Systems, Power and Energy

Supervisors

Dr Andrew Feeney

Dr Paul Prentice

Description

Supervisors

Dr Andrew Feeney

Dr Hadi Heidari

Description

Wearable healthcare devices are forecast to be dominant in health monitoring over the coming years. Ultrasonic wearables will be vital for domestic monitoring of health indicators such as blood pressure. Significant progress has already been made in harnessing the properties of piezoresistive materials, but a fundamental limiter is the requirement of external power, restricting the patient or end-user experience. The goal of this doctoral research project is to develop unobtrusive and self-powered wearable technology based on piezoelectric materials, by replacing bulk-form piezoelectric materials with layered, advanced composites. The project will involve fabrication of piezoelectric sensors, with characterisation of the electromechanical and physical properties using techniques such as impedance spectroscopy, piezoelectric and surface roughness analysis. The piezoelectric devices will be embedded into a flexible polymer (such as PDMS) with development and implementation of power management circuitry, ensuring that small movements can be reliably measured.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Andrew Feeney

Dr Daniel Mulvihill

Description

Ultrasonic devices are an essential technology in applications across medicine, industrial processing, and sensing. However, those devices which are designed for low ultrasonic frequencies (approximately 20 – 100 kHz), tend to be optimised for operation in one resonant mode. They require precise control of geometry and material properties in order to tune device parameters such as resonance frequency and amplitude. The objective of this project is to engineer new multifunctional ultrasonic transducers with adaptive properties by using shape memory materials (SMMs). These are materials which can be trained to change state in response to a specific stimulus, such as temperature or stress. It is anticipated that the incorporation of SMMs into novel designs of ultrasonic transducer will open several new industrial and medical applications.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Andrew Feeney

Dr Paul Prentice

Description

Deep Eutectic Solvents (DESs) are a class of liquids, many of which are biodegradable and environmentally benign, with remarkable solvent properties. Traditionally, they have been popular for dissolving plants for valuable chemicals, where ultrasound can enhance dissolution, producing higher yields and reaction times compared to processing without ultrasound. However, sonication in these solvents in the mineral processing field or in the recycling of e-waste must be investigated. This doctoral research project will explore sonocatalysis of DESs in mineral processing from both ores and e-waste, promoting significant reductions in the environmental impact of metal / mineral processing, realising a new highly-scalable green technology.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Patrick Harkness

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Granular materials are among the most widely-traded substances, as they include many fuels, foods, and feedstocks. They also cover large parts of the Earth, both surface and subsurface, and parts of other asteroids, moons, and planets as well.

These materials are difficult to handle. They can behave as quasi-fluids, but more often they are almost uniquely challenging. If you have ever tried to press your finger directly into sand, without wiggling, you will discover that their stiffness rapidly increases. Similarly, they can stop the rotation of a drilling auger, and jam in the chutes of handling systems.

One solution may be ultrasonic vibration. Our initial studies have shown that sonicated tools can ‘fluidise’ granular materials, making them flow almost like a liquid. With viscosity reduced, penetrators and augers can operate more easily. We can push through the materials, and handle them as we wish, with lower forces and less power. We may even be able to pump them like liquids. This has the potential to facilitate both trade and planetary exploration, where landers might have to drill through regolith using low forces and torques in a low gravity environment.

This project will require the design of resonant ultrasonic tools, and the construction of autonomous mechatronic drilling rigs to carry out multiple penetrometry and augering tests in granular materials such as glass microspheres. Applications to use external, variable-gravity facilities are also anticipated.

Supervisors

Dr Steven L Neale
Dr John Mercer (MVLS)

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

This PhD project will develop miniaturised ultrasonic sensing devices for vascular stents. The goal is to produce a device that can continually monitor the state of the stent, detecting if a blockage is reforming.

Despite significant improvements in healthcare provision, cardiovascular disease (CVD) remains the number one cause of death in the World. Atherosclerosis is the pathological condition that underlies two thirds of heart attacks and strokes, which in turn contribute to more than 4.3 million premature deaths in Europe per annum. The economic burden to the European Union for cardiovascular disease is estimated at over €196 Billion. The current clinical approaches of stenting coronary artery plaques with a small metal stent device or heart by- pass surgery have significant risk and associated costs. This proposal attempts to build towards a smart stent device that can be deployed using existing NHS catheterisation facilities, yet provides advanced technological properties that are predicted to reduce patient morbidity and mortality. A stent which can interact and report on its own vessel status, such as when the vessel re-blocks or clots would be transformative. In this project we will investigate how placing a miniature ultrasonic transducer within the stent before it is deployed could help to monitor the health of the vessel and the stent within it. Current research on smart stents include using the electrical impedance change associated with the build up of tissue around the stent to detect if it is becoming re-blocked however the ultrasonic approach may provide greater information about the amount and position of material building up giving key clinical data informing on whether an intervention is necessary.

The project will build on an existing interdisciplinary collaboration between Dr Steven L Neale who is in Medical and Industrial Ultrasonics (MIU) in the School of Engineering and Dr John Mercer in the Institute of Cardiovascular and Medical Sciences. The work will involve building the miniaturised ultrasonic transducer and testing it using a variety of vascular stents and tissue phantoms and will be based both within the school of Engineering and the British Heart Foundation building and as such it is suitable for an enthusiastic and diligent Engineering/Physics student with interests in developing technology for biomedical applications.

Supervisors

Dr Steven L Neale
Professor Colin Mcinnes
Dr Euan W McGookin

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The PhD project will develop an Artificial Intelligence (AI) to control a swarm of small robots. The AI will be demonstrated and tested through the creation of an Optoelectronic Tweezers (OET) test-bed which will allow the physical robot swarm to attempt specific assembly tasks, evaluating how the coordination of the individuals helps to attain the goal set.

The OET system allows the control of microscopic particles through light patterned electrical fields. Highly polarisable metallic particles are attracted to areas of high electrical field which appear when the OET device is illuminated thus the position of the particles can be controlled by changing the light pattern applied. This “touch free” control is ideal for testing control strategies as the individual elements can be rearranged simply by applying the desired pattern in light. In this project we will be considering each discrete particle as a microscopic actuator able to push a small electronic component or its neighbouring actuators. We will think of these actuators as “microbots” which can be programmed to perform assembly tasks by creating an AI which control the light pattern applied to the device. The assembly goals that we will be targeting will include pushing individual electronic Surface Mount Technology (SMT) components into their positions in a circuit so that the swarm of microscopic robots will be able to achieve a macroscopic assembly goal. We will also test out small scale versions of larger world control scenarios such as simulating search and rescue tasks by creating microscopic mases for the swarm to explore. This will give us the ability to test out large swarms of particles in a real-world environment and see how they cope with the inevitable problems that are associated with large swarms such as some individual robots not performing in the manner expected.

The project will be in Dr Steven L Neale’s micromanipulation research group where we have experience with the Optoelectronic Tweezers system (https://www.gla.ac.uk/schools/engineering/staff/stevenneale/) and all necessary training in microfabrication and micromanipulation will be provided. This project will develop two different control strategies for the microbot swarm and test the against each other which will be developed with the help of Prof Colin McInnes and Dr Euan McGookin who both have experience of different robotics control strategies. The project will involve both developing the AI algorithms and also physical experimentation to demonstrate them and so will require an enthusiastic and dedicated student with an interest in robotics.

Supervisors

Dr Paul Prentice
Dr Helen Mulvana

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Acoustically activated microbubbles (and advanced particles based on microbubbles) for localised and non-invasive drug delivery to a range of organs (including brain and pancreas), continue to attract significant and world-wide research attention, including numerous in-vivo and preclinical trials. In publications generated by this research, it is common for therapeutic bioeffect to be reported in parallel with some representation (often the noise-spectrum) of the acoustic emissions detected from the driven microbubble population, such that the bubble activity can be classified or quantified for correlation to the drug delivery, or degree of tissue damage.

Somewhat remarkably, however, the behaviour of microbubble populations within blood vessels, exposed to therapeutic ultrasound – and in particular, the acoustic emissions generated – are still poorly understood. Clearly, this deficit in knowledge hinders refinement and optimisation of exposure and detection protocols, but is also critical to understanding the mechanisms underpinning microbubble mediated therapies.

This project is dedicated to addressing this deficit, using two state-of-the-art ultra-high speed cameras, a range of hydrophone detectors (including with complex calibration for magnitude and phase response), and research-enabled diagnostic imaging arrays, to interrogate and characterise microbubble activity in capillary models. Dual high-speed imaging will allow unprecedented investigations of phenomena at varying timescales – for example, clustering dynamics under the action of secondary radiation forces during the initial acoustic exposure, and temporally resolved cluster oscillations (at image acquisition rates of up to 10 million frames per second) for direct correlation to the detected acoustic emission.

Therapeutic ultrasound parameters including frequency, pressure amplitude and pulsing duty cycle, as well as microbubble concentration, and flow rate (to mimic blood circulation) will all be systematically studied. The work will make use of anatomically accurate flow phantoms, already in use to study the physiological effects of blood vessel geometry and flow on microbubble dynamics and in vivo delivery data arising from a separate study to use microbubbles for delivery to the rat placenta.

Advanced drug delivery vehicles will also be investigated in the latter stages of this project, including

1. Acoustic Cluster Therapy (ACT^TM; combining commercial GE healthcare microbubble agent, Sonazoid, with drug-loaded vapourisation droplets), via an existing industrial collaboration with manufacturers; Phoenix Solutions AS (Oslo, Norway).
2. SPION (superparamagnetic iron oxide nanoparticles) microbubbles as novel multimodal contrast agent for magnetomotive ultrasound imaging

Supervisors

Prof Margaret Lucas

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Ultrasonic devices for surgery applications rely on resonant structures. This results in the surgical tips being simple geometries, usually straight or with a single bend. Many surgical procedures require devices to reach locations in the body that are difficult to access and therefore a flexible device, able to move along a tortuous path to the site of surgery would have very significant advantages.

This project investigates a completely new approach to the design of ultrasonic surgical devices. The surgical tips will be driven by new, innovative transducers that can enable the device itself to be flexible. The project will research the capabilities of innovative transducers to deliver sufficient ultrasonic excitation and the optimal vibrational motion to the surgical tip to perform precision cutting of tissue. A key focus of the research will be in miniaturisation of devices for minimal access surgeries.

