70 undergraduate courses in the 2025–26 Undergraduate Calendar, and 42 graduate courses in the Spring 2026 Graduate Calendar.
Undergraduate
SYDE 101Communications in Systems Design Engineering-Written and Oral0.25 units
This course introduces first-year students to systems design engineering with a focus on the engineering profession, and technical communication skills. Through in-class and independent activities leading to formative and summative assessments, students will consider and reflect on communication intention, audience, content, medium, format, and tone to demonstrate and improve upon their listening, written and oral communication skills for academic, engineering, and professional context.
SYDE 101LCommunications in Systems Design Engineering-Visualization0.25 units
This laboratory course introduces students to visual communication methods relevant to engineering analysis and design. Through in-class and independent activities leading to formative and summative assessments, students will consider and reflect on communication intention, audience, content, medium, format, and level of detail to demonstrate and improve upon their skills in graphing, freehand sketching, technical drawing, and computer-aided design (CAD).
Systems design first-year students will meet with a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed.
Differential calculus: limits, continuity, derivatives, differentials, applications. Sequences and series: convergence, power series, Taylor expansions. Simple numerical methods. Introduction to integration, indefinite and definite integral, techniques of integration.
SYDE 113Elementary Engineering Mathematics0.25 units
Functions: trigonometric, exponential, log, inverse functions. Mathematical notation and proof techniques. Metric spaces. Geometry and algebra: root-finding, vectors, coordinate systems, lines and planes, conic sections, complex numbers. Introduction to numerical computation: binary and floating point numbers and arithmetic, accuracy and sources of error.
Computer systems, problem solving, data and programs, structured programming, arrays, matrices and pointers, correct and efficient algorithms, data structures.
Multidisciplinary system design, the design process, problem definition, life-cycle design, design specification, concept/design generation and evaluation, design for manufacturing and assembly, system modelling and analysis, introduction to mechanical design, prototyping, safety and responsibility in engineering design, design documentation.
Design of human-machine environments, design to reduce human error. Analytical methods of determining user needs in systems with humans. Information processing and human sensory processes and consideration of these elements in the design of systems with humans. Human physical capabilities and consideration of these in ergonomic design.
Basic concepts of mechanics, vectors. Statics of particles. Rigid bodies and force systems, equilibrium of rigid bodies. Analysis of trusses and frames. Distributed forces, centroids, and moments of inertia. Friction. Virtual work.
Kinematics of particles, rectilinear and curvilinear motion. Kinetics of particles, application to space mechanics. Energy and momentum methods. Systems of particles. Kinematics and kinetics of rigid bodies; planar motion.
Systems design second-year students will meet a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed.
Systems design second-year students will meet a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed.
Vector differential calculus: divergence and curl, line and surface integrals, fundamental theorems, applications. First order ordinary differential equations: analytical techniques and applications. Second order linear ordinary differential equations: systems of equations and applications. Numerical methods for solving ordinary differential equations (ODEs).
Elementary probability theory. Random variables and distributions. Binomial, Poisson, and normal distributions. Elementary sampling. Statistical estimation. Tests of hypotheses and significance. Regression. Goodness-of-fit tests. Analysis of experimental measurements.
Algorithms and data structures emphasizes the following topics: structured software design, data structures, abstract data types, recursive algorithms, algorithm analysis and design, sorting and searching, hashing, problem-solving strategies and NP-completeness.
Models and analysis of linear systems. Discrete time systems, continuous time systems; difference and differential equations; impulse and frequency response. Complex frequency, functions of complex variables, transform domain techniques: Z transforms; Fourier analysis, Laplace transform. Transfer functions and frequency response, frequency domain analysis of linear systems; sampling theory, stability, and linear filters.
This course will help students understand how others think about technology, and then use this knowledge to make better choices when designing. This impact course focuses on identifying, understanding, and analyzing the interactions and impacts among technology, society and the environment for current and emerging technologies using theoretical and evidence-based analyses. Connections among systems of systems engineering, impact analyses, evidence-based analyses, needs assessment, the design process, advocacy, and professional engineering will be discussed and applied.
Topics include microeconomics, supply, demand and external costs in the context of systems design. Sustainability indicators, entrepreneurship, engineering economics, comparison of alternatives, project schedules and developing a business plan.
