 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Data-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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6111) | 02-SEP-2024 - 06-DEC-2024 Tu 09:00AM - 11:50AM | Rm 201, E4 | LAI, Zhilu | 20 | 19 | 1 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| EXCLUSION | INTR 5220 |
| CO-LIST WITH | INTR 5220 |
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6114) | 02-SEP-2024 - 06-DEC-2024 Tu 09:00AM - 11:50AM | Rm 102, E1 | YAN, Jia YANG, Liuqing | 15 | 4 | 11 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | IOTA 6910C |
| DESCRIPTION | The 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6115) | 02-SEP-2024 - 06-DEC-2024 Mo 09:00AM - 11:50AM | Rm 201, E1 | HU, Guobiao | 30 | 8 | 22 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course teaches the design and implementation of efficient, scalable, and fault-tolerant distributed systems. Topics include models for distributed communication and computing, synchronization, and consensus algorithms. The course will also cover relevant applications, including platforms for distributed Machine Learning such as Ray, and large-scale data and stream processing systems such as Apache Flink and Google Dataflow. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6103) | 02-SEP-2024 - 06-DEC-2024 Th 09:00AM - 11:50AM | Rm 202, E4 | KUTSCHER, Dirk | 20 | 12 | 8 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6113) | 02-SEP-2024 - 06-DEC-2024 Mo 03:00PM - 05:50PM | Rm 202, W4 | GAO, Shijian | 30 | 7 | 23 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course introduces students to the fundamentals of discrete-time signal processing, for both linear time invariant (LTI) and non-LTI systems. For LTI systems, the topics include the sampling theorem, Fourier transform, convolution, and spectrum analysis, which lays the foundation for OFDM in wireless communications. Advanced topics will also be covered for time-variant systems, such as Heisenberg transform, Wigner distribution and Fractional Fourier transform, and their applications in radar, sonar and the OTFS modulation in wireless communications. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6112) | 02-SEP-2024 - 06-DEC-2024 Th 09:00AM - 11:50AM | Rm 202, W1 | GONG, Zijun YANG, Liuqing | 30 | 15 | 15 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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). |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6107) | 02-SEP-2024 - 06-DEC-2024 Tu 01:30PM - 04:20PM | Rm 150, E1 | XING, Hong | 30 | 8 | 22 | 0 |
| VECTOR | [2-1-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6105) | 02-SEP-2024 - 06-DEC-2024 Fr 09:00AM - 10:50AM | Rm 201, E3 | CHANG, Tengfei | 20 | 8 | 12 | 0 | |
| T01 (6106) | 02-SEP-2024 - 06-DEC-2024 Fr 11:00AM - 11:50AM | Rm 201, E3 | CHANG, Tengfei | 20 | 8 | 12 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6108) | 02-SEP-2024 - 06-DEC-2024 Mo 10:30AM - 01:20PM | Rm 101, W4 | YU, Jiadong | 30 | 9 | 21 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course explores cutting-edge techniques for creating efficient machine-learning models to address the growing demand for real-time decision-making and localized processing across diverse application fields, including IoT/robotics/smart manufacturing systems and beyond. Key topics include model compression, pruning, quantization, neural architecture search, knowledge distillation, on-device fine-tuning, transfer learning, application-specific acceleration techniques, etc. Through hands-on projects, students will learn to optimize and adapt deep learning models for resource-constrained devices while maintaining accuracy and performance. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6102) | 02-SEP-2024 - 06-DEC-2024 Mo 01:30PM - 04:20PM | Rm 223, W1 | CHEN, Huangxun | 30 | 20 | 10 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6110) | 02-SEP-2024 - 06-DEC-2024 Mo 06:00PM - 08:50PM | Rm 102, E1 | CUI, Ying | 40 | 32 | 8 | 0 |
| VECTOR | [0 credit] |
|---|---|
| DESCRIPTION | A 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6116) | 02-SEP-2024 - 06-DEC-2024 Fr 01:30PM - 02:50PM | Rm 147, E1 | TYSON, Gareth John | 50 | 50 | 0 | 0 |
| VECTOR | [1-3 credit(s)] |
|---|---|
| DESCRIPTION | An independent research project carried out under the supervision of a faculty member on an Internet of Things topic. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6117) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |
| DESCRIPTION | Master'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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6118) | TBA | TBA | TBA | 999 | 11 | 988 | 0 |
| DESCRIPTION | Original 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6119) | TBA | TBA | TBA | 999 | 4 | 995 | 0 |