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 soft-switching 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 real-life applications such as space, telecommunications, information processing and renewable energy generation.

Topics

• Variable Frequency Resonant Converters
• Modeling of Resonant Converters
• Phase-shift 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

• Variable Frequency Resonant Converters
• Modeling of Resonant Converters
• Constant Frequency Phase Shift Resonance
• Constant Frequency APWM
• Soft Switches Converters
• Auxiliary Resonant Converters
• SSOC Resonant Converters

Description

Introduction. Power semiconductor devices. Line- and force-commutated converters. High power ac/dc and dc/ac converter structures and switching techniques. Principles of HVDC and HVAC systems. Large and small scale stabilities, sub-synchronous resonances, inter-area oscillations, voltage sags, and harmonic instability. Voltage, power angle, and impedance control, phase balancing, and power factor correction by means of solid-state power converters. Flexible AC Transmission Systems (FACTS). Three term hours, lecture, Fall.

Description

Analytical methods for nonlinear systems; nonlinear difference equation models: functional expansions and Volterra, Wiener and Fourier-Hermite 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 non-destructive testing. Three term-hours, lecture, Fall.

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
   1. Spatial description and transformations.
   2. Serial manipulators: Forward and inverse kinematics, Jacobian and singularities, Dynamics using Euler-Lagrange method.
2. Robot Control
   1. Position control methods: Centralized and decentralized control, Multivariable control, Robust control, Stability in the sense of Lyapunov, Variable structure control, Adaptive control.
   2. Force control methods: Hybrid control, Impedance control, Parallel force/position control.
3. Telerobotics and Haptics
   1. 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.
   2. Four-channel control formalism, Traditional control architectures, Trade-off between stability and performance
   3. Issue of time-delay, Proposed solutions: passivity-based, optimization-based, predictive-based methods and supervisory control
   4. Adaptive and variable parameter control methods
   5. Issue of rate mode control, Stability and performance
   6. 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.
3. M.W. Spong and M. Vidyasagar, "Robot Dynamics and Control," Wiley, 1989.
4. 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:

• Test - 15%
• Assignments - 30%
• Project/Study - 55%

Test: A mid-term will be held around week 6 on spatial descriptions, kinematics and dynamics. Date/Location: TBD.

Assignments: 3-4 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.

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.

Description

Electric circuit theory and electromagnetic theory are the two fundamental theories upon which all branches of electri-cal 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 com-puter engineering programs. Circuit theory is also valuable to students specializing in other areas of the physical sci-ences because circuits are perfect and easy-to-understand 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 fundamen-tal 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 non-random 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

1. J.G. Proakis and D.G. Manolakis, "Digital Signal Processing: Principles, Algorithms and Applications;" 4th edition, Prentice Hall, 2007.
2. C.M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006.
3. Rabiner & Juang, "Fundamentals of Speech Recognition," Prentice Hall, 1993.
4. Huang, Acero, & Hon, "Spoken Language Processing," Prentice Hall, 2001.
5. S. Haykin, "Adaptive Filter Theory," 4th ed., Prentice Hall, 2002.
6. S.L. Marple, Jr., "Digital Spectral Analysis with Applications", Prentice Hall, 1987.
7. Gray & Davisson, "An Introduction to Statistical Signal Processing," 2004, downloadable from www-ee.stanford.edu/~gray/sp.pdf
8. Stark & Woods, "Probability and Random Processes with Applications to Signal Processing," 3rd ed., Prentice Hall, 2001.

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

Description

In this course we will study discrete-event processes, such as computer systems and manufacturing systems, that require control to induce desirable behaviour. Informally, a discrete-event 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 discrete-event systems are taken from computer science and mathematics and include automata or finite-state 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 discrete-event control problems; centralized control problems; modular supervision; partial observation; nonblocking solutions; decentralized control problems; computational complexity of DES control problems; timed discrete-event systems; and control using a limited-lookahead approach. Small-scale examples will be used to motivate material, and connections with application areas such as manufacturing systems and communication protocols will be emphasized. A software package developed to solve small problems in discrete-event systems will be used to illustrate the mechanics of solving DES control problems. We will show how the course material can be used to model applications such ascommunication protocol verification, 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. Some familiarity with automata theory, graph theory, or propositional logic is a bonus but is not essential. Students are expected to be competent at mathematical proofs (especially mathematical induction), formal reasoning and logical arguments.

