Course subject: Systems Design Engineering (SYDE)

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.

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.

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.

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 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.

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.

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.

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.

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 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.

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.

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 provides specific knowledge in microelectromechanical systems (MEMS) and devices including microactuators, microsensors, micro-domain forces, microfabrication, and their actuation principles. Application domains of MEMS in RF, optics, and biosensing will be discussed. Specific topics include MEMS actuation mechanisms such as electrostatic, electromagnetic, thermal, and piezoelectric; and sensing mechanisms such a peizoresistive, capacitve, optical, and bio-transducer. Topics such as lithography, thin-film deposition methods, etching techniques will be taught. The course covers practical examples, device architecture, fabrication design rules and fabrication procedures.

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.