Engineering and built environment PhD project opportunities

Dr Shabnam Sadeghi Esfahlani

Prof Hassan Shirvani

Alireza Sanaei

AI, Robotics and Automation. Impact themes: Health, Performance, and Wellbeing Sustainable Futures.

Research Topic: Enhancing mobile robot autonomy: exploring advanced algorithms for unstructured terrain navigation using deep learning.

Rationale: In the contemporary age of robotics, navigating unstructured terrains remains a significant challenge. While robots perform well in structured environments with clear paths and markers, terrains that are uneven, unpredictable, or filled with obstacles pose numerous complications. Leveraging the capabilities of advanced machine learning, particularly deep learning, offers a promising solution to this enduring challenge. Through the development and optimization of SROBO, this research seeks to push the boundaries of what is currently possible in robotic navigation, offering more versatile and adaptable robot deployment in real-world scenarios.

1. Develop a deep learning architecture tailored for real-time processing and optimized for mobile robotic platforms.
2. Investigate image segmentation techniques suitable for identifying varying terrains and their respective challenges.
3. Explore object classification methods to discern obstacles and navigate around or interact with them intelligently.
4. Benchmark and compare the performance of SROBO against traditional navigation algorithms in diverse terrains.
5. Assess the robustness of the developed algorithms against different lighting conditions, weather scenarios, and other environmental factors.

1. Dataset Compilation and Augmentation: Use a combination of available datasets for terrain and obstacle recognition and develop a custom dataset by operating SROBO in various terrains and conditions.
2. Deep Learning Model Development: Design and implement convolutional neural networks (CNNs) and potentially recurrent neural networks (RNNs) for sequential decision-making in navigation.
3. Image Segmentation: Employ semantic segmentation techniques to divide an image into segments, aiding in identifying paths, obstacles, and potential danger zones.
4. Object Classification: Utilize state-of-the-art classification models to identify and categorize obstacles, helping the robot decide on interaction strategies.
5. Real-time Integration and Testing: Integrate the developed models into SROBO's onboard computer and conduct real-world navigation tests in various terrains.
6. Performance Assessment: Deploy traditional navigation algorithms on SROBO as a comparison metric to evaluate the efficiency, accuracy, and reliability of the newly developed deep learning-based algorithms.

1. A refined version of SROBO with enhanced capabilities in autonomously navigating unstructured terrains.
2. A comprehensive understanding of the advantages and limitations of using deep learning techniques for robotic navigation in challenging environments.
3. A set of best practices and guidelines for deploying machine learning in mobile robots, particularly those operating in unpredictable terrains.

Contribution to the Field: This research aims to contribute significantly to the field of mobile robotics, offering a fresh perspective on navigating challenging terrains. The lessons learned from enhancing SROBO could potentially be applied to other robotic platforms, paving the way for more adaptable, intelligent machines in various applications, from exploration to disaster response.

Chelmsford

This project is self-funded with the aim of completing in 3 years. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Sufian Yousef

Smart cities, 5G Networks

This topic goes in parallel with end-to-end performance and the ability for a network to know what device a user is using, what application is being used, the physical location and speed, and adapting the network performance to best serve those parameters. Work in that area is already ongoing. Big data analytics are already an area of interest for 5G, and researchers expect data-based intelligence to become more prevalent as part of contextual awareness. However, researchers acknowledged that as more and more content providers move to encrypted content, network providers have less visibility into those data streams. This is a very active area of research, and much can be inferred about the content of a data stream based on its behaviour. Many researchers expect to see content providers and network operators come around to sharing more information with one another, because both sides ultimately want an excellent end-user experience.

Data mining is considered to be one of the key enablers for the next generation of mobile networks. The building of knowledge models is expected to tackle the complexity of these networks and enable their dynamic management and operation.

Recently, this research area has attracted a lot of interest and several models have been proposed by the research community.

5G mobile networks target the provision of tailor-cut solutions not only for the telecommunications sector but also for the so called “vertical industries” (e.g., intelligent transportation systems, smart factories, the health sector, etc.). This result will be achieved by deploying multiple network slices over the same network infrastructure. Thus, 5G networks will be considerably more complex than the previous generations.

