Electronics and Instrumentation Group

Group Leader(s): Professor Khaled Hayatleh

About us

The Electronics and Instrumentation Group is focused on researching highly original and innovative solutions to real-world problems involving information / signal capture and processing. Current projects (amongst others) include:

MRI image enhancement
Electrical Impedance Tomography for non-radiation based body imaging
biomedical signal artefact minimisation using digital signal processing and artificial intelligence techniques
driver distraction monitoring using computer vision and artificial intelligence
an artificial intelligence based road sign recognition system for autonomous vehicles
removal of patient movement generated artefacts in electrocardiogram systems using a novel electrode arrangement

Circuit board

Part of

Related courses

Engineering (MPhil / PhD / Masters by Research / PhD by Published Work)
Electronic Engineering BEng (Final Year Entry) (BEng (Hons))

Leadership

Dr Steve Barker

Visiting Academic - DT-Research-ECG Biosignal Capture Design

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Professor Khaled Hayatleh

Professor of Electronic Engineering

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Membership

Staff

Name	Role	Email
Mohamed Ben-Esmael	Lecturer in Electronics Engineering	ben.esmael@brookes.ac.uk
Dr Nabil Yassine	Senior Lecturer in Electric Vehicles & Postgraduate Engineering Subject Corordinator	nyassine@brookes.ac.uk

Electrical Impedance Mammography

The project is aimed at developing an alternative technique for breast cancer detection based on electrical impedance imaging.

The advantages of an impedance imaging system over traditional X-ray mammography (portability, low cost, little or zero patient discomfort, no known patient risk and no known side effects) make this technology a welcome addition to the tools available in the fight against breast cancer.

The research is devoted to the design, construction and testing of a novel and optimised electrical impedance mammographic sensor which meets all requirements for CE (European Conformity) certification and to the development and adjustment of a computationally efficient image reconstruction algorithm which could be used to detect the size and the location of breast tumours in real time.

Sensing head of the latest mammographic sensor

Vehicular monitoring for enhanced traffic flow control

We are researching into solutions for traffic monitoring based on the Internet of Things. Low energy sensor nodes, which can be embedded in the road or deployed on the roadside are being developed; and alternative low power radio technologies for backhauling data from these sensor nodes to the cloud are being investigated. This can be used for traffic counting, traffic profiling and also for informing vehicles about road conditions.

A motion artefact minimisation system for biomedical monitoring equipment

We aim to produce a system to minimise artefacts due to patient/electrode relative motion. This includes, but is not limited to:

investigating and analysing the causes of artefacts (unwanted signals)
assessing the current approaches in minimising these artefacts
designing a system that maximises the signal to noise ratio of ECG signals by subtracting the motion noise signals from the original signal with the help of Strain Gauge sensors to detect the X-plane and Y-plane movements.

Low power and high signal-to-noise ratio biomedical analog front end design techniques

All medical equipment using electrodes attached to patients requires high-precision, low-power amplifiers (HPLPAs). Without a high level of precision, the captured signals could become distorted, which could possibly lead to misinterpretation and even misdiagnosis.

Another important consideration is the power consumption. With a growing need for continuous monitoring and hence ‘wearable technologies’, it is very important to minimise power consumption in order to maximise battery life.

This aim of this project is to design and develop a low power, high Common-Mode Rejection Ratio, high signal-to-noise ratio analog front end system for biomedical applications.

Artificial intelligence techniques for driver fatigue detection

This research is looking at developing a warning system that records the level of driver fatigue and informs the driver when the fatigue levels surpass a threshold - and it becomes dangerous to drive. This is done by monitoring the driver’s blink rate and head tilt. This design has an advantage over existing systems since user feedback will be faster and more accurate.