Supervisors

Prof Margaret Lucas

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

A significant research effort in the Ultrasonic Group is developing new ultrasonic surgical devices, particularly for miniaturised minimal access surgeries involving bone cutting procedures. Recent research has been successful in incorporating a shape memory alloy (SMA), Nitinol, in a novel ultrasonic cymbal transducer. The transducer is capable of being operated in the same mode of vibration at two distinct resonant frequencies through a phase change in the material as a result of a small change in temperature.

This project will investigate how the temperature change required for the phase change can be controlled and minimised through the choice of material and will also investigate other phase change materials. Research will also focus on how the phase change could be achieved through small changes to the loading of the transducer.  Methods of driving the transducer to deliver the required phase change will also be researched. The project will develop alternative transducer designs that can incorporate SMAs for dual or multiple frequency operation. The overall aim will be to deliver small surgical devices that can cut both soft and hard tissues with a single ultrasonic surgical device.

Supervisors

Prof Colin McInnes

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Asteroids offer to provide material resources to support a range of future space ventures, spanning metals for in-orbit manufacturing and water for in-situ production of propellant. Our prior studies have considered the dynamics of asteroid disassembly using rotational self-energy.

This project will investigate strategies to disassemble rubble pile asteroids using an N-body simulation of the physics of the rubble pile. Disassembly may be required for resource processing, or to reconfigure material for manufacturing structures such as habitats. Such strategies will include free-flying units which remove masses in a serial or parallel fashion, while the rubble pile relaxes into a new minimum energy state after each mass is removed.

 Key research questions include:

• What are the physical limitations on the disassembly of rubble pile asteroids given their gravitational binding energy?
• What strategies can be devised for disassembly using either single or multiple free-flying robotic platforms operating serially or in parallel?
• Can the dynamics of binary asteroids be leveraged to initiate and engineer the flow of material between asteroids?

The project will combine mathematical modelling and simulation to investigate these research questions. Candidates should therefore have a strong aptitude for and interest in mathematical modelling and simulation. The project will be embedded within a large research group pursuing a programme of novel research on emerging space technologies. 

Supervisors

Prof Colin McInnes

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Femtospacecraft offer to deliver a broad range of new mission applications spanning space physics, Earth remote sensing and planetary science. A key issue will be the development of strategies to actively control both the orbit and attitude of such small devices.

This project will investigate novel obit and attitude control strategies based on our Mercury 3.5 x 3.5 cm femtospacecraft. The platform comprises a microcontroller with integrated communications, MEMs attitude sensing and 3-axis magnetic actuation. Key research questions include:

• What attitude control laws are suitable for resource-limited femtosatellite? This task will include both modelling, simulation and laboratory experiments
• What is the trade-off between energy/volume used and performance of the attitude control system?
• How can the orbit of a resource-limited femtosatellite be actively controlled and how can the physics of the space environment be leveraged for such tasks?
• How can spatial patterns be formed in swarms of large numbers of such devices to enable new applications of femtosatellite technology?

The project will combine mathematical modelling, simulation and some laboratory-scale testing using an air-bearing and Helmholtz cage to investigate these research questions. Candidates should have strong aptitude in mathematical modelling and simulation and an interest in pursuing laboratory experimentation. The project will be embedded within a large research group pursuing a programme of novel research on emerging space technologies.

Supervisors

Dr Matteo Ceriotti

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Quantum computing one of the most important emerging technologies: a step change in our ability to solve difficult problems, in the same way conventional computers have been in the sixties. Conventional computers rely on bits, which can carry on/off information; quantum computers use quantum bits, or “qubits”, which can represent several states at once, exploiting the superposition effect of quantum theory. This allows them to work much faster than conventional computers, and adding more qubits make quantum computers exponentially faster, allowing them to solve problems that are so difficult that are out of reach for ordinary calculators.

In the space mission design, the trajectory design problem is a difficult one, even more so when multiple bodies and/or targets have to be selected from a set (e.g. multiple planetary swing-bys, multiple moon or asteroid tours, multiple satellite servicing and/or disposal): this creates a mixed combinatorial-continuous problem, where the combinatorial part is (broadly speaking) a variant of the classic Travelling Salesperson Problem (TSP), to select the sequence of bodies/targets, and in order to evaluate each sequence, a continuous optimisation sub-problem is to be solved. Quantum computing has the potential to dramatically improve the solution of this problem, my exploiting the superimposition of multiple possible paths at once.

As progress is being made into the hardware to make functional quantum computers, scaling up the number of qubits, this PhD will explore the formulation and solution of space mission design problems through a quantum computing. We aim to answer the following research questions:

• What quantum computing framework(s) can be used for space mission trajectory design?
• How can we leverage on and inject quantum computing to the space mission trajectory design problem, particularly when multiple bodies/targets are involved?
• How can trajectory design problems be encoded through a quantum algorithm?
• To what extent a full trajectory design problem can be implemented as (and take benefit from) a quantum algorithm?

Ultimately, we will assess to what extent, injecting quantum computing into the optimisation problem, we obtain a quantum advantage, both in terms of optimality of solution, and computational cost, for this specific application (narrow advantage).

The ideal candidate will have a background in computing science or similar discipline, with a strong interest in space technology and exploration, or vice-versa a background in space trajectory design with strong interest in computing science and programming.

Supervisors

Dr Kevin Worrall

Dr Gerardo Aragon Camarasa (School of Computing Science)

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

With the current push towards space for both private and government organizations, and the recent increase on initiatives to the industrialization of space, there will be an important need for humans to be supported by robotic systems. Understanding and mastering the unique properties that will intervene in the robot behaviour is essential to offer a fully autonomous robotic system which will be expected to work with no human intervention while being robust, accurate and responsive.

The work will consider the different advantages of both traditional and AI-based control methodologies to support the development of a vision-based control system that is able to control a robot manipulator within the space environment during in-orbit assembly tasks. The expected outcome of this work will be a simulation environment of a suitable setup and a practical real-life implementation.

This project will engage with recent research studies on the field on autonomous robotics, building in-orbit structures, satellite assembly and support studies on manufacturing in space. This project can also engage with users beyond space, with advanced manufacturing research being a potential area to explore.

Background in either control engineering mechatronics, computing science, and/or space engineering is highly recommended. In order to be eligible to apply for the School of Engineering Scholarship, an excellent CV is required.

RESEARCH LINES

This project explores the following lines of research:

• Robotic arms for manufacturing in space
  
  This line of research focuses on the analysis of the dynamics, kinematics, and grasping methodologies of the robotic arms while on orbit. This addresses problems related to autonomous robotics, target capture strategy, tackling a moving orbiting object, mathematical approach to the robotic arm dynamics, and contact forces. In addition, the major physical interactions while executing tasks on orbit such as building in-orbit structures, satellite assembly, and space manufacturing, will be considered.
   
• Approaches for controlling robotic manipulators in space
  
  This line of research focuses on the analysis and exploration of traditional and AI-based control methodologies, intelligent control algorithms, and an integrated vision-based control system. This addresses problems related to the vision system embedded in the robot, environment simulation, and parameters such as speed, torque, vibration, and attitude disturbance.

Supervisors

Dr Matteo Ceriotti

Dr Kevin Worrall

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Intersatellite links (ISLs) are telecommunication routes between different satellites which allow a swarm or constellation of satellites (or agents) to effectively become a network of relay nodes. ISLs can be used to share data amongst different nodes of a network; one possible aim is to maximise the bandwidth between two specific agents in the network, or between an agent and an external entity (e.g. a ground station in the space scenario). With these links in place, satellites in large-enough constellations can communicate with relevant ground stations in quasi-real-time, regardless of whether the ground station is in line-of-sight and/or range. It is clear that the extent of the usefulness of ISLs depends on the effectiveness of the routing strategy employed. The main difficulty in utilizing ISLs is the fact that in most satellite constellations, the network topology is time-varying; links will constantly be found/lost as each satellite progresses along its own orbit, hence the effectiveness of the routing strategy becomes key to exploiting the availability of ISLs.

This PhD will investigate distributed algorithms for the autonomous optimisation of ISLs within a satellite constellation. Previous research [http://eprints.gla.ac.uk/159120] has looked into the use of Ant Colony Optimisation, a bio-inspired technique that mimics the behaviour of ants foraging for food; the PhD will expand this research and assess and compare the use of other optimisation methods. It will also investigate the effect of constraints introduced into the network (such as unavailability of one or more nodes) and develop techniques to cope with them optimally. One of the paramount aspects to consider is that the system should be able to self-optimise itself (fully-distributed) without the need of a central controlling node. In this way, the loss of one or more agents does not prevent the swarm to continue to find optimal solutions.

The techniques developed for the satellite scenario can readily be extended to other applications with different agents, such as autonomous vehicles, drones, sensors, etc.

Background in computing science, applied mathematics and/or space engineering is highly recommended. In order to be eligible to apply for the School of Engineering Scholarship, an excellent CV is required.

Supervisors

Prof Colin McInnes
Dr James Beeley
Dr Kevin Worrall

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Early work on biomorphic autonomous spacecraft considered the use analogue circuits to mimic simple spiking neural networks. It has been shown that such biomorphic systems can demonstrate quite complex emergent behaviour and can be robust to failures. While our work on 3x3 cm PCB-satellites currently uses conventional microcontrollers, the use of biomorphic control may enable even smaller, yet capable devices.

This project will firstly investigate the use of biomorphic control for ultra-small, centimetre-scale micro-spacecraft and then further develop our ideas to consider a large networked swam of devices. Key research questions include:

• How can low-level behaviours be embedded in individual centimetre-scale micro-spacecraft; for example de-tumbling, Sun-pointing, target-pointing and orbit control?
• How can interaction between the low-level biomorphic control of members of a large swarm of such devices lead to emergent, complex high-level behaviour?
• What niche applications can be foreseen which leverage the benefits of biomorphic control while competing against the performance of conventional spacecraft swarms?

The project will combine modelling, simulation and laboratory-scale testing to investigate these research questions. Candidates should have an interest in modelling and simulation and an enthusiasm for laboratory experimentation. The project will be embedded within a large group pursuing a programme of novel research on emerging space technologies.

