Taste of Research Summer Scholarships

2025 Projects - School of Electrical Engineering and Telecommunications

Electrical Engineering & Telecommunications Research Areas

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Electrical Engineering & Telecommunications Projects

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Project Title:	AI Agents for Network Observability
Name of Supervisor:	Dr. Minzhao Lyu
Email of Supervisor:	minzhao.lyu@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Vijay Sivaraman
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	This project develops AI agents to autonomously generate actionable insights for campus network operation by analysing real-time network telemetry measured from the university campus, such as the usage of signalling packets like DNS, volumetric telemetry of subnets and hosts, and performance telemetry of video, live stream, gaming, conferencing, GenAI, and other services. For example, the AI agents will be capable to make reasoning and decision on actionable insights for network capacity planning by measuring the trends in popular video streaming services (Netflix, Disney+, Stan, Prime, etc.), Live Streaming (Twitch, sporting games), Online Gaming (CS:GO, CoD, Fortnite, etc.), conferencing (Zoom, Teams, etc.), and GenAI (ChatGPT, Github Copilot, etc.), in terms of viewing patterns, and how they change by time-of-day, day-of-week, week-of-term. etc, or the AI agents can make real-time detection on cyberattacks and suggest remedying actions from volumetric and signalling packet statistics.
Research Environment:	This project will be carried out in a vibrant group that includes not just PhD and honours-thesis students at UNSW, but also commercial personnel from UNSW spin-out Canopus Networks that is building truly disruptive network traffic analytics platforms. You will get to play with live network traffic and develop AI agents for network operators, and your solutions will be tested and deployed in real operational networks.
Novelty and Contribution:	.
Expected Outcomes:	Expected outcomes include: (a) analysis of network traffic characteristics in the UNSW campus for the use case you work on; (b) development of AI agents to extract actionable insights from network telemetry for network capacity planning, trouble shooting, or cybersecurity.
Reference Material Links:	The UNSW research team has written many research articles which can be found at Dr. Minzhao Lyu’s website at https://minzhaolyu.github.io/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Characterisation and Modelling of Heat and Thermal Noise in Silicon Quantum Processors
Name of Supervisor:	Nard Dumoulin Stuyck
Email of Supervisor:	n.dumoulin@unsw.edu.au
Name of Joint/Co-Supervisor:	Arne Laucht
Email of Joint/Co-Supervisor:	a.laucht@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	MEMS, Micro & Nano Technologies
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Silicon quantum-dot (QD) arrays are at the forefront of quantum computing research due to their exceptional transistor integration, long coherence times, and high fidelity. However, as we move towards large-scale integration, managing heat within these arrays becomes a critical challenge. This project aims to characterise and model the thermal dynamics in QD arrays, ensuring the stability and efficiency of quantum operations.
Research Environment:	This project offers a unique opportunity to delve into the cutting-edge field of quantum computing. By focusing on the characterisation and modelling of thermal dynamics in silicon QD arrays, you will contribute to the development of scalable and reliable quantum computing platforms. This research not only enhances your understanding of quantum mechanics and thermal dynamics but also equips you with practical skills in device fabrication and measurement techniques.
Novelty and Contribution:	.
Expected Outcomes:	1. Characterise Heat Sources: Identify and analyse various heat sources in QD arrays, such as thermal noise through wires, microwave signal losses, and charge sensor operations. 2. Thermal Modelling: Develop comprehensive models to predict the thermal behaviour of QD arrays under different operational conditions. 3. Thermal Conduction Measurement: Integrate local heaters and thermometers into QD arrays to accurately measure and understand the thermal conduction characteristics of the device. If you are passionate about quantum computing and eager to tackle real-world challenges, this project is for you. Join us in pioneering the future of quantum technology and making significant strides towards practical and scalable quantum computers.
