Taste of Research Summer Scholarships

2026 Projects - School of Electrical Engineering and Telecommunications

Electrical Engineering & Telecommunications Research Areas

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Electrical Engineering & Telecommunications Projects

No School Research Area

Project Title:	A Unified Framework for Underwater Acoustic Channel Simulation, Analysis, and Hardware Emulation
Name of Supervisor:	Dr Shane Xie
Email of Supervisor:	yixuan.xie@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof Jinhong Yuan
Email of Joint/Co-Supervisor:	j.yuan@unsw.edu.au
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:	Please refer to project information on the Faculty Taste of Research - Advertised Taste of Research areas: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
Research Environment:	The candidate will join the Wireless Communications Research Group within the School of Electrical Engineering and Telecommunications at UNSW, working under the supervision of Prof. Jinhong Yuan, Dr Shane Xie, and their research team. The group maintains a vibrant and collaborative research environment, comprising several PhD candidates and experienced research associates engaged in advanced wireless and signal processing topics. The student will work closely with other HDR and thesis students conducting related research in the laboratory, enabling regular knowledge exchange and peer support. In addition, a researcher from the Defence Science and Technology Group (DSTG) will provide joint supervision and technical guidance, ensuring strong alignment with real?world defence and industry research needs.
Novelty and Contribution:	.
Expected Outcomes:	Underwater channel simulator Report/publication/patent USRP channel emulator (Optional)
Reference Material Links:	Stojanovic, et al. Underwater Acoustic Communication: A review https://www-nature-com.wwwproxy1.library.unsw.edu.au/articles/s44287-024-00122-w Bellhop Acoustic Toolbox: http://oalib.hlsresearch.com/AcousticsToolbox/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	AI-Based Microwave Sensing for Agricultural Infestation Detection
Name of Supervisor:	A/Prof Shaghik Atakaramians
Email of Supervisor:	s.atakaramians@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Amus Goay, Dr Qigejian Alfred Wang,
Email of Joint/Co-Supervisor:	Dr Deepak Mishra, A/Prof Shaghik Atakaramians
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:	Hidden contaminants such as grass seeds or small rocks trapped in wool or lodged in livestock skin can cause infections, reduce wool quality, and lead to economic losses for farmers. Detecting these infestations early, quickly and non-invasively is a major challenge in agriculture. This project explores how microwave sensing combined with machine learning can be used to detect such hidden contaminants. Instead of using optical imaging, this approach relies on analysing microwave signals measured using a Vector Network Analyzer (VNA). When microwaves interact with materials, they produce measurable spectral responses. The key challenge is how to process and interpret this raw measurement data to identify meaningful patterns linked to infestation. In this project, the student will investigate how to: • Process raw microwave measurement data obtained from a VNA using MATLAB • Clean, visualise, and organise spectral data for analysis • Extract relevant features from measured frequency spectra • Apply machine-learning techniques to classify and detect infestations • Evaluate detection accuracy and system performance This project is strongly data-driven and combines signal processing with artificial intelligence. The student will gain practical experience in handling experimental measurement data and transforming it into useful diagnostic information. The project is suitable for students interested in electrical engineering, data science, applied physics, or computer engineering. Basic MATLAB or programming experience and an interest in data analysis will be highly beneficial.
Research Environment:	The project will be conducted within the Terahertz Innovation Group in School of Electrical Engineering and Telecommunications, led by A/Prof Shaghik Atakaramians. The student will work closely with Dr Amus Goay, Dr Qigejian Alfred Wang, Dr Deepak Mishra, A/Prof Shaghik Atakaramians, also interact with industry partners. The research setting integrates experimental measurements using a VNA with computational analysis in MATLAB. The student will gain exposure to real measurement data, structured data analysis workflows, and machine-learning model development within an active research environment.
Novelty and Contribution:	.