Supervisors

Prof. Manosh C Paul (Manosh.Paul@glasgow.ac.uk)

Description

A transition from carbon-based fuels to low-carbon/carbon-neutral alternatives such as hydrogen/ammonia/hydrogen-blend fuels has been identified as an important strategy for decarbonising the heating/transport sectors including aviation. However, when compared to any other hydrocarbon fuel, hydrogen/ammonia combustion in a typical system is significantly challenging, and is often affected by a number of factors such as flame destabilisation/instability, flammability limit, heat transfer characteristics. Another significant problem in a hydrogen based combustion system is NOx production, and while a lean mode operation is favourable, a conventional lean burner may be more susceptible to flame destabilisation. To address these challenges, this project aims to develop a next-generation efficient, low-cost and NOx-free combustion system for hydrogen/ ammonia-based fuels, taking into account the challenges of heat/transport (e.g. LGV/Marine/Aviation) decarbonisation. For the development of burner design and examination of its performance under various operating and fuel circumstances, the research methodology will integrate advanced numerical and CFD techniques. The ultimate goal is to find the best flame and burner configuration, providing the best performance while also maintaining a super ultra-low NOx target. The numerically predicted results will be validated through experimental data as well as other methods.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Supervisors

Prof. Manosh C Paul (Manosh.Paul@glasgow.ac.uk) 

Description

The UK Government's recent Ten Point Plan for a Green Industrial Revolution emphasised the critical importance of low/zero carbon hydrogen in achieving the "net-zero" emission target by 2050. However, this is significantly challenging when the global hydrogen production is mostly dependent on fossil fuels. There is a considerable potential to use municipal solid waste (MSW) and biomass waste as a fuel (renewable) resource to generate hydrogen, and when combined with CCUS (carbon capture, utilisation and storage), it has the potential to provide negative emissions. This research project aims to develop and implement novel technical approaches for producing net-negative emission hydrogen from MSW/biomass waste. The research methodology will integrate robust thermochemical kinetics with numerical and CFD (computational fluid dynamics) methodologies, all of which will be supported and confirmed by experiment.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Supervisors

Professor Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The UK became the first major economy in the world to pass laws to end its contribution to global warming by 2050. In Scotland, the government plans to reduce its greenhouse gas emissions to net-zero by 2045. It is expected that District Heating Networks (DHN’s) will be a big part for decarbonising heating sectors, using renewable heat sources such as ground/ air and water source heat pumps, geothermal power, solar thermal, and industrial heat recovery (including waste water treatment).

This project will focus on developing novel concepts of 5th generation district heating networks, which utilises multiple low carbon heating sources, such as heat from waste water, river water, data centre, off-peak electricity. The project will focus on developing numerical models and tools for system simulations and optimisation. Power-to-heat concept and thermal energy storage technologies will be introduced to utilise the off-peak wind power generation. The interactions between such heat network with the grid will be then investigated.

Supervisors

Professor Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

A group of heat engines (or refrigerators), including Stirling engines and coolers, thermoacoustic engines and coolers, pulse-tube coolers and son on, employ oscillatory flows of a gas (e.g., pressurised helium) to execute thermodynamic cycles to convert heat energy to power (or consumes power to produce refrigeration effects). However, the heat transfer associated with such high amplitude oscillatory flows are not fully understood. In the current practice, a so-called “Iguchi assumption” (i.e., the flow history does not influence the flow at the next instant) is used to simplify the fluid dynamics and heat transfer process, and thus those correlations of steady flow such as friction factors and Nusselt numbers are used for designing the such engines and coolers.

The “Iguchi assumption” becomes invalid when the velocity and frequency are higher according to our previous research, namely the flow history strongly affects the flow state at next instant; and that the Reynolds number is no longer the only parameter controlling the state of the flow. Therefore, the correlations and experimental data obtained using steady flow are invalid for designing devices that involve oscillatory flows with a high amplitude or frequency. However, very little research has been conducted on high frequency oscillatory flows, especially experimental data and correlations are rarely available in literature. This project will focus on the experimental research of heat transfer of oscillation gas flows using advanced techniques such as particle image velocimetry (PIV), filling the knowledge gap in this area.

Supervisors

Professor Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The UK has set an ambitious target to cut its greenhouse gas emissions, and the government’s strategy is to increase both the power and heat generation from renewables. However, these sources suffer from their intermittence, causing a time mismatch between supply and demand. Power-to-heat concept becomes an attractive low-cost solution, e.g., “wrong time” electricity is used to power heat pumps to produce and store heat for later use. As this stored heat will be directly used for space heating and domestic hot water, there is no need to convert it back to electricity, leading to a higher round-trip efficiency than alternative electricity storage technologies which suffer at least two conversion losses.

Cost-effective heat storage products are essential for facilitating the uptake of intermittent power and heat generation. Phase Change Materials (PCMs) are the most promising form of heat storage technologies. However, challenges remain that inhibit the development of cost-effective heat storage products for broad commercial uptake in the domestic heating equipment market. The poor heat transfer resulting from the extremely low thermal conductivity of PCMs leads to low power density, while the commonly used metal heat exchangers are heavy, complicated and expensive.

This project will address these challenges through developing efficient but inexpensive additive materials for enhancing the thermal conductivity of PCMs. It will involve numerical modelling and simulations using CFD software and experimental research using high speed camera and infrared camera.

Supervisors

Xiaolei LiuDr Shengrong Bu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The mix of energy supply worldwide has been changed dramatically in the last few decades. In the UK, in order to tackle climate change and increasing energy consumption, there has been a clear movement from fossils towards renewable and sustainable energy sources. Wind energy, for example, accounting for 98% of Scottish electricity demand in October 2018, has established a world-class record.

Compared with onshore wind turbines, offshore wind could provide a relatively larger capacity and a lower level of noise pollution. Offshore wind energy conversion systems are more sophisticated and new methodologies are urgently required based on more advanced analytics. In light of recent developments in the wind energy sector, it is becoming extremely difficult to ignore the existence of data science, which will still be a fast growing field over the next 10 years. More specifically, it has been widely applied to wind speed/power forecasting & predictions, conversion systems optimization, and fault detection & diagnosis.

This project will first investigate the simulation of wind turbine operations based on advanced numerical methods (e.g. CFD, FEA and multi-body method, etc.). Based on the simulated load effects, responses in normal operation and extreme conditions will be solved by a coupled model of the wind turbine system, which is significant for operation and maintenance. Finally, high-frequency SCADA data will be collected for data science/mining based on principles with Python3 and Machine Learning established by various Intelligent frameworks.

If you are new to programming (mainly Python3) and have a passion to learn/practice it with real on-site data, you are welcome to apply. If you already have a basic knowledge of general computer programming (no matter Python, Fortran, Matlab, C or C++), you will get chances to strengthen your knowledge through applying the most state-of-the-art Artificial Intelligent algorithms (focusing on Machine Learning).

Applicants should have a strong academic background in mechanical engineering, civil engineering, electrical engineering, ocean engineering, naval architecture, mathematics or a related subject at a Master’s level, or holding a BEng/BSc degree, or equivalent.

Applicants should send their application directly to Dr Xiaolei Liu, Xiaolei.Liu@glasgow.ac.uk

Applications should include:

• Cover Letter
• CV

Supervisors

Dr Siming You

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Sustainable food, water, and energy supplies for remote villages remain a great challenge in some developing countries. The development of self-sustaining food, water, and energy systems serves as one of the promising solutions and is receiving an increasing attention in recent years. This project will be based on typical remote villages in several developing countries and aims to develop bespoke concepts of feed-water-bioenergy nexus according to the environmental and resource background of villages. Biochemical (e.g., anaerobic digestion and aerobic digestion) and thermochemical bioenergy technologies (e.g., pyrolysis and gasification) will be included in the system design. The nexus will be optimised using multi-objective methods such as large-scale mixed-integer linear and nonlinear programming. The economic and environmental feasibility of the nexus will be evaluated using cost-benefit analysis and life cycle assessment.

Supervisors

Dr Siming You

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Sustainable management of municipal solid waste (MSW) has become one of the major challenges for megacities. Gasification, anaerobic digestion, and pyrolysis serves as environmentally friendly bioenergy technologies for MSW treatment. It could convert carbonaceous MSW into valuable products such as biogas, bio-oil, synthesis gas, biochar, etc. Biogas, bio-oil and synthesis gas could be further converted into electricity and heat, while biochar has been recognized as an effective carbon abatement tool upon its application in soil. Decentralized energy supply has been regarded as an important component of future smart grid systems. However, decentralized energy production could be economically challenging considering the economy of scale. This project will compare the economic, environmental, and social performance of centralized and decentralized bioenergy systems in both developed and developing countries. Different system and supply chain configurations will be proposed, and a decision support tool will be used to make the comprehensive comparison from the perspectives of different types of stakeholders (i.e. policy makers, investors, and consumers).

Supervisor

Dr Yihuai Zhang 

Description

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Supervisor

Dr Yihuai Zhang 

Description

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Supervisor

Dr Yihuai Zhang 

Description

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Supervisors

Professor Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The thermal efficiency of modern internal combustion engines is limited to 20-40%. For a typical medium-size passenger vehicle in urban traffic conditions, 33% of the thermal energy from fuel combustion within the engine is carried away by exhaust gases and 29% is carried away by cooling water and heat radiation. The temperature of the exhaust gases from internal combustion engines usually vary from 500 to 900 ◦C. This makes the exhaust gases very attractive for energy harvesting applications. A thermoacoustic engine is essentially the acoustic equivalent of the Stirling engine. It employs a delicately designed acoustic network to force the gas parcels within the regenerator to experience a thermodynamic process similar to the Stirling cycle. In this way, it can convert thermal energy to mechanical power. The acoustic power can then be utilised to drive linear alternators to produce electricity. Thermoacoustic engines have several advantages over conventional Stirling engines such as simplicity, reliability and low cost. This project firstly will develop an improved model to optimise whole system, and will then focus on the optimisation of the heat exchanger which extracts heat from the exhaust gases. Novel designs need to be explored and examined, both numerically and experimentally, to minimize the additional backpressure applied to the engine and to ensure that a high degree of heat transfer efficiency is maintained. A prototype of such a thermoacoustic generator will be built and tested and the experimental results will be compared to the simulations in order to further improve the modelling.

Supervisors

Dr Shengrong Bu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The smart grid can optimize electricity generation, transmission, and distribution; reduce peaks in power usage; and sense and prevent power blackouts. Grid modernization will bring significant direct savings and other benefits.

