★ 3.0

(second term,

3-0-0)

Introduction to basic physical and technological aspects of medical imaging. Emphasis on computed transmission and emission tomography, magnetic resonance, and ultrasound imaging. These methods are developed and contrasted in terms of how imaging information is generated, detected, and processed and how different hardware configurations and other factors limit image quality. Relative diagnostic potential of the imaging methods is also discussed in relation to future prospects of each method.

★ 3.0

(second term,

3-1s-0)

Introduction to rehabilitation techniques for assisting individuals with physical disabilities to reach, stand and walk. Biomechanics of intact and pathological movements and the use of assistive devices such as exoskeletal orthotics, neuroprosthetic devices and locomotor training are emphasized. Students are exposed to the concepts of biomechanical modeling, motion analysis, electrical stimulation, control systems, neuroregeneration, and pharmacology. Prerequisite: BME 320 and BME 320 321 or consent of Instructor.

★ 3.0

(either term,

3-0-3)

Introduction to the geometry and photometry of the 3D to 2D image formation process for the purpose of computing scene properties from camera images. Computing and analyzing motion in image sequences. Recognition of objects (what) and spatial relationships (where) from images and tracking of these in video sequences. Prerequisites: CMPUT 201 or 275; one of CMPUT 340, 418, ECE 240, or equivalent knowledge; one of MATH 101, 115, 118, 136, 146 or 156, and one of MATH 102, 125, or 127.

★ 3.0

(first term,

3-0-0)

Introduction to the principles of biophysical instrumentation. Various sensors are examined including strain gauges, inductive, capacitive, thermal, and piezoelectric sensors. Methods of measuring blood pressure are discussed. Origin of biopotentials; membrane and action potentials. Measurement of bioelectrical signals such as the ECG and EMG. Electrical safety, noise, impedance matching, and analog-to-digital conversion. Applications of electrodes, biochemical sensors, and lasers. Prerequisite: ECE 203 or E E 250 or consent of the Instructor. Credit may be obtained in only one of ECE 405 or EE BE 512.

★ 3.8

(either term,

3-0-3/2)

Extension of sampling theory and the Fourier transform to two dimensions, pixel operations including gray-level modification, algebraic and geometric transformations. The design of spatial filters for noise reduction, image sharpening and edge enhancement, and some discussion of interpolation techniques. An introduction to the concepts of image restoration from known degradations and the reconstruction of images from parallel and fan projections. Prerequisite: ECE 340 or E E 338 or consent of Instructor. Credit may be obtained in only one of EE BE 540 or ECE 440.

★ 3.8

(either term,

3-0-3/2)

Human visual/audio perception and multimedia data representations. Basic multimedia processing concepts, multimedia compression and communications. Machine learning tools for multimedia signal processing, including principle component analysis and Gaussian mixture modeling. Applications to human-computer interaction, visual-audio, and visual-text processing. Prerequisites: ECE 220 or CMPUT 275, ECE 342, MATH 102 or equivalent knowledge. Credit may be obtained in only one of ECE 442 or E E 442.

★ 3.8

(either term,

3-0-3/2)

Intelligent systems for automatic control and data analysis. The concepts of vagueness and uncertainty, approximate reasoning, fuzzy rule-based systems and fuzzy control. Strategies for learning and adaptation, supervised and reinforcement learning, self-organization and the selection of neural network architectures. Discussion of the principles of search and optimization, evolution and natural selection and genetic algorithms. Introduction to hybrid intelligence. Applications of intelligent systems for pattern recognition, classification, forecasting, decision support, and control. Credit may be obtained in only one of CMPE 449 or ECE 449.

★ 3.0

(either term,

3-0-0)

Microfluidic and nanobiotechnological devices. Fabrication techniques for devices: self-assembly, lithographic technologies. Applications of nanobiotechnology in computing, electronics, human health, environment and manufacture. Prerequisites: MATH 201 or PHYS 230. Credit may be obtained in only one of ECE 455 or E E 455.

★ 3.0

(either term,

3-0-0)

Overview of microelectromechanical (MEMS) systems, applications of MEMS technology to radio frequency, optical and biomedical devices. Basic MEMS building blocks, cantilever and clamped-clamped beams. Actuation mechanisms of mechanical microdevices, thermal and electrostatic. The thin film fabrication process, deposition, lithography, etching and release. MEMS in circuits, switches, capacitors, and resonators. Prerequisites: ECE 370 or E E 315 or PHYS 381, and one of MAT E 201, PHYS 244, MEC E 250. Credit may be obtained in only one of ECE 458 or E E 458.

★ 3.8

(either term,

3-0-3/2)

Basic concepts of computer-integrated intervention. Surgical CAD/CAM, assist and simulation systems. Actuators and imagers. Medical robot design, control and optimization. Surgeon-robot interface technology. Haptic feedback in surgical simulation and teleoperation. Virtual fixtures. Time delay compensation in telesurgery. Cooperative manipulation control. Overview of existing systems for robot-assisted intervention and for virtual-reality surgical simulation. Prerequisite: ECE 360 or ECE 462 or E E 357 or E E 462 or consent of the Department. Credit may be obtained in only one of ECE 464 or E E 464.

★ 3.0

(either term,

3-0-0)

Network topologies. Layered architectures and the Open Systems Interconnection (OSI) reference model. Peer-to-peer protocols, medium access control protocols, and local area network standards. Packet switched networks and routing, the TCP/IP suite of protocols. Credit may be obtained in only one of ECE 487, CMPUT 313 or CMPE 487.