At the same time, the scientific community has identified that big data solutions can significantly improve the operation and management of existing and future mobile networks. Data mining is used to discover patterns and relationships between variables in large data sets. Several mechanisms that include statistical analysis, artificial intelligence and machine learning are applied in the data set to extract essentially knowledge from the examined data. data are collected from a number of network components. These data may include a variety of information fields such as the quality of the wireless channel, the network load, accounting information, configuration and fault indications, the profile of the subscribers, etc. These data are stored and updated regularly. When collected, they are passed through a pre-processing phase. During this phase transformation, discretization, normalization, outlier detection and dimensionality reduction is executed. The outcome of this phase is then passed to a data analysis phase where a model is built to extract knowledge from the processed data. For example, the result of this process will be the identification of situations where the occurrence of some specific events causes some specific result. The knowledge model may also include some solutions for specific situations (e.g., force the network components to place high moving users to macro cells). The list of the knowledge discovery results can then be communicated to either policy, management or control modules. These modules can use this information in order to optimize the operation of the network and improve the performance.

Chelmsford

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Ahad Ramezanpour

Sustainable Technology and Manufacturing

The low-grade heat recovery systems, recover part of waste water heat and save energy consumption in the form of preheating a cold water input to shower or boiler. The heat recovery unit is a heat exchanger which could be horizontally or vertically designed. The efficiency of the units are functions of design and size envelope influencing convective heat transfer rate and surface area. The adverse effect of pressure drop and physical contaminants in the efficiency of the units in long term requires a holistic view on the design of waste water heat recovery units. On top of this, low energy recovery and high-cost result in a high pay-back period for these units of up to 40 years.

This research focuses on holistic research on technological consideration and innovation of the waste water heat recovery units with a view on pay-back period and commercialisation aspects.

An extensive literature review on the subject and proposed innovations in design to reduce cost while maintaining or increasing efficiency are expected followed by detailed analysis of the selected design both numerically, using computational fluid dynamics, and experimentally, designing, building and testing a test rig. The project enjoys support and contact from industrial companies with excellent track record in the field.

The expected outcome of the research is innovation in the form of intellectual property, scientific publications, and proposed prototype for commercialisation of the final design. Considering that long pay-back period and the high initial cost is the major issue for mass use of domestic waste water heat recovery systems, the project has major environmental impacts by making a product viable and recovering/saving energy on a large scale.

Chelmsford

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Sufian Yousef

Smart cities, 5G Networks

As the industry explores more flexible, automated network solutions, this part of the evolution toward 5G capabilities is already underway and researchers expect it to be fundamental to 5G. However, researchers pointed out that research questions remain on the best network architectures for different applications. Ultra-low-latency applications such as autonomous driving may require highly distributed networks simply due the geographical distributions, while applications that can tolerate higher latency could be served from fewer central locations.

Traditional networks make use of network configuration, bearer and QoS information to satisfy user requests, but the Distributed Cloud (DC) architecture additionally employs user and network context information such as where, when, why who and what is being requested as well as the user’s location and location type to service requests. The network is also able to make use of learned intelligence gathered from these additional resources both at the device and in the network.

The DC network will provide communications connection using both fixed and wireless bearers where available and will enable interconnection with internet, cloud and new content distribution networks. 5G has a main requirement of highly flexible, ultra-low latency and ultra-high bandwidth virtualised infrastructure in order to deliver end-to-end services.

Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies are the key enablers to federate heterogeneous experimental facilities and to integrate both network and cloud resources to offer advanced end-to-end 5G services upon multi-domain heterogeneous networks and distributed centres (DC). SDN has emerged as the most promising candidate to improve network programmability and dynamic adjustment of the network resources. SDN is defined as a control framework that supports the programmability of network functions and protocols by decoupling the data plane and the control plane, which are currently integrated vertically in most network equipment.