Reference Material Links:	tbc
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Conceptualisation and Modelling of a Homopolar Magnetic Bearing with Improved Lift-Up Performance
Name of Supervisor:	Dr Clay Chu
Email of Supervisor:	g.chu@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Programming Languages and Software Engineering
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Active Magnetic Bearings (AMBs) enable frictionless, wear-free operation, making them ideal for high-speed transportation, turbomachinery, and energy systems. They offer active vibration control, high power density, and extended lifespan. However, a key challenge in AMB technology is the high transient power required for rotor lift-up, impacting energy efficiency and scalability. This project aims to reduce lift-up power consumption by exploring alternative homopolar magnetic bearing (HMB) topologies and geometries. The student will conduct analytical modelling and finite element simulations to evaluate new designs that enhance lift-up performance, system efficiency, and rotor stability. By improving power efficiency and dynamic response, this research will contribute to the development of next-generation magnetic bearings for high-speed and energy-efficient applications.
Research Environment:	The student will work in the UNSW Electric Drive Lab, collaborating with researchers in magnetic bearings, motor drives, and power electronics. They will utilize advanced transducers to analyze rotor stability and lift-up characteristics. High-power, high-speed inverters will enable controlled magnetic bearing excitation, while dSPACE MicroLabBox and microprocessor controllers will facilitate real-time control implementation. High-bandwidth measurement devices and digital oscilloscopes will capture transient responses, ensuring precise system evaluation. A three-phase power supply with protection systems will provide a safe testing environment, supporting the development of efficient and high-performance homopolar magnetic bearings.
Novelty and Contribution:	.
Expected Outcomes:	• Research and perform a comparative analysis of magnet topologies. • Build a model of the selected electromagnet. • Perform electromagnetic simulations using multiphysics simulation platforms, e.g., ANSYS. • Outline findings in a comprehensive report.
Reference Material Links:	"S. Noh, J. H. Park, K.-H. Shin, and H.-w. Cho, ""Comparative study on heteropolar/homopolar magnetic bearings for high-speed rotating applications,"" AIP Advances, vol. 14, no. 3, p. 035031, Mar. 2025. S. Debnath, U. Das, P. K. Biswas, B. Aljafari, and S. B. Thanikanti, ""Design and Control of Multicoil Active Magnetic Bearing System for High-Speed Application,"" Energies, vol. 16, no. 11, p. 4447, May 2023. T. Lembke, ""Design and Analysis of a Novel Low Loss Homopolar Electrodynamic Bearing,"" Ph.D. dissertation, Royal Institute of Technology, Stockholm, Sweden, 2005."
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Design of a low-complexity equalizer for AFDM-based 6G communication systems in high-mobility scenar
Name of Supervisor:	Dr. Akram Shafie
Email of Supervisor:	akram.shafie@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Jinhong Yuan, Mr. Chengyang Zhang
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Affine frequency division multiplexing (AFDM) has recently emerged as a promising modulation technique to address challenges in high-mobility 6G communication environments, such as unmanned aerial vehicles (UAVs), high-speed railways, autonomous vehicles, and non-terrestrial networks. AFDM multiplexes information symbols within the affine domain using a sequence of chirps and enables effective path separation in the affine domain at the receiver. Despite its potential, existing AFDM receivers often rely on computationally intensive equalization techniques, especially compared to the single-tap equalization used in OFDM systems. This project aims to explore the design of an AFDM transmission scheme that enables low-complexity equalization. The approach involves strategic selection of AFDM chirp parameters, novel symbol arrangement design at the transmitter, and performing low-complexity equalization in a newly defined domain where channel-induced interference is minimized. Matlab-based simulations will be performed to model, validate, and assess the system’s performance.
Research Environment:	The student will be hosted at the Wireless Communications Lab (WCL) within the School of Electrical Engineering and Telecommunications at UNSW. The lab fosters a dynamic research environment, with several PhD students, senior research associates, and academics actively working on the related topics in wireless communications.
Novelty and Contribution:	.
Expected Outcomes:	1) Research Contributions: Development of a low-complexity equalizer for AFDM systems, accompanied by a poster presentation and a brief video summarizing key findings. 2) Publications: Submission of a short paper to a Tier-1 IEEE communication conference, followed by a full-length journal paper extending the conference work, to be submitted within two months after project completion. 3) Student Training & Development: The student will gain hands-on experience with MATLAB Simulink packages and statistical signal processing, along with deep exposure to wireless communication technologies, fostering their interest in pursuing higher-degree research (HDR/PhD).
