Registration Information:
 All graduate AND undergraduate courses must be registered on paper using an Academic Change Form 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.
Timetable 20182019 (PDF document)
Courses offered at Royal Military College
To register for a course at RMC, complete and submit the application form
ECE Graduate Courses
 ELEC 831  Power Electronics
Description
Power electronics plays a key role in our modern society. It is helping us in building modern infrastructures that are not only providing us a comfortable life but are also environmental friendly. This course presents some of the advanced work in the field of resonant and softswitching converters. We will see how this field has evolved through the years in terms of power electronics converter topologies and control techniques. We will also see how this field has impacted many reallife applications such as space, telecommunications, information processing and renewable energy generation.
Topics
 Variable Frequency Resonant Converters
 Modeling of Resonant Converters
 Phaseshift modulated resonant converters
 Asymmetrical PWM resonant converters
 Naturally commutated soft switching converters
 Auxiliary Commutated Soft Switching Converters
Evaluation
There will be two independent projects for this class. Students will present their projects to the class. The course grade will be based on the class presentations and submissions of project reports.
Files
 ELEC 843  Control of Discrete Event Systems
Course Website
Description
In this course we will study discreteevent processes, such as computer systems and manufacturing systems, that require control to induce desirable behaviour. Informally, a discreteevent system (DES) is a process (or set of processes) that starts out in some initial state, and is transformed from state to state by the occurrence of discrete events. Such a system can be thought of as a set of sequences of events, each sequence describing a series of actions that occur within the system. Control amounts to inhibiting the behaviour of such processes by disabling events (or preventing certain actions from occurring). Standard models for the control of discreteevent systems are taken from computer science and mathematics and include automata or finitestate machines, directed graphs, Petri nets, modal logic (such as temporal logic) and algebras.
Topics covered in the course will include some of the following: basic automata and formal language theory; modeling of plants and supervisors for discreteevent control problems; centralized control problems; modular supervision; partial observation; nonblocking solutions; decentralized control problems; computational complexity of DES control problems; timed discreteevent systems; and control using a limitedlookahead approach. Smallscale examples will be used to motivate material. We will show how the course material can be used to model applications such as communication protocol verification, system secrecy , factory automation, concurrency control, and emergency response to medical outbreaks.
Much of the material that will be used in this course comes from automata theory and formal languages and the mathematics used is discrete mathematics and is closest in flavour to algebra, as opposed to calculus. Students are not expected to have any specific course prerequisites or formal background; however, students are expected to be competent at mathematical proofs (especially mathematical induction), formal reasoning and logical arguments. Some familiarity with automata theory, graph theory, or propositional logic is a bonus but is not essential.
Outline
1. Introduction (1.3)
 What are discreteevent systems (DESs)?
 Why should we study how to control them?
 What types of mathematical models are used to represent DESs?
 Automata, finitestate machines, directed graphs (2.12.4)
 Petri nets (4.14.2)
 Mathematical Logic
 Algebra
2. Automata Theory and the Theory of Formal Languages (2.12.4)
 Formal languages (2.2.1)
 Regular expressions (2.4.2)
 Automata (2.2.2)
 How can automata and languages be used to describe DESs?
 The relationship between finitestate automata and regular languages (2.4)
 Nondeterministic finitestate automaton (NFA); converting NFA to languageequivalent deterministic finitestate automation (DFA) (2.3.3)
3. DES Control Problems (3.13.3)
 Basic RamadgeWonham concepts: how to model the process to be controlled (as an automaton), how to model "desired behaviour", how to formulate control problems (3.13.2)
 Concept of legal language (3.3)
 Concept of supervisor (3.2)
 Generated versus marked behaviour (3.2.1)
 Concept of nonblocking solutions (3.2.1)
 Ways to combine several processes: shuffle, intersection, synchronous product (2.3.2)
4. Centralized DES Control Problems (3.43.5)
 Motivating examples
 Concept of controllability (3.4.1)
 Concept of supremal controllable sublanguage (3.4.3)
 Formulations of centralized DES problems and their solutions (3.4.43.4.5)
 Computing supremal controllable sublanguage (3.5.3)
 Cat and Mouse example
5. Modular Supervision (3.6)
 Supervisor conjunction
 Nonblocking solutions (3.6)
 Smallscale manufacturing system example
6. Centralized DES Control Problems with Partial Observation (3.7)
 Motivation, discussion of why it's harder to control system that cannot be fully observe
 Concept of observability (3.7.1)
 Formulations of centralized, partial observation DES problems and their solutions (3.7.4)
 Suboptimal solutions: concept of "normality" (3.7.5)
 Small example: trains on subway tracks
 Failure diagnosis as an example of partial observation (2.5.3)
7. Decentralized DES Control Problems (3.8)
 Local versus global specification
 Motivating examples
 Formulations of decentralized DES problems
 Concepts of decomposability, coobservability (3.8.1)
 How to solve decentralized DES problems
 Communication protocol verification example
 Computational complexity (3.8.5)
 Using formal reasoning about knowledge and modal logic to model decentralized DES problems
8. Timed DESs
 How to model DESs where events have time bounds (thus permitting realtime constraints to be realized)
 Smallscale industrial automation problem
9. Limited Lookahead, Online Control and Dynamic DESs
 What is limited lookahead?
 What is online control?
 What if plant being controlled changes over time?
