The department’s research aim is to continue developing its research culture, capacity and international reputation. We are committed to engaging in knowledge generation, applicable research and enhanced societal impact activities. Expertise is housed in the research centre and visual computing lab and it is primarily focused around smart data analytics and visualisation, disruptive technologies and smart applications, cyber security, autonomous systems and robotics, telecommunications, computer vision, augmented and virtual reality.
If you would like to find out more about our research, please get in touch.
The research centre has been formed to support the development of an environment of international excellence in the areas of computer and data science with a particular emphasis on smart data analytics, data representation and visualisation, cyber security, interoperable and interconnected distributed applications and technologies and smart infrastructures, AI and autonomous robotics, telecommunications, IoTs and other disruptive services and technologies. The research centre is structured into five high-level research groupings:
Smart data analytics
The Smart Data Analytics Research Group focusses on core and applied aspects of Data Analytics, Data Science and Machine Learning.
Overview
The Smart Data Analytics Research Group aims to promote and shape research relevant to the theory and practice of methods and tools for data collection, enrichment, processing and storage. It focuses on developing application-driven methods, algorithms and protocols that enable efficient collection, transfer and curation of images, videos, text, time series, data streams and other types of unstructured or semi-structured data.
The Group has expertise on a multitude of fields in the broad research areas of Machine Learning, Data Mining, Natural Language Processing, Social Media mining, Optimisation algorithms, Metaheuristics and AI Ethics. The Group’s research activity is inherently interdisciplinary. Application domains of current interest include medicine & healthcare, psychology, software analysis, astronomy, physics and unmanned aerial vehicles (UAVs) for geography.
Research areas:
Real-time machine learning
Resource-efficient machine learning
Classification Imbalance
outlier detection
novelty detection (novel classes in dynamically-changing data)
one-class learning
Big data
Data mining, pattern recognition & semantic analysis
Members of the group are supervising and have supervised many MRes and PhD students on research projects related to the research and application areas mentioned above. Members are always interested in supervising new PhD students.
To discuss potential PhD topics under Smart Data Analytics, please contact Professor Yannis Korkontzelos. For other enquiries related to PhD admissions, please contact Professor Ella Pereira, Director of Research.
Smart systems and applications
Research focus and activities
The focus of our group is on developing novel autonomous and smart systems, capable of collating, communicating, and computing information streams. The new paradigm of hybrid, complex and intelligent systems are expected to provide solutions to many health, industry and automation challenges, thus improving quality of life and wellbeing. However, developing interconnected/distributed systems have become increasingly complex and require expertise in the key enabling technologies such as Internet of Things (IoT), fog, cloud and AI.
Our group members with expertise in the areas of Distributed Systems, Distributed Middleware, Cloud computing, IoT and IoT enabled smart systems, applied AI/ML/Deep Learning, ontologies, semantic web and biometrics share interest in addressing challenges posed by the complexity of delivering highly reliable and dependable technological innovations and smart solutions. We carry out both fundamental and applied research.
With the strong focus on applications for digital health and industry 4.0 and its system based approach, the group compliments and synergise with the work carried out by colleagues from other research groupings within the department, such as smart analytics, computer vision and those from engineering disciplines. We believe that complexity involved in developing and delivering smart applications very often requires interdisciplinary approach to research and problem solving. To support of our interdisciplinary activities, internally, we are working with the Health Research Institute. Externally, we have established collaborations with industry, NHS, academic partners, stakeholders and research users.
Research areas
Fog/Edge/Cloud computing
Distributed systems
Applied AI/Machine Learning
Internet of Things (IoT)
Body/Sensor Networks
Ontology and semantics technologies
Digital health
Healthcare applications
Distributed Application/Systems development
Multimedia Networks
Mobile Applications – Mobile Healthcare
Future Internet – IoT
Industry 4.0
Cyber Physical Systems
Smart Cities
Autonomous transportation systems
Digital Twins
Environmental/climate-change/Disaster management systems
Members
Professor Ella Pereira (Group Leader)
Dr Muhammad Awais
Professor Nik Bessis
Professor Marcello Trovati
Dr Nonso Nnamoko
Dr Sarah McHale
Dr Peter Matthew
Dr Huazhong Zhang
Dr Mohzin Raza
Dr Xutao Deng
Wilson Costa (PhD student, Graduate Teaching Assistant)
Our research group has attracted close to £1M of external funding from several sources, such as InnovateUK, EPSRC and the Department for Education.
KTPs with Country Range Group LTD and BEC Integration LTD
MRes/PhD research
The group members have extended experience of supervising MRes and PhD studies and welcomes proposals on the topics related to the groups research activities and interests, above.
For any PhD or other enquiries please contact Professor Ella Pereira, Director of Research.
