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ELEC

Electronic and Computer Engineering

ELEC 5010
Introduction to the Design & Implementation of Micro-Systems
[3-0-1:3]
Exclusion(s)
MECH 5950
Description
Introduction to the concept of micro-systems. Dimensional scaling and its implications. Multi-physics modeling. Micro-fabrication techniques. Introduction to Coventor, a numerical simulation package for micro-systems. The design, implementation and testing of a micro-device.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Analyze and design MEMS systems.
ELEC 5040
Advanced Analog IC Analysis and Design
[3-0-0:3]
Exclusion(s)
EESM 5120
Background
ELEC 4420 and ELEC 4510
Description
Noise analysis; Advanced op-amp design techniques; Analog VLSI building blocks: multipliers, oscillators, mixers, phase-locked loops, A/D and D/A converters; Passive filter design; Frequency scaling; Active filter design.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Apply large-signal analysis and small-signal analysis in analyzing key analog integrated circuits and VLSI building blocks, including amplifiers, filters, ADCs.
- 2.
  Master advanced design techniques for analog key analog integrated circuits and VLSI building blocks.
- 3.
  Design, analyze, lay out, and debug analog key analog integrated circuits and VLSI building blocks with state-of-the-art performance.
- 4.
  Understand design comparison and trade-off for key analog integrated circuits and VLSI building blocks.
- 5.
  Use software tools, including HSPICE, Cadence, Matlab, to design, simulate, and lay out integrated circuits and VLSI building blocks.
ELEC 5050
Advanced CMOS Devices
[3-0-0:3]
Prerequisite(s)
ELEC 3500
Description
Principles and characteristics of semiconductor devices found in State-of-the-Art ICs. Emphasis is on deep-submicron MOS device design, characterization and modeling. Important issues such as short channel effects, high-field behavior, hot carrier effects, reliability and device scaling for present and future technology will be covered.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Make projects on the future CMOS technology.
- 2.
  Perform 2-D device simulation to generate the characteristics of semiconductor devices.
- 3.
  Describe the theory governing device reliability and aging process.
- 4.
  Describe the role of device models in the process of circuit simulation.
- 5.
  Explain the process of device parameter extraction to match the device model output to physical data.
ELEC 5070
Microelectronics Fabrication Technology
[3-0-0:3]
Description
Process technologies in IC fabrication: epitaxial growth; chemical-vapor and physical-vapor deposition of films; thermal oxidation; diffusion; ion implantation; microlithography; wet/dry etching processes; process integration of MOS and bipolar technologies.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the science and engineering of micro-fabrication technology.
ELEC 5080
Integrated-Circuit Fabrication Laboratory
[2-0-6:4]
Prerequisite(s)
ELEC 5070
Description
Laboratory course requiring hands-on work in fabricating MOS transistors. Process modules including photolithography, dry etching, wet etching, metal sputtering, oxidation, diffusion and low-pressure chemical-vapor deposition will be covered. Student will also learn to characterize the fabricated devices.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Gaining practical experience of micro-fabrication technology.
ELEC 5090
Advanced Photonics Technologies
[3-0-0:3]
Description
A brief review of modern optics theories, Fourier optics based devices and systems, fundamentals of laser physics, optoelectronics, nonlinear optics and laser spectroscopy.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate capability to apply the fundamental principles of optics and photonics on technology applications.
- 2.
  Demonstrate capability to apply the fundamental principles of quantum electronics on technology applications.
- 3.
  Demonstrate capability to apply the fundamental principles of laser physics on technology applications.
- 4.
  Demonstrate capability to apply the fundamental principles of nonlinear optics on technology applications.
ELEC 5110
Nanoelectronic Materials for Energy Technologies
[3-0-0:3]
Co-list with
ENEG 5200
Exclusion(s)
ENEG 5200
Background
ELEC 3500
Description
Conventional and unconventional fabrication of nanostructures including electron beam lithography, nanoimprint, chemical synthesis, self-assembly, etc.; size dependent electronic and optoelectronic properties of nanomaterials; large-scale assembly and integration of nanomaterials for electronics; energy harvesting and storage devices using nanoelectronic materials.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the difference between nanomaterials and micro/bulk materials.
