37 graduate courses in Waterloo’s course catalog for Spring 2022, from UW Open Data. uwloo can’t read that term’s Graduate Calendar, so this stands in for it; the calendar itself is in Waterloo’s archive.
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.
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.
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 theory and application 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, and multivariate (multiple inputs-multiple output) models. 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.
Antireq: SYDE 730 Topic 2 - Remote Sensing Systems
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.
Antireq: SYDE 740 Topic 9 - Collaborative Systems Design
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 Systems
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.
This course is intended to provide an understanding of the principles of optimal control and how they are used in various engineering applications. Dynamic programming, variational approach and Pontryagin's Minimum Principle, linear quadratic optimal control, discrete-time optimal control, constrained optimal control systems and model predictive control are introduced. Numerical methods for optimal control problems are also discussed briefly.
Engineering design project course where students work in small groups or individually 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.
SYDE 660ASystems Design Graduate Workshop 1 - AI and Machine Learning
Engineering Design Project course where students work individually or 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 & presentations, project reviews, and design embodiment demonstrations.
SYDE 660BSystems Design Graduate Workshop 1 - Biomedical Systems
Engineering Design Project course where students work individually or 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 & presentations, project reviews, and design embodiment demonstrations.
SYDE 660CSystems Design Graduate Workshop 1 - Human Factors
Engineering Design Project course where students work individually or 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 & presentations, project reviews, and design embodiment demonstrations.
Engineering Design Project course where students work individually or 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 & Physical Systems design project with regular mentorship, project update reports & presentations, project reviews, and design embodiment demonstrations.
Engineering Design Project course where students work individually or 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 & Signal Processing design project with regular mentorship, project update reports & presentations, project reviews, and design embodiment demonstrations.
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.
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 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.
Antireq: SYDE 770 Topic 14 - Adv. Topics in Image Proc.
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.
Antireq: SYDE 770 Topic 7 - Topics in Particle Filtering
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.
Antireq: SYDE 770 Topic 9 - 3D Computer Vision and Imaging
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).
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.
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.
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.