Cyber security
The Cyber Security Research group is committed to carry out internationally recognized, high-quality research on security and privacy in computer systems and networks. We collaborate with academic and industrial partners in the UK and worldwide in different research and knowledge transfer projects. The research activities of the group focus on designing and evaluating secure and privacy friendly systems, networks, and applications, by proposing novel approaches in these areas. Several of our publications appeared in reputed journals and conferences. Along with the research activities, we teach several modules in undergraduate and postgraduate security programmes at Edge Hill University.
Research areas
Blockchain based security and privacy enhancing technologies and applications
The concept of enterprise and permissioned blockchains opens many opportunities to design and develop decentralised applications to provide higher degree of data control and transparency for the users. Another advantage is that smart contracts can be used to automate security verification and notification in enterprise environments. We investigate the application of this decentralised concept, and design new applications such as distributed collaborative intrusion detection systems (IDSs), and decentralised data protection mandate enforcement engines.
Security and privacy by design
System design goes through several stages before it is finally followed by implementation and production. It is crucial that any flaw in the design is recognised and fixed at the early stage to avoid production and implementation delay and additional cost. Our research covers different areas of system design, including threat and risk assessment, user-centred design and fully automated or computer aided security and privacy verification methods.
Security and privacy problems of Internet of Things and smart services and applications.
Internet of Things (IoT) applications help making our life more convenient and safer, and enable smart city, smart homes, or smart health-care initiatives worldwide. Potential design flaws in these applications and services pose high security and privacy risks to the system users. To prevent this, we design and evaluate secure and privacy friendly approaches for IoT applications and developing testbeds for performance evaluations.
Security and privacy problems of biometrics systems and applications.
Biometrics systems are broadly used for authentication purposes. However, applying them correctly as part of a complex security system is not trivial. Incorrect application or installation can pose security and privacy risks and violate data protection mandates. We focus on designing and developing approaches and algorithms to improve the effectiveness of biometrics authentication, and model different types of biometric systems to evaluate their security and privacy properties based on mathematical methods.
Members
Dr Vinh-Thong Ta (Group Leader)
Dr Mark Liptrott
Dr Peter Matthew
Dr Muhammad Usman
Dr Sarah McHale
Dr Nonso Nnamoko
Professor Marcello Trovati
Current projects
Our research group has attracted approximately £250K of external funding from several sources, such as Higher Education Academy and the Department of Business, Energy and Industrial Strategy.
Cyber Security Student Club
The Cyber Security Student Club gathers talented students who expressed strong interest in the field of cyber security. Core members meet to expand their knowledge by discussing and solving practical exercises and problems in system and network security, to socialize, and in general, to have fun with others of similar interest. Members also prepare for Capture the Flag (CTF) competitions and creating practical ethical hacking exercises.
If you are interested in joining the club, please contact Dr Vinh-Thong Ta. Note: The club is only open for students of the Computer Science Department at Edge Hill university.
MRes/PhD research
Members of the group are supervising and have supervised many MRes and PhD students on research projects related to the research and application areas, above. Members are always interested in supervising new PhD students.
To discuss potential PhD topics under Cyber Security, please contact Dr Vinh-Thong Ta. For other enquiries related to PhD admissions, please contact Prof Ella Pereira, Director of Research.
Robotics and autonomous systems
The main goal of the Robotics and Autonomous Systems (RAS) research group is to provide innovative solutions and develop novel AI-driven robotics systems to address real-world challenges in a broad range of contexts. We are a newly established interdisciplinary group, looking for synergies and collaborations with national and international industrial and academic partners to transfer knowledge and technology and co-produce impactful research. Our research activities are supported by a suite of state-of-the-art technologies and facilities for robot design and construction.
Research areas
Robot mechatronic design
Bio-inspired robots
Unmanned aerial vehicles
Underwater robotics
Machine vision in robotics
Soft robotics
Swarm Robots
Robots for inspection tasks and hazardous environments
Industrial robotic automation
Members
Dr Ammar Al Mhdawi (Group Leader)
Dr Swagat Kumar(Group co-leader)
Dr Ardhendu Behera
Professor Nik Bessis
Dr Nonso Nnamoko
Current projects
Members of our research group have attracted approximately £250K of external funding from several sources, such as UKIERI and RCUK.
Automation, robotics, and mechatronics are important technologies that underlie a wide range of applications in manufacturing and automation, as well as autonomous vehicles and robots for surgery. Currently, we are seeking research PhD students to work on novel robotics projects. To discuss potential MRes and PhD topics under Robotics and Autonomous Systems, please contact Dr Ammar Al Mhdawi. For other enquiries related to PhD admissions, please contact Professor Ella Pereira, Director of Research.