- 2.
  Learn the fabrication methods of various kinds of nanomaterials.
- 3.
  Understand the origin of size dependent physical properties of nanomaterials.
- 4.
  Understand working principle of solar cells and the role nanostructures can play in improving device performance so acquire capability to design nanostructured solar cells.
- 5.
  Understand the working principle of other energy harvesting technologies include thermoelectrics and triboelectrics and their applications.
- 6.
  Understand the working principle of energy storage devices including batteries and supercapacitors.
ELEC 5120
Semiconductor Power and Energy Conversion Technologies
[3-0-0:3]
Co-list with
ENEG 5250
Exclusion(s)
ENEG 5250
Description
Analysis of power semiconductor device technologies in the context of electric power conversion and transmission; emphasis on the understanding of the critical roles of semiconductor device technologies in power and energy conversion. The mainstream silicon and emerging semiconductor power devices technologies; material properties, device structure design, advanced fabrication techniques, and device characteristics. Critical device-circuit interaction issues and basic power electronics circuits will be covered focusing on the role of these circuits in electric power conversion and transmission.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the roles of power semiconductor devices in the context of electric power conversion and transmission.
- 2.
  Understand the structure and operating principles of power semiconductor rectifiers and switches.
- 3.
  Apply semiconductor physics to analyze the critical performance parameters of power semiconductor devices.
- 4.
  Understand the mainstream silicon and emerging semiconductor power device technologies, material properties, advanced fabrication properties.
- 5.
  Understand the basic power electronic circuits and the critical device-circuit interaction issues.
ELEC 5140
Advanced Computer Architecture
[3-0-0:3]
Previous Course Code(s)
ELEC 6910K
Background
Background knowledge in ELEC 2300 (Computer Organization) or COMP 2611 (Computer Organization)
Description
The course introduces the important building blocks in modern computing systems including superscalar processor pipeline, memory hierarchies, network design in the multicore‐processors. The design techniques, evaluation metrics and optimization techniques will be discussed in detail with the example of real computer systems. The students will gain not only theoretical knowledge through lectures, but also hands‐on experiences through projects.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain the processor design and the collaboration between software and hardware.
- 2.
  Evaluate and analyze the performance of a computer system.
- 3.
  Develop the simple improvement of a computer architecture design.
- 4.
  Optimize the performance of program running on the commercial computer systems.
ELEC 5160
Digital VLSI System Design and Design Automation
[3-0-0:3]
Prerequisite(s)
ELEC 4410
Exclusion(s)
EESM 5020
Background
ELEC 2200
Description
Structured design styles; specification, synthesis and simulation using Hardware Descriptive Language (HDL); Structural chip design and system design; Circuit design of system building blocks: arithmetic unit, memory systems; clocking and performance issues in system design; Design-Automation tools and their applications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Recognize, understand the flow of designing complex digital system such as System-on-a-chip (SOC).
- 2.
  Understand the use of high-level hardware descriptive language in design digital system.
- 3.
  Use commercial ECAD tools to design, synthesize, analyze, simulate and debug simple digital systems.
- 4.
  Understand the design of the VLSI architecture for basic digital arithmetic building blocks.
- 5.
  Understand the circuit and architecture design of different types of memory blocks.
- 6.
  Understand the techniques of designing low-power and high-performance digital circuits and systems.
ELEC 5180
RF/Microwave Circuit Design and Measurement
[3-0-3:4]
Background
ELEC 3100, ELEC 3400, ELEC 3600 and ELEC 4420
Description
Introduction to techniques for analyzing, engineering and testing of circuits for RF/microwave frequencies using CAD tools. The lab provides hands-on CAD/simulation, building and testing of low-noise amplifier, mixer, VCO, filter, IF AGC, detectors and other circuits discussed in lecture.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the concepts of the wave and distributive properties of various components (passive and active) and circuits at radio- and microwave frequencies.
- 2.
  Master the new concepts and new terminologies for RF/microwave circuit analysis, such as transmission line theory, S-parameters, Smith chart, impedance matching, etc..
- 3.
  Analyze and design RF/microwave front-end functional blocks such as linear and low-noise amplifiers, mixers, oscillators, using computer-aided design tools.
- 4.
