Projects

Capstone thesis project description

University of Sydney

Title: Development of hand-held devices based on UWB, mmWave, THz or WiFi signals to see-through walls for human activity monitoring

In this project, we aim to develop a low-cost X-ray vision wireless hand-held device. The developed hand-held device can be used to see through walls to track moving human bodies. The technique would be based on a concept similar to radar and sonar imaging, instead of using high power signal, this one would use low power Wi-Fi or mmWave/THz, UWB signals to track the movement of people behind walls and closed doors. When an RF signal is transmitted towards a wall, due to the absorbing property of the walls, only a small part of the signal can be penetrated through the wall and can be reflected back when the signal reaches any objects that happen to be moving around in the other room. Based on the reflected signal, we can detect the moving objects.

Supervisor: Zihuai Lin. Email: zihuai.lin@sydney.edu.au

Requirements: up to 2 students are required for this project. The students participating in this project should have good knowledge of wireless communications, RF engineering, wireless local area networks and wireless sensor networks. Matlab C++ and Python programming skills are essential.

Reference

1. X. Wang and Z. Lin, “Nonrandom microwave ghost imaging,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8,pp. 4747–4764, 2018.

2. X. Wang and Z. Lin, “Microwave Surveillance based on Ghost Imaging and Distributed Antennas” IEEE Antennas and Wireless Propagation Letters, Volume: 15, March 2016. pp. 1831 - 1834

3. R. Luo, Z. Zhang, X. Wang, Z. Lin, “Wi-Fi Based Device-free Microwave Ghost Imaging Indoor Surveillance System” accepted at 2018 28th International Telecommunication Networks and Applications Conference (ITNAC).

4. Z. Zhang, R. Luo, X. Wang, and Z. Lin, “Microwave Ghost Imaging via LTE-DL Signals, accepted by International Conference on Radar 2018 August 2018, Brisbane

5. S. Liu, Z. Lin, "LDPC Coding Assisted Ghost Imaging ", submitted to IEEE Transactions on Computational Imaging, Feb. 2023.

Title: Precise Indoor Positioning based on moving Robots and UWB/mmWave/THz/WiFi techniques

Recently, indoor positioning is attracting considerable attention from both research and industry. Logistics, health-care applications, search and rescue, military services, tracking of objects and people, gaming and entertainment are a few examples of applications which can benefit from having precise localization information. However, in indoor environments, traditional services provided by e.g. GPS usually are not available, unreliable or inaccurate. For this reason, alternative solutions need to be developed. In this project, we aim to use Ultra-Wideband (UWB)/mmWave/THz/WiFi techniques and/or moving robots to realize precise indoor positioning.

Supervisor: Zihuai Lin. Email: zihuai.lin@sydney.edu.au

Requirements: up to 3 students are required for this project. The students participating in this project should have good knowledge on wireless sensor networks, communication theory and signal processing. Matlab, C++, Python programming skills are essential.

Reference

1. X. Shi, B. Anderson, G. Mao, Z. Yang, J. Chen, and Z. Lin, “Robust Localization Using Time Difference of Arrivals”, IEEE Signal processing letters, Vol. 23, Issue 10, Oct. 2016, pp. 1320 –1324. The latest impact factor is 2.528

2. D. Zhai and Z. Lin, “RSS-based Indoor Positioning with Biased Estimator and Local Geographical Factor”, Proceedings of the 22nd International Conference on Telecommunications (ICT 2015), Sydney, Australia

Title: Reflective Intelligence System (RIS) for 6G THz Cellular Networks

With explosive growth of wireless networks and the Internet, network resource utilization becomes one of the critical design issues. RIS has emerged as a promising tool for the design of next generation communication networks. There is a need to develop systematic design of RIS THz array techniques for real wireless energy-efficient networks. In this project, we plan to systematically design RIS channel estimation, beamforming mechanisms for cellular networks and related signal processing algorithms, as well as demonstrate the feasibility and benefits of integrating the developed RIS schemes into practical systems. The research outcomes are likely to result in significant improvements in network performance

Supervisor: Zihuai Lin. Email: zihuai.lin@sydney.edu.au

Requirements: up to 3 students are required for this project. The students participating in this project should have good knowledge of wireless cellular networks, communication theory, and signal processing. Matlab, C++ and Python programming skills are essential. The students with average marks above 75 are preferred.

