Registration Information:

  • All graduate AND undergraduate courses must be registered on paper using an Academic Change Form (PDF) and approved by your supervisor.
  • When registering for a course, you must indicate whether the course will be primary or secondary to your program
  • All courses which are part of your required program must be listed as primary
  • All primary courses require a pass mark of B- or 2.7 or 70%
  • Courses taken outside the department and that are used to meet degree requirements should be in a related field to the student's research and must have course instructor's approval
  • Students who audit graduate courses may be required to participate in assignments but not final examinations; consult the instructor beforehand.
  • It is the student's responsibility to adhere to the guidelines for dropping and adding courses by the relevant deadlines.

Graduate Timetable, Fall 2021 (PDF) - Remote Courses-Zoom links please email: fraser.d@queensu.ca or cheryl.wright@queensu.ca 

Courses offered at Royal Military College 2021-2022 (PDF) - Offered ONLY remotely

RMC course descriptions

To register for a course at RMC, complete and submit the application form

ECE Graduate Courses

Fall 2021 Courses
Winter 2022 Courses
Not Currently Offered
ELEC 825 - Machine Learning and Deep Learning

Instructor:

X. Zhu

Description

Basic machine learning concepts in supervised and unsupervised learning; discriminative and generative models; backpropagation, FFN, CNN, RNN, autoencoders; regularization technologies; attention-based models, Transformer, Capsule Networks; pretraining and self- supervised models; Generative Adversarial Networks (GANs), variational autoencoders; applications.

Prerequisite

ELEC 326 or equivalent, or permission of the instructor

ELEC 832 - Modeling & High Control of Switching Power Converters

Instructor:

Y.-F. Liu

Description

This course covers the modeling and control techniques for switching power converters. Switching power converters are non-linear and time varying system. Small signal models and large signal models are needed in order to design an optimal closed loop system. Stability issues will be discussed for a power system composed of several non-linear power electronic circuits. Control methods play very important role in achieving optimal dynamic performance. Different control techniques for switching power converters will be analyzed. In addition to the conventional analogue control method, (such as direct duty cycle control, peak current programmed control, average current mode control, etc.), digital control (such as fuzzy logic control, sliding mode like control, etc) will also be analyzed. The course will also analyze digital control techniques for AC-to-DC power converters in order to achieve power factor correction. It is expected that each student will do a design project using one or more of the techniques covered in the course.

Evaluation

Homework (30%), Project (25%), Paper presentation (15%), Final exam (closed book, 30%)

Text Book

Class notes, Research Papers

Course Notes

Fundamentals of Power Electronics Slides

ELEC 848 - Control Systems Design for Robots & Telerobots
NOTE:

The Fall 2021 lectures have been scheduled for 4:30-6:00 pm Wednesdays and Thursdays. For the first two weeks the lectures will be online and synchronous. The first lecture will take place on Wednesday September 8, 2021. To receive a Zoom link please contact the course instructor at khz[at]queensu[dot]ca.

Instructor

K. Hashtrudi-Zaad

Description

This course provides an overview of manipulator modeling, and presents and analyzes various control architectures designed for robots and telerobots. Topics include introduction to robotics, serial manipulator forward and inverse kinematics, Jacobian, singularities and dynamics, robot position and force control methodologies and their stability analyses; introduction to telerobotics and haptics, haptic devices and their specifications, network modeling of telerobotic systems, stability and performance measures, bilateral control architectures, issues of communication delays and dynamic uncertainties and proposed treatments, rate control.

Course Overview

1.Robot Modeling

a.Spatial description and transformations.

b.Serial manipulators: Forward and inverse kinematics, Jacobian and singularities, Dynamics using Euler-Lagrange method.

2.Robot Control:

a.Position control methods: Centralized and decentralized control, Multivariable control, Robust control, Stability in the sense of Lyapunov, Variable structure control, Adaptive control.

b.Force control methods: Hybrid control, Impedance control, Parallel force/position control.

3.Telerobotics and Haptics:

a.Introduction to telerobotics and applications, Haptic devices and their specifications, Network modeling of telerobotic systems, Kinesthetic and task-based performance measures, Stability and stability robustness.

b.Four-channel control formalism, Traditional control architectures, Trade-off between stability and performance

c.Issue of time-delay, Proposed solutions: passivity-based, optimization-based, predictive-based methods and supervisory control

d.Adaptive and variable parameter control methods

e.Issue of rate mode control, Stability and performance

f. Current research topics

Material

Textbooks:
  • 1.B. Siciliano, L. Scavicco, L. Villani, and G. Oriolo, "Robotics," 2009. Available online through Queen's Library.
  • J.J. Craig, “Introduction to Robotics: Mechanics and Control,” 2004.
  • M.W. Spong and M. Vidyasagar, "Robot Dynamics and Control," Wiley, 1989.
  • M.W. Spong, S, Hutchinson and M. Vidyasagar, "Robot Modeling and Control," Wiley, 2006.
  • Courses Recommended:

    Any introductory courses in linear control systems (e.g. ELEC-443 or MECH-350 or MTHE-332) and in robotics (e.g. ELEC-448 or MECH-456).