  Implement, characterize and verify the RF/microwave circuits designs, facilitated by hands-on experience in the lab.
ELEC 5190
Solid State and Semiconductor Electronics
[3-0-0:3]
Background
ELEC 4510
Description
Crystal Lattices; lattice vibration and thermal properties of crystals; free-electron theory; electrons in periodic lattices; carrier transport; metal semiconductor contacts and semiconductor surfaces; optical processes.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Know the special terms (and meaning) used in semiconductors and devices.
- 2.
  Describe the device operational principles and the characteristics of semiconductor devices.
- 3.
  Carry out project research (literature search) on special semiconductor materials and devices.
ELEC 5210
Advanced Topics in Nanoelectronics
[3-0-0:3]
Background
ELEC 4510
Description
Introduction to state-of-the-art development in the broad area of nanoelectronics, including concepts and devices for spin electronics and quantum information science. Students are expected to demonstrate the capability of applying fundamental principles to understand advanced electronic devices through hands-on homework projects.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate the capability to understand new developments in the broad area of nanoelectronics in terms of their significance, content, and objectives.
- 2.
  Identify fundamental principles of new electronic devices.
- 3.
  Develop intuitions about new electronic devices through performing hands-on homework projects.
ELEC 5240
Advanced Display Technologies
[3-0-0:3]
Previous Course Code(s)
ELEC 6910V
Background
Basic understanding of calculus and algebra
Description
Introduction of the human visual system, Colorimetry and photometry, Introduction of the modern TFTs, Modern AMLCD, AMOLED, Fluorescence and phosphorescence, Introduction of Electrophoretic displays, Color electrophoretic displays, Nano-material for displays, Electroluminescence and Photoluminescence, Quantum dot, Quantum rods, State-of-the-art development in the area of display technology: High-resolution displays (4k, 8k, and 10k), Local backlight dimming, Introduction to AR/VR display solutions, Holographic displays, Flexible displays etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Obtain basic knowledge of display technology.
- 2.
  Understand the design principle of LCD, OLED and LEDs.
- 3.
  Understand the design principle of nanomaterials for display and LEDs.
- 4.
  Understand the principles of AR/VR and Flexible Display.
ELEC 5280
High Frequency Circuit Design
[3-0-0:3]
Background
ELEC 3100, ELEC 3400, ELEC 4180 and ELEC 4630
Description
High frequency circuit design for wireless applications. S-parameters, front-end amp, VCO, PLL, power amplifier, and integration issues will be covered.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop clear understanding of wireless communication system design including link budget (Shannon's channel capacity limit, Friis's formula for radio frequency signal propagation), modulation scheme, and multi-access techniques.
- 2.
  Analyze and design radio-frequency wireless circuit including low-noise amplifier, mixer, voltage-controlled oscillator, PLL, and power amplifier.
- 3.
  Demonstrate capability to analyze and design RF circuits using state-of-the-art computer-aided design (CAD) software.
- 4.
  Identity the device characteristics and circuit specifications that directly dictate the overall wireless system performance.
- 5.
  Evaluate and optimize the performance of RF transistor and passive components including inductors and transformers.
ELEC 5300
Stochastic Processes
[3-0-0:3]
Background
ELEC 2600
Description
Borel/sigma fields. Sequences of random variables and convergence. Spectral factorization. Karhunen-Loeve Expansion. Stationarity, ergodicity and spectral estimation. Mean square estimation and Kalman filtering. Entropy. System identification.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate an in-depth understanding of the theoretical foundation of the methods of multiple random variables and stochastic processes.
- 2.
  Demonstrate mastery of, and critical understanding of, the relevant methods in random signal representation and analysis.
- 3.
  Critically apply the methods of multiple random variables and stochastic processes to address important questions in optimal filtering and parameter estimation.
- 4.
  Demonstrate the ability to address ECE-relevant theoretical questions using rigorous mathematical notations and derivation steps, and deduce important engineering insights from the mathematical solutions.