One of the key challenges faced by the smart grid is to integrate large-scale renewable energy generation while maintaining grid reliability. Unlike conventional generators, renewable energy generators are highly intermittent and uncontrollable, making them difficult to widely integrate. Potential solutions include improved forecasts, demand shaping, electricity storage, and optimal grid operation. Addressing renewable energy integration into the smart grid alone is a compelling problem for a PhD project to consider. The project can first investigate the fundamental limits of grid reliability in the face of generation uncertainty, analyze existing options, and then design novel mechanisms.

Plug-in electric vehicles (PEVs) are becoming a promising alternative to conventional automobiles. However, PEVs not only significantly increase the average electricity consumption, but also generate very bursty demand patterns. It is critical to address challenges caused by the rapid growth of PEVs to the power grid. A potential interesting focus for a PhD candidate is to design charging strategies for PEVs to maximize customer benefit while satisfying system constraints. The work could be extended to consider PEV scheduling as a mechanism to partly absorb renewable energy variability.

In the smart grid, large amount of data with various communication requirements need to be transmitted, therefore, communication infrastructure is critical. However, many issues in the field of smart grid communication are still not well addressed. One potential research topic for the PhD candidate is to study the interactions and tradeoffs of scalability and reliability of networks, and then propose novel network design strategies and also design novel algorithms to achieve scalable and reliable network communication infrastructure in the smart grid.

The aim of this project is to propose solutions that will help increase utilization of renewable energy and reduce greenhouse emissions for both the power and transportation industries at minimum cost and maximum reliability.

Supervisors

Dr Neil Burnside
Dr Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Thermal energy efficiency and sustainability is a major challenge, particularly for countries such as Scotland where heating represents nearly two thirds of overall energy demand. Abandoned flooded mines are present over vast areas of the UK and typically represent high-level environmental and financial liabilities. However, these remnants of our carbon-intensive industrial past also provide an exciting opportunity to develop sustainable low-carbon energy resources through geothermal energy production and thermal energy storage. This project will develop innovative strategies for evaluating the geothermal potential of flooded mine workings.

Thanks to Glasgow’s extraordinary industrial heritage, many parts of the city, including the University, are positioned over a large network of abandoned, flooded coal mine workings. This project will use the University of Glasgow (UoG) as its major case study. The UoG has a campus wide Combined Heat and Power (CHP) district heating scheme which heats buildings, generates a large percentage of campus electricity and makes a significant contribution to the UoG’s carbon footprint reduction target (20% per year). Efforts are underway to achieve further carbon emission reductions for the current scheme and to ensure low-carbon energy for a £1 billion plus campus expansion.

As to be expected with the local climate, the campus has a seasonally high heat demand in the winter months. Excess ‘waste’ heat generated by the existing CHP system could potentially be turned into a valuable resource to help meet campus winter heat demand if it could be successfully captured and stored.

A major challenge for heat storage is the capacity required to store enough useable heat for a long enough duration. Due to the warm embrace of the surrounding geology, the flooded mine workings insulate groundwater from seasonal variations in surface temperature. Add to that the enormous volumes of water involved (several million m³), and these flooded mine workings represent a fantastic opportunity for inter-seasonal thermal energy storage if their hydrological nature can be robustly characterised.

Supervisors

Dr Manosh C Paul
Dr Nader Karimi
Dr Siming You

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Production of carbon neutral fuels is an essential step in reducing carbon emissions to atmosphere and mitigation of the global warming. Gasification is a key technology in realising this ambition and is therefore under intensive development worldwide. The School of Engineering at the University of Glasgow has a strong track record in the field of gasification and combustion engineering, with a number of ongoing collaborative projects focusing on biomass, waste and underground coal gasification. Gasification, which is a partial oxidation process, usually takes place at temperature 450-1350°C with very little air or oxygen, by which carbonaceous sources of energy are converted to synthesis gas (syngas) which ideally should comprise a well combination of hydrogen (H₂) and carbon monoxide (CO). However, currently there is a lack of clear understanding of the gasification thermochemical processes (such as drying, pyrolysis, combustion and reduction) which lead to the production of impurities and emissions. A key research question that will be addressed in this PhD project is how to get the gasification process robust enough, thus enabling to produce sufficiently clean syngas from various feedstocks. How to reduce/remove the tar formation? Also how to make it CO₂ neutral/negative? These are the challenging questions to be addressed through the project. The student will contribute to the development of advanced thermochemical as well as computational fluid dynamics (CFD) based techniques to first understand the gasification processes and then investigate how to improve the processes through systematic parametric optimisations. Gasification experiment will be performed to validate the modelling results, and combustion performance of produced syngas will also be investigated for potential downstream applications leading to the efficient generation of combined heat and power.

The candidate should have a strong academic background in mechanical, chemical or aeronautical engineering, or applied physics and mathematics. For further information please contact Dr Manosh Paul (Manosh.Paul@glasgow.ac.uk).

Supervisors

Dr Manosh C Paul
Dr Nader Karimi

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Flow transition with thermal energy transport is a common phenomenon which occurs in almost every energy engineering system e.g. in cooling electronic devices, heat exchangers, solar thermal energy, nuclear reactor, thermal energy storage, and so on. This may occur in different flow conditions e.g. laminar, turbulent, with/without influence of any external effects such as the gravitational force. Understanding the transportation process of heat and mass energy is thus crucially important for improving the performance of any thermal engineering systems. This proposed PhD project aims to study the various flow phenomena which may be complex at some conditions/applications due to the interaction between the system’s operation and energy transportation. The research will therefore initially focus on the development of highly advanced computational fluid dynamics (CFD) based numerical methods with the aim to investigate those complex phenomena. Most recently in-house developed advanced large eddy simulation (LES) and direct numerical simulation (DNS) codes will be extended further, thus allowing investigation of the fundamental aspects of the problem associated with the fluid mechanics and heat transfer. The research may also be extended further by utilising an alternative fluid such as nanofluid to investigate the performance against a base fluid (e.g. air, water). This will further involve the study of multi-phase flow with an effect of a combination of the various fluid conditions such as particle size and concentration of nanofluid. A possible extension of the study will be the investigation of phase change martial/heat pipe technology for efficient heat energy storage application – one of the key energy strategies for the UK Government.

The candidate should have a strong academic background in mechanical, chemical or aeronautical engineering, or applied physics and applied mathematics. For further information please contact Dr Manosh Paul (Manosh.Paul@glasgow.ac.uk).

Supervisors

Dr Keliang Zhou
Prof Yun Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Advanced power electronic converters, which can precisely and efficiently convert, control, and condition electricity, play a key role in the successful grid integration of different distributed generators, loads, and transmission devices. The global electricity processed by power electronic converters would be up to 80% in the very near future. The power quality and even the stability of electrical power systems would be affected and even determined by massive interfacing power converters. As a consequence, power converters highly demand optimal control strategies for periodic voltages/currents compensation to assure good power quality and stable power system operation. Simple but very effective periodic controllers offer attractive control solutions to power converters. The proportional–integral–derivative (PID) controller is the commonly used periodic controller in industrial applications.

The project is dedicated to comprehensively investigate the control, compensation, and filtering of periodic signals in power electronic power processing, aiming to provide a general proportional-integral-derivative control solution to periodic signal compensation in extensive engineering applications, such as ultrahigh accuracy nano-positioning, grid integration of renewable generation via power converters, power quality systems, and so on.

Supervisors

Dr Keliang Zhou
Dr Shufan Yang

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Addressing challenges from greenhouse emission and energy security, more and more distributed renewable energy generators such as solar photovoltaics and wind turbines are integrated into the grid. However, due to their intermittent nature, high penetration of those alternative sources will cause a number of problems to the grids such as supply and demand mismatches, voltage and frequency violations, which results in equipment damages and network instability.

Electric vehicles provide a feasible solution to tackle the problems of high penetration of renewable energy generation in the smarter grids. Bidirectional Grid-to-Vehicle and Vehicle-to-Grid services could mitigate the variation due to renewables.

Supervisors

Dr Ian Watson
Dr Julian Dow

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Biomass and waste feedstocks represents a significant opportunity to produce sustainable sources of energy whilst extracting valuable platform chemicals using biorefinery concepts.  The work will undertake novel approaches to extract liquid biooils using a range of thermochemical based processes and investigate applications that optimise the end energy and product yield.  Thermochemical treatments include: torrefaction, pyrolysis and gasification.  Process modelling will be done to determine the impact of feedstock and process treatment on the end product and will be supported by experimental work to identify novel applications of extracts.

Supervisors

Dr Ying Liu
Prof Yun Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption focuses on the need to improve the efficiency of resources (machines).The potential for energy reducing at the system-level has been largely ignored. At this level, operational research methods can be employed as the energy saving approach.

Job shops are widely used in the manufacturing industry, especially in small and medium enterprises. In the future, the uncertainty within the system will be increased as the result of mass customisation and personalisation. Optimisation techniques to solve the uncertainties and maintain the robustness of the manufacturing system will become increasingly important.

Reducing the electricity consumption in a dynamic job shop will be studied in this research. Existing dynamic scheduling algorithms will be extended to reduce the electricity consumption and improve productivity for job shops where the components arrive at the production system at randomly distributed times. This will extend the applicable range of the developed multi-objective optimisation methodology to include stochastic manufacturing systems which are widely used in the real manufacturing world.

Thus, in this project, meta-heuristics based optimisation approaches which include electricity consumption as an objective to minimise when uncertainties such as machine breakdown occur in the production system at randomly distributed times will be developed. Reinforcement learning will be used to identify the pattern of uncertainties in the manufacturing system.

Supervisors

Dr Ying Liu
Prof Yun Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Energy is one of the most vital resources for manufacturing. In the last 50 years, the consumption of energy by the industrial sector has more than doubled and industry currently consumes about half of the world’s energy.

Flexible job shops are widely used in the manufacturing industry, especially in small and medium enterprises. For instance, original equipment manufacturers in the aerospace industry usually employ the flexible job shop manufacturing system for their capability to satisfy the increasingly diversified customer demands. In the future, the requirement on the system flexibility will be increased to adapt mass customisation and personalisation. On-line decision making for the flexible manufacturing system will become increasingly important.