Reference Material Links:	[1] A. Bemani, N. Ksairi, and M. Kountouris, “AFDM: A full diversity next generation waveform for high mobility communications,” in Proc. IEEE Int. Conf. Commun. (ICC) Workshop, Montreal, Canada, Jun. 2021, pp. 1–6. https://ieeexplore.ieee.org/document/9473655 [2] A. Bemani, N. Ksairi, and M. Kountouris, “Affine frequency division multiplexing for next generation wireless communications,” IEEE Trans. Wireless Commun., vol. 22, no. 11, pp. 8214–8229, Mar. 2023. https://ieeexplore.ieee.org/document/9562168 [3] C. Zhang, A. Shafie, D. Mishra, J. Yuan, “Unveiling AFDM modulation: Operational insights and parameter designs”, in Poster Session of IEEE AusCTW, Perth, Australia, Feb. 2025. https://drive.google.com/file/d/1cWkOFdXAFMZUBQtBAwvwLh9IezkQ_ugT/view?usp=drive_link
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Developing Speech-Based AI Models for Indigenous Australian Languages
Name of Supervisor:	Eliathamby Ambikairajah
Email of Supervisor:	e.ambikairajah@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Signal Processing & Control
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Unlike humans, speech AI systems require extensive data to learn a language. Consequently, AI models are typically developed for high-resource languages like English and Chinese, which have a large amount of voice recordings. This project aims to develop AI models for low-resource languages, such as Aboriginal languages using limited voice recordings, so that these low-resource languages are represented within the AI landscape.
Research Environment:	Our Speech and Behavioural Processing Research Laboratory is internationally recognised for its research in automatic emotion and mental state inference from speech and behavioural signals, pronunciation detection, speaker and language identification and cochlear signal processing. You will work closely with an academic supervisor(s) from the UNSW Speech Group and our speech research collaborator from the University of Melbourne.
Novelty and Contribution:	.
Expected Outcomes:	In this project, students will develop a solid understanding of signal processing, machine learning, and deep learning, while gaining practical skills in speech signal processing and coding.
Reference Material Links:	Bartelds, M., et al. "Making more of little data: Improving low-resource automatic speech recognition using data augmentation", ACL 2023.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	High-density implants for large-scale brain recording and stimulation
Name of Supervisor:	David Tsai
Email of Supervisor:	d.tsai@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	We are developing brain machine interfaces capable of interacting (recording and stimulating) with neurons at scale and with high spatiotemporal resolution, achieved through micro/nano-fabricated microelectrode arrays and CMOS microelectronics circuits. The microelectrode arrays are custom fabricated in a range of form factor, ranging from penetrating silicon probes for deep brain recordings, to soft flexible arrays for low-invasiveness cortical surface recording. A key aspect of the project is component packaging. Namely, bringing together the electrode array and the CMOS circuit to create reliable electrical connections. Once the packaging step has been done, it is essential that we verify the quality of these connections and adjust the packaging strategy if needed. This project is tightly integrated with several nationally / internationally funded research within our team, with the goal of developing low-noise and low-power CMOS ICs for recording neural signals and for stimulating neurons.
Research Environment:	This is a team project involving several researchers, graduate students, research assistants and thesis students, spanning Biomedical Engineering, and Electrical Engineering & Telecommunications
Novelty and Contribution:	.
Expected Outcomes:	The ToR project will involve developing code (in python) and working with custom CMOS integrated circuits and electrode arrays we have developed in-house. You will also be involved in silicon chip testing using a probe-station in a cleanroom environment, using specialized characterisation equipment. You should have a background in electrical engineering, comfortable with working on circuit boards, good at python, and have some exposure to microprocessor architectures. Good hand dexterity with working in very small devices would be a plus.
Reference Material Links:	https://biomicrosyslab.org
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Integrated sensing and communications (ISAC) for underwater acoustic systems
Name of Supervisor:	Dr. Akram Shafie
Email of Supervisor:	akram.shafie@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Jinhong Yuan, Mr. Kehan Huang
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	The future of the marine industry heavily relies on advanced underwater sensing and communications technologies to effectively monitor maritime activities for resource management and decision-making processes. However, the efficacy of current underwater communications and sensing systems faces significant challenges, primarily due to multipath propagation, large delays, severe Doppler shift and scaling. This project aims to develop a reliable and efficient underwater communication and sensing system aided by orthogonal delay-Doppler division multiplexing (ODDM) modulation and frequency modulated continuous wave (FMCW) signals. Novel algorithm for data-aided sensing and joint channel estimation and data detection techniques will be explored.