    Grading:

    Test20%

    Assignments36%

    Project/Study44%

    Test:

    A test will be held on spatial descriptions, transformations, kinematics and dynamics.

    Assignments:

    Three or four assignments on robot and telerobot control will be handed out, collected and marked.

    Project/Study:

    This part consists of a group simulation project or survey. The deliverables are an oral presentation and a report.

    ELEC 852 - Broadband Integrated Circuits

    Instructor:

    A. Freundorfer

    Description

    Topics covered include S-parameter design method; filters, equalizers and amplifiers; broadband design applications of microwave integrated circuits (MIC) with emphasis on lightwave transmitters and receivers; broadband adaptive filtering for lightwave systems; monolithic microwave integrated circuits (MMIC) techniques; comparison between MIC and MMIC.

    Objectives

    • Understand limitations of electronic elements and their parasitics. Parasitic extraction.
    • RF, microwave and electromagnetic modelling and there limitations. Many examples in IC and PCB designs.
    • Getting the most out of your active devices. Increasing fT and fmax of FETs and BJTs.
    • Broadband amplifier design techniques. Dealing with Miller through unilaterlization and neutralization. Parasitic absorption. Applications to filters and mixers.
    • High speed digital topologies.

    Evaluation

    50% Assignments and 50% Project.

    ELEC 855 - Nanoelectronics and Nano-Devices

    Instructor:

    S. Kabiri Ameri

    Description

    This course teaches the fundamentals of electron devices in nanometer regime. The course will cover introduction to the nanoelectronics, basics of quantum mechanics and band theory of solids. The concept of Coulomb blockade, many electrons phenomenon, ballistic and spin transport will be discussed and single electron transistor, quantum dots, nanowire and quantum wells based devices will be taught.

    Course outline:

    Introduction to nanoelectronics (1 Lecture)

    • Applications of nanoelectronics
    • Fabrication approaches
    • • Top-Down
    • • Bottom-up

    Introduction to Quantum mechanics (5 Lectures)

    • Operators in quantum mechanics
    • Eigenfunctions and Eigenvalues
    • Measurement probability
    • Schrodinger’s equation
    • Multiple particle systems
    • Spin and angular momentum

    Band theory of solids (6 Lectures)

    • Solids crystalline structures
    • Electrons in periodic potential: the Bloch theorem
    • Kronig-Penney potential
    • Metals, insulators and semiconductors
    • Band structure of graphene and carbon nanotubes

    Coulomb blockade and single electron transistor (6 Lectures)

    • Coulomb blockade
    • Tunneling junction
    • Single electron transistor
    • Carbon nanotube transistor

    Many electrons phenomenon (5 Lectures)

    • Density of states
    • Quantum statistics

    Semiconductor quantum wells, quantum dots and nanowires (5 Lectures)

    Ballistic transport, nanowire transport and spin transport (5 lectures)

    • Classical and semi classical transport
    • Ballistic transport
    • Carbon nanotubes and Nanowires
    • Spintronic

    Required textbook:

    “Fundamentals of Nanoelectronics,” Goerge W. Hanson, Prentice-Hall, New Jersey, 2009

    Recommended reading:

    • Modern Physics for Engineers,” Singh J., Wiley-Interscience, New York, 1999
    • 2. “Solid State Electronic Devices,” Ben G. Streetman and Sanjay K. Banerjee, Pearson Education Limited, Boston, 2016
    • 3. “Modern Quantum Mechanics,” J. J. Sakurai and Jim Napolitano, Cambridge University Press, 2017

    Marking plan:

    • Assignments 20 %
    • Course project 20 %
    • Midterm 25 %
    • Final Exam 35 %
    ELEC 856 - Introduction to Nanophotonics

    Course Website


    Instructor

    M. Alam

    Description

    The course will provide an overview of the principles of operation of current nanophotonic devices, and recent advances in nanophotonics. Topics covered will include: light-matter interaction, optical waveguides, modeling of nanophotonic devices, light propagation in periodic and anisotropic media, coupled mode devices, plasmonics, metamaterial and metasurface. Emphasis of the course will be on the underlying physics behind the operation and design of nanophotonic devices.

