Undergraduate Courses 2023-24

Undergraduate courses marked with [BLD] or [SPO] may be offered in the mode of blended learning or self-paced online delivery respectively, subject to different offerings. Students should check the delivery mode of the class section before registration.

• ELEC 1010

  Electronic and Information Technology

  3 Credit(s)
  Previous Course Code(s)

  CORE 1240

  Prerequisite(s)

  a passing letter grade in LANG 1401 OR LANG 1402 OR LANG 1403 OR LANG 1404 OR LANG 1002 (prior to 2022-23)

  Description

  This general-education course introduces the basics of electronic and information technology and their applications to daily-life consumer electronics and communication devices. Contents include the representation of signals in the time and frequency domains; digitization of information; coding for data compression and error protection; transmission of signals; cellular mobile phones and wireless communications; and the Internet. It is expected that through studying these technologies and how they address the problems encountered in the information technology area, students will also grasp the skills in solving problems with engineering approach and spirit and appreciate how these technologies impact the society.
• ELEC 1030

  The Rise of Autonomous Robots

  3 Credit(s)
  Description

  This course is designed to give students with little or no technical background a general understanding about robots and their potential impact. Our society is on the verge of a new era of technology convergence: robotic devices are envisioned to become a nearly ubiquitous part of our day‐to‐day lives. New technologies such as wireless communication and voice and visual recognition will make robots become behaviour‐based, cognitive and biologically-inspired humanoids. Fundamental social, economic, and technological issues of a human‐machine society will be identified and discussed in interactive sessions. The learning process is designed to transcend conventional boundaries between technology and other disciplines, and will be facilitated by a series of demonstrative sessions presenting students with opportunities to observe, evaluate, examine, and interact with a variety of commercial robots and humanoids. Guided by internal and external experts, students will focus on a specific social, business or technology issue, identifying and exploring potential solutions enabled by robotic and automation technology. In this exploration process, students will be encouraged to apply their personal background and interests, and possibly experiment with robot kits.
• ELEC 1095

  Special Topics

  1-4 Credit(s)
  Description

  Selected introductory topics in Electronic and Computer Engineering. May be repeated for credit, if different topics taken.
• ELEC 1100

  Introduction to Electro-Robot Design

  4 Credit(s)
  Description

  The course introduces the fundamental knowledge on the design, implementation and evaluation of a robot and its sub-systems. It covers the basic principles of analog and digital circuits as well as robot sensing and control mechanisms. Students have to apply the knowledge and principles learned to design and build a functional robot by the end of the course. Students who have completed ELEC 2200, ELEC 2350, ELEC 2400, ELEC 2420, or ELEC 3310, must obtain instructor's approval to take this course.
• ELEC 1200

  A System View of Communications: from Signals to Packets

  4 Credit(s)
  Corequisite(s)

  (COMP 1021 OR COMP 1022P) AND (MATH 1003 OR MATH 1014 OR MATH 1020 OR MATH 1024)

  Mode of Delivery

  [SPO] Self-paced online delivery
  [BLD] Blended learning

  Description

  Have you ever wondered what technologies go into your mobile phone or a WiFi hotspot? Through hands on work with a simple but fully functional wireless communication system, you will understand the basic engineering tools used and tradeoffs encountered in the design of these systems. This course is centered on weekly laboratories, each designed to introduce an important concept in the design of these systems. The lab sessions are supported by two one-hour lectures and a tutorial that introduce the concepts for the next laboratory, as well as reviewing and expanding the concepts learned in the previous laboratory.
• ELEC 2100

  Signals and Systems

  4 Credit(s)
  Prerequisite(s)

  MATH 2011 OR MATH 2023 OR MATH 2111 OR MATH 2350 OR MATH 2351 OR MATH 2352

  Exclusion(s)

