User profiles for Roderick Murray-Smith
Roderick Murray-SmithProfessor of Computing Science, University of Glasgow, School of Computing Science Verified email at glasgow.ac.uk Cited by 12724 |
[BOOK][B] Multiple model approaches to nonlinear modelling and control
R Murray-Smith, T Johansen - 2020 - books.google.com
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 …
ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide …
[HTML][HTML] Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges
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 …
brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity …
Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting
…, JQ Candela, R Murray-Smith - Advances in neural …, 2002 - proceedings.neurips.cc
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 …
Gaussian process model.-step ahead forecasting of a discrete-time non-linear …
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
…, R Shorten, R Murray-Smith - IEEE Transactions on …, 2000 - ieeexplore.ieee.org
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 …
they are identified from experimental data. It is shown that there exists a close relationship …
Derivative observations in Gaussian process models of dynamic systems
E Solak, R Murray-Smith… - Advances in neural …, 2002 - proceedings.neurips.cc
Gaussian processes provide an approach to nonparametric modelling which allows a
straightforward combination of function and derivative observations in an empirical model. This is …
straightforward combination of function and derivative observations in an empirical model. This is …
Gaussian process model based predictive control
J Kocijan, R Murray-Smith… - Proceedings of the …, 2004 - ieeexplore.ieee.org
Gaussian process models provide a probabilistic non-parametric modelling approach for
black-box identification of non-linear dynamic systems. The Gaussian processes can highlight …
black-box identification of non-linear dynamic systems. The Gaussian processes can highlight …
A dose of reality: Overcoming usability challenges in vr head-mounted displays
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 …
displays (HMDs) in a survey of 108 VR HMD users. Users reported significant issues in …
Dynamic systems identification with Gaussian processes
…, A Girard, B Banko, R Murray-Smith - … and Computer Modelling …, 2005 - Taylor & Francis
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 …
process (GP) prior model. This model is an example of the use of a probabilistic non-parametric …
[HTML][HTML] Deep learning for real-time single-pixel video
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 …
enabling the use of state-of-the-art detector technologies and providing a potentially low-cost …
The fully convolutional transformer for medical image segmentation
A Tragakis, C Kaul, R Murray-Smith… - Proceedings of the …, 2023 - openaccess.thecvf.com
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 …
modalities. Challenges posed by the fine-grained nature of medical image analysis mean that …