40 graduate courses in the Fall 2025 Graduate Calendar.
SYDE 600Systems Theory, Models, Research & Design0.50 units
The introduction of systems theory (Graph theoretic methods and formulation of system equations) to the solving and modelling of problems via design and research methods. Research methods include the formulation of good problems, critical analysis of existing literature and methods, the formulation of ideas and research plans and papers. In addition to problem formulation and definition, design also includes needs analysis, criteria, generation of alternative solutions, feasibility analysis, optimization, selection, and implementation & solution.
SYDE 620Fundamentals of Continuous System Models0.50 units
Solution of systems of ordinary differential equations (ODEs), nonlinear systems, qualitative analysis, phase plane, stability, and nonhomogeneous systems. Series solutions of ODEs; Legendre polynomials, and Bessel functions. Fourier expansions and integral, orthogonal functions, and Sturm-Liouville systems. Partial differential equations: parabolic, hyperbolic and elliptic equations and their solution techniques.
Review of mathematical and computational preliminaries; sources of error in floating-point arithmetic; solution of linear equations, eigen value problems, singular value decomposition, non-linear equations, ordinary differential equations and issues in designing mathematical software. The emphasis in this course will be on solution techniques rather than modelling and equation formulation.
SYDE 625Tools of Intelligent Systems Design0.50 units
The course outlines fundamentals of intelligent systems design using tools of computational intelligence and soft computing. These include fuzzy logic, neural networks, genetic algorithms and other hybrid techniques such as neuro fuzzy systems and fuzzy-generated algorithms.
The theories and applications of time-series modelling are presented for describing phenomena measured at discrete points in time. The types of time-series models include stationary auto regressive moving average (ARMA), nonstationary, special families of seasonal, transfer function-noise (multiple inputs-single output), intervention, multivariate (multiple inputs-multiple output) models and machine learning methods for time series. Applications are used for explaining how the foregoing models are fitted to both natural and socio-economic time series by following the identification, estimation and diagnostic check stages of model construction. Other topics include simulation in engineering design, forecasting in the operation of large-scale projects, and environmental impact assessment.
This course is intended to give a broad treatment of the subject of practical optimization. Emphasis will be given to understanding the motivation and scope of various optimization techniques for constrained and unconstrained problems. The methods discussed include, but are not limited to: Newton's method and its variants, secant methods and conjugate gradient methods for unconstrained problems; active set methods, penalty methods and Lagrangian methods for constrained problems. In order to use, adapt and modify these methods, details that affect their performance will be discussed.
A survey of modern quantitative remote sensing using optical, infrared, and microwave radiation. The principles and technologies for acquiring and understanding remotely sensed image data are discussed. Physical principles of EM propagation and interaction between the radiation and terrestrial and atmospheric materials. Principles and operation of sensor systems. Principles of pattern recognition and image processing techniques unique to remote sensing. Applications of remote sensing to monitoring vegetation, soil, oceans, and inland waters, and snow and ice.
This course introduces fundamentals and important techniques for experimental design and statistical analysis used in systems design engineering. Topics include measurements, variables, main and interaction effects, sources of errors, experimental control, T-test, ANOVA, regression, factorial and fractional factorial designs, response surface methods, and robust parameter design. Techniques frequently used in experiments both with and without human participants will be introduced. Examples will be selected from research publications. Students will learn the importance and proper methods of experimental design through studying examples in the field of systems design engineering and practice with different software tools.
This course examines the fundamentals of modern perspectives on interface design for complex systems using current methods in cognitive engineering. We discuss Cognitive Work Analysis, Brunswick's' Lens Model, Goal Directed Task Analysis, Situation Awareness Oriented Design, Naturalistic Decision Making, Contextual Inquiry, Macro-cognitive Methods, Activity Theory, Concept Mapping, Cognitive Task Analysis, Social Network Analysis and their application to different types of human engineering problems. Students in this course will learn multiple methods in cognitive engineering with an emphasis on knowing the differences in foundation, assumptions and appropriate application of the methods. Students will be expected to apply the methods in a realistic research context, applying for ethics clearance and working with actual participants. Examples of appropriate topics may include understanding how people work with complex or automated systems models. Finally this course discusses aspects of the current research environment in cognitive engineering, with the objective of developing successful future researchers in this area.
Interaction of humans with technological systems often takes place within the broader context of a collaborative setting. Therefore, there is an increasing trend for the design of these systems to incorporate and support group interactions. This course will emphasize the study of collaboration from an interdisciplinary perspective and the derivation of system design criteria. Topics will include group theories, collaboration requirements (including communication, coordination, team awareness), quantitative and qualitative research methods (including laboratory studies, surveys, ethnographic research methods), data analysis, and collaboration technologies.