ELEC 5360
Principles of Digital Communications
[3-0-0:3]
Exclusion(s)
EESM 5536
Background
Probability theory
Description
The aim of this course is to provide an in-depth treatment of the theoretical basis, analysis, and design of digital communication systems. The first half of the course will focus on the theoretical foundations of a basic digital communication system, including source coding, modulating and channel coding, and introductory information theory. The second half will deal with advanced techniques including orthogonal frequency division multiplexing (OFDM), multi-antenna communications, spread-spectrum communications, and cooperative communications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Comprehend M-ary digital modulation schemes.
- 2.
  Identify the fundamental principles related to digital communication technology.
- 3.
  Comprehend digital communications transmission over fading channels.
- 4.
  Comprehend technical specifications and understand how and why practical digital communications systems are designed.
ELEC 5450
Random Matrix Theory and Applications
[3-0-0:3]
Previous Course Code(s)
ELEC 6910H
Background
UG-level probability (e.g., ELEC 2600 in ECE) is expected. No prior knowledge of wireless communications or signal processing is required
Description
This course gives an introduction to random matrix theory (RMT), which has become a very important tool in communication systems, signal processing and a wealth of (high dimensional) statistical applications. Topics include: introduction to RMT models in engineering; eigenvalue distributions; Wishart and related distributions; finite-dimensional and large-dimensional techniques. Applications include wireless communications, array processing, robust covariance estimation, principal component analysis, signal detection, data analysis applications to financial and biomedical engineering.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the underlying principles of random matrix theory and the distinction with respect to classical statistical theory.
- 2.
  Derive random matrix properties of practically-important random matrix models.
- 3.
  Apply random matrix theory to solve engineering problems.
- 4.
  Apply random matrix concepts to analyze and interpret high dimensional data sets.
- 5.
  Apply software tools to simulate and visualize random matrix properties and to numerically validate mathematical theories.
ELEC 5460
Stochastic Optimization for Wireless Systems and Federated-Learning
[3-0-0:3]
Background
ELEC 4110 or equivalent
Description
Stochastic Optimization plays a critical role in radio resource optimization of wireless networks, optimal control theory as well as financial engineering (portfolio optimization). This course will focus on the stochastic optimization theory and the application to the design and optimization of next generation wireless systems and federated learning applications. Topics covered include (A) Physical Layer Modeling: review of information theory for wireless fading channels, MIMO spatial diversity and spatial multiplexing, (B) Theory of Stochastic Optimization: classifications and motivating examples of stochastic optimizations [Type I stochastic Optimization and Type II stochastic optimization problems], theory of Stochastic Approximation, Stochastic Gradient, (C) Applications of Type I SO: Robust optimizations and Federated Learning: (D) Applications of Type II SO: Markov Decision Process, Stochastic Stability and Delay-optimal wireless resource control.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Learn the basic theory of stochastic optimization.
- 2.
  Learn the basic comm and information theory for the sake of modeling of the PHY.
- 3.
  Learn the basic techniques of stochastic optimization formulation for comm-related problems.
- 4.
  Learn the basic techniques of stochastic optimization algorithm designs.
ELEC 5470
Convex Optimization
[3-0-0:3]
Co-list with
IEDA 5470
Exclusion(s)
IEDA 5470
Background
Linear algebra (also basic digital communications and basic signal processing)
Description
Convex optimization theory with applications to communication systems and signal processing: convex sets/functions/problems; Lagrange duality and KKT conditions; saddle points and minimax problems; numerical algorithms; primal/dual decomposition methods. Applications: filter design; robust beamforming; power control in wireless systems; design of MIMO systems; GP duality in information theory; network utility maximization. For PG students in second year or above.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Learn the basic theory of convex optimization.
- 2.
  Read a paper and understand the optimization component in a critical way.
- 3.
  Learn fundamental optimization techniques such as majorization-minimization method, robust optimization, sparse optimization, low-rank optimization, SDP relaxation, and decomposition methods.
- 4.
  Learn specific applications such as low-rank optimization for matrix completion, portfolio optimization, sparse regression in index tracking, SDP relaxation for maximum likelihood detection, graph learning from data, Internet as a convex optimization problem, support vector machine for classification, etc.
- 5.
  Deal with a new problem using the learned optimization tools.