 How limited lookahead policies and online control can be applied to dynamic DESs
 Truck company scheduling example
10. Other Applications of DES
 Truck dispatching for mining industry
 Emergency response protocols for epidemiological outbreaks
 Concurrency control in software development
 Supervisory control of biological pathways
 Using DES to maintain secrecy
Student Accessibility
Queen’s University is committed to achieving full accessibility for persons with disabilities. Part of this commitment includes arranging academic accommodations for students with disabilities to ensure they have an equitable opportunity to participate in all of their academic activities. If you are a student with a disability and think you may need accommodations, you are strongly encouraged to contact Student Wellness Services (SWS) and register as early as possible. For more information, including important deadlines, please visit the Student Wellness website at:http://www.queensu.ca/studentwellness/accessibilityservices/
Academic Integrity
It is your responsibility to adhere to academic integrity. Copying other people's work (in whole or in part) is plagiarism and is not allowed. Facilitating or allowing your work to be plagiarized is also an infringement of academic integrity. See the link to the SGS guidelines on academic integrity:http://www.queensu.ca/calendars/sgsr/Academic_Integrity_Policy.html
 ELEC 852  Broadband Integrated Circuits
Description
Topics covered include Sparameter 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 864  WDM Fibre Optic Communications Systems
Description
The course will include the following topics:
 Topic 1: Introduction: semiconductor lasers, photodiodes, optical fibers
 Topic 2: WDM transmission system, receiver performance, Qfactor, sensitivity
 Topic 3: Modulators and transmitter chirp
 Topic 4: Dispersion management and compensation, impact on WDM system performance
 Topic 5: Fiber nonlinear effects (scattering, Kerr)
 Topic 6: Interplay between fiber nonlinearities, dispersion, and transmitter chirp.
 Topic 7: Optical amplification (doped fiber amplifier, Raman, parametric amplification)
 Topic 8: Polarization mode dispersion Project
 Students can select from a list of suggested topics or any topic in the area of optical communications (with instructor’s consent).
 The project will be evaluated by a number of criteria (specifics to be determined). Criteria include thoroughness of background literature search, novelty of proposed research, and quality of research (theory and/or simulation and/or experiment).
Marking Scheme
Homework Assignments  0%
Class Project  50%
Final Exam  50%Course Material
FiberOptic Communication Systems, 4th Edition, Govind P. Agrawal, Wiley, 2010, ISBN 9780470505113
Lecture Notes
Research Literature  ELEC 867  Data Communications
Course Website
ELEC867 Fall 2018Course Instructor
Dr. ILMIN KIM
Office: Walter Light Hall, Room 409
Tel: 5336000, ext. 77672
Email: ilmin.kim[at]queensu.ca
Lab: Wireless Information Transmission Laboratory (WITL)Time and location:
Wednesday 2:30  5:30 PM, WLH 620
Office Hours:
TBD
Course objectives:
 To provide the fundamental theories of modulation, demodulation, and detection in digital communications systems
 To provide the foundation for the power efficiency/bandwidth efficiency/complexity tradeoffs in digital communications systems.
 To provide the fundamentals of OFDM and wireless communications theories
Course topics:
 Introduction
 Baseband Demodulation/Detection
 Bandpass Modulation and Demodulation/Detection
 Baseband Representation of Bandpass Signals
 Bandlimited Channels and InterSymbol Interference (ISI)
 Power and Bandwidth Tradeoffs
 TrellisCoded Modulation
 Wireless Digital Communications
 OFDM
 Adaptive Modulation
 Other Topics: Synchronization, CDMA, UWB, etc.
Textbooks:
 B. Sklar, Digital Communications, 2nd edition, Prentice Hall.
The textbook has been placed on reserve for 1 day loans at the Engineering & Science Library.
Other books:
 J. G. Proakis and M. Salehi, Communication systems engineering, 2nd edition, Prentice Hall
 J. G. Proakis and M. Salehi, Digital communications, 5th edition, McGrawHill
 M. K. Simon and M.S. Alouini, Digital communication over fading channels, 2nd edition, Wiley
 A. Goldsmith, Wireless communications, Cambridge University Press
 D. Tse and P. Viswanath, Fundamentals of wireless communication, Cambridge University Press
 All books have been placed on reserve for 1 day loans at the Engineering & Science Library.
Grading policy:
 Homework: 10%
 Term project: 10%
 Midterm exam: 30%
 Final exam: 50%
The responsibility to adhere to academic integrity:
Please see the following FEAS guidelines: engineering.queensu.ca/policy/Honesty.html
Disability accommodations:
Queen’s University is committed to achieving full accessibility for persons with disabilities. Part of this commitment includes arranging academic accommodations for students with disabilities to ensure they have all equitable opportunity to participate in all of their academic activities. If you are a student with a disability and you think you may need accommodations, you are strongly encouraged to contact the Disability Services Office (DSO) and register as early as possible. For more information, including important deadlines, please visit the DSO website at: http://www.queensu.ca/studentwellness/accessibilityservices
 ELEC 873  Cluster Computing
Description
This course covers topics related to highperformance computing (HPC) systems, from traditional to heterogeneous clusters, parallel programming models, highperformance networking and communication subsystems, and topologyaware and poweraware HPC. Research papers from literature, a term paper and presentation, project (optional), critique, and handson programming on clusters will play an important role in the learning process.
Text: Lecture notes, book chapters, journal and conference papers
Topics
 Introduction to Cluster Computing
 Parallel Programming Paradigms (MPI, OpenMP, PosixThreads, PGAS)
 Scalability Analysis, Performance Metrics and Benchmarks
 Interconnection Networks, Highspeed Interconnects and Messaging Layers
 Collective Communications, and Onesided Communications
 Latency Tolerance and Overlapping Techniques
 Heterogeneous (GPU, Xeon Phi) Computing
 Topologyaware, and Poweraware HighPerformance Computing
 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 stateoftheart 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 midDecember (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 twocolumn 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).
Contact
 Dr. Ahmad Afsahi, P.Eng.
 Professor and Chair of Graduate Studies
 Department of Electrical and Computer Engineering
 Queen's University
 Office: Walter Light Hall, Rm 403
 Tel: 6135333068
 Email: ahmad.afsahi[AT]queensu.ca
 ELEC 876  Software Reengineering
Description
This course covers software reengineering 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 reengineering 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 reengineering research. The topics include software refactoring strategies, migration to Object Oriented platforms, quality issues in reengineering processes, migration to networkcentric environments, and software integration.
Text: Lecture notes, book chapters, journal and conference papers
Structure
 Introduction to software reengineering
 Program comprehension
 Software reengineering techniques in source code transformation and refactoring strategies
 Software metrics & quality
 Reengineering economics
 Techniques for the migration of legacy systems into network centric environments
 Software integration issues and enabling technologies in webenabled 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 Aging", by David Lorge Parnas, International Conference on Software Engineering, 1994.
