems of components. Analytical models for systems analysis,
lifetime distributions, repairable systems, warranties, preventive maintenance and
inspection. Software reliability and fault tolerant computer systems. Prerequisite:
ESE 503 or permission of instructor. Fall, 3 credits, grading ABCF.
ESE 541 Digital System Design
This course is only for students in the Optoelectromechanical Systems Eng. program
and cannot be used to fulfill any ESE degree requirement. This course provides an
introduction to digital and computer systems. The course follows a top-down approach
to presenting design of computer systems, from the architectural-level to the gate-level.
VHDL language is used to illustrate the discussed issues. Topics include design hierarchy
and topdown design, introduction to hardware description languages, computer-aided
design and digital synthesis, basic building blocks like adders, comparators, multipliers,
latches, flip-flops, registers etc., static and dynamic random access memory, data
and control buses, fundamental techniques for combinational circuit analysis and design,
sequential circuit design procedures, and programmable logic devices. Testing of digital
designs is addressed throughout the course. A mini project will complement the course.
Spring, 3 credits, grading ABCF.
ESE 542/MEC 525 Product Design Concept Development and Optimization (cross listed)
This course will concentrate on the design concept development of the product development
cycle, from the creative phase of solution development to preliminary concept evaluation
and selection. The course will then cover methods for mathematical modeling, computer
simulation and optimization. The concept development component of the course will
also cover intellectual property and patent issues. The course will not concentrate
on the development of any particular class of products, but the focus will be mainly
on mechanical and electromechanical devices and systems. As part of the course, each
participant will select an appropriate project to practice the application of the
material covered in the course and prepare a final report. Prerequisite: Undergraduate
electrical or mechanical engineering and/or science training. Fall, 3 credits, grading
ESE 543 Mobile Cloud Computing
Introduction to the basic concepts of mobile cloud computing, including: 1. The mobile
computing technology used in modern smart phones; 2. The cloud computing technologies
used in existing data centers; 3. The synergy of mobile and cloud computing and its
applications; 4. Programming on smart phone utilizing data center services. Students
will gain knowledge of: the fundamental principles, the major technologies that support
mobile cloud computing, the current challenges and primary areas of research within
the field, and a basic understanding of the role of mobile cloud computing in the
context of the everyday living. Spring, 3 credits, grading ABCF.
ESE 544 Network Security Engineering
An introduction to computer network and telecommunication network security engineering.
Special emphasis on building security into hardware working with software. Topics
include encryption, public key cryptography, authentication, intrusion detection,
digital rights management, firewalls, trusted computing, encrypted computing, intruders
and virus. Some projects. Prerequisite or co-requisite: ESE 546 OR ESE 548 Fall, alternate
years, grading ABCF
ESE 545 Computer Architecture – CORE COURSE
The course covers uniprocessor and pipelined vector processors. Topics include: hierarchical
organization of a computer system; processor design; control design; memory organization
and virtual memory; I/O systems; balancing subsystem bandwidths; RISC processors;
principles of designing pipelined processors; vector processing on pipelines; examples
of pipelined processors. The course involves a system design project using VHDL. Prerequisite:
ESE 318 or equivalent. Spring, 4 credits, grading ABCF.
ESE 546 Networking Algorithms and Analysis
An introduction to algorithms and analysis for computer and telecommunication networks.
Continuous time and discrete time single queue analysis. Algorithms for public key
cryptography, routing, protocol verification, multiple access, error, codes, data
compression, search. Prerequisite: ESE 503 or permission of instructor. Fall, 3 credits,
ESE 547 Digital Signal Processing
A basic graduate course in Digital Signal Processing. Sampling and reconstruction
of Signals. Review of Z-Transform theory. Signal flow-graphs. Design of FUR and IIR
filters. discrete and fast Fourier transforms., Introduction to adaptive signal processing.
Implementation considerations. Prerequisites: Senior level course in signals and systems.
Fall, 3 credits, grading ABCF.
ESE 548 Computer Networks
To present basic network principles and methods in a top-down approach. The course
will introduce the material of high-level network applications, and motivate students
to find out about networking aspects inside these applications. The course will provide
the details of network services from the top layer (TCP/UDP) to lower layer (Ethernet).
