This work presents approaches to modelling and control problems arising from conditions of
ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide …

In recent years, new research has brought the field of electroencephalogram (EEG)-based
brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity …

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric
Gaussian process model.-step ahead forecasting of a discrete-time non-linear …

Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when
they are identified from experimental data. It is shown that there exists a close relationship …

Gaussian processes provide an approach to nonparametric modelling which allows a
straightforward combination of function and derivative observations in an empirical model. This is …

Gaussian process models provide a probabilistic non-parametric modelling approach for
black-box identification of non-linear dynamic systems. The Gaussian processes can highlight …

We identify usability challenges facing consumers adopting Virtual Reality (VR) head-mounted
displays (HMDs) in a survey of 108 VR HMD users. Users reported significant issues in …

This paper describes the identification of nonlinear dynamic systems with a Gaussian
process (GP) prior model. This model is an example of the use of a probabilistic non-parametric …

Single-pixel cameras capture images without the requirement for a multi-pixel sensor,
enabling the use of state-of-the-art detector technologies and providing a potentially low-cost …

We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean that …