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A range of opportunities exist for new PhD candidates to become involved in key research projects within GBDTC, including the selected projects listed below.
Please contact us at Eugenia.Tan@uts.edu.au to discuss these or other potential projects.
Reconfigurable beamforming antenna arrays for space-borne vehicles
Sponsored by the Australian Research Council (ARC), this project is to develop a novel reconfigurable antenna array structure and beamforming algorithms to increase the distance and data rates of wireless communications between air-borne/space-borne vehicles.
TeraHertz (THz) antennas
THz is regarded as the next frontier for wireless communications and sensing. The challenges are in ultra-high gain antenna design, integrated design of the antennas and THz front-end, and a new generation of wireless system architecture to manage the characteristics and THz waves. Sponsored by industry, this project will address these challenges through theoretical and experimental studies.
Base station antennas for 5G wireless communications
The cellular wireless communications industry is moving towards 5G, which in turn demands more advanced antennas, bringing challenges relating to massive antenna arrays, the capability to deal with more bands, and low cost mm-wave antenna systems. This project aims to develop new antenna technologies to meet the capacity requirements of future 5G systems.
Base station antennas using metamaterial
Base station antennas are the key devices in wireless cellular communications that provide a link between base stations and mobile stations. The multi-generations of wireless communications systems have posed ever-increasing challenges to the design of base station antennas, particularly around wide bandwidth, beam consistency and polarisation isolation. Sponsored by industry, this research will investigate new antenna configurations and meta-material structures to meet these changing needs.
Frequency and pattern reconfigurable antenna for cognitive radio systems
It is suggested that two separate antennas are required for cognitive radio systems: a wideband antenna for scanning the spectrum, and a narrowband frequency reconfigurable antenna for communicating. This research looks at ways to combine the two antennas together to reduce the size and complexity of the system”s radio frequency (RF) front-end, using a single-port fed antenna that can switch between the wideband and narrowband operations. Additionally, this new development will allow the main beam for each narrow band operation to be steered according to the channel conditions.
Self-interference cancellation for full duplex wireless communications
Full duplex has emerged as a new communications paradigm shift, and is anticipated to significantly increase information transmission capacity compared to conventional half duplex radios. This research investigates theory and implementation techniques for RF and baseband interference cancellation, enabling bi-directional transmission with the same frequency band. Additional uses for full duplex in wireless systems, such as active radar systems, will result in revolutionary architecture innovation and significant performance improvement.
Massive hybrid antenna array for mm-wave communications
Wireless communications using millimetre wave (mm-wave) frequency bands have developed from niche market applications into potentially global applications in mobile broadband systems. Wireless system designers have embarked on research into fifth generation (5G) cellular systems to meeting exponential growth in demand for high data rates and mobility required by new wireless applications. This research project will enable the 5G vision, with a focus on development and delivery of a low cost massive hybrid antenna array implementation and effective digital signal processing techniques.
High speed signal processing and implementation for wireless communication systems
Increasing demand for high speed wireless communications systems means that new signal processing algorithms and hardware implementations are necessary to meet low cost and high performance requirements. This research project will investigate effective digital signal processing algorithms and efficient hardware design to realise high speed wireless communications in real-time. PhD candidates should have both theoretical background and hardware implementation experience.
Fine-grained activity recognition and object tracking
Recognising human activities from video streams is an important task for home automation, robotics and surveillance. Increasingly, human activities need to be recognised at a fine grain, for example, telling apart a person holding a cup of coffee from a person holding a foreign object. In this PhD project, the candidate will investigate the synergistic use of activity recognition and tracking techniques to achieve accurate, fine-grained recognition of activities. The outcome will be a technology that can be flexibly employed in a variety of human-computer applications.
Joint activity recognition and summarisation of videos
Recognising human activities in video clips is an important task for social media analysis. Moreover, being able to automatically select a few frames to summarise each clip (“storyboarding”) can add extra useful information for an end user. In this PhD project, the candidate will explore how to provide activity recognition and video summarisation together, leveraging the constructive interactions of these two tasks.
Word embedding for natural language processing
Encoding the semantic and structural relationships between words can prove useful for many natural language processing (NLP) tasks. A successful approach “embeds” the words into a vector space where semantic and structural similarities between words are preserved. In this PhD project, the candidate will explore different embedding approaches and their impact on the accuracy of NLP tasks such as named entity recognition and part-of-speech tagging.
The Internet of Things is growing around us every day. Soon not only smart phones and computers, but objects of all kinds, will be communicating with and sharing data between themselves and with the cloud. An absolutely critical part of that data are the recorded timestamps of when events occur: did the car hit its brakes before the lights went red, or not? did my fridge decide to throw out my leg of lamb before the power failure, or because of it? did my stock order really go through before the crash? IoT applications will grow into an enormously diverse and complex system, and timing will be a key to making applications effective and reliable, and in maintaining order over chaos.