The areas to be covered are computer networks introduction, network applications,
transport layer, network layer, link layer (LAN), wireless networks, and network security.
Students are required to implement two projects for date transfer. Students will also
use network tools to inspect communication networks in action. Spring 3 credits, grading
ESE 549 Advanced VLSI System Testing
This course is designed to acquaint students with fault diagnosis of logic circuits.
Both combinatorial and sequential circuits are considered. Concepts of faults and
fault models are presented. Emphasis is given to test generation, test selection,
fault detection, fault location, fault location within a module and fault correction.
Spring, 3 credits, grading ABCF.
ESE 550 Network Management and Planning
This course provides an introduction to telecommunications and computer network management
and planning. Network management is concerned with the operation of networks while
network planning is concerned with the proper evolution of network installations over
time. Network management topics include meeting service requirements, management operations,
management interoperability and specific architectures such as Telecommunications
Management Network (TMS), and Simple Network Management Protocol (SNMP). Network planning
topics include planning problem modeling, topological planning design, heuristic and
formal solution techniques. Fall, 3 credits, grading ABCF.
ESE 552 Interconnection Networks
Formation and analysis of interconnect processing elements in parallel computing organization.
Topics include: SIMD/MIMD computers, multiprocessors, multicomputers, density, symmetry,
representations, and routing algorithms. Topologies being discussed include: Benes,
Omega, Banyan, mesh, hypercube, cube-connected cycles, generalized chordal rings,
chordal rings, DeBruijn, Moebius graphs, Cayley graphs and Borel Cayley graphs. Prerequisite:
ESE 545 or equivalent. Fall, 3 credits, grading ABCF.
ESE 553 A/D and D/A Integrated Data Converters
This is an advanced course on analog integrated circuit design aspects for data converters.
Topics include: continuous and discrete-time signals and systems; sampling theorem;
ideal A/D and D/A converters; specifications and testing of data converters; basic
building blocks in data converters: current sources and mirrors, differential gain
stages, voltage references, S/H circuits, comparators: Nyquist D/A and A/D converters:
principles of data conversion and circuit design techniques; over sampling data converters:
low-pass and band-pass delta-sigma modulators, decimation and interpolation for delta-sigma
data converters. The attending students must be acquainted with principles of transistor
operation, function of simple analysis. Familiarity with SPICE is required. Fall,
3 credits, grading ABCF.
ESE 554 Computational Models for Computer Engineers – CORE COURSE
This course covers mathematical techniques and models used in the solution of computer
engineering problems. The course heavily emphasizes computer engineering application.
Topics covered include set theory, relations, functions, graph theory and graph algorithms,
computational complexity, ordering relations, lattices, Boolean algebras, combinations
and algebraic structures. Fall, 3 credits, grading ABCF.
ESE 555 Advanced VLSI Circuit Design – CORE COURSE
Techniques of VLSI circuit design in the MOS technology are presented. Topics include
MOS transistor theory, CMOS processing technology, MOS digital circuit analysis and
design and various CMOS circuit design techniques. Digital systems are designed and
simulated throughout the course using an assortment of VLSI design tools. Prerequisite:
BS in Electrical Engineering or Computer Science. Spring, 3 credits, grading ABCF.
ESE 556 VLSI Physical and Logic Design Automation
Upon completion of this course, the students will be able to develop state-of-the-art
CAD tools and algorithms for VLSI logic and physical design. Tools will address design
tasks such as floor planning, module placement and signal routing. Also, automated
optimization of combinational and sequential circuits will be contemplated.
Prerequisite: BS in Computer Engineering/Science or Electrical Engineering. Spring,
3 credits, grading ABCF.
ESE 557 Digital Signal Processing II: Advanced Topics
A number of different topics in digital signal processing will be covered, depending
on class and current research interest. Areas to be covered will include the following:
parametric signal modeling, spectral estimation, multirate processing, advanced FFT
and convolution algorithms, adaptive signal processing, multidimensional signal processing
for inverse problems. Students will be expected to read and present current research
literature. Prerequisite: ESE 547 or permission of instructor. Spring, 3 credits,
ESE 558 Digital Image Processing I
Covers digital image fundamentals, mathematical preliminaries of two-dimensional systems,
image transforms, human perception, color basics, sampling and quantization, compression
techniques, image enhancement, image restoration, image reconstruction from projections,
and binary image processing. Prerequisite: BS in engineering or physical or mathematical
sciences. Spring. 3 credits, grading ABCF.
