61 courses in the 1994–95 Undergraduate Calendar, read from the scanned original. The text comes from OCR, so check the scanned page before relying on a detail.
Engineering
Undergraduate Officer D.W. Stashuk, ext. 5566
Introductory Notes 1. The numbering of Systems Design
Engineering courses is as follows.
a) If the course is given in the “A” term, the number in the units place is odd; otherwise, it is even.
b) The number in the 10's place refers to the field of the subject matter of the course, according to the following codes.
7 topics in applied mathematics 2 computer systems
3 socio-economic systems
4 human systems
Course Descriptions Systems Design Engineering
5 physical systems 6 the design of engineering systems 7 communication and information systems
8 engineering sciences 9 laboratories
c) The number in the 100’s place generally refers to the year in the Program in which the student will encounter the course.
2. The majority of Systems Design courses are given on the basis of 3 formal lectures and 1 tutorial hour each week. The department endeavours to ensure that the formal course schedule remains below 30 hours per week in each term. Beyond this, other, less formally scheduled, meetings between students and faculty are required. It is expected that the average student will spend, in tota/, between 45 and 55 hours per week on her/his studies.
Systems Design first year students will meet with a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed. Non-credit courses.
The limit, continuity, and inverse functions. Integral calculus: fur?damental theorems, integral as an area, irWefinite integrals, methods of integration. Areas, volume, work, impulse and rgy; polar coordinates; sequences, series and convergence.
Intermediate and me@ value theorems, U'Hépital’s rule. Taylot/Maclaurin and other power series, convergence and applications. Functions of tw? variables, partial derivatives, Jacobian, gradient, extrema, series. i
Systems of linear equations and matrices, vector spaces. Computalonal aspects of linear algebra, theoretical ‘¥spects: linear transformations, determinan’s. eigenvalues and eigenvectors, inner preduct spaces.
Computer systems, problem solving, data and programs, structured programming, arrays, matrices and pointers, correct and efficient algorithms, data structures.
Ergonomics - the human-machine environment, human sensory processes, information processing, motor function. introduction to biomedical engineering. Introduction to cognitive ergonomics.
Defining systems design, systems concepts, system sciences. Systems problem solving: identification, analysis and design. Graphics: engineering drawing, projections, descriptive geometry, points planes and lines.
Basic concepts of mechanics, vectors. Statics of particles. Rigid bodies and force systems, equilibrium of rigid bodies. Analysis of trusses and frames. Distributed forces, centroids and moments of inertia. Friction. Virtual work.
Kinematics of particles, rectilinear and curvilinear motion. Kinetics of particles, application to space mechanics. Energy and momentum methods. Systems of particles. Kinematics and kinetics of rigid bodies; planar motion. Vibrations.
Concepts of electronic structure, atomic and molecular bonding, shapes of molecules; reactivity, energetics, chemical behaviour. Acidity, basicity and buffer solutions. Solids: ‘ionic and covalent crystals, crystal defects. Oxidation and corrosion. Organic chemistry — common functional groups and reactions, including polymerforming reactions; fibers, plastics and rubber.
Digital tecnnology, combinatorial logic, binary aritnmetic, synchronous sequential Circuits, design methodology, algorithmic State machines, microcomputer interfacing.
Systems Design second-year students will meet a faculty member designated as their class Professor. Performance in assignments, conceptual difficulties with courses, interrajation of coursework, later work and eng'Neering practice will be discussed. Ncin-credit courses.
First order differential equations, existence and uniqueness theorems, solution methods, second and higher order differential equations. Forced and free solutions to differential equations, transient and steady state solutions, applications, systems of differential equations. Laplace transforms with applications to differential equations.
Probability models, random sampling, distribution functions, independent experiments, conditional probability, Bayes Theorem. Discrete and continuous variates; mean and variance, covariance and correlation.
Likelihood methods, two parameter likelihoods, frequency properties, tests of significance. Analysis of Normal measurements. Applications to statistical quality and process control.
Structured software design, overview of programming systems and computer organization; data structures; hashing, sorting and searching; algorithm complexity, evaluation and design; event-driven and object-oriented programming; concurrency. Laboratories devoted to implementing a medium-sized programming project.
