ADVANCED PROJECTS

May be repeated for up to 6 points of credit. Graduate-level projects in various areas of electrical engineering and computer science. In consultation with an instructor, each student designs his or her project depending on the students previous training and experience. Students should consult with a professor in their area for detailed arrangements no later than the last day of registration.

• Instructor: James Teherani
  h-index: None Info
• Instructor: Yannis P Tsividis
  CULPA: 4 Wikipedia Info
• Instructor: David G Vallancourt
  CULPA: 28 Info
• Instructor: Wen I Wang
  CULPA: 1 Info
• Instructor: Xiaodong Wang
  CULPA: 15 Info
• Instructor: John Wright
  CULPA: 1 h-index: 44 Info
• Instructor: Charles A Zukowski
  CULPA: 5 Wikipedia Info
• Instructor: Gil Zussman
  CULPA: 3 h-index: 39 Info
• Instructor: James Anderson
  h-index: 18 Info
• 1-4 points

MASTER'S THESIS

Research in an area of Electrical Engineering culminating in a verbal presentation and a written thesis document approved by the thesis instructor. Must obtain permission from a thesis instructor to enroll. Thesis projects span at least two terms: an ELEN E6001 or E6002 Advanced Project followed by the E6003 Master’s Thesis with the same instructor. Students must use a department recommended format for thesis writing. Counts towards the amount of research credit in the MS program.

• Instructor: Dion Khodagholy Araghy
  Info
• Instructor: Gil Zussman
  CULPA: 3 h-index: 39 Info
• 3 points

ADV ANALOG INTEGRATED CIRCUITS

Integrated circuit device characteristics and models; temperature- and supply-independent biasing; IC operational amplifier analysis and design and their applications; feedback amplifiers, stability and frequency compensation techniques; noise in circuits and low-noise design; mismatch in circuits and low-offset design. Computer-aided analysis techniques are used in homework(s) or a design project.

• R 4:10 p.m.-6:40 p.m.
• Instructor: Harish Krishnaswamy
  Info
• 3 points

Analog-Digital Interfaces in VLSI

Analog-to-digital and digital-to-analog conversion techniques for very large scale integrated circuits and systems. Precision sampling; quantization; A/D and D/A converter architectures and metrics; Nyquist architectures; oversampling architectures; correction techniques; system considerations. A design project is an integral part of this course.

• T 7 p.m.-9:30 p.m.
• Instructor: Timothy Dickson
  Info
• 3 points

MICROWAVE CIRCUIT DESIGN

Introduction to microwave engineering and microwave circuit design. Review of transmission lines. Smith chart, S-parameters, microwave impedance matching, transformation and power combining networks, active and passive microwave devices, S-parameter-based design of RF and microwave amplifiers. A microwave circuit design project (using microwave CAD) is an integral part of the course.

• M 4:10 p.m.-6:40 p.m.
• Instructor: Yves Baeyens
  h-index: 31 Info
• 3 points

ADV DIGITAL ELECTRONIC CIRCUIT

Advanced topics in the design of digital integrated circuits. Clocked and non-clocked combinational logic styles. Timing circuits: latches and flip-flops, phase-locked loops, delay-locked loops. SRAM and DRAM memory circuits. Modeling and analysis of on-chip interconnect. Power distribution and power-supply noise. Clocking, timing, and synchronization issues. Circuits for chip-to-chip electrical communication. Advanced technology issues that affect circuit design. The class may include a team circuit design project.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Mingoo Seok
  h-index: 31 Info
• 3 points

PRINCPLS SEMICONDUCTR PHYSCS I

Designed for students interested in research in semiconductor materials and devices. Topics include energy bands: nearly free electron and tight-binding approximations, the k.p. method, quantitative calculation of band structures and their applications to quantum structure transistors, photodetectors, and lasers; semiconductor statistics, Boltzmann transport equation, scattering processes, quantum effect in transport phenomena, properties of heterostructures. Quantum mechanical treatment throughout.

• R 7 p.m.-9:30 p.m.
• Instructor: Wen I Wang
  CULPA: 1 Info
• 3 points

VLSI DESIGN LABORATORY

Design of a CMOS mixed-signal integrated circuit. The class divides up into teams to work on mixed-signal integrated circuit designs. The chips are fabricated to be tested the following term. Lectures cover use of computer-aided design tools, design issues specific to the projects, and chip integration issues. This course shares lectures with E4350, but the complexity requirements of integrated circuits are higher.

