The aim of this project is the development of a novel machine learning-based optical IPS system for adult healthcare in intelligent buildings. The proposed digital embedded system should be low cost and incorporate bespoke optical elements as secondary optics for increased position accuracy.
The topic of energy efficiency is gaining in importance globally due to ecological and economic reasons. One of the trends to reduce electricity bills and CO2 emissions is the replacement of non-efficient sources of illumination with energy efficient ones. Light Emitting Diodes (LEDs) in particular are becoming pervasive not only because of their reduced energy consumption, low cost and long life spans, but also because they can be used to transmit data. Furthermore machine intelligence (machine learning) is further optimising energy efficiency within electronic systems.
One of the technologies in which LEDs have great potential as both data transmission sources and lighting devices is indoor positioning systems (IPS), which can be used in a wide variety of applications. Optical IPS have great potential, for instance, in the field of health where they can be used to track equipment and/or patients in hospitals and care homes. This is particularly relevant in aging societies where an increasing number of people are developing conditions related to dementia.
Machine learning techniques can allow improved accuracy within application domains. However, a common issue with machine learning involves significantly high implementation computational processing requirements. This significant level of computational requirements can be reduced to improve power consumption with designing specific hardware architecture within the specific, unique and problematic infrastructure of IPS applications.
The proposed project aims to improve accuracy in low cost optical IPS systems by combining novel free-form lenses with LEDs within luminaires. The improved accuracy will explore machine learning to achieve low power implementation on novel embedded digital system architecture.
Research Strategy and Research Profile
Glasgow Caledonian University’s research is framed around the United Nations Sustainable Development Goals, We address the Goals via three societal challenge areas of Inclusive Societies, Healthy Lives and Sustainable Environments. For more. This project is part of the research activity of the Smart Connectivity and Sensing Research Group.
How to Apply
Applicants will normally hold a UK honours degree 2:1 (or equivalent); or a Masters degree in a subject relevant to the research project. Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required. Some research disciplines may require higher levels.
Candidates are encouraged to contact the research supervisors for the project before applying. Applicants should complete the online GCU Research Application Form, stating the Project Title and Reference Number (listed above).
Please also attach to the online application, copies of academic qualifications (including IELTS if required), 2 references and any other relevant documentation.
Please send any enquiries regarding your application to: [email protected]
Applicants shortlisted for the PhD project will be contacted for an interview.
For more information on How to apply and the online application form please go to https://www.gcu.ac.uk/research/postgraduateresearchstudy/applicationprocess/
Dr. Ryan Gibson [ECR], Department of Electrical and Electronic Engineering, [email protected]