About
Professor Lijie Li
Professor Lijie Li
Developing MEMS transducers, optical and radio frequency (RF) MEMS devices and systems.
Developed several MEMS devices that have many promising applications in optical, radio frequency (RF) and bioscience fields, such as in miniaturized optical imaging tools, energy harvesting devices, and spectroscopic biosensing applications.
Current research efforts are focused on:
The module reviews linear modulations, channel models for radio wave propagation in wireless communications, and the receiver design principles. The transmission diversity techniques are also included. In the second part, the techniques used in optical wireless communications are explained.
This module provides an introduction to several AI algorithms for engineering/physics problems. The specific engineering problems chosen to demonstrate the benefits offered by AI algorithms are 1) interpretation/processing of data from distributed sensors; 2) optimisation of material/device properties through physics informed neural network. The module teaches students basic statistical skills underpinning machine learning/artificial intelligence such as probability analysis and regression, as well as several case studies that use existing AI software to analyse engineering problems. Two assessments (exam and individual project, each carries 50%) for each term, designed to examine understanding of the basic machine leaning concepts and using the software to solve engineering problems, take place in the middle of term and end of term. Emphasis is placed on the use of existing software for tackling engineering problems.
Micro and Nano Electro-Mechanical Systems (MEMS/NEMS) are technology that integrates electrical and mechanical components and they offer many novel and diverse applications ranging from display technologies to sensor systems.
2018 - Present