Introduction to neuroanatomy and processes that are core to perception, memory, executive functions, language, decision making, and action. Introduction to stress and emotions, regulation of thought and behaviour, and reward processing. Case studies in Addiction, Depression, Dementia, ADHD, and Dyslexia. Role of neuroimaging and brain lesions in demonstrating the functioning of different pathways and regions of interest within the brain. Use of experiments to test hypotheses concerning brain activities and computations. Conducting a literature review and reporting experimental research, use of elementary statistics, and satisfaction of research ethics requirements.

Introduction to basic mechanical parts and mechanisms: gears, cams, bearings, linkages, actuators and motors, chain and belt drives, brakes and clutches, hydraulics and pneumatics. Tutorials on engineering drawing, sketching, and CAD/CAM in SolidWorks: views and drawing types, 2D sketching, 3D modeling and engineering drawing generation, modeling of assembly and motion analysis/animation. Conceptual design examples and mechanical engineering design process, including selection and applications of mechanisms. Dissection and reverse engineering of selected mechanical devices, mechanisms, and subsystems. Competitive group design project including technical report and 3D printing.

Instruction and assessment of communication centered around course deliverables that will form part of an ongoing design portfolio.

Introduction to algorithms (principles involved in designing, analyzing, and implementing algorithms). Basic data structures (lists, sets, maps, stacks, queues). Graphs and graph search. Decision algorithms (greedy methods and approximation algorithms). Sorting, divide-and-conquer, and recursive algorithms. Trees, heaps, and priority queues. Hashing and hash tables. Algorithmic analysis: big-O complexity. Numerical methods as examples of algorithms and big-O analysis (matrix inversion, matrix decomposition, solving linear system of equations).

Introduction to object-oriented programming using the Java programming language with heavy emphasis on practical application; variable types; console and file input/output; arithmetic; logical expressions; control structures; arrays; modularity; functions; classes and objects; access modifiers; inheritance; polymorphism; common data structures; regular expressions; GitHub; Java Swing; unit testing; introduction to complexity analysis; introduction to parallel computing; design and implementation of programs relevant to industrial engineering needs according to strict specifications.

Introduction to deterministic operations research. Formulations of mathematical models to improve decision making; linear and integer programming; the simplex method; the revised simplex method; branch-and-bound methods; sensitivity analysis; duality; network models; network simplex method; Dijkstra's algorithm; Prim’s and Kruskal’s algorithms; deterministic dynamic programming; applications of deterministic OR in machine learning; common metaheuristics.

Modeling and analysis of systems subject to uncertainty using probabilistic methods. Derivation and application of Bernoulli and Poisson processes, Markov chains, Markov decision processes, Monte Carlo simulation, and queuing models. Applications to engineering, health care, finance, and management.

Corrosion and degradation of materials; Phase transformation and strengthening mechanisms; Mechanical failure, fatigue, creep, impact; Electrical, thermal, magnetic, optical properties of materials; Composite materials.

A course in probability and statistics for Engineering Science students focusing on building solid probabilistic and statistical foundations both mathematically and in terms of engineering application. Topics include: sample space, events, definitions of probability, conditional probability, Bayes' theorem, important classes of discrete and continuous random variables and their distributions, joint, conditional, and marginal distributions, expectation, moment generating and characteristic functions, transformations of random variables, central limit theorem and approximations. Graphical methods, quantile plots, point and interval estimation of population parameters, method of maximum likelihood. Hypothesis testing, simple and multiple regression, correlation analysis, and introduction to Bayesian statistics.

Classifications of mechanisms, velocity, acceleration and force analysis, graphical and computer-oriented methods, gears, geartrains, cams, flywheels, mechanism dynamics.

Instruction and assessment of engineering communication that will form part of an ongoing design portfolio.

Engineering applications of thermodynamics in the analysis and design of heat engines and other thermal energy conversion processes within an environmental framework; Steam power plants, gas cycles in internal combustion engines, gas turbines and jet engines. Fossil fuel combustion, Alternative fuel combustions, fusion processes and introduction to advanced systems of fuel cells.

Introduction to quality engineering. Quality standards and certification. TQM. Modeling processes with simulation. Making inferences about product quality from real or simulation output data. Introduction to statistical process control. Control charts for variables and attributes. Process capability analysis. Lot Acceptance Sampling.

Engineering applications of thermodynamics in the analysis and design of heat engines and other thermal energy conversion processes within an environmental framework. Steam power plants, gas cycles in internal combustion engines, gas turbines and jet engines. Refrigeration, psychrometry and air conditioning. Fossil fuel combustion and advanced systems includes fuel cells.

Fluid statics, pressure measurement, forces on surfaces. Kinematics of flow, velocity field, streamlines. Conservation of mass. Fluid dynamics, momentum analysis, Euler and Bernoulli equations. Energy and head lines. Laminar flow. Flow at high Reynolds numbers, turbulence, the Moody diagram. External flows. Boundary layers. Lift and drag. Flow separation.

Exact and numerical analysis of steady and transient conduction in solids. Solutions of one-dimensional and multidimensional systems. Principles of convection and solutions under laminar and turbulent flow over flat plates and inside and over pipes. Free convection. Thermal radiation between multiple black and grey surfaces. Analysis of open-ended design problems for improving thermal transport in commercial products.

Exploration of design strategies, decisions, and best practices for the creation of sustainable products that do not harm the public, now or future generations. Understanding of Life Cycle Assessment to make justifiable and quantitative claims about the environmental and societal impacts of a product or design decision. Best practices for clear communication of engineering decisions and demonstrate these in written and oral form as part of an ongoing design portfolio.