ELEC 5510
Switch Mode Power Converters
[3-0-0:3]
Background
ELEC 2100 AND ELEC 3400
Description
DC-DC conversion: topologies, continuous and discontinuous conduction modes, steady state analysis, loop gain analysis and relevant mathematical tools, stability and compensation; AC-DC conversion: power factor correctors.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Analyze and design the power stage of switch mode power converters.
- 2.
  Analyze and design the feedback compensator of switch mode power converters.
- 3.
  Apply and understand the limitation of averaging techniques in modeling a periodically switching system.
ELEC 5520
Power Management Integrated Circuit Design
[3-0-0:3]
Background
ELEC 4420 and ELEC 4430
Description
Integrated circuit techniques for power management components such as voltage references, linear voltage regulators, low dropout regulators, switch mode power converters and switched-capacitor power converters.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Analyze and design power management integrated circuits and systems.
- 2.
  Use simulation tools to help design power management integrated circuits and systems.
- 3.
  Communicate effectively design choices and considerations via written reports.
ELEC 5530
Mixed-Signal Integrated Bio-Sensory Circuit Design
[3-0-0:3]
Previous Course Code(s)
ELEC 6910C
Background
ELEC 4420
Description
The course aims to systematically introduce major issues of mixed-signal circuit designs and their applications in bio-medical and sensory systems. The first half course is dedicated to mixed-signal IC design. The course starts with 2 review classes on OPAMP design, filter design and circuit noise. Then, the course covers topics on pipelined ADC, Sigma-delta ADC, and SAR ADC. The second half course is dedicated to sensory and bio-medical IC design. The topics include bio-potential detection, implants, DNA detection, CCD, CMOS imaging, and CT/SPECT.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate in-depth understanding of fundamental theories of advanced mixed-signal IC design, and their application to critical mixed-signal circuit blocks.
- 2.
  Demonstrate understanding of the technological status of mixed-signal IC design field.
- 3.
  Execute critical analysis of state-of-the-art mixed-signal IC designs.
- 4.
  Develop skills of mixed-signal IC circuits design through practicing design projects in the course.
ELEC 5540
High Tech Innovation and Entrepreneurship
[3-0-0:3]
Previous Course Code(s)
ELEC 6910I
Exclusion(s)
CSIT 6000C, EESM 5810 (prior to 2019-20), ELEC 6910N, SBMT 6010K
Description
This interdisciplinary class combines a technical survey of emerging technologies/innovation with practical high-tech entrepreneurship training. It surveys a few major areas of innovation that will change the future landscape of the high-tech industry, with notable guest lecturers describing business cases and providing an industrial perspective. The class also introduces practical entrepreneurship principles for business development. Students will learn important skills such as building teams and attracting talent, developing a product/technology roadmap, marketing and selling an idea, company structuring, managing rapid growth, venture fund raising, forming strategic partnerships, and developing and intellectual property strategy. Students will form multi-disciplinary teams to write real-world business plans. Each team will develop a business model and execution plan based on its members' interests.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify what high technology innovative business is and how it is different to the traditional business model.
- 2.
  Identify two different perspectives of immitative and innovative business in high technology innovative business.
- 3.
  Identify the strategic processes of building a high technology and innovative business.
- 4.
  Identify all components required for a business plan and how to strategically align each component to build a robust business plan.
- 5.
  Analyze the competitive advantage of a business by applying Industry analysis, SWOT analysis, Value Chain analysis and VRIO analysis.
- 6.
  Develop and present a robust business plan by building all of the components of a business plan.
ELEC 5600
Linear-System Theory
[3-0-0:3]
Background
ELEC 2100, MATH 2350 and MATH 2352
Description
Introduces modern system theory, with applications to control, signal processing and related topics. Basic system concepts, state-space and I/O representation, properties of linear systems, controllability, observability, minimality, transfer-function matrices, state and output feedback, stability, observers, optimal regulators.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Get acquainted with advanced linear algebra applicable to a broad range of engineering problems.
- 2.
  Get trained in rigorous mathematical reasoning.
- 3.
  Learn the basic concepts and techniques of dynamical system theory.
ELEC 5640
Robot Manipulation
[3-0-0:3]
Co-list with
MECH 5561
Exclusion(s)
MECH 5561
Description
Extensive introduction to robot manipulation theory from a geometric viewpoint. Rigid-body kinematics; spatial and body representation of rigid-body velocities; coordinate transformations; forward kinematics of open-chain manipulators; solution of inverse kinematics; robot workspaces; nonlinear decoupling control and force control.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the fundamentals of robot kinematics and dynamics.
