PhD Projects SEIT

Scholarships of $35,000 (AUD) are available for PhD students  who have achieved Honours 1/High Distinction in their UG program and/or have completed a Masters by Research.

Multisource big spatial data analysis

The field of earth observation (EO), or remote sensing, is now facing significant challenges in the processing of image data for end user purposes because of the rapidly escalating numbers of missions and sensors, and because of the range of different types of sensor being orbited.

Investigating Application of Possibility theory to Systems Engineering Risk Management

Complex systems are characterized by many future possibilities each with a tiny probability. The traditional risk analysis that considers probability versus impact might not be helpful when the number of futures is too many. This project aims at finding parallel risk management methods based on possibility theory rather than probability theory.

The aim of this project is to design an intelligent control system that can optimise the trajactory of an insect inspired flapping wing system. Owing to the high speed of the flapping, a high bandwidth control system is required which may be implemented on a Field Programmable Gate Array or neuromorphic hardware. Using machine learning and evolutionary techniques, the system will learn how to best control the angle of attack and flapping motion to most efficiently produce thrust.

Design and Analysis of Efficient Coding Schemes for Internet of Things

The aim of this project is to research coding techniques for device to device cooperative communications in IoT. Specifically, this project will investigate techniques and methods to improve the throughput and efficiency of the cooperative communications in IoT.

Evolution of water-jets from the centrebow and demihull prior to wetdeck slamming event

Over the past three decades there has been increased military and commercial interest in lightweight high-speed ships, mainly due to their ability to provide fast sea transportation and relatively high payload capacity. Australia is an acknowledged world leader in the innovative design and construction of large high-speed aluminium catamarans, such as the vessels developed by Incat Tasmania and Austal.

Dynamic collapse of metallic lattice structures

Metallic lattice materials have shown promise for lightweight sandwich panels that provide protection against blast and shock propagation. However, little is known of their dynamic spall characteristics (when shock-compressed) and their collapse under dynamic loading.

Milimeter wave location-aware communication systems for the fifth generation of mobile communication

The fifth generation of mobile communication (5G) is being designed with a trend towards using millimetre frequency bands (mmWave) with a large number of antennas at the transmitter and receiver. Due to its low scattering and reflective nature, mmWave channels are spatially sparse with communication occurring via only a few propagation paths.

Progressive Damage Modelling and Crash Simulation for Laminated Composite Structures

The project is concerned with the development of a modelling approach to the simulation of the dynamic response of thin-walled composite structural components subjected to crushing loads. The progressive damage model should be developed and implemented into a FE code using a material characterisation process that is based on the material’s experimentally recorded behaviour.

Quantum light source using coherent control

Quantum information technology exploits quantum phenomena to create new devices in computation, communication, and metrology. Quantum state of light (or a photon) is a promising information career because of the relative easiness of creations and manipulations of photons, and direct applications to communication.

Deep Learning and Neural Networks for Multi-modal Human-Swarm Data Fusion

A human-swarm interaction generates very large real-time data streams including image, voice, EEG, human physiological data, task and swarm data, and interaction data. In our paper below, we took steps towards fusing two sources of data, images and timeseries/signal sensorial data. This project will need to systematically develop deep learning and neural network models and architectures to design general deep networks for multi-modal data fusion.

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