 "Software Maintenance and Evolution: a Roadmap", by K.H.Bennett and V.T Rajlich, The Future of Software Engineering, ACM Press 2000.
 "Reverse Engineering and Design Recovery: A Taxonomy", by Elliot J. Chikofsky and James H. Cross II, IEEE Software, Vol. 7, January 1990.
 "Reengineering: Defining an Integreated Migration Framework", by William M. Ulrich, CASE: Trends Magazine, May/June 1991.
 "A Unified Interprocedural Program Representation for a Maintenance Environment", by Mary Jean Harrold, and Brian Malloy, IEEE Transactions on Software Engineering, Vol. 19, No. 6, June 1993.
 "Interprocedural Slicing using Dependence Graphs", by Susan Horwitz, Thomas Reps, and David Binkley, ACM Transactions on Programming Languages and Systems, Vol. 12, No.1, Jan. 1990.
Software Reengineering Tools
 ELEC 879  Wearable IoT Computing
Description
This course focuses on recent advances and computing trends in wearable technologies, mobile devices, the Internet of Things (IoT), smart homes, and smart vehicles. The history, background, and applications of these systems are reviewed, followed by the description of common sensing technologies often utilized in these devices. Signal/timeseries analysis techniques, machine learning algorithms, and computing methods that are often utilized in such applications will be covered in detail. The course is highly applied and students will complete a project and present their results.
The course is an option for graduate students and the delivery will be primarily in the form of lectures, as well as studentled seminars and presentations. It is anticipated that this course will appeal to students from the Department of Electrical and Computer Engineering, as well as students from other departments such as Computing, Mechanical Engineering, and Mining.
Evaluation Criteria:
 Project and presentation: 50%
 Seminar: 20%
 Final Exam: 30%
Covered Topics:
 Introduction, background, and applications:
 Mobile devices
 Wearables for activity monitoring, health, entertainment, and rehabilitation
 Virtual reality (VR) and augmented reality (AR)
 IoT devices, smart homes, and smart vehicles
 Common sensors available in mobile, wearable, and IoT devices:
 Inertial measurement unit (IMU): accelerometer, gyroscope, and magnetometer
 Photoplethysmography (PPG)
 Electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG)
 Electrical bioimpedance (EBI) and galvanic skin response (GSR)
 Textile sensors
 Stretch and pressure sensors
 State of the art algorithms used in wearables and IoT devices:
 Preprocessing: denoising, feature extraction, dimensionality reduction, etc.
 Machine learning (supervised and unsupervised)
 Other computingrelated concepts in wearables and IoT:
 Resourceconstrained computing
 Cloud computing
 HCI
 Casestudies
 ELEC 811  Biological Signal Analysis
Description
The nature of biological signals and how these signals are detected, recorded and processed to extract information, are introduced in this course. A particular biosignal  the electromyogr am (EMG) associated with skeletal muscle contraction  will be covered in depth, including models of EMG generation; time and frequencydomain characteristics of the signal; physiologica l and nonphysiological factors which affect the EMG; and EMG analysis via standard and advanced signal processing techniques.
Objectives
 how biological potentials are generated, detected and recorded
 physiologicallybased EMG signal modeling
 EMG signal characteristics  the EMG as a stochastic signal
 timedomain, frequencydomain and timefrequency analysis of EMG signals
Course Outline
 Biosignal generation and muscle structure and function
 Electrodes and biosignal recording
 EMG modeling; stochastic signals
 EMG signal processing
 Other topics  e.g. muscle mechanics  time permitting
 ELEC 823  Signal Processing
Description
Electric circuit theory and electromagnetic theory are the two fundamental theories upon which all branches of electrical engineering are built, including computer engineering. Many branches of electrical engineering such as power, electric machines, control, electronics, communications, and instrumentation, are based on electric circuit theory. Therefore, the basic electric circuit theory is "the" foundation and starting point for what follows in electrical and computer engineering programs. Circuit theory is also valuable to students specializing in other areas of the physical sciences because circuits are perfect and easytounderstand models for the study of energy systems in general. This is also partly due to the common applied mathematics, physics, and topology involved. This course builds on fundamental physics and mathematics from APSC 112, APSC 171, APSC 172, and APSC 174.
Background Preparation
Students taking this course should have a strong grounding in probability and random variables (ELEC 326, ELEC 861, reference books 7 & 8 below) and in basic digital signal processing (ELEC 421, the nonrandom signal processing part of reference book 1 below). Proficiency in computer programming is essential.
ELEC 421 info: Motivations: Why study DSP? and What is Signal Processing?
Textbook
No textbook prescribed. The reference books below are listed roughly in decreasing relevance to the materials in this course.
Marking Scheme (Tentative)
Homework 20%, project 40%, exam 40%
Please familiarize with the rules and policies on academic honesty.
Reference Books
 J.G. Proakis and D.G. Manolakis, "Digital Signal Processing: Principles, Algorithms and Applications;" 4th edition, Prentice Hall, 2007.
 C.M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006.
 Rabiner & Juang, "Fundamentals of Speech Recognition," Prentice Hall, 1993.
 Huang, Acero, & Hon, "Spoken Language Processing," Prentice Hall, 2001.
 S. Haykin, "Adaptive Filter Theory," 4th ed., Prentice Hall, 2002.
 S.L. Marple, Jr., "Digital Spectral Analysis with Applications", Prentice Hall, 1987.
 Gray & Davisson, "An Introduction to Statistical Signal Processing," 2004, downloadable from wwwee.stanford.edu/~gray/sp.pdf
 Stark & Woods, "Probability and Random Processes with Applications to Signal Processing," 3rd ed., Prentice Hall, 2001.
 ELEC 830  Emerging Technologies in Power Grid
Description
Renewable energy generation; wind and Photovoltaic energy conversion; energy storage; distributed energy generation; hybrid systems; Power electronics interfaces and control. Grid connected distributed sources. Standalone operation of distributed sources and microgrid systems. System protection. Economical dispatch. Centralized and decentralized control. Smart grid.