Research Environment:	The candidate will work closely with the wireless communications research group within the School of Electrical Engineering and Telecommunications at UNSW. The research group has a vibrant research ethos with several PhD students and a few senior research associates.
Novelty and Contribution:	.
Expected Outcomes:	1) The student is expected to gain design knowledge of digital communication systems for underwater environments. 2) One short paper in a tier-1 IEEE communication conference and one full transaction-type journal paper 3) The student will gain hands-on experience with MATLAB Simulink packages and statistical signal processing, along with deep exposure to wireless communication technologies, fostering their interest in pursuing higher-degree research (HDR/PhD).
Reference Material Links:	[1] H. Lin and J. Yuan, “Orthogonal delay-Doppler division multiplexing modulation,” IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 11 024–11 037, 2022. [2] S. E. Zegrar, S. Rafique, and H. Arslan, “OTFS-FMCW waveform design for low complexity joint sensing and communication,” in Proc. IEEE PIMRC, 2022, pp. 988–993.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Machine Learning-Based Modelling of Distributed Energy Resources (DERs)
Name of Supervisor:	Dr. Ahmed Musleh
Email of Supervisor:	a.musleh@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr. Zahra Rahimpour
Email of Joint/Co-Supervisor:	z.rahimpour@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	The rapid integration of Distributed Energy Resources (DERs), such as solar photovoltaics (PV), is reshaping modern power grids. However, the stochastic nature, variability, and decentralised operation of DERs pose challenges to grid stability, forecasting, and energy management. Traditional modelling methods fail to capture the dynamic behaviour and real-time interactions of DERs, necessitating advanced data-driven solutions. This project proposes the development of machine learning (ML)-based models as a Digital Twin (DT) technology to enhance predictability, control, and optimisation of DERs. The Digital Twin framework will create a real-time virtual replica of DERs, enabling continuous monitoring, simulation, and predictive analytics. The main objective is to develop high-fidelity ML models to predict DER behaviour under varying grid conditions. By combining real-time data streams from smart meters, weather forecasts, and historical grid data, this research will enable scalable, intelligent solutions for improving DER management, strengthening grid resilience, and supporting a sustainable energy transition.
Research Environment:	The student will be hosted at the cyber-physical smart grid lab at the school of Electrical Engineering and Telecommunications (333, G17) working alongside talented researchers and research students. The student will be supported by this team and will receive the proper training accordingly.
Novelty and Contribution:	.
Expected Outcomes:	The project will deliver Digital Twin ML models for accurate DER behaviour forecasting under various grid conditions.
Reference Material Links:	[1] Ana P. Talayero, Julio J. Melero, Andrés Llombart, and Nurseda Y. Yürü?en, “Machine Learning models for the estimation of the production of large utility-scale photovoltaic plants”, Solar Energy, v. 254, 2023. [2] Suanpang, Pannee, and Pitchaya Jamjuntr. 2024. "Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities" Sustainability 16, no. 14: 6087.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Multiphysics Analysis and Optimization of a High-Speed IPMSM for EV Applications
Name of Supervisor:	Dr Clay Chu
Email of Supervisor:	g.chu@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof Rukmi Dutta
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	The rapid electrification of transportation has driven the demand for high-performance Interior Permanent Magnet Synchronous Machines (IPMSMs), particularly for electric vehicle (EV) applications. Achieving high efficiency, power density, and reliability requires careful optimization of both the electrical and mechanical aspects of the motor. This project aims to extend the existing 4-pole DAB-type rotor topology to explore 6-pole and 8-pole configurations, leveraging multiphysics optimization to enhance both mechanical robustness and electromagnetic performance. The study will involve a comprehensive comparative analysis of the three rotor configurations, evaluating key parameters such as torque ripple, efficiency, thermal performance, structural integrity, and manufacturability. A key outcome of this project will be the development of a parametric rotor design model, allowing for flexible topology generation based on user-defined inputs. This will provide a valuable tool for future design iterations, supporting next-generation high-speed motor development for EV applications.
