Master of Research (MRes) Technology is a postgraduate course that will allow you to focus your research interests on one or two areas of technology and work towards translating your learning into research related outputs – such as a submission for a peer-reviewed publication; a peer reviewed research/knowledge transfer grant application, or a presentation.

MRes Technology can be studied either full time (1-year) or part time (2-years), with start dates in September and January each year. You will develop a wide variety of skills, experience and competence on this course, and the MRes will provide a thorough grounding for students moving towards Doctoral (PhD) studies, or pursuing research related activities as a career.

The course is taught within the Faculty of Technology and many of the projects listed on this page are linked to research that is undertaken in our School of Computing. If you'd like to propose your own idea for a research project in the fields of computing and technology, email Dr Alice Good to discuss feasibility and potential supervisors.

A novel digital forensic tool for assessing the suspect's IT competency level

Project supervisor: Dr Fudong Li

Over the last few years, the number of computer assisted crimes has increased significantly; as a result, investigators need to examine a large number of computing devices which can be time consuming.

One of the factors that could be used by the investigator to determine how much time should be spent during the triage phase is the suspect’s IT competency level, such as less time for a novice users computer and more time for an advanced users device.

With the aim of advising the investigator to allocate sufficient time when triaging a new case, this project aims to design and develop a novel forensic tool that can be used for assessing the suspect’s IT skill level based on the information collected during the device seizure phase and also the data presented at the forensic image.

An advanced analysis tool for digital forensics

Project supervisor: Dr Fudong Li

Over the past 15 years, digital forensics has experienced increased challenges, including expanding data storage, the prevalence of embedded flash storage, the need to analyse multiple devices, and the use of encryption.

These are an extra burden on the work of the human investigator who carries out the manual and time consuming analysis process, resulting many outstanding cases. This project will investigate the existing analysis tools of digital forensics and develop a novel advanced analysis tool that aims to remove some of the human cognitive load and offer informed decisions.

Blocks-based program construction for a textual language

Supervisor: Dr Matthew Poole

Blocks-based programming languages such as Scratch are attractive environments in which to learn to program. Chunking of code into blocks reduces cognitive load and drag-and-drop program construction avoids syntax errors. Many learners though need to program with traditional textual languages. There exists some recent work on a blocks-based environment for introductory procedural programming in Python.

This project will investigate design ideas to enable object-oriented programming (for example, in Python) using blocks. Core issues to be investigated include visual representations of objects and block-based structuring of classes. Design ideas will be prototyped and evaluated. Candidates should be familiar with Python and JavaScript.

Clinical outcome modelling

Supervisors: Professor Jim Briggs and Professor David Prytherch

The Centre for Healthcare Modelling and Informatics (CHMI) at the University of Portsmouth is a long-established health informatics research and innovation group.

In collaboration with Portsmouth Hospitals and other partners, our work in clinical outcome modelling has supported the development of the VitalPAC vital signs collection system and the National Early Warning Score (NEWS), which recommended by the Royal College of Physicians, among many other projects.

We offer several projects involving applying statistical or data science techniques to clinical datasets in order to derive new clinical knowledge or improve clinical practice.

Data mining methods for risk prediction in intensive care units

Supervisor: Dr Mohamed Bader

This study aims to investigate the development of data mining methods for analysing large-scale medical data.

The study will focus on the Intensive Care Unit (ICU) as it is one of most data-intensive units in the healthcare system. The study will be based on the Philips eICU and Mimic database which contains more than 3 million ICU stays and billions of medical measurements that is collected from more than 400 hospitals.

Research students will join an existing team working on risk and mortality prediction in ICUs. The team has access to an existing preprocessed ICU database that is compatible with several data mining and data analytics tools

Deep fuzzy modelling

Supervisor: Dr Alexander Gegov

Deep learning has gained significant attention within the computational intelligence community over the recent years. Its success has been mainly due to the increased capability of modern computers to collect, store and process large volumes of data. This has led to a substantial increase in the effectiveness and efficiency of data management. As a result, it has become possible to achieve high accuracy for some benchmark learning tasks such as object classification and image recognition within a short time frame.

The most common implementation of deep learning has been through neural networks due to the ability of their layers to perform multiple functional composition as part of a multistage learning process. In spite of the significant recent advances in deep learning discussed above, there are still some open problems and serious limitations.

