MPhil-PhD

Basic telephony; concepts of switching, transmission, multiplexing and concentration; circuit switching, time-space-time switching; virtual-circuit/label switching; crossbar/bus/shared-memory switches; Ethernet switches at edge and metro; switching characteristics of interconnection networks; parallel switching control in sorting, concentration, multicasting and distribution.

Advisory note: Students are expected to have background in Signals and Systems. Course Exclusion: IERG4020 or ESTR4318.

This is a graduate level course in computer networks. It covers advanced technologies, theory and applications of networking protocols and systems. It studies recent developments and emerging trend in various networking research areas such as:

Architecture and Economic aspects of the Internet; Network management, Operation and Traffic Engineering ; Network Measurement and Monitoring ; Domain-specific networking architectures and technologies, e.g., Data-Center, Content Distribution Networks and their corresponding challenges and solutions ; Network Algorithmics and Protocol Design ; Operating Systems and Hardware Support for Networking ; Modeling and performance analysis of protocols, systems and services. Each student is expected to complete a Term-Project as part of the course assessment.

Advisory: Students are expected to have basic background in computer networks. Exclusion: IEMS5715.

This course is a graduate level introduction to probabilistic models and inference algorithms, which constitute a common foundation for many methodologies in machine learning and related fields (e.g. computer vision, natural language processing, and data mining).

The course begins with a detailed exposition of probabilistic graphical models, then proceeds with various inference methods, including variational inference, belief propagation, and Markov Chain Monte Carlo (MCMC). In the second part of the course, we then discuss the connections between probabilistic models and risk minimization, as well as how optimization-based methods can be used in large-scale model estimation. Finally, the course will briefly discuss nonparametric models, e.g. Gaussian processes, and their use in practical applications.

Advisory: Basic knowledge on linear algebra, probability theory, optimization are required.

Optical networking principles and network elements: add-drop multiplexers, cross-connects. Optical signal processing techniques and enabling technologies for transport and network functions. Optical access networks: passive optical networks, radio-over-fiber networks, fiber-to-the-home/premise networks. Optical metro and core networks: SDH/SONET, GMPLS, gigabit Ethernet, optical flow/burst/label/packet switching. Optical networking operation, administration, management and planning techniques (OAM&P) including: wavelength assignment & routing, multicast, grooming, fault management, connection management, performance management and network resource optimization. All-optical networks.

Advisory: Students are expected to have background in Optical Communications.

This course covers classic and new channel coding, and related modulation schemes. Topics include Reed-Solomon codes, convolutional codes, concatenated codes, low-density parity-check (LDPC) codes, and optionally, OFDM, MIMO, and network coding.

Exclusion: MATH4260 and IERG4200.

This is a systems course that will enable students to have in-depth understanding of key information processing algorithms and their implementation for Internet of Things (IoT) systems. The topics cover 1) overview of basic signal processing algorithms such as FFT and digital filters; 2) advanced information processing algorithms such as acoustic and visual signal processing, spatial sensing, machine learning etc.; 3) their implementation on cutting-edge IoT platforms and key system issues of such as energy efficiency and real-time in the contexts of a set of key IoT applications such as smart health, environmental monitoring, smart homes/buildings, smart cities etc.

Students will work on an individual or team project to build an end-to-end IoT system. The project should have a significant information processing component and include implementation on real IoT platforms such as Raspberry Pi, Ardunio, wearable devices, smartphones etc, and. In addition to homework and lab assignments, students will also read and discuss latest publications in the areas of Internet of Things, Cyber-Physical Systems, mobile systems, and ubiquitous computing.

Advisory: Students are expected to have basic understanding on signals and systems, signal processing or imaging processing and Internet of Things System.

This module introduces the principles of networking protocol design under the mobile/ wireless environment. Mobile networking protocol design for the MAC, Network, Transport, Session and Application layers will be covered. The course will follow a "problem-and-solution" approach in which key generic problems created by the mobile/ wireless environment on each protocol layer are first introduced. Alternative solutions as well as their associated trade-offs, are then illustrated via real-world examples. Specific problems and their practical solutions to be studied include: Multi Access Control in a wireless environment, Mobility Management for infrastructure-oriented wireless networks (e.g. location tracking and handover), Routing in Mobile Ad Hoc networks, Wireless Transport protocol design, Session and Service control for Multimedia Wireless networks. Emerging Mobile networking Technologies and Future Directions will also be discussed.

Advisory: Pre-requisite includes Computer Networks (IERG3310) and Digital Communications (IERG3010) OR their equivalents OR the consent of the instructor.

Examples of network coding. Acyclic networks: linear network codes and desirable properties, existence and construction, static network codes. Cyclic networks: convolutional network codes. Relations between network coding and classical algebraic coding theory.