SDN proposes a logically centralized architecture where the control entity (SDN controller) is responsible for providing an abstraction of network resources through Application Programming Interfaces (API). One of the main benefits of this architecture resides on the ability to perform control and management tasks of different wireless and wired network forwarding technologies (e.g., packet/flow switching or circuit switching) by means of the same network controller. The OpenFlow protocol is the most commonly deployed protocol for enabling SDN. It offers a logical switch abstraction, mapping high-level instructions of the protocol to hide vendor-specific hardware details, which mitigates inter-operability issues commonly found in multi-vendor deployments. This abstraction enables SDN to perform network virtualization, that is, to slice the physical infrastructure and create multiple co-existing network slices (virtual networks) independent of the underlying wireless or optical technology and network protocols. In a multi-tenant environment, these virtual networks can be independently controlled by their own instance of SDN control plane (e.g., virtual operators).

This project can be investigated by Matlab, NS3 Simulation and hardware setup.

Chelmsford

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Sufian Yousef

Smart cities, 5G Networks

There is a substantial amount of spectrum at very high frequencies, which makes it very attractive, but the engineering challenges are intense. This spectrum offers “huge opportunities and huge challenges,” especially the big challenge of the spectrum’s vulnerability to shadowing.

If there is not a line-of-sight link between the access point and the user device, then the connection basically goes to zero - unless there is a reflection off a very flat wall nearby. Researchers and engineers will have to figure out how to leverage that spectrum while providing the consistent high quality of experience, adding that performance over distance will be important as well. Researchers expect systems that can harness such spectrum over a meter or two of distance from the base station will start to appear soon, such as WiGig, but it will be important to design systems that can utilize the high-frequency spectrum at a distance of, say, 100 metres.

There is a high possibility to allocate 24.75-27.5GHz, 66-71GHz and 81-86GHz to 5G in ITU. There is currently a fierce competition of 5G among telecom powers. 5G is urged to fulfil enhanced needs of high data rata, easy usage and low latency. There are three methods to enlarge the capacity of 5G: higher spectrum efficiency, denser coverage and more spectrum resource. The spectrum efficiency of 4G is already very high. Although it can be developed, it can’t meet the growing requirement of the service. More base stations are on the way, they can increase the capacity of the system, but they’re not enough to meet the requirements of wide band and electromagnetic compatibility. As a result, it is urgent to allocate more frequencies for 5G. Frequency resource is essential for 5G deployment. It needs much more frequencies for large scale deployment of 5G. There is little frequency resource that can be allocated to 5G below 6GHz. But there are plentiful frequencies in the millimetre wave band. Millimetre wave band starts from 30GHz, but under some conditions, the band above 10GHz is also called millimetre wave band. For a long time, the characteristic of millimetre wave band is regarded as not suitable for mobile service (MS) because of its high transmitting loss and limitable coverage. Atmospheric attenuation, rain attenuation, tree leaves attenuation, and building penetration loss, contributes to the transmitting loss. But multiple antennas and beam forming can spread the distance of coverage in the Line Of Sight (LOS) in millimetre wave band. Degrading diffraction compensating by strong reflection with beam forming can fulfil the connection out of sight. This means that the low band remains the main operating band in 5G, meanwhile, the millimetre wave band can supplement the coverage with high capacity and high data rate in dense outdoor and indoor scenarios.

Chelmsford

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Shabnam Sadeghi Esfahlani

Dr Muhammad Naguib Bin Ahmad Nazri

Dr Shahzad Gishkori

AI, Robotics and Automation. Impact themes: Health, Performance, and Wellbeing Sustainable Futures.

Research Topic: Empathetic Robotics: designing and testing human-centric assistive robots for healthcare applications.

Rationale: The realm of healthcare presents a myriad of challenges and opportunities for robotics. As patient care demands increase, the potential for robots to play a vital role in addressing care needs, especially in sensitive tasks, becomes evident. However, the sheer mechanical and operational nature of robots can make their integration into healthcare settings challenging. The Assistive Feeding Robot experience underlines the necessity of not only functional but also empathetic robotic aids. This research aims to bridge the gap, creating robots that don't just 'work' but 'care', ensuring better acceptance, improved patient experience, and more effective healthcare delivery.

