Communications Course Catalogue

Updated for 2011-12

I. Fundamentals

ECE521H1  Inference Algorithms
Professor B. Frey
Squared error and the Gaussian probability distribution. Maximum likelihood estimation. Logistic regression, neural networks, radial basis function networks. Occam’s razor, validation, bagging, Bayesian techniques. Auto-encoders, principal components analysis, clustering. The EM algorithm. Matrix factorization. Markov models, hidden Markov models, the forward-backward algorithm, the Viterbi algorithm. Factor graphs, Bayesian networks, variable elimination, the sum-product algorithm, the max-product algorithm. Estimating graphical models. Applications to image classification, image processing, object tracking, speech recognition, telecommunications and genomics.

ECE1500H Stochastic Processes
Professor R.H. Kwong
This course provides an introduction to stochastic processes and probabilistic modelling, with emphasis on communication system applications. Topics include probability space, random variables, distributions, moments, sequences of random variables, stochastic convergence, concept of a stochastic process and examples, stationarity, ergodicity, power spectra, systems with stochastic inputs, Karhunen-Loeve expansion, mean-square estimation, filtering, Markov chains and processes, queueing.

ECE1501H Error Control Codes
Professor F.R. Kschischang
This course provides an introduction to error control techniques, with emphasis on decoding algorithms. Topics include algebraic coding theory: finite fields, linear codes, cyclic codes, BCH codes and decoding, Reed-Solomon codes; iterative decoding: codes defined on graphs, the sum-product algorithm, low-density parity-check codes, turbo codes.

ECE1502H Information Theory
Professor W. Yu
This course deals with fundamental limits on communication, including the following topics: entropy, relative entropy and mutual information: entropy rates for stochastic processes; differential entropy; data compression; the Kraft inequality; Shannon-Fano codes; Huffman codes; arithmetic coding; channel capacity; discrete channels; the random coding bound and its converse; the capacity of Gaussian channels; the sphere-packing bound; coloured Gaussian noise and water-filling; rate-distortion theory; the rate-distortion function; multiuser information theory.

ECE1505H Convex Optimization
Professor W. Yu
This course provides a comprehensive coverage of the theoretical foundation and numerical algorithms for convex optimization with engineering applications. Topics include: convex sets and convex functions; convex optimization problems; least-square problems; optimal control problems; Lagrangian duality theory. Karush-Kuhn-Tucker (KKT) theorem; Slater's condition; generalized inequalities; minimiax optimization and saddle point; introduction to linear programming, quadratic programming, semidefinite programming and geometric programming; numerical algorithms: descent methods, Newton's method, interior-point method; convex relaxation; applications to communications and signal processing.

ECE1508H Special Topics in Communications: 4G LTE for Mobile Broadband
Course Instructor: Professor I. Maljevic
Course Coordinator: Professor E.S. Sousa
This course covers Radio Access Network (RAN) aspects of the 4G Long Term Evolution (LTE) mobile broadband communication systems. In addition to learning the important aspects of modern mobile networks such as network design concepts, physical and L2/L3 layers, the students will get substantial exposure to the practical material 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. The RF concepts of channels, frequency band plans and the same basic network planning will be included as well. Moreover, we will go over the architectural solutions, remote radio heads (distributed radio solutions), and important hardware components in the network. Programming/numerical exercises are a part of the course, and a large selection of course projects is intended to complement the material covered in the lectures.
Please note that this course is open to M.Eng. students only

ECE1508H Special Topics in Communications: Multiuser Information Theory
Professor A. Khisti
This course is designed to give students an introduction to source and channel coding techniques in information theory. Coding theorems for point to point channels will be reviewed. Advanced topics such as multiuser and MIMO channels, information theoretic secrecy, multi-terminal source coding, delay sensitive communications, channel dispersion, and applications to wireless communication systems will be covered.

