MRes Projects - Engineering
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 Engineering. If you'd like to propose your own idea for a research project in the fields of computing and technology, email Dr Linda Yang to discuss feasibility and potential supervisors.
- A multi-agent framework for practical artificial intelligence
- Accurate 3D printed models for dental practice
- Better bones and muscles: Physical activity monitoring for older people
- Capture and reuse of tacit knowledge and experience using a computational approach
- Context-sensitive information retrieval and service in mobile ad hoc societies
- Design for sustainability: An integrated approach
- Developing targeted exercise programmes to combat degenerative joint diseases in the aging population
- Digital volume correlation (DVC) strain measurements in bone and biomaterials
- Fracture mechanics assessment of cracking in nuclear graphite
- High-resolution XCT imaging and mechanics of teeth
- Image-based authentication system
- Image characterisation of nano-scale fibrous bioengineering structures
- Intelligent monitoring system to predict potential catastrophic incidents
- Intelligent real time energy saving
- Knowledge-enabled intelligent system for wind turbine condition monitoring and fault diagnosis
- Mechanical and/or tribological properties at true nanoscale
- Multifunctional surfaces: Can man-made coatings self-adapt to external stimulus, and maintain required performance
- Multiple criteria decision making
- Pre-clinical analysis of a novel hip-stem design
- Pre-clinical analysis of orthopaedic implants: A multi-scale approach
- Sharing control of a powered-wheelchair between a wheelchair user and an intelligent sensor system
- XCT assessment of laser cutting techniques in dentistry
Supervisor: Professor Adrian Hopgood
The aim of this project is to build a multi-agent framework for hybrid artificial intelligence (AI).
Hybrid AI systems use multiple techniques collaboratively, e.g. rules, probabilistic modelling, case-based reasoning, fuzzy logic, genetic algorithms, neural networks, and deep learning. Most real-world problems are multi-facetted and therefore require such a hybrid approach. Blackboard systems are a popular model, in which software agents communicate via an area of shared memory, i.e. the ‘blackboard’. As each agent can have a different specialism using its own technique, this approach mimics an interdisciplinary team of experts rather than a single human.
Prior work has led to the development of a software framework known as DARBS (Distributed Algorithmic and Rule-based Blackboard System), which can form the start point for this project. However, several updates and improvements are now needed. An additional exciting possibility would be the development of a new portable version for mobile devices.
Supervisors: Dr Gianluca Tozzi and Prof Chris Louca
Polymeric inks have been produced and tuned to achieve high-resolution multi-jet 3D printing, with the potential to replicate microarchitecture and structural performance of biological tissues. Recently, we have developed a pioneering material updating algorithm (MUA) used in combination with finite element (FE) modelling for multi-material 3D printing ; allowing 3D printed polymers to replicate bone morphology and mechanics with unprecedented resolution.
- Peña Fernández et al., 2017. Bone & Joint. Vol. 99-B no. SUPP 1 34.
This project aims to apply high-resolution multi-material 3D printing to produce precise and mechanically competent tooth-gum-bone systems that would provide advanced substrates to enhance training and education in dentistry.
The project will be conducted in collaboration with the University of Portsmouth Dental Academy, using 3D printing, X-ray microscopy and licensed software available at the Zeiss Global Centre at the University of Portsmouth.
The student will report to Dr Gianluca Tozzi and Prof Chris Louca, but will operate in a vibrant environment including PhD students, Post-doctoral researchers and other academics in the field of engineering and the University's Dental Academy, which will provide expertise and guidance throughout the project.
Supervisors: Professor Raymond Lee, Dr J Luo
Osteoporosis is a clinical condition that weakens bones and muscles, often leading to serious consequences such as fractures. Physical activity has been shown to improve the strength of bones and muscles and consequently the risks of fractures. Mobile apps are frequently used to monitor and encourage physical activity. They normally only provide simple information such as the number of steps taken in a time period, but not mechanical loading information related to the health of bones and muscles. They may also not be entirely suitable for older adults which require different design specifications.
