COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONData-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab. In-class lab demonstrations will also be provided.

IOTA 5002 - Data-driven Modeling: Learning from Sensor Data (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6158) Tu 09:00AM - 11:50AMRm 201, E3LAI, Zhilu3017130 
COURSE INFO
VECTOR[3-0-0:3]
EXCLUSIONINTR 5220
CO-LIST WITHINTR 5220
DESCRIPTIONThis course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.

IOTA 5003 - Wireless Connectivity for Mobile Autonomous Things (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6160) Tu 09:00AM - 11:50AMTBAYAN, Jia
YANG, Liuqing		153120
The classroom is W4202.
 
COURSE INFO
VECTOR[3-0-0:3]
PREVIOUS CODEIOTA 6910C
DESCRIPTIONThe distributed power supply for millions and billions of remote & off-grid electronics (such as wireless IOT node sensors) is challenging. Harvesting sustainable energy from the ambient environment provides the possibility of designing battery-free devices. This course will introduce the vibration energy harvesting technology developed in the past two decades to students. As the fundamentals, commonly used energy transduction mechanisms will be first introduced to the students. The students will also learn various modeling methods, including lumped parameter modeling, equivalent circuit modeling, and finite element modeling.

IOTA 5005 - Introduction to Energy Harvesting Technology (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6161) Mo 09:00AM - 11:50AMRm 201, W1HU, Guobiao3015150 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course provides a comprehensive overview of array processing techniques and their applications in various fields, including wireless communications, radio astronomy, biomedical imaging, and audio and speech signal processing. Students will explore topics such as wave propagation, data modeling, matrix decomposition, adaptive filtering, beamforming, direction finding, microphone and radar array processing, factor analysis, and biomedical array processing. The course emphasizes the interdisciplinary nature of array processing and its relevance in the context of IoT, preparing students to be equipped with the knowledge and skills necessary to analyze and process data from array-structured systems, contributing to advancements in array processing across diverse domains.

IOTA 5007 - Introduction to Data Processing in Array-structured Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6162) We 04:30PM - 07:20PMRm 201, W1GAO, Shijian307230 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course lays theoretical foundation for students with general EE background to pursue research advances involving wireless communication-based systems, e.g., 4G/5G mobile communication, IoT and WLAN. In this course, concepts of wireless channels, its modelling as well as channel capacity for point-to-point/multi-user/MMO communications will be systematically introduced. Along with understanding of these theories, the principles and state-of-the-art technologies to combat fading and interference including general diversity techniques, OFDM, and multiple access schemes will be conveyed. Finally, a few advanced topics for 5G-and beyond (B5G) communication system design will be briefly introduced, such as massive MIMO and reconfigurable intelligent surface (RIS).

IOTA 5105 - Fundamentals of Wireless Communications (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6164) We 10:30AM - 01:20PMRm 201, W4XING, Hong306240 
COURSE INFO
VECTOR[3-0-0:3]
EXCLUSIONINTR 5320
CO-LIST WITHINTR 5320
DESCRIPTIONThis course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.

IOTA 5108 - Incremental Learning and Adaptive Signal Processing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6166) Th 09:00AM - 11:50AMTBAGONG, Zijun
YANG, Liuqing		202180
The classroom is E2228.
 
COURSE INFO
VECTOR[2-1-0:3]
DESCRIPTIONThis course teaches the basic concepts of wireless sensor and actuator networks (WSANs), and how swarm intelligence is applied over WSAN. The course content includes the typical architecture of the hardware WSAN devices, the general communication mechanism and examples of applications using WSANs to realize swarm intelligence.

IOTA 5109 - Wireless Sensor and Actuator Networks Toward Swarm Intelligence (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6167) Fr 09:00AM - 10:50AMRm 101, W2CHANG, Tengfei208120 
T01 (6168) Fr 11:00AM - 11:50AMRm 101, W2CHANG, Tengfei208120 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course focuses on applying reinforcement learning (RL) to cyber-physical systems (CPS), which integrate computation, networking, and physical processes. It covers the fundamentals of RL and its application in designing intelligent decision-making algorithms for CPS. Topics include model-based and model-free RL approaches, safe RL practices to ensure the safety of CPS, and the integration of RL with other techniques, such as model predictive control in CPS. The course will also explore different applications of RL in CPS.

IOTA 5201 - Reinforcement Learning for Intelligent Decision Making in Cyber-Physical Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6169) Tu 01:30PM - 04:20PMRm 149, E1YU, Jiadong403910 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.

IOTA 5501 - Convex and Nonconvex Optimization I (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6170) Mo 03:00PM - 05:50PMRm 202, E4CUI, Ying4013270 
COURSE INFO
VECTOR[0 credit]
DESCRIPTIONA series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Graded P or F.

IOTA 6101 - Internet of Things Seminar I (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6171) Mo 01:30PM - 02:20PMRm 134, E1LI, Hongyu
TYSON, Gareth John		504820 
COURSE INFO
VECTOR[1 credit]
DESCRIPTIONAn independent research project carried out under the supervision of a faculty member on an Internet of Things topic.

IOTA 6900B - Independent Study (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6689) TBATBATBA202180
Instructor Consent Required
 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course explores the intersection of privacy, security, and the rapidly evolving landscape of foundation models—large-scale AI systems like ChatGPT and DeepSeek that power modern applications. Students will gain foundational knowledge of these models, including their architecture, training methodologies, and real-world applications. The course will then dive into emerging threats, such as data privacy risks (e.g., model inversion, membership inference) and security vulnerabilities (e.g., adversarial attacks, poisoning). We will examine state-of-the-art defense strategies, including robust training techniques, cryptographic protections, and privacy-preserving frameworks, while addressing ethical, regulatory, and practical challenges. By the end of the course, students will be equipped to identify, analyze, and mitigate privacy and security risks in foundation models, balancing innovation with responsible deployment.

IOTA 6910H - Privacy and Security Threat to Foundation Models (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6172) Th 06:30PM - 09:20PMRm 202, W4HE, Xinlei302730 
COURSE INFO
DESCRIPTIONMaster's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

IOTA 6990 - MPhil Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6048) TBANo room requiredTBA999209790 
COURSE INFO
DESCRIPTIONOriginal and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

IOTA 7990 - Doctoral Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6049) TBANo room requiredTBA999129870