The main goal of this project is to address the multi-objective flexible job shop scheduling problems with reducing energy consumption and its related cost as part of the objectives. Electricity consumption and electricity cost reduction have not been well investigated in the multi-objective scheduling approaches for a typical flexible job shop manufacturing system. The lack of a more fundamental energy saving oriented flexible job-shop model and its related scheduling techniques is a significant gap in the current research which needs to be addressed.

A dispatching rule is a rule that prioritises all the jobs that are waiting for processing on a machine, which is widely used in the manufacturing system for decision support, especially for the on-line environment. The prioritisation scheme may take into account the job’s attributes, the machines’ attributes as well as the current time. Compared to exact algorithms and meta-heuristics, dispatching rules are easy to implement and fast to calculate, and can be used in real time to schedule jobs. In other words, dispatching rules usually can deliver reasonably good solutions in a relatively short time.

Thus, in this project, dispatching rules which include electricity consumption as an objective to minimise when jobs arrive at the flexible production system at randomly distributed times will be developed. Techniques like genetic programming will be used to construct the composite dispatching rules. Reinforcement learning will be used to identify the electricity consumption pattern of assets in the manufacturing system.

Supervisors

Dr Peifeng Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Open-cell titanium alloy porous structures (foams) are attractive materials for applications such as sound damping, heat exchange, impact energy dissipation and tissue engineering. The structures with periodic unit cells can be manufactured via additive manufacturing (3D printing) such as selective laser melting (SLM). The combined slurry coating and powder metallurgy approach can produce porous structures with irregular unit cell topologies. It has been a challenge to select the appropriate manufacturing route for titanium porous structures as different routes lead to dissimilar final properties.

This project aims to comparatively evaluate the properties of titanium porous structures fabricated by the different manufacturing processes. Research will focus on both additive manufacturing for periodic unit cell topologies and slurry coating with powder metallurgy for irregular topologies. Mechanical, thermal, and/or acoustic properties of porous structures will be quantitatively characterised and compared. In particular, the effect of processing parameters will be investigated to improve the manufacturing processes. Numerical simulation such as FE, CFD will also be used to explore the properties of titanium porous structures.

Supervisors

Dr Peifeng Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Additive manufacturing (3D printing) of metals is one of the significant research focuses in the Materials and Manufacturing Group. Selective laser melting (SLM) has been successfully used to manufacture lightweight metallic structures with complex geometries, such as microlattice structures, which can potentially be used in aerospace components and biomedical implants. Despite the numerous investigations on bulk mechanical properties of SLM metals, there is a scarcity of research on the underlying deformation and failure micromechanisms that determine the bulk behaviour.

This project aims to investigate the underlying micromechanisms on the deformation and failure process of metals (e.g., titanium alloy, aluminium alloy and stainless steel) made by the SLM technique using advanced experimental characterisation approaches such as in-situ SEM. Research will focus on how the microstructure and micro-texture in SLM metals in very small length scales affect the micromechanisms on both the elastic and plastic deformation behaviour. The constitutive behaviour for SLM metals will also be formulated for FE modelling of SLM components to predict their service performance.

Supervisor

Dr Philip Harrison

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Description

Advanced composites are attracting a huge amount of interest in the automotive sector where government legislation on emissions means that light-weighting is now a primary driver in the design process. Due to their enhanced damage tolerance, fast production times and potential recyclability, advanced thermoplastic composites are of particular interest. However, current computer aided manufacture modelling tools for these materials are inaccurate and the lab time required to characterise their forming behaviour for input into computer simulations is prohibitive. The goal of this project will be to predict the comprehensive forming mechanics of advanced thermoplastic composites directly from the matrix rheology and fibre volume fraction of the composite, a capability that will lead to significant reductions in design and manufacture costs, facilitating the wider use of advanced thermoplastic composites in the automotive sector and ultimately contributing to a greener economy. With the skills and experience gained during the project, can expect excellent employment opportunities in both the aero and automotive sectors.

Supervisor

Dr Philip Harrison

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Description

Composite materials are an exciting and fast-moving research topic. Developments in this area are driving economic growth and transforming society in a number of fundamental ways; from the reduction of carbon emissions through light-weighting, to the development of multi-functional smart composites that can self-sense and self-heal damage for low-cost in-service maintenance. This project will focus one of the most promising fully-automated low cost   manufacture   techniques in manufacturing advanced composites,   namely,   sheet   thermoforming. For the PhD student, the crux of the project will be to develop a mapping interface allowing fibre angle predictions following sheet forming to be fed into subsequent mechanical   simulations for structural analysis and for the prediction of warpage due to thermally generated residual stresses. The student will gain expertise in composite manufacturing, computational modelling and material characterisation. Computational modelling will involve FEA and coding in Matlab and python. Once the mapping algorithm is implemented, genetic algorithms will be used to optimise structural performance (minimise mass) and control warpage. With the skills and experience gained during the project, can expect excellent employment opportunities in both the aero and automotive sectors.

Supervisors

Dr Kumar Shanmugam

Funding

Description

Supervisors

Dr Kumar Shanmugam

Funding

Description

Supervisors

Dr Yusuf Sambo

Prof Muhammad Imran

Description

Mobile networks are generally overprovisioned to accommodate for reasonable increase in user density and the associated rise in traffic. However, in heavily crowded events, mobile networks face increase in both voice and data traffic by orders of magnitude, which severely degrades the performance of the network through increase in transmission delay, packet loss, dropped calls and reduced throughput. To overcome this, network operators deploy additional base stations within the crowded area to reduce the load on existing base stations and increase capacity, but this approach requires significant planning which is time consuming and expensive.

This PhD will leverage the principles of self-organising networks, whereby networks have the ability to autonomously configure, optimise and heal themselves with minimal human intervention, to design algorithms that enable the deployment of pop-up networks. This would speed up network commissioning/decommissioning, reduce personnel cost as well as provide quick and autonomous response to capacity demands.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Yao Sun

Lei Zhang

Muhammad Imran

Description

Software defined networking has been widely accepted as a novel technology to virtualize future mobile networks into multiple end-to-end network slices within a common physical infrastructure. Each network slice should be uniquely configured communication resources and network functions with the aim of providing tailored service for a specific communication scenario. Considering the scenario of multiple service co-existence, it is quite challenging to determine the optimal network slice configurations with limited communication resources thus to satisfy the diversified service requirements as well as the strict isolation constraint among these slices.

In this project, it is expected to design machine learning algorithms to cope with the slice resource allocation problem for co-existence of multiple services in future networks. Due to the dynamic nature of network environments, machine learning especially reinforcement learning should be adopted, thus the network operators/devices can continuously interact with the environments and thus obtain an optimal solution by using a trial-and-error learning process. In addition, under the cases with limited data, large action/state space, and/or no central controller, novel machine learning algorithms such as distributed learning, meta learning, federated learning, etc. should be investigated in this project.

An ideal candidate should have experience in wireless communications/networking and computer science. Strong background in mathematics, machine leaning, telecommunication industry experience is also desirable.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Qammer H. Abbasi

Ahmed Zoha

Muhammad Ali Imran

Description

Fifth generation (5G) mobile networks will soon exploit the millimetre wave (mmWave) frequency spectrum (24-30 GHz) to meet the growing demands of data traffic. However, due to the extremely inefficient nature of mmWave channels mainly due to severe pathloss, highly directive antenna systems need to be deployed at both the base station and the user end. Beamforming is typically achieved through massive multi-in multi-out (MIMO) systems using phased antenna arrays in which the beam direction is controlled by radiofrequency (RF) chain blocks of the communication system. For mmWave systems, the RF chain is highly complex and introduces latency in the network due to large computational times.  In this proposal, we aim to investigate plasma-based antenna arrays in which the radiation beam can be reconfigured by changing the electronic properties of plasma material. It is expected that plasma antenna arrays will result in ultrafast beamforming which will be an integral component of mmWave networks with cell sizes ten times smaller than the sub-6 GHz systems.

The congested nature of urban infrastructure of most of the larger UK cities requires a mobile network deployment that comprises small cells. For mmWave systems, the reliable range of communication becomes even lower (few hundred metres). Therefore, the significance of a low-latency beam forming method to establish of high-performance network is more than ever.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Qammer H. Abbasi

Ahmed Zoha

Muhammad Ali Imran

Description

The number of wireless devices demanding high data-rate has increased significantly in recent years. Many use cases that require high speed and reliable communication spontaneously continue their rise, which subsequently demand high bandwidth. Previous network generations (4G and earlier) primarily utilized the sub-6 GHz frequency spectrum, with a bandwidth of as high as 750 MHz For today’s applications, this band can be classified as too narrow. Exploitation of the millimetre wave (mm-wave) frequency band has been considered as a potential solution since the band has an abundant spectrum. However, the mm-wave band is sensitive to the blockage and high pathloss, due to which the potentials of the band cannot be fully utilized. In order to use mm-wave effectively, mobile network having a small cell size has to be considered to increase coverage and reliability; hence, deployment of an ultra dense structure of base stations (BS) becomes integral to the solution. However, the deployment of many small cell BS brings its own challenges, chief among them is the issue of handover (HO).
In this proposal, we aim to solve the HO problem through artificial intelligence (AI) using reinforcement learning techniques. Through analytical studies, we will develop datasets that simulate highly populated urban environments requiring the deployment of ultra dense mm-wave networks. A primary objective will be to assess the performance of device to device (D2D) communication architecture through evaluation of the AI generated models.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Qammer H. Abbasi

Hasan T. Abbas

Muhammad Ali Imran

Description

In a moving cellular network environment such as one a user is inside a transport vehicle, the antenna beams configured for the mobile terminals and the base station are not always fixed. Contrary to this, in a typical access-point deployment, both the access-point and the mobile client are equipped with omni-directional antennas. Potential challenges in this traditional deployment are, i) more access-points are needed due to the small cell radius, ii) mutual interference between adjacent cells, iii) high deployment cost to lay cables, poles, power, etc. and iv) high maintenance cost due to more equipment. An agile antennas system is proposed to overcome these challenges.
We propose a method to achieve the best configuration of the beamforming antenna for use in the transportation environment with a lot of movement, where different application scenarios may require different physical coverage beam and communication range as, for example, an access-point installed at the track-side of a single track may require 180° coverage instead of 360°, a right-angle road turn may require just 90° beam coverage. In addition, the antenna gain of choice can be chosen from the low/mid/high gain arrays to match the application needs. Furthermore, each array in the proposed agile antenna structure will be designed with beamforming capability for integration with an overall beamforming frontend. This method allows optimum interference performance and lowest deployment and maintenance cost to suit the volatile transportation environment.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Qammer H. Abbasi

