Projects

Nanotechnology and Electronics Research Group

Active projects

Assessment of Quality of Experience of High Dynamic Range Images Using the EEG and Applications in Healthcare

Research student: Shaymaa S Al-Juboori
Course: PhD, Computing and Electronics
Funding: Iraq Ministry of Higher Education and Scientific Research
Start date: 1 October, 2014
Supervisors: Professor Emmanuel Ifeachor (Director of studies) and Dr Lingfen Sun

Project description
Recent years have witnessed the widespread application of High Dynamic Range (HDR) imaging, which like the Human Visual System, has the ability to capture a wide range of luminance values. Areas of application include home-entertainment, security, scientific imaging, video processing, computer graphics, multimedia communications, and healthcare. However, in practice, HDR content cannot be displayed in full on standard or low dynamic range (LDR) displays, and this diminishes the benefits of HDR technology for many users. To address this problem, Tone-Mapping Operators (TMO) are used to convert HDR images so that they can be displayed on low-dynamic-range displays and preserve as far as possible the perception of HDR. However, this may affect the visual Quality of Experience (QoE) of the end user and this is a vital issue in image and video applications.

The aim of the project is to investigate the assessment of the visual QoE of HDR images on small screen devices (e.g. smart phones) using the electroencephalogram (EEG) and to explore how to exploit the outcome of this in novel applications in healthcare (e.g. to detect colour vision deficiency).

The EEG is a promising approach that can be used to assess quality implicitly. Unlike traditional subjective methods using the Mean Opinion Score (MOS), the EEG provides valuable insight into the link between perceived quality and how the user feels at the physiological level. However, there is a need to better understand the nature of the recorded neural signals and their associations with user-perceived quality. Nevertheless, the EEG can provide additional and complementary information that will aid understanding of human perception of content. Furthermore, it has the potential to facilitate real-time monitoring of QoE.

Figure: HDR Image of Tamar Bridge

Human Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease

Research student: Ali H. Husseen Al-nuaimi
Course:   PhD, Computing and Electronics
Funding: Iraq Ministry of Higher Education and Scientific Research
Supervisors:   Professor Emmanuel Ifeachor (Director of studies), Dr Lingfen Sun and Dr Emmanuel Jammeh

Project description
Alzheimer’s disease (AD) is a progressive disorder that affects cognitive brain functions and develops many years before the clinical symptoms become evident. A biomarker that provides a quantitative measure of changes in the brain in the early stages of AD would therefore be useful for early diagnosis. However, giving the large numbers of people affected by AD there is a need for a low-cost, robust, and easy to use biomarkers to detect AD in its early stages. Recent guidelines promote the use of biochemical and neuroimaging biomarkers to improve the diagnosis of AD. But, cerebral spinal fluid testing for AD is invasive and neuroimaging (e.g., positron emission tomography-PET) is expensive and available only in specialist centres. Blood-based biomarkers have shown promising results in terms of AD diagnosis, but these are not yet fully developed and low-cost biosensors to detect such biomarkers do not yet exist.

Potentially, electroencephalogram (EEG)-based biomarkers can be used to fulfil the above needs. The EEG is low-cost, widely available and contains valuable information about brain dynamics in AD. Thus, it can be used as the basis for a first line decision-support tool in AD diagnosis to complement CSF and neuroimaging biomarkers.

This project aims to develop robust EEG biomarkers to detect AD based on analysis of changes in the EEG. The most characteristic features in AD are slowing of the EEG activities, a decrease in coherence, and a reduction in complexity. These changes can be quantified as biomarkers of AD.

Figure: EEG Measures

Graphene based materials for water filtration

Research student: Jonathan Bloor
Course: MPhil/PhD in Computing and Electronics
Funding: University of Plymouth (GD105237-105)
Start date: 1 October, 2017
Supervisors: Dr David Jenkins (Director of Studies), Professor Richard Handy and Dr Shakil Awan

Project description
The graphene based water filtration project aims to use aerogel structures for the adsorption of the toxic metals to clean up potable water in rural India. The functionalisation of graphene results in specific complex coordination’s with target toxic metals. The overall aim of this PhD is to confirm the maximum adsorption of the graphene aerogel structure and to what degree specificity can be engineered. The regeneration of the material is key to metal recovery and the sustainability of the product. Graphene oxide and other graphene related nanomaterials have been shown to be effective in the remediation of a multitude of toxic metals and organic chemicals, indicating that this technology is applicable in developed countries as well as water stressed nations.