1. To design a framework for empathetic robot behavior in healthcare settings, ensuring sensitive and compassionate human-robot interaction.
2. Analyze patients' emotional and psychological responses to robotic aids, focusing on their needs, fears, and expectations.
3. Develop and prototype design modifications for the Assistive Feeding Robot, incorporating features that promote empathetic interaction.
4. Test and evaluate the updated Assistive Feeding Robot in real-world healthcare settings, collecting quantitative and qualitative data on its efficacy and acceptance.
5. Extend findings to propose guidelines for the development of future human-centric assistive robots across various healthcare applications.

1. Literature Review: Examine existing literature on human-robot interaction in healthcare, focusing on patients' perceptions, experiences, and expectations.
2. Framework Development: Establish a set of principles and guidelines for empathetic robot behavior based on the literature review and expert consultations.
3. Design Iteration: Apply the framework to design and prototype modifications for the Assistive Feeding Robot.
4. Field Testing: Deploy the modified robot in healthcare settings, observing its interactions with patients and collecting feedback.
5. Data Collection and Analysis: Use a combination of surveys, interviews, and observation notes to gather insights. Employ statistical and thematic analysis to process the collected data.
6. Guideline Formulation: Based on research findings, draft a comprehensive guideline for designing empathetic robotic aids in healthcare.

1. An enhanced Assistive Feeding Robot with improved features for empathetic human-robot interaction.
2. A robust framework for empathetic robot behavior in healthcare environments.
3. Comprehensive guidelines for the development and design of human-centric assistive robots in healthcare settings.
4. Insights into patients' perspectives regarding robotic aids, informing future robot design and deployment strategies.

Contribution to the Field: This research seeks to set a new standard for robotic aids in healthcare. By emphasizing empathy, the study aims to ensure that technology truly aligns with human needs, fostering genuine improvements in healthcare outcomes and experiences. The guidelines produced could serve as a beacon for future robotic developments in sensitive environments.

Chelmsford

This project is self-funded with an aim to complete in 3 years.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Jennifer Martay

Dr Shabnam Sadeghi-Esfahlani

Alireza Sanaei

Dr Stephen Hughes (HEMS)

Healthcare, Telemedicine, Quality of life

The lockdown due to Covid-19 has highlighted the importance of telemedicine: being able to collect and analyse data with the patent and doctor at different locations. The NHS will continue telemedicine after lockdown ends, particularly for regular monitoring of long-term patients, due to telemedicine being “convenient, accessible, and cost-effective” (1).

The ability to remotely collect and analyse data is also beneficial to research. For example, longitudinal studies (participants required to regularly come into the lab for repeated measurements) often had low participant retention levels. These retention levels can be improved by allowing participants to collect the data at home in their own time.

In this PhD, you will develop methods of using everyday devices to collect biomechanics data, which will then be sent for remote, automatic analysis (you will also create these analysis methods). Developing and validating these data collection and analysis methods will be major objectives of this PhD. A third major objective will be then applying your methods to conduct a longitudinal study into an aspect of healthcare.

(1) Technology Enabled Care Services. (n.d.). Available at https://www.england.nhs.uk/tecs/ [Accessed 15 March 2021].

Chelmsford

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Sufian Yousef

Smart Cities

The smart city is an alluring image of the future, where traffic lights, smart meters for utilities, public transport, smart street lights, traffic tracking sensors in roads, collected data on pollution and health, earthquake early warning and flood control in some cases are expected sources of data management and control through wired and wireless networks.

The software and hardware technologies are allowing exponentially rising numbers of smart devices to be connected to the Internet for the sake of smart city applications and services. The collected data from devices, sensors, smart units will be at different formats and quality requirements. This can be a source of hindrance for an effective use of the collected data. The scalability of the data collection and analysis methods that needs higher level of abstraction can be a challenge. Semantic Web technologies can provide reasoning about security, information and knowledge extraction and interoperability.

Smart Cities process large amounts of data streams which raise serious privacy and security concerns for everybody involved. Many attempts are needed to be made to ensure the security of the people’s data. There are issues that categorise data, for example, data reliability, data ownership and service provider trustworthiness. Some researchers have warned that smart cities could be more vulnerable to hacking than the smartphones of today.

