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Compressed learning techniques for next generation connected autonomous mobility


Department of Electronic Engineering

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Dr Y Ko Applications accepted all year round Self-Funded PhD Students Only

About the Project

The exploding data traffic is expected to grow at around daily 3 Petabytes by 2022, by a range of the emerging autonomous mobility applications such as connected autonomous vehicles, healthcare autonomy, warehouse robots, and AR/VR entertainment. Such autonomous mobility systems rely increasingly on sophisticated new networking and communications technologies to manage a huge daily dataset and their unpredictable patterns, especially in uncontrolled environment. Deep learning (DL) tools are motivated to extract hidden patterns of a huge dataset, which cannot be performed by human experts. On the other hand, the state-of-the-art practices in deep learning, e.g., deep neural networks (DNNs), are inaccessible to mobile users who cannot customise a big dataset of deep neural networks to their unique patterns and needs. Thus, there is a strong need to process data in close proximity to source (rather than at centralized DNNs) and, ‘autonomously’ reduce the amount of data to be transferred between devices.

This PhD project will research into new autonomous-communication techniques whose designs are inspired by compressed sensing and deep learning principles. The main objective of this project is to develop new opportunities for applying machine (deep) learning algorithms to compressed sensing hyper-dimensional communication mechanisms, and the developed opportunities will unleash the potential of the today’s hyper-dimensional modulation and network coding techniques towards next generation autonomous mobility systems which will often run in uncontrolled environment with presence of growing uncertainties. The results will be exploited to provide significant advance that can radically transform the ‘static’ nature of today’s multi-dimensional modulation and networking paradigms. The project will challenge how today’s multi-dimensional system designs are autonomously adapted in deployed environments by mimicking human ability to effectively learn, fine-tune and adapt skills at runtime, which can also be feasible.

Entry requirements:
Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics, Music Technology or a closely related subject.

How to apply:
Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the Electronic Engineering website https://www.york.ac.uk/electronic-engineering/postgraduate/funding/ for details about funding opportunities at York.
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