The School offers the following courses. Not all courses are offered each semester.
Linear Systems Theory
State-space analysis of finite dimensional continuous and discrete time linear systems. Linear vector spaces, linear operators, normed linear spaces, and inner product spaces. Fundamentals of matrix algebra; generalized inverses, solution of Ax=y and AXB=Y, least square and recursive least square estimation, induced norm and matrix measures, functions of a square matrix, Cayley-Hamilton and Sylvester's theorems, Singular Value Decomposition (SVD) with applications. Analytical representation of linear systems, state-space formulation, solution of the state equation and determination of the system's response. Controllability, observability, duality, canonical forms, and minimal realization concepts. Stability analysis and the Lyapunov's method. Prerequisite: Graduate standing.
The application of theories in probability, random variables and stochastic processes in the analysis and modelling of engineering systems. Topics include: a review of probability and random variables; random deviate generation; convergence of random sequences; random processes; auto correlation and power spectral-density; linear systems with stochastic inputs; mean-square calculus; AR and ARMA models; Markov chains; elementary queuing theory; an introduction to estimation theory. Areas of application include digital communications, speech and image processing, control, radar and Monte Carlo simulations. Prerequisite: Graduate standing.
Writing for Publication
Through discourse analysis and simulation of the publication process, ENSC 803 enables the analysis and refinement of writing processes and written styles when preparing journal articles, oral conference presentations, and poster presentations in professional contexts. Students will write and revise an article suitable for publication in a professional journal, design a poster presentation, and design and deliver an oral conference presentation. Additionally, students will blind review a peer's journal article and will participate in a series of team-based discourse analysis exercises. ENSC 803 will also cover departmental requirements and University regulations related to thesis completion and submission. This course cannot be used as one of the course requirements towards the degree.
Advanced Digital Communications
This course discusses the fundamental techniques used in the physical layer of a digital communication system. The main topics are as follow: digital modulation, including complex baseband representations, the concept of the signal space, optimal demodulation, bit error probability analysis, as well as timing and carrier recovery; error control techniques, including soft decision decoding and the Viterbi algorithms; and various kinds of equalization (linear, decision feedback, and maximum likelihood sequences estimation). Sub topics of the equalization section include pulse shaping and eye diagrams. The emphasis may vary slightly in different offerings. Prerequisite: ENSC 428 or equivalent. ENSC 802 (as a corequisite) or permission of instructor.
Information measures: entropy, relative entropy, mutual information, entropy rate, differential entropy. Asymptotic Equipartition Property. Lossless data compression: Kraft inequality, Huffman code, Shannon code, Arithmetic coding. Channel capacity: binary symmetric channel, binary erasure channel, Shannon's channel coding theorem, Gaussian channel, feedback. Prerequisite: STAT 270 or equivalent.
Statistical Signal Processing
Processing techniques for continuous and discrete signals with initially unknown or time-varying characteristics. Parameter estimation; Bayes, MAP, maximum likelihood, least squares the Cramer-Rao bound. Linear estimation, prediction, power spectrum estimation, lattice filters. Adaptive filtering by LMS and recursive least squares. Kalman filtering. Eigenmethods for spectral estimation. Implementation issues and numerical methods of computation are considered throughout. Prerequisite: ENSC 802 and 429 or their equivalents.
Deep Learning Systems in Engineering
Covers machine learning basics, generalization theory, training, validation and testing. Introduces artificial neural networks, feedforward networks, convolutional networks, and types of layers in deep models. Provides overview of hardware architectures for deep learning: architectural and memory calculations; regularization and optimization of deep learning models. Analyzes recurrent and discursive networks. Culminates in a major project focusing on engineering applications of deep learning in signal processing, communications, biomedical engineering, robotics, or other areas. Students with credit for CMPT 880 - Special Topics in Computing Science: Deep Learning may not take this course for further credit. Prerequisite: MATH 251 or ENSC 280 or ENSC 380 or permission of instructor.
Subband and Sparse Signal Processing
Theory and applications of subband and sparse signal processing. Topics include: subband signal processing (wavelets and filter banks), sparse and redundant signal representations and reconstructions, and the latest research topics. Prerequisite: ENSC 429 or equivalent.
Engineering Management for Development Projects
This course focuses on the management and reporting activities of typical engineering development projects. Through seminars and workshops it builds the student's skills at estimating project cost and schedule, keeping a project on track, and handing over the completed project to a customer or another team. A writing workshop emphasizes techniques for writing proposals, and writing and controlling documentation. Note that ENSC 820 will not count towards the course work requirement of students enrolled in the MASc program.
This course is a mandatory for MEng students.
Network Protocols and Performance
This course covers the techniques needed to understand and analyse modern communications networks. The main topics are as follow: practical techniques for the design and performance analysis of data communication networks; performance analysis of error control, flow and congestion control, and routing; networks of queues using stochastic and mean value analysis; polling and random access LANs and MANs; wireless networks; broadband integrated services digital networks and asynchronous transfer mode; optical networks. Prerequisite: ENSC 802 or permission of instructor.
Techniques needed to understand and analyze modern data communications networks. Basic architecture of packet networks and their network elements (switches, routers, bridges), and the protocols used to enable transmission of packets through the network. Techniques for collection, characterization, and modeling of traffic in packet networks. Aspects of traffic management, such as call admission control and congestion control algorithms in packet networks and the influence of traffic on network performance. Prerequisite: ENSC 427 or permission of the instructor.