Research Environment:	The student will be hosted at the UNSW Electric Drive Lab, working alongside researchers in electric machines and motor optimization. They will access motor loading test facilities, including HBM (200 Nm) and TQM (120,000 rpm, 1.2 Nm) torque transducers, high-speed inverters, dSPACE MicroLabBox controllers, and high-bandwidth measurement devices. A three-phase power supply with protection systems ensures safe testing. This hands-on experience will enhance their high-speed IPMSM design and optimization skills, contributing to the development of next-generation electric vehicle traction motors.
Novelty and Contribution:	.
Expected Outcomes:	Review of IPMSM technological developments over last 5 years Parametrised model of 6-pole and 8-pole double-tied-arch IPMSM rotors An optimised high-speed IPMSM design based on real-world EV requirements (Sunswift EV)
Reference Material Links:	"UNSW Newsroom: New very-high-speed motor offers improved power density for use in electric vehicles (link: https://www.unsw.edu.au/newsroom/news/2022/09/new-very-high-speed-motor-offers-improved-power-density-use-electric-vehicles#:~:text=The%20maximum%20power%20and%20speed%20achieved%20by%20this,fastest%20IPMSM%20ever%20built%20with%20commercialised%20lamination%20materials.) G. Chu, R. Dutta, D. Xiao, J. E. Fletcher, and M. F. Rahman, “Development and Optimisation of a Mechanically Robust Novel Rotor Topology for Very-high-speed IPMSMs,” IEEE Transactions on Energy Conversion, 2023, doi: 10.1109/TEC.2023.3258463."
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Silicon quantum dot simulator for quantum computing application
Name of Supervisor:	Dr Kok Wai Chan
Email of Supervisor:	kokwai@unsw.edu.au
Name of Joint/Co-Supervisor:	Mr Cédric Bohémier
Email of Joint/Co-Supervisor:	c.bohemier@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	MEMS, Micro & Nano Technologies
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Silicon CMOS quantum dot qubit device hold immense potential for the realization of full-scale quantum computers. However, to reach full potential, quantum processors need to scale from their current size of tens of quantum bits (qubits) to thousands and even millions of qubits. The fabrication and characterization efforts become enormously exhausting as we scale-up to realize a universal quantum processor. Therefore, it is useful to build a silicon quantum dot device simulator to feedback on the actual device (digital clone) characteristic and assist in auto-tuning and machine learning projects. The simulator can also be used for educational purposes.
Research Environment:	The research group is led by Prof. Andrew Dzurak with a dynamic team members of research staff and students. The group is also closely affiliated to a start-up company, Diraq with plenty of support and interaction in both academic and industry aspects.
Novelty and Contribution:	.
Expected Outcomes:	The student will build a simulator for single to multi quantum dot in silicon for quantum computing application. Background knowledge in programming languages, in particular Python, will be useful to lead this project. This project builds upon existing simulator with macros and graphical user interfaces which produced single sweep of single-electron transistor turn-on characteristic, coulomb oscillations and double quantum dot charge stability diagram. The student will expand and add features to the simulator such as 2-dimensional sweep and increase in number of quantum dots. The simulator should be able to cater for parametrized qubit design to study effect of different architecture. In later stages, the simulator will be used to generate training data for a customized quantum dot machine learning model.
Reference Material Links:	1. Angus et al ""Gate-Defined Quantum Dots in Intrinsic Silicon"" Nano Lett. 2007, 7, 7, 2051 - 2055 https://pubs.acs.org/doi/abs/10.1021/nl070949k 2. Lim et al ""Observation of the single-electron regime in a highly tunable silicon quantum dot"" Appl. Phys. Lett. 95, 242102 2009 https://aip.scitation.org/doi/10.1063/1.3272858 3. Yang et al “Generic Hubbard model description of semiconductor quantum-dot spin qubits” Phys. Rev. B 83, 161301(R) 2011 Generic Hubbard model description of semiconductor quantum-dot spin qubits \| Phys. Rev. B 4. van der Wiel et al “Electron transport through double quantum dots” Rev. Mod. Phys. 75, 1 2002 Electron transport through double quantum dots \| Rev. Mod. Phys.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Spin Qubit Simulation in Quantum Dots
Name of Supervisor:	Henry Yang
Email of Supervisor:	henry.yang@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr. Gerardo Paz Silva
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	MEMS, Micro & Nano Technologies
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Spin qubits in quantum dots present a promising avenue for quantum computing due to their relatively long coherence times. However, their operational efficiency is hindered by decoherence mechanisms such as nuclear spin hyperfine interactions and spin-orbit coupling effects. This project utilizes advanced simulation tools to analyse the noise contributions affecting qubit performance. The objective is to develop and optimise control sequences to mitigate these noise sources, thereby enhancing the qubits' overall performance and stability for quantum computing applications.