    Course Outline

    Light-matter interaction

    • Constitutive relations, Dielectric function, Kramers-Kronig relation
    • Optical properties of metals, semiconductors and dielectrics

    Optical waveguides

    • Slab waveguides
    • Two dimensional waveguides, Effective index method, Transfer matrix method
    • Numerical modeling: Finite difference time domain, Finite element, Beam propagation method

    Coupled waveguide devices

    • Coupled mode theory
    • Directional couplers
    • Grating analysis using coupled mode theory

    Light propagation in periodic media

    • Optics of dielectric layered media
    • Bragg gratings
    • Two- and three-dimensional photonic crystals

    Light propagation in anisotropic media

    • Index ellipsoid, Ordinary and extraordinary waves
    • Electro-optic effect in anisotropic media
    • Applications

    Plasmonics

    • Localized and guided surface plasmon polaritons
    • Guiding and focusing of light below the diffraction limit
    • Plasmonics for biosensing

    Metamaterial and metasurface

    • Electric and magnetic metamaterials, Negative refractive index, Superlens
    • Metasurface and phase engineering

    Fabrication of nanophotonic devices

    • Epitaxial growth of semiconductors
    • Thin film deposition
    • Photo lithography and electro beam lithography
    • Dry and wet etching

    Required textbook:

    B. A. Saleh, and M. C. Teich, Fundamentals of Photonics, John Wiley & Sons Inc., 2007

    Recommended reading:

  • S. V. Gaponenko, Introduction to Nanophotonics, Cambridge University Press, 2010.
  • L. Novotny, Principles of nano-optics, Cambridge University Press, 2012.
  • D. L. Lee, Electromagnetic principles of integrated optics, John Wiley & Sons Inc., 1986.
  • Marking plan:

    • Assignments 25 %
    • Course project 25 %
    • Midterm 20 %
    • Final Exam 30 %
    ELEC 866 - Signal Detection and Estimation

    Instructor

    S.D. Blostein

    Description

    This is a general introduction to the theory and application of detection and estimation involving probabilistic / statistical inference as needed for engineering problems, focusing on systems involving signal processing, communications, biomedical, control, tracking, radar and navigation systems. Formulations and methodologies for the solutions of problems as well as understanding of underlying assumptions and limitations of concepts presented are emphasized. It it assumed that students have had prior exposure to basic concepts in probability and random processes as typically encountered in an upper-year undergraduate or entry-level graduate course. It also assumed that students have had exposure to signals and systems including linear time invariant systems, filtering and frequency domain descriptions as found in a core ECE or related undergraduate program.

    Midterm Exam (covering Weeks 1-7): 30% 
    Final Exam: 60% 
    Assignments (approximately every 2 weeks): 10% 

    Course Schedule 

    Weeks 1-3: Background: elementary concepts from vector spaces, optimization, least squares, and linear systems, including transmission of wide sense stationary random processes. 

    Weeks 4-7: Hypothesis testing. signal detection in discrete time including problem formulations for applications in communications, signal and image processing, and others. Performance evaluation methods for signal detection, theory of optimal stopping, sequential, and quickest detection. 

    Weeks 8-10: Parameter estimation, including Bayesian, nonrandom, and maximum likelihood. Performance of estimators and asymptotic properties. 

    Weeks 11-12: Topics in maximum likelihood estimation, expectation-maximization, signal estimation in discrete time, including Kalman filtering, Wiener filtering, linear estimation. 

    Applications include communications, sensor array processing, image processing and target tracking. 

    Main Reference Texts:

    1. "Fundamentals of Statistical Signal Processing: Estimation Theory", Kay,Prentice Hall, ISBN: 0 -13-345711-7.
    2. "An Introduction to Signal Detection and Estimation" H. V. Poor, Springer-Verlag, ISBN: 0-387 -94173-8.
    3. Course notes package containing lecture notes and a variety of other reference material available first week of term.
    ELEC 872 - Artificial Intelligence & Interactive Systems

    Instructor:

    A. Etemad

    Description

    Fundamental concepts and applications of intelligent and interactive system design and implementation. Topics include: (1) Sensors and Signals in Interactive Systems (2) Data Preprocessing: data acquisition, filtering, missing data, source separation, feature extraction, feature selection, dimensionality reduction; (3) Machine Learning: supervised learning, ensemble learning, multi-task learning, unsupervised learning; (4) Identity Recognition; (5) Activity Recognition and Analysis; (6) Affective Computing.

    PREREQUISITE: ELEC 326 or equivalent, or permission of the instructor.