 

Figure: Graphene aerogels ready for SEM imaging (left). Graphene aerogel monolith (right).

Graphene Sensors for Alzheimer’s Disease Multiplexed Biomarker Detection

Research student: Theodore Swanta Bungon
Course: MPhil/PhD in Computing and Electronics
Funding: International Student (GD110025-106)
Start date: 1 October, 2018
Supervisors: Dr Shakil Awan (Director of studies), Dr Toby Whitley and Dr Paul Davey

Project description
The aim of this project is to fabricate Graphene Field Effect Transistor (GFET) Biosensors that can be utilized to detect a variety of biomolecules such as DNA and Protein, specific to neurodegeneration especially Alzheimer’s disease (AD).

Graphene is a single atomic plane of graphite with high mobility, low electrical noise, exceptional surface-to-volume ratio and biocompatibility which makes graphene potentially an ideal platform for a variety of biosensing applications. We typically fabricate the GFET sensors on Si/SiO2 substrate through processes of photolithographic patterning, evaporation of chromium and sputtered gold contacts and metal lift-off technique to form the biosensing devices. These are then characterised using atomic force microscopy, scanning electron microscopy and Raman spectroscopy to determine the quality, uniformity, defects and doping of the graphene channel material. Functionalisation and immobilisation of the

GFET sensors, including antigen biosensing, is monitored using 2-probe, 4-probe and back-gated measurements with a Keysight parameter analyser interfaced to a Cascade probe station. The fabricated GFET biosensors are generic, selective, and low-cost and could find applications in a broad range of point-of-care (PoC) medical diagnostics such as neurodegeneration cancer and cardiovascular disorders.

Figure: Fabricated GFET Biosensors (left). DC 4-Probe System (right).

Intelligent data analysis and Alzheimer’s disease biomarker discovery

Research student: Chima Stanley Eke
Course: MPhil/PhD in Computing and Electronics
Funders: EU Marie Curie (BBDIAG)
Start date: 1 October, 2017
Supervisors: Professor Emmanuel Ifeachor (Director of Studies), Dr Xinzhong Li, Dr Camille Carroll and Dr Stephen Pearson.

Project description
Alzheimer’s disease is the leading cause of dementia. Over 50 million individuals currently suffer from dementia worldwide. There is currently no cure for Alzheimer’s disease but efforts are being made to develop new interventions. Such interventions are aimed at the early stages of the disease prior to extensive cell damage in individuals with the disease. This necessitates early diagnosis of disease subjects to enable selection of suitable candidates to participate in clinical trials.

This project focuses on applying artificial intelligence-based data analysis techniques to identify blood-based biomarkers that may assist in the identification of early disease subjects and predicting likely course of the disease progression. Blood-based methods may serve as a cost-effective and non-invasive approach that may be implemented in point-of-care devices to complement more sophisticated approaches.

Figure: Conceptual diagram of AI-based real-time biosensing platform

Graphene Antennas and Multifunctional Sensors 

Research student: Benjamin O’Driscoll
Course: MPhil/PHD in Computing and Electronics
Funding: University of Plymouth (GD110025-104)
Start date: 1 October 2018
Supervisors: Dr Toby Whitley (Director of Studies), Dr Shakil Awan and Dr Paul Davey

Project description
Graphene has shown how valuable it is as a sensing material in a wide variety of applications including its use as a back-gated channel material in graphene field effect transistors (GFETs) optimised to detect a variety of biomolecules. By measuring the resistance change through the conducting graphene channel when analytes are immobilised onto the surface of graphene, signals that correspond to binding events can be detected. Graphene based biosensors aim to utilise the material’s sensitivity, linear current-voltage (I-V) characteristics and biocompatibility for the next generation of early detection screening devices.

Currently, several GFET biosensors require the laborious method of measuring the resistance change through semiconductor device analysers interfaced with probe stations. This detection method would allow swift and repeatable measurements to be conducted which would aim to alleviate the need for expensive and specialist equipment, consequently facilitating the development of this technology into a popular method for the early screening of a wide variety of diseases in the future.