ESE 561 Theory of Artificial Intelligence
Problem solving by searching, game trees, constraint satisfaction problems, uncertain
knowledge and reasoning, probabilistic reasoning, probabilistic reasoning over time,
Markov decision processes, partially observable Markov decision processes, reinforcement
learning, generalized reinforcement learning. Fall, 3 credits, grading ABCF.
ESE 562 AI Driven Smart Grids
The course focuses on artificial intelligence (AI) applications to power system analysis,
planning and operation. Topics include basics of AI and smart grid, data preprocessing,
predictive analytics, AI driven static analytics such as optimal dispatch, state estimation
and security assessment, and AI-based dynamical analytics such as transient stability
assessment, dynamic model discovery and emergency control. Emerging topics, including
transfer learning, data-driven formal methods, learning-based cybersecurity and big
data platform, are also discussed. Fall, 3 credits, grading ABCF.
ESE 563 Fundamentals of Robotics I
This course covers: homogenous transformations of coordinates; kinematic and dynamic
equations of robots with their associated solutions; control and programming of robots.
Prerequisite: Permission of instructor. Fall, 3 credits, grading ABCF.
ESE 564 Artifical Intelligence for Robotics
Artificial Intelligence (AI) is intelligence demonstrated by machines, unlike the
natural intelligence displayed by humans and animals. Research in AI focuses on the
development and analysis of algorithms that learn and perform intelligent behavior
with minimal human intervention. This course aims to introduce students some basic
techniques and algorithms in AI including probabilistic inference, planning and search,
localization, tracking and control, and their applications to robotics. Pre-/Co-requisite: Probability and Random Processes, Linear Algebra, Feedback Control.
Success in this course also requires some mathematical fluency with background in
linear systems (e.g., ESE 502 or instructor approval) and programming experience (fluent
in at least one programming language, e.g., Python and MATLAB). Spring, 3 credits, grading ABCF.
ESE 565 Parallel Processing Architectures
The course provides a comprehensive introduction to parallel processing. Topics include;
types of parallelism, classification of parallel computers; functional organizations,
interconnection networks, memory organizations, control methods, parallel programming,
parallel algorithms, performance enhancement techniques and design examples for SIMD
array processors, loosely coupled multiprocessors, tightly coupled multiprocessors
will be discussed; a brief overview of dataflow and reduction machines will also be
given. Prerequisite: ESE 545 or equivalent. Spring, 3 credits, grading ABCF.
ESE 566 Hardware-Software Co-Design of Embedded Systems
This course will present state-of-the-art concepts and techniques for design of embedded
systems consisting of hardware and software components. Discussed topics include system
specification, architectures for embedded systems performance modeling and evaluation,
system synthesis, and validation. The course is complemented by three mini-projects
focused on designing and implementing various co-design methods. Prerequisite: ESE
545, ESE 554 and ESE 333. Fall, 3 credits, grading ABCF.
ESE 568 Computer and Robot Vision
Principles and applications of computer and robot vision are covered. Primary emphasis
is on techniques and algorithms for three-dimensional machine vision. The topics include
image sensing of three- dimensional scenes, a review of two-dimensional techniques,
image segmentation, stereo vision, optical flow, time-varying image analysis, shape-from-shading,
texture, depth-from- defocus matching, object recognition, shape representation, interpretation
of line drawings, and representation and analysis of 3D range data. The course includes
programming projects on industrial applications of robot vision.
Prerequisite: BS in Engineering or Physical or Mathematical Sciences. Fall, 3 credits,
ESE 569 Translational Bioinformatics
Advanced technologies have driven rapid increases in the quantities of biomedical
data. Translational bioinformatics develops the specified computational and analytic
methods to transform these large-scale datasets into biomedical applicable information
and knowledge. It is one of major applications of machine learning and data mining.