 ELEC 832  Modeling and Control of Switching Power Converters
Description
This course covers the modeling and control techniques for switching power converters. Switching power converters are nonlinear 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 nonlinear 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 ACtoDC 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
 ELEC 841  NonLinear Systems: Analysis and Identification
Description
Analytical methods for nonlinear systems; nonlinear difference equation models: functional expansions and Volterra, Wiener and FourierHermite kernels; kernel estimation techniques; identification of cascades of linear and static nonlinear systems; use of Volterra series to find region of stability of nonlinear differential equations; applications to pattern recognition, communications, physiological systems, and nondestructive testing. Three termhours, lecture, Fall.
 ELEC 848  Control Systems Design for Robots and Telerobots
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.
0. Course Overview
1. Robot Modeling
a. Spatial description and transformations.
b. Serial manipulators: Forward and inverse kinematics, Jacobian and singularities, Dynamics using EulerLagrange 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 taskbased performance measures, Stability and stability robustness.
b. Fourchannel control formalism, Traditional control architectures, Tradeoff between stability and performance
c. Issue of timedelay, Proposed solutions: passivitybased, optimizationbased, predictivebased 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.
2. J.J. Craig, “Introduction to Robotics: Mechanics and Control,” 2004.
2. M.W. Spong and M. Vidyasagar, "Robot Dynamics and Control," Wiley, 1989.
3. 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. ELEC443 or MECH350 or MTHE332) and in robotics (e.g. ELEC448 or MECH456).
Grading:
Test 15%
Assignments 30%
Project/Study 55%
Test: A midterm will be held around week 6 on spatial descriptions, kinematics and dynamics. Date/Location: TBD.
Assignments: 34 assignments on robot control will be handed out, collected and marked.
Project/Study: consists of (i) individual or group project or study, and (ii) class discussions on selected key topics intelerobitics and haptics. The deliverable on item (i) are an oral presentation and a report.
 ELEC 854  Microwave Circuits and Systems
Description
This is a graduate course on the theory and design of very highspeed circuits and systems. Practical applications of microwave circuits for communications systems, biotelemetry, radio/radar imaging and radio astronomy instrumentation will be discussed over the course of the term. The course begins with coverage of fundamental concepts needed for general microwave circuit design and then proceeds to discuss specific circuit concepts. Prerequisites − an advanced undergraduate course in analog circuits or permission of instructor.
Prerequisites − an advanced undergraduate course in analog circuits or permission of instructor.
Coursework
Student performance will be evaluated through a term design project, takehome assignments and quizzes. Lecture duration: − two 75minute lectures per week. For the specific time and location of the lectures, consult the graduate timetable published online on the departmental website. Course website − course materials such as CAD tutorials, reference materials, assignments, and solutions will be distributed to students through the D2L (Brightspace) content management system.
Lecture duration:
Two 75minute lectures per week. For the specific time and location of the lectures, consult the graduate timetable published online on the departmental website.
Course Website:
Course materials such as CAD tutorials, reference materials, assignments, and solutions will be distributed to students through the D2L (Brightspace) content management system.
Contact
Dr. Carlos Saavedra, P.Eng.
Professor Department of Electrical and Computer Engineering
Walter Light Hall, Room 518
Queen’s University Kingston, ON Canada K7L 3N6email: saavedra@queensu.ca
 ELEC 866  Signal Detection and Estimation
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 upperyear undergraduate or entrylevel 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 17): 30%
Final Exam: 60%
Assignments (approximately every 2 weeks): 10%Course Schedule
Weeks 13: Background: elementary concepts from vector spaces, optimization, least squares, and linear systems, including transmission of wide sense stationary random processes.
Weeks 47: 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 810: Parameter estimation, including Bayesian, nonrandom, and maximum likelihood. Performance of estimators and asymptotic properties.
Weeks 1112: Topics in maximum likelihood estimation, expectationmaximization, 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:
 "Fundamentals of Statistical Signal Processing: Estimation Theory", Kay,Prentice Hall, ISBN: 0 133457117.
 "An Introduction to Signal Detection and Estimation" H. V. Poor, SpringerVerlag, ISBN: 0387 941738.
 Course notes package containing lecture notes and a variety of other reference material available first week of term.
 ELEC 875  Design Recovery and Automated Evolution
Description
Design recovery is the extraction of a design model from the artifacts of an existing software system. This design model is used to continue the evolution of the system. The model can be used in the planning and impact analysis stage, while making the changes and to test the result. The extracted design model can also be used to automate each of these tasks to varying degrees. Topics include design models, design recovery techniques, software evolution tasks, the semantics of programming languages and execution environments, and source code transformation.
There will be about 5 weeks of lectures followed by a midterm. The remaining 6 weeks will be seminar format where students will survey literature and prepare reports. Students will take turns leading class discussions on the literature. Students will also do a project singley or in pairs, including a class presentation of the project. The marking scheme is:
Midterm 25% Reports 30% Class Participation 15% Project 30% Course Schedule
This is a very general outline of topics for the first 5 weeks. The content of the remainder of the course depends on the papers chosen by the students.
 Introduction to Design Recovery
Motivation, General Description, Legacy (Vintage) System Issues, Software Evolution Tasks (Planning & Impact Analysis, Modification, Testing)  General Design Recovery
Motivation, General Models (examples: Datrix, Hungarian and Dagstuhl Middle Models)  Programming Language and Execution Environment
 Storage Semantics, Control Semantics, Preprocessor, Formatting, Comments, Environment Interaction Program Transformation
 Techniques, Grammar Based Transformation, Graph Transformations, TXL, Progres
 Empirical Studies
 Tools
Rigi, Revolve, CPPX, PBS, LS/2000, and others. Exchange Formats (GXL , etc.)
Project Suggestions
This section has some suggested projects. The student is encouraged to come up with thier own ideas as well.