Models and analysis of linear systems. Discrete time systems, continuous time systems; difference and differential equations; impulse and frequency response. Complex frequency, functions of complex variables, transform domain techniques.
Z transforms; Fourier analysis, Laplace transform. Transfer functions and frequency response, frequency domain analysis of linear systems; sampling theory, stability, and linear filters.
Introduction to mechanical response of materials and stress-strain relationship. Behaviour of prismatic members in tension, compression, shear, bending and torsion. Shear-force and bending-moment diagrams. Work and energy methods. Introduction to instability.
Course Descriptions Systems Design Engineering
Fundamental concepts in systems involving fluid flow. Basic treatment of statics, kinematics and dynamics of fluids. Conservation of mass, momentum and energy for a control volume. Dimensional analysis and similarity. Flow in pipes and channels. Brief introduction to boundary layers, lift and drag, ideal and compressible flow.
Introduction to the fundamental laws of electricity, magnetism and optics; electric fields, voltage, resistance, current, properties of conductors and semiconductors, capacitance, properties of dielectrics, magnetic fields, Faraday'’s Law and inductance, properties of magnetic materials; electromagnetic waves and the nature of light. geometrical optics: reflection and refraction, physical optics: interference and diffraction.
Active and passive circuit elements, Kirchhoff’s laws, mesh and nodal circuit analysis, principle of superposition; step response of first and second order networks; sinusoidal steady state analysis using complex impedance phasors; inputoutput relationships, transfer functions and frequency response of linear systems; operational amplifiers, operational amplifier circuits using negative or positive feedback; diodes, operational amplifier circuits using diodes; analog signal detection, conditioning and conversion systems; transducers, difference and instrumentation amplifiers, active filters, A/D and D/A conversion.
Systems Design third year students will meet with a faculty member designated as their class professor. Performance in assignments, conceptual difficulties with courses, interrelation of coursework, later work and engineering practice will be discussed. Non-credit courses.
16:149
Mathematical foundations for engineering design and decision making. Emphasis is on basic concepts and optimization methods. Classification of techniques. Concepts of system and model, and the analysis and design process. Theory of value. Multiple criteria and trade-offs. Dealing with uncertainty and time. Computational techniques. Applications, principles and practice.
introduction to numerical techniques for engineering problems. Topics covered include: source of computational error; solutions to linear and non-linear equations; matrix factorization; eigensystems; numerical interpolation and approximation; numerical integration, solution of ordinary and partial differential equations. Introduction to data structures and their application.
Data structures techniques and their role in the design of algorithms, arrays, lists, trees and graphs, sorting and searching algorithms, evaluation and analysis of algorithms, application to engineering problems.
This course is designed to satisfy Engineering Economics requirements of the Canadian Accreditation Board. Price and output decisions. Choosing among alternative inputs and production processes. Evaluating alternative investments, equipment service life, and new products.
Multiple regression analysis. Assumptions, use of indicator variables, variable selection techniques, analysis of variance. Introduction to experimental design, including block designs, factorial arrangements of treatments.
Ergonomics applications in industrial environments. Human performance and fatigue in industrial accident prevention. Human systems reliability in the industrial workplace. Human-machine function and human stress, adaptation and reliability. Industrial workstation design. Case studies of industrial ergonomics problems. Ergonomics in industrial systems design.
Introduction to systems modelling and analysis. Graph theoretic models and formulation of system equations. State space formulation and solution. Time and frequency domain solutions. Application to engineering systems.
Classical and state space representations of control systems. Stability, controllability, observability and sensitivity. Routh-Hurwitz and root-locus methods. Frequency domain behaviour, Bode plots, Nyquist stability criteria. Pole placement, PID, phase-lead and phase-lag controllers.
The subject matter is similar to SY DE 351 except the development is based on other physical systems such as mechanical and hydraulic systems. Mixed nodal, state formulation and solution. Relationship to classical approaches to modelling systems for other physical systems.
The methodology of design: defects, needs and the problem definition; criteria and generation of alternative solutions; feasibility analysis; optimization; selection, implementation and solution. The lecture material is supplemented by a term long design project done in small groups.