ECE 1508H Special Topics in Communications: Advanced Machine Learning
Professor B. Frey
Algorithms for automatically analyzing images, video, biological sequences, biological sensory data, audio, communication signals, text, and other types of data should take into account the uncertain relationships between inputs, intermediate representations, and outputs. Probability theory can account for these uncertainties, and provides a way to pose information processing problems as the computational task of learning an appropriate probability model and computing conditional probabilities using the model. Complex probability models for real-world applications often involve millions of random variables and intractable density functions, so probabilities cannot be computed using straightforward approaches. This course examines the fundamental concepts of graph-based formulations of complex probability models and introduces computationally efficient techniques for computing probabilities and estimating parameters in these models. The following topics will be covered: Linear regression and Bayesian linear regression; Decision making and estimation under uncertainty; Maximum likelihood learning; Neural networks and kernel methods; Bayesian networks; Markov random fields; Factor graphs; Probabilistic inference and why its "optimal"; Marginalization versus maximization; Bayesian learning; The elimination algorithm; Probability (belief) propagation and the sum-product algorithm; The EM algorithm for MAP estimation; Factor analysis (and principal components analysis); Mixtures of Gaussians and generalized mixture models; Hidden Markov models; The forward-backward algorithm and the Baum-Welch algorithm; Kalman filtering and adaptive Kalman filtering; The leaf-root-leaf algorithm in trees; Approximate inference techniques and the generalized EM algorithm; Loopy belief propagation; Iterated conditional modes; Mean field methods; Variational techniques; Bethe and Kikuchi free energy minimization; Convexified free energies; Inference using linear programming; Markov chain Monte Carlo techniques. In addition to introducing new concepts in conjunction with toy examples, we will survey applications in the following areas: Image and video analysis; Bioinformatics; Speech recognition and enhancement; Digital Communication. Prerequisites include introductory courses in probability, statistics, calculus and linear algebra. Some background in information theory and continuous optimization will be helpful, but not necessary. Grading: Assignment 1: 20%; Assignment 2: 20%; Midterm exam: 30%; Final exam: 30%.

ECE 1508H Special Topics in Communications: Seminar in Security Technology and Policy
Coordinators: Prof. D. Hatzinakos, Prof. K. Plataniotis, Prof. A. Clement (FIS), Prof. M. Kumar (UTM)
This is an interdisciplinary course based on a series of seminars addressing the problems of identity, privacy and information security. It consists of topics in three areas: A) Security Technologies, B) Security Policies, and C) Security Sciences.It is a required course for graduate students taking the M.Eng in Communications with concentration in Integrated Security Technologies and Policies, jointly offered by the Faculty of Applied Science and Engineering and the Faculty of Information Studies. Seminars open to general attendance will be scheduled regularly during the first part of each lecture. The second part of the lecture will be restricted only to students enrolled to the course. Enrolled students will be required to actively participate in each seminar, read and access assigned material and work on a term project assignment. Prerequisites: Course coordinator approval.

ECE1508H Special Topics in Communications: Biometric Systems
Professor D. Hatzinakos
Course description: This is an introductory level course for graduate students or practitioners to gain knowledge and hand-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. Prerequisites: Signals and Systems (ECE216), Probability and random variables (ECE302), Digital Signal Processing (ECE431), or equivalent. For non-ECE students course instructor approval.

ECE1510H  Advanced Inference Algorithms
Professor B. Frey
Advanced concepts in machine learning and probabilistic inference. An introductory course on inference algorithms or machine learning should be taken prior to this course. Topics covered: Probability models, neural networks, graphical models, Bayesian networks, factor graphs, Markov random fields (MRFs). Structured models, convolutional networks, transformations as hidden variables, bivariate and trivariate potentials, high-order potentials. Exact probabilistic inference, variable elimination, sum-product and max-product algorithms, factorizing high-order potentials. Approximate probabilistic inference, iterated conditional modes, gradient-based inference, loopy belief propagation, variational techniques, expectation propagation, sampling methods (MCMC). Learning in directed and undirected models, EM, sampling, contrastive divergence. Deep belief networks. Applications to image processing, scene analysis, pattern recognition, speech recognition, computational biology.
Prerequisite:  ECE521 or equivalent

II. Signal Processing

ECE 1511H Signal Processing
Professor D. Hatzinakos
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. Prerequisites: ECE 310F, ECE 431S, ECE 302S or equivalent.