 Design recovery of an example system (Open Souce, Public Domain, or Student Provided)
 Use of an existing tool to perform a maintenance task
 An analysis tool that uses the output of an existing tool to perform further analysis
Course Schedule and Readings
Week 1
T.J. Biggerstaff, "Design Recovery For Maintenance and Reuse", IEEE Computer, 22(7), July 1989, pp. 3699.
(available in the Douglas Library and on IEEE Xplore [direct link]).This week will focus on Design Recovery as a topic, Design Models Organization and General Terminology
Week 2
Singer, J., Lethbridge, T., Vinson, N. and Anquetil, N., "An Examination of Software Engineering Work Practices", CASCON '97, Toronto, October, pp. 209223.
Lethbridge, T. and Singer, J. (1997), "Understanding Software Maintenance Tools: Some Empirical Research", Workshop on Empirical Studies of Software Maintenance (WESS 97), Bari Italy, October, pp. 157162.
R. Ferenc, S. Sim, R. Holt , R. Koschke, T. Gyimothy, "Towards a Standard Schema for C/C++", 8th Working Conference On Reverse Engineering (WCRE'01) , Stuttgart, Germany, October, pp. 4958.
Week 3
T. Lethbridge, E. PlÃ¶dereder, S. Techelaar, C. Riva, P. Linos, S. Marchenko, Ã¢â‚¬Å“The Dagstuhl Middle ModelÃ¢â‚¬Â [here]
H. Fahmy, R.C. Holt and J.R. Cordy, "Wins and Losses of Algebraic Transformations of Software Architectures", Proc. ASE'2001, IEEE 16th International Conference on Automated Software Engineering, San Diego, November 2001, pp. 5162. [here]
Board Pictures, C1 C2 C3, Java1, Java2 Java3
As mentioned in class, the C diagram is missing the Sets and Uses edges. There should be a sets edge between main and x and a uses edge between main and x and a uses edge betwen Foo and s.
Wed Slides
Week 4
Michael L. Van De Vanter, "The Documentary Structure of Source Code" Journal of Information and Software Technology, Elsevier, Volume 44, No 13, pp. 767782
Queen's has a subscription to the Elsevier Journals. You must be on a Queen's Computer or using the secure proxy server in order to download the paper. Go to the Elsevier website. In the Journal or Book title search box, type "Information and Software Technology", then select Volume 44, Issue 13. You can then download the Van de Vanter Paper.
Vaclav Rajlich, N. Wilde, "The role of Concepts in Program Comprehension" Proc. 2002 International Workshop on Program Comprehension (IWPC'02), June 2002, Paris, 271278.
I.T. Bowman, R. Holt, N.V. Brewster, "Linux as a Case Study: It's Extracted Software Architecture", Proc. 21st International Conference on Software Engineering (ICSE'99), May 1999, Los Angeles, pp. 555563
Available fromÂ IEEE Xplore.
Week 5
Bull, R.I.; Trevors, A.; Malton, A.J.; Godfrey, M.W. Semantic grep: regular expressions + relational abstraction 9th Working Conference on Reverse Engineering(WCRE 2002), October, 2002, Richmond, Virginia, pp. 267 276.
Philipp Schugerl, "Scalable Clone Detection Using Description Logic" Proceeding IWSC '11 Proceedings of the 5th International Workshop on Software Clones, 201, pp 4753 (Available at ACM digital library). Library access from Queen's IP address.
Week 6
The exam is this week. It is closed book except for the DMM specification document.
Previous exam
Week 7
Sorry for the delay. I has some other issues to deal with during reading week and a touch of the flu. Please do your best to read the papers this week. The deadline for the critical sumaries is Friday.
Tues: Groups 1, 4
Roy et al, NICAD: Accurate Detection of NearMiss Intentional Clones Using Flexible PrettyPrinting and Code Normalization â€“ 16th International Conference on Program Comprehension. 2008
Zhang et al, Predicting Consistent Clone Change, IEEE 27th International Symposium on Software Reliability Engineering (ISSRE)
Wed: Groups 2,3
Ali Mesbah, Software Analysis for the Web: Achievements and Prospects,Â IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)
Sheneamer et al, A Detection framework for semantic code clones and obfuscated code Expert Systems with Applications, Volume 97, 1 May 2018, Pages 405420
Week 8
Tues: Groups 5,6
Minelli, Roberto, Andrea Mocci, and Michele Lanza. "I know what you did last summer: an investigation of how developers spend their time." Proceedings of the 2015 IEEE 23rd International Conference on Program Comprehension. IEEE Press, 2015. (6)
Codoban et al. Software History under the Lens: A Study on Why and How Developers Examine It, ICSME 2015. (5)
Wed: Groups 1, 4
Stevenson et al, Models are code too: Nearmiss clone detection for Simulink models, ICSM 2012. (4)
Gueheneuc et al., A comparative framework for design recovery tools,Conference on Software Maintenance and Reengineering (CSMR'06), Bari, 2006, pp. 10 pp.134. (1)
Week 9
Tues: Groups 2,3
Yandrapally et al, Robust Test Automation using Contextual Clues, ISSTA’14, pp 304314 (2)
Wed: Groups 5,6
Mondal, Manishankar, Chanchal K. Roy, and Kevin A. Schneider. "Bug Propagation through Code Cloning: An Empirical Study." Software Maintenance and Evolution (ICSME), 2017 IEEE International Conference on. IEEE, 2017.(6)
Marcus et al, An Information Retrieval Approach to Concept Location in Source Code, WCRE 2004.
Week 10
Tues: Groups 1, 4
Wed: Groups 2,3
Week 11
Tues: Groups 5,6
Selim, Gehan MK, James R. Cordy, and Juergen Dingel. "How is ATL Really Used? Language Feature Use in the ATL Zoo." Proceedings of the 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems. IEEE Press. 2017.(6)
Wed: Groups 1, 4
Week 12
Tues: Groups 2,3
Wed: Groups 5,6
SulÃr, MatÃºÅ¡, and Jaroslav PorubÃ¤n. "RuntimeSearch: Ctrl+ F for a running program." Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. IEEE Press, 2017. (6)
 Introduction to Design Recovery
 ELEC 880  Machine Learning for Natural Language Processing
Description
Human (or natural) language data permeate almost all aspects of our daily life. This course covers basic machine learning approaches to modelling natural language, including fundamental supervised and unsupervised methods for modelling sequences and structures in the data. Based on this, students learn how to develop various applications such as chatbots and information extraction systems. The course will also include stateoftheart artificial intelligence and deep learning approaches to natural language processing.