This course introduces large-scale biomedical data resources and management, data
processing and modeling, data mining and machine learning approaches in translational
bioinformatics, and provides the hands-on projects for students to practice these
approaches for real-world biomedical data. Fall, 3 credits, grading ABCF.
ESE 575 Advanced VLSI Signal Processing Architecture
This course is concerned with advanced aspects of VLSI architecture in digital signal
processing and wireless communications. The first phase of the course covers the derivation
of both data transformation and control sequencing from a behavioral description of
an algorithm. The next phase reviews the general purpose and dedicated processor for
signal processing algorithms. This course focuses on low-complexity high-performance
algorithm development and evaluation, system architecture modeling, power-performance
tradeoff analysis. The emphasis is on the development of application-specific VLSI
architectures for current and future generation of wireless digital communication
systems. An experimental/research project is required.
Prerequisite: ESE 355 or equivalent. ESE 305 or ESE 337 or equivalent. ESE 306 or
ESE 340 or equivalent. ESE 380 or equivalent. Spring, 3 credits, grading ABCF.
ESE 576 Power System Dynamics
The course provides the background for understanding power system dynamics and numerical
simulation techniques. Topics include the numerical integration for large scale power
networks, numerical oscillation and its solution, power system component modeling,
frequency-dependent transmission network, nonlinear elements, network equivalents,
power network stability, simulation of power electronic inverters, and microgrid stability
& control. The area of real-time simulation for cyber-physical power infrastructures
will also be discussed. The course involves term project. Fall, 3 credits, grading
ESE 577: Deep Learning Algorithms and Software
This course is an introduction to deep learning which uses neural networks to extract
layered high-level representations of data in a way that maximizes performance on
a given task. Deep learning is behind many recent advances in AI, including Siri’s
speech recognition, Facebook’s tag suggestions and self-driving cars. Topics covered
include basic neural networks, convolutional and recurrent network structures, deep
unsupervised and reinforcement learning, and applications to problem domains like
speech recognition and computer vision. Classes will be a mix of short lectures and
tutorials, hands-on problem solving, and project work in groups. Fall, 3 credits,
ESE 579 Advanced Topics in Translational Bioinformatics
This course introduces the current applications of machine learning and data mining
techniques in biomedical data science, discusses the latest translational research
areas and progresses, and provides the hands-on team projects for graduate students
to explore, design and practice their data-driven solutions for the cutting-edge research
topics in biomedical data science. Fall, 3 credits, grading ABCF.
ESE 580, 581 Microprocessor-Based Systems, Engineering I and II
This course is a study of methodologies and techniques for the engineering design
of microprocessor-based systems. Emphasis is placed on the design of reliable industrial
quality systems. Diagnostic features are included in these designs. Steps in the design
cycle are considered. Specifically, requirement definitions, systematic design implementation,
testing, debugging, documentation and maintenance are covered. Laboratory demonstrations
of design techniques are included in this course. The students also obtain laboratory
experience in the use of microprocessors, the development of systems, circuit emulation
and the use of signature and logic analyzers. Fall, Spring, 4 credits, each semester,
ESE 585 Nanoscale Integrated Circuit Design
This course describes high performance and low power integrated circuit (IC) design
issues for advanced nanoscale technologies. After a brief review of VLSI design methodologies
and current IC trends, fundamental challenges related to the conventional CMOS technologies
are described. The shift from logic-centric to interconnect-centric design is emphasized.
Primary aspects of an interconnect- centric design flow are described in four phases:
(1) general characteristics of on-chip interconnects, (2) on-chip interconnects for
data signals, (3) on-chip power generation and distribution, and (4) on-chip clock
generation and distribution. Existing design challenges faced by IC industry are investigated
for each phase. Tradeoffs among various design criteria such as speed-power-noise-area
are highlighted. In the last phase of the courses, several post-CMOS devices, emerging
circuit styles, and architectures are briefly discussed. At the end of the course, the students will have a thorough understanding
of the primary circuit and physical level design challenges with application to industrial
IC design. Prerequisite: ESE 555 or ESE 330 and ESE 355. Spring, 3 credits, grading
ESE 586 Microgrids
Advanced modeling, control, resilience and security technologies useful for the grid
modernization from a unique angle of microgrid design, analysis and operation. Smart
inverters, microgrid architec- tures, distributed energy resources modeling, microgrid
hierachical control, microgrid stability, fault management, resilient microgrids through
programmable networks, reliable networked microgrids, and cyber security.