The focus of the course is on in-depth explorations of primary methods of data collection used in human factors engineering research and product design. We will begin with various philosophical positions regarding human factors for testing and evaluation as they relate to both industrial and research applications. We will discuss the applications and implications of data collection involving human participants; the limitations of statistical analyses; and the potential application of human factors research as guidelines for product or system design. Students will be encouraged to explore human factors methods related to their own area of research.
In this course, linear graph theory is used to model the topology of 2-d and 3-d systems of rigid bodies connected by mechanical joints, springs, dampers, and actuators. Graph-theoretic methods are then used to systematically derive the kinematic and dynamic equations; the numeric solution of these equations provides a simulation of the system's motion. Topics include: review of kinematics, dynamics and graph theoretic (GT) methods; application to one-dimensional mechanical systems; GT representation of two-dimensional components and systems; formulation and solution of governing system equations; extension to three dimensional mechanical systems with flexible bodies and mechatronic components; application to kinematic and dynamic analysis of mechanisms, robotic manipulators, vehicles and satellites.
SYDE 654Graphic Theoretic Models for Complex Systems0.50 units
This course extends material in SY DE 551 to include complex systems, systems with uncertainty and systems design issues. Material covered includes: non-linear systems models, their formulations and solutions; higher-order sensitivity models and solutions; second moment analysis and robust design methods for systems with probabilistic components. Examples are taken from electro-mechanical disciplines.
SYDE 655Optimal and Learning-Based Control0.50 units
This course provides an understanding of the principles of optimal control while introducing the key ideas of learning-based control and discussing intersections between these two broad areas. Variational approach, Pontryagin's Minimum Principle, linear quadratic optimal control, the principle of optimality, dynamic programming, and the Hamilton-Jacobi-Bellman equation are covered. Model predictive control is presented. A brief introduction to learning for control applications is provided, along with introducing iterative learning control. Model-based and model-free reinforcement learning (RL) techniques, connections between RL and optimal control ideas, and approximate RL solution methods for complex problems are discussed.
SYDE 660Systems Design Graduate Workshop 10.50 units
Engineering design project course where students work in small groups applying the principles of engineering problem solving, research methods, systems analysis including modelling, simulation, optimization and design. There is a strong focus on a major design project with regular mentorship, project update reports and presentations, project reviews, and design embodiment demonstrations.
Prerequisites
Must have completed the following:
SYDE600 - Systems Theory, Models, Research & Design (0.50)
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE660A - Systems Design Graduate Workshop 1 - AI and Machine Learning (0.50)
SYDE660B - Systems Design Graduate Workshop 1 - Biomedical Systems (0.50)
SYDE660C - Systems Design Graduate Workshop 1 - Human Factors (0.50)
SYDE660D - Systems Design Graduate Workshop 1 - Mechatronic & Physical Systems (0.50)
SYDE660E - Systems Design Graduate Workshop 1 - Vision, Image & Signal Processing (0.50)
SYDE 660ASystems Design Graduate Workshop 1 - AI and Machine Learning0.50 units
Engineering design project course where students work in small groups applying the principles of engineering problem solving, research methods, systems analysis including modelling, simulation, optimization and design. There is a strong focus on a major AI and Machine Learning design project with regular mentorship, project update reports and presentations, project reviews, and design embodiment demonstrations.
Prerequisites
Must have completed the following:
SYDE600 - Systems Theory, Models, Research & Design (0.50)
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE660 - Systems Design Graduate Workshop 1 (0.50)
SYDE660B - Systems Design Graduate Workshop 1 - Biomedical Systems (0.50)
SYDE660C - Systems Design Graduate Workshop 1 - Human Factors (0.50)
SYDE660D - Systems Design Graduate Workshop 1 - Mechatronic & Physical Systems (0.50)
SYDE660E - Systems Design Graduate Workshop 1 - Vision, Image & Signal Processing (0.50)
SYDE 660BSystems Design Graduate Workshop 1 - Biomedical Systems0.50 units
Engineering design project course where students work in small groups applying the principles of engineering problem solving, research methods, systems analysis including modelling, simulation, optimization and design. There is a strong focus on a major Biomedical Systems design project with regular mentorship, project update reports and presentations, project reviews, and design embodiment demonstrations.