This course discusses the design and realization of security and privacy services in practical large-scale systems. Topics include: Online Identity and Authentication Management ; Safe Browsing ; Geolocation privacy ; Mobile payment systems with Smartcard/ Near Field Communications (NFC) ; e-cash ; Best privacy practices for Online Social Networks and Mobile applications ; Cloud Computing security and privacy: Trustworthy Cloud Infrastructure; Secure Outsourcing of Data and Computation ; Data Provenance; Virtual Machine security. Additional cyber security services/applications such as e-voting systems, secure and anonymous routing systems, digital rights management will also be covered.

Advisory: Students are expected to have basic background in Cyber Security.

This course is an introduction to digital forensics and cyber crime investigation. It will discuss techniques, methods, procedures and tools for applying forensic science and practice to the acquisition and analysis of evidence existing in digital form for the purposes of cyber crime investigation. Specific topics include computer (hard disk, file-systems) forensics, network/intrusion forensics, mobile device forensics, and a brief introduction to multimedia forensics. Techniques for detecting, tracking, dissecting and analyzing malware and other malicious cyberspace activities will also be covered.

Advisory: Students are expected to have basic background in C/C++ programming skills.

This course introduces the basics of microeconomics, game theory, and mechanism design, with applications in wireless communication networks and Internet. The detailed topics include market mechanisms, consumer surpluses, profit maximization, welfare maximization, pricing, strategic form games, dominator strategy equilibria, Nash equilibrium, Bayesian games, repeated game, social choice functions, incentive compatibility, the revelation theorem, auction design, and network externality.

Advisory: Students are expected to have basic background in optimization. Exclusion: IERG6280.

Entrepreneur characteristics; product innovation: factors driving innovation, creation and evaluation of new product ideas, risk assessment of commercialization, critical factors for success; business planning: market assessment and strategy, business model, product planning, financial planning, cash flow; financing options, negotiation and deals; formation of a new venture: team, company and product building; execution and dealing with reality; exit strategies; case studies related to innovation and entrepreneurship in information engineering.

Exclusion: SEEM3450 or IEMS5712.

This course aims to cover the fundamental topics relevant to Reinforcement Learning (RL), a computational learning approach where an agent tries to maximize the total amount of reward it receives while interacting with the complex and uncertain environments. The course content includes the basics of Markov Decision Processes, model-based and model-free RL techniques, policy optimization, RL distributed system design, as well as the case studies of RL for game playing such as AlphaGo, traffic simulation, and other robotics applications.

Advisory: Students are expected to have solid foundation on signal processing.

This course aims to cover advanced topics on blockchain. The focus will be on advanced topics like permissionless blockchain, Ethereum, smart contract, mining pool, permissioned blockchain, anonymity, new consensus, sidechain, ripple, offchain and lightning network.

Advisory: Students are expected to have solid foundations on operating systems and database systems. Exclusion: CSCI5590 or FTEC5520.

The course will introduce to the students advanced topics in Information Engineering. The detailed course contents may be changed from year to year depending on the current development and the teacher specialty. Offerings with different subtitles can be taken for credit, up to a maximum of two times. This course is independent of IERG6130. Students can take either course or both.

Advisory: Retaking the course for the 2nd time requires obtaining special approval from the course instructor before enrollment. The credit units completed will be counted toward the calculation of Term GPA as well as cumulative GPA no matter students pass or fail the course.

The course will introduce to the students advanced topics in Information Engineering. The detailed course contents may be changed from year to year depending on the current development and the teacher specialty. Offerings with different subtitles can be taken for credit, up to a maximum of two times. This course is independent of IERG6120. Students can take either course or both.

Advisory: Retaking the course for the 2nd time requires obtaining special approval from the course instructor before enrollment. The credit units completed will be counted toward the calculation of Term GPA as well as cumulative GPA no matter students pass or fail the course.

The course introduces the basic tools and techniques used in Network Information Theory. In particular the following topics are addressed: Multiple Access Channels, Broadcast Channels, Interference Channels, Channels with State, Relay Channels, and other topics. The course will be mathematical with an emphasis on proving theorems.

Advisory: A prior undergradaute level course in probability and/or information theory is highly recommended.

This course will introduce to the students advanced topics in Computer Networks. The detailed course contents may be changed from year to year depending on the current development.

This course will introduce to the students advanced topics in Information Processing. The detailed course contents may be changed from year to year depending on the current development.

The course covers the following topics: Construction of measures, integration, transformation, product spaces, distributions, expectation, Borel-Cantelli lemmas, characteristic functions, weak convergence, independence, weak law of large numbers, strong law of large numbers, central limit theorem, conditional expectation, Markov chains, stopping times and renewal times, martingales, martingale convergence Theorems, Doob's decomposition theorem, up-crossing inequality, and Birkhoff's ergodic theorem. The focus will be on mathematical rigor and development of all the tools to prove the results formally.

Advisory: Students are expected to have basic background in probability and real analysis at undergraduate level.