  ELEC 2100H

  Mode of Delivery

  [BLD] Blended learning

  Description

  This is an introductory course for signal and system analysis. The course covers signal analysis tools including continuous- and discrete-time Fourier series and Fourier transform, and Laplace Transform; interactions between signals and linear time invariant (LTI) systems, and differential and difference equations as LTI systems, sampling theorem; and application examples in communication and control systems. MATLAB introduced as an integral part of this course.
• ELEC 2100H

  Honors Signals and Systems

  4 Credit(s)
  Prerequisite(s)

  (ELEC 1100 OR ELEC 1200 OR ELEC 2400) AND Grade A- or above in (MATH 2011 OR MATH 2023 OR MATH 2111 OR MATH 2350 OR MATH 2351 OR MATH 2352)

  Exclusion(s)

  ELEC 2100

  Mode of Delivery

  [BLD] Blended learning

  Description

  This is an accelerated and intensive course on signals and system analysis. The course covers continuous- and discrete-time Fourier series, Fourier transform, Laplace Transform, interactions between signals, linear time invariant systems, differential and difference equations, and the sampling theorem. There will also be an introduction to advanced concepts and related applications, such as various transforms used in image processing and modulation techniques used in communication systems, which will allow students to develop a deeper understanding of the fundamentals of signals and systems. MATLAB will be introduced as an integral part of this course.
• ELEC 2200

  Digital Circuits and Systems

  4 Credit(s)
  Prerequisite(s)

  ELEC 1100 OR ELEC 1200

  Description

  Design of combinatorial and sequential logic circuits; introduction to logic families (TTL and CMOS); programmable logic devices; special digital systems. Laboratory assignments make extensive use of computer-aided design (CAD) tools for design, simulation and testing.
• ELEC 2300

  Computer Organization

  4 Credit(s)
  Corequisite(s)

  ELEC 2200

  Exclusion(s)

  COMP 2611

  Description

  This is an introductory course to computer organization. The topics covered include instruction-set-design, digital design and computer arithmetic, controller and datapath design, memory systems, input-output systems, interrupts, pipelining, performance analysis, assembly language programming, and survey of advanced architectures.
• ELEC 2350

  Introduction to Computer Organization and Design

  4 Credit(s)
  Prerequisite(s)

  ELEC 1100

  Exclusion(s)

  COMP 2611, ELEC 2300, ISDN 4000F

  Description

  This is an introductory course to computer hardware and software organization. The topics covered include computing systems, computing programing, hardware-software collaboration, computer arithmetic, computer hardware organizations and operations, parallel processing, memory technologies and organization, and technology trends.
• ELEC 2400

  Electronic Circuits

  4 Credit(s)
  Prerequisite(s)

  ELEC 1100 AND (MATH 1003 OR MATH 1014 OR MATH 1020 OR MATH 1024)

  Corequisite(s)

  PHYS 1114 OR PHYS 1314

  Exclusion(s)

  ELEC 2420

  Description

  Fundamental electronic concepts for DC and AC circuits, KVL and KCL, Thevenin and Norton Theroems, linearity and superposition, nodal and mesh analyses, sinusoidal steady state and phasor, transient analysis, transfer functions and Bode plots, op-amps, diodes, MOS transistors and related circuits.
• ELEC 2420

  Basic Electronics

  3 Credit(s)
  Prerequisite(s)

  MATH 1014 OR MATH 1020 OR MATH 1024

  Corequisite(s)

  PHYS 1111 OR PHYS 1112 OR PHYS 1312

  Exclusion(s)

  ELEC 2400

  Description

  Basic electronic concepts and components; DC, AC and transient analyses of analog electronic circuits; operational amplifiers and circuits; digital electronics includes binary number systems, Boolean algebra, and combinational and sequential logic. For non-ECE students only.
• ELEC 2600

  Probability and Random Processes in Engineering

  4 Credit(s)
  Prerequisite(s)

  MATH 1003 OR MATH 1014 OR MATH 1020 OR MATH 1024

  Corequisite(s)

  MATH 2011 OR MATH 2023

  Exclusion(s)