Outline

1. Introduction (1.3)

• What are discrete-event systems (DESs)?
• Why should we study how to control them?
• What types of mathematical models are used to represent DESs?
• Automata, finite-state machines, directed graphs (2.1-2.4)
• Petri nets (4.1-4.2)
• Mathematical Logic
• Algebra

2. Automata Theory and the Theory of Formal Languages (2.1-2.4)

• 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 finite-state automata and regular languages (2.4)
• Nondeterministic finite-state automaton (NFA); converting NFA to language-equivalent deterministic finite-state automation (DFA) (2.3.3)

3. DES Control Problems (3.1-3.3)

• Basic Ramadge-Wonham concepts: how to model the process to be controlled (as an automaton), how to model "desired behaviour", how to formulate control problems (3.1-3.2)
• 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 (3.4-3.5)

• Motivating examples
• Concept of controllability (3.4.1)
• Concept of supremal controllable sublanguage (3.4.3)
• Formulations of centralized DES problems and their solutions (3.4.4-3.4.5)
• Computing supremal controllable sublanguage (3.5.3)
• Cat and Mouse example

6. Modular Supervision (3.6)

• Supervisor conjunction
• Nonblocking solutions (3.6)
• Small-scale manufacturing system example

5. 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, co-observability (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 real-time constraints to be realized)
• Small-scale 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

Syllabus

Links to Recommended DES Software

IDES: Developed at Queen's by K. Rudie's research group.

TCT: Developed at University of Toronto by W.M. Wonham's research group. XP and Linux versions available (by following links to either XPTCT or LTCT).

DESUMA: Developed at the University of Michigan by S. Lafortune's research group and at Mount Allison University by S.L. Ricker's research group.

Supremica: Developed at Chalmers University of Technology by Akesson and Fabian's research group.

Supplemental Course Notes

First Lecture (in Adobe pdf format)

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/accessibility-services/

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

Description

This is a graduate course on the theory and design of very high-speed 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. Pre-requisites − an advanced undergraduate course in analog circuits or permission of instructor.

Pre-requisites − an advanced undergraduate course in analog circuits or permission of instructor.

Coursework

Student performance will be evaluated through a term design project, take-home assignments and quizzes. Lecture duration: − two 75-minute lectures per week. For the specific time and location of the lectures, consult the graduate timetable published on-line 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 75-minute lectures per week. For the specific time and location of the lectures, consult the graduate timetable published on-line 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 3N6

e-mail: saavedra@queensu.ca

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 term-hours, lecture, Winter.

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 electromyogram (EMG) associated with skeletal muscle contraction - will be covered in depth, including models of EMG generation; time- and frequency-domain characteristics of the signal; physiological and non-physiological factors which affect the EMG; and EMG analysis via standard and advanced signal processing techniques.

Objectives

• how biological potentials are generated, detected and recorded
• physiologically-based EMG signal modeling
• EMG signal characteristics - the EMG as a stochastic signal
• time-domain, frequency-domain and time-frequency 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

Course Materials

• Week 1 lecture slides
• Course notes: biosignal generation
• Week 2 lecture slides

Description

From low-level image processing to high-level 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.

Description

This is a graduate level course on Adaptive and Array Signal Processing. This course addresses the following topics: A very short review of Discrete-Time 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 Least-Mean-Squared (LMS) and Recursive-Least-Squares (RLS) algorithms, tracking and convergence analysis of the generalized LMS-type algorithms in mean-squared-error 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.

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 human-human collaboration. Multimedia signal processing embodiments abound, e.g., sensor signal processing and information extraction for portable/wearable devices; multimedia content generation, distribution, and playback; point-to-point 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 human-human communication and human-machine 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.

Description

Overview of advanced telecommunication networks and powering requirements: central office equipment, optical networks, Fiber-In-The-Loop 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 term-hours, lecture.

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 system-on-chip designs; and a look at the future of silicon high-speed circuits. Three term-hours, lecture.

Prerequisites

ELEC-483 or equivalent

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, Co-channel 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 multi-user detection, Cellular coverage planning, Link quality measurements and handoff initiation, Introduction to satellite mobile communications, Third generation global mobile communication standards. Three term-hours, lecture.