ECE 1512H Digital Image Processing and Applications
Professor K.N. Plataniotis
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. Prerequisites: ECE 310F, ECE 311S or equivalent.

ECE 1514H Spectral Analysis and Array Processing
Professor D. Hatzinakos
Spectrum estimation is an important area of digital signal processing that finds applications in sonar and radar, geophysics and oil exploration, radioastronomy, biomedicine, speech and image processing. This course will cover the basic principles and wide variety of signal processing techniques developed for spectral analysis. Topics include: definitions of power spectrum; conventional spectrum estimation methods; maximum likelihood method of Capon; maximum entropy method; parametric modeling of time series; AR and ARMA spectrum estimation; harmonic decomposition techniques; duality between spectral analysis and array processing; signal and noise subspace methods in array processing. Higher-order-spectral analysis methods and applications. Prerequisites: ECE 310F, ECE 431S, ECE 302S or equivalent.

ECE 1515H Smart Antennas
Professor R.S. Adve
This course will cover the principles and applications of adaptive processing for smart antennas. Applications of interest will include wireless communications and airborne/spaceborne radar. Topics will include antenna array concepts, signal models (including modulation, multiple access schemes, fading), optimal v/s adaptive beamforming (including blind and semi-blind schemes), SMI, reduced rank techniques, non-statistical methods, direction finding. Another aspect of this course will be the impact of electromagnetic coupling on the performance of signal processing algorithms including mutual coupling and array errors.

ECE 1516H Visual Data Engineering
Professor K.N. Plataniotis
This course will introduce graduate students to the fundamentals of visual data representation, visual object determination, object-based coding, visual object classification and association. Particular emphasis will be placed on emerging application areas such as object based video streaming and transcoding, visual data authentication, and indexing and retrieval.

ECE 1517H Biometric Systems
Professor K.N. Plataniotis
This is an introductory level course for graduate students or practitioners to gain knowledge and hand-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.

ECE1518H Seminar in Identity, Privacy and Security
Prof. K. Plataniotis, Prof. D. Hatzinakos, Prof. A. Clement (FIS), invited speakers
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.

III. Communications

ECE 1520H Data Communications I
Professor R.S. Adve
This is an introductory course on digital transmission. Topics include: signal space concepts, signals with memory and Markovian models, power spectrum of digital signals, bandwidth, baseband transmission: intersymbol interference and Nyquist pulse shaping, optimum coherent symbol detection, binary and M-ary modulation, differential and noncoherent demodulation, error rate - bandwidth efficiency comparisons, sequence detection and the Viterbi algorithm. Equalization. Multi-carrier modulation. Prerequisites: ECE 417S or equivalent.

ECE 1521H Statistical Communication Theory
Staff
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. Prerequisites: ECE 302S or equivalent.

ECE 1522H Data Communications II
Staff
This is a continuation of ECE 1520F. Topics include – Spectrum control: line codes, partial response signaling, continuous phase modulation, scrambling. Trellis coded modulation. Synchronization: carrier, bit and frame, fading channels: optimal receivers and diversity techniques.

ECE 1523H Coded Modulation
Professor F.R. Kschischang
This course is intended as an introduction to coded modulation techniques. Coded modulation schemes combine powerful channel coding techniques with appropriate modulation schemes to achieve power- and/or bandwidth-efficient transmission of information over noisy channels. Topics covered will include: signal theory, modulation, Gaussian channel capacity, block and convolutional codes, hard- and soft-decision decoding techniques, block- and trellis-coded modulation, multidimensional lattices, shaping, coded modulation for fading channels.