  ELEC 2600H, MATH 2421

  Description

  An introduction to statistical inference and random processes in electrical engineering, including the necessary probabilistic background. Random variables, distribution and density functions, characteristic functions, conditional statistics, expectation, moments, stochastic processes.
• ELEC 2600H

  Honors Probability and Random Processes in Engineering

  4 Credit(s)
  Prerequisite(s)

  Grade A- or above in MATH 1014 OR MATH 1020 OR MATH 1024

  Corequisite(s)

  MATH 2011 OR MATH 2023

  Exclusion(s)

  ELEC 2600, MATH 2421

  Description

  This course is an accelerated and intensive course on probability and random processes. There will be an introduction to statistical inference and random processes in electrical engineering, including the necessary probabilistic background. The course also covers random variables, distribution and density functions, characteristic functions, conditional statistics, expectation, moments, stochastic processes.
• ELEC 2910

  Academic and Professional Development I

  0 Credit(s)
  Description

  A compulsory, one year course for Electronic Engineering and EE (Information and Communication Engineering) students only. This course is designed to provide academic advising to students and/or to develop students' communication skills in interacting with the technical and non-technical audiences in their professional careers. Graded P or F.
• ELEC 2991

  Industrial Experience (Electronic Engineering)

  0 Credit(s)
  Previous Course Code(s)

  ELEC 1991

  Description

  Full-time internship training for a period of at least six weeks in an organization or company recognized by the Department for providing qualified internship training relevant to the electronic and computer engineering profession. Students must also complete the USTSEEB Safety Training module. Cantonese or other working languages may be used in off-campus trainings and internships, in some situations. For students in the BEng in Electronic Engineering program under the four-year degree only. Internship coordinator's approval is required for enrollment in the course. Graded P, PP or F.
• ELEC 3100

  Signal Processing and Communications

  4 Credit(s)
  Prerequisite(s)

  (ELEC 2100 OR ELEC 2100H) AND (ELEC 2600 OR ELEC 2600H)

  Description

  The course provides a comprehensive overview of signal processing and communications using quantitative modeling and analysis. Topics include: 7 layer communications model, discrete Fourier transform and z-transform, IIR and FIR filter design techniques and realizations, complexity and implementation considerations of FFT and FIR/IIR, source coding, digital modulation, PSD and spectrum, effects of noise to communication system designs, detection theory, matched filter, signal space and error analysis, channel models and channel coding. Application examples are provided to illustrate on how practical communication systems are designed using these quantitative tools. Design projects are set up so that the students can apply theory learnt in the class to physical problems. MATLAB CAD tools are being used as an integral part of this course.
• ELEC 3120

  Computer Communication Networks

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4120

  Prerequisite(s)

  COMP 1021 OR COMP 1022P

  Exclusion(s)

  COMP 4621

  Description

  Overview of computer networks: network architecture and switching techniques. Introduction to the Internet, network programming, and layer architecture. Application layer: HTTP, FTP, SMTP, and CDN. Transport layer: TCP and UDP. Network layer: IP routing, NAT, and DHCP. Data link layer and local area networks: MAC protocols, Ethernet, and hubs/bridges/switches.
• ELEC 3180

  Data-Driven Portfolio Optimization

  3 Credit(s)
  Alternate code(s)

  IEDA 3180

  Prerequisite(s)

  (IEDA 2520 OR IEDA 2540 OR ELEC 2600) AND (MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)

  Description

  Modern portfolio theory started with Harry Markowitz's 1952 seminal paper "Portfolio Selection." He put forth the idea that risk-adverse investors should optimize their portfolio based on a combination of two objectives: expected return and risk. Until today, that idea has remained central in portfolio optimization. However, the vanilla Markowitz portfolio formulation does not seem to behave as expected in practice and most practitioners tend to avoid it. During the past half century, researchers and practitioners have reconsidered the Markowitz portfolio formulation and have proposed countless of improvements and alternatives such as robust optimization methods, alternative measures of risk, regularization via sparsity, improved estimators of the covariance matrix, robust estimators for heavy tails, factor models, volatility clustering models, risk-parity formulations, index tracking, etc. This course will explore the Markowitz portfolio optimization in its many variations and extensions, with special emphasis on Python programming.
• ELEC 3200