Research Environment:	The research group comprises a dynamic team of academics, research staff, and students, providing a collaborative and supportive environment for cutting-edge research.
Novelty and Contribution:	.
Expected Outcomes:	Utilize simulation techniques to explore the impact of noise on the dynamics of spin states, which is critical for quantum information processing. Apply time evolution on spin qubits to understand noise influences. Develop optimised control strategies to significantly improve the fidelity of qubit operations.
Reference Material Links:	Guido Burkard, Thaddeus D. Ladd, Andrew Pan, John M. Nichol, and Jason R. Petta, Semiconductor spin qubits, Rev. Mod. Phys. 95, 025003 (2023)
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Terahertz Imaging for Grass Seed Infestation Detection
Name of Supervisor:	Wendy Lee
Email of Supervisor:	wendy.lee@unsw.edu.au
Name of Joint/Co-Supervisor:	Shaghik Atakaramians
Email of Joint/Co-Supervisor:	s.atakaramians@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Signal Processing & Control
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	The presence of grass seed infestations in livestock poses significant biosecurity and economic challenges to the Australian agricultural industry. Traditional detection methods rely on manual inspection and histopathological analysis, which can be time-consuming, labour-intensive, and prone to human error. This project aims to leverage terahertz (THz) imaging technology to develop a non-invasive, rapid, and highly sensitive detection method for identifying infested grass seeds in sheep wool and skin. We will use a state-of-the-art THz camera from INO to capture high-resolution imaging data. By utilizing the unique spectral signatures of biological tissues and foreign contaminants in the THz range, this project seeks to establish proof-of-concept imaging techniques that enhance accuracy and efficiency in agricultural diagnostics.
Research Environment:	This project requires no prior knowledge of crucial system parameters, such as the specific THz spectral characteristics of different biological tissues. The primary focus will be on learning and extracting relevant features from THz imaging data to develop an efficient detection framework. The data acquisition process using the INO THz camera will be combined with image processing techniques to identify distinguishing patterns between infested and non-infested samples... More info on Faculty Taste of Research website (https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas)
Novelty and Contribution:	.
Expected Outcomes:	• Proof-of-Concept THz Imaging System: Develop and validate an initial THz imaging setup optimized for detecting grass seed infestations in biological samples. • Preliminary Algorithm Development: Implement an initial classification algorithm to automate infestation detection based on THz spectral data. • Comparative Analysis: Benchmark THz imaging performance against conventional detection methods (raster scanning) to assess improvements in speed, accuracy, and feasibility. • Feasibility Assessment: Evaluate the practical applications and scalability of THz imaging for field deployment in agricultural settings. • Final Report & Recommendations: Document findings, challenges, and recommendations for further research or potential commercialization.
Reference Material Links:	"1) Thigale, S., Wang, Q., Mishra, D., Goldys, E. M., & Atakaramians, S. (2023). Terahertz imaging: a diagnostic technology for prevention of grass seed infestation. Optics Express, 31(22), 37030-37039. https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-22-37030&id=540968 2) Wang, Q., Goay, A. C. Y., Mishra, D., Goldys, E. M., & Atakaramians, S. (2025). Diagnosing Grass Seed Infestation: Convolutional Neural Network Based Terahertz Imaging. IEEE Access. https://ieeexplore.ieee.org/document/10843713 3) Lee, W. S., Ferrante, A., Withayachumnankul, W., & Able, J. A. (2020). Assessing frost damage in barley using terahertz imaging. Optics Express, 28(21), 30644-30655. https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-21-30644&id=440158 "
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Projects offered by other Engineering Schools that may be of interest are:

Graduate School of Biomedical Engineering

School of Computer Science and Engineering