Basic physical principles of transport phenomena. Heat and mass transfer methods for physical systems. Time and volume averaging. Dimensional analysis.

The course focuses on modelling and solving numerically different practical engineering problems, including electrical and electronic circuits, sensors, mechanical stress and thermal analysis. Topics covered include: the mathematical and computational foundations of the numerical approximation and solution of scientific problems; regression and interpolation; integration and differentiation; solution of large scale systems of linear and nonlinear equations; modelling and solution with sparse equations; explicit schemes to solve ordinary differential equations; and simple optimization.

The question of "how safe is safe enough?" has no simple answer. In response, this course develops the bases by which we can assess and manage risk in engineering. Course deals with fate and transport issues associated with risk, as relevant to engineering and how these aspects are employed in the making of decisions. Students are expected to have already taken a relevant undergraduate course in statistics (STAT*2040, STAT*2120, or equivalent).

Laminar and turbulent flow. Turbulence and turbulence modelling. Boundary-layer flow. Compressible flow. Potential flow.

Numerical solution of partial differential equations of flow through porous media; flow of heat and vibrations; characterization of solution techniques and analysis of stability; convergence and compatibility criteria for various finite difference schemes.

Boundary-value problems. Methods of approximation. Time dependent problems. Isoparametric elements. Numerical integration. Computer implementation. Mesh generation and layouts. Two-dimensional finite elements.

A study of theoretical and experimental methods for characterizing the dynamic behaviour of engineering systems. Distributed and lumped parameter model development. Digital simulation of systems for design and control.

Digital image processing techniques including filtering and restoration; physics of image formation for such modalities as radiography, MRI, ultrasound. Offered in conjunction with ENGG*4660. Extra work is required for graduate students.

The course objective is to train the student in preparing, delivering and evaluating technical presentations. Each student is required to: (a) attend and write critiques on a minimum of six technical seminars in the School of Engineering; and (b) conduct a seminar, presenting technical material to an audience consisting of faculty and graduate students in the school. This presentation will then be reviewed by the student and the instructor.

A course of directed study involving selected readings and analyses in developing knowledge areas which are applicable to several of the engineering disciplines in the School of Engineering.

Computer vision studies how computers can analyze and perceive the world using input from imaging devices. Topics covered include image pre-processing, segmentation, shape analysis, object recognition, image understanding, 3D vision, motion and stereo analysis, as well as case studies.

Kinetics of biological reactions, reactor dynamics and design. Food rheology and texture; water activity and the role of water in food processing; unit operations design-thermal processing; and drying, freezing and separation processes.

Modelling and design of fermenter systems. Topics include microbial growth kinetics, reactor design, heat and mass transfer. Instrumentation and unit operations for feed preparation and product recovery. An ndergraduate course in each of microbiology, heat and mass transfer, and biochemistry or bioprocess engineering is required for this course.

Rheology and rheological properties. Contact stresses between bodies in compression. Mechanical damage. Aerodynamic and hydro-dynamic characteristics. Friction.

This course serves as a graduate introduction into combinatorics and optimization. Optimization is the main pillar of Engineering and the performance of most systems can be improved through intelligent use of optimization algorithms. Topics to be covered: Complexity theory, Linear/Integer Programming techniques, Constrained/Unconstrained optimization and Nonlinear programming, Heuristic Search Techniques such as Tabu Search, Genetic Algorithms, Simulated Annealing and GRASP.

Instrumentation systems. Transducers. Amplifier circuits. Recording methods. Spectroscopy & colorimetry. Radiation, humidity, pH and noise measurements. Chromatography.

Application of heat and mass transfer, fluid flow, food properties, and food- processing constraints in the design and selection of food process equipment. Development of process specifications for the control of the flow of heat and moisture and the associated microbial, nutritional and organoleptic change in foods. Food system dynamics and process development.

A course of directed study involving selected readings and analyses in developing knowledge areas of food engineering.

A project course in which a problem of advanced design or analysis in the area of biological engineering is established, an investigation is performed and a final design or solution is presented.

A course of directed study involving selected readings and analyses in developing knowledge areas of biological engineering.