  System Modeling, Analysis and Control

  4 Credit(s)
  Prerequisite(s)

  (ELEC 2100 OR ELEC 2100H) AND [MATH 2350 OR (MATH 2111 AND MATH 2351)]

  Exclusion(s)

  CENG 3110, MECH 3610

  Description

  This course introduces basic concepts, tools and techniques for modeling, analysis, and control of dynamical systems. The course starts from the use of differential equations to model continuous time systems. Examples from a variety of Electronic and Computer Engineering disciplines will be given to illustrate the modeling process. Then, basic tools needed for analyzing the behavior of dynamical systems will be presented. Finally, techniques for controlling their behavior will be introduced. Throughout the course, laboratory experiments demonstrating the use of these analysis/design tools will be included.
• ELEC 3210

  Introduction to Mobile Robotics

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4010K

  Prerequisite(s)

  (ELEC 2600 OR ELEC 2600H) AND MATH 2111

  Description

  The course is to introduce the basic concepts of autonomous navigation used in mobile robotics. Course content includes navigation paradigm, common sensors, Bayes theory, Kalman filter, robot mapping, SLAM, motion planning, and software platforms for robotics research.
• ELEC 3300

  Introduction to Embedded Systems

  4 Credit(s)
  Prerequisite(s)

  COMP 2611 OR ELEC 2300 OR ELEC 2350 OR ISDN 4000F

  Description

  This course is designed to teach techniques on how to integrate machine-level software and hardware in ARM-core microcontroller based systems. It makes use of industry-standard techniques and technologies, from which students can interface, design and program microcontroller systems. The task of the course will be to complete five laboratory experiments which address different aspects of hardware/software interfacing, and one large microprocessor/microcontroller based project which should result in the design and implementation of a small working embedded system.
• ELEC 3310

  Digital Fundamentals and System Design

  4 Credit(s)
  Prerequisite(s)

  ELEC 1100

  Exclusion(s)

  ELEC 2200, ISDN 4000D

  Description

  Design and synthesis of digital circuits with main emphasis on sequential logic taught through project-based learning approach. Laboratory assignments make extensive use of VHDL and FPGAs and prepare students for an open-ended project undertaken in the remaining part of the course.
• ELEC 3400

  Introduction to Integrated Circuits and Systems

  4 Credit(s)
  Prerequisite(s)

  ELEC 2400

  Description

  This course presents an overview, applications, fundamentals and design flow of the state-of-the-art integrated circuits (IC) and systems. Course contents include fabrication process; diodes, bipolar transistors and MOS transistors and modes of operations; and fundamental of analog, digital and mixed-signal IC design.
• ELEC 3450

  Introduction to Smart Electric Power Systems

  3 Credit(s)
  Prerequisite(s)

  ELEC 1100 OR PHYS 1114 OR PHYS 1314

  Description

  This is an introductory course for electric power systems and smart grid. The course includes the following topics: power concepts for ac systems, generation, transmission, distribution, and utilization of electric power, system aspects of synchronous machines, transmission lines, transformers, and motors. Power flow and contingency states. Smart grid concepts, role of information technology in smart grid applications, smart metering, smart buildings and homes.
• ELEC 3500

  Integrated Circuit Devices

  4 Credit(s)
  Prerequisite(s)

  ELEC 2400

  Mode of Delivery

  [SPO] Self-paced online delivery
  [BLD] Blended learning

  Description

  This is an introductory course for semiconductor device operation principles and technology in common electronic products such as integrated circuit (IC), digital camera, solar cell, memory elements, smartcard, etc. Topics covered include Semiconductor properties, IC fabrication technology, PN junctions, Bipolar Junction Transistors (BJT), MOSFETs, CCD and the future technology trend in the electronic industry.
• ELEC 3600