- 2.
  Understand the basics of robotic planning and control.
- 3.
  Implement robotic solutions on real robot platforms.
- 4.
  Advance technical writing and presentation skills.
ELEC 5650
Introduction to Networked Sensing, Estimation and Control
[3-0-0:3]
Previous Course Code(s)
ELEC 6910E
Background
ELEC 2600 AND ELEC 3200
Description
The course gives an introduction to the analysis and design of sensing, estimation and control systems in a networked setting. It consists of three parts: the first part introduces necessary background knowledge in communication networks, sensor networks, linear state estimation, MAP and ML estimators, Kalman filtering, and modern control theory; the second part focuses on analysis of network effect to remote state estimation and control; the third part presents some advanced topics including distributed state estimation and resource allocation through scheduling.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand critical issues in modern networked control applications.
- 2.
  Distinguish the differences between MAP, ML, MMSE, and LMMSE estimators and know their respective application scenorios.
- 3.
  Design a stabilizing controller for a linear Gaussian control systems using Kalman filter and LQR regulator.
- 4.
  Quantify data packet drop rate and maximum delays which may lead to instability of a networked control system.
ELEC 5660
Introduction to Aerial Robotics
[3-0-3:3]
Previous Course Code(s)
ELEC 6910P
Background
Linear algebra; Probability; MATLAB programming skills; C++ programming skills
Description
This course gives a comprehensive introduction to aerial robots. The goal of this course is to expose students to relevant mathematical foundations and algorithms, and train them to develop real-time software modules for aerial robotic systems. Topics to be covered include rigid-body dynamics, system modeling, control, trajectory planning, sensor fusion, and vision-based state estimation. Students will complete a series of projects which combine into an aerial robot that is capable of vision-based autonomous indoor navigation.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Recognize the history and development of aerial robotics.
- 2.
  Explain fundamentals in rigid body dynamics and aerial robot modeling.
- 3.
  Explain computer vision and state estimation techniques for aerial robots.
- 4.
  Implement trajectory planning and feedback control methods for aerial robots.
- 5.
  Implement real-time visual-inertial state estimators for aerial robots.
- 6.
  Analyze performance of algorithm implementations, and improve performance in an iterative manner.
- 7.
  Design and construct a complete autonomous aerial robot system under resource constraints.
- 8.
  Debug and fix real-time robotics system.
ELEC 5670
Robot Perception and Learning
[3-0-0:3]
Previous Course Code(s)
ELEC 6910R
Co-list with
COMP 5215
Exclusion(s)
COMP 5223
Description
This course introduces the essential theoretical frameworks, methods, concepts, tools and techniques used to enable robotic perception and behavior, with particular emphasis on applications in autonomous mobile robots. The course starts from Bayesian programming and probabilistic methods, and then moves on to cover generic machine learning, especially deep learning. It also includes coverage of reinforcement learning. Important libraries for hands-on experiments for mobile robotic systems will be introduced. The students will have the opportunity to test their algorithms and implementations on real platforms.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Discuss the importance and complexity of probabilistic modelling and information processing.
- 2.
  Develop first-hand know-how during class about modern robotic platforms, such as Turtlebot.
- 3.
  Demonstrate good comprehension of fundamentals of basic machine learning techniques by lecture assignments.
- 4.
  Demonstrate the ability to apply practical algorithms to realise basic perception for robotic systems by projects.
- 5.
  Apply knowledge in computer science, signal processing and practical programming.
- 6.
  Use the aggregate knowledge to perform robotic tasks to operate simulated robots in simulated environment scenarios.
ELEC 5680
Advanced Deep Learning Architectures
[3-0-0:3]
Previous Course Code(s)
ELEC 6910T
Co-list with
COMP 5214
Exclusion(s)
COMP 5214
Description
This course focuses on advanced deep learning architectures and their applications in various areas. Specifically, the topics include various deep neural network architectures with applications in computer vision, signal processing, graph analysis, and natural language processing. Different state-of-the-art neural network models will be introduced, including graph neural networks, normalizing flows, point cloud models, sparse convolutions,and neural architecture search. The students have the opportunities to implement deep learning models for some AI-related tasks such as visual perception, image processing and generation, graph processing, speech enhancement, sentiment classification, and novel view synthesis.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand a broad range of advanced deep learning models.