This course focuses on the theory and the applications of colloid and interface science in the environmental, chemical, and food sectors. Major topics include the forces of interactions between colloids, the stabilization and destabilization of emulsions and foams, and polymeric fluids and gels.

This course provides a theoretical and practical understanding of advanced state and parameter estimation theory. Topics include, but are not limited to: linear and nonlinear models, system and measurement noise distributions, observers, optimal filters, robust strategies, and written communication skills. Students should have background knowledge in linear algebra, programming, and systems and control theory.

The course covers analytical tools for integrated production planning and control. The analytical tools include mathematical models for inventory control, determination of order quantities and safety stocks, inventory replenishment systems, demand forecasting, materials requirements planning and manufacturing resource planning (MRP/MRPII), production planning, lot sizing, dispatching, scheduling, and order releasing. The course also covers contemporary manufacturing philosophies such as Just-in-Time systems, lean and Agile manufacturing, digital manufacturing, and Industry 4.0.

A course of directed study involving selected readings and analyses in developing knowledge areas of mechanical engineering.

Fundamental concepts related to supply chain management are covered. Among the topics covered will be defining supply chains, managing quality, forecasting, green/sustainable supply chains, Industry 4.0, artificial intelligence and optimization in supply chain.

The basic techniques and regulations surrounding quality control in a generic manufacturing environment are covered. Topics covered include: total quality management including relevant ISO regulations, six sigma, reliability, statistical process control, acceptance sampling, and 2k factorial design of experiments. Students are expected to have already taken a relevant undergraduate course in statistics (STAT*2120 or equivalent). Offered in conjunction with ENGG*4050. Extra work is required for graduate students.

Research methodologies used in bioengineering are reviewed and assessed in the context of a diverse range of applications: biomechanics, control and instrumentation, ergonomics, diagnostic tools, biomaterials and food safety. The scientific method is discussed in terms of defining research problems, appropriate tests and hypotheses, experimental methods, data analysis and drawing conclusions. The objective is to guide students as they develop a coherent research proposal and deepen their understanding of the breadth of the discipline.

This course provides an overview of micro and nanotechnologies and how they can be used in biological and biomedical sciences. Various inventions, designs, and engineering approaches are discussed. The course is intended to bridge the gap between engineering/physical sciences and biology/biomedicine.

The course covers numerous topics in image processing (image enhancement, segmentation, registration, classification, etc.) with specific examples on applications to medical images (e.g., brain tumour detection, cardiac functional imaging, and image-guided surgery). It is intended for graduate students from various backgrounds who wish to acquire basic knowledge in image processing.

An independent project carried out under the supervision of a Biomedical Engineering faculty member in which an advanced literature review, data analysis, experiment, simulation, and/or design project is completed. Regular meetings, final report, and presentation required.

A course of directed study involving selected readings and analyses in developing knowledge areas of biomedical engineering.

Course covers: switched reluctance motor, brushless motor, linear motor, axial flux motor, and harmonic drive motor with applicable actuators. Other topics introduced include: Electromagnetic micro power generation, design and analysis of cooling systems and control mechanism. Background in electromagnetism required.

This course covers materials related to mechatronics systems in terms of dynamics, control, sensing, estimation. The course covers advanced topics in these areas and provides students the tools to model, analyze, and control these systems. The focus is on vehicles and robots (mobile robots).

An independent project carried out under the supervision of a Mechatronics Engineering faculty member in which an advanced literature review, data analysis, experiment, simulation, and/or design project is completed. Regular meetings, final report, and presentation required.

This course provides students with practical experience in designing and modeling of heat exchangers for different applications. Students will apply theory and knowledge of heat and mass transfer, thermodynamics, and fluid mechanics to the design of heat exchanger devices for different applications. Students are expected to have already taken relevant undergraduate courses (ENGG*2230, ENGG*3260, ENGG*3370 and ENGG*3430, or equivalents).

Theoretical and hands-on experience in bio-renewable energy areas prepares students from diverse backgrounds for a career in the biorefinery industry, academia, or entrepreneurial endeavors. Also deals with the technologies of converting biomass into upgraded energy, value added products, fuels, and chemicals. Thermodynamics background helpful.

