Special topics offered by graduate units in area of Communications. Content of courses may change each time they are offered and are developed to cover emergeing issues or specialized content.

Signal processing techniques using special purpose digital hardware and general purpose digital computers are playing an increasingly important role. The course deals with some introductory and some advanced topics in the area. In particular, it presents the characterization of random discrete time signals. It provides an introduction to traditional and modern statistical discrete time signal processing frameworks, including processing with second-, higher-, and fractional lower-order statistics. It discusses sampling and multirate signal conversion; linear prediction and optimum linear filters; least squares methods for system modeling and design; theory and applications of adaptive filters. It also deals with applications in signal and image processing and analysis.

This course will present the concepts of the main processing techniques for digital image processing. It will cover image enhancement and restoration, digital filtering (linear and nonlinear), local space operators, image analysis, and elements of vision. It will also describe the impact of digital image processing to the more important fields of application.

An Introduction to the basic theory, the fundamental algorithms, and the computational toolboxes of machine learning. The focus is on a balanced treatment of the practical and theoretical approaches, along with hands on experience with relevant software packages. Supervised learning methods covered in the course will include: the study of linear models for classification and regression and neural networks. Unsupervised learning methods covered in the course will include: principal component analysis, k-means clustering, and Gaussian mixture models. Techniques to control overfitting, including regularization and validation, will be covered.

This is an introductory level course for graduate students or practitioners to gain knowledge and hands-on experiences in biometric systems and security applications. Topics include: Introduction to important biometric security technologies and policies, biometric modalities and signal processing, biometric solutions and applications, biometric encryption and cryptosystems, biometrics identity analysis, and privacy considerations.

This interdisciplinary course examines issues of identity, privacy, and security from a range of technological, policy, and scientific perspectives, highlighting the relationships, overlaps, tensions, tradeoffs and synergies between them. Based on a combination of public lectures, in-depth seminar discussions and group project work, it will study contemporary identity, privacy and security systems, practices and controversies, with such focal topics as biometric identification schemes, public key encryption infrastructure, privacy enhancing technologies, identity theft risks and protections, on-line fraud detection and prevention, and computer crime, varying between offerings.

This course presents an introduction to the principles and applications of detection and estimation theories. The main thrust is to show how statistical models can be used to provide optimal and suboptimal signal processing structures for digital communication systems operating over noisy channels. Topics covered include: classical detection theory and hypothesis testing, parameter estimation, binary and M-ary digital modulation, detection in coloured noise, coherent and non-coherent structures, detection of random signals in random noise, EM algorithm.

In last decade telecom industry has gone through transformational changes that started with the introduction of the concept of software defined networking or SDN and the emergence of Big Data as well as Machine Learning techniques. With hyper-scalers like Google and Amazon in the horizon, the landscape for traditional Telco service providers are changing. The course is primarily about this change and its profound impacts in telco service providers from different angles, including architecture, service design, business model, security, and privacy. The SDN journey starts by network programmability, that is why the first part of will be walking the students through different steps of building a programmable network. Having programmable network we will have to start building intelligence by introducing closed loop control logics, the second part of the course deals with ideas around creating multilayer control logics, where we employ concepts of Big Data and Machine Learning to create innovative services. Given that SDN is meaningless without proper abstraction and interface modeling, we will discuss model driven approach to network management and from there we open the door to discuss orchestration strategies. Nowadays all telco discussions end with 5G; therefore, we explain 5G with the focus on the role of SDN there, followed by some important 5G use cases including smart cities and IoT. In the last part of the course we zoom into software defined security aspects, as well as a discussion on new methods of creating innovative services. the course will be concluded by discussing some operational aspects of SDN and the role of AI and Machine Learning there.

Special topics offered by graduate units in area of Software Engineering. Content of courses may change each time they are offered and are developed to cover emerging issues or specialized content.

The course explores the theoretical and practical procedures for designing adaptive systems. Topics include decision theory, parameter estimation, supervised learning, unsupervised learning, state-space models, adaptive signal detection, channel characterization, iterative detection, forward-backward adaptive algorithms.

This course teaches the fundamentals of network performance and analysis. The topics are: traffic modeling for voice, video and data, self-similarity and long range dependence in the internet, queueing systems, large deviations and buffer management, multiple access communications, scheduling and processor sharing, routing and dynamic programming, vehicular networks.