The purpose of this study was to develop a mobile app which employs an algorithm that we have previously developed to monitor mechanical loading (Bone 2014, 67:41-45), and to correlate the signals of the input sensor (typically attached to the wrist) with the mechanical loadings in the bones. We shall look at how this will suit the specific needs of the older people, and whether this can encourage health promotion in this population.
Supervisor: Dr Hongwei Wang
Design knowledge reuse holds the key to facilitating new product development and support innovation. While most of current research work on managing design knowledge is focused on capturing and reusing explicit knowledge, e.g. CAD models, reports and calculation sheets, there exist great challenges for capturing collective knowledge from the design processes of complex engineering systems.
This project aims to investigate how tacit design knowledge that generally exists in experienced engineers’ minds can be effectively and efficiently captured, represented and reused using a computational approach. This will involve developing models for representing design knowledge as well as methods for effectively reusing knowledge. The proposed models and methods will be implemented in a prototype system and evaluated using data from real-world product design projects.
Supervisor: Dr Linda Yang
This project aims to provide intelligent context-sensitive information searching and services in mobile ad hoc societies.
The challenge is to utilize information coming from different sources to provide proactive information services adapted to the user’s context such as activities and location. In many cases, this involves dealing with large amounts of sensor and location data and information resources that are distributed across multiple sites.
This research focuses on developing new search, classification and context-sensitive recommendation technologies that will allow mobile information systems to be truly usable and valuable. The project will apply and evaluate the proposed theory in the practical settings towards end-users scenarios, expecting to deliver exploitable and ready-to-use products and services.
Supervisor: Dr Hongwei Wang
Decision making in engineering design increasingly needs to consider environmental issues and cater for the need for sustainable development. However, current research has a focus on high-level frameworks highlighting key issues to consider while there is a lack of research into how designers can be supported with an integrated approach which can provide in-context information.
This project aims to develop an integrated computational approach which will enable the development of computer tools to achieve informed decision-making by considering a range of environmental issues and reusing knowledge generated from previous projects.
Developing targeted exercise programmes to combat degenerative joint diseases in the aging population
Supervisors: Professor Jie Tong; Dr P Heaton, Consultant Orthopaedic Surgeon, Pilgrim Hospital Boston, Lincolnshire; Dr A Cossey, Consultant Orthopaedic Surgeon, Spire Portsmouth Hospital, Portsmouth
According to Arthritis Research UK, a total of 8.75 million people in the UK have osteoarthritis and the number is increasing. The burden of replacement surgery will be unmanageable unless concerted efforts are made in public health research so that non-NHS interventions may be developed to delay the progress of such diseases.
This project aims to carry out computer modelling of hip/knee joints and to identify relationships between specific muscle force and joint reaction force. The knowledge will be used to guide the development of targeted exercise programmes in collaboration with clinicians. The designed exercise programmes will target selected groups of muscles so that joint pressures may be reduced, which will delay the progression of joint diseases as well as alleviate the symptoms such as joint pains.
Supervisor: Dr Gianluca Tozzi
Digital volume correlation (DVC) is rapidly growing in the biomechanical evaluation of biological tissues and biomaterials. In fact, with the rapid development of in vitro/in vivo biomechanical imaging protocols, DVC has become a powerful tool to measure full-field internal deformations in trabecular/cortical bone, whole bones, biomaterials and bonebiomaterial systems [1, 2]. The potential that DVC has to offer to biomechanics is impressive and ranges from the perfect framework for full-field validation of local properties predictions in computational models (i.e. finite element analysis) for in silicomedicine, to novel diagnostic tools for risk of fracture assessment in clinical imaging.
- Tozzi et al., 2017. JMBBM. 67:117-126
- Dall’Ara et al., 2017. Frontiers in Materials. doi: 10.3389/fmats.2017.00031
This project aims to explore new areas in DVC application to bone and biomaterials such as:
- Optimisation of DVC parameters for strain calculation
- DVC performance in fast x-ray computed tomography
The project will be conducted using x-ray microscopy and licensed software available at the Zeiss Global Centre at the University of Portsmouth.
Supervision: The student will report to Dr Gianluca Tozzi, but will operate in a vibrant environment including PhD students, Post-doctoral researchers and other academics at the field of engineering that will provide expertise and guidance throughout the duration of the project.