ESE 587 Hardware Architectures for Deep Learning
This course focuses on the design and implementation of specialized digital hardware
systems for executing deep learning algorithms. The course is divided into three sections.
First, students will study field-programmable gate arrays (FPGAs) and related tools.
Second, the course will present an overview of modern deep learning algorithms and
applications (e.g., visual object recognition, or speech recognition). Third, students
will apply this knowledge to complete a significant design project implementing and
optimizing a deep learning algorithm on an FPGA. Prerequisite: ESE 507. Spring, 3
credits, grading ABCF.
ESE 588 Fundamentals of Machine Learning
The fundamentals of machine learning are introduced including learning with parametric
models, online learning: stochastic gradient descent family of methods; classification;
logistic regression; the naïve Bayes classifier; the nearest neighbor rule; classification
trees; boosting methods; sparsity aware learning: concepts and methods; learning in
reproducing kernel Hilbert spaces; Bayesian learning; variational approximation, sparse
Bayesian learning, relevance vector machines; neural networks and deep learning; the
backpropagation algorithm; convolutional neural networks; recurrent neural networks;
adversarial training; dimensionality reduction; PCA; ICA; nonlinear dimensionality
reduction. Prerequisite: Stochastic Processes and Data Structures. Spring, 3 credits,
ESE 589 Learning Systems for Engineering Applications
The course presents the main methods used in automated (machine) learning for engineering
applications. The course discusses representation models for learning, extraction
of frequent patterns, classification, clustering and application of these techniques
for diverse engineering applications, such as Intranet-of-Things, electronic design
automation, and healthcare. The covered topics include an overview of learning systems,
learning representations i.e. ontologies, regression models, stochastic models and
symbolic models, data preparing techniques, different frequent pattern extraction
methods, supervised and unsupervised classification, and basic and advanced clustering
algorithms. The course is organized as three modules, each module being centered on
a specific theme. Students will learn the characteristics of the enumerated topics,
and devise and implement software programs for discussed techniques as part of their
project work for the course. Student projects will be assessed using standard benchmarks.
Spring, 3 credits, grading ABCF.
ESE 590 Practical Machine Learning and Artificial Intelligence
This course provides a broad introduction to the state-of-the-art of machine learning
methods through lectures and labs, where the lectures summarize the theoretical foundations
of the methods and the emphasis is on their practical use. Students work in teams
and utilize modern tools to develop a specific application in artificial intelligence.
3 credits, grading ABCF.
ESE 591 Industrial Project in OEMS Engineering
Students must carry out a detailed design of an industrial project in Optoelectromechanical
Systems engineering. A comprehensive technical report of the project and an oral presentation
are required. Fall, 3 credits, grading ABCF.
ESE 592 Distributed Computation, Control and Learning over Networks
Network science is an interdisciplinary research area, which typically deals with
large- scale complex networks. This course covers fundamental problems in distributed
computation and control, including consensus and distributed averaging, distributed
optimization, discusses the rendezvous problem and formation control, and explores
recent development in distributed machine learning over networks. Pre- or Corequisite:
Linear Algebra, Applied Calculus. 3 credits, grading ABCF.
ESE 597/PHY 693 High Power RF Engineering
The course provides an essential review of the properties of low and medium power
RF waves and components including transmission lines, waveguides and cavities, and
then proceeds to highlight the properties and limitations under high power RF conditions.
The principal deleterious effects taking place at high power levels are caused by
arcing (a high peak power effect) and the ohmic dissipation in the metal walls (a
high average power effect). Exceeding the power handling capacity of the RF components
can result in expensive repairs. Methods of mitigating or avoiding these expensive
repairs are discussed. Important applications of high power rf are discussed in depth.