ECE1524H  Service Provider Networks
Course Instructor: A. Tizghadam
Course Coordinator:  Professor A. Leon-Garcia
This course covers the concepts of networking from a service provider’s standpoint. The proper design, operation, and security of a network are key elements to the success of a service provider, and are equally important for the safety and health of Internet as a whole. This course provides an overview of these key elements. The course consists of two major blocks: 1) introduction to fundamental concepts in data networks (with emphasis on important topics from service provider’s point of view), and 2) networking in the service provider domain. The three steps of design, operation, and security will be addressed in both blocks. Block 1 provides a theoretical background, and in block 2 the students learn how to design and operate a healthy network and offer services as a service provider. Topics in the first block include: fundamental concepts of networks, resource management in networks, switching & routing, network services, and security issues in data networks. In second block the major topics are: service provider’s network design, service life-cycle, network engineering versus traffic engineering in the service provider domain, and competitiveness of service provider.
Please note that this course is open to M.Eng. students only

ECE1528H Special Topics in Data Communications: Stochastic Network Calculus
Professor J. Liebeherr
The network calculus has evolved into an elegant framework for analytical performance evaluation of data networks. It provides methods to determine resource requirements of traffic flows in a network using an envelope description for arrivals and service. Particular strengths of the network calculus are that it can elegantly characterize delays and backlog in a network of multiple nodes, and that it can accurately describe the behavior of a wide variety of link scheduling algorithms. More recently, stochastic extensions of the network calculus have resulted in the discovery of new scaling laws of network delays, and an end-to-end analysis of a network with heavy-tailed traffic. Different from queueing theoretic approaches to network analysis, which often seek an exact characterization of traffic and service, a network calculus analysis seeks to obtain bounds for performance metrics, thus reducing the complexity of the analysis. The course will cover the deterministic and stochastic network calculus, as well as elements of min-plus linear system theory. Applications of network calculus include the design of networks with service differentiation, real-time systems, and wireless networks. The course also covers applications of min-plus system theory for developing methods for bandwidth estimation in wired and wireless networks.

ECE 1528H Special Topics in Data Communications: Stochastic Networks
Professor B. Liang
An introduction to analysis, control, and optimization in communication networks using stochastic models. We cover both classical queueing networks and recent advances in stochastic network modeling and optimization. Topics include Jackson and Whittle networks, reversible network processes, Palm probabilities, space-time Poisson models, stochastic geometry, network utility maximization, Lyapunov stability, and stochastic network optimization. Pre-requisite: ECE1500/ECE537 or equivalent with continuous-time Markov chains.

ECE 1529H Adaptive Systems for Signal Processing and Communications
Professor K.N. Plataniotis
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.

ECE 1530H Multi-User Detection
Staff
This is a course introducing fundamental concepts and state-of-the-art research in the detection of non-orthogonal signals originating from a number of independent sources. The problem exists primarily in code-division multiple-access (CDMA) wireless communications systems. Topics covered include spread spectrum communications, Rake receiver, maximum-likelihood (ML) receiver, decorrelating detection, minimum mean squared error (MMSE) detection, adaptive detection, multi-stage interference cancellation, iterative detection of coded CDMA, space-time multiple access and multi-user precoding techniques.

ECE 1531H Quantum Information Theory
Professor H-K. Lo
This is a first course on quantum information and communications theory. Topics covered include: (1) basics of quantum mechanics and quantum information, (2) resource model of quantum information processing, (3) entanglement and entanglement distillation protocols, (4) quantum cryptography and security proofs.

