QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 15 matching student topics
Displaying 13–15 of 15 results
Cobot contact tasks through multi-sensory deep learning
Contact tasks like grinding, polishing and assembly require a robot to physically interact with both rigid and flexible objects. Current methods relying on force control have difficulty achieving consistent finishing results and lack robustness in dealing with non-linear dynamics inherent in how the material is handled. This project will take a new approach that detects and diagnoses the dynamical process through deep learning fusion of multi-sensory data, including force/tactile, visual, thermal, sound, and acoustic emission; and generate corrective process parameters …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Data reasoning to extend domain knowledge in deep learning
A wide variety of companies now use personalized prediction models to improve customer satisfaction, for example, detecting cancer relapses, Detecting Attacks in Networks (e.g., SDN) or understanding Customer Online Shopping Behaviour. However, the dramatic increase in size and complexity of newly generated data from various sources is creating a number of challenges for domain experts to make personalized prediction.For example, early detection of cancer can drastically improve the chance and successful treatment. Recently, supervised deep learning has brought breakthroughs in …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Productive reproducible workflows for deep learning-enabled large-scale industry systems
Deep learning is a mainstream to increase the capability of industry systems, particularly for those with massive data input and output. It is seen that many tools are now claimed to be freely available and could facilitate such process of development and deployment significantly with scalability and quality.However, limited attention has been on developing reproducible and productive workflows to identify the tools and their values towards large-scale industry systems. In this project, we will explore how to design such a …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
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