Expected Outcomes:	By the end of the project, the student is expected to: • Understand the fundamentals of microwave sensing using a VNA • Develop skills in MATLAB-based signal processing • Extract meaningful features from spectral measurement data • Implement a basic machine-learning model for infestation detection • Present results in a report or presentation Strong outcomes may contribute to ongoing research on intelligent sensing solutions for agriculture and livestock monitoring.
Reference Material Links:	[1] Thigale, S., Wang, Q., Mishra, D., Goldys, E.M. and Atakaramians, S. (2023). Terahertz imaging: a diagnostic technology for prevention of grass seed infestation. Optics Express, 31(22), 37030–37039. [2] Wang, Q., Goay, A.C.Y., Mishra, D., Goldys, E.M. and Atakaramians, S. (2025). Diagnosing Grass Seed Infestation: Convolutional Neural Network-Based Terahertz Imaging. IEEE Access, 13, 16094–16102.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	AI-based Network Behaviour Characterisation for Secure Management of Connected Systems
Name of Supervisor:	A/Prof Hassan Habibi Gharakheili
Email of Supervisor:	h.habibi@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:	Please refer to project information on the Faculty Taste of Research - Advertised Taste of Research areas: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
Research Environment:	This project will be conducted within a vibrant and collaborative research group comprising PhD students, honours students, postdoctoral researchers, and academic staff at UNSW. The team also works closely with industry partners interested in trialling and evaluating research outcomes in real operational networks. You will have access to commercial IoT devices and real network traffic datasets (both live and pre-recorded packet traces). The research environment emphasises theoretical development and practical validation, enabling your software tools and algorithms to be tested in realistic network conditions. Regular group meetings, research discussions, and mentorship from senior researchers will support your learning and research development.
Novelty and Contribution:	.
Expected Outcomes:	Expected outcomes of this project include: 1. Development of: (a) novel algorithms for characterising the network behaviour of individual IoT device types, (b) methods for identifying relationships and dependencies between different IoT devices, (c) techniques for improving the interpretability and controllability of machine learning-based traffic classifiers. 2. Prototype software tools for analysing and classifying IoT network traffic. 3. A written scientific technical report documenting research findings. High-quality results may contribute to publications in international conferences or journals, and selected prototypes may be trialled in live operational networks through industry collaboration.
Reference Material Links:	The UNSW research team has published many research articles in this area, which can be found at A/Prof. Gharakheili’s website: https://www2.ee.unsw.edu.au/~hhabibi/publications.html. A recommended starting point is our recent paper: A. Sivanathan et al, “Real-Time and Trustworthy Classification of IoT Traffic Using Lightweight Deep Learning”, IEEE Transactions on Network Science and Engineering (https://www2.ee.unsw.edu.au/~hhabibi/pubs/jrnl/25tnse.pdf).
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Constructing Radio Maps for 5G Communications
Name of Supervisor:	Wei Zhang
Email of Supervisor:	w.zhang@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:	This project focuses on constructing radio maps for 5G communications. Radio maps are spatial databases that characterize signal power, channel gain, and interference levels across a geographic area. The key research is reconstructing accurate radio maps from sparse and irregular measurements. This research will explore data-driven approaches, including machine learning techniques, to learn spatial correlations and reconstruct radio maps from limited samples. The expected contributions are novel algorithms for radio map construction validated using 5G measurements, providing practical frameworks for enhancing network intelligence and performance.
Research Environment:	This research will leverage computational resources with machine learning frameworks, alongside access to some 5G measurement datasets and collaboration with Postdoc for writing research papers.
Novelty and Contribution:	.
Expected Outcomes:	Expected outcomes include new machine learning-based methods for radio map construction, experimental validation, and contributions to more efficient network planning and interference management.