Initially this project has focussed on the familiarisation of the fabrication techniques which include photolithography, sputtering, plasma and chemical etching and evaporation. These techniques have been applied on many occasions to fabricate several sets of GFETs from graphene samples provided by various collaborators including the University of Cambridge. Once the standard procedures for the measurements to characterise the electrical and structural properties of the GFETs are completed, the focus of this project over the course of the next six to twelve months will be on the bio-functionalisation of GFETs and antennas.

Figuew: Fabricated GFET biosensor (left) and characteristic spectrum of monolayer graphene with inset showing Raman Spectroscopy System at the University of Plymouth (right).

Graphene-based biosensors for quantitative characterisation of DNA methylation

Research student: Mina Safarzadeh
Course: MPhil/PhD in Computing and Electronics
Start date: 1 October, 2018
Supervisors: Professor Genhua Pan, Dr David Jenkins, Dr Adrian Ambroze

Project description
The objectives of Mina’s project are to develop a simple and inexpensive biosensor for quantitative detection of DNA methylation. Two types of graphene DNA sensors, an electrochemical-based electrode made of reduced graphene oxide flakes, and a conductance based backgated graphene field effect transistor (gFET), will be explored in the project with the aim to produce a reliable and sensitive sensing device for DNA methylation. Mina is an early stage researcher (ESR) in the AiPBAND network, funded by the European Research Council under the umbrella of the Marie Sklodowska-Curie Action Initial Training Networks (MSC-ITN, part of Horizon 2020) which aims to train a new generation of entrepreneurial and innovative researchers.


Figure: A schematic of biosensors

Graphene based biosensors for detection of blood biomarkers of Alzheimer’s disease

Research student: Jagriti Sethi
Course: MPhil/PhD in Computing and Electronics
Funding: EU Marie Curie (BBDIAG)
Start date: 1 October, 2017
Supervisors: Professor Genhua Pan (Director of Studies) and Dr Yinghui Wei

Project description:

Alzheimer’s disease (AD) is one of the major forms of dementia affecting millions of people worldwide. Preclinical diagnosis of AD, before significant brain damage, is a key requirement for developing disease-modifying drugs and preventive strategies. Under the Blood Biomarker-based diagnosis tools for early stage Alzheimer’s disease (BBDiag), our aim is to develop novel and innovative diagnostic strategies for the detection of biomarkers.

Graphene is a two-dimensional allotrope of carbon consisting of monolayer of atoms arranged in a honey-comb lattice. It is an attractive material for biosensors due to its amazing properties such as mechanical strength, large surface to volume ratio, chemically inert surface, high electron mobility, thermal conductivity, large scale CVD production, ease of surface functionalisation and low intrinsic electrical noise. Additionally, graphene sensors can also be easily integrated into a POC technology for high throughput screening of biological samples (plasma, serum etc). This ensures miniaturization of the system and promotes automation and parallelization for a multiplexed platform.

We have developed both back gated graphene field effect transistors (gFETs) and modified screen printed electrodes (SPEs) for the detection of important neurochemical indicators of AD such as Aβ1-42 and Clusterin. The measurements for gFETs are done with Keysight parameter analyser interfaced to a 4-probe station and for SPEs is done using Dropsens Analyser. The developed sensing platforms are highly sensitive and provide a tool for the rapid and reliable detection of biomarkers for minimally invasive, cost and time effective point of care diagnostic devices.

Figure: Graphene based FET and SPEs biosensor

Efficient and Novel CVD-graphene Based Devices

Research student: Ahmed Suhail
Course: MPhil/PhD in Computing and Electronics
Funding: HCED institution
Start date: 1 October 2014
Supervisors: Professor Genhua Pan (Director of Studies) and Dr David Jenkins

Project description:
Since 2016, during my project, the transferred CVD-graphene (Gr) has been successfully achieved as a first time at the clean room/Plymouth university. It has been worked on the preparation of efficient and novel CVD-graphene based devices. This has been achieved through transferring high quality CVD-graphene using novel techniques developed during my project. Many techniques such as Raman, AFM, SEM and XPS have been applied to investigate the quality of CVD-graphene. Additionally, several novel techniques have been developed to be used in the fabrication process and analysing of CVD-graphene based devices. For graphene-based field effect transistors (gFETs), it has been efficiently used for investigating transferred graphene and detecting biomarkers. For graphene /Si Schottky junction solar cell, it has been powerfully prepared with a recorded efficiency of 14%.