Supervisors: Professor Jie Tong
The UK advanced gas-cooled reactor nuclear power stations are graphite moderated. Throughout reactor life, fast neutron irradiation and radiolytic-oxidation increase the risk of cracking in graphite components, particularly in the keyway roots. Understanding cracking in nuclear graphite is of considerable importance in safe operation of nuclear power plants. The project will examine how a crack propagates under complex loading conditions towards prediction of crack path and growth rates. The project will offer an opportunity of working with some of the latest technologies including in situ mechanical testing within a microCT chamber, digital volume correlation and advanced finite element modelling.
Supervisors: Dr Gianluca Tozzi and Professor Chris Louca
High-resolution X-ray Computed Tomography (XCT) has been extensively used to characterise morphology of mineralised tissues such as bone, down to their ultrastructure . Recent technical advances like phase-contrast X-ray Tomography were used to enable contrast enhancement between very small or chemically similar material phase and greater resolution for soft tissues, without the need for staining agents . In addition, image-based mechanical properties of mineralised tissues can be evaluated in great detail by employing in situ loading devices. Surprisingly, the 3D investigation of tooth structure and function with sub-micron resolution is limited, despite the clinical relevance that such knowledge brings.
- Georgiadis et al., 2016. J. R. Soc. Interface http://dx.doi.org/10.1098/rsif.2016.0088
- Zaslansky et al., 2010. Dental Materials. 26, e1-e10
This project aims to explore in detail the structure and mechanics of teeth in order to apply this knowledge to the clinical situation, including the impact of oral disease conditions and various treatment modalities, on mineralized tissues, to inform clinical practice and surgical procedures.
The project will be conducted in collaboration with the University of Portsmouth Dental Academy, using state-of-the-art X-ray microscopy and licensed software available at the Zeiss Global Centre at the University of Portsmouth.
The student will report to Dr Gianluca Tozzi and Prof Chris Louca, but will operate in a vibrant working environment including PhD students, Post-doctoral researchers and other academics in the field of engineering and the University's Dental Academy, which will provide expertise and guidance throughout the duration of the project.
Supervisor: Dr Linda Yang
This project aims to support existing research in authentication.
Textual passwords have been the most widely used authentication method for decades. However, a strong textual password is hard to memorize and recollect. Image-based passwords were proved to be easier to recollect. As a result users can set up a complex authentication password and are capable of recollecting it after a long time even if the memory is not activated periodically.
In this project the student will be expected to implement an authentication system based on graphic images, which will protect users from becoming victims of shoulder surfing attacks.
Supervisors: Dr John Chiverton
Nano-scale structures such as electro-spun fibres or fibres found in biological tissues can be observed with various imaging modalities such as with a scanning electron microscope (SEM) or nano-scale or perhaps micro X-ray CT (XCT). Imaging of these fibrous materials is an important tool in the bioengineering domain, because measurements can be obtained from the images that can help quantify the characteristics of the materials.
You will be expected to develop software using C++ or Matlab in conjunction with Python to characterise various important aspects of these materials for various different image acquisition processes. You will be supervised by Dr John Chiverton with particular emphasis on software and algorithm development. You will also interact with PhD, post-doctoral students and academics with strong interests in bioengineering research and teaching.
The project may also make use of data obtained using our x-ray microscopy and licensed software available at the Zeiss Global Centre here at the University.
Supervisor: Dr David Sanders
This project aims to create a predictive system overlaid on trend analysis of historic data to predict the probable future condition of trait ‘states’ and so predict potential situations that may lead to catastrophe. Achieved by:
- Investigating the classification of common traits that lead to catastrophe.
- Grouping traits under headings based on those used by the Health and Safety Executive (HSE).
- Amalgamate trait information with a qualitative fault tree to provide a known current ‘state’.
- Creating a monitoring system to represent trait levels combined into a single weighted state’.
- Creating a new way of presenting trait issues using 2D surface plots and dashboards.
- Creating an intelligent monitoring system through representation of a weighted sum of traits verlaid on a qualitative fault tree developed for a specific catastrophic event.