Course covers fluid-structure interaction problems with an emphasis on analytical and numerical methods. Topics include vortex and turbulence induced vibration, galloping and flutter, fluid-elastic instability, and acoustic resonance. Various case studies and applications will be discussed. Background in fluid mechanics and vibrations required.

Examination of principles governing fuel cell technology and the technical challenges associated with developing fuel cell systems. Topics include the chemical thermodynamics and electrochemical kinetics of fuel cells, the evolution of fuel cell technology, and fuel cell system design. Background in materials and thermodynamics required.

Course covers general conservation equations for studying the flow and heat transfer through porous media. Application and case studies of porous materials will be discussed. Modelling techniques will be shown for a particular application area. Background in Heat Transfer required.

Many complex engineering, operations, and business systems can be modeled as discrete-event systems. Efficient management and operation of these systems requires simulation to study their performance. Case studies and applications will be presented and discussed.

A project course in which a problem of advanced design or analysis in the area of mechanical engineering is established, an investigation is performed and a final design or solution is presented.

This course provides an introduction to developing applications for mobile devices. The emphasis will be on the fundamentals of mobile application programming. This is primarily a project-based course in which the goal is to produce a working app by the end of the course. The purpose of this course is to create new inter-disciplinary applications of mobile devices. Graduate students from all disciplines at the University of Guelph are invited to take the course for credit.

This course is designed to develop the fundamental and applied knowledge needed to understand the pillars of sustainability and local/global order. It covers topics in economic systems, finance, investments, risk, law, human behaviour, economic development, economic cycles, monetary policy, trade, and externalities of the economy (society, environment, and ecology).

This course provides a background for the reflective engineer who wants to explore the positive effect of ethics on engineering practice. It helps design practitioners understand how technology and its impact on society can be positively shaped by consideration of non-engineering values during the design process and how these values can be introduced into designs.

This course provides engineering students with an understanding of the concepts, principles, and practices for project management. It introduces an understanding and appreciation of the importance of managing projects, project teams, the project management systems and tools, the various components of the project management process, and professional codes of conduct and ethics. The emphasis is on the techniques most frequently used in the context of, both internal and external organizational roles of a project manager.

The course presents solid modelling of parts with increasing complexity and their assembly to form a final design. The simulation analysis of components and assemblies will also be covered in the course. The course also presents the underlying mathematics of the geometric modelling of curves and surfaces.

Biomechanical Design from concept through prototyping and testing. This course will investigate and apply techniques used for biomechanical design including reverse engineering, solid modelling, geometric tolerancing, testing and rapid prototyping.

Network traffic modeling. Transient and steady-state analysis of Markov chains. Queueing analysis. Admission and access control. Flow control protocols. Congestion control. End-to-end performance bounds analysis.

This course introduces engineering students to leadership concepts and theory in the context of application to the engineering profession and practice. The focus is on developing practical leadership knowledge, skills and attitudes, starting from the personal level and extending to application in the organizations and society. The content is presented and assessed through a blend of lectures, readings, case studies, discussions, presentations, workshops, reflective practice and a major project.

This course empowers engineering graduate students to communicate both within engineering disciplines and to broader audiences, such as the public, industry, and government. Students cover a variety of genres, including peer-reviewed academic journal articles, research proposals, reports, job applications, and poster and conference presentations.

This course examins the fundamental principles of metal and alloy solidification. Aspects of nucleation, grain, growth, dendrite formation in casting and welding processes are examined. Thermal analysis, solidification defects and alloy characterization are also covered. Students are expected to have already taken undergraduate courses in materials science and manufacturing processes (ENGG*2120 and ENGG*2180, or equivalents).

Introduces advanced design methodologies applicable to mechanical systems. Includes the following topics: materials selection; specialized design methods such as concurrent engineering, design for reliability and life cycle design; application of biologically inspired modeling, optimization methods and finite element analysis; integration of various tools to solve a specific engineering problem; implications of design decisions on sustainability and environment; and utilizing different software packages. Students are expected to have already taken undergraduate courses in materials science and machine design (ENGG*2120 and ENGG*3280, or equivalents).

