This will be a group project oriented course that focuses on the development of tools for solving a practical financial engineering problem. In particular, a decision support system will be developed that integrates both the mathematical and statistical modeling techniques learned in the option along with relevant computing technologies. Problems that contain a real-time economic decision making component will be emphasized, but does not necessarily or explicitly involve financial markets. An important goal of the capstone is the articulation of the requirements to non-specialists as an exercise in communication with non-technical members of an organization.

An experience in engineering practice through a significant design project whereby student teams meet specific client needs through a creative, iterative, and open-ended design process. The project must include:
• The application of disciplinary knowledge and skills to conduct engineering analysis and design,
• The demonstration of engineering judgment in integrating economic, health, safety, environmental, social or other pertinent interdisciplinary factors,
• Elements of teamwork, project management and client interaction, and
• A demonstration of proof of the design concept.

An experience in engineering practice through a significant design project whereby students teams meet specific client needs or the requirements of a recognized design competition through a creative, iterative, and open-ended design process. The project must include:

The demonstration of engineering judgement in integrating economic, health, safety, environmental, social or other pertinent interdisciplinary factors,

Elements of teamwork, project management and client interaction, and

A demonstration of proof of the design concept.

An opportunity to conduct independent research under the supervision of a faculty member in MIE. Admission to the course requires the approval of a project proposal by the Undergraduate office. The proposal must: 1) Explain how the research project builds upon one or more aspects of engineering science introduced in the student's academic program, 2) provide an estimate of a level of effort not less than 130 productive hours of work per term, 3) specify a deliverable in each term to be submitted by the last day of lectures, 4) be signed by the supervisor, and 5) be received by the Undergraduate Office one week prior to the last add day.

Note: Approval to register for the fourth-year thesis course (MIE498H1 or MIE498Y1) must be obtained from the Associate Chair - Undergraduate and is normally restricted to fourth year students with a cumulative grade point average of at least 2.7.

An opportunity to conduct independent research under the supervision of a faculty member in MIE. Admission to the course requires the approval of a project proposal by the Undergraduate office. The proposal must: 1) Explain how the research project builds upon one or more aspects of engineering science introduced in the student's academic program, 2) provide an estimate of a level of effort not less than 130 productive hours of work per term, 3) specify a deliverable in each term to be submitted by the last day of lectures, 4) be signed by the supervisor, and 5) be received by the Undergraduate Office one week prior to the last add day.

Note: Approval to register for the fourth-year thesis course (MIE498H1 or MIE498Y1) must be obtained from the Associate Chair - Undergraduate and is normally restricted to fourth year students with a cumulative grade point average of at least 2.7.

The course is designed for Students with no or little Computational Fluid Dynamics (CFD) knowledge who want to learn CFD application to solve engineering problems. The course will provide a general perspective to the CFD and its application to fluid flow and heat transfer and it will teach the use of some of the popular CFD packages and provides them with the necessary tool to use CFD in specific applications. Students will also learn basics of CFD and will use that basic knowledge to learn Fluent Ansys CFD software. Most CFD packages have a variety of modules to deal with a specific type of flow. Students will be introduced to different modules and their specific applications. They will then be able to utilize the CFD package to simulate any particular problem. Ansys software will be the commercial package that will be used in this course. Ansys Fluent is the most common commercial CFD code available and most of the engineering companies use this code for their research & development and product analysis.

This course will cover the design, modeling, fabrication, and control of miniature robot and micro/nano-manipulation systems for graduate and upper level undergraduate students. Micro and Nano robotics is an interdisciplinary field which draws on aspects of microfabrication, robotics, medicine and materials science.

In addition to basic background material, the course includes case studies of current micro/nano-systems, challenges and future trends, and potential applications. The course will focus on a team design project involving novel theoretical and/or experimental concepts for micro/nano-robotic systems with a team of students. Throughout the course, discussions and lab tours will be organized on selected topics.

This course will present the fundamental basis of microelectromechanical systems (MEMS). Topics will include: micromachining/microfabrication techniques, micro sensing and actuation principles and design, MEMS modeling and simulation, and device characterization and packaging. Students will be required to complete a MEMS design term project, including design modeling, simulation, microfabrication process design, and photolithographic mask layout.