This course addresses the practical aspects of design and technical communication. By using a series of workshops culminating in a comprehensive final project, students gain the hands-on experience necessary for prototyping and design validation. Topics include generating technical specifications, material selection, fastener basics, motor selection, electronic switching, reading data sheets and schematics, and advanced microcontroller implementation.
SYDE 283Physics 3: Electricity, Magnetism and Optics0.50 units
Introduction to the fundamental laws of electricity, magnetism, and optics; electric fields, voltage, resistance, current, properties of conductors and semiconductors, capacitance, properties of dielectrics, magnetic fields, Faraday's Law and inductance, properties of magnetic materials; electromagnetic waves and the nature of light, geometrical optics; reflection and refraction, physical optics; interference and diffraction.
The course will present how the fundamentals of chemistry are applied to materials science and engineering. Concepts such as chemical bonding, crystal structure, phase diagram, redox reaction, and electrochemistry will be introduced in the context of materials science. Properties, processing and structure of metals, semiconductors, polymers, ceramic, nanomaterials and biomaterials will be presented.
Introduction to mechanical response of materials and stress-strain relationship. Behaviour of prismatic members in tension, compression, shear, bending and torsion. Shear-force and bending-moment diagrams. Introduction to instability.
SYDE 292Circuits, Instrumentation, and Measurements0.50 units
Active and passive circuit elements, Kirchhoff's laws, mesh and nodal circuit analysis, principle of superposition; step response of first and second order networks; sinusoidal steady state analysis using complex impedance phasors; input-output relationships, transfer functions and frequency response of linear systems; operational amplifiers, operational amplifier circuits using negative or positive feedback; diodes, operational amplifier circuits using diodes; analog signal detection, conditioning and conversion systems; transducers, difference and instrumentation amplifiers, active filters, A/D and D/A conversion.
A work-term poster is composed and presented at the Work-term symposium. The poster and presentation provide an opportunity for students to effectively communicate and reflect on their engineering experience gained during their co-op work terms. In the poster and presentation, students draw connections between the theoretical aspects of engineering taught in the classroom and the practical applications of that theory in the workplace.
Prerequisites
Complete all of the following
Must have completed the following:
PD11 - Processes for Technical Report Writing (0.50)
Systems design third-year students will meet with a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed.
Systems design third-year students will meet with a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed.
Vector spaces, linear independence, linear maps and matrix representations. Inner product spaces and orthogonality. Gramm-Schmidt algorithm and orthogonal projection. Interpolation and curve-fitting. Eigenvalues and eigenvectors, diagonalization, singular value decomposition. Applications and numerical methods.
Software requirements specification; software architecture; design patterns; software testing and quality assurance; software maintenance; design of efficient algorithms and methods for their analysis, mathematical algorithms, string processing algorithms, geometrical algorithms, exhaustive search and traversal techniques, introduction to lower bound theory and NP-completeness. Case studies and engineering examples.
Review of basic Normal theory, t, chi-squared, and F distributions. Simple linear regression. Lack of fit. Analysis of variance. Multiple linear regression, variable selection techniques, indicator variables, diagnostics. Brief introduction to non-linear regression, factorial experimentation.
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed the following:
BME213 - Statistics and Experimental Design (0.50)
Earned a minimum grade of 70% in each of the following:
Introduction to systems modelling and analysis. Graph theoretic models and formulation of system equations. State space formulation and solution. Time and frequency domain solutions. Application to engineering systems.
Classical and state space representations of control systems. Stability, controllability, observability and sensitivity. Routh-Hurwitz and root-locus methods. Frequency domain behaviour, Bode plots, Nyquist stability criteria. Pole placement, PID, phase-lead and phase-lag controllers.
SYDE 361Systems Design Methods 1: Needs Analysis and Prototyping0.50 units
The methodology of design, situation of concern; needs analysis and problem definition, engineering analysis and generation of alternative solutions, design prototyping, and design documentation. The lecture material is supplemented by a term-long design project done in small groups that develops hands-on experience with electromechanical prototyping.
SYDE 362Systems Design Methods 2: Testing, Verification, and Validation0.50 units
Engineering design project course where students work in small groups applying the principles of engineering problem solving and design, with a focus on testing and design evaluation, and an introduction to benchmark testing and applied design optimization. Student projects will provide hands-on experience with design verification, validation, and performance measurement and analysis using engineering tools.
An introductory course in engineering thermodynamics structured for students in systems design. Classical thermodynamics is presented as the systematic study of energy; its use, degradation, and waste. Introduction to heat transfer by conduction, convection, and radiation. Applications focus on problems of energy and environment. The concepts of statistical thermodynamics are introduced.