Reference Material Links:	https://ieeexplore.ieee.org/document/11008499 https://ieeexplore.ieee.org/document/11397522
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Design and Development of a Semi Automated Electronic Rig for Testing Electrically Tuneable Lenses
Name of Supervisor:	Shaghik Atakaramians, Wendy Lee
Email of Supervisor:	wendy.lee@unsw.edu.au
Name of Joint/Co-Supervisor:	Peter Wagner, Arthur Ho, Maitreyee Roy
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:	Electrically Tuneable Lenses (ETLs) are a core enabling technology for digital phoropters and next generation ophthalmic devices. A digital phoropter is an electronic vision testing device that automatically adjusts lenses to measure a person’s eyesight more accurately and efficiently than a manual phoropter. To accurately assess ETLs performance, a reliable and semi automated measurement system is needed. This is a joint project with the School of Optometry and the School of Electrical Engineering and Telecommunications (EE&T), The EE&T student will focus on the end to end engineering development of a research grade measurement rig for ETLs. The completed system will be used in collaboration with a parallel Optometry project that will carry out the optical characterisation.
Research Environment:	Multidisciplinary engineering and optometry research laboratory with access to electronics prototyping, mechanical fabrication, embedded systems development, and optical testing facilities.
Novelty and Contribution:	.
Expected Outcomes:	Key Responsibilities: • Design and prototype a semi automated ETL measurement rig • Create mechanical components using 3D CAD modelling and fabrication (3D printing, machining) • Implement microcontroller based control of motors/actuators for positioning the ETL • Build firmware for precise motion control and sensor data acquisition • Develop PC based software for hardware control and automated measurement routines
Reference Material Links:	https://www.corning.com/worldwide/en/products/advanced-optics/product-materials/corning-varioptic-lenses/varioptic-technology.html
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Designing and Testing a Lens for a Terahertz Antenna
Name of Supervisor:	Dr Dominik Vogt
Email of Supervisor:	d.vogt@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:	In this project, you will work on designing and building a specialised lens that guides terahertz waves—very high-frequency waves used in advanced imaging, sensing, and communications (6G). These waves can be hard to control, so the lens you create will help focus and direct them more effectively. Your work will involve two main parts: 1. Computer Simulations – You will use tools such as MATLAB and CST Microwave Studio to trace how rays travel through your lens design and to test how well different shapes and materials might perform. 2. Hands-On Lab Work – After designing the lens, you will 3D-print a prototype and then test it in the Terahertz Laboratory to see how it performs in real life. What You Will Learn • How to model and simulate optical components using MATLAB and CST Microwave Studio • How to 3D-print a physical prototype of your lens • How to perform experimental testing in a specialised terahertz laboratory
Research Environment:	Supervision and Social Environment You will work closely with Dr Dominik Vogt, who will support you throughout the entire project—from learning the basics to completing your prototype and experiments. You will also be part of the Terahertz Innovation Group, a vibrant and friendly research team with deep expertise in designing optical elements and terahertz devices. The group regularly collaborates, discusses ideas, and shares practical know-how, creating a supportive environment for learning new skills. You will be able to ask questions freely, get help from team members, and experience the collaborative nature of real research.
Novelty and Contribution:	.
Expected Outcomes:	The goal of the project is to design, build, and evaluate a lens that works together with a terahertz antenna, improving its ability to send and receive terahertz signals.
Reference Material Links:	https://www2.ee.unsw.edu.au/terahertz/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Fast Terahertz Imaging for Smart Agriculture and Livestock Monitoring
Name of Supervisor:	A/Prof Shaghik Atakaramians
Email of Supervisor:	s.atakaramians@unsw.edu.au
Name of Joint/Co-Supervisor:	r Wendy Lee, Dr Qigejian Alfred Wang
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:	How can we “see” inside plants or detect hidden contaminants in wool without cutting, damaging, or touching them? This project explores the use of terahertz waves, a special type of electromagnetic waves that can see through many non-metallic materials, to solve real-world challenges in agriculture and farming. This project is developing fast terahertz imaging systems that can monitor water content in plants and detect hidden infestations such as grass seeds, small rocks, or debris in wool or on livestock skin. During this project, the student will learn how to: • Control an advanced terahertz source using MATLAB • Set up an experimental optical platform in the lab • Operate a terahertz camera to capture images • Process and analyse images to extract meaningful information This is a hands-on experimental and computational project. You will gain experience in optics, imaging systems, programming, and data analysis, while contributing to research that could improve agricultural productivity and animal welfare. Students with an interest in physics, engineering, photonics, computer science, or applied mathematics are encouraged to apply. Basic programming skills (especially MATLAB) and curiosity about how technology solves real-world problems will be highly beneficial.
