This course is designed to present an overview of some advanced computer architectures and their underlying design principles. Issues discussed will include scalability and performance evaluation. The underlying technologies such as processor and memory hierarchy, cache and shared memory, and advanced pipelining techniques will be presented. Examples of high performance vector processors, multicomputers and massive parallel processors will be compared. Some novel architectures such as VLIW, fault tolerant systems and data flow machines will also be elaborated.

Data mining provides useful tools for the analysis, understanding and extraction of useful information from huge databases. These techniques are used in business, finance, medicine and engineering. This course will introduce the techniques used in data mining. Topics will include clustering, classifi cation, estimation, forecasting, statistical analysis and visualization tools.

This course will cover fundamental knowledge and advanced topics in image processing and computer vision, including feature detection, segmentation, motion estimation, panorama construction, 3D reconstruction, scene detection and classification, color image processing and restoration. Applications in computer graphics will also be introduced, including image transformation, and camera calibration. Basic concepts of related algorithms and mathematic background will be discussed.

This course aims to introduce important topics in computer and network security from an applied perspective. Topics include: (i) applied cryptography (e.g., cryptographic primitives, programming with OpenSSL), (ii) network security (e.g., unauthorized accesses, large-scale network attacks, firewall & intrusion detection systems), (iii) web security (e.g., HTTP session management and web attacks), and (iv) system security (e.g., buffer overflow, passwords, file system security), (v) wireless security (e.g., WiFi security, wireless broadband network security). The course also discusses latest applied security topics depending on the current research trends.

This course surveys the current research in information retrieval for the Internet and related topics. This course will focus on the theoretical development of information retrieval systems for multimedia contents as well as practical design and implementation issues associated with Internet search engines. Topics include probabilistic retrieval, relevance feedback, indexing of multimedia data, and applications in e-commerce.

This course aims at teaching students the state-of-the-art big data analytics, including techniques, software, applications, and perspectives with massive data. The class will cover, but not be limited to, the following topics: advanced techniques in distributed file systems such as Google File System, Hadoop Distributed File System, CloudStore, and map-reduce technology; similarity search techniques for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technology; managing advertising and recommendation systems for Web applications. The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services, or other scientific applications.

The course introduces fuzzy logic and applications. Fuzzy expert systems. Fuzzy query. Fuzzy data and knowledge engineering. Fuzzy control. Genetic algorithms and programming and their applications. Parallel genetic algorithms. Island model and coevolution. Genetic programming. Introduction to emergent computing.

This course provides an introduction to the important concepts, theories and algorithms of pattern recognition. The topics cover Bayesian decision theory, maximum likelihood and Bayesian parameter estimation, support vector machine, boosting, nonparametric pattern recognition methods, and clustering. It also includes applications of pattern recognition in different fields. Students taking this course are expected to have the background knowledge of calculus, linear algebra, probability and random process as a prerequisite.

This course is designed to introduce the Advanced Microwave Engineering. Topics will be selected from the following: Linearization techniques for RF power transmitters, high frequency circuit packaging, microwave filter design, LTCC/MCM technology, computer-aided design of microwave circuits, electromagnetic simulation.

This course is a review of semiconductor physics. The course content covers the following topics. Electrons in nanostructures: density of states, quantum confinement, transport properties, nanocontacts, Coulomb blockade. Nanoscale fabrication and synthesis: lithography, nanopatternning, epitaxy and heterostructure, self-assembly, other techniques. Nanoscale characterization: scanning probe microscopy and other microscopic techniques, nanoscale electrical measurements. It also introduces nanoscale devices such as nano-MOSFETs; carbon nanotube devices, nanowire- and nanoparticle-based devices, organic thin film devices, molecular electronic devices, their applications, and commercialization.

This course is an overview of fiber communication technology. This course content covers fiber transmission impairments, introduction to nonlinear optics, second order and third order nonlinear phenomena, lightwave propagation in nonlinear media, optical signal processing in communications and specialty fibers.

Introduction. Shannon’s information measures. Entropy rate of a stationary process. The source coding theorem. Kraft inequality. Huffman code. Redundancy of a prefix code. The channel coding theorem. Rate-distortion theory. Universal data compression.

This course starts with a review of Markov chain, random walk, Poisson process, martingale, and limit theorems. The main content includes a few major topics: Markov process (also called continuoustime Markov chain), renewal theory; queueing theory, and Brownian motion.

This course provides an extensive introduction to basic principles and advanced techniques in the physical layer of wireless communications. Topics to be covered include channel coding, MIMO and space-time processing, OFDM and multicarrier systems, spread spectrum and CDMA, channel capacity, opportunistic scheduling and diversity schemes.

