Integrated Circuits and Systems Projects

Intelligent Bio-Signal Processing

While AI has paved its way to some bionic applications mostly through software post-processing, close to none of today’s electronic implants can be named “Intelligent”! We believe with the large amount of data that is available in any medical field and remarkable advances in AI and the expertise that we have in that as well as electronic technology shrinkage to a remarkable scale, there is a real chance that we may ultimately be able to let an implant to “learn and understand” streams of neural data and make decision with high accuracy.

Team Members: Prof. Omid Kavehei

Contact: Prof. Omid Kavehei ([email protected])

A Ternary Neural Network Accelerator

The aim of this project is to develop a field programmable gate array (FPGA) based prototype accelerator supporting neural networks with ternary weights. It will be a precursor to a custom integrated circuit (IC) that can achieve improved speed and power consumption over graphics processing units (GPUs). The prototype will enable accurate estimation of performance achievable by a future IC, in particular establishing memory bandwidth and processing element requirements.

Team Members: Prof. Philip Leong

Contact: Prof. Philip Leong ([email protected])

Side Channel Attacks, Remote Power Attacks and Countermeasures

This project focuses on security attacks targeting embedded systems that illegally gain access to information or destroy information. While Advanced Encryption Standard (AES) is mathematically shown to be quite secure, AES circuits and software implementations are vulnerable to side channel attacks. The research explores differing power-based attacks, various countermeasures, and implementation of these countermeasures in hardware and software.

Team Members: Prof. Sridevan Parameswaran

Contact: Prof. Sridevan Parameswaran ([email protected])

Integrated Microwave Photonics for Signal Processing, Sensing, and Machine Learning

This project explores the development of advanced photonic integrated circuits (PICs) and microwave photonics to deliver innovative solutions for signal processing, sensing, and machine learning. It specifically targets critical challenges in processing radiofrequency, microwave, millimeter-wave, and digital signals directly in the optical domain. By leveraging unique optical and electronic characteristics of integrated photonics, the research aims to achieve compact, wideband-tunable, and high-speed photonic devices that significantly surpass the size, power consumption, and speed limitations of traditional systems. The resulting PIC technologies will enable transformative applications in high-performance signal processing, ultra-sensitive sensing platforms, and photonics-enhanced machine learning, opening new frontiers in next generation intelligent technologies.

Team Members: Prof. Xiaoke Yi

Contact: Prof. Xiaoke Yi ([email protected])

Nanofabrication and Heterogeneous Integration

This project investigates advanced nanofabrication techniques and innovative approaches to the heterogeneous integration of electronics and photonics, aiming to seamlessly combine diverse device functionalities onto a unified integrated platform. By addressing critical challenges such as device compatibility, signal interfacing, thermal management, and precise nanoscale fabrication, this research leverages the complementary strengths of electronics and photonics. The integrated micro/nanosystems resulting from this work will offer substantial improvements in miniaturization, system performance, multifunctionality, and energy efficiency. These advancements are anticipated to drive transformative innovations in high-speed communications, smart sensing systems, and data processing, paving the way toward next-generation integrated technologies.

Team Members: A/Prof. Liwei Li

Contact: A/Prof. Liwei Li ([email protected])

Unobtrusive Sensor Systems and AI for Healthcare

This project focuses on developing unobtrusive sensor systems and AI data processing for healthcare applications, including wearable sensor systems, health monitoring systems for people from neonates to elderly, and physiological and behavioral data sensing and analysis. The research aims to create intelligent health sensing, AI for health informatics and multimodal approaches for health regulation, patient health monitoring, and home monitoring.

Team Members: Prof. Wei Chen

Contact: Prof. Wei Chen ([email protected])

State Estimation and Planning in Dynamic Environments

This project focuses on developing foundation methodologies and algorithms to enable robotic systems to build real-time detailed 3D representation of the environment, a problem known as simultaneous localization and mapping (SLAM). The research approach draws from recent advances in stochastic systems, information theory, high performance computing, machine learning, and linear and nonlinear algebra.

Team Members: Dr. Viorela Ila

Contact: Dr. Viorela Ila ([email protected])

Embedded Robotic Imaging Systems

This project explores how new imaging technologies can help robots see in new ways, encompassing the design, fabrication, and deployment of optics, sensing, and embedded digital systems tailored to specific tasks. Leveraging custom sensing configurations allows traditionally serial, iterative, and complex processes to be carried out by parallel, low-latency, and low-power digital circuits. This work delivers next-generation sensing allowing robots to operate in new domains and in new ways, including privacy-preserving vision and robust low-latency vision in challenging conditions.

Team Members: Dr. Donald Dansereau

Contact: Dr. Donald Dansereau ([email protected])

Electrical Impedance Tomography for Medical Applications

This project focuses on understanding the electrical properties of biological tissue to better address a range of major health challenges relating to cardiovascular disease, cancer, and nutrition. The research aims to lead to the development of new devices to improve diagnosis and treatment of health problems, particularly in the area of low-cost devices for home health care.

Team Members: Prof. Alistair McEwan

Contact: Prof. Alistair McEwan ([email protected])

Table-top Nanometrology for Computational Imaging

This project aims to establish a table-top nanometrology platform that integrates coherent diffraction imaging with advanced computational reconstruction techniques. By leveraging high-harmonic generation light sources, the system enables non-destructive, high-resolution imaging of nanostructures in laboratory settings. It supports diverse applications ranging from semiconductor metrology to biological analysis.

Team Members: A/Prof. Steve Shu

Contact: A/Prof. Steve Shu ([email protected])

Group Coordinator

For general inquiries about collaboration opportunities, please contact:

Prof. Omid Kavehei
Email: [email protected]
Phone: +61 2 9351 XXXX