Research Environment:	The project will be conducted within the Terahertz Innovation Group in School of Electrical Engineering and Telecommunications, led by A/Prof Shaghik Atakaramians. The student will work closely with Dr Wendy Lee, Dr Qigejian Alfred Wang and A/Prof Shaghik Atakaramians, also interact with PhD students and other researchers in a collaborative laboratory setting. The research environment combines experimental optics, advanced instrumentation, and computational data analysis. Students will have access to state-of-the-art terahertz sources and imaging equipment and will gain exposure to real research workflows from experiment design to data interpretation.
Novelty and Contribution:	.
Expected Outcomes:	By the end of the project, students are expected to: • Understand the fundamentals of terahertz imaging • Gain practical experience operating a terahertz imaging system • Develop basic skills in MATLAB-based instrument control • Learn image post-processing techniques to extract physical information • Present their findings in a report or presentation
Reference Material Links:	[1] Thigale, S., Wang, Q., Mishra, D., Goldys, E.M. and Atakaramians, S. (2023). Terahertz imaging: a diagnostic technology for prevention of grass seed infestation. Optics Express, 31(22), 37030–37039. [2] Wang, Q., Goay, A.C.Y., Mishra, D., Goldys, E.M. and Atakaramians, S. (2025). Diagnosing Grass Seed Infestation: Convolutional Neural Network-Based Terahertz Imaging. IEEE Access, 13, 16094–16102.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Green Designs for UAV-assisted Backscatter Communications
Name of Supervisor:	Dr Deepak Mishra
Email of Supervisor:	d.mishra@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:	This project involves the following two aspects of wireless communication: a) Backscatter communication (BSC) systems that comprise a power-unlimited reader and low-power tags. This technology thrives on its capability to use low-power and passive devices like envelope detectors, dividers, comparators, and impedance controllers, instead of more costly and bulkier conventional radio frequency (RF) chain components such as local oscillators, mixers, and converters. The BSC systems generally comprise a power-unlimited reader and low-power tags. As passive tags do not have transmission circuitry, they rely on carrier transmission from the emitter to power itself and backscatter its data to the reader by appending information to the backscattered carrier. b) Unmanned Aerial Vehicle (UAV) networks: The growing demand for a higher data rate has presented a considerable challenge to the traditional cellular and IoT networks. UAVs are considered the next-generation systems to enhance current coverage because, owing to the aerial nature of UAVs; they can maintain line-of-sight (LoS) connection with the ground users leading to enhanced coverage and efficiency. Therefore, using UAVs as an aerial access point is desirable to improve wireless services and coverage in hotspot areas.
Research Environment:	Considering no prior knowledge of crucial system parameters like tags’ location & channel statistics, the underlying different channel, location & mobility related parameters will be learned to present a distributed algorithm based on the deep reinforcement learning approach to be implemented at the UAV while getting the desired cooperation of the BSC tags. This will be a software simulation-based task so that it can be implemented with the help of an undergraduate student under my guidance over two months as it will not require a complex mathematical framework. The interested student needs to be good in Matlab programming along with having basic knowledge in wireless communications and Machine Learning. I will be providing the lectures myself to teach the student about the advanced concepts that will be used as a part of his/her training for this project.
Novelty and Contribution:	.