 

Figure. Fabricated GFET biosensor (left) and graphene/Si Schottky junction solar cell (Right).

Smart Mixing - Artificial Intelligence in Live Music Mixing Systems

Research student: Christopher Towell
Course: MPhil/PhD, Computing and Electronics
Funders: Royal Commission Industrial Fellowship & Allen and Heath
Start date: 1 April, 2019
Supervisors: Professor Emmanuel Ifeachor (Director of Studies) and Dr Lingfen Sun

Project description
Expectations of sound quality are continually growing for both pre-recorded and live audio. This has led to increasingly complex mixing desks with a huge number of parameters that a sound engineer can control. The aim of the project is to develop a novel automated mixing system, based on artificial intelligence and machine learning techniques, with performance similar to that of an expert sound engineer when mixing realistic live audio.

 

Figure: Live audio mixing (Tom Howart and Bryan Ferry, Credit, Lee Wilkinson Photography).

Selected completed projects

Discovering biomarkers of Alzheimer’s disease by statistical learning approaches,
Long J, PhD, 2019.
Supervisors: Dr Z Li and Professor E Ifeachor.

High efficiency graphene,
Suhail A, PhD, 2019.
Supervisor: Professor G Pan.

Surface Plasmon Resonance….
Nasih Hma Salah, PhD 2015.
Supervisors Dr D Jenkins and Professor Richard Handy.

Low Level Laser Therapy,
Ruwaidah Musstaf, PhD, 2018.
Supervisors Dr D Jenkins and Professor A Jha.

Geraphene Biosensors,
Li B, PhD, 2016.
Supervisor: Professor G Pan.

Video content and context-aware QoE prediction for HEVC video over IP networks,
Anegekuh L. PhD, 2015.
Supervisors: Dr L Sun and Professor E Ifeachor.

Video quality prediction for video over wireless access networks (UMTS and WLAN),
Khan A, PhD, 2011.
Supervisors: Professor E Ifeachor and Dr L Sun.

A framework for bioprofile analysis over Grid,
Hu P, PhD, 2008.
Supervisors: Professor E Ifeachor and Dr L Sun.

Enhancement of perceived quality of service for voice over internet protocols systems,
Qiao Z, PhD, 2008.
Supervisors: Professor E Ifeachor and Dr L Sun.

Resource-efficient strategies for mobile ad-hoc networking,
Li Z, PhD, 2007.
Supervisors: Professor E Ifeachor and Dr L Sun.

Artificial intelligence based modelling of musical instruments and sound design,
Hamadicharef B, PhD, 2005.
Supervisor: Professor E Ifeachor.

Early detection of dementia using the human electroencephalogram,
Henderson G, PhD 2005.
Supervisor: Professor E Ifeachor.

Speech quality prediction for voice over internet protocol networks,
Sun L, PhD, 2004.
Supervisor: Professor E Ifeachor.

Closed loop control of total intravenous anaesthesia,
Dong C, PhD, 2003.
Supervisor: Professor E Ifeachor and Professor R Sneyd.

Evaluation of intelligent medical systems,
Tilbury, T J, PhD, 2002.
Supervisor: Professor E Ifeachor.

Intelligent techniques for reducing uncertainty in retrospective CTG analysis,
Skinner, MPhil, 2002.
Supervisor: Professor E Ifeachor.

Harris S P. Natural algorithms in digital filter design,
Harris S P, PhD, 2001.
Supervisor: Professor E Ifeachor.

Investigation into digital audio equaliser systems and the effects of arithmetic and transform error on performance,
Clark R J, PhD, 2001.
Supervisor: Professor Ifeachor.

Automated interpretation of the background EEG using fuzzy logic,
Riddington E, PhD, 1998.
Supervisor: Professor E Ifeachor.

Intelligent pattern analysis of foetal electrocardiogram,
Outram, N J, PhD, 1997.
Supervisor: Professor E Ifeachor.

Intelligent techniques for handling uncertainty in the assessment of neonatal outcome,
Garibaldi J M, PhD, 1997.
Supervisor: Professor E Ifeachor.

Intelligent fetal monitoring and decision support in the management of labour,
Keith R D F, PhD, 1993.
Supervisor: Professor E Ifeachor.

Knowledge based digital signal processing of human EEG,
Hellyar M T, PhD, 1991.
Supervisor: Professor E Ifeachor.