You will join a timing research project, part of the Network Timing Laboratory research effort, to develop a robust and accurate timing system for the IoT ecosystem. This encompasses both the timing approach within IoT devices, and the Internet-based support infrastructure. It will build on the extensive experience in Internet timing, including a state of the art timing testbed, at UTS, and involves the cooperation of both national and international partners.
Required background: excellent knowledge of computer networking, excellent programming skills, and knowledge of time series analysis or related disciplines.
A stipend is a available to outstanding candidates.
Drone Energy Autonomy
Outstanding students are sought to work on an exciting new project in Drone Energy Autonomy, whose goal is the development of a reliable, autonomous infrastructure allowing UAVs (with a focus on quadcopters) to operate as if their range were unlimited. The goal is ambitious and transformative and involves significant experimental and theoretical challenges.
Required background: a recent mechatronics or robotics undergraduate degree, strong practical skills in multi-rotor UAV development, a strong background in control, and a can-do attitude with a determination to have an impact in the space. Significant experience using C++, Python, Linux, and ROS is necessary.
A stipend is a available to outstanding candidates.
Trusted Timing for the Internet of Things
The Internet of Things is expanding rapidly. Soon IoT enabled objects of all kinds will be communicating and sharing data between themselves and the cloud. A critical part of that data are the recorded timestamps of when events occur, and a key question is, can those timestamps be trusted? The security implications of timestamps which have been fabricated, tampered with, or are otherwise unreliable are enormous, given that decisions by individuals, companies, and software will be based on them in myraid ways. For example the ordering of financial transactions, acceptance of outdated passwords, timely delivery of parcels, or the predicted location of people could all fail if timestamps are wrong.
You will join a timing research project to develop a secure and trusted timing system for the IoT ecosystem. This encompasses both the security issues within IoT devices, and of the Internet-based support infrastructure. It will build on the extensive experience in Internet timing, including a state of the art timing testbed, at UTS, and involves the cooperation of both national and international partners.
Required background: extensive knowledge of computer networking, excellent programming skills, and knowledge of time series analysis or related disciplines, and a grounding in traditional network security.
For security threats which follow a known pattern, a rule or signature based approach can be an effective countermeasure, for example to detect and block attacks in applications like network intrusion detection. When the attack type is unknown however, its detection is a far more difficult and subtle problem, often complicated by a lack of clear definitions enabling `signal’, `noise’ and `anomaly’ from being distinguished.
You will develop and evaluate methodologies based on innovative approaches to anomaly detection.
These will include non-linear filtering, sketching, sampling, and wavelet analysis. The results will be applied to datasets from Internet data and bio-signals, and will contribute creatively to the arsenal of methods used Data Analytics.
Required background: strong mathematical background in areas such as statistics, time series analysis, optimisation, machine learning, and well as strong computer science knowledge and programming experience.
Information Theoretic Security
Data confidentiality is a critical component of secure data transfer, and much of the digital economy, for example secure financial transactions, relies upon it. It is typically provided using encryption methods involving secret keys, for example in the well known RSA cryptosystem. A lesser known, but very powerful basis lies in Shannon’s Information Theory. Information Theoretic Security is based on the idea of the Wyner Wiretap channel, where an eavesdropper, ‘Eve’, has access to the secret transmission through a channel which is noisier than that between the communicating parties. By intricate and clever coding methods, that extra noise can be made to act like an intrinsic secret key. This approach is capable in theory of providing unbreakable security without the need for secret keys to be exchanged.
The project will built on recent work showing how multiple separate channels (like those found in smart phones: Wifi, cellular, bluetooth..) can be exploited to provide a system that provides Information Theoretic security guarantees. The research will focus on expanding and applying these results so that they can be used in real networks to secure the future digital economy.
Required background: strong mathematical background in information theory and optimisation, and well as strong computer science knowledge and programming experience. A background in computer networking and cryptography would be a strong advantage. A stipend is a available to outstanding candidates.
Graph theory is a mature discipline with applications in many domains, and is often used to model empirical data. A common feature of real-world datasets however, for example in social networking, is that they are only partially observed, that is they have elements missing. This can be thought of as a statistical sampling of the underlying graph, and there are many important problems in how to infer characteristics of the full graph from such incomplete measurements. Even if the datasets are very large, the scale of the true underlying graph is often much, much larger, so this is a non-trivial problem, with important implications for the limits of data analytic methods.
This project will develop scalable methods of graph inference, for example graph matching, capable of tackling truly large graphs. It will also extend them into the relatively unexplored area of hyper-graphs, where the concept of a link from graph theory (effectively a pair of nodes) is generalised to any set of nodes.
Hyper-graphs offer much richer modelling options than graphs, and will play an important role in the study of the rich, huge, data sets arising from the digital world.
Required background: strong mathematical background in graph theory and optimisation, and well as strong computer science knowledge and programming experience. A background in statistics would be an advantage. A stipend is a available to outstanding candidates.