This is a graduate-level course on cryptography. It focuses on the definitions and constructions of various cryptographic schemes and protocols, as well as their applications. Useful tools for securing practical systems and emerging techniques in the applied research community will be introduced. No prior knowledge of security, cryptography, or number theory is required.
– Introduction: a brief history, applications in distributed systems; basic number theory
– Symmetric-key encryption: definition, information-theoretic security, Entropy, PRNG
– Provable security: bounded adversary, random oracle model, basic primitives, reduction
– Public-key encryption: modelling security, Diffie-Hellman protocol, hybrid encryption
– Authentication: Hash function, collision-resistance, MAC, unforgeability
– Public-key infrastructure: certificate management, deployment, and revocation issues
– More schemes: Fiat-Shamir transformation, Cramer-Shoup encryption, identity-/attribute-based encryption, certificateless encryption, proxy re-encryption , broadcast
– Privacy-enhancing cryptography: zero-knowledge proof, anonymous credentials
– Pairing-based cryptography: elliptic curve basic, short signature, searchable encryption

This course covers the design of advanced optical fiber communication systems. Topics include: optical signal characterization and spectral efficient optical modulation formats, high-speed signal transmission & multiplexing techniques, linear & nonlinear fiber effects and fiber transmission impairments, basic guided-wave optoelectronics and novel integrated optical devices (tunable lasers, planar lightwave circuits, silicon photonics), optical signal amplification, regeneration and performance monitoring techniques, coherent optical communications and enabling digital signal processing techniques, and examples of optical subsystems for optical networks.

This course provides a comprehensive overview of robotics for postgraduate level study. The course covers the fundamental concepts and methods to analyze, model, and control of robotic mechanisms. Specific topics include kinematics, inverse kinematics, dynamics, trajectory generation, individual and multivariable control, interaction force control, and sensors. Students will also involve in hands-on programming project to reinforce the basic principles developed in lectures as well as develop robot algorithm implementation skillsets. The course will also expose students to the latest and advanced developments in robotics such as medical robotics, dynamic parameter identification.

Linear system theory and design is the core of modern control approaches, such as optimal, robust, adaptive and multivariable control. This course aims to develop a solid understanding of the fundamentals of linear systems analysis and design using the state space approach. Topics covered include state space representation of systems; solution of state equations; stability analysis; controllability and observability; linear state feedback design; observer and compensator design, advanced multivariable control systems design, decoupling and servo control. This course is a must for higher degree students in control engineering, robotics or servo engineering. It is also very useful for those who are interested in signal processing and computer engineering.

This course provides a broad overview of microfabrication and microelectromechanical systems. Topics include introduction to basic micromaching techniques such as photolithography; isotropic and anisotropic wet etching; dry etching; physical and chemical vapor deposition; electroplating; metrology; statistical design of experiments; MEMS release etching; stiction; and MEMS device testing. The course also reviews important microsensors, microactuators and microstructures. Topics include accelerometers; pressure sensor; optical switches; cantilever beams; thin-film stress test structures and bulk micromaching test structures. Lastly, the course introduces the fundamentals of central dogma of molecular biology; cell and tissue biology; and principles of transduction and measurements of molecules, cells and tissues.

This course introduces engineering design and design procedure, design innovation and TRIZ, axiomatic design, nature’s design and complex systems, design analysis (modeling and simulation), statistical analysis, design optimization, statistical design optimization, and Design for Six Sigma (DFSS). Practical examples of design and applications are provided in the course such as pendulum, bicycle, windmill and propulsion.

In this course we will develop the basic machineries needed for formulating and analyzing various optimization problems. Topics include convex analysis, linear and conic linear programming, nonlinear programming, optimality conditions, Lagrangian duality theory, and basics of optimization algorithms. Applications from different fields, such as computational economics and finance, combinatorial optimization, and signal and image processing, will be used to complement the theoretical developments. No prior optimization background is required for this class. However, students should have a workable knowledge in multivariable calculus, basic concepts of analysis, linear algebra and matrix theory.

This course focuses on biomechanics (biostatics, biodynamics, mechanics of biological solids), biomaterials (metals, ceramics, synthetic polymers, natural polymers, composites; characterization of biomaterials; biomaterial scaffolds for regenerative medicine) & clinical applications in the musculoskeletal system (including, sports, traumatology, and rehabilitation), cardiovascular system, and dentistry.