  Electromagnetics: From Wireless to Photonic Applications

  4 Credit(s)
  Prerequisite(s)

  (MATH 2011 OR MATH 2023) AND MATH 2351 AND PHYS 1114

  Description

  This area course introduces applied electromagnetics from fundamentals to applications. Topics include: Gauss', Faraday's and Ampere's laws; electrostatics and magnetostatics; Maxwell's equations; electromagnetic plane wave propagation; transmission lines; radiation and antenna fundamentals; light wave fundamentals. Students will also acquire hands-on experience to electromagnetics through laboratory sessions.
• ELEC 3810

  Data Science for Neural Engineering

  3 Credit(s)
  Alternate code(s)

  BIEN 3310

  Prerequisite(s)

  (BIEN 2310 OR ELEC 2600 OR ELEC 2600H OR MATH 2111) AND (BIEN 3300 OR CENG 3300 OR LIFS 3150 OR MATH 2411)

  Exclusion(s)

  BIEN 4310, ELEC 4830

  Description

  This is an introductory course on data science and its applications in neural engineering. The course introduces the fundamentals of data science, principles of neuroscience, and the technologies and implementations of neural engineering. The topics include probability, random variables, statistical detection and estimation, random process, structure and function of the nervous system, encoding and decoding, population coding, neural network, plasticity and learning, neural interfaces and rehabilitation.
• ELEC 3910

  Academic and Professional Development II

  0 Credit(s)
  Description

  Continuation of ELEC 2910. Graded P or F.
• ELEC 4010

  Special Topics

  1-4 Credit(s)
  Description

  Selected topics in Electronic and Computer Engineering. May be repeated for credit, if different topics taken.
• ELEC 4110

  Digital Communications and Wireless Systems

  3 Credit(s)
  Prerequisite(s)

  ELEC 2100 OR ELEC 2100H

  Description

  Representation of signals, optimum detection of signals in noise, matched filtering, error probability calculations for digital modulation. Multilevel modulation schemes, comparison of digital communications systems, mobile and wireless channels, diversity techniques, spread-spectrum communications, Resource Partitioning in Multiuser systems (FDMA, TDMA, CDMA) and their applications in cellular mobile and wireless personal communications.
• ELEC 4130

  Machine Learning on Images

  3 Credit(s)
  Prerequisite(s)

  ELEC 2100

  Corequisite(s)

  ELEC 3100 OR ELEC 3200

  Exclusion(s)

  COMP 4421, MATH 4336

  Description

  This course introduces methods to process images on a computer. Topics include the formation and quantification of digital images, morphological image processing, image enhancement in the spatial and frequency domain, image restoration, color image processing, image compression, image segmentation, image representation and description, the image pattern classification using classic machine learning approaches, such as minimum-distance, and Bayes classifiers, Support Vector Machine et al., the modern approaches implemented using deep neural networks. This course provides a new perspective of comparing image processing technique to human vision system in terms of information perception and decision making. Students are expected to obtain knowledge of digital image processing, and hands-on experience on programming and presentation skill through the final project. This course is mathematics-oriented. It requires basic knowledge of linear algebra, calculus and linear filtering. Familiarity with the programming language MATLAB is needed.
• ELEC 4150

  Information Theory and Error-Correcting Codes

  3 Credit(s)
  Prerequisite(s)

  ELEC 2100

  Description

  Communication and information theory; self and mutual information measures; channel models and capacity; source coding; hamming codes; cyclic codes; BCH and Reed-Solomon codes; convolutional codes and the Viterbi algorithm; burst error correction; Turbo coding.
• ELEC 4210

  Control System Design

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4010G

  Prerequisite(s)

