This project aims to develop an athlete monitoring system.

This project aims to develop a new fuzzy modelling approach using the curvature values. This PhD project is co-funded by Yobo Technology Ltd., China and the university. UK Principle Investigator: Dr. Longzhi Yang, and Thailand Principle Investigator: Dr. Tossapon Boongoen

PI: Prof. Lynne Coventry, I'm one of the Co-I
The Academic Centres of Excellence in Cyber Security Research (ACEs-CSR) scheme is sponsored through the Department for Digital, Culture, Media and Sport. It is one of a number of initiatives outlined in the UK Government's £1.9 billion National Cyber Security Strategy 2016-2021, 'Protecting and Promoting the UK in a Digital World', which outlines how Government is working with academia and industry to make the UK more resilient to cyber attacks.
After successfully meeting the scheme’s tough requirements, Northumbria has now been recognised as an ACE-CSR. The Northumbria Cyber Security Research Group leads the University’s research across this area. This multi-disciplinary group combines technical research on biometric encryption, wireless sensor networks, web security protocols, and image recognition, with human-centred work on usable security, privacy, trust and behaviour change. The group’s work identifies both the virtual and physical risks associated with connected smart cities and complements other work ongoing at the University relating to the digital living space, which explores the intersection of people, place and technology in the digital and urban environment.

Collaborated with a local company in developing a digital solution for for effluent treatment recommendation

Collaborated with a local company in developing a digital solution for running product recommendation

This project aims to develop an open cyber security research and practice platform. Prinicple supervisor: Dr. Longzhi Yang

Smart meters have been partially deployed to UK homes to support consumers in managing their energy and expenditure intelligently, to help implement the CO2 emission reduction goal. However, the information from smart meters is still not enough for efficient intelligent home appliance control, as the residents’ living behaviour patterns, as well as the surrounding environments also have great influences on the control decisions for home appliances. Despite of the advance in machine learning and expert system technologies, the extraction of decision patterns based on residents’ behaviours and the surrounding environments is still of great challenge as every resident has their own living style and each dwelling has its own unique characteristics.

Take intelligent home heating systems as an example. In order to accurately and economically control the home heating systems such that a property can always be properly pre-heated when the residents getting home whilst no energy is unnecessarily wasted on heating empty dwellings, the control system should be developed based on a well understating of the information from smart meters (such as electricity supply/demand pattern) and the residents’ living styles, and be timely updated when the supply/demand pattern or the living styles change. However, it is unrealistic for system manufacturer to collect highly personalised data and to consequently produce customised heating controllers for each household, such that the heating controller can make accurate decisions based on the residents’ unique living style in addition to the current electricity supply/demand pattern and surrounding environment.

This research proposes an algorithm which would enable intelligent home heating system to efficiently learn the residents’ behaviour patterns associated with the information from smart maters in a dynamic and adaptive manner. Consequently, mass-production of intelligent heating controllers is allowed. In particular, these devices are initialised by the most general and common rules which are suitable for the majority of people. Then the intelligent controller will learn more customised details in real time after it is deployed. Also, once the electricity supply/demand pattern and the living style or the users of the controller have changed, the controller will be able to catch the changes up in real time. It has been reported that around 40% residential energy use is consumed to deliver ‘unused’ energy services. The proposed approach will potentially provide significant help in reducing energy waste and CO2 emission but in the same time improving the quality of life.

PI: Dr. Longzhi Yang

Simpson Group is one of the UK’s leading manufactures of posters and 3D displays for promotions. The company has a number of customers we are all very familiar with, such as Next, Morrison's, and Danbenhams. A promotion usually involves multiple display objects, and a display object is typically produced in a sequence of operations. In addition, an operation can be alternatively conducted by different capable machines with different costs. This project developed a software prototype which intelligently schedules the manufacturing production line use CI techniques and thus improves production efficiency.

This project aims to teach robots to write in an analogy to how humban being writes. PI: Dr. Fei Chao at Xiamen University, China

Interaction based on motion analysis. PI: Dr. Hubert P.H. Shum at Nothumbria University

MEXT Top Global University Project Scholarship: £5,500, Contributing Researcher (PI: Prof. Shigeo Morishima). Received from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)To support 1 Japanese PhD (Waseda University) working in the UK for 6 months .