The aim of this course is about nonlinear and intelligent control systems for mechatronics applications (mixture of theory and applications). Students will also learn about nonlinear systems and important concepts associated with them. Important control techniques both for lienar and nonlinear systems will be taught (focus will be on nonlinear). Applications of various control techniques for vehicles and robotic systems will be taught as well. This course is suitable for students who have some background in control and mechatronics (ENGG*2400 and ENGG*3410, or equivalents).

The aim of this course is to provide students with an introduction to algorithms and techniques of machine learning particularly in engineering applications. The emphasis will be on the fundamentals and not specific approach or software tool. Class discussions will cover and compare all current major approaches and their applicability to various engineering problems, while assignments and project will provide hands-on experience with some of the tools.

In this course, operating principles and design techniques of analog integrated circuits are introduced with emphasis on device and system modelling. These circuits include analog and switched-capacitor filters, data converters, amplifiers, oscillators, modulators, circuits for communications, sensor readout channels, and circuits for integrated memories. It is recommended that students are familiar with the fundamentals of linear systems, circuit analysis, and electronic devices.

This course will introduce the principles of VLSI MOSFET digital design from a circuit and system perspective. Advanced topics include: power issues related to each level of design abstraction; voltage and frequency scaling; power to speed tradeoffs; ASIC digital design flow; Verilog intergrationintegration; ASIC case studies. It is recommended that students are familiar with the fundamentals of digital circuits and electronic devices.

This course serves as a graduate introduction into reconfigurable computing systems. It introduces students to the analyses, synthesis and design of embedded systems and implementing them using Field Programmable Gate Arrays. Topics include: Programmable Logic devices, Hardware Description Languages, Computer Aided Design Flow, Hardware Accelerators, Hardware/Software Co-design techniques, Run Time Reconfiguration, High Level Synthesis. It is recommended that students are familiar with the fundamentals of digital design and hardware description languages.

This course is intended for graduate students who have some knowledge and interest in robotics. The course covers modelling, design, planning control, sensors and programming of robotic systems. In addition to lectures, students will work on a term project in which a problem related to robotics systems will be studied.

This course explores the fundamental principles and advanced techniques in mobile robotics, focusing on kinematic modeling and motion control. It delves into navigation strategies, including path planning, obstacle avoidance, and environment decomposition. Students study localization methods such as odometry, map representation, map construction, and an introduction to probabilistic map-based localization. The course also covers Kalman filter-based localization and alternative localization systems. Additionally, it provides an in-depth examination of computer vision, encompassing imaging fundamentals, image representation, feature extraction, pattern recognition, motion analysis from 2D image sequences, image segmentation, object pose estimation, and applications in virtual reality.

This course focuses on control system design for robotic manipulators, equipping students with the theoretical foundations and practical techniques necessary to develop advanced controllers. Key topics include vector mathematics, coordinate transformations, kinematics, and robot dynamics. The course covers a range of position control strategies, such as PD control, computed torque control, adaptive control, sliding mode control, time-delay control, and disturbance observer-based control. Additionally, it explores force control methodologies, including impedance control and hybrid force control. Students implement and evaluate these controllers through MATLAB/Simulink-based simulations, gaining hands-on experience in analyzing robot dynamics and optimizing control performance.

This course provides a comprehensive introduction to industrial robotic manipulators, covering fundamental concepts and advanced techniques. Topics include robot kinematics and differential kinematics, as well as the statics, dynamics, and control of robotic arms. The course explores various actuation methods, real-time joint control strategies, and sensor technologies for both contact and non-contact applications. Additionally, students learn about task planning, robot programming, and real-world industrial applications of robotic systems. Through theoretical foundations and practical implementations, this course equips students with the skills to analyze, design, and control industrial robots effectively.

Soft real-time systems, hard real-time systems, embedded systems, time handling and synchronization, deadlines, preemption, interruption, RTS languages, RTS/ operating systems, system life-cycle, petri nets, task scheduling and allocation, fault-tolerance, resource management, RTS/search techniques, dealing with uncertainty.

Discrete-time signals and systems, z transform, frequency analysis of signals and systems, fourier transform, fast fourier transform, design of digital filters, signal reconstruction, power spectrum estimation.