Engineering design project course where students work in small groups applying the Principles of engineering problem solving, systems analysis, simulation, optimization " and design to a problem of their own choosing. Students have individual project supervisors as well as an overall coordinator who provides the framework for the term projects.
The generation and forming of surfaces. Concept and design of tooling for manufacturing processes. The influence of materials on processes and choice of processes on design. The behaviour of materials in processes. Process limitation, tolerances, accuracy, surface finish and economics. Cost in manufacturing.
Course Descriptions Systems Design Engineering
Pattern recognition as a process of data analysis. Pattern features as components in a random vector representation. Classification techniques: distance measures in feature space, probabilistic (Bayesian) decision theory, linear discriminants. Clustering and feature extraction. Applications: optical character recognition, speech recognition, industrial robot vision, medical diagnosis, remote sensing and satellite image analysis, fault detection and diagnosis in complex systems such as nuclear reactors.
An introductory course in engineering thermodynamics structured for students in Systems Design. Classical thermodynamics is presented as the systematic study of energy; its use, degradation, and waste. Applications focus on problems of energy and environment. The concepts of statistical thermodynamics are introduced briefly and their connections with information theory are described.
Introduction to dynamic analysis of mechanical systems; review of planar kinematics and dynamics; basic concepts in kinematics of mechanical systems; position, velocity, and acceleration analysis of two-dimensional linkages and machines; dynamics of rigid body systems; solution of equations of motion to obtain time response and reaction forces; application to planar mechanisms, robots, and vehicles; extension to three-dimensional systems; computer-aided simulation and animation; introduction to advanced concepts in multibody dynamics.
An introduction to the understanding of the properties and applications of engineering materials. Atomic bonding and packing; crystal defects and microstructure; elasticity, plasticity, strength and fracture; strengthening methods and transformations; fast fracture, toughness, fatigue and creep; oxidation and corrosion; case studies of materials in design.
Systems Design fourth year students will meet with a faculty member designated as their class professor. Conceptual difficulties, the interrelation of course work and engineering practice will be discussed. Non-credit courses.
The objective of this course is to introduce the students to current intelligent system concepts. Artificial intelligence systems in areas such as natural language understanding, speech understanding, machine vision and learning will be discussed. Methods and tools for building expert systems will be introduced.
Prereq: SY DE 324 or equivalent
Design of efficient algorithms and methods for their analysis, mathematical algorithms, string processing algorithms, geometrical algorithms, exhaustive search and traversal techniques, introduction to a lower bound theory and NP-completeness, examples from engineering problems. Prereq: SY DE 324 or equivalent
Theory and algorithms for non4inear constrained optimization problems: convex set, convex functions, convex programming, Kuhn-Tucker conditions, duality, quadratic programming, quasi-Newton methods, geometric programming, dynamic programming.
The objective of this course is two-fold: firstly to impress on the students that most processes in the environment occur as random processes and secondly to develop in the students the capability to analyze such processes. The course will review theory of random variables and introduce concepts of random processes and time series analysis. Physical phenomena in the environment and their random nature will be discussed with examples from the hydrologic cycle, air circulation and ocean circulation.
Concept of system safety and safety as a system component in design and industry. Liabilities and safety legislation. Worker's Compensation: cost, disability, benefits, adequacy. Management and safety responsibilities. The changing role of safety personnel. Hazards, their determination, categories and elimination. Industrial safety and accident protection. Plant safety and emergency planning. Accident investigation. The hazards of toxic materials, their types, mechanisms, detection and measurement. Toxicology and the workplace, industrial safety standards and codes.
This course develops an understanding of the fundamental concepts of biomedical engineering through the review of the basic functioning aspects of several major physiological systems, through the analysis of mathematical models used to represent the systems and through the study of techniques used to measure pertinent parameters of these systems. in addition, a number of current clinically used medical imaging techniques are covered.
The major physiological systems covered include the neuromuscular, cardiovascular and respiratory systems. Imaging techniques analyzed include X-ray, CT Scan, ultrasound, magnetic resonance imaging and positive emission topography.