Fundamental concepts in systems involving fluid flow. Basic treatment of statics, kinematics and dynamics of fluids. Conservation of mass, momentum and energy for a control volume. Dimensional analysis and similarity. Flow in pipes and channels. Brief introduction to boundary layers, lift and drag, ideal and compressible flow.
Systems design fourth-year students will meet with a faculty member designated as their class professor. Conceptual difficulties, the interrelation of course work and engineering practice will be discussed.
Systems design fourth-year students will meet with a faculty member designated as their class professor. Conceptual difficulties, the interrelation of course work and engineering practice will be discussed.
SYDE 411Optimization and Numerical Methods0.50 units
Advanced methods for systems of linear equations. Root-finding methods. Linear programming, simplex and interior-point methods. Local and global optimization methods. Constrained optimization. Multiobjective and multidisciplinary design optimization.
SYDE 461Systems Design Capstone Project 10.50 units
The first half of a two-term engineering design project continuing the systems design project sequence. Students work in small groups applying the principles of systems design engineering to a situation of concern of their own choosing. Students have individual project supervisors as well as an overall co-ordinator who provides the framework for the term assessments.
SYDE 462Systems Design Capstone Project 20.50 units
The second half of a two-term engineering design project continuing the systems design project sequence. Students work in small groups applying the principles of systems design engineering to a situation of concern of their own choosing. Students have individual project supervisors as well as an overall co-ordinator who provides the framework for the term assessments.
SYDE 499Elective Systems Design Research Project0.50 units
A major undergraduate individual systems design engineering research project carried out as a technical elective (TE) under the supervision of a faculty member. Students are expected to demonstrate initiative and responsibility. An oral presentation of results and a written report are the minimum requirements. The faculty supervisor or department may set other requirements. Students will arrange for a faculty supervisor prior to registration.
SYDE 522Foundations of Artificial Intelligence0.50 units
The objective of this course is to introduce students to fundamental concepts of artificial intelligence. An overview of different learning schemes will be provided, including supervised and unsupervised algorithms. The focus of this course will be on dimensionality reduction, clustering, classification, deep and shallow artificial neural networks, and reinforcement learning. Ethical aspects of artificial intelligence will be discussed.
SYDE 531Design Optimization Under Probabilistic Uncertainty0.50 units
Optimization methods for real world problems have to deal with probabilistic uncertainty either due to data uncertainty or manufacturing uncertainty or both. Maximizing the expected value of the objective function subject to reliability (or risk) constraints is commonly used in such design or decision-making problems. Common methods used are stochastic programming, stochastic dynamic programming, chance-constraints, yield optimization and tolerance design. Example applications are selected from water management, energy systems, financial engineering, and manufacturing.
The overwhelming majority of societal, ecological and climate issues of pressing importance are complex systems; nonlinear dynamic interacting systems poorly characterized by linear analyses and Gaussian statistics. This course introduces the mathematics needed to understand such interactions, including nonlinear dynamics, critical and bifurcation behaviours, large-scale systems and power-law distributions. The mathematical methods will be motivated by extensive examples and case studies such as global warming, energy shortages, desertification, overpopulation, poverty, and economic instability, to be investigated from a systems engineering perspective that will connect mathematical concepts to real-world examples.
Formal methods for studying engineering decision-making problems involving multiple participants and multiple objectives. Topics include the graph model for conflict resolution, normal game form, metagame analysis, games with misperceptions, preference elicitation, human behaviour under conflict, evolution of a conflict, decision-making under uncertainty, sensitivity analyses, multiple criteria decision analysis, group decision and negotiation, coalition analysis, decision support systems, and real-world applications of the foregoing concepts.
Traditional electric energy systems comprising generation, transmission, and distribution are examined. Emerging technologies and trends are considered, such as competition, smart grids, energy storage, demand side management, renewable energy, and P2P. Future challenges and possibilities are then explored. Issues will be explored from technical, economic, environmental, sustainability, and social perspectives.
SYDE 535Computational Simulations for Societal and Environmental Systems0.50 units
This course will introduce and apply the fundamentals of computational simulation for complex systems. Data collection and analysis, conceptual modelling, computational modelling, output analysis, and communication of results to non-expert audiences will be covered extensively. Students will develop computational simulations to explore potential solutions for complex societal problems, identifying emergent scenarios, costs, and benefits with the goal of informing decision-makers.