This course offers an introduction to Micro-Electromechanical Systems (MEMS) and nanotechnology, focusing on their fundamental principles, design methodologies, fabrication techniques, and real-world applications. Key topics include the working principles of MEMS and nanoscale systems, microfabrication and micromachining processes, and the selection of materials for MEMS and nanotechnology. Students explore advanced techniques in designing and manufacturing micro- and nano systems, as well as their diverse applications across industries such as healthcare, electronics, and automation. By bridging theory with practical insights, this course equips students with a solid foundation in MEMS and nanotechnology for research and industrial innovation.

Neural dynamics and computation from a single neuron to a neural network architecture. Advanced neural networks and applications. Soft computing approaches to uncertainty representation, multi-agents and optimization.

This course will start with state space analysis of multi-input multi-output control systems. Then state space design will be presented. After that, nonlinear control systems and soft computing based intelligent control systems will be studied. Finally, hybrid control systems, H infinite control and uncertainty and robustness in control systems will be addressed.

This project-based course provides a comprehensive introduction to Model Predictive Control (MPC) theory, guiding students through its development, implementation, and practical applications. The course covers system identification techniques for modeling both linear and nonlinear systems, forming the foundation for advanced MPC design. Students explore and implement various MPC algorithms, including Simplified MPC, Dynamic Matrix Control (DMC), General Predictive Control (GPC), and other conditioning methodologies. Through hands-on projects, participants gain valuable experience in designing, tuning, and applying MPC strategies to real-world control problems, equipping them with essential skills for modern control engineering.

A project course in which a problem of advanced design or analysis in the area of Engineering Systems and Computing is established by the student, an investigation is performed, and a report on the final design or solution selected is presented.

A course of directed study involving selected readings and analyses in developing knowledge areas of Engineering Systems and Computing.

This course aims at providing a solid introduction to the field of Reinforcement Learning (RL). Students become familiar with the key ideas and techniques in RL, understand how RL approaches would provide intelligent decisions to various problems, and gain knowledge on the challenges and the state-of-the-art approaches including Deep Reinforcement Learning (DRL). Students are recommended to have already taken a relevant undergraduate course (STAT*2120 or equivalent).

Continuous stormwater management models and model structure. Catchment discretization and process disaggregation. Pollutant build-up, wash off and transport. Flow and pollutant routing in complex, looped, partially surcharged pipe/channel networks including pond storage, storage tanks, diversion structures, transverse and side weirs, pump stations, orifices, radical and leaf gates and transient receiving water conditions (including tides). Pollutant removal in sewer networks, storage facilities and treatment plants.

Analysis of fate mechanisms associated with environmental contaminants. Focus on substances which are generally considered to be hazardous to humans, or other animal life at low concentrations. Study of physicochemical properties and fate estimation on control and remediation strategies. Quantitative analysis of contaminant partitioning and mass flows, including cross-media transport and simultaneous action of contaminant fate mechanisms.

Analysis of analytical and computational models used to predict the fate of airborne contaminants; role of air quality models for the solution of engineering-related problems; analysis of important boundary layer meteorology phenomena that influence the fate of air pollutants; conservation equations and mathematical solution techniques; model input requirements such as emissions inventories; Gaussian models; higher-order closure models; Eulerian photochemical grid models.

The engineering principles of renewable energy technologies including wind, solar, geothermal and biomass will be examined, including technology-specific design, economic and environmental constraints. Students will compare the relative merits of different energy technologies and gain a knowledge base for further study in the field.

This course will define the different types of hazardous wastes that currently exist and outline the pertinent legislation governing these wastes. Information will be presented on different ways to handle, treat and dispose the hazardous waste, including separation, segregation, minimization, recycling and chemical, physical, biological, and thermal treatment. Also to be discussed are hazardous waste landfills and site remediation technologies. Specifics include design and operation of hazardous landfill sites, handling and treatment of leachate, comparison of pertinent soil remediation technologies. Case studies will be reviewed.

This design course will discuss advanced technologies not traditionally covered during an undergraduate curriculum. An important consideration will be the reuse of water.