The course involves project. Prerequisite: A basic course in microwaves. Fall, 3 credits,
ESE 597 Practicum in Engineering (Internship) Non-Regular Course
This course is for part-time and full-time students who will be on Curricular Practical
Training (CPT). CPT is defined as training that is an integral part of an established
curriculum. Participation is in private corporations, public agencies or non-profit
institutions. Students will be required to have a faculty coordinator as well as a
contact in the outside organization, to participate with them in regular consultations
on their project and to submit a final report to both. Registration must have the
prior approval of the Graduate Program Director. Fall and Spring and Summer, variable
credit. Grading, S/U.
ESE 599 Research (for students in the Master’s program) Non-Regular Course
Fall and Spring, variable and repetitive credit, Grading S,U.
ESE 610 Seminar in Solid-State Electronics
Current research in solid-state devices and circuits and computer-aided network design.
Fall and Spring, 3 credits, grading .
ESE 670 Topics in Electrical Sciences
Varying topics selected from current research topics. This course is designed to give
the necessary flexibility to students and faculty to introduce new material into the
curriculum before it has attracted sufficient interest to be made part of the regular
course material. Topics include: a) Biomedical Engineering; b) Circuit Theory; c)
Controls; d) Electronics Circuits; e) Digital Systems and Electronics; f) Switching
Theory and Sequential Machines; g) Digital Signal Processing; h) Digital communications;
i) Computer Architecture; j) Networks; k) Systems Theory; l) Solid State Electronics;
m) Integrated Electronics; n) Quantum Electronics and Lasers; o) Communication Theory;
p) Wave Propagation; q) Integrated Optics; r) Optical Communications and Information
Processing; s) Instrumentation; t) VLSI Computer Design and Processing. Fall and Spring,
variable and repetitive credit.
ESE 691 Seminar in Electrical Engineering
This course is designed to expose students to the broadest possible range of the current
activities in electrical engineering. Speakers from both on and off campus discuss
topics of current interest in electrical engineering.
Fall and Spring, 1 credit, repetitive, grading S,U
ESE 697 Ph.D. Practicum in Teaching - *3 credits are required for Ph.D. degree
This course provides hands-on experience in classroom teaching. Other activities may
include preparation and supervision of laboratory experiments, exams, homework assignments,
and projects. Final report that summarizes the activities and provides a description
of the gained experience and a list of recommendations is required. 3 credits, grading
*Prerequisite: G5 status and Permission of Graduate Program Director. Students must
inform the department TWO weeks prior to the beginning of each semester, if they plan
on taking ESE 697. The graduate program director will then assign you to a course.
ESE 698 Practicum in Teaching Non-Regular Course
This course enables graduate students to gain experience in teaching and interacting
with students enrolled in an electrical and computer engineering courses. Students
enrolled in ESE 698 are expected to perform various teaching duties required by the
course instructor, such as attending lectures, providing office hours, holding review/recitation
sessions, assisting in lab sections, grading, etc. Fall, Spring and Summer, variable
and repetitive, grading ABCF.
ESE 699 Dissertation (Research On Campus) Non-Regular Course
Students should register for this if the major portion of their research will take
place on Stony Brook University campus, Cold Spring Harbor or Brookhaven National
Fall and Spring, variable and repetitive credit, grading S,U.
ESE 700 Dissertation Research (Off Campus – Domestic) Non-Regular Course
Students should register for this when a major portion of their research will take
place off-campus but in the United States and/or U.S. provinces (please note that
Brookhaven National Labs and Cold Spring Harbor are considered on-campus). All international
students who register for ESE 700 must enroll in one of the graduate student employee
insurance plans and should be advised by an International Advisor. Fall and Spring,
variable and repetitive credit, grading S,U.
ESE 701 Dissertation Research (Off Campus – INTERNATIONAL) Non-Regular Course
Students should register for this when a major portion of their research will take
place outside of the United States and/or U.S. provinces. In these cases, domestic
students have the option of the health plan and may also enroll in MEDEX.
International students should note the following:
• International students who are in their home country are NOT covered by the mandatory
health plan and must contact the Insurance Office for the insurance charge to be removed.
• International students who are not in their home country ARE charged for the mandatory
health insurance. If they are to be covered by another insurance plan, they must file
a waiver by the second week of classes. The charge will only be removed if the other
plan is deemed comparable.
• All international students must receive clearance from an International Advisor.
Fall and Spring, variable and repetitive credit, grading S,U.
ESE 800 Full-Time Summer Research