Expected Outcomes:	1) One short paper in tier-1 IEEE communication conference with the undergraduate student. A poster presentation and a brief video highlighting the main findings will also be provided. 2) One full transaction-type journal paper based on the extension of the conference paper to be led by me within the two months after the end of this project. 3) Training of an
Reference Material Links:	Please refer to project information on the Faculty Taste of Research - Advertised Taste of Research areas: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	IoT-Integrated Battery Management System for Real-Time Monitoring and Diagnostics of Lithium-Ion Bat
Name of Supervisor:	Associate Lecturer, Pablo Poblete Durruty
Email of Supervisor:	p.poblete_durruty@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:	This project offers an undergraduate student the opportunity to work with a researcher with academic and industry experience on the further development of a battery management system with IoT capabilities. The work will build on an existing functional prototype that has already been developed and experimentally tested. The student will be part of a research and development environment and will contribute to translatig this BMS design into a more simpler version focused for teaching pourposes, while including IoT connectivity for remote monitoring via WiFi. The student will support battery testing and characterisation, assist in implementing and assessing state-of-charge estimation algorithms, and contribute to the integration of IoT connectivity for remote data access. Through this project, the student will gain practical experience in battery systems, real-time algorithm implementation, embedded systems, and software development.
Research Environment:	Laboratory
Novelty and Contribution:	.
Expected Outcomes:	1) Printed circuit board of a battery management system for a 24V battery. 2) Human Machine Interface to monitor and configure the battery management system via remote software interfaces.
Reference Material Links:	N/A
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Learning to Communicate: An End-to-End Deep Learning Approach for Image Transmission and Classificat
Name of Supervisor:	Dr. Zhitong Ni
Email of Supervisor:	zhitong.ni@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr. Tom Wang, Dr. Yixuan Xie, Prof. Jinhong Yuan
Email of Joint/Co-Supervisor:	tom.wang@unsw.edu.au
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 explores how artificial intelligence (AI) can be used to improve the way machines communicate visual information. The student will work closely with a senior researcher and join other undergraduate and postgraduate students, providing an opportunity to learn and collaborate in a supportive environment. The aim of the project is to develop a system that can “learn to communicate” images efficiently using deep learning. Instead of focusing on transmitting every pixel perfectly, the system will learn to send only the most important features needed for a computer to correctly recognize the image’s content. The student will gain hands-on experience in training neural networks, testing how telecommunication systems work within limited throughput, and comparing the performance with traditional image transmission methods. By the end of the project, the student will have a solid understanding of both modern AI techniques and the principles of task-oriented telecommunications while contributing to a first-of-its-kind research study in this emerging area.
Research Environment:	Please refer to project information on the Faculty Taste of Research - Advertised Taste of Research areas: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
Novelty and Contribution:	.
Expected Outcomes:	An end-to-end emulation package via Python demonstrating accurate image classification in the receiving end.
Reference Material Links:	[1] Semantic Communications: Principles and Challenges, https://arxiv.org/pdf/2201.01389 [2] Digital Communications by John Proakis
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Light-weight Motor Drive for High-Performance EVs
Name of Supervisor:	Clay Chu
Email of Supervisor:	g.chu@unsw.edu.au
Name of Joint/Co-Supervisor:	Rukmi Dutta
Email of Joint/Co-Supervisor:	rukmi.dutta@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Fluid Dynamics and Thermal Engineering
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	High-performance electric vehicles require motor drives that are compact, lightweight, and highly efficient, particularly in motorsport environments such as Formula SAE, where packaging constraints and vehicle mass significantly affect performance. Current commercial high-performance inverters are often costly and not fully optimised for student-built vehicles. This project will provide research experience in the conceptual design and modelling of a high-power-density 30 kW PMSM motor controller, targeting improved efficiency, reduced weight, and compact integration compared with existing solutions. The student will investigate SiC-based switching technologies suitable for operation up to 600 VDC, advanced DC-link capacitor configurations, thermal management strategies, and mechanical integration requirements to ensure robustness under vibration and acceleration. The student will work closely with a team of HDR (PhD) students and researchers in power electronics and electric drives, gaining exposure to research methods, modelling tools, and system-level design approaches. The outcome will be a research-informed conceptual inverter architecture and comparative performance analysis that can inform future high-performance EV powertrain development.