 ELEC 824  Machine Vision
Description
From lowlevel image processing to highlevel machine vision. Topics covered include: image formation and representation; gradient operators, edge detection and feature extraction; stereovision and epipolar geometry;projective vision; range image acquisition and registration; pose determination and object recognition; image retrieval; applications.
 ELEC 826  Adaptive and Array Signal Processing
Description
This is a graduate level course on Adaptive and Array Signal Processing. This course addresses the following topics: A very short review of DiscreteTime Signals and Systems, and fundamental concepts of optimal linear (Wiener Filters) filters. Eigenanalysis that is an essential mathematical tool for the study of adaptive and array processing, the LeastMeanSquared (LMS) and RecursiveLeastSquares (RLS) algorithms, tracking and convergence analysis of the generalized LMStype algorithms in meansquarederror sense, fundamental concepts of array signal processing (wave propagation, wavenumber), Beamforming, Source localization and spectral estimation. Each student will have a project related to adaptive and/or array signal processing.
PREREQUISITES: To follow the course, in addition to basic notions of digital signal processing, the student is expected to have some familiarity with the basic notions of probability and linear algebra. Three term hours, lecture.
 ELEC 827  Multimedia Signal Processing
Description
Burgeoning internetworking and proliferation of smart devices has multiplied the scope and instances of applications that employ multimedia signal processing functions. These functions can be found embedded in networked machines that interact with humans and mediate humanhuman collaboration. Multimedia signal processing embodiments abound, e.g., sensor signal processing and information extraction for portable/wearable devices; multimedia content generation, distribution, and playback; pointtopoint and multipoint communications over wireless networks and the Internet. An overarching theme of this course is the human centered aspect of multimedia, in terms of its ultimate users and source of signals. The focus on auditory and visual signals enables learning specific signal processing approaches and techniques, thereby laying a foundation to work with a variety of existing and emerging interface modalities. Thus, this course will cover human perception and signal production modeling and analysis; machine learning techniques for information extraction; coding for data compression and transmission; anthropomorphic machine intelligence, etc. Through a course project, each student will apply the lecture materials to study a class of signals of his/her choosing, where "signal" is broadly defined (see here for what constitutes a "signal").
Objectives
Study of multimedia signal processing for network mediated humanhuman communication and humanmachine interaction (HMI). Topics covered include: overview of multimedia applications and processing functions; speech production; human auditory and speech perception; image formation; human visual perception; perceptual quality and user experience modeling; speech and image analysis and synthesis methods; lossless and lossy compression techniques; coding for communication and storage; sensing modalities for HMI; machine learning algorithms for information extraction and understanding.
Prerequisites
Students taking this course should have taken an introductory course to probability and random variables, and digital signal processing. Proficiency in computer programming (e.g., using Matlab) is necessary as the course project requires running computer simulations to process signals.
 ELEC 836  Power Systems Design for Telecommunications
Description
Overview of advanced telecommunication networks and powering requirements: central office equipment, optical networks, FiberInTheLoop systems, and hybrid fiber/coax networks. Powering alternatives: low frequency distribution, dc distribution and high frequency distribution. System modeling and simulation. Stability of the power system. Special emphasis will be placed on the design techniques using practical examples.
Prerequisites
ELEC 431 or permission of instructor. Three termhours, lecture.
 ELEC 853  Silicon RF and Microwave Circuits
Description
This course presents an introduction to the design of RF and microwave circuits using silicon technologies. Topics include: an overview of silicon technologies; the design of passive structures including transmission lines, inductors, and couplers; considerations in the layout of active devices; examples of the design of circuit components on silicon; system design including integrated systemonchip designs; and a look at the future of silicon highspeed circuits. Three termhours, lecture.
Prerequisites
ELEC483 or equivalent
 ELEC 858  Principles of Microwave Imaging and Remote Sensing
Description
This course is an overview of the physical and engineering principles of microwave imaging and remote sensing. Topics include: electromagnetic wave propagation, polarized and partially polarized waves, polarimetric synthesis and decomposition, wave diffraction, wave scattering from smooth and rough boundaries, scattering and emission of waves from natural surfaces, passive microwave detectors, radar fundamentals, radar altimeters, radar image construction, polarimetric radar, nonideal imaging effects such as speckle and geometric distortion. Applications of microwave imaging to the earth sciences will be discussed.
Objectives
Review of Maxwell's equations, polarized waves, polarimetric synthesis and decomposition, wave scattering from smooth and rough boundaries, scattering and emission of waves from the earth (soil, vegetation, oceans, ice, etc.), passive microwave imaging systems (radiometers), active microwave imaging systems.
Prerequisites  At least one undergraduate, semesterlong, course in electromagnetics
Course Material
Required Text:
 Iain H. Woodhouse, Introduction to Microwave Remote Sensing, CRC Press,
New York: 2006.
Reference books on 3hour reserve at Douglas Library:
 John R. Jensen, Remote Sensing of the Environment: an Earth Resource Perspective, Pearson Prentice Hall, Upper Saddle River, NJ: 2007.
Library call number: QE33.2.R4 J46 2007  Arthur P. Cracknell, Introduction to Remote Sensing, CRC/Taylor & Francis,
New York: 2007.
Library call number: G70.4.C73 2007.
CAD Software:
 Matlab will be used for assignments. This software is available in the Bain lab in Walter Light Hall.