In particular, effectiveness is usually adversely affected when the data is not well defined due to inherent noise, uncertainty, ambiguity, vagueness and incompleteness. This has an adverse impact on efficiency due to the necessity to define the data better by means of additional collection, analysis and cleaning. The reduced effectiveness and efficiency undermines the ability of deep learning to address real life tasks that are safety critical or time critical.

Besides this, deep leaning has been used mainly in a passive manner for the purpose of observing the environment but it almost has not been used in an active manner for the purpose of changing the environment. Finally, deep learning models often have poor transparency which makes them difficult for understanding and interpretation by non-technical users.

The aim of this project is to address some of the problems and limitations discussed above with the help of deep fuzzy models. The latter have been around in different forms and under different names such as hierarchical fuzzy systems and fuzzy networks. These models are well suited for performing multiple functional composition at both crisp and linguistic level. Moreover, they have the potential of handling effectively and efficiently data that is not well defined due to the use of a fuzzy approach. Also, deep fuzzy models can be used in both passive and active manner with regard to the environment due to their generic structure. Finally, these models have a high level of transparency due to their rule based nature.

Emerging darknets

Supervisor: Dr Gareth Owenson 

Darknets are often championed as places of freedom and liberty, but the reality is that they more often than not provide a safe place for criminals to trade and publish their wares.

Darknets are designed to make it difficult for law enforcement to understand what is going on and where actors are located. In this project, you will examine new and emerging darknets to analyse them for vulnerabilities and develop attacks that can be utilised by law enforcement.

You will have experience programming in a language such as C or Java, understand networking concepts like TCP and UDP, and have at least a basic understanding of cryptographic primitives (for example, public/private key, block cipher, etc).

Impact of virtual communities on health outcomes

Supervisor: Dr Alice Good

With rapidly evolving technological communications, virtual communities are transcending many aspects of our lives, including commercial, social, education, health and wellbeing.

Health related virtual communities and Electronic Support Groups (ESGs) offer a peer to peer community support forum that enables people to seek and offer advice and support relating to specific health areas.

The aim of the project is to carry out a systematic review on the health benefits of these communities and groups when used as an adjunct support intervention.

Intelligent motion planning for the robot grasps

Supervisor: Dr Zhaojie Ju

Different shapes and colours of manipulated objects provide a challenge for dexterous robot grasps, which are crucial to many robot applications, such as home assistance, industrial robot tasks, robot entertainment and robot healthcare.

The aim of this project is to find the best way to grasp objects and hold them while moving, using machine learning algorithms. An experimental robotic arm with a gripper end-effector will be provided and used to grasp different shapes of objects.

Investigating the challenges, opportunities and impact AI can have on the education sector

Supervisors: Petronella Beukman and Dr Mihaela Cocea 

How will the education sector respond to AI?

One of the simplest but most impactful things AI can do for the educational space, is to speed up the administrative processes for educators. Some of the more complex applications focus on the development of intelligent tutoring systems that use test responses to personalise how students navigate through materials and assessments targeting the skills the students need to develop, as well as the introduction of teaching robots to teach linguistics to young learners.

The aim of this research is to investigate the challenges, opportunities and impact for AI in a range of educational settings, from to enhancing autonomous learning to improving communication and interaction between teaching robots and students.

A student should have experience in the field of AI as well as an understanding/interest in pedagogy and psychology.

Machine learning for digital marketing

Supervisor: Dr Mohamed Bader 

With the continuous growth of e-commerce and online sales, digital media is rapidly becoming the core marketing venue for many retailers companies. This growth has allowed retailers to gather data about their customers and their shopping behaviour. However, analyzing and mining this data is still a challenge. This project aims to use machine learning and data mining methods for developing product recommender systems and analyzing the various digital marketing data. The project will be based on real-world data and will run in collaboration with industrial partners.

Mobile apps and wellbeing

Supervisor: Dr Alice Good

Mobile applications are increasingly being used to help manage wellbeing. Examples of projects in this area within the School of Computing have looked at the potential of apps in facilitating reminiscence therapy for people with Alzheimers and supporting adherence to Post Natal Depression interventions.

We recognise the importance of designing theory based apps that are specifically tailored towards the needs of the intended user group. We offer projects that evaluate the effectiveness of these apps from both practitioners’ and caregivers’ perspectives. We also offer projects that focus on providing an evaluation of mobile apps that support wellbeing from both a theoretical and usability perspective. Ideas for proposals for new mobile apps to support wellbeing are also welcome.