Introduction to the fundamentals of HVAC system operation and the relationship between these systems, building occupants and the building envelope. Fundamentals of psychrometrics, heat transfer and refrigeration; determination of heating and cooling loads driven by occupant requirements and the building envelope; heating and cooling equipment types and HVAC system configurations; controls and maintenance issues that influence performance; evaluation of various HVAC systems with respect to energy and indoor environmental quality performance.

The issue of design and development of AI systems that have beneficial social impact will be discussed and analyzed. The focus will not be on the mechanics of AI algorithms, but rather on the implementation of AI methods to address societal problems. Topics to be covered will include: Safeguarding of human interests (e.g., fairness, privacy) when AI methods are used; partnering of humans and AI systems to implement AI effectively; evaluation of AI assisted interventions; practical considerations in the selection of AI methods to be used in addressing societal problems. The issues that arise in implementing AI for beneficial social impact will be illustrated in a set of case studies aimed at creating beneficial social impact. Class activities will include lectures, seminars, labs, and take-home assignments.

Introduction to combustion theory. Chemical equilibrium and the products of combustion. Combustion kinetics and types of combustion. Pollutant formation. Design of combustion systems for gaseous, liquid and solid fuels. The use of alternative fuels (hydrogen, biofuels, etc.) and their effect on combustion systems.

Thermodynamics and electrochemistry of fuel cell operation and testing; understanding of polarization curves and impedance spectroscopy; common fuel cell types, materials, components, and auxiliary systems; high and low temperature fuel cells and their applications in transportation and stationary power generation, including co-generation and combined heat and power systems; engineering system requirements resulting from basic fuel cell properties and characteristics.

This course is designed to provide an integrated multidisciplinary approach to Advanced Manufacturing Engineering, and provide a strong foundation including fundamentals and applications of advanced manufacturing (AM). Topics include: additive manufacturing, 3D printing, micro- and nano-manufacturing, continuous & precision manufacturing, green and biological manufacturing. New applications of AM in sectors such as automotive, aerospace, biomedical, and electronics.

Application of conservation relations and momentum balances, dimensional analysis and scaling, mass transfer, heat transfer, and fluid flow to biological systems, including: transport in the circulation, transport in porous media and tissues, transvascular transport, transport of gases between blood and tissues, and transport in organs and organisms.

Note: This course is on hiatus and is not scheduled to be offered until further notice.

An examination of the relation between behavioural science and the design of human-machine systems, with special attention to advanced control room design. Human limitations on perception, attention, memory and decision making, and the design of displays and intelligent machines to supplement them. The human operator in process control and the supervisory control of automated and robotic systems. Laboratory exercises to introduce techniques of evaluating human performance.

Introduction to data mining and machine learning algorithms for very large datasets; Emphasis on creating scalable algorithms using MapReduce and Spark, as well as modern machine learning frameworks. Algorithms for high-dimensional data. Data mining and machine learning with large-scale graph data. Handling infinite data streams. Modern applications of scalable data mining and machine learning algorithms.

The course is designed for students who are interested in more advanced studies of applying wave principles to engineering applications in the field of non-destructive testing (NDT) and imaging (NDI). Topics will cover: Review of principles and characteristics of sound and ultrasonic waves; thermal waves; optical (light) waves; photons: light waves behaving as particles; black body radiation, continuous wave and pulsed lasers. The course will focus on NDT and NDI applications in component inspection and medical diagnostics using ultrasonics, laser photothermal radiometry, thermography and dynamic infrared imaging.

Challenges of meeting net-zero, fundamentals of markets, structures and participants, spot markets, economic dispatch, day-ahead markets, optimal unit commitment, forward markets, settlement process, storage and demand management, renewable and distributed energy resources, trading over transmission networks, nodal pricing, reliability resources, generation and transmission capacity investment models, capacity markets.

This course takes a 360° perspective on product design: beginning at the market need, evolving this need into a concept, and optimizing the concept. Students will gain an understanding of the steps involved and the tools utilized in developing new products. The course will integrate both business and engineering concepts seamlessly through examples, case studies and a final project. Some of the business concepts covered include: identifying customer needs, project management and the economics of product design. The engineering design tools include: developing product specifications, concept generation, concept selection, Product Functional Decomposition diagrams, orthogonal arrays, full and fractional factorials, noises, interactions, tolerance analysis and latitude studies. Specific emphasis will be placed on robust and tunable technology for product optimization.

