Course subject: Systems Design Engineering (SYDE)

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 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.

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.

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.

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.

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.

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.

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 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.

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).

This course involves the rigorous grounding in the theory and practice of MEMS design as well as ways of extending MEMS (micro-electro-mechanical systems) to apply to the design of NEMS (nano-electro-mechanical systems). Modeling and simulation processes as they apply to MEMS and NEMS are presented. Concepts covered include basics of statics and dynamics necessary to construct lumped-mass models, an introduction to the use of reduced-order models in MEMS/NEMS design, the use of these modeling techniques with the use of commercial FEM software (COMSOL, ANSYS, and Coventor) in the simulation and design of MEMS/NEMS, and discussing the most effective uses and limitations of each of these approaches. The course involves building effective MEMS by design not by trial and error. Analytical tools for exploring the possibilities of nano-electro-mechanical systems (NEMS) are introduced.