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Text analysis for engineering education

Textual data contains a large amount of information which is embedded. This text information is easily extracted by humans but is difficult for machines to interpret. Using various textual analysis methods some information can be drawn out from pieces of text.

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

AI-Based Data Analysis on Multiple Imaging Modalities

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. According to the World Health Organization (WHO), it is estimated CVD takes 17.9 million lives every year. In Australian, the statistical data from the Australia Heart Foundation shows CVD is a major cause of death in Australia. It occupies 26% of all deaths, responsible for an average 118 deaths every day. Four of the main types of CVD are coronary heart disease, strokes and transient ischaemic attack, peripheral …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Developing predictive models, methods and analytics for complex sports data

A 3-year strategic partnership on sports data science between the Centre for Data Science (CDS), the Australian Institute of Sport (AIS) and the Queensland Academy of Sport (QAS) was launched in the past few months. With a drive towards data informed decision making across the high performance sports network nationally, a number of collaborative, interdisciplinary research and scholarship opportunities ranging from VRES, to honours, masters and PhD have developed.

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Adversarial attacks to machine learning based models in cybersecurity

Modern Intrusion Detection Systems (IDSs) rely on machine learning for detecting and defending cyber-attacks in information technology (IT) networks. However, the introduction of such systems has introduced an additional attack dimension; the trained IDS models may also be subject to attacks.The act of deploying attacks towards machine learning based systems is known as Adversarial Machine Learning (AML) [1]. The aim is to exploit the weaknesses of the pretrained model which has “blind spots” between data points it has seen during …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Computer Science

Statistical methods for detecting Antarctic ecosystems from space

Satellite images are a frequent and free source of global data which can be used to effectively monitor the environment. We can see how the land is being used, how it’s being changed, what’s there – even where animals are in the landscape. Using these images is essential, particularly for regions where data is expensive to collect or difficult to physically access, like Antarctica. In Antarctica and the sub-Antarctic islands, satellite images can be an easy and quick way to …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

A new physics informed machine learning framework for structural optimisation design of the biomedical devices

The machine learning based computer modelling and simulation for engineering and science is a new era. The optimisation analysis is widely used in the design of structures.

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Mechanical, Medical and Process Engineering
Research centre(s)
Centre for Biomedical Technologies
Centre for Biomedical Technologies

Group activity classification in sports with machine learning

Within a team sports environment, coordinated behaviours between teammates are crucial to success. To accurately detect and correctly classify group behaviours, the actions of the individuals must be considered. Within a sporting environment the problem is further complicated by the presence of an opposing team, who are seeking to counter and disrupt the other team.While there is a substantial volume of research concerning activity detection and related tasks such as segmentation and anticipation from video footage, these tasks are less …

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Path prediction in sports with machine learning

The ability to predict the future movement of an agent has many applications, from anticipating behaviours within an autonomous driving environment to detecting abnormal behaviours in a security setting. Within sports, being able to predict paths has applications for match analysis, strategy planning, and automated broadcasting.Path prediction is typically performed at an individual level, predicting the future path of each person (or the ball) in turn. While such predictions may consider the locations and past movements of other players (both …

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it is highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:detecting target animalscounting herd animalsclassifying land useassessing …

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Machine learning for understanding and predicting behaviour

Understanding behaviour and predicting events is a core machine learning task, and has many applications in areas including computer vision (to detect or prediction actions in video) and signal processing (to detect events in medical signals).While a large body of research exists exploring these tasks, a number of common challenges persist including:capturing variations in how behaviours or events appear across different subjects, such that predictions can be accurately made for previously unseen subjectsmodelling and incorporating long-term relationships, such as previously …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Green polymer-inorganic composite materials

Composite materials are widely researched and widely used in applications such as aircraft, automobiles, ships, structural components and even the space industry.There is a need to create new composite materials which are environmentally friendly and do not use fossil fuel based products. Moreover, the properties of the composites need to be improved while at the same time minimising the costs involved.Consequently our research group is working on composite materials which not only include inexpensive inorganic fillers from the mining sector …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Mechanical, Medical and Process Engineering

When does computer vision fail?

Computer vision models predict where objects are in an image, and what those objects are. These vision models give robots the ability to perceive their environment and choose safe and smart actions based on this perception.Computer vision models can fail silently when exposed to unexpected or difficult environments - e.g. changes in camera viewpoints, changes in lighting, or when seeing new objects that haven't been seen before. This raises concerns about the safety of using vision models in the real …

Study level
Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

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