This course will cover basic principles in the design of mobile communication systems, included in the various generations of cellular systems from 1G to 5G. The radio propagation environment: basic radio propagation considerations, Rayleigh and Rician statistics, power spectral density, small scale and large scale signal variation, delay spread, Doppler spread, and angular spread, coherence bandwidth, coherence time, and coherence space; MIMO channel modeling. Link issues: modulation techniques including OFDM, diversity, interleaving, forward error correction. Principles of spread spectrum systems and CDMA. System issues: spectral sharing schemes, frequency re-use, noise, and interference analysis, call and packet oriented capacity analysis, and basic scheduling approches including proportional fair. Drop oriented network simulation models. Basic aspects of cellular system standards such as GSM, WCDMA, LTE, and 5G new radio. Familiarization with software radio architecture of commercially available systems including RF chip architecture, FPGA and host processing. RF bands covered, local oscillator management and phase lock, RF filtering, down-coversion, IQ sampling, and digital filtering, frequency and phase synchronization, and demodulation. Issues in the implementation of antenna arrays and massive MIMO. The course will have various exercises based on software radio and matlab including the analysis of real off-the air cellular system pilot and synchronization signals.

This course is one of two companion courses on network softwarization offered simultaneously. The first course (this one) introduces concepts and principles of network softwarization while the second course focuses on hands on experience with technology enablers. The courses will be offered simultaneously in four universities, namely University of Waterloo, University of Toronto, Université Laval, and École des Technologies Supérieures (ETS).

An introduction to stochastic analysis techniques for complex networked systems, covering queueing networks, random graphs, and stochastic geometry. Main topics include Jackson and Whittle networks, reversible network processes, network utility maximization, stochastic network optimization, Erdös–Rényi and Gilbert models, graph evolution and connectivity, random subgraphs, random point processes, Laplace functionals, marked point processes, and Palm probability.

This course covers Radio Access Network (RAN) aspects of the 5G New Radio (NR). Important RF parameters like power flux density, electrical field and various power definitions are introduced and their relationship to regulatory requirements and standards based usage are covered in great detail. Also, various RF impediments such as the noise figure, out of band emissions and ACS/ACLR are introduced. The link budget, receiver sensitivity, channel models and how they relate to 5G systems are explained. Spectrum and RF characteristics of 5G NR are an important part of the course. Moreover, we will go over the architectural solutions, remote radio heads (distributed radio solutions), and important hardware components in the network. Throughout the course, students will get substantial exposure to the practice-based content not commonly found in the textbooks. The course will offer an insight into the important industry standards and initiatives, trials, and the global vendor/operator status in terms of product development and network deployments. A large selection of course projects and guest lectures from major infrastructure vendors and operators are intended to complement the material covered in the lectures.

This course provides an in-depth coverage of modern mobile air-interfaces, focusing mainly on the fourth (4G) and fifth generation (5G) of cellular networks. Following the introduction to multicarrier transmission, the key elements of layer 1, 2, and 3 of air interfaces of the 4G and 5G systems are covered in detail. Frequency division duplex and time division duplex solutions are compared and contrasted, and the differences between two main frequency ranges (i.e., below and above 6 GHz) are highlighted. Finally, the last segment of the course covers some more advanced topics, such as carrier aggregation, dual connectivity, massive machine type communication, and ultra-reliable low latency communication. Students will get the latest updates from the 3GPP standardization process as they become available, and study the impact of these changes on the performance improvement of mobile networks. Additionally, students will be exposed to practical problems that operators and infrastructure vendors are facing on daily basis. Two course projects will help students to supplement the learning material within the area of their own interest. Also, guest lecturers from major infrastructure vendors and operators will be invited to complement the lecture material.

Special topics offered by graduate units in area of Control. Content of courses may change each time they are offered and are developed to cover emerging issues or specialized content.

This course is an introduction to the control of discrete, asynchronous, nondeterministic systems like manufacturing systems, traffic systems, and certain communication systems. Architectural issues (modular, decentralized, and hierarchical control) are emphasized. The theory is developed in an elementary framework of automata and formal languages, and is supported by a software package for creating applications.

This is the first course of a two-term sequence on stochastic systems designed to cover some of the basic results on estimation, identification, stochastic control and adaptive control. Topics include: stochastic processes and their descriptions, analysis of linear systems with random inputs; prediction and filtering theory: prediction for ARMAX systems, the Kalman filter and the Riccati equation; stochastic control methods based on dynamic programming; the LQG problem and the separation theorem; minimum variance control.

Special topics offered by graduate units in area of Control. Content of courses may change each time they are offered and are developed to cover emergeing issues or specialized content.