★ 3.0

(either term,

3-0-0)

Concepts of reliability, failure rate, maintainability, and availability. Properties of various statistical distributions and their applications in reliability engineering. Failure data analysis techniques including probability plotting. Load and strength interference in mechanical component design. System reliability models and system reliability evaluation methods. Optimal system design considering reliability issues. Prerequisite: STAT 235 or equivalent.

★ 3.0

(either term or Spring/Summer,

3-0-0)

Introduction to project management tools, techniques, templates, and methodologies. This course examines the eight knowledge areas of the Project Management Institute (PMI) which provide an integrated approach to managing engineering projects. Prerequisites: One of ENGG 310, 401 or ENG M 310, 401.

★ 3.0

(either term,

3-0-0)

Fundamental methods for the analysis of human systems in industrial engineering. Human-machine interaction. Engineering of the workplace and the work environment. Motion and time study. Standards in ergonomics and work design.

★ 3.8

(either term or Spring/Summer,

3-3s/2-0)

Introduction to process safety. Basic concepts of fires, explosions and releases. Introduction to process hazards analysis, methodologies and tools. Overview of process safety management frameworks. Case studies and industrial tour(s) demonstrate the application of specialized tools and methodologies in complex industrial operations across all engineering disciplines. Seminars develop competencies and proficiencies in applying these specialized methodologies and tools towards proactive risk management. Requires payment of additional student instructional support fees Refer to the Tuition and Fees page in the University Regulations section of the Calendar. Prerequisite: ENGG 404 or consent of the instructor.

★ 3.5

(either term,

3-1s-0)

Terminology, welding processes and materials considerations, mechanisms of welding including the welding arc, molten metal issues, mass and energy balances, heat transfer, basics of procedure development, design of weldments, codes and standards, non-destructive testing, guest lectures from industrial practitioners and specialists. Pre-requisites: Completion of 2 years in any engineering discipline or consent by Instructor.

★ 3.5

(either term or Spring/Summer,

2-0-3)

Primary manufacturing processes including casting, forming, machining, powdered metallurgy and surface technology, interactions between design, materials (metals, polymers, ceramics, composites) and processes, selected field trips and laboratory activities. Requires payment of additional student instructional support fees. Refer to the Tuition and Fees page in the University Regulations section of the Calendar. Prerequisite: MEC E 260.

★ 4.0

(either term or Spring/Summer,

1-2s-4)

Design of machine components for ease of manufacture. Application of measurement, inspection, and reverse engineering techniques. Preparation of working drawings for manufacturing. Introduction to machining operations, including hands-on machine shop practice. Evaluation of design performance. Sections offered at an increased rate of fee assessment; refer to the Tuition and Fees page in the University Regulations sections of the Calendar. Prerequisites: MEC E 260, 265, 300, and 301.

★ 3.0

(either term or Spring/Summer,

3-0-0)

Boundary layer flow, vorticity, circulation and aerodynamic lift, wing theory, aeronautical applications. Prerequisite: MEC E 330 or 331.

★ 4.5

(either term or Spring/Summer,

3-0-3)

Model selection and simplification, grid generation and grid independence, transient and advection terms treatment, turbulence modeling, verification and validation, best practices. Hands-on experience with commercial CFD codes to demonstrate the application of: theory, proper setup and analysis. Prerequisites: MEC E 390, and 331 or equivalent.

★ 3.0

(either term,

3-0-0)

This course is concerned with methods used to predict the uncertain nature of business trends in an effort to help managers make better decisions and plans. Such efforts often involve the study of historical data and manipulation of these data to search for patterns that can be effectively extrapolated to produce forecasts. This is a business statistics course that covers all aspects of business forecasting where the emphasis is on intuitive concepts and applications. Topics covered include the family of exponential smoothing methods, decomposition methods, dynamic regression methods, Box-Jenkins methods and judgmental forecasting methods (e.g. the Delphi method). Because forecasting is best taught through practice, the course contains numerous real, relevant, business oriented case studies and examples that students can use to practice the application of concepts. Prerequisites: MGTSC 312, MGTSC 352 or OM 352.

★ 3.0

(either term,

3-0-0)

A problem-solving course which introduces the student to deterministic and stochastic models which are useful for production planning and operations management in business and government. Note: Students are expected to have basic familiarity with microcomputer applications. Prerequisite: MATH 154 or equivalent and STAT 161 or equivalent. Students may not receive credit for both OM 252 and OM 352.

★ 3.0

(either term,

3-0-0)

Computer modelling of management systems in such functional areas as accounting, finance, marketing and operations. Basic concepts of deterministic and probabilistic (Monte Carlo) simulation and their applications. Microcomputer implementation of case studies using spreadsheets particularly emphasized. Required term project. Prerequisites: MGTSC 312 (or equivalent STAT course), MGTSC 352 or OM 352; and FIN 301 or ACCTG 311. Not to be taken by students with credit in MGTSC 422.

★ 3.5

(,

3-0-0)

The course aims to cover medical image processing and analysis techniques, including de-noising, registration, segmentation, and 3D reconstruction, applicable in diagnostic imaging modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). The course will also cover machine learning topics related to medical image analysis. Clinical examples in cardiovascular, musculoskeletal, and brain imaging will be discussed. Prerequisite: Linear algebra and knowledge in Python programming language or consent of the Department.