22 smart-grid positions at Ulster University

AI Technology for Smart Labelling and Root Cause Analysis in a Semiconductor Wafer Manufacturing Process

the successful PhD applicant will work within the Smart Nano Manufacturing Northern Ireland Consortium (https://www.smartnanoni.com ). SmartNanoNI is a £40 million Northern Ireland consortium, collaborating

Towards Trusted Cognitive Intelligence for User-centric Smart Systems

-Physical Systems in Smart Environments, Monograph, ISBN:978-3-030-19407-9, Springer, 2019. M. Garcia-Constantino, A. Konios, C. Nugent, Modelling Activities of Daily Living with Petri nets, 2018 IEEE

Using digital twins to optimise activity recognition within smart environments

Adaptation of Binary Sensors in Smart Environments Through Activity Alignment, IEEE Access, vol. 8, pp. 228804-228817, 2020, doi: 10.1109/ACCESS.2020.3046181. Ng, Wing W.Y., Shichao Xu, Ting Wang, Shuai Zhang

Adaptive Smart Retrofitting for Historical Buildings: Innovative Integration of Sustainable Wall Technologies with Energy AI-based feedback system. ​​

., & Santaniello, D. (2022). A Deep Learning Approach to Protecting Cultural Heritage Buildings Through IoT-Based Systems. 2022 IEEE International Conference on Smart Computing (SMARTCOMP), Helsinki, Finland, pp

​​Utilizing Process Mining and Modelling Techniques for Sensor-Based Patient Activity Recognition within the Smart Home Environment

, Cham. ​​Shuai Zhang, Sally I. McClean, Bryan W. Scotney (2012): Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living in Smart Homes, IEEE Transactions

Advancing Medical Image Analysis through Hexagonal Structured Deep Learning Networks

analysis, including segmentation, classification, and disease detection. To date, research conducted within deep learning for medical image analysis primarily utilises a regular square grid representation

Assessing energy efficiency of buildings – role of remote sensing data and geospatial methods

analysis. Environment and Planning B: Urban Analytics and City Science, 44(2), 227-255. Few, J., McKenna, E., Pullinger, M., Elam, S., Webborn, E., & Oreszczyn, T. (2022). Smart Energy Research Lab: Energy

​​Digital urban planning: reimagining planning processes through the utilisation of generative AI​

of Urban Planning and Smart Cities (IJUPSC), 2(2), 19-33. http://doi.org/10.4018/IJUPSC.2021070102 ​Willis, K.S. and Aurigi, A. (2018) Digital and Smart Cities. Routledge. ​Wilson, A., & Tewdwr-Jones, M

​​AI-Driven Approaches to Enhance Human Activity Recognition​

smart object to detect atomic activities in the smart home, 2019, Pervasive and Mobile Computing, 56, 1, , 10.1016/j.pmcj.2019.03.006. Patterson, T., Khan, N., McClean, S., Nugent, C., Zhang, S., Cleland

​​Generative Modelling for Synthetic Data Generation ​

., Nugent, C., Arias-Fonseca, S., & Jaramillo-Rueda, N. (2022). Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia