Undergraduate Courses 2025-26

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

  Exclusion(s)

  any ELEC courses of 2000-level or above

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the key technological developments of electronic and information technology which have reshaped industries, societies and the world
  • 2.
    Identify the fundamental principles related to electronic and information technology and how the technology changes our life and applies in daily life
  • 3.
    Apply MS Excel to solve simple engineering problems
  • 4.
    Describe engineering knowledge on up-to-date electronic and information technology
  • 5.
    Appraise and understand the problem solving approach used in engineering discipline, in particular in the electronics and information technology area
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Obtain a holistic view of the robotic future
  • 2.
    Recognize and understand the various components of robotic systems and how they work together with their basic functions
  • 3.
    Comprehend fundamentals of basic autonomous systems and robots and their development principles
  • 4.
    Enhance the knowledge in engineering, practical programming, and system evaluation
  • 5.
    Develop skills of concept exploration, critical thinking and teamwork; Appreciate the potential value of robotic technology to the society
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Equip with broad and useful knowledge to various topics which are not covered by existing courses
  • 2.
    (Each offering under the umbrella will have specific learning outcomes)
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the history and development of major ECE fields.
  • 2.
    Apply the fundamental circuit concepts to compute the output of basic electronic circuits.
  • 3.
    Analyze, design, and debug simple analog circuits, and design and program for simple digital control strategies.
  • 4.
    Build a real engineering system following a hierarchical design principle.
  • 5.
    Work in a team environment, learn and practice effective project management.
  • 6.
    Execute a complete project from problem formulation, design/implementation, up to verification and documentation.
• ELEC 1150

  Information Technology Revolution: Past, Present and Future

  3 Credit(s)
  Previous Course Code(s)

  CORE 1203, ENGG 1150

  Description

  This course introduces the scientific foundation, technology breakthroughs, and basic concepts in information technology, while leading students to reflect on the impacts of information technology in our daily life. Topics include electromagnetic science, the transistor, the integrated circuit industry, computing machines, representation of information in digital formats, communication systems, cryptography, and modern applications such as social networking, big data, virtualization, blockchain, AI, autonomous and intelligent systems, and quantum computing. The focus is to identify the key technological advances in information technology and understand how these advances led to revolutionary changes in our life and society.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Identify the key advances in information technology and related enabler in various technology
  • 2.
    Describe how advances in information technology are applied to different fields and the revolutionary changes they brought to those fields
  • 3.
    Evaluate the key benefits and potential drawback or issues underlying these changes
  • 4.
    Recognize the future trend in information technology development and possible benefits and issues associated with these development trends
• ELEC 1200

  A System View of Communications: from Signals to Packets

  4 Credit(s)
  Corequisite(s)

  (COMP 1021 OR COMP 1022P OR COMP 1023) AND (MATH 1003 OR MATH 1014 OR MATH 1020 OR MATH 1024)

  Mode of Delivery

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

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Examine a voice communication system to identify the practical context of key theoretical concepts in ECE.
  • 2.
    Identify typical problems and tradeoffs encountered in electronic and computer engineering systems.
  • 3.
    Analyze simple approaches to address a range of problems and tradeoffs.
  • 4.
    Use software tools, such as MATLAB, to investigate potential solutions to problems and tradeoffs in order to validate an analysis, and to handle cases not amenable to simple analysis.
  • 5.
    Work in a cooperative setting on real hardware or by simulations where the simplifying assumptions used in theoretical analysis may be violated, and assess the benefits and limitations of such analysis.
• ELEC 1300

  Introduction to Microelectronics and Integrated Circuits

  4 Credit(s)
  Corequisite(s)

  MATH 1014 AND (PHYS 1114 OR PHYS 1314)

  Exclusion(s)

  ELEC 3400, ELEC 3500

  Description

  This course introduces the foundations and applications of microelectronics and integrated circuits (ICs). Topics are divided into five categories, covering (i) historical perspectives of microelectronics devices and ICs; (ii) electronic materials (from atoms to solids, semiconductors, crystal growth); (iii) solid-state devices (diodes, photo-active devices, bipolar junction transistors, field effect transistors); (iv) classifications and applications of ICs (digital/logic, analog, memory); (v) modern technologies (artificial intelligence, quantum, etc.).

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Explore the historical perspectives and future trends of the microelectronics and integrated circuits industry
  • 2.
    Analyze the electronic and transport properties of crystalline solids
  • 3.
    Describe the operating principles and functionalities of common solid-state electronic devices
  • 4.
    Understand the classifications and applications of various types of integrated circuits
  • 5.
    Describe the key techniques and considerations behind the fabrication of modern electronic devices and integrated circuits
• ELEC 1910

  Academic and Professional Development I

  0 Credit(s)
  Description

  A compulsory, one year course for Electronic Engineering (ELEC) and Microelectronics and Integrated Circuits (MEIC) 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, PP or F.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Engage in discussions on academic and professional issues, and effectively express opinions and concerns
  • 2.
    Express their views on the study related issues about electronic and computer engineering
  • 3.
    Become a platform to practice their English communication skills, in speaking and listening, on both academic and non-academic topics
• 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

  Cross-Campus Equivalent Course

  ROAS 2100

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Describe Basic Continuous Time and Discrete Time signals and different ways to make use of and manipulate them.
  • 2.
    List the Properties of LTI systems and to determine the output of an LTI system using the impulse response and the convolution sum/integral, and the frequency response and transform.
  • 3.
    Correctly apply the appropriate transform (FS, DTFS, FT, DTFT) to produce a Frequency domain representation for continuous-time/discrete-time and periodic/aperiodic signals, and relate basic operations in the time and frequency domains.
  • 4.
    State and prove the sampling theorem.
  • 5.
    Analyze differential and difference equations as causal LTI systems and to realize them in different block diagram forms.
  • 6.
    Apply theories learnt to the analysis of communication systems including Amplitude Modulation and Frequency Division Multiplexing, the communication channel, mechanical systems, and new problems.
  • 7.
    Use the Software Tools Matlab to manipulate, process, analyze and plot signals.
• ELEC 2350

  Introduction to Computer Organization and Design

  4 Credit(s)
  Prerequisite(s)

  ELEC 1100

  Exclusion(s)

  COMP 2611, ISDN 4000F

  Cross-Campus Equivalent Course

  MICS 2070

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the typical computer hardware and software components and computer technology trends
  • 2.
    Understand typical instruction set architecture and assembly programming method
  • 3.
    Use computer arithmetic techniques to represent and process data in computers
  • 4.
    Use typical methods to evaluate computer performance
  • 5.
    Use a typical computer system design flow to systematically develop single-cycle processor architectures including datapath and control for an instruction set
  • 6.
    Systematically develop basic multi-cycle pipelined processor architectures for an instruction set and handle hazards
  • 7.
    Understand memory hierarchies and use cache to handle temporal and spatial locality in programs
• 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

  Cross-Campus Equivalent Course

  ROAS 2200

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Apply the fundamental circuit concepts to compute the output of basic electronic circuits in response to a DC input signal.
  • 2.
    Recognize sinusoidal steady state characteristics of basic electronic circuits using phasors and compute the output of basic electronic circuits in response to an AC input.
  • 3.
    Compute the transient responses of basic electronic circuits consisting of capacitors and inductors.
  • 4.
    Compute the characteristics of basic electronic circuits consisting of operational amplifiers and diodes.
  • 5.
    Employ electronic instruments and perform experiments.
  • 6.
    Apply CAD tools to simulate and analyze electronic 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize basic concepts of electronic components and circuits
  • 2.
    Analyze DC, AC and transient behaviors of electronic circuits
  • 3.
    Recognize basic logic functions and logic gates
  • 4.
    Analyze and design combinational and sequential logic circuits
  • 5.
    Employ electronic instruments to perform experiments
• 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 (prior to 2022-23), 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the mathematic basis of probability models and their application to engineering.
  • 2.
    Manipulate probability models to solve engineering problem.
  • 3.
    Recognize probabilistic experiments and develop relevant probability models for representing such experiments.
  • 4.
    Use Python as a software tool to manipulate, process, analyze and plot quantities relating to engineering probability models.
• ELEC 2910

  Academic and Professional Development II

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Maintain a clear understanding on academic and professional matters of both general and individual concern via meeting with their advisors.
  • 2.
    Be familiar with the matters related to their study of electronic and computer engineering.
  • 3.
    Improve English communication skills, in speaking and listening, on both academic and nonacademic topics.
• ELEC 2991

  Industrial Experience (Electronic Engineering)

  0 Credit(s)
  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Use tools or platforms commonly used in the engineering industry in order to solve engineering and business problems in an efficient, economical, and practical way
  • 2.
    Be well-equipped to enter and become productive members of the work force
  • 3.
    Be aware of the professional practices and ethical responsibilities of engineers
  • 4.
    Gain experience applying their knowledge of mathematics, science and electronic and computer engineering in an industrial setting
  • 5.
    Cooperate with people from various disciplines and backgrounds
• ELEC 2992

  Industrial Experience (Microelectronics and Integrated Circuits)

  0 Credit(s)
  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 microelectronics and integrated circuits profession. Students must also complete the USTSEEB Safety Training module. Cantonese or other working languages may be used in off-campus training and internships, in some situations. For students in the BEng in Microelectronics and Integrated Circuits program under the four-year degree only. Internship coordinator's approval is required for enrollment in the course. Graded P, PP or F.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Use tools or platforms commonly used in the engineering industry in order to solve engineering and business problems in an efficient, economical, and practical way
  • 2.
    Be well-equipped to enter and become productive members of the work force
  • 3.
    Be aware of the professional practices and ethical responsibilities of engineers
  • 4.
    Gain experience applying their knowledge of mathematics, science and electronic and computer engineering in an industrial setting
  • 5.
    Cooperate with people from various disciplines and backgrounds
• ELEC 3100

  Signal Processing and Communications

  4 Credit(s)
  Prerequisite(s)

  (ELEC 2100 OR ELEC 2100H) AND (ELEC 2600 OR ELEC 2600H (prior to 2022-23))

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the modeling of communication links as well as understand why we are interested to study communication systems.
  • 2.
    Be familiar with both mathematical and qualitative concepts regarding analog communication systems as well as digital communication systems.
  • 3.
    Understand the difference, pros and cons between analog and digital communication systems.
  • 4.
    Understand how practical communication systems (analog and digital) are designed as well as explaining why these systems are designed that way.
  • 5.
    Understand how to utilize the mathematical tools of random variables and random process to quantify the performance of communication systems under noise.
  • 6.
    Use software tools (such as Matlab) to design and quantify the performance of communication systems.
  • 7.
    Apply the concept of signal space to qualitatively explain the design of digital communication systems.
• ELEC 3120

  Computer Communication Networks

  3 Credit(s)
  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the key technological developments in networking technology.
  • 2.
    Understand the fundamental principles for constructing a computer network.
  • 3.
    Develop network programming skills for various applications.
• ELEC 3130

  Digital Image Processing

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4130

  Prerequisite(s)

  ELEC 2100

  Corequisite(s)

  ELEC 3100 OR ELEC 3200

  Exclusion(s)

  COMP 4421, MATH 4336

  Cross-Campus Equivalent Course

  ROAS 4760

  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, object recognition and face detection. This course is mathematics‐oriented. It requires basic knowledge of linear algebra, calculus and linear filtering. Familiarity with the programming language MATLAB is needed.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the formation and quantification of digital images.
  • 2.
    Understand the basic principles behind spatial filtering and frequency domain filtering.
  • 3.
    Use filtering concept to enhance images and restore noisy-corrupted images.
  • 4.
    Understand the difference between binary images and gray value images, as well as the difference between gray value images and color images.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Acquire basic knowledge on optimization problems.
  • 2.
    Be able to solve optimization problems in practice with Python.
  • 3.
    Learn about financial data and modeling techniques.
  • 4.
    Learn about portfolio design in financial systems.
  • 5.
    Be able to employ the theoretical and practical knowledge on optimization for portfolio design.
  • 6.
    Learn more sophisticated portfolio optimization formulations and solve them.
• ELEC 3200

  System Modeling, Analysis and Control

  4 Credit(s)
  Prerequisite(s)

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

  Exclusion(s)

  CENG 3110, MECH 3610

  Cross-Campus Equivalent Course

  ROAS 3700

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    model simple dynamical systems using differential equations as well as understand the importance of the models in system analysis, synthesis, and simulation.
  • 2.
    Manipulate the models of LTI systems in different forms, such as differential equations, transfer functions, and block diagrams, analytically and by CAD tools (such as MATLAB).
  • 3.
    Understand feedback as a ubiquitous tool to control a system.
  • 4.
    Understand the importance of stability in a physical system and understand how to achieve stability using feedback control.
  • 5.
    Complete a real feedback control task from modeling, controller design, simulation, and implementation using CAD tools (such as MATLAB).
  • 6.
    Understand the robustness and performance issues in a control system and the ways to address these issues.
• ELEC 3300

  Introduction to Embedded Systems

  4 Credit(s)
  Prerequisite(s)

  COMP 2611 OR ELEC 2350 OR ISDN 4000F

  Cross-Campus Equivalent Course

  ROAS 3180

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the marketing and engineering views of embedded system applications.
  • 2.
    Explain the building blocks of embedded systems and analyse their interfacing techniques with simple external devices.
  • 3.
    Explain and compare different up‐to‐date computer interfacing technologies.
  • 4.
    Use CAD tools to program and emulate the performance of the micro‐controller.
  • 5.
    Execute a complete project in team from problem formulation, time management, design/implementation, up to verification and documentation.
• ELEC 3310

  Digital Fundamentals and System Design

  4 Credit(s)
  Prerequisite(s)

  ELEC 1100

  Exclusion(s)

  ISDN 4000D

  Cross-Campus Equivalent Course

  MICS 3430

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Describe and analyze sequential logic circuits
  • 2.
    Design, model, and simulate sequential logic circuits using RTL design and HDL languages
  • 3.
    Design, implement, and test sequential circuits and systems using FPGAs
  • 4.
    Develop a model engineering system following a hierarchical design principle
• ELEC 3350

  Principles of Machine Learning

  3 Credit(s)
  Prerequisite(s)

  COMP 1021 AND (ELEC 2600 OR MATH 2111 OR MATH 2121 OR MATH 2131)

  Exclusion(s)

  COMP 4211, EMIA 4110, MATH 4432

  Description

  An introductory course which provides a comprehensive overview of the fundamental concepts and techniques in machine learning, covering supervised learning, unsupervised learning, and reinforcement learning. Students will explore various algorithms and models, such as classification, regression, neural networks, clustering, dimensionality reduction, Markov decision processes, and reinforcement learning. The course emphasizes both theoretical understanding and practical applications, with hands-on programming assignments and a final project. By the end of the course, students will have a solid foundation in machine learning and be equipped with the skills to analyze and solve real-world problems using machine learning techniques.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Differentiate between supervised learning, unsupervised learning, and reinforcement learning, and understand their respective objectives and key characteristics
  • 2.
    Apply fundamental modeling techniques and algorithms in machine learning to solve real-world problems
  • 3.
    Implement machine learning algorithms using Python/Pytorch
  • 4.
    Evaluate the performance of machine learning models using appropriate metrics and validation techniques, and interpret the results to draw meaningful conclusions
  • 5.
    Recognize the potential applications of machine learning in various domains and appreciate the ethical considerations and limitations associated with these techniques
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the operations of diodes and transistors in integrated circuit functional blocks and systems.
  • 2.
    Distinguish and employ large‐signal analysis and small‐signal analysis in analyzing a circuit.
  • 3.
    Analyze and design basic CMOS analog and digital building blocks and simple mixed‐signal systems.
  • 4.
    Analyze, design and debug analog and digital circuit building blocks.
  • 5.
    Apply software tool, such as Pspice, to design, simulate and analyze integrated circuit functional blocks.
  • 6.
    Access to a reference book and McGraw-Hill software for additional learning and support.
• ELEC 3410

  CMOS VLSI Design

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4410

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the advantages and critical importance of CMOS technology for very-large-scale integration.
  • 2.
    Understand the physical structure and operation of digital CMOS integrated circuits.
  • 3.
    Use a computer-aided-design tool for developing and characterizing CMOS integrated circuits.
  • 4.
    Design and demonstrate high-performance and compact digital CMOS integrated circuits.
  • 5.
    Understand the basic principles and current challenges in CMOS technology scaling.
• ELEC 3500

  Integrated Circuit Devices

  4 Credit(s)
  Prerequisite(s)

  ELEC 2400

  Cross-Campus Equivalent Course

  MICS 3090

  Mode of Delivery

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

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    communicate with the language of semiconductor (diode, MOSFET, doping, Fermi‐level, drift‐diffusion, memory, etc.
  • 2.
    Describe the basic principles of some common circuit active elements plus photo active devices (solar cell, LED, CCD).
  • 3.
    Describe the effects of changing the key physical parameters of diode, MOSFET, and memory on the trend of their characteristics.
  • 4.
    Match a given model to measurement data by selecting relevant parameters.
  • 5.
    Understand basic lab operations (including cleanroom and device testing) in integrated circuits 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Solve the main problems of the electric and magnetic fields distribution for different device constructions, taking into account the boundary problems.
  • 2.
    Be familiar with the Maxwell equations both in the integral and differential forms as the fundamental laws of the Electromagnetism and to give a physical evidence that lead to their appearance.
  • 3.
    Present a basic description of the electromagnetic wave propagation in various materials, including conductors, dielectrics and magnetic materials.
  • 4.
    Apply the basic principles of the electromagnetism to the development of the transfer devices of the electromagnetic energy, such as transmission lines and antennas.
• 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 (prior to 2022-23) OR MATH 2111) AND (BIEN 3300 OR CENG 3300 OR LIFS 3150 OR MATH 2411)

  Exclusion(s)

  BIEN 4310 (prior to 2022-23), 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Obtain a holistic view of Cognitive neuroscience.
  • 2.
    Recognize the neuroanatomy and understand the basic functions.
  • 3.
    Comprehend methods of cognitive neuroscience.
  • 4.
    Understand basic neural coding theory and computation method.
  • 5.
    Enhance the knowledge in related area, such as neural engineering.
• ELEC 3910

  Academic and Professional Development III

  0 Credit(s)
  Description

  Continuation of ELEC 2910. Graded P or F.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Maintain a clear understanding on academic and professional matters of both general and individual concern via meeting with their advisors.
  • 2.
    Be familiar with the matters related to their study of electronic and computer engineering.
  • 3.
    Improve English communication skills, in speaking and listening, on both academic and nonacademic topics.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Equip with broad and useful knowledge to various topics which are not covered by existing courses.
  • 2.
    (Each offering under the umbrella will have specific learning outcomes.)
• ELEC 4110

  Digital Communications and Wireless Systems

  3 Credit(s)
  Prerequisite(s)

  ELEC 2100

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the key technological developments of digital communications and wireless systems.
  • 2.
    Identify the fundamental principles related to digital communication technology.
  • 3.
    Use Matlab to solve simple simulation problems in digital communications.
  • 4.
    Comprehend technical specifications and understand how and why practical wireless systems are designed.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Have an in-depth understanding of time-domain and frequency-domain methods as well as their relationships in dynamic system modeling, analysis and control
  • 2.
    Use several techniques for control system design
  • 3.
    Understand further the importance of feedback and its limitations
  • 4.
    Skillfully use CAD tools (such as MATLAB and SIMULINK) in control system modeling, analysis and control
  • 5.
    Equip themselves with experience in controlling real physical systems
• ELEC 4220

  Robotics: Modeling, Control and Planning

  3 Credit(s)
  Prerequisite(s)

  ELEC 3200

  Cross-Campus Equivalent Course

  ROAS 4220

  Description

  In this course, fundamental disciplines of modern robotics are introduced: mechanics, control, and computing. These components are integrated to analysis, design, and control of mobile robots and manipulators to serve engineering or scientific needs. Students will learn: (1) how to use mathematical methods to model mobile robots and manipulators and to plan their motion; (2) how to process sensor information and design feedback controllers and planners; and (3) how to implement algorithms through computer systems to achieve autonomy. As class projects, students will be encouraged to perform simulations using MATLAB and to carry experiments on mobile robots and manipulators.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the history and development of the robotics field
  • 2.
    Identify the basic building blocks of robots and concepts of robotics
  • 3.
    Analyze, design and debug building modules for robots
  • 4.
    Design and build a robot that meets a given set of specifications
  • 5.
    Work in a team environment: learn and practice effective project and time management
  • 6.
    Execute a complete project from problem formulation, design/implementation, up to verification and documentation
• ELEC 4240

  Deep Learning in Computer Vision

  3 Credit(s)
  Alternate code(s)

  COMP 4471

  Prerequisite(s)

  (COMP 1023 OR COMP 2011 OR COMP 2012 OR COMP 2012H) AND (MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)

  Cross-Campus Equivalent Course

  COMP 4471, ROAS 4471

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the basics of deep neural networks.
  • 2.
    Train deep neural networks on several computer vision tasks.
  • 3.
    Use deep learning as a tool to solve a research problem of their interests.
• ELEC 4260

  Intelligent Robots and Embodied AI

  3 Credit(s)
  Prerequisite(s)

  ELEC 3130 AND ELEC 3350

  Exclusion(s)

  ELEC 3210 (prior to 2025-26)

  Description

  AI plays an essential role in robotics to enable them to understand and interact with the surrounding environment. This course gives an introduction to the application of AI in robotics. It focuses on vision-based (and learning-based) techniques to enable robot navigation and object manipulation.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the basic principles of robot navigation, grasping, and manipulation
  • 2.
    Learn to formulate and apply machine learning techniques for robotics applications
  • 3.
    Learn to implement machine learning algorithms on robot platforms (in both simulator and real robots)
  • 4.
    Learn to test and improve various alogrithms with experiments
  • 5.
    Understand and apply vision based robot navigation algorithms
  • 6.
    Understand and apply vision based robot grasping and manipulation algorithms
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Know the design flow of FPGA based design, basic architecture of FPGA device.
  • 2.
    Learn the basics of Verilog, a hardware description language (HDL).
  • 3.
    Learn how to develop RTL design based on Verilog, and implement the design on FPGA.
  • 4.
    Learn the high‐level synthesis design flow.
  • 5.
    Know how to analyze the performance of a FPGA based design.
• ELEC 4350

  AI Processor Architecture

  3 Credit(s)
  Prerequisite(s)

  ELEC 2350

  Description

  Artificial Intelligence (AI) techniques have achieved great success in a wide range of applications like computer vision, natural language processing, and scientific computing. Traditional processors are not optimized for AI tasks, which can result in slow performance and high energy consumption. To unlock the full potential of AI, both academia and industry have developed many AI processors for efficient AI computing from edge to cloud, with specialized architecture for the complex computations of AI applications. This is an introductory course to advanced processor architecture for AI computing. The topics covered include AI algorithm basics, processing element, dataflow, memory system, software hardware co-design for AI processors. This course will also introduce benchmarking and recent advances of AI processors.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the AI computing basics and challenges
  • 2.
    Understand the programming and dataflow of AI processors
  • 3.
    Understand the hardware components of AI processors
  • 4.
    Survey and present at least one of the major AI processor technology trends
  • 5.
    Acquire the typical methods to design and evaluate AI processor performance
• ELEC 4420

  Analogue Integrated Circuits Design and Analysis

  4 Credit(s)
  Prerequisite(s)

  ELEC 3400

  Cross-Campus Equivalent Course

  MICS 4040

  Description

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

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Analyze basic analog circuit schematics composed of MOS transistors; know how to compute the gain, bandwidth, input/output voltage range, PSRR, CMRR with a given circuit schematic.
  • 2.
    Design basic analog circuits, including single‐stage amplifiers, two‐stage amplifiers, reference circuits, etc.
  • 3.
    Understand what feedback in analog circuits is, identify corresponding feedback types and the sources of instabilities.
  • 4.
    Understand noise and its origin in analog circuits, calculate noise contribution from different sources.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize magnetic quantities such as magnetic flux, permeability and reluctance, and compute magnetic quantities relating to inductors and transformers.
  • 2.
    Recognize and compute electrical quantities such as power and work done related to both DC and AC circuits.
  • 3.
    Compute operating parameters and characterize the performance of power converters and regulator circuits.
  • 4.
    Analyze and design component parameters for power converters and regulator circuits.
  • 5.
    Apply software (CAD) tools to design, simulate and analyze power converters and regulator circuits.
• ELEC 4450

  Practical Considerations of Analog Integrated Circuit Design

  3 Credit(s)
  Previous Course Code(s)

  ELEC 4010O

  Prerequisite(s)

  ELEC 3400

  Description

  The performance of analog integrated circuits is fundamentally tied to the behavior of key building blocks, such as current mirrors and differential pairs. These components depend critically on the matching properties of paired MOSFETs. In advanced CMOS technology, these properties are influenced not only by channel width and length but also by layout-dependent effects and parasitic elements unique to scaled processes. This course explores the impact of these phenomena on analog circuit behavior, equipping students with practical methodologies to mitigate their influence and optimize circuit design.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the layout view and cross-sectional view of deeply scaled PMOSFETs, NMOSFETs, and isolated-NMOSFET.
  • 2.
    Identify parasitic components in deeply scaled CMOS technologies and understand their impact on analog circuit performance.
  • 3.
    Understand the impact of short-channel effects, layout dependent effects, and process variations, on analog circuit performance.
  • 4.
    Analyze and compute the impact of process variations on analog circuit performance such as accuracy of a current mirror and input offset voltage of an amplifier.
  • 5.
    Apply various design and layout methodologies to minimize the impact of short-channel effects, layout dependent effects, and process variations, on analog circuit performance.
  • 6.
    Apply industry software, Cadence, to design analog circuits and evaluate their performance under the impact of short-channel effects, layout dependent effects, and process variations.
• ELEC 4510

  Semiconductor Physics for Solid-State Electronics

  3 Credit(s)
  Prerequisite(s)

  ELEC 3500

  Exclusion(s)

  ELEC 4010Q

  Description

  This course covers fundamental semiconductor physics relevant to modern electronics and provides a physical understanding of advanced solid-state devices. Topics include quantum mechanics of electrons in solids, crystalline structures, band theory of semiconductors, electron statistics and dynamics in energy bands, carrier transport, and semiconductor heterostructures. Background in basic calculus, linear algebra, and probability is assumed.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the formation and properties of semiconductor crystals.
  • 2.
    Interpret the electronic band structure of semiconductor crystals.
  • 3.
    Associate the electronic band structure to the properties of semiconductor materials and devices.
  • 4.
    Analyze carrier dynamics and transport in semiconductors.
  • 5.
    Construct energy band diagrams of semiconductor heterostructures.
  • 6.
    Understand the physics and operation of advanced semiconductor devices such as heterojunction bipolar transistors and heterojunction field-effect transistors.
• ELEC 4520

  Integrated Circuit Fabrication Technology

  3 Credit(s)
  Prerequisite(s)

  ELEC 3500

  Cross-Campus Equivalent Course

  AMAT 4570

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the operation of a cleanroom.
  • 2.
    Recognize the basic operation principles of semiconductor fabrication equipment.
  • 3.
    Understand the process modules available in IC fabrication.
  • 4.
    Design process flows of IC fabrication technologies.
  • 5.
    Evaluate effects of process parameters on final transistor characteristics.
  • 6.
    Apply the measurement skills for microelectronic devices and IC characterization.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the current energy situation
  • 2.
    Understand the current energy supply chain using fossil fuels and its environment consequences
  • 3.
    Understand some current alternative technologies related to energy generation
  • 4.
    Understand the operation of solar cells and related semiconductor physics
  • 5.
    Analyze and simulate operation of solar cells
  • 6.
    Understand the operation of thin film solar cells
  • 7.
    Solve homework problems based on class discussions and lecture notes
  • 8.
    Perform research and complete a term project on a topic relevant to the course
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Explain key theoretical concepts relating to optics and applications of optical technology, including the nature and propagation of light, and optical instrumentation.
  • 2.
    Observe key optical phenomena experimentally and build a variety of optical instruments.
  • 3.
    Analyze simple optical systems consisting of lenses, stops, reflectors and prisms, determine and use principal points and focal points, and calculate and describe optical aberrations.
  • 4.
    Analyze and design systems for measurement of polarization, precision measurement based on interference, optical thin film, interferometer, etc.
  • 5.
    Analyze Fraunhofer diffraction patterns, determine the spatial resolution of an imaging system, design optical gratings and build an optical spectrometer.
• ELEC 4620

  Photonics and Optical Communications

  4 Credit(s)
  Prerequisite(s)

  ELEC 3600

  Cross-Campus Equivalent Course

  AMAT 4580

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Explain the fundamental principles and concepts of photonics in the context of optical communications.
  • 2.
    Utilize a range of components in photonics and optical communications.
  • 3.
    Communicate effectively using appropriate technical terminology and conventions in photonics and optical communications.
  • 4.
    Self-learn on key current developments in photonics and optical communications.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the broad role that an electric engineer can play in biomedical engineering.
  • 2.
    Describe and analyze biomedical applications from electrical, chemical and mechanical engineering perspectives.
  • 3.
    Recognize how engineering and mathematics can be applied to the analysis and constructive manipulation of biological systems and the development of biomedical therapies.
  • 4.
    Design a variety of biomedical instruments via comprehensive labs.
  • 5.
    Work collaboratively in an interdisciplinary setting.
  • 6.
    Undertake more advanced courses in biomedical engineering.
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Explain the differences of five common imaging modalities, namely X-ray imaging, computer tomography, nuclear medicine imaging, ultrasound imaging and MRI, in terms of application setting and targets of measurement.
  • 2.
    Calculate radiation and attenuation of X-ray in body and explain the working principle of X-ray imaging.
  • 3.
    Carry out Fourier transform and Radon transform to objects with different geometric shapes (such as circle and ellipse), and apply Fourier slice theorem and filtered back projection to reconstruct the CT images of objects from parallel projection and fan projection.
  • 4.
    Calculate the mass defect and the corresponding energy, explain the radioactive decay law, and describe the image formation process of nuclear imaging using mathematical formula.
  • 5.
    Solve the plane wave equation and the spherical wave equation, and mathematically describe the ultrasound imaging process.
  • 6.
    Explain the nuclear magnetic resonance (NMR) at both microscopic level and macroscopic level, describe the key difference between NMR and magnetic resonance imaging (MRI), draw the timing diagram of MRI pulse sequences (such as 90-free induction decay (FID) pulse sequence, spin-echo pulse sequence, and inversion-recovery sequence) and describe the principles of controlling gradients (such as slice selection gradient, frequency encoding gradient, and phase encoding gradient).
• ELEC 4830

  Statistical Signal Analysis and Applications in Neural Engineering

  3 Credit(s)
  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the basic concepts of probability, random processes, expectation and averages
  • 2.
    Obtain a holistic view of neural engineering
  • 3.
    Recognize the neuroanatomy and understand the basic functions
  • 4.
    Comprehend fundamentals of basic neural coding theory and computation methods
  • 5.
    Analyze neural data and implement on neutrally-controlled device/robot
  • 6.
    Enhance the knowledge in neural engineering, signal processing and practical programming
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Deploy the deep learning algorithms to perform fundamental image recognition tasks
  • 2.
    Display proficiency in utilizing at least one of the deep learning frameworks, such as PyTorch or TensorFlow
  • 3.
    Acquire a foundational understanding of multimodality medical images
  • 4.
    Acquire a foundational understandings of deep learning basics
  • 5.
    Acquire a basic understanding of deep learning applications in medical image analysis such as semi-supervised learning, domain generalizable approaches
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the importance and difficulties in applying learned skills/knowledge to solve practical problems
  • 2.
    Understand the steps in solving a practical problem from background research to problem solution
  • 3.
    Execute a complete project &om problem formulation, design/implementation, up to verification, documentation and presentation
  • 4.
    Work in a team environment: learn and practice effective project and time management
  • 5.
    Identify the contribution of the project
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Recognize the importance and difficulties in applying learned skills/knowledge to solve research problems
  • 2.
    Understand the steps in solving a research problem from literature review to problem solution
  • 3.
    Execute a complete project from problem formulation, design/optimization, up to verification, thesis preparation and presentation
  • 4.
    Identify the novelty/contribution of the thesis
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Use tools or platforms commonly used in the engineering industry In order to solve engineering and business problems in an efficient, economical, and practical way
  • 2.
    Be well-equipped to enter and become productive members of the work force
  • 3.
    Be aware of the professional practices and ethical responsibilities of engineers
  • 4.
    Gain experience applying their knowledge of mathematics, science and electronic and computer engineering in an industrial setting
  • 5.
    Cooperate with people from various disciplines and backgrounds to work in a team environment
  • 6.
    Understand the steps in solving a practical problem from background research to problem solution
  • 7.
    Execute a complete project from problem formulation, design/implementation, up to verification, documentation and presentation
  • 8.
    Identify the contribution of the project
• ELEC 4920

  Microelectronics Project

  3 Credit(s)
  Corequisite(s)

  ELEC 3500

  Description

  The goals of this course are to provide a detailed understanding and hands-on laboratory experience in the wide range of technical processes and characterization techniques that comprise modern microelectronics and integrated circuits. The course is intended to (a) be the primary core course for students seeking to pursue a career in microelectronics and integrated circuit fabrication and testing, and (b) provide overview background materials for students interested in other related aspects of the semiconductor industry, including VLSI design, microprocessor architectures, micro-electro-mechanical systems (MEMS), and optoelectronic devices. Instructor’s approval is required for enrollment in the course.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the operation of surface and bulk micromachining techniques: depositions, oxidation, wafer bonding, optical lithography, wet and dry etching, lift-off, and structure release
  • 2.
    Understand the process for manufacturing silicon IC wafers from raw quartzite. Calculate the impurity doping density of a silicon wafer or epitaxial layer from four-point probe measurements of the sheet resistance
  • 3.
    Understand the chemistry of photoresists and explain the differences, particularly in development, between positive and negative resists
  • 4.
    Understand the advantages of electron beam lithography for design, development, and manufacturing
  • 5.
    Understand the chemical process for wet chemical etching and design a process to obtain a specific etch rate and profile
  • 6.
    Understand the implications of the binary phase diagrams of important elements and silicon and demonstrate the ability to calculate liquid and solid distributions by the lever rule
  • 7.
    Fabricate a selection of microelectronic & IC devices, including diodes, diffused capacitors, Schottky diodes, pn junction diodes, npn bipolar transistors, p-MOS transistors, n-MOS transistors, CMOS devices, and ring oscillators
  • 8.
    Satisfactorily test and characterize the devices fabricated and apply statistical criteria to provide upper and lower limits to process sensitivity
• ELEC 4930