Masood ur Rehman

Muhammad Ali Imran

Description

Intelligent reflecting surface (IRS) is a revolutionary and transformative technology to enable smart walls concept for wireless communication coverage enhancement, in addition to achieving spectrum and energy-efficient wireless communication. It is the first time ever we can control the wireless channels and mitigate the negative effects of multipath channel, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently without the need of radio-frequency (RF) chains. It can provide low cost, green, flexible and small-size solutions for further wireless communications. The aim of this research work is to design beamforming algorithms and develop hardware testbed for the IRS. The proposed work will explore various meta-surface unit cells which could effectively manipulate the electromagnetic waves and design a control board with IRS for beamforming applications.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Petros Karadimas

Description

Secure data exchange between communicating vehicles is one of the greatest technical challenges pending to be addressed prior to mass production of fully autonomous vehicles. The security solution has to be energy-efficient and adaptable to any wireless propagation environment in which connected autonomous vehicles (CAVs) operate. The proposed communication security solution relies on symmetric cryptographic key establishment and authentication enhancement by exploiting the physical layer characteristics of the wireless propagation environment. In the international literature, it has been named as physical layer security (PLS) and proven to be an ideal candidate for secure communications with strict constrains on computational resources and power consumption. Starting from a very thorough literature review, the PhD candidate will have to understand and become familiar with the most recent advances in PLS and how this can be implemented in CAVs. Accordingly, the PhD candidate will have to understand the algorithmic solutions and steps involved in the key establishment and authentication enhancement processes, including vehicular channel modeling, estimation and simulation, received signal quantization, information reconciliation and privacy amplification. The final goal is to design a cryptographic key establishment algorithm and evaluate its performance according to certain key performance indicators such as key generation rate and key entropy. This project is suitable for applicants with interdisciplinary interests in wireless communications, security/cryptography and programming/algorithms. Indicatively, applicants should have good performance in the following subjects: Communication Principles/Theory, Digital Communications, Security/Cybersecurity and Cryptography, Programming/Algorithms and Software Engineering.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Petros Karadimas

Description

Antennas are the corner stone of wireless communications as they are responsible for transmitting and receiving the electromagnetic wave that carries the information message. Although a very classical topic with more than 100 years of history since the first wireless transmission, the design of optimum antennas remains a timely issue. Multiple input-multiple output (MIMO) communication systems have been employed to offer parallel data streams and increase data rate. Particularly, for connected autonomous vehicles (CAVs), compact antennas supporting MIMO communications should be integrated in the on-board unit (OBU). Starting from a very thorough literature review, the PhD candidate will have to understand the radiation mechanisms of antennas and become familiar with the Maxwellian basis of antenna analysis and design. Accordingly, the PhD candidate will study existing MIMO antennas for CAVs and evaluate them according to certain key performance metrics (KPMs) including the diversity antenna gain (DAG) and channel capacity (CC). The aforementioned step of studying and evaluating existing state-of-the art MIMO antennas will enable the PhD candidate to gain significant experience to progress to the next level. The next level and ultimate goal of this project is the PhD candidate to come up with novel brand new MIMO antennas (at least three) that will show better performance, i.e., higher DAG and CC compare to the existing ones. This project is suitable for applicants with interests and good background in electromagnetics and electromagnetic designs and particularly in electromagnetic wave propagation, antennas and antenna arrays. Indicatively, applicants should have good performance in the following subjects: Electromagnetic Theory and Fields, Microwave and mm-Wave Transmission Systems and Devices, Communication Principles/Theory, Engineering Mathematics.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Petros Karadimas

Description

Antennas are the corner stone of wireless communications as they are responsible for transmitting and receiving the electromagnetic wave that carries the information message. Although a very classical topic with more than 100 years of history since the first wireless transmission, the design of optimum antennas remains a timely issue. Multiple input-multiple output (MIMO) communication systems have been employed to offer parallel data streams and increase data rate. Particularly, in 5G portable devices, such as mobile phones and tablets, a compact design should be integrated in the limited device space. Starting from a very thorough literature review, the PhD candidate will have to understand the radiation mechanisms of antennas and become familiar with the Maxwellian basis of antenna analysis and design. Accordingly, the PhD candidate will study existing MIMO antennas for 5G portable devices and evaluate them according to certain key performance metrics (KPMs) including the diversity antenna gain (DAG) and channel capacity (CC). The aforementioned step of studying and evaluating existing state-of-the art MIMO antennas will enable the PhD candidate to gain significant experience to progress to the next level. The next level and ultimate goal of this project is the PhD candidate to come up with novel brand new MIMO antennas (at least three) that will show better performance, i.e., higher DAG and CC compare to the existing ones. This project is suitable for applicants with interests and good background in electromagnetics and electromagnetic designs and particularly in electromagnetic wave propagation, antennas and antenna arrays. Indicatively, applicants should have good performance in the following subjects: Electromagnetic Theory and Fields, Microwave and mm-Wave Transmission Systems and Devices, Communication Principles/Theory, Engineering Mathematics.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Petros Karadimas

Description

Orthogonal Frequency Division Multiplexing (OFDM) is a signalling technique that exploits orthogonal carriers to transmit information and enhance received signal diversity and consequently increase received signal-to-noise ratio (SNR). In wireless mobile communications, carrier orthogonality is violated due to the inherent Doppler spread arisen by the temporal variability of the wireless channel. This effect causes degradation of received signal quality and becomes more evident in scenarios with very high mobility such those of connected autonomous vehicles (CAVs). However, the increased Doppler spread in CAVs provides an alternative signal diversity mechanism by using spread spectrum signalling and characterized as Doppler diversity. Starting from a very thorough literature review, the OFDM technique will be theoretically studied and analyzed to understand the important parameters affecting its performance in CAV scenarios. The PhD candidate should come up with a solution compensating the increased Doppler spread in such scenarios. The project will then investigate the implementation of a novel OFDM architecture by incorporating an extra feature that is capable to exploit the inherent Doppler diversity in CAVs. Thus, a hybrid "two-dimensional" OFDM architecture will arise with two degrees of freedom, i.e., those due to orthogonal carriers and those due to Doppler diversity in each carrier, offering potentials for improved performance compared to the standard OFDM. Both the hybrid and the standard OFDM architectures should be then implemented/simulated in an appropriate software tool (e.g., Matlab, Labview). A comparative study of both architectures will demonstrate the performance improvement (if any) of the hybrid against the standard OFDM architecture. This project is suitable for applicants with interests and good background in wireless communication systems and particularly in the physical layer of wireless communications. Indicatively, applicants should have good performance in the following subjects: Communication Principles/Theory, Digital Communications, Digital Signal Processing, Statistics and Stochastic Processes, Engineering Mathematics.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Oluwakayode Onireti

Prof Muhammad Ali Imran

Description

High data rate in the future cellular network will be enabled by ultra-dense small cells operating in the millimetre and terahertz bands. However, cellular networks with ultra-dense small cells have significant challenges in terms of high overheads, mobility and handover management, and excessive energy consumption when all small cell base stations are kept active. This PhD research will investigate and develop memory-driven smart handover protocols for next-generation cellular networks.
The smart and proactive approach to handovers for cellular networks can be achieved by leveraging a memory-full network, i.e., the user-side and network-side memory. With the former, the mobility and behavioural history of the user can be leveraged in a future handover process to reduce the handover latency and signalling overhead cost. Other user context information includes information on user mobility and user service requirements. On the other hand, the network memory can be leveraged for information related to types and position of devices, activity status, hotspots location and density, traffic profile, energy consumption, resource availability, and historical network performances. With historical network information, sleeping small cells be activated in advance for the handover process.

The PhD research will utilize both the user and the network side information to develop smart handover techniques for cellular networks.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Masood Ur Rehman

Muhammad Ali Imran

Description

Global Navigation Satellite Systems (GNSS) have seen a remarkable growth in recent years. GPS (US), GLONASS (Russia), GALILLEO (Europe Union), BEIDOU (China) and a number of regional navigational satellite systems are fulfilling the navigation and positioning requirements worldwide. Antennas play a pivotal role in the effectiveness of the GNSS systems and are an integral part of all modern wireless devices for applications ranging from geodetic surveys to wearable monitoring/surveillance equipment, agriculture & food safety to automation. The GNSS antennas covering frequencies from 1.1 to 1.6 GHz are therefore, in high demand to make use of potential advantages of interoperability and satellite availability of different GNSS systems along with the emerging IoT and 5G & Beyond systems especially in restricted and difficult environments.

An efficient multiband and reconfigurable antenna system that not only meets the standard requirements of such systems in terms of the bandwidth, axial ratio, beamwidth, gain, form factor, flexibility and body conformity while minimising the geometric dilution of precision, phase centre error, group delay variations and offering high efficiency is an extremely sought after solution. This project will explore such a solution and investigate methods to overcome interference and jamming to further improve the system reliability.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Masood Ur Rehman

Lina Mohjazi

Muhammad Ali Imran

Description

Increased development of wearable sensors for physiological monitoring has spurred complementary interest in the detection of biochemical indicators of health and performance. In a sports performance setting, many athletes exercise at specific intensities as informed by blood lactate (BLac) levels. The BLac data is used to determine ‘training zones’ and guide individual exercise intensity for a given session. Traditionally individual blood lactate data relative to exercise intensity is obtained within a laboratory environment by undertaking an incremental exercise test or through the analysis of capillary blood samples.

A novel solution to capture continuous BLac data and therefore, informing the athlete and coaching staff of ‘live’ physiological load can enhance the athlete’s performance significantly through better understanding of training needs, adaptation of best available exercise routines and reduced test times.This project will address the design, development and implementation of a low-cost, efficient, robust and novel wearable 5G mmWave sensor system for lactate monitoring.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Masood Ur Rehman

Qammer H. Abbasi

Muhammad Ali Imran

Description

The use of Implantable Medical Devices (IMDs) has risen in recent years due to the advantages of real time observation of bio-telemetric data to diagnose and treat a wide range of medical. Some examples include, diabetic monitoring, remote drug delivery, heart failure monitoring and brain-computer interface devices.