This course is an advanced, graduate level, course dealing with the important concepts associated with groundwater flow in fractured rock and field methods for characterizing groundwater flow and quantifying transport in bedrock at both the borehold and flow system scales. Fractured rock hydrology pertains to numerous engineering challenges from mining, waste containment, upstream oil/gas geothermal water supply and watershed/ecosystem management. Students are recommended to have already taken a relevant undergraduate course (ENGG*2230, ENGG*3220 or ENGG*3670).

Introduction to current groundwater issues, definition of terms, review of fundamental equations describing fluid and contaminant transport in saturated groundwater zones. Mathematical techniques (analytical, FE and FD) for the solution of the fundamental equations. Application of numerical groundwater models to a variety of situations. Case studies. Review of groundwater models used in industry.

This course concerns groundwater flow systems and the role of aquitards with and without pumping for water supply. Representative geologic domains will be examined using multiple types of evidence to discern flow system characteristics and present various conceptual models from field-based research studies. Students are recommended to have already taken a relevant ungergraduate course (ENGG*2230, ENGG*3220 or ENGG*3670).

A course of directed study involving selected readings and analyses in developing knowledge areas of environmental engineering.

Deterministic hydrological models. Function of watershed models for hydraulic design, environmental assessment, operation of water control structures, flood warning. Calculation algorithms.

This course covers techniques used to measure rates of movement and amounts of water occurring as precipitation, soil water, ground water and streamflow. Available measurements of water quality are surveyed. Calculation procedures involved in the use of indirect indicators of water quantity and quality individually and in combination are described.

Basic concepts, energy principle; momentum principle; flow resistance; non-uniform flow; channel controls and transitions; unsteady flow; flood routing.

Explores the multi-disciplinary principles of stream and wetland restoration and the tools and techniques for restoration design. Restoration design is approached from a water resources engineering perspective with emphasis on hydrological and hydraulic techniques. Numerous case studies are examined as a means to identify more successful design approaches.

Students will be able to (i) describe processes related to soil erosion by water, (ii) describe processes related to fluvial sedimentation, (iii) evaluate and prescribe structural and non- structural control methods, and (iv) run at least one soil erosion/fluvial sedimentation computer model if the course is satisfactorily completed.

A project course in which an advanced design problem in the area of watershed engineering is established, a feasibility investigation performed and a final design presented.

A course of directed study involving selected readings and analyses in developing knowledge areas of water resources engineering.

This seminar course provides students with exposure to current and emerging research topics in the field of engineering management. The Master of Engineering Management Graduate Program Coordinator invites academic and industry speakers to present their work and perspectives on the role of engineering management in the profession in weekly meetings. Students are expected and encouraged to engage in conversations and participate actively during the presentations and seminars. The capstone culminates in an experiential case study simulation of an engineering management challenge and the presentation of its solution to faculty and industry professionals. This course should be taken in a student's last semester of the MEM.

A project course in which a problem of advanced design or analysis in the area of environmental engineering is established, an investigation is performed and a final design or solution is presented.

This course focuses on applying the knowledge gained in advanced engineering science and design courses on team-based engineering projects that may be community-based or industry sponsored. Students collect and analyze information and synthesize solutions taking into account significant technological, commercial, socio-economical, and environmental considerations. Additionally, the teams prepare a project proposal with a clear problem definition and methodology that may be completed in ENGG*6970.

This course builds upon knowledge foundations and team-based project proposals from ENGG*6960. Student design teams work in consultation with a faculty advisor to develop a design concept through detail design, prototyping (virtual or physical) and testing phases. Students apply advanced engineering science knowledge and develop skills in computer-assisted design, reverse engineering and additive manufacturing. The course culminates with submission of a written report, delivery of an oral presentation and a prototype demonstration. Lecture-based case studies are drawn from across engineering disciplines to illustrate fundamental design principles of reliability, safety, sustainability and cost.

This course addresses specialized topics in one or more aspects of Computer Engineering not covered by other graduate courses. Includes selected readings and thorough analyses in emerging knowledge areas, advanced engineering tools, and current technical developments. May be repeated for credit as topics vary.

An independent project carried out under the supervision of a Computer Engineering faculty member in which an advanced modelling or design problem and the desired outcomes are defined, possible solutions are synthetized and analyzed, and a final model or design is evaluated. Regular meetings, final report, and presentation required.