The integration of human factors into engineering projects. Human Factors Integration (HFI) process and systems constraints, HFI tools, and HFI best practices. Modelling, economics, and communication of HFI problems. Examples of HFI are drawn from energy, healthcare, military, transportation, and software systems. Application of HFI theory and methods to a capstone or other design project, including HFI problem specification, concept generation, and design solution selection through an iterative and open-ended design process. (Note: project topic subject to lecturers’ approval.)

This course observes: conservation of mass, momentum, energy and species; diffusive momentum, heat and mass transfer; dimensionless equations and numbers; laminar boundary layers; drag, heat transfer and mass transfer coefficients; transport analogies; simultaneous heat and mass transfer; as well as evaporative cooling, droplet evaporation and diffusion flames.

MIE 561 is a "cap-stone" course. Its purpose is to give students an opportunity to integrate the Industrial Engineering tools learned in previous courses by applying them to real world problems. While the specific focus of the case studies used to illustrate the application of Industrial Engineering will be the Canadian health care system, the approach to problem solving adopted in this course will be applicable to any setting. This course will provide a framework for identifying and resolving problems in a complex, unstructured decision-making environment. It will give students the opportunity to apply a problem identification framework through real world case studies. The case studies will involve people from the health care industry bringing current practical problems to the class. Students work in small groups preparing a feasibility study discussing potential approaches. Although the course is directed at Industrial Engineering fourth year and graduate students, it does not assume specific previous knowledge, and the course is open to students in other disciplines.

This course takes a practical approach to scheduling problems and solution techniques, motivating the different mathematical definitions of scheduling with real world scheduling systems and problems. Topics covered include: job shop scheduling, timetabling, project scheduling, and the variety of solution approaches including constraint programming, local search, heuristics, and dispatch rules. Also covered will be information engineering aspects of building scheduling systems for real world problems.

This course explores analytic and numerical solution techniques for heat/mass diffusion and vibration/wave equations. Emphasis is placed on intuitive derivation of these equations, and analytic solution techniques like separation of variations, eigenfunction expansions, Fourier analysis, integral transforms, coordinate transforms, and special functions. Numerical solutions are introduced via finite difference methods. A key learning outcome of this course is understanding the central role that analytic solutions play in developing intuition about engineering physics, and how this is a fundamental step in learning to verify, validate, and properly use advanced computational modelling tools.

Smart materials are characterized by new and unique properties that can be altered in response to environmental stimuli. They can be used in a wide range of applications since they can exceed the current abilities of traditional materials especially in environments where conditions are constantly changing. Smart manufacturing refers to the use of the holistic integration of modern technologies with the data analytics, automation and computing to form a new efficient and adaptable manufacturing framework. This course is designed to provide an integrated introduction to smart materials and manufacturing, and provide a strong foundation for further studies and research. Topics include: smart materials processing and design; mechanical, thermal, electrical, magnetic and optical smart materials systems with applications in sensors, soft robotics, energy systems; introduction to industry 4.0 and Smart Factory, Internet of Things (IoT) platforms, advanced human-machine interfaces, wearables, smart sensors, smart machines.

Methods of analysis for decision making in the face of uncertainty and opponents. Topics include subjective discrete and continuous probability, utility functions, decision trees, influence diagrams, bayesian networks, multi-attribute utility functions, static and dynamic games with complete and incomplete information, bayesian games. Supporting software.

This course is to provide fundamental concepts and mathematical frameworks for sequential decision making of a team of decision makers in the presence of uncertainty. Topics include Markov decision processes, reinforcement learning, theory of games and stochastic games, multi-agent reinforcement learning and decentralized Markov decision processes. The course places an emphasize on conceptual understanding of core concepts and expects students to be able to implement the concepts to demonstrate their understanding.

A comprehensive introduction to the global minerals industry using international regulatory requirements as a thematic structure. Engineering applications together with current and emerging issues are emphasized throughout. Principal topics include: mineral resources in the economy; stakeholder concerns and responsible mining; mineral exploration; surface and sub‑surface mine development and operation; fundamentals of mineral processing; mineral industry finance.

This is a seminar series that will introduce students to the community, upper-year experience, and core fields of Mineral Engineering. Seminar presenters will represent the major areas in Mineral Engineering and will also be drawn from an array of groups, including students, staff, faculty, and alumni. The format will vary and may include application examples, case studies, career opportunities, and research talks. The purpose of the seminar series is to provide first year students with some understanding of the various options within the Department to enable them to make educated choices as they progress through the program. This course will be offered on a credit/no credit basis.