Design of implant antennas is very challenging. Implant antennas are usually designed for operation in a single tissue type. Each tissue has varying electrical properties. The distribution and amount of different tissues is subject specific and vary greatly patient-to-patient depending on sex, age, weight, position, etc. An implant antenna’s performance is strongly dependant on the tissue immediately surrounding it with potential changes in antenna input impedance, radiation pattern fragmentation, reduced radiation efficiency and polaristion distortion. This work aims to design compact and efficient implantable antennas that maintain their impedance and radiation characteristics in a broad range of tissue types in the sub-6GHz/60GHz frequency bands for future medical applications.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Masood Ur Rehman

Ahmed Zoha

Muhammad Ali Imran

Description

Wireless technologies are fast becoming an integral part of our daily lives. The concept of Inetrnet of Things in Smart Cities strongly rely on users’ real time location to offer better services and improved communications leading to the term Location Based Services. The applications of Location Based Services range from social networking and marketing to vehicle-to-anything communications.

This project will look into the intelligent ways to improve on the real time localisation techniques (e.g. GPS, Wi-Fi, and 5G) and hence the proximity estimation in indoor as well as outdoor settings primarily on the physical layer through smart antenna design. Use of deep learning to assess the available localisation signals and means to enhance them through intelligent reflective surfaces, beamforming and error reduction would be investigated.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Lei Zhang

Description

Blockchain, as the backbone technology of Bitcoin digital currency, has become a revolutionary decentralized data management framework. It is history's first permanent, decentralized, global, trustless ledger of records that may reshape the future digital economy and transforming society. Blockchain technology can solve the long-lasting security and overhead issues in the IoT systems, as a result, enabling decentralized, automatic, massive connected mobile IoT ecosystems with the wide applications from smart contract, supply chain, healthcare, digital identity, to digital voting, etc.  

In this project, the classic consensus mechanisms will be extended into the wireless communication scenarios, to serve as a theoretical foundation for blockchain-enabled IoT ecosystems. In addition, the unique requirements in blockchain protocol will motivate a new beyond 5G or 6G communication protocol dedicated for the scenarios to maximizing the communication spectrum efficiency and minimizing the cost and power consumption at the low cost IoT devices.

The project is cross-disciplinary that involves communication engineering, IoT, computer science, etc. The methodology proposed in this work will bridge the gap between the blockchain and IoT systems through wireless communications. It can catalyse the future research in all aforementioned related topics.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Lei Zhang

Yao Sun

Muhammad Imran

Description

This project will focus on privacy-preserving machine learning (ML) algorithms for Internet of Things (IoT). In particular, distributed learning algorithms will be applied for data sharing among IoT devices, for different application scenarios. Privacy will be one of the primary performance metrics to be investigated and optimised. In addition, algorithms will be improved to reduce the required communication/computing resource in training to achieve a lower power consumption and a lower latency. Distributed ledger technology (blockchain) will be used as a framework to establish a sustainable ecosystem to enable the data sharing and trading among the devices, and further enhance the privacy. Both analytical and simulation results will be provided to guide the practical system deployment.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Lina Mohjazi

Ahmed Zoha

Muhammad Ali Imran

Description

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology to prolong the lifetime of energy-constrained wireless networks and to offer an unprecedented opportunity to connect the global world via a massive number of low-power heterogeneous smart devices, enabled by the internet of Things (IoTs). In a SWIPT-enabled system, a wireless node is powered up by a received Radio Frequency (RF) signal and, simultaneously, information processing is carried out using the same signal. Recently edge computing is envisioned to be integrated with IoTs to provide efficient and secure services for a large number of end-users, and edge computing-based architecture is considered for the future IoT infrastructure. The aim of the PhD research is to design novel beamforming strategies for SWIPT-enabled mobile edge computing (MEC) systems. The proposed work will explore various aspects related to MEC IoT applications such as data offloading and resource allocation schemes. The performance of these algorithms will be evaluated in terms of carefully selected key performance indicators.

This project is suitable for applicants that have experience in wireless communications and signal processing. Strong background in mathematics and Maltlab/C programming are also desirable.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Hasan T Abbas

Qammer H. Abbasi

Muhammad Ali Imran

Description

By the year 2024, mobile data traffic in the world will grow nine times than what it is today. As a result, next generation wireless networks (NGWNs), starting with fifth generation (5G), should immensely improve in terms of high data-rates, latency, and reliability. Furthermore, with the advent of direct communication technologies such as machine-to-machine and vehicle-to-everything, wireless communications nodes can communicate directly in local premises, independent of core networks. Communication in the terahertz (THz) frequency spectrum (300 GHz – 3 THz) communication will enable extremely high data rates. However, owing to the severe pathloss of THz systems, the network cell size will become extremely small requiring a number of antennas for reliable coverage. Therefore, there is a growing need of highly efficient and highly directive THz antennas not only at the base station but at the user end as well, that will ensure minimal interference and maximum performance. In this proposal, we aim to investigate massive multi-in multi-out (MIMO) based very large phased array antenna systems in which plasmonic materials such as graphene act as radiating element. One of the main advantages of using plasmonic antenna arrays is that they are highly reconfigurable in space, hence providing extremely fast and adaptive beamforming. Moreover, artificial intelligence, which is expected to become a pivotal component of NGWNs such as 6G, will be introduced at the physical layer so that radiation from the antenna arrays could be efficiently configured.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Guodong Zhao

Muhammad Imran

Description

The 5th and 6th generation cellular networks (5G and 6G) with impressive performance provide great potentials to exchange data and skills over wireless networks. Tele-robotics is one of them which spans wide range of applications such as remote operation in Industry 4.0, healthcare, education, etc, especially in case of infectious diseases. Afflictive effects of Covid-19 on socio-economic life over last few months proves the importance of remote operation especially in case of emergency situations, where people cannot physically be present on site. Thus, there will be strong demand and a large market for remote controlled robotics in the future. However, real-time robotic control requires ultra-reliable and low-latency communications to satisfy control performance requirements. The biggest challenge is to have reliable and low-latency remote robotic control over wireless links. In this project, Mobile Edge Computing (MEC) techniques will be studied to address the challenge from the perspective of co-design of communication protocols, robotic systems, and inference engines, where machine learning, artificial intelligent (AI), real-time analysis, optimization techniques will be used. Students are expected to design and test their ideas using the 5G testbed and tele-robotic testbed at University of Glasgow.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Bo Liu

Muhammad Imran

Description

AI techniques will play a significant role in future electronic engineering, but there is a gap between off-the-shelf AI techniques and real-world electronic design. Filling this gap to transform AI techniques for electronic design from laboratory to industry is the goal of AI-driven Design Lab (ADL), which has a sound track record.  

This year, several PhD projects are available.

• For students with a sound mathematical background and will devote themselves into machine learning and intelligent optimization research, you will be guided to invent core algorithms for next-generation AI-driven antenna/filter/analog IC design.
• For students with a sound knowledge of analog IC manual design, you will be guided to become the first generation analog IC designers with AI tools and novel methodologies, and even the invertors of them.
• For students with a sound knowledge of filter design, you will be guided to develop next-generation mixed intelligence filter and multiplexer design techniques.
• For students interested in applications, you will be guided to become the pioneer of introducing AI-driven design techniques to novel electronic design areas.
• For students with a software engineering background, you will be guided to become software engineers with expertise in intelligent computing and CAD techniques, making you stand out from common software engineers.

You are encouraged to contact Dr. Bo Liu (Bo.Liu@glasgow.ac.uk) for project details.

ADL is collaborating with academic and industry leaders. Involvement with these PhD projects is expected to generate an impressive track record for the PhD student’s future career, either from an industry or academic point of view.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

The project could be funded by industry partners, which will be updated regularly. The potential students should contact Dr. Bo Liu for latest information.

Supervisors

Dr Ahmed Zoha

Dr Lei Zhang

Muhammad Ali Imran

Description

Given the ubiquitous smart mobile gadgets and Internet of Things (IoT) devices that are expected to be empowered by 5G networks offers an unprecedented opportunity to contribute data and computing resources for a range of AI/ML-driven applications that demands faster training and inference for near real-time response. The real-world performance of any ML-driven application depends on the amount and the relevance of the training data. This often involves transfer of large amount data from the devices to the server that not only results in substantial network footprint but can lead to privacy issues. Even in cases where all the required data is available, reliance on a centralized dataset for maintenance and retraining purposes can be costly and time consuming. In this PhD project, we aim to exploit the distributed, on-device learning framework called federated learning (FL) for the purpose of training a deep neural network that can infer psychomotor impairment/mental disorders by capitalizing on on-device sensing modalities. The envisioned model performs multi-modal data fusion from data sources including keyboard typing patterns captured in-the-wild, geo-locations, accelerometer readings and heart-rate variability for predicting depression scores. FL enables learning of the shared prediction model across all mobile devices while the user-sensitive data is retained locally during the training of the deep learning model and only the exchange of model parameters across the different clients are aggregated and distributed by one or more central entities.. The PhD student will develop a privacy-aware on-device FL framework that can be integrated with remote mental disorder monitoring tools to minimize the risk of exposing sensitive user-information while reducing data transfer and still achieving on-par accuracy in terms of prediction.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Ahmed Zoha

Qammer H Abbasi

Muhammad Imran

Description

Non-Invasive human activity monitoring has become a major area of interest over the past few decades. Researchers have come to the realization that traditional visionary sensing technology despite all their benefits have major drawbacks such as lack of privacy, high installation cost and energy consumption. Motivated to address these challenges, there has been a lot of interest recently to develop privacy aware and non-intrusive solution. Non-invasive activity monitoring solutions making use of ubiquitous technologies such as Radio Frequency signals and WiFI shows great promise since they do not need additional infrastructure deployment and do not raise any evident privacy concerns. RF-sensing has shown great promise in detecting critical events including falls and vitals without invading the privacy of vulnerable individuals for in-home/care non-invasive health monitoring. Vulnerable individuals include the elderly and people with cognitive or physical impairments, or those with multi-morbidity conditions. This project aims to develop a non-invasive human activity monitoring system with as strong focus on developing artificial intelligence and machine learning driven solutions that can exploit data from off-the-shelf RF sensing devices including Radar, WiFi or USRP. The proposed system analyses the magnitude and phase variations of the continuous stream of multiple subcarriers and correlates these changes to infer daily activities patterns via advanced gait and motion analysis. An inference engine will be developed using state-of-the-art machine learning techniques to classify human activities in near real-time based on the evolving subcarrier patterns.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Ahmed Zoha

Dr Lina Mohjazi

Muhammad Ali Imran

Description

This PhD project investigates a novel research direction that leverages vision to help overcome the critical challenges of the next-generation wireless communication systems. In particular, this project considers millimetre wave (mmWave) and sub-terahertz communication systems, hinges on realizing unprecedented low-latency and high-reliability requirements. These systems face two important challenges: (i) the mmWave an sub-terahertz signals sensitivity to link blockages (ii) the large training overhead associated with selecting the optimal beam.