- 2.
  Have in-depth knowledge of the differences and similarities of deep learning architectures in various areas.
- 3.
  Apply learned deep learning models to solve a problem of interests with a written technical report and presentation.
- 4.
  Write programs in Tensorflow or Pytorch to implement several given deep neural network architectures.
- 5.
  Combine different neural network architectures to solve a problem.
ELEC 5810
Introduction to Bioinformatics Algorithms
[3-0-0:3]
Mode of Delivery
[BLD] Blended learning
Description
This is an introductory course on computational biology at the molecular level. It will cover basic biological knowledge, important biological questions, common data acquisition techniques, popular data analysis algorithms and their applications. The major content of this course is computation-oriented.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the fundamentals of bioinformatics.
- 2.
  Demonstrate mastery of, and critical understanding of, relevant scientific literature related to bioinformatics.
- 3.
  Summarize the key points from related literature, make comparisons among different papers, and carry out insightful discussions.
ELEC 5820
Microfluidics and Biosensors
[3-0-0:3]
Previous Course Code(s)
ELEC 6910D
Co-list with
BIEN 5820
Exclusion(s)
BIEN 5820
Background
Basic Physics
Description
Introduction to Microfluidics and Biosensors; Overview of microfabrication materials & techniques; microfluidic principles; miniaturized biosensors; micro total analysis system (µTAS) & lab-on-a-chip (LOC) for clinical and research applications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe core concepts in the field of Microfluidics & Biosensors.
- 2.
  Describe trends and motivations in the field of Microfluidics & Biosensors.
- 3.
  Analyze/critique research articles in the field of Microfluidics & Biosensors.
- 4.
  Formulate a potential solution for a clinical problem using Microfluidics & Biosensors.
ELEC 5900
Modern Engineering Research Methodologies
[3-0-0:3]
Exclusion(s)
EESM 5770
Description
The course provides a high-level description of modern engineering research practices. It covers topics including research mentality, the scientific method, evaluating research topics, literature search, report writing, presenting data, publication, research management, research ethics and technology transfer.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe the scientific method for doing research.
- 2.
  Distinguish and categorize different types of researches.
- 3.
  Evaluate the quality of research papers.
- 4.
  Develop a research proposal independently.
- 5.
  Identify different components in a published paper.
- 6.
  Write a critical review of a research paper.
- 7.
  Analyze the arguments concerning research ethics.
- 8.
  Explain the process of technology transfer.
ELEC 6770
Professional Development in Electronic and Computer Engineering
[0-1-0:1]
Description
This one-credit course aims at providing research postgraduate students with basic training in teaching skills, research management, career development, and related professional skills. This course consists of a number of mini-workshops. Some department-specific workshops will be coordinated by Department of ECE. Graded PP, P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Acquire teaching skills, research management, career development, and related professional skills.
ELEC 6900
Independent Study
[1-3 credit(s)]
Description
Selected topics in electronic and computer engineering studied under the supervision of a faculty member. Graded P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Acquire knowledge of special topics related to electronic and computer enginnering.
ELEC 6910-6940
Special Topics
[1-4 credit(s)]
Description
Selected topics of current interest. May be repeated for credit, if different topics are covered.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Acquire knowledge of special topics related to electronic and computer enginnering.
ELEC 6950
Departmental Seminar
[1-0-0:0]
Description
Series of seminar topics presented by students, faculty and guest speakers. Graded P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Acquire, enhance, and practice presentation skills.
ELEC 6990
MPhil Thesis Research
Description
Master's thesis research supervised by a faculty member. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Carry out Master's thesis research supervised by a faculty member.
- 2.
  Defend the thesis research conducted by the Thesis Examination Committee.
ELEC 7990
Doctoral Thesis Research
Description
Original and independent doctoral thesis research. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Carry out original and independent doctoral thesis research.
- 2.
  Defend the thesis research conducted by the Thesis Examination Committee.

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