 Iain H. Woodhouse, Introduction to Microwave Remote Sensing, CRC Press,
 ELEC 861  Probability, Random Variables and Stochastic Processes
Description
The review of probability theory including probability spaces, random variables, probability distribution and density functions, characteristic functions, convergence of random sequences, and laws of large numbers. Fundamental concepts of random processes including stationarity, ergodicity, autocorrelation function and power spectral density, and transmission of random processes through linear systems. Special random processes, including Gaussian processes, with applications to electrical and computer engineering at a rigorous level. Three termhours, lecture, Winter.
 ELEC 862  Wireless Mobile Communications
Description
This course covers wireless mobile and satellite communication systems. The main topics of this course are: Introduction to the basic concepts of wireless mobile systems and standards, Propagation modeling, Cochannel interference, Modulation techniques with applications to mobile communications (PSK, ASK, OFDM, etc.), Digital signaling on flat fading channels and diversity techniques, Equalization and digital signaling on ISI channels, Error probability performance analysis, CDMA and multiuser detection, Cellular coverage planning, Link quality measurements and handoff initiation, Introduction to satellite mobile communications, Third generation global mobile communication standards. Three termhours, lecture.
 ELEC 863  Topics in Optical Communications
Grading Scheme
 Assignments: 20%
 Project Report: 30%
 Final Exam: 50%
The final exam is openbook and 3 hours in duration.
Course Material
All course material is on the onQ course homepage.
 ELEC 865  Coding Theory
Description
his course will be an introductory course on Error Control Coding. The field of error control coding targets the problem of reliable transmission of information over time or space in the presence of noise. The birth of this area from both mathematical as well as engineering perspective can be regarded the same as that of Information Theory: the year of 1948 when Claude Shannon published his landmark paper on communications over noisy channels. Shannon's work in essence was quite a tease! His central result was that if the rate of communication is kept below a certain value (Channel Capacity), reliable communication can be achieved if one chooses proper channel encoding/decoding. He never said what would this proper scheme be for any given channel. Researchers have been addressing this for more than half a century starting with primarily algebraic structures (algebraic coding) and shifting towards random and heuristic schemes (turbo, turbolike, and lowdensity paritycheck Codes) lately. We put the emphasis on the decoding as Elwyn Berlekamp observes "from a practical standpoint, the essential limitation of all coding and decoding schemes proposed to date has not been Shannon's capacity but the complexity of the decoder. For this reason, efforts have been directed toward the design of coding and decoding schemes which could easily be implemented." In this course we will study a number of efficient encoding/decoding strategies which have proven important in practice with a categorization on the notion of decoding. The course will roughly consist of the following subjects:

Introduction to channel coding: Channel capacity, Shannon theory, performance bounds, discrete and analog channel models

Algebraic decoding: Harddecision decoding, algebraic coding theory, group theory, codes over fields, codes over rings/groups, linear binary block codes, cyclic codes, BCH codes, RS codes

Trellis decoding: Softdecision decoding, graphical representations of codes: trellis diagram, Tanner/factor graph, the Viterbi algorithm, convolutional codes, list decoding, sequential decoding, coded modulation

Iterative decoding: Codes over graphs, the forwardbackward algorithm, softoutput decoding, lowdensity paritycheck codes, turbo codes, repeataccumulate codes, block turbo coding, fountain codes, EXIT analysis
PREREQUISITES: Knowledge of digital communications, basic information theory, probability theory, stochastic processes, and (abstract) algebra [group theory] would be a plus (and at times crucial). Also MATLAB and C will be needed for your projects and home works. LATEX is a must for some of the projects (scribe) and reports.
Course Material
 Course notes, slides, and a number of papers
 E. R. Berlekamp, Algebraic Coding Theory, McGrawHill, New York, 1968
 R. E. Blahut, Theory and Practice of Error Control Codes, AddisonWesley, 1983
 S. Lin and D. J. Costello, Jr., Error Control Coding, Prentice Hall, 2nd Edition, 2004
 R. G. Gallager, LowDensity ParityCheck Codes, MIT Press, Cambridge, MA, 1963
 R. Johannesson and and K. Sh. Zigangirov, Fudamentals of Convolutional Coding, IEEE Press, 1999
 D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003
 F.J. MacWilliams and N. J. A. Sloane, The Theory of ErrorCorrecting Codes, NorthHolland, 1978
 J. Pearl, Probabilistic Reasoning in Intelligent Systems: Netwroks of Plausible Inference, Morgan Kaufmann, 1988
 W. W. Peterson and E. J. Weldon, Jr., Error Correcting Codes, MIT Press, Cambridge, MA, 1972
 V. Pless and W. C. Huffman, Eds., Handbood of Coding Theory, Elsevier, 1998
 B. Vucetic and J. Yuan, Turbo Codes, Principles and Applications, Kluwer Academic Publishers, 2003
 S. B. Wicker and S. Kim, Fundamentals of Codes, Graphs, and Iterative Decoding, Kluwer Academic Publishers, 2003
 S. B. Wicker, Error Control Systems for Digital Communication and Storage, Prentice Hall, 1995
 T. Richardson and R. Urbanke, Modern Coding Theory, Cambridge University Press, 2008

 ELEC 868  Simulation of Optical Communications Systems
Description
This course focuses on the study of various components that goes into an effective simulation tool for next generation optical communications systems. While the underlying constitution blocks for devices and subsystems will be introduced, the emphasis will be on the investigation and engineering choice of appropriate systemlevel models that will deliver a highperformance optical simulation system that is both computationally efficient, and highly realistic of the targeted physical optoelectronic physical phenomena. Various numerical models for devices (e.g. transmitter, receivers, fiber, filter) will be introduced with the aid of simulation tools (e.g. OptiSystem or equivalent, Matlab) as the course progresses, and their respective contributions to the overall system performance will be examined.
The objective of this course is to provide students with an understanding of the simulation of optical communications systems. It will introduce the students to the underlying principles of optoelectronic devices, waveguide propagation, digital communication basics and coding. It will present current mathematical modeling of devices and components useful for the simulation of a fullfledged optical communication system. Both through theory and using the modeling software (OptiSystem or equivalent) as a basis for simulation tool, students are expected to develop an understanding of the critical aspects and tradeoffs that characterizes optical communication systems. The modeling software has cosimulation with MATLAB so additional models/functions can be included as the course develops.
