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Research Thesis Topic

Machine Learning Techniques for Wireless Communications and Biomedical Signal Classification


Topic ID
436

Thesis Topic/Title
Machine Learning Techniques for Wireless Communications and Biomedical Signal Classification

Description

This PhD project aims to conduct research in artificial intelligence and machine learning (AI/ML) algorithms towards developing a prototype for human-to-machine interfacing and brain-to-brain communication.

The candidate will conduct research on developing novel artificial intelligence (AI) and machine learning (ML) based strategies for wireless communication and biomedical signal classification. The project will involve developing AI/ML algorithms in an experimental test-bed to demonstrate key concepts of the project.

This project will identify and address many challenges associated with vicarious transmission of information from people to people without using known human sensory channels or physical interactions. Key focus areas include developing a test-bed for capturing, training and testing brain signals for communication; designing and implementing suitable online/off-line training methods to interpret brain signals to human-readable languages.

The project will suit a Master of Research, or a PhD student interested in AI and machine learning. A student with a background in AI, machine learning, electrical engineering, physics, computing or data science is highly sought. The project is highly innovative and will feed into ongoing research within our team. It may have opportunities to collaborate with industry organisations and a strong emphasis on publications.

Our research team (Advanced Data Analytics) has a strong track record and industry collaborations in the application of machine learning for telecommunications, health, agriculture, solar radiation, air quality, etc.



Principal Supervisor

Associate Supervisors

Research Affiliations
  • School of Agricultural, Computational and Environmental Sciences

Field of Research
  • Artificial Intelligence
  • Artificial Intelligence and Image Processing
  • Communications Engineering
  • Communications Technologies
  • Computer Vision And Multimedia Computation
  • Electrical Engineering


Application Open Date
04/09/2021

Application Close Date
04/09/2022

USQ Scholarship Applications

Other Scholarship Funding Details
Domestic HDR Stipend Scholarship; International PhD Fees Scholarship

Pre-approved for Ethics
Not Applicable

Admission Requirements

Please review the admission requirements for the academic program associated with this Thesis Topic

For more information contact the project supervisor Professor Ravinesh Deo at University of Southern Queensland

Email: ravinesh.deo@usq.edu.au.

Or Visit

USQ’s Advanced Data Analytics Group https://eportfolio.usq.edu.au/view/view.php?id=116719




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