Semiconductor Device Theory
Detailed treatment at the graduate level of semiconductor fundamentals and theory. Electronic properties and characteristics of selected semiconductor devices: pn junctions, Schottky barrier junctions, silicon-based heterojunctions and ohmic contacts; bipolar junction transistors; field effect transistors; heterostructures; charge coupled devices and microwave devices. Prerequisite: PHYS 365 or permission of instructor.
Integrated Circuit Technology
Review of semiconductor physics. Technology of semiconductor devices and integrated circuits: material evaluation, crystal growth, doping, epitaxy, thermal diffusion, ion implantation, lithography and device patterning, and thin film formation. Design and fabrication of active and passive semiconductor devices, packaging techniques and reliability of integrated circuits.
Analog Integrated Circuits
Models for integrated circuit activity and passive devices and their implementation; computer aided design tools and their use in designing analog integrated circuits; analysis of single transistor amplifiers; current sources, current mirrors, and voltage references; op-amps characteristics, analyses and circuit design examples; frequency response of integrated circuits; noise in integrated circuits; low power integrated circuits; non-linear analog integrated circuits. The students will be required to either design, fabricate and test simple analog ICs in the microelectronics lab, or do a project which involves the design, analysis, modeling and simulation of an analog integrated circuit. Prerequisite: ENSC 850 or permission of instructor.
Digital CMOS Integrated Circuits
MOS device electronics. Second Order Effects in MOS transistors. BJT device electronics. Static and transient analysis of inverters. Digital gates, circuits and circuit techniques. Speed and power dissipation. Memory systems. Gate arrays, semicustom and customized integrated circuits. CAD tools. Students are required to complete a project. Prerequisite: ENSC 850 or permission of the instructor.
Integrated Microsensors and Actuators
Microelectronic transducer principles, classification, fabrication and application areas. Silicon micromachining and its application to integrated microelectronic sensors and actuators. CMOS compatible micromachining, static, dynamic and kinematic microactuator fabrication. Integrated transducer system design and applications. Students will be required to complete a micromachining project in the microfabrication lab at ENSC. Prerequisite: ENSC 475 and ENSC 495 or permission of instructor.
Biomedical Microdevices and Systems
This course introduces students to microdevices and systems with applications in biology, chemistry, and medicine. Topics include microfabrication techniques of biocompatible materials including polymers; microfluidic theory and components; electro-osmotic flow and separation techniques; system integration; and a selection of key applications including micro total analysis systems, cell and tissue applications, implantable/transdermal devices, biosensors, and biotechnology (PCR, DNA chips). Prerequisite: Recommended: ENSC 495/851 or ENSC 854.
Advanced Multimedia Compression
The theory and applications of multimedia compression and transmission. Topics include: basic information theory, transforms, quantization, entropy coding, various coding standards, and design of multimedia communication systems. Prerequisite: ENSC 429 or equivalent.
MEng Course Option Portfolio
Students in the course option of the MEng program develop a portfolio of their MEng graduate work. This includes a brief report submitted to the Graduate Program Committee that describes the work undertaken in each course and how the overall set of courses contributes to their areas of expertise and future careers. Graded on a satisfactory/unsatisfactory basis. Prerequisite: Students may only register for the ENSC 870-0 during their final term.
PhD Qualifying Examination
Qualifying examination for admission to doctoral candidate standing in the School of Engineering Science. A written thesis proposal is to be submitted to the Supervisory Committee and presented orally no earlier than two weeks after submission. The proposal's defence will be judged according to the feasibility and scientific merits of the proposed research, and demonstration of a sophisticated understanding of general material in the student's major area of research. Graded on a satisfactory/unsatisfactory basis. Prerequisite: ENSC PhD student.
A main goal of computational robotics is to automatically synthesize robot motions to achieve a given task. This course discusses geometric and algorithmic issues that arise in such an endeavour. For example: how can a robot plan its own collision-free motions? How does it grasp a given object? How do we account for uncertainty? The course employs a broad range of tools from computational geometry, mechanics, algorithms and control. The material covered also finds applications in designing devices for automation and in computer animation. The course involves a substantial project which exposes students to practical and implementational issues involved in building automatic motion planning capabilities for robotic systems. Prerequisite: ENSC 488 and a basic course in data structures and algorithms, or permission of the instructor.
MEng Project (Completion)
Students who do not complete ENSC 897 in one term must enroll for this course in all subsequent terms. The tuition for ENSC 896 is half of that of ENSC 897. Graded on a satisfactory/unsatisfactory basis.
Graded on a satisfactory/unsatisfactory basis.
Graded on a satisfactory/unsatisfactory basis.
Graded on a satisfactory/unsatisfactory basis.
Special Topic Courses
The following courses are usually combined classes with undergraduate courses. The topics vary each term.
- ENSC 893 Special Topic I
- ENSC 894 Special Topic II
- ENSC 895 Special Topic III
Directed Studies Courses
The following courses are the class code for directed studies. Please fill out the ENSC Directed Studies 891/892 form to apply for directed studies. A course proposal should be included with your application. Directed Studies guidelines can be found here: Directed Studies Guidelines and Proposal Template.
- ENSC 891 Directed Studies I
- ENSC 892 Directed Studies II
The following courses and modules are associated with the SFU Engineering Science Communication Program. If you have a specific question regarding the content of a course or module, please contact the instructors in the Communication Program.
- ENSC 803
- ENSC 820
Supplementary Communication Modules
This module introduces you to a range of stylistic features commonly found in technical documents that affect readability and rhetorical effectiveness. It addresses issues of style, focusing on the order of ideas within paragraphs and sentences and on clear, concise expression of thoughts. It also directs your attention to how people typically read and the problems your style may pose for them.