  ELEC 3200 OR MECH 3610

  Description

  In the lectures, the following topics will be covered: time-domain and frequency-domain system modeling and analysis, optimal control, robust control, computer aided control designs, digital control. In the experiments, the students will be asked to design and implement controllers for a magnetic suspension system, an inverted pendulum system, and a tower crane system.
• ELEC 4220

  Introduction to Robotics: From Mobile Robots to Manipulators

  4 Credit(s)
  Prerequisite(s)

  ELEC 3200

  Description

  This course introduces basic building blocks of robots: actuators (DC and servo motors), sensors (proprioceptive/exteroceptive, passive/active); controller platforms as well as mechanical modules; and basic concepts of robotics: rigid motion and configuration space, kinematics, dynamics, trajectory generation and planning, locomotion, localization and mapping, navigation and control. Using these basic building blocks and robotic concepts, students will learn how to design and build a robot prototype that meets certain design specifications. Design examples include a mobile robot to engage in a competition, a robotic manipulator in a typical assembly task, an unmanned aerial vehicle (UAV) in a surveillance application, etc.
• ELEC 4230

  Deep Learning for Natural Language Processing

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4010I

  Prerequisite(s)

  (COMP 2012 OR COMP 2012H) AND (ELEC 2600 OR ELEC 2600H)

  Exclusion(s)

  COMP 4901I

  Description

  This course covers the basic theories and applications of natural language processing including speech processing using deep learning. It also introduces the topic of ethics in Artificial Intelligence. Topics include distributional semantics with word embeddings, text classification, emotion recognition, sentiment analysis from text and speech, language modeling, machine translation, and question answering. We will also cover multi-linguality and multi-modality, end-to-end chatbots, and task-oriented dialogue systems. The topics in Deep Learning ranges from feed forward neural networks, convolutional neural networks, recurrent neural networks, sequence-to-sequence neural networks, and the latest architecture in DL. Students will learn about various natural language processing (NLP) topics, how to design and build a deep learning model using PyTorch and Python, and the basics of software engineering. At the end of the course, students will work alone or in pairs to implement a deep learning model for various NLP research tasks, and describe their methods and results in a conference paper format. Successful groups will be able to submit their papers to an international conference.
• ELEC 4240

  Deep Learning in Computer Vision

  3 Credit(s)
  Alternate code(s)

  COMP 4471

  Prerequisite(s)

  (COMP 2011 OR COMP 2012 OR COMP 2012H) AND (MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)

  Description

  Deep learning has significantly advanced the performance of computer vision system from object recognition to image processing. This course covers the basics and various applications of deep learning in computer vision. Students will study the details of convolutional neural networks as well as recurrent neural networks and train deep networks with end-to-end optimization, and learn deep learning based approaches for both high-level and low-level computer vision tasks such as image recognition and image enhancement. Through programming projects, students will implement, train, and test deep neural networks on cutting-edge computer vision research. Students would be required to study or do research in a final course project related to deep learning and computer vision and present their work by the end of the course.
• ELEC 4310

  Embedded System Design

  4 Credit(s)
  Prerequisite(s)

  [COMP 2611 AND (ELEC 2200 OR ELEC 3310)] OR ELEC 2300 OR ELEC 2350 OR ISDN 4000F

  Description

  In this course, students will learn the important concepts and modern design practices of embedded computing systems. They will see how a complex embedded system can be systematically developed as a union of software and hardware. The course will cover several fundamental topics, such as design targets, hardware/software co-design methodology, common design techniques, processors, architectures, and physical implementations. It will also cover several advanced topics, such as behavioral modeling, low-power techniques, and systems-on-chip.
• ELEC 4320

  FPGA-based Design: From Theory to Practice

  3 Credit(s)
  Prerequisite(s)

  ELEC 2350 OR ELEC 3310

  Description

  This course introduces the basic theory and design skills for FPGA-based design. The course aims to equip the students with enough knowledge and skills for the real world engineering using FPGA devices. Major topics include introduction to reconfigurable computing, hardware description language, FPGA device, and mapping flow. Students will gain hands-on experiences of the complete FPGA-based design cycle, from design specification, synthesis, implementation and simulation in this course.
• ELEC 4410

