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Smart air monitoring system for the detection of microbial pathogens based on image processing and deep learning


School of Engineering and the Built Environment (SEBE)

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Dr A Kerrouche , Prof H Yu No more applications being accepted Self-Funded PhD Students Only
Edinburgh United Kingdom Bioinformatics Biomedical Engineering Electrical Engineering

About the Project

Air quality plays an important role in our health especially with the current worldwide covid-19 pandemic. One large consideration for filtration systems is size of particulate matter (PM), the microscopic aerosol particles made up of solids and liquids that need to be eliminated. The demand for virus and bacteria monitoring inside buildings is more prevalent to ensure people’s safety. The actual performance of industrial air filtration systems e.g. in hospitals are specified at > 6 air changes/hr, as part of general new guidelines in ventilation policy to reduce Covid-19 risk indoors. The time required for clearance of aerosols, and thus the time after which the room can be entered without a filtering face piece (FFP3: Filter Standard Respirator Class 3) respirator, can be determined by the number of air changes per hour (ACH) as outlined in World Health Organization (WHO) guidance. In general wards and single rooms there should be a minimum of 6 air changes per hour, in negative-pressure isolation rooms there should be a minimum of 12 ACH. The project aims to develop a small-portable multi-sensor system, which includes heating, cooling, light detector and chip location for the detection of fluorophores only liberated if the specific target (the pathogen) is present in the sample. The PhD student will design the global system architecture using AutoCad 2D/3D computer-aided design software and exploiting e.g. simple fuzzy-logic rules. Other technologies will be investigated such as ultrasound and UV light to clean the filter. This project will study re-usable air filtration technology such as oil-coated filtration system exceeding High-Efficiency Particulate Air (HEPA) standards for Covid-19 particles. The system will allow a rapid test, directly in the sample, detection of optical signal emission, data display and storage by telemetry technology.

Academic qualifications

A first degree (at least a 2.1) ideally in Electronic/Optical Engineering, Computer Science, Bioinformatics with a good fundamental knowledge of Image processing and bioinformatics..

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

· Experience of fundamental Strong machine learning background

· Competent in Matlab, Python, Java and/or C++

· Knowledge of Biomedical Optics

· Good written and oral communication skills

· Strong motivation, with evidence of independent research skills relevant to the project

· Good time management

Desirable attributes:

Practical research expertise in an optical lab and/or a clean-room environment


Funding Notes

Self funded

References

Abdelfateh Kerrouche , MP Desmulliez and H. Bridle “Megasonic sonication for cost-effective and automatable elution of Cryptosporidium from filters and membranes”. J Microbiol Methods. 2015 Nov;118:123-7
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