Remote dexterous robotic hand control

Supervisor: Dr Zhaojie Ju

Dexterous robotic hands are able to perform certain actions as human do. This project is aimed at transferring human hand skills to a robotic hand, where a surface electromyography (sEMG) sensing system is used as a way to capture human hand motions. The hand motions will be analysed and recognised for the robot to perform a desired task using machine learning and pattern recognition algorithms.

Sentiment analysis from textual data

Supervisor: Dr Mihaela Cocea

The amount of data we produce has sharply increased giving rise to the big data era. IBM has estimated that 80% of this data is “unstructured”, with text being one of the most prevalent format, yet our machine learning algorithms perform less well on textual data than other types of data, especially when focusing on tasks capturing opinions and other subjective aspects. This project will investigate different machine learning algorithms for classification of textual data, with a particular interest in fuzzy classifiers which can deal with ambiguity. Candidates should have experience in machine learning or data mining, or be willing to learn about them.

Task design for an intelligent sports assistant robot

Supervisor: Dr Zhaojie Ju

Mobile robots with manipulators will be very popular in future applications, such as assistive home robots and sports robots. In this research, a robotic arm on a track base with a gripper as the end-effector will serve as an experimental sports auxiliary. This project will study a ping-pong ball collecting task, investigating both ball detection path planning with methodologies in artificial intelligence.

The application of augmented reality in apprenticeship training

Supervisor: Athanasios Paraskelidis

Emerging technologies such as Augmented Reality (AR) provide a unique opportunity to improve accessibility to teaching material, encourage independent learning and speed up the learning process. This project innovates by bringing AR technology into engineering apprentice students’ teaching and learning experiences. It will demonstrate how AR technology could revolutionise the delivery of quality teaching material to students in a digital form. The use of AR in learning applications has been documented for schools and higher education but it has not been represented in apprentice training.

Exploring the possibilities of using AR in this domain is expected to benefit apprentices and their instructors alike, and to contribute to the literature on use and effectiveness of AR learning objects. Previous experience on Augmented and/or Mixed Reality is desirable but not essential. Candidate(s) with experience on Java/JavaScript programing are particularly welcome.

Touchscreen-optimized real programming environment

Supervisor: Dr Jacek Kopecky

One of the major limitations of mobile devices (smartphones, tablets) compared to laptops and desktop computers is that it is not easy to create real programs on touchscreen-oriented devices. This project would research ways in which interactions with hand-held devices (such as touch, voice, etc.) could substitute typing when programming, as touchscreen typing in modern programming languages is painfully slow.

Traffic flow fingerprinting

Supervisor: Dr Gareth Owenson

A range of protocols on the Internet now use encryption to hide the content of messages. Whilst encryption is principally a net-gain for society, protecting people’s information, for law enforcement it presents a difficulty in conducting authorised surveilance.

In this project, you will examine techniques of analysing timing and sizes of encrypted messages to generate fingerprints for particular types of activity and/or destination using machine learning. You will then deploy this in the real world and evaluate its efficiency.

You will have experience programming in a language such as C or Java, understand networking concepts like TCP and UDP, and have at least a basic understanding of cryptographic primitives (e.g. public/private key, block cipher, etc).

Understanding patterns of inconsistent data quality in electronic healthcare records

Supervisor: Dr Philip Scott

Many hospitals have seen the use of digitized medical records (scanned paper) as a means to save money on administration and improve access to records. In the United Kingdom (UK), Government policy has repeatedly promoted the move away from paper records in health care. However, published UK experience has shown that clinical usability of the digitized hospital record can be poor and potentially have negative effects on operational processes. Even full electronic patient records (EPRs) have had detrimental impact on clinical productivity, both in the USA and recent UK implementations.

Web accessibility audit

Supervisor: Dr Alice Good

Web accessibility continues to be an issue for many users. Despite the availability of standards and guidelines there is still a significant shortfall of compliance. Where non-compliance to design standards prevails, there will always be users who are faced with barriers. Learning potential, inclusion and empowerment are all issues that are affected by inaccessible web pages. An investigation commissioned by the Disability Rights Commission (DRC) found that 81 per cent of websites fail to meet the most basic standards for accessibility (2004). We are interested to identify how web accessibility has improved since then. The aim of this research is to carry out an audit of selected websites and evaluate their accessibility using a range of user centred methods.

Other research projects

MRes Technology research projects are offered in the following areas:

Please note, these lists are not exhaustive and you'll need to meet and discuss the project you're interested in with a member of research staff before you apply.

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