In this project, we aim to develop a vision-aided machine learning system allowing the next-generation wireless network to develop a sense of its surrounding. This is done by employing cameras at the mmWave base stations and leveraging their visual data to help overcome the beam selection and blockage prediction challenges. The student will develop skills in artificial intelligence and machine, specifically in deep neural networks for vision processing in order to predict mmWave beams and blockages directly from the camera RGB images and the sub-6GHz channels.

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Supervisors

Dr Shengrong Bu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Rapid growth in commercial data services over several decades has lead to the “Big Data” era. Among various data sources, mobile wireless networks are one of the primary data contributors. Due to the increasing applications supported by smart phones and other smart devices, mobile data traffic is expected to increase 5 times within next 5 years.

5G wireless networks are under research to address the following challenges: higher capacity and data rate, lower end-to-end latency, larger numbers of connected devices and better quality of experience for users. The requirements for 5G are on a completely different scale compared to existing deployed 4G networks. Various novel techniques, e.g., small cells, cloud-based radio access networks, and software-defined networking will be used in 5G networks to address the challenges. With the use of these techniques, high levels of signaling traffic will be generated, and this traffic will increase 50 percent faster than mobile data traffic. Therefore, 5G wireless networks will face even more difficult challenges in the big data space.

Initial work has been proposed two approaches to manage big data traffic in 5G networks: 1) exploit various signal processing methods to reduce data traffic within the network. 2) improve wireless system design so that spectrum efficiency, computing capacities and link capacity will be increased. The growing data volume is expected to overwhelm these combined processing and link capacity improvements. Therefore, it is urgent to seek fundamentally novel approaches to address big data related problems.

For this project, the PhD candidate will have the chance to contribute to an exciting new research area: big data aware 5G wireless networks. Compared to the existing work where big data is considered as a burden, this research area will consider the opportunities provided by big data. Specifically, the scholar will research how to extract important individual and social features from wireless data traffic. Novel data analytics methods need to be proposed for data from 5G networks to handle their high dimensionality and complex features. Second, the candidate will exploit the new extracted context information for optimizing and managing resource allocation in 5G wireless networks to improve wireless service quality and network efficiency.

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Although there is significant progress in the provision of higher data rates, low latency and energy/spectral efficient wireless communication systems over the years, the emergency management system (EMS), which is conventionally based on wireless communication, has often been left behind. Given that current platforms are susceptible to being destroyed by disasters, it is paramount to implement robust EMSs to handle such situations. Moreover, current platforms require human intervention, making them expensive to operate and maintain in times of disaster. The next generation EMS needs to be an effective and efficient critical communication platform that provides situational awareness and coordination. Fortunately, the advent of the 5G mobile system presents a new opportunity for research and the design of a distributed, autonomous and resilient EMSs.

A single point of failure is a major concern in most centralised solutions and its detrimental effect is even more pronounced in emergency scenarios. Hence for improved resilience, this PhD will focus on proposing distributed data delivery and resource management algorithms that are suitable for emergency situations. The fundamental theoretical underpinning of stochastic geometry will be used to model the nodes and the network entities’ locations along with appropriate traffic models to develop theoretical, as well as practical understanding of the system. The performance of these algorithms will be evaluated in terms of carefully selected key performance indicators. 

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Millimetre wave (mmWave) communication is one of the enablers to meet the requirements of future communication systems, in particular, to provide the high bandwidth necessary to achieve the expected high data rates for extreme broadband. However, to overcome the adverse propagation conditions at higher frequencies, high gain directional beamforming is required at the transmitter and receiver. This kind of directional, coordinated communication leads to a new design paradigm for future systems, which is much more complex than their counterparts operating at lower frequencies.

This PhD will address the handover and mobility management in mmWave cellular networks. In particular, the directivity nature of the base station and user equipment beams will be exploited for developing novel handover and mobility management solutions. The architectures to be considered will include the dual base station option, which integrates the mmWave and microwave frequencies on the same node. Inter-frequency (mmWave and microwave) deployment scenarios without such integration will also be analysed. The PhD will investigate the performance of such architectures in terms of the initial cell discovery, inter-frequency measurement energy consumption, handover failures and seamless mobility. The impact of proactive content caching on handover and mobility management in such deployment will also be analysed.

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Mobile networks are generally overprovisioned to accommodate for reasonable increase in user density and the associated rise in traffic. However, in heavily crowded events, mobile networks face increase in both voice and data traffic by orders of magnitude, which severely degrades the performance of the network through increase in transmission delay, packet loss, dropped calls and reduced throughput. To overcome this, network operators deploy additional base stations within the crowded area to reduce the load on existing base stations and increase capacity. Although this approach brings about considerable relief to the network, deployment is very time consuming and expensive.

Due to the nature of cellular networks being heterogeneous, this PhD will incorporate the principles of self-organisation (configuration, optimisation and healing) with the use of entities such as small cells, fixed relays, Unmanned Aerial Vehicles (drones) and even mobile devices in crowded networks, in order to reduce human intervention, as well as deployment time and cost. A load aware modelling of the resource allocation for emergency scenario will also be carried out using stochastic geometry.

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The ultra-dense deployment requirement of future cellular networks implies multifarious complexity and a significant rise in cost of operation. One way to address this challenge is via novel Radio-Access-Network (RAN) architecture such as the Control and Data plane Separated (CDS) architecture, and equipping them with the next generation Self-Organising Network (SON) functions.

In CDS architecture, small cells (Data Base Station – DBS) provide data plane to high rate users on an on-demand basis. Overlaying larger cells (Control Base Station – CBS) provide control plane to high rate users, and both control and data plane to low rate users. The CDS architecture enables having localised database at the CBS that consist network and user measurement such as received signal strength and quality reports, DBS load conditions and user mobility report among others. Intelligence extractable from the database can be used by a CBS to perform a number of SON functions such as dynamic switching on/off of DBS, self-healing (cell outage detection and compensation) and self-optimisation (i.e., coverage, energy efficiency and radio resource optimisation).

This PhD will investigate the use of machine learning for SON functions in the context of CDS. Advanced mobility prediction techniques and protocols related to resource reservation, predictive DBS handover, and predictive DBS switch on/off will be designed for the CDS architecture. The novel algorithms will be validated on the 5GSON testbed being built at the University.

Supervisors

Dr Lei Zhang
Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The evolution of the first four generations of cellular systems has successfully connected people and brought us into a cyber world. This is essentially based on information exchange, which allows us to communicate to people and access the internet almost at anytime and anywhere. In future fifth generation (5G) cellular networks, a significant number “things”, e.g., sensors and actuators, are expected to be connected, which allows us to interact with the physical world in a read-time fashion. In other words, we are stepping into a cyber-physical world, which offers many emerging applications, such as tactile internet, industrial automation, self-driving vehicles, remote surgery, smart grid, and more. The essential function that enables these emerging applications is the real-time wireless control, which is beyond the conventional cellular networks that are designed for supporting massive high data-rate information exchange.

This project is to design ultra-low latency and high reliable communication solutions for wireless control. The joint design between control and communication will be considered. Specifically, the basic feedback control and wireless communication model will be built. Then, we will analyse the dynamic interactions between the two sub-systems, where both computer simulation and theoretical analysis will be conducted. The project also designs algorithms to optimize overall control-wireless performance in terms of latency, throughput and reliability, etc. In addition, we expect to build a test bed to verify the developed algorithm, where a robot is controlled through a shared wireless network.

 An ideal candidate should have experience in wireless communications and signal processing.  Strong background in mathematics, Maltlab/C programming, control and telecommunication industry experience is also desirable.

Supervisors

Dr Lei Zhang
Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The next generation wireless communication (5G) and beyond will enable fully mobile and connected society, which is required to support a variety of very diverse user cases in terms of throughput, latency, reliability, availability and scalability. Three main communication scenarios have been identified by 3GPP with each having huge potential applications, i.e., enhanced mobile broadband (eMBB) (e.g., supporting mobile HD movie), massive machine type communications (mMTC) (e.g., supporting internet of things communications), and ultra reliable low latency communications (URLLC) (e.g., supporting vehicular to vehicular or virtual reality communications). Millimetre wave (mm-wave), at the frequency band 30–300 GHz, has been proposed as an enabler for many different communication scenarios. Although abundant unlicensed spectrum available in mm-wave band provides excellent possibility to fulfil the capacity requirements of the next generation wireless systems, the channel characteristics in these bands are significantly different from lower frequency channels. Exploiting large antenna array to combat the severe path loss in mm-wave communications is one viable option. For example, hundreds of antennas can be equipped within both access point and mobile station sides (i.e., massive MIMO system) to achieve very high beamforming gain by forming a very narrow beam pointing to the target. Moreover, the narrow beam width can be used for positioning in the scenario of vehicular and drone communications. However, it also causes severe problems for random access and user tracking. In this project, a wide omnidirectional beam pattern will be designed for both LoS and nLoS mm-Wave communications to alleviate the overhead and delay in the beam training. In addition, by taking the user moving trajectory and behaviour into the consideration, beam tracking based on machine learning algorithms will be proposed to fast track moving targets (e.g., car, drone and mobile phone, etc.) to provide high and consistent quality of experience communications. One of the challenge in such communication scenario is the communication and positing algorithm joint design by using the same signal.

For this project, the PhD candidate will have the chance to contribute to an exciting new research area, machine learning algorithms assisting millimetre wave communications for 5G and beyond, the work will include design, modelling and implementation of millimetre communication algorithms to support 5G communication scenarios and other unforeseen applications in the future digital society.

An ideal candidate should have experience in wireless communications and signal processing.  Strong background in mathematics, Maltlab/C programming, telecommunication industry experience is also desirable.