Topics include decomposition techniques, graph theoretic methods of analysis, tearing of large systems into subsystems, multiport and multiterminal component representations; examples are drawn from practical large-scale systems.
State equations for two-terminal component systems; time varying and non-linear components; analytical solutions for state models; numerical and analog methods of solution.
System modelling, simulation techniques for continuous and discrete systems; special purpose computer languages for systems simulation; examples and applications in a variety of areas.
e
Course Descriptions Systems Design Engineering
The first half of a two term engineering
- design project continuing the systems
design workshop sequence. An interim progress report is presented at the end of the first term.
The concluding half of the fourth year Systems Design Workshop.
A continuation of SY DE 311, with emphasis on stochastic operdtions research models. Topics will include: introductory time series analysis and queuing theory, Markov decision processes, and stochastic programming. Models for optimization of large systems under uncertainty.
Important concepts in graph theory, their properties, relationships among them and useful graph algorithms are given in the context of various applications. Applications include but are not restricted to graph theoretic solutions to electrical network equations, sparse matrix techniques, graph models for fault diagnosis, optimum distribution of traffic through networks and other network flow problems.
Issues and directions in computer aided design and engineering (CAD/CAE); principles underlying the design of CAD systems. CAD systems architecture and data structures. Fundamentats of interactive computer graphics with application to engineering design and analysis software: graphical interfaces; geometrical transformations and projections; representation of lines, curves, surfaces and solids; graphical rendering techniques. Automated assembly and solution techniques for linear systems. The course usually involves a major project in which students develop a working CAD system.
Techniques from game theory for assessing the social and political influences in engineering decision making. Topics include metagame analysis, games with mistaken information, sensitivity analysis, dynamic games, probabilistic considerations, bargaining and real-world applications of all the foregoing concepts.
16:151
The course presents concepts of systems modelling and applies them to environmental subsystems such as energy, physical and bio-ecosystems, and to socio-economic systems. The course emphasizes the symbiotic aspects of socio-economic and environmental systems which form the basis for analysis and design of such complex projects.
Cross-listed as ENV E 420
The purpose of this course is to provide a comprehensive survey of human mental capabilities with applications to humanmachine systems. Topics covered include: signal detection and absolute judgement, decision making, perception of verbal material, non-verbal perception, memory, attention and perception, mental workload, selection of action, reaction time and human error, continuous manual control, process control and automation, learning and skill acquisition.
This course introduces the key rules for interface design, and the theory on which these rules are based. The focus is on human-computer interaction, but results about display and control devices and human performance are discussed and illustrated in the context of machines in general. Design strengths, weaknesses, compromises and tradeoffs are explored for a variety of representative user interfaces. Major topics: role of the user interface, relevant results in human cognition, devices for input and output, models for system users, interaction styles and techniques, design principles for user interface software and graphic displays.
Fundamental concepts and definitions in the stability theory of deterministic systems. Classification of systems and stability criteria. Nonlinear behaviour, static and dynamic bifurcations, and catastrophes. Secondary bifurcations and imperfection sensitivity. Hopf bifurcations, and introduction to chaos.
Review of particle dynamics; variational methods; Hamilton's Principle; Lagrange’s equations. Dynamics of rigid bodies; gyroscopic motion. Simple and multiple degree of freedom vibratory systerns; model analysis for discrete systems; extension to continuous systems.
Finite difference methods as they are applied to boundary value problems in solid mechanics and heat transfer analysis. Use of the finite difference method in the solutions of systems of higher order differential equations. The finite element method as it is applied to problems from structural and thermal analysis. Foundations and important principles of the finite element methods.
Beginning with a discussion of quantitative models of imaging systems, this course moves on to apply methods of linear systems theory and signal processing to image processing. Simple spatial domain techniques as well as spatial frequency domain methods and digital filter design for image enhancement and restoration are discussed. Special topics in application areas of machine vision (segmentation and feature extraction), remote sensing, medical imaging and vision models are presented throughout the term.
Prereq: SY DE 252 or equivalent
Ukrainian
For courses in Ukrainian see Germanic and Slavic Languages and Literatures.
Course Descriptions Ukrainian - Women’s Studies