  CMOS VLSI Design

  3 Credit(s)
  Prerequisite(s)

  ELEC 2400

  Description

  CMOS process and design rules; MOS device electronics; CMOS circuit and logic circuit characterization and performance estimation; VLSI design and verification tools. Laboratory work will be centered on industry standard tools.
• ELEC 4420

  Analogue Integrated Circuits Design and Analysis

  4 Credit(s)
  Prerequisite(s)

  ELEC 3400

  Description

  Multiple-stage operational amplifiers, frequency response, feedback analysis, stability and compensation, Slew rate, advanced amplifier design techniques, analog VLSI building blocks.
• ELEC 4430

  Integrated Power Electronics

  3 Credit(s)
  Prerequisite(s)

  ELEC 3400

  Description

  Power computation, diodes and rectifier circuits, power factor correctors, switch mode power converters, magnetic components, switch capacitor power converters, linear regulators, and integrated circuit techniques for controller design.
• ELEC 4510

  Semiconductor Materials and Devices

  3 Credit(s)
  Prerequisite(s)

  ELEC 3500

  Description

  This is an introductory course for semiconductor materials and devices. The course content includes the following topics: the growth and properties of semiconductor crystals; the theory of the electronic structures of atoms and solids; the energy band and conduction mechanisms in semiconductors; the physics of junction diodes; excess carriers; bipolar junction transistors (BJT); metal oxide semiconductor field-effect transistors (MOSFET).
• ELEC 4520

  Integrated Circuit Fabrication Technology

  3 Credit(s)
  Prerequisite(s)

  ELEC 3500

  Description

  For UG students only. The course is intended to provide students with fundamental knowledge in device and integrated circuits (IC's) fabrication. The class covers the modules of device fabrication (including clean room concept, cleaning procedures, diffusion, lithography, wet processing, dry etching, chemical vapor deposition, sputtering) and process integration to form IC's. The lab section will bring the students with hands-on experience in IC fabrication facilities in Nanoelectronics Fabrication Facility of HKUST.
• ELEC 4530

  Fundamentals of Photovoltaic and Renewable Energy

  3 Credit(s)
  Prerequisite(s)

  ELEC 3500

  Description

  Introduction of solar and other renewable energy generation. Silicon and other semiconductor solar cells. Physics and circuit modeling. Energy storage and distribution.
• ELEC 4610

  Engineering Optics

  4 Credit(s)
  Prerequisite(s)

  ELEC 2400

  Exclusion(s)

  PHYS 3038

  Description

  An introductory course in optics covering fundamentals of geometrical and physical optics. Topics include: review of geometrical optics, first order optical system and analysis, aberration, aperture and field stops; Basic wave theory, diffraction, interference, polarization, dispersion; fundamentals of optical instrumentation.
• ELEC 4620

  Photonics and Optical Communications

  4 Credit(s)
  Prerequisite(s)

  ELEC 3600

  Description

  To introduce optoelectronics and fiber optics for communications. Topics include optical fibers, optical sources, optical detectors, and passive components for wavelength-division multiplexing. Laboratory gives hands-on experience in handling optical fibers, lasers and detectors, micro-optical components, opto-mechanical equipment, and building wavelength-division-multiplexed optical links.
• ELEC 4810

  Introduction to Biosensors and Bioinstrumentation

  4 Credit(s)
  Prerequisite(s)

  ELEC 2400 OR ELEC 2420

  Description

  This course builds on the fundamental knowledge of biosensors and bioinstrumentation. Lectures and hands-on laboratory experiments cover: (1) Basic concepts of biomedical signal analysis; (2) Measurements of bioelectrical, biomechanical and biochemical signals for medical diagnosis and clinical monitoring; (3) Principles of biosensors and biochips; (4) Simple design of new bioinstrumentation and biosensor to solve biomedical problems.
• ELEC 4820