Prerequisites
Must have completed the following:
SYDE600 - Systems Theory, Models, Research & Design (0.50)
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE660 - Systems Design Graduate Workshop 1 (0.50)
SYDE660A - Systems Design Graduate Workshop 1 - AI and Machine Learning (0.50)
SYDE660C - Systems Design Graduate Workshop 1 - Human Factors (0.50)
SYDE660D - Systems Design Graduate Workshop 1 - Mechatronic & Physical Systems (0.50)
SYDE660E - Systems Design Graduate Workshop 1 - Vision, Image & Signal Processing (0.50)
SYDE 660CSystems Design Graduate Workshop 1 - Human Factors0.50 units
Engineering design project course where students work in small groups applying the principles of engineering problem solving, research methods, systems analysis including modelling, simulation, optimization and design. There is a strong focus on a major Human Factors design project with regular mentorship, project update reports and presentations, project reviews, and design embodiment demonstrations.
Prerequisites
Must have completed the following:
SYDE600 - Systems Theory, Models, Research & Design (0.50)
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE660 - Systems Design Graduate Workshop 1 (0.50)
SYDE660A - Systems Design Graduate Workshop 1 - AI and Machine Learning (0.50)
SYDE660B - Systems Design Graduate Workshop 1 - Biomedical Systems (0.50)
SYDE660D - Systems Design Graduate Workshop 1 - Mechatronic & Physical Systems (0.50)
SYDE660E - Systems Design Graduate Workshop 1 - Vision, Image & Signal Processing (0.50)
Engineering design project course where students work in small groups applying the principles of engineering problem solving, research methods, systems analysis including modelling, simulation, optimization and design. There is a strong focus on a major mechatronic and physical systems design project with regular mentorship, project update reports and presentations, project reviews, and design embodiment demonstrations.
Prerequisites
Must have completed the following:
SYDE600 - Systems Theory, Models, Research & Design (0.50)
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE660 - Systems Design Graduate Workshop 1 (0.50)
SYDE660A - Systems Design Graduate Workshop 1 - AI and Machine Learning (0.50)
SYDE660B - Systems Design Graduate Workshop 1 - Biomedical Systems (0.50)
SYDE660C - Systems Design Graduate Workshop 1 - Human Factors (0.50)
SYDE660E - Systems Design Graduate Workshop 1 - Vision, Image & Signal Processing (0.50)
SYDE 660ESystems Design Graduate Workshop 1 - Vision, Image & Signal Processing0.50 units
Engineering design project course where students work in small groups applying the principles of engineering problem solving, research methods, systems analysis including modelling, simulation, optimization and design. There is a strong focus on a major vision, image and signal processing design project with regular mentorship, project update reports and presentations, project reviews, and design embodiment demonstrations.
Prerequisites
Must have completed the following:
SYDE600 - Systems Theory, Models, Research & Design (0.50)
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE660 - Systems Design Graduate Workshop 1 (0.50)
SYDE660A - Systems Design Graduate Workshop 1 - AI and Machine Learning (0.50)
SYDE660B - Systems Design Graduate Workshop 1 - Biomedical Systems (0.50)
SYDE660C - Systems Design Graduate Workshop 1 - Human Factors (0.50)
SYDE660D - Systems Design Graduate Workshop 1 - Mechatronic & Physical Systems (0.50)
Robust design encompasses the theories and methodologies that make performance measures (responses, critical times, energy, etc.) invariant to uncertainties in design variables (environmental conditions, manufacturing processes, material dimensions and properties, etc.). In this course you will learn how robust design methods and mathematical models of engineering systems help find improved designs at a lower cost. Topics include: the building of simple, efficient, meta-models through computer experiments to replace traditional mechanistic models. Performance measures and their design specifications. Sensitivity and importance analysis to select design variables. Second moment methods using Taylor series to provide parameter (mean) design. Probabilistic methods combined with manufacturing and scrap costs to perform simultaneous parameter design and tolerance allocation. Desirability functions and loss functions to deal with multiple competing performance measures. Integrated design by constrained optimization. Examples come from industrial processes, as well as hydraulic, electrical and mechatronic systems. Mathematics required: Total derivatives, Matrix calculus, Kronecker product, singular value decomposition. Matlab serves as the computing tool. Course notes are available.
SYDE 662Systems Design Graduate Workshop 20.50 units
This course builds upon SYDE 660, SYDE 660A, SYDE 660B, SYDE 660C, SYDE 660D, or SYDE 660E. The project undertaken in this course can either be a continuation of the project in one of these courses or a new project. Expectations in this course are that the design undertaken also includes prototype testing, evaluation, and final design specification.
This course introduces students to both the theory and practice of Critical Design, broadly construed. Critical Design is not a field of its own, but a mode of design thinking that is informed by critical theories and research methods from the arts and humanities. Critical Design can intersect with and draw on established fields of design from graphic and UX design to industrial and urban design. The course begins with an overview of the history of design as critique, before examining the recent emergence of research-creation practices such as speculative design, critical making, discursive design, and applied media theory. The positionality of designers and audiences will be considered in readings and assignments that focus on gender, disability, race, and class. Special attention will be paid to the design of media technologies and the infrastructures that support them, which involves methods in UX design, sustainable hardware design, and digital urban design. Students will demonstrate their knowledge of course materials through writing, design, and light fabrication.