Objectives
 Calculate propagation of an optical signal in an optical fiber link in linear and nonlinear regime.
 Calculate the output field of an optical modulator.
 Calculate the noise accumulated in an amplified link.
 Calculate transfer functions of basic optical filter elements.
 Select and design appropriate optical filters.
 Evaluate the performance of digital optical communications systems based on different modulation formats.
 Calculate the improvement in system performance by introducing advanced signal processing techniques and error correction codes.
Course evaluation:
Evaluation by each instructor:
 15% × 6 = 90%
 Final Project: 10% (presentation in front of all CREATE trainees)
Course Material
Week 1  January 13, 15  Scott Yam
 Optical communication system overview and introduction to system simulation
 Digital representation of signal and noise for system simulations
Week 2  January 20, 22  Leslie Rusch
 Mathematical modeling of noise (e.g., Monte Carlo simulation, simulation of random Poisson and Weiner processes, simulation of electrical and optical noise)
 Optical noise in context of EDFAs in WDM systems
Weeks 3 and 4  January 27, 29 and February 3, 5  David Plant
 Semiconductor lasers
 Deterministic and stochastic rate equation models, mathematical models and implementations for system simulations
 Laser performance metrics (output power, bandwidth, linewidth, RIN, chirp)
 DFB lasers, tunable lasers, SOAs
Weeks 5 and 6  February 10, 12 and February 17, 19  John Cartledge
 Optical modulation formats
 Optical modulators, amplitude and/or phase modulation, mathematical models and implementations for system simulations
 Modulator specifications and performance metrics
Weeks 7  February 24, 26  Lawrence Chen
Optical fibers
 Linear and nonlinear propagation, mathematical models and implementations for system simulations (dispersion, selfphase modulation, crossphase modulation, fourwave mixing)
*No class March 3, March 5
Week 8  March 10, 12  LaRochelle
 Operational principle of EDFA
 Mathematical models and implementations for system simulations
 Amplifier performance metrics (saturation, output power, noise, crosstalk)
Weeks 9  March 17, 19  Lawrence Chen
Optical fibers Modeling
 Numerical solution of propagation equations: splitstep Fourier method
 Fiber specifications and performance metrics
Week 10  March 24, 26  LaRochelle
 Types of optical filters (thinfilm interference filters, integrated waveguide filters, fiber Bragg gratings, arrayedwaveguidegratings)
 Mathematical models and implementations for system simulations
 Filter performance metrics
Week 11  March 31, April 2  Yam
 Receivers and system performance for various modulation formats (BER, Qfactor, OSNR sensitivity)
 System performance w.r.t. parameters (dispersion, nonlinearity)
Week 12  April 7, 9  Rusch
 Forward error correction (encoding/ decoding/ implementations)
 Impact of coding on system performance.
Week 13  April 14, 16  All Students
 Project presentation (active participation mandatory)
Institutions, Professors, Contact Info:
Queen's University John Cartledge  Profile and Contact Info: John Cartledge
 Scott Yam  Profile and Contact Info: Scott Yam
McGill University
 David Plant  Profile and Contact Info: David Plant
 Lawrence Chen  Profile and Contact Info: Lawwrence Chen
Laval University
 Leslie Rusch  Profile and Contact Info: Leslie Rusch
 Sophie LaRochelle  Profile and Contact Info: Sophie LaRochelle
 ELEC 869  MIMO Communications Systems
Description
Electric circuit theory and electromagnetic theory are the two fundamental theories upon which all branches of electrical engineering are built, including computer engineering. Many branches of electrical engineering such as power, electric machines, control, electronics, communications, and instrumentation, are based on electric circuit theory. Therefore, the basic electric circuit theory is "the" foundation and starting point for what follows in electrical and computer engineering programs. Circuit theory is also valuable to students specializing in other areas of the physical sciences because circuits are perfect and easytounderstand models for the study of energy systems in general. This is also partly due to the common applied mathematics, physics, and topology involved. This course builds on fundamental physics and mathematics from APSC 112, APSC 171, APSC 172, and APSC 174.
Objectives
Objectives and goals in this course are as follows:
 You will have a thorough understanding of basic ideal circuit elements: resistors, capacitors, inductors, and voltage and current sources
 You will understand the two fundamentals laws of circuits: KCL and KVL; and will apply them to analyze simple circuits
 You will be able to calculate the voltages, currents, energies, and powers of elements of a welldesigned circuit and make sense of them
 You will distinguish between passive and active elements
 You will learn a number of systematic circuit analysis techniques and will be able to apply them to solve simple circuits
 You will learn a number of methods to simplify and reduce circuits to "equivalent" circuits which are easier to understand and analyze
 You will be able to analyze and explain the behaviour of circuits with up to two capacitors and/or inductors (more precisely: first and secondorder circuits) and will be able to explain the practical issues involving such responses in electrical engineering industry
 You will understand the principle of superposition and will apply it to analyze circuits and explain responses
 You will differentiate between the response of a circuit to initial conditions and that due to inputs such as a step function (sudden application of a voltage or current)
 You will appreciate the significance of the maximum power transfer theorem and will use it to design circuits which are optimal in the sense of power absorption
 You will differentiate between the transient and steadystate portions of a response
 You will be able to analyze circuits in response to sinusoidal inputs in steadystate and will appreciate the significance of such responses in electrical engineering practice
Fourth Year Courses
Fourth year courses listed below may be taken by graduate students for credit, subject to the regulations set forth in the departmental prescription and those of the School of graduate Studies and Research.
External Course List
Department of Chemical Engineering
Proceed to Queen's Department of Chemical Engineering WebpageDepartment of Mechanical Engineering
Proceed to Queen's Mechanical Engineering Department WebpageDepartment of Mathematics and Statistics
Proceed to Math Department WebpageRobert M. Buchan Department of Mining
Proceed to Robert M. Buchan Department of Mining CoursesSchool of Computing
Proceed to Queen's School of computing WebpageRoyal Military College
Proceed to RMC webpage