Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks (2022)
Journal Article
Wootton, A. J., Day, C., & Haycock, P. W. (2022). Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks. Structural Health Monitoring, 21(6), 2910-2921. https://doi.org/10.1177/14759217221080718

The degradation of roads is an expensive problem: in the United Kingdom alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available technique... Read More about Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks.

Optimizing Echo State Networks for Static Pattern Recognition (2017)
Journal Article
Wooton, A. J., Taylor, S. L., Day, C., & Haycock, P. W. (2017). Optimizing Echo State Networks for Static Pattern Recognition. Cognitive Computation, 391-399. https://doi.org/10.1007/s12559-017-9468-2

Static pattern recognition requires a machine to classify an object on the basis of a combination of attributes and is typically performed using machine learning techniques such as support vector machines and multilayer perceptrons. Unusually, in thi... Read More about Optimizing Echo State Networks for Static Pattern Recognition.