Research Environment:	The student will work in the UNSW Electric Drive Lab in the School of EE&T, alongside HDR students and researchers specialising in power electronics and electric drives. The lab provides electric-drive dynamometer test facilities with HBM torque transducers (up to 200 Nm) and a high-speed torque transducer (up to 120,000 rpm) to evaluate drive system performance. High-power, high-speed inverters and dSPACE MicroLabBox controllers enable rapid control prototyping and testing of motor control algorithms. The student will also use high-bandwidth measurement equipment and digital oscilloscopes to analyse switching behaviour and system dynamics. A three-phase power supply with protection systems ensures safe testing of high-power electric drive prototypes.
Novelty and Contribution:	.
Expected Outcomes:	A conceptual design of a compact, high-efficiency 30 kW PMSM motor controller supported by modelling and comparative analysis. The student will gain experience in power electronics and electric drive research while producing documented design insights to inform future high-performance EV inverter development.
Reference Material Links:	https://www.saea.com.au/formula-sae-a https://www.grants.gov.au/Ga/Show/faab47e1-86c9-4fe8-8659-48e5976173fd https://www.unsw.edu.au/newsroom/news/2022/09/new-very-high-speed-motor-offers-improved-power-density-use-electric-vehicles
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Machine Learning-aided Integrated Sensing and Communications
Name of Supervisor:	Dr Akram Shafie
Email of Supervisor:	akram.shafie@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr. Zhitong Nie, Prof. Jinhong Yuan
Email of Joint/Co-Supervisor:	j.yuan@unsw.edu.au
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:	Please refer to project information on the Faculty Taste of Research - Advertised Taste of Research areas: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
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 machine learning model for accurate sensing in dynamic channels, accompanied by a poster presentation and a brief video summarising key findings. 2) Publications: Submission of a short conference paper, to be submitted within two months after project completion. 3) Student Training & Development: The student will gain hands-on experience with MATLAB, Python, 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] K. Huang, A. Shafie, M. Qiu, E. Aboutanios and J. Yuan, "A Novel ISAC Waveform Based on Orthogonal Delay-Doppler Division Multiplexing With FMCW," in IEEE Transactions on Wireless Communications, 2026 [2] Q. Cheng, Z. Shi, J. Yuan and H. Lin, "MIMO-ODDM Signal Detection: A Spatial-Based Generative Adversarial Network Approach," in IEEE Transactions on Wireless Communications, Sept. 2024
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Reconfigurable High Throughput LDPC Channel CODEC for 5G Communication Systems
Name of Supervisor:	Dr Shane Xie
Email of Supervisor:	yixuan.xie@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof Jinhong Yuan
Email of Joint/Co-Supervisor:	j.yuan@unsw.edu.au
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:	Please refer to project information on the Faculty Taste of Research - Advertised Taste of Research areas: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
Research Environment:	The candidate will join the Wireless Communications Research Group within the School of Electrical Engineering and Telecommunications at UNSW, working under the supervision of Prof. Jinhong Yuan, Dr Shane Xie, and their research team. The group maintains a vibrant and collaborative research environment, comprising several PhD candidates and experienced research associates engaged in advanced wireless and signal processing topics. The student will work closely with other HDR and thesis students conducting related research and discussions in the laboratory, enabling regular knowledge exchange and peer support.
Novelty and Contribution:	.