  Integrated Circuit Design Project I

  3 Credit(s)
  Corequisite(s)

  ELEC 3400 AND ELEC 4410

  Description

  Students will work on the chip design and layout in this course. Students will attend weekly lectures to gain the essential knowledge required for IC tape-out. ESD circuit design and layout; I/O design and pads; The basics of analog layout; Power/ground routing & DRC/LVS checks from a tapeout perspective; Digital test structures and the role of dedicated power/GND pins; Analog test structures; Test structures for circuit and device reliability; Built-in self-test, design for testability, and automatic test pattern generation; Passive components and layout; Circuit-level verification techniques & Verilog-A; Chip-level verification: An introduction to mixed-mode simulations; Floor-planning and chip assembly. Instructor’s approval is required for enrollment in the course.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Complete an integrated circuit chip from specification to tape-out
  • 2.
    Use a given process development kit to its full potential using a fullcustom design flow
  • 3.
    Optimize an integrated circuit to meet given powerperformance-area constraints
  • 4.
    Understand and address challenges associated with the integration of digital and analog blocks on a chip
  • 5.
    Perform focused simulations to verify timing, functionality, and signal integrity with the effective use of Verilog-A
  • 6.
    Build suitable test structures for chip characterization
  • 7.
    Clearly present solutions to design problems via reports and class presentations, both individually as well as in a group
• ELEC 4931

  Integrated Circuit Design Project II

  3 Credit(s)
  Prerequisite(s)

  ELEC 4930

  Description

  Continuing ELEC 4930, students will prepare for chip testing. This preparation will include PCB design and fabrication, getting acquainted with chip testing strategies/techniques and equipment, writing a test plan, and writing RTL for an FPGA platform. Overview of chip testing & the different types of IC tests; Interfacing with an FPGA platform and RTL modules; Guidelines for low/medium frequency generic PCB design; PCB layout techniques for low/medium frequency and dc measurements; Understanding key tests: shorting condition, leakage condition, supply conditions, and input and output conditions; Writing an exhaustive test plan; Labs will focus on getting acquainted with different types of chip measurement & testing equipment, PCB design & fabrication, and FPAG platform setup. Instructor’s approval is required for enrollment in the course.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Design and layout a printed circuit board to facilitate testing of an integrated circuit chip
  • 2.
    Interface a test chip with an FPGA platform to perform tests
  • 3.
    Use various chip testing equipment to fully characterize an integrated circuit
  • 4.
    Draft an exhaustive chip test plan
  • 5.
    Demonstrate understanding of key tests that are performed on integrated circuit chips for their characterization
  • 6.
    Demonstrate understanding of limitations of measurements, lab safety precautions, and handling instructions for various equipment types
  • 7.
    Effectively communicate their solutions via reports and presentations
• ELEC 4932

  Integrated Circuit Design Project III

  3 Credit(s)
  Prerequisite(s)

  ELEC 4931

  Description

  Continuing ELEC 4931, students will perform chip testing and draft the datasheet. Students will perform detailed chip characterization following the exhaustive test plan they designed earlier in ELEC 4931. Perform detailed chip characterization on multiple dies, including at-speed and dc tests. Undertake tests to gather data on variability. Perform chip-level and system-level tests in an environmental chamber under varying VDD, temperature, and humidity levels. Carry out post-processing of measured data to gain further insight into chip behavior and performance. Log test results in a product datasheet. Instructor’s approval is required for enrollment in the course.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Follow a test plan to systematically perform full chip characterization
  • 2.
    Gain hands-on experience with configuring test equipment to conduct various tests
  • 3.
    Extract data on chip variability to obtain a deeper insight into the fabrication process
  • 4.
    Understand the importance of post processing of measured data to better understand chip performance metrics
  • 5.
    Characterize a particular use case (application) of an integrated circuit starting with the chip specification
  • 6.
    Draft a formal datasheet for an integrated circuit
  • 7.
    Clearly present chip characterization results in a class presentation
• 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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Learn to do research via engaging in a literature search of a special topic related to the Electronic and Computer Engineering, including reading and understanding the resarch papers.
  • 2.
    Define a research topic and identify problems in different aspects and conduct a research project.