Reference

1. X. Wang, Z. Lin, F. Lin, and L. Hanzo, Hybrid 3D Beamforming Relying on Sensor-Based Training and Channel Estimation for Reconfigurable Intelligent Surface Aided TeraHertz MIMO systems. arXiv preprint arXiv:2201.10757.

      2. Y. Hu, P. Wang, Z. Lin, M. Ding, "Performance Analysis of Reconfigurable Intelligent Surface Assisted Wireless System with Low-Density Parity-Check Code," accepted  by IEEE Communications Letters, June, 2021.

Title: Development of AI-based Digital Heart

This project will develop an AI-based digital heart to identify the strengths and weaknesses of the current technology which will be served as a foundation for improvement and development of future AI-enabled ECG in deep-learning convolutional neural networks. The most desirable outcome is a fully automated AI-enabled ECG system that mimics human-like interpretation of ECGs to assist medical practitioners in making greater diagnosis and clinical decisions.

1. Mobile phone APP development based on Android system.

Here you need to develop an APP for the Android system, the basic function is that you can use the APP to connect with the wearable ECG patch and perform some simple data analysis, etc.

2. Mobile phone APP development based on iOS system

Similar to the first project, now the platform is changed to iOS, the APP is particular for iPhone, iPad, etc

3. Computer-based user interface

Develop AI-based computer software which can automatically diagnose heart diseases. The above three subprojects are highly related.

Supervisor: Branka Vucetic and Zihuai Lin. Email: branka.vucetic@sydney.edu.au; zihuai.lin@sydney.edu.au

Requirements: up to 6 students are required for this project. The students participating in this project should have good knowledge on smart phone APP development. Programming skills are essential.

Reference

1. L. Meng, K. Ge, Y. Song, D. Yang, and Z. Lin, Long-term wearable electrocardiogram signal monitoring and analysis based on convolutional neural network, the IEEE Transactions on Instrumentation & Measurement, Vol. 70, April 2021, DOI:10.1109/TIM.2021.3072144

2. Z. Chen, Z. Lin, P. Wang, and M. Ding, Negative-ResNet: noisy ambulatory electrocardiogram signal classification scheme, Neural Computing and Applications, Vol. 33, Issue 14, July 2021, pp. 8857-8869

3. P, Wang, Z. Lin, Z. Chen, X. Yan, M. Ding, A Wearable ECG Monitor for Deep Learning-Based Real-Time Cardiovascular Disease Detection, https://arxiv.org/abs/2201.10083

4. X. Yan, Z. Lin, P. Wang, Wireless Electrocardiograph Monitoring Based on Wavelet Convolutional Neural Network, Proceedings of the IEEE WCNC 2020.

Title: Internet of Things (IoT) in Smart Hospitals for Pressure Injury Monitoring

This project will develop novel solutions for IoT based pressure injury monitoring to enable smart hospitals, smart healthcare and provide in-patients with good treatment experience. IoT is a key enabler for our future smart hospital and healthcare to effectively overcome the major problems, such as nursing staff shortness, impractical physical care environments and difficulties in identifying patients’ needs, etc.  In order to enable a fast uptake of the IoT pressure injury monitoring systems, key issues, such as data collection and sensing, pressure injury prediction modelling,  and hospital validation, should be addressed. These problems are the major technological obstacles that are preventing industrial partners from further expanding their business in the hospital IoT area.

1. A hospital-specific pressure injury IoT platform: We will develop a hospital IoT with a large-scale hybrid cloud system (combining Edge and Cloud computing) to meet the QoS requirement of the provided services and addressing the unexpected transmission latency caused by Internet connections.

2. Smart mat: We will develop a smart mat for pressure injury monitoring. The proposed smart mat consisting of a pressure sensor array and a moisture sensor array will be tested and validated in the Westmead hospital. The collected data will be transmitted to the cloud via WiFi or other wireless networks for data processing and analysis. Our developed deep learning algorithms for smart mat will be used to obtain the weight and moisture distribution of the inpatient’s body as well as the respiratory rate.

3. Pressure injury prediction: Based on the data collected in steps 1 - 2 and the PI prediction model obtained in Task 3, we will predict the pressure injury development in a real scenario. The results from different sensing techniques will be compared with those obtained using Waterlow scale. The comparison will provide insight to further improve the proposed algorithms and designs.

4. Hospital validation: Our developed pressure injury IoT system will be validated at Westmead hospital.