Three-dimensional stress transformation, strain energy, energy methods, finite element method, asymmetric and curved beams, superposition of beam solutions, beams on elastic foundations, buckling, fracture mechanics, yield criteria, stress concentration, plane stress and strain.

This course presents analysis of complex circuits and application of circuit principles to design circuits for mechanical engineering systems. Discussions will center around circuits and instrumentation. In-depth discussions will be given on a number of topics: (1) Mechatronics design applications of circuit principles; (2) Network theorems, node-voltage, mesh-current method, Thévenin equivalents; (3) Operational amplifier circuits; (4) 1st and 2nd order circuits; (5) Laplace transform, frequency response; (6) Passive and active filter design (low- and high-pass filters, bandpass and bandreject filters); (7) Interface/readout circuits for mechanical engineering systems, sensors, instrumentation; (8) Inductance, transformers, DC/AC machines; (9) Digital circuit and data sampling introduction.

Application of information and interaction design principles in interactive systems. Focus on design and methods for understanding user needs, making sense of user research, prototyping, evaluation methods and iterative design. The course will include in depth coverage of rapid prototyping, scenario-based design, usability inspection methods, summative and formative usability evaluation, and comparison tests. Eye tracking, remote testing and experience/ journey mapping will be introduced.

A detailed analysis will be made of several cases in which human factors methods have been applied to improve the efficiency with which human-machine systems operate. Examples will be chosen both from the area of basic ergonomics and from high technology. Emphasis will be placed on the practical use of material learned in earlier human factors courses.

A study of the fundamental behaviour of the major semiconductor devices (diodes, bipolar junction transistors and field effect transistors). Development of analysis and design methods for basic analog and digital electronic circuits and devices using analytical, computer and laboratory tools. Application of electronic circuits to instrumentation and mechatronic systems.

The course covers the software lifecycle of user-centered, computer-based information systems. Topics include software development methodologies, requirement engineering, use case analysis, process modelling, data flow diagrams, UML, design, model-driven architecture, and implementation. The course will emphasize user-centered perspectives and effective communication across the software lifecycle of information systems.

This course provides an understanding of the principles and techniques of information modelling and data management, covering both relational theory and SQL database systems (DBMS), as well as entity-relation conceptual modelling. The course also provides an introduction to graph databases (RDF, SPARQL, and knowledge graphs), as well as UML class diagrams. The laboratory focuses on database application development using SQL DBMS, OLAP queries and data modelling.

This course focuses on understanding and applying multiple perspectives for organizing, assessing, designing, and implementing integrated distributed information systems to support an organization's objectives. The emphasis is on; 1) understanding how Business Process Management techniques and tools can contribute to align an organization's business and information technology perspectives; 2) designing, developing, and deploying Business Processes as information systems. The course introduces Blockchain technologies, an emerging class of distributed information system providing the foundation for decentralized applications. Students will work in the laboratory to develop business processes that integrate blockchain smart contracts. The business processes will be specified using process modeling languages such as BPMN (an industry standard diagrammatic notation). Students will implement and test executable business processes that combine code generated from process models with additional programming.

This course provides students with knowledge and skills for understanding, analyzing, and solving decision making problems which involve economic concepts. These problems deal with deciding among alternatives in engineering projects with respect to costs and benefits over time. The overarching goal of the course is preparing engineers with the skills and knowledge for analyzing economic decisions quantitatively and making suitable decisions by acknowledging and incorporating the ramifications of factors like interest, depreciation, taxes, inflation, and risk in engineering projects.

Study of work systems design in new and existing organizations. Consideration will be given to fundamental organizational theory topics such as structure, lifecycle, culture, and ethics. These concepts will be the foundation for an understanding of concepts such as bureaucracy, incentives, innovation, international business, trends in technology, and hiring. An emphasis will be placed on applying these concepts to real-world organizational examples and case studies.

Principles for developing, testing and using discrete event simulation models for system performance improvement. Simulation languages, generating random variables, verifying and validating simulation models. Statistical methods for analyzing simulation model outputs, and comparing alternative system designs. Fitting input distributions, including goodness of fit tests. Role of optimization in simulation studies.

This course focuses on features of production/service systems and methods of modelling their operation; the material flow, information flow and control systems. Topics include demand forecasting, inventory management, supply chain management, capacity planning, and lot size planning. Emphasis will be placed on the modelling aspects of operations management, as well as the application of analytical methods in the design of production/service systems. Students will be asked to address open-ended design problems in various activities of the course.

Linear programming extensions: goal programming. Cooperative game theory (shapley values and nucleolus), interior point methods, large scale decomposition methods (Lagrangian relaxation, Dantzig-Wolfe decomposition, benders decomposition and column generation), stochastic programming. Karush-Kuhn-Tucker (KKT) conditions with application to quadratic programming and bi-level programming. Mathematical Programming formulation choices. Markov Decision Process (MDP) problems.

The course provides an introduction to circuit analysis and design for mechatronics applications. The focus is on building a working knowledge of: (1) op-amp circuits, (2) step response, steady-state response, and frequency response, (3) passive and active filter design, and (4) applications of the above to mechatronics systems, including sensors and instrumentation. The course will continue with a study of the fundamental behaviour and specific applications of the major semiconductor devices, including (5) diodes and (6) field effect transistors. Additional ‘design assignments' will require students to design real-world viable circuits for mechatronics applications, and laboratory experiments will present additional applications for all circuits being studied.

This course focuses on the integration of the results from earlier operations research courses and an assessment of the different methods with regard to typical applications. The course is taught using the case method. Students are expected to analyze cases based on real applications on their own, in small groups and during lecture sessions, and solve them using commercial software packages.