- Create a standardised approach to the investigation of catastrophes and standardise some terminology used for disaster investigation and reporting to improve feedback and reporting.
- Carry out experimental test trials to verify that a predictive system has been created.
- Assess results. Report, publish, disseminate and document.
Supervisor: Dr David Sanders
Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, fewer than 1 unit is turned into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed.
This research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). The research will combine real time ambient sensing with artificial intelligence (AI) and knowledge management (KM) to automatically improve efficiency in energy intensive manufacturing. Ambient data provides detailed information on performance. AI will make sense of that data and automatically act. KM will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity.
Create new intelligent techniques to save energy in compressed air systems.
- Achieve a breakthrough in energy management for manufacturing. Combine shop-floor infrastructure and flexible ambient sensing with AI to monitor the infrastructure and sensors and make some automatic decisions. Then integrate KM tools. Investigate integrating independent compressed air generating and distributing circuits and intelligent control methods to reduce idle time and maintain pressure. Investigate heat recapture. Install sequence controls for compressors and investigate the use of reciprocating compressors for partial loads.
- Use ambient sensing to monitor performance and the environment. Investigate and compare centralized and distributed automatic leak-measuring. Model different sized buffer tanks improve compressor loading. Create a flexible ambient sensor system by interfacing to existing sensors and introducing new sensors using wireless micro sensors to monitor consumption, loads and variables. Create data for machine learning and store in a recurrent neural network able to learn simple tasks. For example, monitor air and control systems to identify leaks, and check joints, filters, valves, fittings and hose connections.
- Create AI to evaluate performance and the environment. Investigate AI systems to monitor sensors and environment, make some automatic decisions based on deep reinforced learning, and interact with the KM system and human users. Investigate Case Based Reasoning to provide confidence weightings for a Decision Making System. Automatically identify changes to loads due to blocked filters, leaks or new process requirements by asking questions such as: "Are there any leaky connections or fittings?", "Are air filters clean?", "Is air pressure too high?", Can some hoses and couplings be removed to reduce leakage?", "Are loads correct?", and other questions described in the application. Use statistical machine learning to find patterns in the data.
Supervisor: Dr Hongwei Wang
Wind energy is one of the fastest-developing renewable energies across the world particularly in developing countries where there is an increasing pressure from environmental issues, and it has been anticipated that by 2020 about 12 percent of global electricity supply will be from wind power. However, the operation and maintenance costs of wind turbines are generally very high due to harsh working conditions and difficulties of accessing and replacing components. As such, this has raised the need to develop advanced condition monitoring and fault diagnosis systems to identify and resolve faults effectively and efficiently.
Even though some studies have been done on condition monitoring and fault diagnosis, most of them have been focused on data collection and strategies of flagging alerts and there is a lack of systems that use intelligent methods to accurately predict potential faults based on historical data and effectively capture diagnosis knowledge for effective reuse.
In an attempt to address such a gap, this project aims to develop the framework, models, and computational methods for an intelligent system for wind turbine condition monitoring and fault diagnosis. Specifically, it involves the development of a systematic framework for such a system to outline the various issues such as data collection and processing, modelling of diagnosis knowledge and computational methods for reasoning and analysis.
Supervisors: Dr Jurgita Zekonyte, Dr Jovana Radulovic, Dr Aleksander Krupski
Measuring mechanical properties of materials on a very small scale is a difficult, but in modern times becomes an increasingly important task. There are only a few existing technologies for non-destructive tests to determine the properties for various materials ranging from plastics and nanocomposites to organic species, human hair and other biological tissues. One of those techniques is atomic force microscopy (AFM). The aim of this study will be to use AFM to measure defined properties (hardness, modulus, friction, wear, etc.) for materials of interest such as (but not limited) fibres, graphene, skin, nanoparticles, etc. It will also involve extensive data analysis, and numerical calculations. The project is mainly experimental, but students who are interested in molecular dynamic simulations are welcome to contact supervisor.