SYDE 536Modelling Transportation Systems0.50 units
Overview of frameworks used to model the global and community transportation systems followed by four case studies: Bayesian modelling of collision rates; discrete choice modelling of travel mode; operating mode-based time series modelling of tailpipe emissions, and simulation of traffic operations including congestion.
SYDE 537Artificial Life: Biology and Computation0.50 units
Artificial life is the study of the simulation and synthesis of living or life-like systems. This course treats the basic principles of biology and computation in nature that underpin the organization of living systems in life as we know it, as it might exist elsewhere in the universe, and in digital or artificial media. We explore the mechanisms within living individuals that grow and change in a complex environment. This provides a variety of methods for understanding, modelling, and designing complex adaptive systems, whether naturally occurring or engineered, in simulation, in physical systems, with a view to applications in artificial life as the foundation for artificial intelligence and biological modelling.
This course focuses on the design of computer interfaces for simple to complex systems. Examples of applications are used to illustrate theoretical approaches. Main topics include forms of visual display; auditory display and soft controls; context, navigation, and layout; development techniques; design for engagement.
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed at least 1 of the following:
BME162 - Human Factors in the Design of Biomedical and Health Systems (0.50)
This course focuses on the role engineering psychology research plays in design of the information displays and devices associated with simple and complex cognitive tasks. Main topics include signal detection and target location tasks, navigation tasks, training tasks, communication tasks, human error, stress and mental workload, supervisory control, and situational awareness.
SYDE 544Biomedical Measurement and Signal Processing0.50 units
This course develops an understanding of biomedical measurements through the examination of electromyographic (EMG), electroencephalographic (EEG), and electrocardiographic (ECG) signals. Measurement of human-body position, force, and pressure, and related instrumentation will also be presented. Signal processing techniques will be discussed in the context of extraction and application of useful biomedical signals.
This course approaches the design of tasks, tools, products, and systems from a user-centered design perspective. Emphasis is on the human factors and usability methods, and techniques that can and should be applied throughout the iterative design process. While design issues pertaining to human-computer interaction are discussed, the methods presented can be applied to the design of almost any user interface. Major topics include user research methods for usability and user experience, inspection methods, user testing, applied statistical analysis.
Introduction to modelling and analysis of neurophysiological systems. Models of synaptic plasticity and learning, with focus on the similarities and differences between neuroscience and deep learning.
Newtonian and Eulerian formulation of particle and rigid body kinematics and dynamics. Energy (Lagrangian and Hamiltonian) formulations of particle and rigid body dynamics; generalized co-ordinates, generalized forces, holonomic constraints, Lagrange multipliers.
This course introduces nonlinear systems with applications in realistic ecological, mechanical, electrical, and chemical engineering systems. The properties of those systems are investigated via analytical and numerical tools, with particular application to MEMS and NEMS actuators & sensors. Students will develop knowledge and appreciation of the following concepts and tools: differences between linear and nonlinear dynamic systems; autonomous and non-autonomous systems; existence and uniqueness of solutions; equilibrium points and limit cycles; phase plane analysis; nonlinear phenomena; Poincare section analysis; multi-frequency oscillations; notions of system stability (e.g., Lyapunov functions).
SYDE 556Simulating Neurobiological Systems0.50 units
This course develops and applies a general framework for understanding neural computation in the context of recent advances in theoretical and experimental neuroscience. Particular emphasis is placed on understanding representation, nonlinear computation, and dynamics in real neurobiological systems. Students will apply signal processing, control theory, linear algebra, probability theory, and similar quantitative tools for the purpose of modelling sensory, motor, and cognitive systems. Topics covered include single neuron function, neural coding, neural dynamics, attractor networks, learning, statistical inference, locomotion, working memory, etc. Familiarity with neural systems is helpful but not essential.
SYDE 572Introduction to Pattern Recognition0.50 units
Pattern recognition as a process of data analysis. Pattern features as components in a random vector representation. Classification techniques; distance measures in feature space, probabilistic (Bayesian) decision theory, linear discriminants. Clustering and feature extraction. Applications; optical character recognition, speech recognition, industrial robot vision, medical diagnosis, remote sensing and satellite image analysis, fault detection and diagnosis in complex systems such as nuclear reactors.