    ELEC 873 - Cluster Computing

    Instructor

    A. Afsahi

    Description

    This course covers topics related to high-performance computing (HPC) systems, from traditional to heterogeneous GPU clusters, parallel programming models, and high-performance communication subsystems, for both HPC and Deep Learning workloads, among others. Research papers from literature, a term paper and presentation, project (optional), paper critique and presentation, and hands-on programming on clusters will play an important role in the learning process.

    Text: Lecture notes, book chapters, journal and conference papers

    Topics

    • Introduction to High-Performance Computing
    • Parallel Programming Models (MPI, OpenMP, PThreads, PGAS, hybrid)
    • Scalability Analysis, Performance Metrics and Benchmarks
    • Interconnection Networks and High-speed Interconnects
    • Software Messaging Layers
    • Collective Communications, and Neighborhood Collectives
    • Latency Tolerance and Overlapping Techniques
    • GPU Computing and Clusters
    • Remote Memory Access (RMA) and MPI Shared Memory
    • Parallel I/O, Support of Clustering, and Availability

    Textbook

    There is no required textbook for this course. Course materials have been gathered from recent research papers and selected chapters of different textbooks.

    Research Paper and Presentation

    Each student will work on a research topic and will present his/her work in class. A wide range of research topics is available. Students may also propose their own topics, which must be acknowledged by the instructor. Your work may be a survey of the state-of-the-art in a particular research area, may include verification of a recent research work, or it may propose some new ideas with experimental results. Your research will be evaluated based on its importance, originality, technical quality, depth of analysis, completeness, and the final research paper and its presentation. Presentations will be typically in the last two weeks of classes; however, you are allowed to continue working on your research paper until mid-December (for the actual date, refer to the course website). Research paper is limited to 8 pages, and students are to write their papers using the standard two-column IEEE Computer Society conference paper template.

    Course Project and Report

    Course project is optional, but it is recommended for students in the field and those who would like to have a deeper knowledge about the design and implementation of ideas/projects in cluster computing. If you opt for the course project, the weight of the final exam will be adjusted accordingly. A wide range of project topics is available. Students may also propose their own project topics, which must be acknowledged by the instructor. Projects must be distinct from the research paper component of the course. They will be evaluated based on their technical quality, completeness, and the final report. You are allowed to continue working on your project report until mid December (for the actual date, refer to the course website).

    Course Website

    Contact

    • Dr. Ahmad Afsahi, P.Eng.
    • Professor
    • Department of Electrical and Computer Engineering
    • Queen's University
    • Office: Walter Light Hall, Rm 410
    • Tel: 613-533-3068
    • E-mail: ahmad.afsahi[AT]queensu.ca
    ELEC 876 - Software Re-engineering

    Instructor

    Y.Zou

    Description

    This course covers software re-engineering techniques and tools that facilitate the evolution of legacy systems. This course is broken into three major parts. In the first part, the course discusses the terminology and the processes pertaining to software evolution. In the second part, the course provides the fundamental re-engineering techniques to modernize legacy systems. These techniques include source code analysis, architecture recovery, and code restructuring. The last part of the course focuses on specific topics in software re-engineering research. The topics include software refactoring strategies, migration to Object Oriented platforms, quality issues in re-engineering processes, migration to network-centric environments, and software integration.

    Text: Lecture notes, book chapters, journal and conference papers

    Structure

    1. Introduction to software re-engineering
    2. Program comprehension
    3. Software re-engineering techniques in source code transformation and refactoring strategies
    4. Software metrics & quality
    5. Re-engineering economics
    6. Techniques for the migration of legacy systems into network centric environments
    7. Software integration issues and enabling technologies in web-enabled and distributed environments.

    Course Material Info

    The course will be consisted of lectures and student presentations. There are about 6 weeks of lectures. The other 6 weeks will be seminar format where students will present assigned research papers. Students will also do a project singly or in pairs, including a class presentation of the project. The marking scheme is:

    Paper Critiques (2 papers per student 10%
    Paper Presentation 10%
    Project Proposal 10%
    Project Progress Reports 10%
    Final Report 50%
    Class discussion 10%

    Resources for Lectures

    Software Reengineering Tools

    1. The Department of Electrical and Computer Engineering may not offer a specific graduate course if registration is less than three students.
    2. Winter Term courses and instructors may change.  Students will be notified.

    Fourth Year Courses

    Fourth year undergraduate courses listed below may be taken by MASc or MEng students for credit toward their program requirements, subject to the regulations set forth in the Departmental prescription for the MASc or MEng program, the Faculty of Engineering and Applied Science Graduate Council, and the School of Graduate Studies.  PhD students cannot take fourth year undergraduate courses for credit toward their PhD program requirements, as per the FEAS Graduate Council Regulation 2.1.4.

    External Course List