The fact that most of the data will be transmitted wirelessly poses a real exposure to all sources of security attacks. This means that a very reliable and strong encryption method that can outperform the current encryption techniques is required. At our University in the Telecommunications Engineering Research Group a new encryption method that uses parallel computing has been designed and tested where the complexity is very high and the resulted delay in the encryption process is negligible. This method is named “Anglia 1”.

In this proposal, the research in advanced computing will consider the security of cloud computing by deployment of data offloading on a middleware that will be supporting a mobile cloudlet which is a medium between the core server and the mobile device. The aim is to ensure cloud computing end-to-end secure and privacy features for trustable data acquisition, transmission, processing and legitimate service provisioning.

This research on the privacy and security of data will be part of designing suitable smart network infrastructure that can have their own security protocols which are able to work harmoniously with the proposed confidentiality and authentication techniques. Gateways play good role in connecting devices to the network. However, an interfacing between gateways and IP has to be stable in routing and resource allocation management. This means that it is essential to convert the current IP protocol to hoist larger address spaces than the current addressing capacity of IPv6 in order to suit large number of connected sensors.

In a nutshell, this proposal is confined to developing a complexity encryption and authentication technique for wireless networks, mobile networks, ad hoc mobile networks and cloud networks by parallel computing encryption, gateways routing of large sensors’ data and the IPv6 address extensions. These proposed research items are related to each other.

Chelmsford

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Dr Shabnam Sadeghi Esfahlani

Dr Matthew A. Timmis

Dr Kjell van Paridon

AI, Robotics and Automation. Impact themes: Health, Performance, and Wellbeing Sustainable Futures.

Research Topic: The Application of Virtual Reality in Training and Safety: an in-depth study into enhancing real-world hazard perception across multiple modalities.

Rationale: The rise of urbanization and the push for sustainable modes of transport have led to an increase in the cycling community. While cycling promotes health and environmental benefits, it brings forth safety challenges, particularly in busy urban landscapes. Traditional safety training methods may not be sufficient to prepare cyclists for the dynamic nature of road environments. Virtual Reality (VR), with its immersive and interactive capabilities, presents an opportunity to revolutionize hazard perception training. By mimicking real-world scenarios in a controlled virtual environment, VR can offer cyclists a comprehensive training experience, thereby potentially reducing on-road incidents.

1. Design and develop the "Hazard Perception Game," a virtual reality application tailored to enhance the hazard perception skills of the cycling community.
2. Investigate the effectiveness of VR as a tool for safety training compared to traditional methods.
3. Assess the translation of hazard perception skills from the virtual realm to real-world cycling scenarios.
4. Analyze user feedback to understand the psychological and cognitive impact of VR-based training on participants.
5. Identify any limitations and propose iterative improvements to the VR training program based on user feedback and performance metrics.

1. VR Development: Collaborate with VR developers to design a realistic urban environment for the "Hazard Perception Game," incorporating common hazards cyclists encounter.
2. Participant Selection and Grouping: Recruit a diverse group of cyclists and divide them into control (traditional training) and experimental (VR training) groups.
3. Training Sessions: Expose the experimental group to a series of VR scenarios, while the control group undergoes traditional hazard perception training.
4. Evaluation: Use a combination of questionnaires, psychological tests, and practical cycling tests in real-world scenarios to assess hazard perception skills.
5. Feedback Collection: Conduct post-training interviews and surveys with participants to gather insights into their experiences, perceptions, and suggestions.
6. Data Analysis: Utilize quantitative and qualitative data analysis techniques to compare the effectiveness of VR versus traditional training and to derive insights from user feedback.

1. An advanced VR application tailored to the needs of the cycling community for enhanced hazard perception training.
2. Evidence-based findings on the efficacy of VR as a training tool for hazard perception in comparison to traditional methods.
3. Insights into the cognitive and psychological impacts of immersive VR training on cyclists.
4. A set of recommendations and guidelines for the integration of VR in safety training programs across various sectors.

Contribution to the Field: This research promises to pioneer the integration of VR in the realm of safety training, particularly for cyclists. The findings could lay the groundwork for broader applications of VR in training across different transportation modalities and industries, driving a paradigm shift in safety training approaches.

Chelmsford

This project is self-funded with the aim to complete in 3 year. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.