IV. Networks

ECE 1540H Digital Telephony
Professor A. Leon-Garcia
This course gives an introduction to all aspects of digital telephone networks. Topics include: Telecommunications services and network structure; voice digitization and digital transmission; signaling; space and time switching; network synchronization; network control and management; Common Channel Signaling System #7, ISDN and BISDN; traffic engineering. Textbook: Digital Telephony, by J.C. Bellamy, Wiley, 1982.

ECE 1541H Communication Networks I
Professor S. Valaee
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.

ECE 1542H Communication Networks II
Professor S. Valaee
Topics include: layering, distributed algorithms, network algorithms, shortest path routing, coping with link failures, optimal routing and topology design, fairness, flow control.

ECE1543H Mobile Communications Systems
Professor E.S. Sousa
This course will cover basic principles in the design of mobile communication systems, such as cellular radio, packet radio networks, and indoor wireless networks. The radio environment: basic radio propagation considerations, Rayleigh and Rician statistics, power spectral density, large scale signal variation, the Doppler effect, coherence bandwidth, coherence time, and coherence space; link issues: modulation, diversity, interleaving, forward error correction; spread spectrum systems, CDMA; system issues: spectral sharing schemes, cellular schemes, noise and interference, call set up and handoff in current cellular systems, digital cellular system standards, fixed wireless networks, wireless LANs.

ECE 1544H Optical Communication Networks
Staff
Building Blocks: Propagation, couplers, taps, filters, lasers, amplifiers. Transmission: optical modulation, detection, coherent and direct detection, shot noise. Broadcast networks, star and bus topologies. Layered architectures and network control, light weight protocols. Multiaccess on fiber, WDMA, TDMA and CDMA. Packet switching on broadcast networks, multihop networks, other multiaccess protocols. Communication over light paths and light trees, wavelength routers, optical switches. Graph algorithms and their use in establishing light paths and light trees.

ECE 1545H Bridges and Routers
Professor J. Liebeherr
Basic concepts. Data link layer issues: Transparent bridges, learning bridges, spanning tree algorithm. Source routing bridges, interworking with . Networtransparent bridgesk layer issues: Service interface, addressing, address resolution protocol, routing algorithms, routing protocols, QOS issues: Integrated services, RSVP; Differentiated Services, Tag Switching and MPLS. Prerequisites: ECE 460 or equivalent.

ECE 1546H Broadband Integrated Networks
Staff
This course in an in-depth examination of the fundamental concepts and implementation issues for development of high speed integrated networks of the future. Topics covered include: Broadband Services and ATM, Traffic Characterization for Broadband Integrated Networks, Broadband ISDN and SONET, Admission and Access Control, ATM Switching Architectures, Routing: Point-to-Point and Multicasting, Congestion and Flow Control, Wireless ATM, Fundamental Concepts of Management and Control for Broadband Communications, ATM network Management Paradigm, Internetworking and Inter-operability Issues.

ECE 1547H Content-Based and Network Security
Staff
Content-based security involves the process of covertly and robustly communicating discreet information through a variety of media such as standard communication channels as well as digital audio, video and images. Network security addresses strategies to secure information during transmission using cryptographic tool-sets and communication protocols. This course introduces both concepts and focuses on a few particular topics relevant to the emerging area of multimedia security. Subject matter includes steganography, covert communications, digital watermarking, authentication applications, electronic mail security, IP security and web security.

ECE1548H Advanced Network Architectures
Professor A. Leon-Garcia
The objective of this course is to present the key trends in the evolution of network architectures and the services and applications they support. Part I. Existing Architectures: Internet; LTE 4G architecture; Cloud computing; Apple and Google application platforms. Future Architectures: The Internet is broken; Future Internet proposals; Experimental networks and testbeds.  Part II. Network Science Design Principles: Introduction to graph theory and optimization; Flow and capacity assignment; Topology design; Robustness and adaptation; Networked markets; Pricing strategies and market power. Part III. Designing Future Networks: Virtual networks; Green networks; Datacenters and computing clouds; Smart grids.

Return to Top