Centre for the Advanced and Smart Technologies (CAST)
The Centre for Advanced and Smart Technologies (CAST) synergises the current and emerging areas of technology (engineering and computer science) research, learning and teaching and knowledge exchange/transfer activities at the University.
About the Centre for the Advanced and Smart Technologies (CAST)
This involves its industrial and academic partners engaging with issues of advanced technology innovation in areas of Engineering and Computer Science. CAST is an inter/multidisciplinary Centre that provides a university-wide research identity for anyone involved in advanced, smart and emerging technologies towards creating a stronger, impactful and sustainable research and enterprise environment. At the heart of most innovative solution in various disciplines and sectors (such as Industry 4.0, medical equipment) is the enabling technology carefully supported by advances in engineering, computer science and/or other multidisciplinary groups. CAST targets to provide a pre-emptive opportunity for generating impactful and practical research solutions towards supporting University of Northampton’s goal of being a Changemaker in addressing the key societal priorities.
CAST has two main focuses:
- To continue and increase high quality sustainable scientific research in the field of advanced technologies powered by engineering and computer science to support the University’s research ambitions by collating advances in research, teaching and enterprise to stimulate new activities in the field.
- To create strong academic, industrial and social collaborations and partnerships within and outside the University and its local communities towards cutting-edge research in advanced technology and to produce research excellence and outputs of high quality with powerful impact as well as research enhanced opportunities to attract external research income through its objectives.
Themes
CAST brings together inter/multidisciplinary research and knowledge transfer activities of academics in a range of technology areas which include Engineering and Computing Science. Through these two key areas, the Centre’s research is focused on:
- Engineering – Lift Technology and High-Performance Engineering (LTHPE) and Non-Destructive Testing (NDT);
- Computer Science – Artificial Intelligence, future networks, embedded systems & Internet of Things (IoT), augmented reality, virtual reality and sustaining the future of computing through innovative STEAM education.
Centre for the Advanced and Smart Technologies (CAST) Leadership
- Professor Michael Opoku Agyeman – Lead
- Professor Stefan Kaczmarczyk – Deputy
Special Interest Groups Leaders
Special Interest Groups (SIGS) are collections of staff members that work together on a single topic/discipline within the activities covered by the wider Centre for Advanced and Smart Technologies. These SIGs engage with their communities through a broad range of activities building platforms of engagement, hosting events and writing publications. Our centre members are interested in:
- Smart Systems and Internet of Things (IoT) – led by Professor Michael Opoku Agyeman
- Artificial Intelligence (AI) and Data Science – led by Associate Professor Mu Mu
- Non -Destructive Testing (NDT) – led by Dr. Abdeldjalil Bennecer
- Lift Technology and High-Performance Engineering (LTHPE) – led by Professor Stefan Kaczmarczyk
- Telecommunications – led by Dr. Triantafyllos Kanakis
- Technology Enhanced Learning – led by Associate professor Suraj Ajit
- Games, Augment and Virtual Reality – Dr. Anastasios Bakaoukas
Adaptive Interference Tolerant Receivers for Asynchronous Cooperative MIMO Communications
Litchfield, C. & Kanakis, T., 26 Mar 2022.
A Deep Neural Network-Based Prediction Model for Students’ Academic Performance
Al-Tameemi, G., Xue, J., Ajit, S., Kanakis, T., Hadi, I., Baker, T., Al-Khafajiy, M. & Al-Jumeily, R., 1 Mar 2022. 6 p.
An Ageing-Aware and Temperature Mapping Algorithm For Multi-Level Cache Nodes
Ofori-Attah, E. & Opoku Agyeman, M., 19 Apr 2022, (Accepted/In press) In: IEEE Access. p. 1-11.
Associations between obstructive sleep apnea and cardiac troponin T levels: a meta-analysis
Salari, N., Hosseinian Far, A., Sharafkhaneh, A., Khaledi-Paveh, B., Mohammadi, M., Ghasemi, H., Rasoulpoor, S., Rasoulpoor, S. & Khazaei, H., 14 May 2022, In: Current Psychology.
A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction
Khalsan, M., Machado, L., SALIH AL-SHAMERY, EMAN., Ajit, S., Anthony, K., Mu, M. & Opoku Agyeman, M., 18 Mar 2022, In: IEEE Access.
Convolutional Neural Network based algorithm for Early Warning Proactive System security in Software Defined Networks. Janabi, A., Kanakis, T. & Johnson, M., 31 Jan 2022, In: IEEE Access. 10, p. 14301 – 14310 10 p., Access-2021-41735.