Multifunctional surfaces: Can man-made coatings self-adapt to external stimulus, and maintain required performance
Supervisors: Dr Jurgita Zekonyte, Dr Jovana Radulovic, Dr Aleksander Krupski
The performance of materials heavily depends on their surface properties and interfacial behaviour with our environment. Examples of common surface problems are friction, shearing, lubrication, abrasion, wetting, adhesion, absorption, etc. Different requirements also mean that materials require different surface properties to perform optimally without loosing efficiency. For example, surfaces have to be water repelling, yet transparent; or coatings should have very low coefficient of friction, but be wear resistant. Due to the increasing demand of more complex surfaces, new materials must have the capability to undergo changes according to external stimuli, yet maintain its main coating performance.
The aim of the project will be to assess the effects of different types of stimulus on advanced materials, and identify if they can exhibit dual surface properties while maintaining required performance predefined by the initial application. This will be achieved through experiments and/or modelling.
Supervisor: Dr David Sanders
Making a decision is a process where alternatives are assessed to select a choice or a course of action to fulfil desired objectives and goals. A suitable decision making process is essential for success in an organization. Unsuitable and deficient decision-making might reduce competitiveness. Multiple Criteria Decision Making (MCDM) has often been considered as a reliable method for decision making. MCDM is a set of methods and procedures by which multiple and conflicting criteria can be incorporated into the decision process. MCDM aims to enable decision makers to solve conflicting real world quantitative and or qualitative multi-criteria problems, and to find best-fit alternatives from a set of alternatives in certain, uncertain, or risky environments.
A new framework to select an appropriate group of candidate MCDM methods for a decisional problem is to be investigated. A new structured framework is proposed based on an analysis of MCDM problems and methods. Investigating that should reveal factors to be addressed when selecting a MCDM method, including problem characteristics and MCDM method characteristics.
Mathematical approaches are to be applied to candidate methods to select the method that will provide the most robust output. Problem characteristics considered in the framework will address the nature of alternative sets (continuous or discrete), type of input set (qualitative, quantitative, or mixed), the nature of the information being considered (deterministic, non-deterministic or mixed), the type of decision problem being addressed (choice, ranking, description or sorting) and the type of preference mode being considered (pairwise comparisons or performance measures).
Moreover MCDM methods, characteristics will address the type of ordering of alternatives (total pre-order, partial semi order or partial interval), the measurement scale (nominal, ordinal, interval or absolute), and the type of preference structure (preference, indifference or incomparability), software availability and ease of use (ease of understanding, user friendliness, previous experience and knowledge, and time needed to apply a method).
Supervisor: Dr Bidyut Pal
The use of the uncemented stems in hip arthroplasty has been increasing. The major complications of uncemented hip-stems, however, are a peri-prosthetic fracture, thigh pain, and proximal femoral stress-/strain-shielding. Previously, Dr Pal has proposed a novel design of an uncemented hip-stem, aiming at reducing such concerns, and improving osseointegration.
We have already conducted some numerical studies to investigate the usefulness of the proposed design. However, there is scope for further work if a fuller picture of the behaviour of the new stem is to be understood. Using computed tomography-based three-dimensional finite element models of an intact proximal femur, and the same femur implanted with the proposed design, the aim of the current study is to investigate the performance of the proposed design, with particular attention to the following objectives.
- Investigation on the primary stability of the stem.
- Investigation of peri-prosthetic bone remodelling and risk of bone fracture under physiologically demanding loading configurations.
- Simulation of different geometry of the stem-tip.
- Evaluation of local or detailed effects of this new stem.
- Comparison with a known stem with an established clinical performance.
Candidates should be able to work on solid modelling and finite element modelling platform.
Supervisor: Dr Bidyut Pal
Despite the success of joint replacement procedures, failures of the reconstructed joints are still a major concern. Mechanical loosening of the implant is responsible for the largest proportion of the failures of the reconstructed joints. The causes of failure of a reconstructed joint may be multi-factorial, but one of the major factors is the fixation method (concerning how implants are secured to the bone).
Implant fixation has primarily been investigated based on the continuum finite element (FE) modelling approach at a macro-scale. In order to investigate the relationship between the bone and the implant more effectively, micromechanical approaches are gaining in popularity, although the precise relationship between the failure of the reconstructed joints and the role of the biomechanical factors are yet to be understood clearly.