This course is intended to provide insights into advanced topics in image processing. The topics discussed include but are not limited to: multi-scale and probabilistic image respresentation and analysis, image restoration, invariant image representation, image reconstruction, image fusion, otical flow, image segmentation, and image registration. Recent research papers and review papers from the field will be sued as complimentary material to weekly lectures.
Images are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. Where these images are acquired from a microscope, telescope, satellite, or medical imaging device there is a statistical image processing task: the interference of something - an artery, a road, a DNA marker, an oil spill - from imagery, possibly noisy, blurry, or incomplete. The goal of this course is to address methods for solving multidimensional statistical problems, emphasizing theory, mathematical modeling, and algorithms. Specific topics of interest include inverse problems, multidimensional modeling, random fields, and hierarchical methods.
This course introduces methods to acquire state (both spatial and temporal) estimations from video streams. Video streams are analyzed as dynamic systems, linear and non-linear. If the system can be approximated as linear and Gaussian in terms of the dynamic noise in the process and measurement, then Kalman filter techniques are used. This refers to sequential state estimation. For nonlinear dynamical systems, the EFK (Extended Kalman filter) can be used by a liberalization of the stat space model. Particle filters are used to address state estimation problems where the systems are non-linear and non-Gaussian. Particle Filters are rooted in Bayesian estimation and Monte Carlo procedures. The course builds upon these techniques in studying visual tracking and the components of Visual SLAM (Simultaneous Localization and Mapping) procedures.
This course focuses on 3D computer-vision and camera/optical-based 3D shape-measurement techniques. Topics include stereo-camera 3D measurement, structures-light techniques, laser-camera range-sensing, fringe-projection phase shifting methods, curve and surface representation, surface fitting, and range-image registration. The measurement methods will include setup of measurement systems and calibration of instrumentation. Techniques will be demonstrated and practical exercises will be given. Biomedical applications will be discussed. Students will complete an individual course project with written report and oral presentation in class.
Pattern recognition addresses the problem of detecting and classifying patterns in data, a process of machine perception in which objects are assigned to classes to which they are most similar. This course introduces the three modern approaches to pattern recognition: statistical, structural and neural. Specific topics include distance and probability based approaches in multidimensional feature spaces, feature extraction, clustering and performance measures; pattern grammars, syntax analysis and grammatical inference; connectionist models, pattern associators, back propagation and self-organizing networks.
This course introduces the fundamental concepts for medical imaging which include medical image formation (X-ray, CT, MRI, sonography); storage and formats (DICOM, DICOM RT, PACS); visualization, detection and analysis (enhancement, segmentation, registration, compression); safety and regulations for imaging devices & software (IEEE standards, Health Canada Licensing, FDA Clearance).
Antirequisites
Not completed nor concurrently enrolled in any of the following:
SYDE 682Advanced MicroElectroMechanical Systems: Physics, Design & Fabrication0.50 units
Course builds critical understanding in microfabrication, MEMS actuation and sensing mechanisms. Microelectromechanical systems and devices, including microactuators, microsensors, micro-domain forces, microfabrication and their actuation principles. Application domains of MEMS in RF, optics and biosensing. Specific topics include MEMS actuation mechanisms such as electrostatic, electromagnetic, thermal and piezoelectric, and sensing mechanisms such as piezoresistive, capacitive, optical and bio-transducer. Lithography, thinfilm deposition methods and etching techniques will be taught. Course covers practical examples, device architecture, fabrication design rules and fabrication procedures.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
Rigorous grounding in the theory and practice of Micro-Electro-Mechanical Systems (MEMS) design, as well as ways of extending them to Nano-Electro-Mechanical Systems (NEMS) design. Analytical tools to explore the possibilities of NEMS. Includes methodology to derive and use MEMS and NEMS models as design tools, basic concepts of statics and dynamics needed to construct lumped-mass models, the use of reduced-order models in MEMS/NEMS design, advantages and disadvantages of these techniques versus commercial software (COMSOL, ANSYS, and Coventor), most effective uses and limitations for each modeling approach.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
The course covers fundamental topics of biocompatibility of materials in medicine (polymers, ceramics, metals, composites, bioengineered materials). Fundamental principles of materials science (bonding, atomic/molecular structure) as well as interfacial engineering of materials will be reviewed. Materials response to biological systems such as corrosion, degradation, leaching and fracture will be studied in the context of specific biomedical applications. The host response to materials (immune, inflammatory response and coagulation) will also be treated. Specific examples of material interactions with biological systems such as bone, blood, skin and the eye will be studied.