  Medical Imaging

  3 Credit(s)
  Prerequisite(s)

  ELEC 2100 AND MATH 2011 AND MATH 2111

  Mode of Delivery

  [BLD] Blended learning

  Description

  This course introduces medical imaging methods to senior undergraduate and graduate students. It covers the following topics: radiation, radiography, computer tomography, radioisotope imaging, diagnostic ultrasound imaging, magnetic resonance imaging, and applications of different imaging modalities. This course requires basic knowledge of linear algebra, calculus, and geometry. Familiarity with a programming language such as MATLAB is needed.
• ELEC 4830

  Statistical Signal Analysis and Applications in Neural Engineering

  3 Credit(s)
  Alternate code(s)

  BIEN 4310

  Prerequisite(s)

  (BIEN 3320 OR MATH 2111) AND (ELEC 2600 OR ISOM 2500 OR LIFS 3150 OR MATH 2411)

  Description

  This is an introductory course on statistical signal processing and its applications in neural engineering. The course introduces the fundamentals of statistical signal processing, principles of neuroscience, and the technologies and implementations of neural engineering. The topics include probability, random variables, vectors and process, expectation, cellular mechanisms and neuroanatomy of the brain, neural coding theory, neural network models, plasticity and learning, neural interfaces and rehabilitation.
• ELEC 4840

  Artificial Intelligence for Medical Image Analysis

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4010N

  Prerequisite(s)

  (COMP 2011 OR COMP 2012 OR COMP 2012H) AND (MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)

  Exclusion(s)

  COMP 5423

  Description

  Medical imaging plays a vital role in the entire spectrum of healthcare, ranging from wellness and screening to early diagnosis, treatment selection, and follow-up. The course delves deeply into the latest advancements in Al for medical image analysis, with a specific focus on deep learning techniques for disease screening and detection using medical images. It will cover various topics, including the fundamentals of deep neural networks, the basics of medical imaging, and an exploration of state-of-the-art deep learning models in the context of different types of medical images. The objective is to equip students from diverse backgrounds with both a conceptual understanding and practical skills in cutting-edge research on Al in healthcare. By providing a comprehensive overview of the field and the fundamental techniques necessary for image processing, analysis, and scientific applications, it aims to empower students to effectively utilize images for scientific discovery and practical purposes.
• ELEC 4900

  Final Year Design Project

  6 Credit(s)
  Description

  Each undergraduate student enrolled in Electronic Engineering and Computer Engineering is required to complete a final year design project before graduation. The project is conducted under the supervision of a faculty member. Credit load will be spread over the project period.
• ELEC 4901

  Final Year Thesis

  6 Credit(s)
  Description

  Each undergraduate student taking the Research Option of the Electronic Engineering program is required to complete an individual thesis and the thesis should summarize his/her work conducted under the supervision of a faculty member. Credit load will be spread over the project period.
• ELEC 4907

  Final Year Project

  9 Credit(s)
  Description

  Each undergraduate student enrolled in the Department of Electronic and Computer Engineering is required to complete a final year project before graduation. The student is expected to submit a project progress report by the end of the first term of the project, and to complete a final project report and to give an oral project presentation at the end. The project is conducted under the supervision of a faculty member. The credit load will be spread over 3 terms.
• ELEC 4910

  Co-op Program

  6 Credit(s)
  Exclusion(s)

  ELEC 4900, ELEC 4901

  Description

  Students in this co-op program will be engaged in practical hands-on training for a period of at least 5 months working in an organization or company that provides qualified training relevant to the electronic and computer engineering profession. Students are required to complete a final year project under the supervision of an industrial supervisor and a faculty member during the co-op. May be graded PP. For ELEC students in their third or fourth year of study only. Approval of the course coordinator is required for enrollment in the course.
• ELEC 4940

  Independent Study

  1-3 Credit(s)
  Description

  Selected topics in electronic and computer engineering studied under the supervision of a faculty member. Enrollment subject to approval by the department.