This research aims to develop a multi-scale modelling framework, connecting models at the micro- and macro- scales to gain insight into implant fixation integrity to inform future implant design. Detailed stress analysis of the natural and implanted bones will be performed using three-dimensional FE models (macro- and micro-scales). The effects of implant surface morphology (cementless type) and micro-structure of bone and cement interdigitation in bone (cemented type) on implant fixation will be explored. A multi-scale framework will be developed to exchange useful information between the macro- and micro-scale models.
Candidates should be able to work on solid modelling and finite element modelling platform. Knowledge of programming would be an advantage
Supervisor: Dr David Sanders
This MRes project will investigate the novel use of shared control, sensors and artificial intelligence to create systems that will significantly and positively impact on the lives of powered-wheelchair users. People will be able to drive for longer and in some cases for the first time.
A new technique that continuously assesses ability will share control between drivers and intelligent sensors. The work will improve access to independent mobility and allow at least some self-initiated mobility even without the spatial awareness and neural ability that is usually required, so that even some blind children will be able to steer without the need for helpers. The research will develop technologies that will enable the next generation of assistive devices to provide natural control through enhanced and intelligent sensor feedback. Choices for a particular driver will be bespoke, based on their characteristics and history.
Create new systems to improve mobility and quality of life for people with disabilities.
- Create AI to interpret what a human user wants to do. This will reduce effort and stress. New digital object-proximity-sensing and some input devices will be created and simple systems to interpret them will be investigated, starting with simple IF THEN systems. Similar methods have been used before but this initial work is necessary for the new research. Fuzzy Systems will be created to interpret hand movements and tremors from among other involuntary movements and this will be the only attempt that has been made using this promising method. Achievement of this would allow many more people to use powered wheelchairs.
- Sensor fusion to interpret the environment. This will reduce tiredness and collisions. A Rule Based System will be investigated first to generate revised instructions to correct for veer; the first time that this has been attempted. New digital object-proximity-sensing systems and effort-reduction systems will be created, along with new input devices, along with AI systems to monitor them. That will improve mobility and allow some disabled people to use powered wheelchairs for the first time.
- Create a simple Decision-Making System (DMS). This will compare outputs from the AI systems and suggest best possible course of action.
- Share control between the disabled driver and the new wheelchair system. Automatically assess the ability of the wheelchair user and establish control gains for the sensor system and human driver by calculating a self-reliance factor depending on ability, tiredness, recent driving performance etc. An avoidance-factor will depend on obstacle proximity, a safety-factor will denote the ability of the driver and an assistance-factor will depend on time spent driving and tiredness. The sensor system will influence the motion of the wheelchair to compensate in those areas. This will be the first time that a wheelchair system has adapted to a disabled human user in real time.
Supervisors: Dr Gianluca Tozzi and Prof Chris Louca
Laser preparation of dental hard tissue is considered to be safer and more comfortable for the patient, compared to the use of traditional dental handpieces, due to less pain and reduced noise and vibrations. In recent years, ultra-short pulsed lasers (e.g. Er:YAG, Er:YSGG) have been introduced in restorative dentistry to overcome the drawbacks of traditional treatment methods . However, how such novel procedures affect the mechanical and chemical composition of the tissue is still not clearly understood .
- Yuan et al., 2016. Scientific Reports. DOI: 10.1038/srep25281
- Suhaimi et al., 2016. AIP Conference Proceedings 1791, 020004
This project aims to evaluate laser preparation procedures on mineralised tissues in dentistry using XCT-based technical advances such as dual-energy for chemical mapping and Digital Volume Correlation for residual strain determination.
The project will be conducted in collaboration with the University of Portsmouth Dental Academy, using x-ray microscopy and licensed software available at the Zeiss Global Centre at the University of Portsmouth.
The student will report to Dr Gianluca Tozzi and Prof Chris Louca, but will operate in a vibrant working environment including PhD students, Post-doctoral researchers and other academics in the field of engieering and the University's Dental Academy, which will provide expertise and guidance throughout the duration of the project.