This course is a mathematical introduction to nonlinear control theory, a subject with roots in dynamical systems theory, mechanics, and differential geometry. The focus of this course is on the dynamical systems perspective. The material covered in this course finds application in fields as diverse as orbital mechanics and aerospace engineering, circuit theory, power systems, robotics, and mathematical biology, to name a few. The course is organized in four chapters, as follows:

1) Vector Fields and Dynamical Systems: Finite dimensional dynamical systems, vector fields, and their equivalence. Existence and uniqueness of solutions of ODEs. 2) Foundations of Dynamical Systems Theory: Invariant sets and their characterization by the Nagumo theorem. Limit sets as a tool to characterize the asymptotic behaviour of bounded orbits. Limit sets of two-dimensional systems: the Poincaré-Bendixson theorem. Poincaré theory of stability of closed orbits. Linearization of vector fields about equilibria. Linearization of vector fields about closed orbits. 3) Foundations of Stability Theory: Equilibrium stability and its characterization by means of Lyapunov’s theorem. Domain of attraction of an equilibrium. The Krasovskii-LaSalle invariance principle. Stability of LTI systems, and exponential stability of equilibria. Converse stability theorems. 4) Introduction to Nonlinear Stabilization: Control-Lyapunov functions. Parametrization of equilibrium stabilizers by CLFs (Artstein-Sontag theorem). Passive systems and passivity-based equilibrium stabilization. Passivity of mechanical control systems. Port-Hamiltonian systems.

The course explores design of control systems that achieve complex specifications. This is an emerging area in control theory that contrasts with traditional control design focused on stabilization and tracking. We introduce linear temporal logic (LTL) and show how LTL specifications capture a rich class of transient and steady-state behaviours of control systems. The LTL control problem is reduced to a hybrid control problem using ideas from computer science to obtain a design that includes high-level discrete algorithms and low-level continuous time controllers. The course covers the most important techniques and tools that come to play in this methodology, including triangulation, behaviour of affine systems on polytopes, the Reach Control Problem (RCP), and flow functions. We explore in depth control synthesis methods for the RCP based on affine, continuous state, and piecewise affine feedbacks. The techniques studied in the course come together to solve the problem of motion planning for a group of quadrocopters.

This course presents recent developments on control of underactuated mechanical systems, focusing on the notion of virtual constraint. Traditionally, motion control problems in robotics are partitioned in two parts: motion planning and trajectory tracking. The motion planning algorithm converts the motion specification into reference signals for the robot joints. The trajectory tracker uses feedback control to make the robot joints track the reference signals. There is an emerging consensus in the academic community that this approach is inadequate for sophisticated motion control problems, in that reference signals impose a timing on the control loop which is unnatural and inherently non robust.

The virtual constraint technique does not rely on any reference signal, and does not impose any timing in the feedback loop. Motions are characterized implicitly through constraints that are enforced via feedback. Through judicious choice of the constraints, one may induce motions that are surprisingly natural and biologically plausible. For this reason, the virtual constraints technique has become a dominant paradigm in bipedal robot locomotion, and has the potential of becoming even more widespread in other area of robot locomotion.

The virtual constraint approach is geometric in nature. This course presents the required mathematical tools from differential geometry and surveys the basic results in this emergent research area. Topics covered will include: Differentiable manifolds and basic operations. Controlled invariant manifolds and zero dynamics of nonlinear control systems. Euler-Lagrange robot models and models of impulsive impacts. Virtual holonomic constraints (VHCs). Constrained dynamics resulting from VHCs, and conditions for existence of a Lagrangian structure. Virtual constraint generators. Stabilization of periodic orbits on the constraint manifold. Virtual constraints for walking robots.

Convex optimization methods based on Linear Matrix Inequalities (LMIs) have dramatically expanded our ability to analyze and design complex multivariable control systems. This course explores material from the broad areas of robust and optimal control, with an emphasis on formulating systems analysis and controller design problems using LMIs. Topics covered will include: Historical context of robust and optimal control; Fundamentals of optimization, linear matrix inequalities and semidefinite programming; Lyapunov equations and inequalities; H-Infinity and H2 performance criteria for dynamic systems; Dissipative dynamical systems; The generalized plant framework for optimal control; LMI solutions of H2 and H-Infinity state and output feedback control problems; Uncertain systems: linear and nonlinear uncertainty modelling, linear fractional representations; Robust stability and performance analysis of uncertain systems; Introduction to integral quadratic constraints.

The related areas of risk-aware control theory, stochastic control theory, and reinforcement learning are becoming ever more important in the modern age of data. In lectures, we focus on studying mathematical foundations and the pros and cons of existing methods to highlight research gaps (e.g., quality-of-approximation guarantees vs. scalability to high-dimensional systems). Lecture topics include introductions to measure theory, Borel spaces, continuous-state Markov decision processes (MDPs), finite-horizon MDP problems, stochastic safety analysis, solution methods via value iteration, risk functionals, risk-aware control theory, and parametric approximation methods. The exploration of additional topics and applications related to stochastic control and reinforcement learning is encouraged through literature critiques and research projects. This course is designed to practice and enhance creative thinking skills and to launch and inspire your research.

Special topics offered by graduate units in area of Computer Hardware Design. Content of courses may change each time they are offered and are developed to cover emerging issues or specialized content.