Beginning with a discussion of quantitative models of imaging systems, this course moves on to apply methods of linear systems theory and signal processing to image processing. Simple spatial domain techniques as well as spatial frequency domain methods, and digital filter design for image enhancement and restoration are discussed. The key methods and problems are surveyed; edge detection, image denoising, image segmentation, image enhancement, image compression, image registration, and feature detection. Applications to machine vision, remote sensing, and medical imaging will be emphasized.
Introduction to deep learning. Regression and classification; backpropagation; convolutional and recurrent networks; optimization and regularization of deep networks; transformers; recent developments in the field.
SYDE 584Physiological Systems and Biomedical Design0.50 units
In this course, students will become familiar with the physiology and anatomical structures of the human body. The structure, functions, and properties of major physiological systems (e.g., musculoskeletal, nervous, cardiovascular, and respiratory) will be presented in relation to modelling physiological systems and the design of biomedical devices (imaging, assistive, and diagnostic). Various aspects of pathology and how they influence measurements will also be introduced.
Prerequisites
Complete all of the following
Students must be in level 3B or higher
Complete 1 of the following
Enrolled in a program offered by Faculty of Engineering
Enrolled in Honours Life Physics - Biophysics Specialization
Antirequisites
Not completed nor concurrently enrolled in any of the following:
Material selection based on material property and design requirements. Design a product and selected material against failure. Select a manufacturing process that meets design requirements and constraints. Incorporate economic analysis and environmental impact in the selection process. Assess life cycle of the product and the selected material. Case studies related to general engineering applications and biomedical applications.
SYDE 599Special Topics in Systems Design Engineering0.50 units
This course deals with selected topics at the undergraduate level in environmental and societal systems, mechatronics and physical systems, or human factors and ergonomics.
Prerequisites
Complete all of the following
Students must be in level 3A or higher
Enrolled in a program offered by Faculty of Engineering
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 610Design of Biomedical Technologies0.50 units
Systems theory and formulation of system dynamics problems. Design and research methods for biomedical technologies. Problem formulation and definition, stakeholder engagement, needs analysis, generation of alternative solutions, feasibility analysis, optimization, selection, and solution implementation.
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.
This course teaches an engineer how to model and simulate the motion of complex multibody systems such as robots, humans, vehicles, and mechanisms. Systematic methods, including conventional matrix approaches and linear graph theory, are used to generate kinematic and dynamic models for 2-d and 3-d systems of bodies connected by joints, springs, dampers, muscles, and actuators. Numerical solutions for the dynamic equations provide a simulation of the system response, which are obtained using commercial software packages. The course concepts are demonstrated through applications to the kinematic and dynamic analysis of mechanisms, human movement, biomechatronic systems, serial and parallel robot manipulators, autonomous and hybrid vehicles, and other multibody systems.
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.
SYDE 670Deployment of Deep Learning Models0.50 units
This course focuses on the deployment of computer vision networks for real-world applications. Students will gain practical knowledge on various computer vision tasks such as semantic segmentation, object detection, optical flow, and depth estimation. The course covers all critical aspects from selecting appropriate network architectures, data preparation and handling, to deployment on edge devices. The course also delves into infrastructure setup, training, and model compression techniques to ensure efficient deployment. By the end of the course, practitioners will be equipped with the skills needed to devise, implement, and maintain robust computer vision solutions in diverse real-world scenarios.
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, nano- and bioengineered materials). Fundamental principles of materials science as well as interfacial engineering of materials will be reviewed. Materials response to biological systems such as corrosion, wear, fracture, degradation, swelling, leaching, biodistribution and accumulation will be studied in the context of specific biomedical applications. The importance of properties and their modulation (e.g. surface modification, interfacial engineering, importance of particle size and surface) and how it affects host response (cell adhesion, inflammatory response, coagulation, immunity) will be discussed. Specific examples of material interactions with biological systems such as bone, blood, brain, skin and the eye will be studied. Engineering materials to improve biocompatibility will be discussed extensively during lecture time as well literature discussion and problem-solving sessions. The course will include how to design appropriate toxicity and biocompatibility studies based on the target (nano)material and tissue. This course is taught in part using problem-based learning techniques. In class exercises/discussion/problem solving session will be used throughout the term. Students are expected to have completed the assignments and/or requested reading to participate in the discussion and problem-solving exercise. Where possible, research articles will also be selected to reflect the research topics of the students registered in the course. Students should have background knowledge in physiological systems and materials.