Expected Outcomes:	FPGA IP core Software verification tool in C/C++ Report/publication/patent
Reference Material Links:	Introducing Low-Density Parity-Check Codes: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.researchgate.net/profile/Sarah-Johnson-64/publication/228977165_Introducing_Low-Density_Parity-Check_Codes/links/0deec51c7c8f92e602000000/Introducing-Low-Density-Parity-Check-Codes.pdf
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Signal Coupling Between Terahertz Fibers for Future 6G Communication Systems
Name of Supervisor:	A/Prof Shaghik Atakaramians
Email of Supervisor:	s.atakaramians@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Qigejian Alfred Wang
Email of Joint/Co-Supervisor:	qigejian.wang@unsw.edu.au
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:	Future 6G communication systems are expected to operate at terahertz frequencies (0.1-10 THz) to achieve ultra-high data rates. A key challenge in these systems is how to efficiently guide signals through terahertz fibers and then transfer (or “couple”) the signal into other components such as antennas. This project focuses on understanding and designing signal coupling between two adjacent dielectric terahertz fibers. The goal is to investigate how a signal can be transferred from one fiber to another with minimal loss, and how the second fiber can be structurally modified to make it easier to connect to downstream components, such as terahertz antennas. For example, the project may explore introducing a low-loss outward bending section that enables practical device integration. Students will learn how to: • Design and simulate terahertz fiber structures using industry-level software such as CST and COMSOL • Study coupling efficiency and signal leakage between adjacent fibers • Modify fiber geometries to improve integration with antennas • Perform measurements using an advanced photonic-based terahertz communication system • Analyze communication performance metrics such as baseband signals and bit-error rate (BER) The outcomes of this study will contribute to the development of terahertz “pinching” antennas and integrated fiber-fed devices, which are promising building blocks for next-generation 6G wireless systems.
Research Environment:	The project will be conducted within the Terahertz Innovation Group in School of Electrical Engineering and Telecommunications, led by A/Prof Shaghik Atakaramians. The student will work closely with Dr Qigejian Alfred Wang and A/Prof Shaghik Atakaramians, also interact with PhD students and other researchers in a collaborative laboratory setting. The research environment combines numerical simulation software (CST, COMSOL), device fabrication facilities (3D printers), and photonic-based terahertz communication system. The student will gain exposure to both simulation-driven design and hands-on testing using a state-of-the-art photonic-based terahertz communication platform.
Novelty and Contribution:	.
Expected Outcomes:	By the end of the project, the student is expected to: • Understand the principles of signal coupling between adjacent terahertz fibers • Gain practical experience in electromagnetic simulation tools (CST and/or COMSOL) • Evaluate coupling efficiency and crosstalk effects • Perform basic communication measurements and BER analysis • Present their findings in a report or presentation Strong outcomes may contribute to ongoing research toward integrated terahertz fiber–antenna systems for 6G applications.
Reference Material Links:	[1] Li, H., Cao, Y., Skorobogatiy, M. and Atakaramians, S. (2025). Terahertz fiber devices. APL Photonics, 10(2). [2] Ge, H., Li, H., Jie, L., Wang, J., Cao, Y., Atakaramians, S., Gong, Y., Ren, G. and Pei, L. (2024). 3D-printed terahertz subwavelength dual-core fibers with dense channel-integration. Journal of Lightwave Technology, 43(5), 2329–2339. [3] Puttnam, B.J., Luis, R.S., Eriksson, T.A., Klaus, W., Mendinueta, J.M.D., Awaji, Y. and Wada, N. (2016). Impact of intercore crosstalk on the transmission distance of QAM formats in multicore fibers. IEEE Photonics Journal, 8(2), 1–9.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Simulating Spin Qubit Control in Silicon Quantum Dots
Name of Supervisor:	A/Prof 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 silicon quantum dots offer a promising pathway for quantum computing due to their relatively long coherence times. However, operational efficiency is challenged by decoherence mechanisms, such as nuclear spin hyperfine interactions and spin-orbit coupling effects. This project employs advanced simulation tools to analyse the noise contributions affecting qubit performance. The objective is to develop and optimize control sequences to mitigate these noise sources, enhancing the qubits' overall performance and stability for quantum computing applications.
Research Environment:	The research will be conducted in the SiMOS Quantum Dot lab, led by Prof. Andrew Dzurak. 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:	Utilise simulation techniques to explore the impact of noise on the dynamics of spin states, critical for quantum information processing. Apply wavefunction evolution or density matrix methods to understand noise influences. Inform the research group on how different noise types affect various operating protocols. Develop optimised control strategies to significantly improve the fidelity of quantum 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) https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.95.025003
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