Matrix analysis and computations are widely used in engineering fields—such as machine learning, computer vision, systems and control, signal and image processing, optimization, communications and networks, and many more—and are considered key fundamental tools. This course covers matrix analysis and computations at an advanced or research level. It consists of several parts. The first part focuses on various matrix factorizations, such as eigendecomposition, singular value decomposition, Schur decomposition, QZ decomposition and nonnegative factorization. The second part considers important matrix operations and solutions such as matrix inversion lemmas, linear system of equations, least squares, subspace projections, Kronecker product, Hadamard product and the vectorization operator. Sensitivity and computational aspects are also studied. The third part explores presently frontier or further advanced topics, such as matrix calculus and its various applications, tensor decomposition, and compressive sensing (or managing undetermined systems of equations via sparsity). In every part, relevance to engineering is emphasized and applications are showcased.

Geometric computing tools have been widely used in modern product design and realization, such as all kinds of Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM) and Computer-Aided Engineering (CAE) software systems. However, the capability of simply using these CAD, CAM and CAE software systems is not sufficient for future products design and manufacturing. This course aims to help students in understanding the principles of geometric computing behind CAD, CAM and CAE systems, and provides students with deep understanding of computational techniques and practical experience in developing novel computational design and manufacturing applications.

This course teaches concepts, models, methods, and applications of computational intelligence. Topics include neural networks, support vector machines, fuzzy systems, simulated annealing, genetic algorithms, and their applications to control, robotics, automation, manufacturing, and transportation.

This course consists of two parts. The first part is analysis of nonlinear systems, which includes state space description of nonlinear control systems, phase plane analysis of second order dynamic systems, Lyapunov ‘s stability theory such as Lyapunov’s first method, second method, Barbalat’s lemma, and total stability. The second part is design of nonlinear control systems, which includes Jacobian linearization, feedback linearization, sliding mode control, and backstepping method.

The contents of this course include overview of smart materials technology, characteristics of smart materials such as piezoelectric materials, magnetorheological fluids, and shape memory alloys. It covers smart actuators and sensors; structural modelling and design; dynamics and control for smart structures; integrated system analysis; and applications in biomedical devices, precision machinery, transportation, and buildings.

This course aims to teach students a range of classical and state-of-the-art topics through a series of examples. The focus will be on how different fundamental topics, such as linear and non-linear control, optimization, path planning, visual servo control, robot kinematics and dynamics, and machine learning, are applied through practical applications within robotics. Different application scenarios that may be used to show different fundamental topics include: mobile manipulation, bio-inspired and humanoid robots, robotic walking, rehabilitation robotics, medical and surgical robots, cable-driven robots, and autonomous ground, water and aerial vehicles.

The field of quantum information science includes quantum control and quantum information. It is a new area of inter-disciplinary research involving physicists, computer scientists, mathematicians and engineers. The course is an introduction to this rapidly expanding field. It covers basic quantum mechanics including quantum entanglement and quantum measurement; the modeling and control of quantum mechanical systems; quantum error correction; quantum communication and quantum information theory.

This course provides both fundamental knowledge of nanomaterials and nanotechnology and advanced topics related to applications. These topics cover basic principles, which include the scaling law, the surface science for nanomaterials, observation and characterization tools for nanomaterials, the nanofabrication techniques, building blocks for nanodevices and systems, etc. In the second half of this course, advanced topics on applying nanomaterials and nanotechnology for applications in mechanical engineering, energy engineering and biomedical engineering will be covered.

Mechanics is the foundation of many emerging research and engineering topics. With the rapid advancement in computing power, numerical methods are preferred to solve differential equations governing the physical process. It opens a whole new domain in industrial design, manufacturing process analysis, material behaviour prediction, etc. This course covers theoretical fundamentals in computational mechanics, including continuum mechanics, finite element methods, and computational plasticity. In addition, the course will also introduce practical skills to applying computational mechanics in research, including multi-physics simulation and advanced finite element simulation techniques.

This course focuses on a suite of materials characterization techniques that are useful in energy and environmental sciences. The main targets of these techniques include functional materials that are used in energy and environmental applications as well as solid, liquid, and gas samples that are involved in energy production and conversion, and pollution monitoring and control. The techniques include mass spectrometry (MS), gas chromatography (GC), high performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR), infrared (IR) spectroscopy, Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron microscopy, and X-ray absorption fine structure (XAFS) spectroscopy. Students will receive lectures on the theory and operation principle of each technique as well as its limitations, and obtain hands-on experience with some of the techniques in supplemental lab sessions.

This course will cover advanced topics in heat transfer and fluid mechanics including overview of macroscopic theory of heat transfer, microscopic picture of heat carriers and their transport, micro- and nanoscale energy transport in solids, chemical thermodynamics, chemical kinetics, multicomponent and multiphase mixtures, basic principles of computational fluid dynamics, turbulence modeling, and airflow simulation in enclosed environments.

In this course, a student is required to meet with his/her supervisor regularly who provides necessary guidance and supervision to write up a thesis and monitors the students’ academic progress.