Communications systems
Communication Systems Research Group focuses on interdisciplinary fundamental and practical research in communication systems and networks. We aim to research and engineer the underlying technology required to enable the effective functionality of next generation of communication systems (6G). The group supports our partners in the design, development and testing of vertical applications and services in multiple user community-driven contexts, including smart healthcare, intelligent transport, industry 4.0 and smart cities.
Research areas
Wireless Systems and Services
Internet of Things
AI in Communication Network
Security in Communication Networks
Satellite Communication and Applications
Satellite and Mobile Networks
Future Disruptive Technologies and Networks
Members
Dr Hassan Malik (Group Leader)
Professor Ray Sheriff (Group Co-leader)
Dr Mohsin Raza
Dr Muhammad Awais
Dr Umar Khan
Brian Akwirry (PhD student)
MRes/PhD research
The group members have extended experience of supervising MRes and PhD studies and welcome proposals on the topics related to the groups research activities and interests. The topics include, but are not limited to:
AI in wireless Network
5G application in Healthcare, Intelligent Transport, Industry 4.0
6G Networks
Satellite Communication
To discuss potential PhD topics, please contact Dr Hassan Malik. For other enquiries related to PhD admissions, please contact Professor Ella Pereira, Director of Research.
Visual Computing Lab
Director: Professor Yonghuai Liu
The lab focuses on the topics in the general areas of 3D computer vision, image processing, pattern recognition, visual data analytics, computer graphics, virtual/augmented reality, human sensing and human-robot social interactions. The research is supported by the latest technologies such as the Computer Augmented Virtual Environment (CAVE) and a range of state-of-the-art devices for data acquisition, output, visualisation, and full immersive experiences including: Tobii Eye X, Vive, Z-Space, Kinects, Hype Box, Emotive, Enobio BCI, Nao Robots, 3D Scanner and printer. It has the following goals:
connecting with stakeholders to impact society
supporting students to test the latest visual computing techniques and acquire research-lead employability skills
empowering researchers to solve theoretical and real-world problems in visual computing and utilising virtual and augmented reality technology.
It aims to:
Visualise the signal, image, shape and video data and their operations with the modern CAVE and other devices.
enhance the visual data for high contrast and removal of unwanted factors,
analyse and convert the visual data into semantically meaningful components, models, measurements, relevant knowledge and/or interesting responses and behaviours
explore data visualisation and AR/VR simulation and applications in the real world such as medical imaging, training, computer aided instruction, CAD, systems automation, additive manufacturing, plant science, robotics, autonomous vehicles, intelligent surveillance, remote sensing, industrial quality assurance, and others.
It currently focuses on, but is not limited to, the following areas:
3D computer vision
3D shape modelling, analysis and measurements
camera calibration
3D model search and query
image processing
Image noise removal, enhancement and super-resolution.
Image analysis, decomposition, segmentation, classification and recognition.
Pattern recognition
Object detection, classification, recognition and tracking
Human behaviour representation, responses and prediction
Human activity/pose representation, estimation, classification, and recognition
CCTV surveillance and management
Lab members
Dr Ardhendu Behera
Professor Yonghuai Liu
Dr Peter Matthew
Dr Peter Vangorp,
Dr Huaizhong Zhang
Data Science STEM Research Centre
Our strategic target for growth in Data Analytics has enabled the department of Computer Science to found and lead an interdisciplinary STEMM research centre, which draws expertise from multiple subjects across the University.
Developing simulation software in order to improve technology enhanced learning of modern computer architecture
Teaching and learning of computer architecture has been enhanced using highly interactive simulations with carefully constructed visualisations and animations. Computer scientists need to understand and observe how different parts of a modern computer system’s architecture and organization fit together, interact and support each other. Unique educational simulation software has been designed, developed and evaluated with these requirements in mind. Since the software and teaching materials have been made public, numerous universities worldwide adopted it in their courses with claimed positive impact on student engagement, course popularity, grades, speed of delivery of curriculum, attendance and peer recognition of best practice.
Improving quality assurance of a large software model through relative debugging
As a result of collaborative commissioned research, the lead developers of a major atmospheric research and operational weather forecasting model have changed their approach to quality assuring model source code. Drawing directly on the research findings, the lead developer has taken the decision to adopt a new approach to the correction of inconsistencies and inefficiencies in source code and to alter the software build procedure to be followed by a large model development community. An additional impact, in the form of improved business competitiveness, is felt by a British software and consultancy company, which has been able to enhance a key tool used in their quality assurance and platform migration work with a global client base.
Facilities
Academics, post-doctoral and PhD researchers have access to cutting-edge facilities including state-of-the-art software (SAS, Matlab, Unity, and others) and hardware (high-end servers, robots, transparent touchscreens, Hololens, VIVEs and the first 4K CAVE installed in a UK academic institution).
The department has also an established community of Postgraduate Researchers undertaking research in different areas of computer science. We operate under a university research governance and ethics framework.