• W 2:40 p.m.-5:10 p.m.
• Instructor: Peter Kinget
  CULPA: 1 h-index: 47 Info
• 3 points

LIGHTWAVE DEVICES

Electro-optics: principles; electro-optics of liquid crystals and photo-refractive materials. Nonlinear optics: second-order nonlinear optics; third-order nonlinear optics; pulse propagation and solitons. Acousto-optics: interaction of light and sound; acousto-optic devices. Photonic switching and computing: photonic switches; all-optical switches; bistable optical devices. Introduction to fiber-optic communications: components of the fiber-optic link; modulation, multiplexing and coupling; system performance; receiver sensitivity; coherent optical communications.

• M 7 p.m.-9:30 p.m.
• Instructor: Xiang Meng
  h-index: 8 Info
• 3 points

PHOTONIC INTEGRATED CIRCUITS

Photonic integrated circuits are important subsystem components for telecommunications, optically controlled radar, optical signal processing, and photonic local area networks. An introduction to the devices and the design of these circuits. Principle and modeling of dielectric waveguides (including silica on silicon and InP based materials), waveguide devices (simple and star couplers), and surface diffractive elements. Discussion of numerical techniques for modeling circuits, including beam propagation and finite difference codes, and design of other devices: optical isolators, demultiplexers.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Michal Lipson
  CULPA: 1 Wikipedia h-index: 114 Info
• 3 points

MODERN CONTROL THEORY

• MW 2:40 p.m.-3:55 p.m.
• Instructor: James Anderson
  h-index: 18 Info
• 3 points

TOPICS DATA-DRIVEN ANAL & COMP

Advanced Deep Learning

Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.

• W 10:10 a.m.-12:40 p.m.
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• 3 points

TOPICS DATA-DRIVEN ANAL & COMP

Deep Learning on the Edge

Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.

• W 1:10 p.m.-3:40 p.m.
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• 3 points

TOPICS DATA-DRIVEN ANAL & COMP

TPC: MATHEMATICS OF DEEP LEARN

Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.

• W 4:10 p.m.-6:40 p.m.
• Instructor: Predrag R Jelenkovic
  CULPA: 1 h-index: 35 Info
• 3 points

INTERNET ECON, ENG & SOCIETY

Mathematical models, analyses of economics and networking interdependencies in the internet. Topics include microeconomics of pricing and regulations in communications industry, game theory in revenue allocations, ISP settlements, network externalities, two-sided markets. Economic principles in networking and network design, decentralized vs. centralized resource allocation, “price of anarchy,” congestion control. Case studies of topical internet issues. Societal and industry implications of internet evolution.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• 3 points

TOPICS IN NETWORKING

5G PROGRAMMABLE NETWORKS

Further study of areas such as communication protocols and architectures, flow and congestion control in data networks, performance evaluation in integrated networks. Content varies from year to year, and different topics rotate through the course numbers 6770 to 6779.

• W 7 p.m.-9:30 p.m.
• Instructor: Doru Calin
  h-index: 26 Info
• 3 points

Sparse and Low-Dimensional Models for Hi

SPARSE MODELS FOR HI-D DA

Overview of theory, computation and applications for sparse and low-dimensional data modeling. Recoverability of sparse and low-rank models. Optimization methods for low-dim data modeling. Applications to imaging, neuroscience, communications, web data.

• M 1:10 p.m.-3:40 p.m.
• Instructor: John Wright
  CULPA: 1 h-index: 44 Info
• 3 points

TOPICS IN SIGNAL PROCESSING

INTRO TO BLOCKCHAIN TECH

Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889.

• F 8:10 a.m.-10 a.m.
• 3 points

TOPICS IN SIGNAL PROCESSING

LARGE-SCALE STREAM PROCESSING

Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889.  Topic: Large Data Stream Processing.

• R 7 p.m.-9:30 p.m.
• Instructor: Deepak S Turaga
  Info
• 3 points

TOPICS-INFORMATION PROCESSING

Reinforcement Learning in Information Systems

Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Javad Ghaderi
  h-index: 17 Info
• 3 points

TOPICS-INFORMATION PROCESSING

TPC: Adv Big Data & AI

Advanced topics spanning Electrical Engineering and Computer Science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.  Topic: Advanced Big Data Analytics.

• F 7 p.m.-9:30 p.m.
• Instructor: Ching-Yung Lin
  h-index: None Info
• 3 points

TPCS-ELEC & COMPUT ENGINEERING

Smart Grid Technologies

Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.

