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Found 42 matching student topics

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Machine learning for data manipulation

This project will design and develop a machine learning architecture for data compression. The developed approach will be benchmarked against standard data compression algorithms. Data might include images and/or text.

Study level
PhD, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Learning with agents and avatars in gameful virtual/augmented/mixed reality applications

Immersive interfaces enhance learning experiences by situating learners into a specific authentic-feeling context; they provide learners with multisensory experiences. Good games excel at teaching players skills required to play the game, and testing the comprehension and application of these skills playfully yet systematically in such authentic contexts. Gameful design is the process of applying the principles of what makes games engaging and motivating to non-game settings.This project will explore using relevant theories and principles (e.g., Flow theory, Self-Determination Theory) as …

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Robotic education for early childhood

This project will further develop a range of robotics, coding and STEM-embedded picture books and associated learning materials for early childhood education purposes.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Enhancing discrete choice models using insights from behavioural economics and psychology

Behavioural economics studies the impact of psychological factors on economic decisions and how these decisions deviate from those implied by classical economic theory. It merges the fields of economics and psychology to provide a better understanding of choice behaviour.When it comes to transportation individuals face long-term choices, such as car ownership and residential/work locations, and short-term choices, such as destination and departure time.Insights from behavioural economics can be applied to these choices to gain a better understanding of the conditions …

Study level
PhD, Master of Philosophy
Faculty
Science and Engineering Faculty
Lead unit
School of Civil Engineering and Built Environment

Mini-autonomous car

You will be further developing the mini-autonomous car at the QUT robotics group, to enable a range of technology demonstrations and public display options.Some of the key capabilities to add or enhance include:navigationpositioningmappingscene understandingpedestrian detectionroad sign and traffic light detectioncontrol and path planning.

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Visual chatbot (AI)

This project aims to create an Artificial Intelligence (AI) agent that can have a natural-language dialogue with humans about the content of an image or a video.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Deep learning for iris recognition

This project investigates deep learning techniques including Convolutional Neural Networks (CNNs) to perform identity recognition based on the iris region.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Speech recognition using deep neural networks

The success of automatic speech recognition (ASR) systems is largely driven by the availability of large-volume, high-quality speech data for building the acoustic model. In particular, recent significant quality improvements of ASR systems have been due to progress in the fields of machine learning and artificial intelligence with deep neural network (DNN) based acoustic modeling.A large volume of data is usually required for training a high-dimensional model using a DNN with many hidden layers and millions of parameters in order …

Study level
Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Wire detection using computer vision for the application of electric wire following by drones for inspection purposes.

We are looking to develop a system that where drones can detect wires using computer vision.We expect this technology to be applied to drones that can follow electrical wires for inspection purposes.

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Camera-based positioning system for autonomous vehicles

This project will develop a camera-based positioning system for ground-based autonomous vehicles, including autonomous cars that enables low latency, highly accurate absolute positioning for vehicles relative to a pre-mapped environment.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Neuroscience-based algorithms for robotics and autonomous vehicles

Increasingly researchers are looking to neuroscience and the brain for inspiration in how they design their deep learning network architectures for robotics and artificial intelligence tasks.This project will design innovative new neural network-based learning approaches to make breakthroughs in robotic and autonomous vehicle capabilities in:sensingscene understandingnavigationSimultaneous Localisation And Mapping (SLAM)other key capabilities.In doing so you will gain invaluable experience and skills in one of the most exciting fields in the world today, along with opportunities for international travel and collaborations.Our …

Study level
PhD, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Data augmentation for training deep learning networks to detect brain lesions from MRI

Detecting lesions from medical imaging is a difficult tasks for radiologists that is time-consuming and subjective. Artificial intelligence (AI) techniques could outperform humans but there is a lack of well-characterised, large datasets available for training purposes.This PhD project will focus on data augmentation techniques using synthetic approaches, as well as weekly supervised learning.This project is part of a large team of researchers involving startups, CSIRO, QUT faculties and several postdoc and PhD students. …

Study level
PhD
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

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