• W 7 p.m.-9:30 p.m.
• Instructor: Sindhu Suresh
  Info
• 3 points

TPCS-ELEC & COMPUT ENGINEERING

Trans Arch for xG Wireles

Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.

• M 7 p.m.-9:30 p.m.
• Instructor: Tanbir Haque
  Info
• 3 points

TOPICS IN ELECTRICAL AND COMPUTER ENGINE

Embedded AI

Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.

• M 10:10 a.m.-12:40 p.m.
• Instructor: Xiaofan Jiang
  h-index: 24 Info
• 3 points

TPCS-ELECTRICAL & COMPUTER ENGINEERING

Motor Drive Systems

Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.

• T 7 p.m.-9 p.m.
• Instructor: Matthias Preindl
  h-index: 26 Info
• 3 points

DEVICE NANOFABRICATION

Device Nanofabrication

This course provides an understanding of the methods used for structuring matter on the nanometer length: thin-film technology; lithographic patterning and technologies including photon, electron, ion and atom, scanning probe, soft lithography, and nanoimprinting; pattern transfer; self-assembly; process integration; and applications.

• R 1:10 p.m.-3:40 p.m.
• Instructor: Dion Khodagholy Araghy
  Info
• 3 points

FIELDWORK

May be repeated for credit, but no more than 3 total points may be used for degree credit. Only for electrical engineering and computer engineering graduate students who include relevant off-campus work experience as part of their approved program of study. Final report required. May not be taken for pass/fail credit or audited.

• Instructor: Xiaofan Jiang
  h-index: 24 Info
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• Instructor: Xiaodong Wang
  CULPA: 15 Info
• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• Instructor: Aurel A Lazar
  h-index: 53 Info
• Instructor: Dion Khodagholy Araghy
  Info
• Instructor: Asaf Cidon
  h-index: 20 Info
• 1-1.5 points

RESEARCH II

Points of credit to be approved by the department. Requires submission of an outline of the proposed research for approval by the faculty member who is to supervise the work of the student. The research facilities of the department are available to qualified students interested in advanced study.

• Instructor: Dimitris Anastassiou
  Wikipedia Info
• Instructor: Keren Bergman
  CULPA: 1 Wikipedia h-index: 69 Info
• Instructor: Shih-Fu Chang
  CULPA: 3 Wikipedia h-index: 122 Info
• Instructor: Javad Ghaderi
  h-index: 17 Info
• Instructor: Christine P Hendon
  Wikipedia h-index: 21 Info
• Instructor: Predrag R Jelenkovic
  CULPA: 1 h-index: 35 Info
• Instructor: Xiaofan Jiang
  h-index: 24 Info
• Instructor: Ethan Katz-Bassett
  h-index: 31 Info
• Instructor: Dion Khodagholy Araghy
  Info
• Instructor: Peter Kinget
  CULPA: 1 h-index: 47 Info
• Instructor: Harish Krishnaswamy
  Info
• Instructor: Ioannis Kymissis
  CULPA: 4 h-index: 48 Info
• Instructor: Aurel A Lazar
  h-index: 53 Info
• Instructor: Michal Lipson
  CULPA: 1 Wikipedia h-index: 114 Info
• Instructor: Nima Mesgarani
  h-index: 35 Info
• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• Instructor: John W Paisley
  CULPA: 1 Info
• Instructor: Matthias Preindl
  h-index: 26 Info
• Instructor: Mingoo Seok
  h-index: 31 Info
• Instructor: Kenneth Shepard
  CULPA: 2 Wikipedia h-index: 64 Info
• Instructor: James Teherani
  h-index: None Info
• Instructor: Yannis P Tsividis
  CULPA: 4 Wikipedia Info
• Instructor: David G Vallancourt
  CULPA: 28 Info
• Instructor: Wen I Wang
  CULPA: 1 Info
• Instructor: Xiaodong Wang
  CULPA: 15 Info
• Instructor: John Wright
  CULPA: 1 h-index: 44 Info
• Instructor: Charles A Zukowski
  CULPA: 5 Wikipedia Info
• Instructor: Gil Zussman
  CULPA: 3 h-index: 39 Info
• Instructor: James Anderson
  h-index: 18 Info
• 0-6 points

Seminar in Data-Driven Analysis and Comp

ADVANCE PROB MACHINE LEARNING

Advanced topics and recent developments in mathematical techniques and computational tools for data science and engineering problems.

• T 4:10 p.m.-6:40 p.m.
• Instructor: John W Paisley
  CULPA: 1 Info
• 3 points