All Outputs (18)

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 UK 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 techniques, each with... Read More about Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks.

A comparative evaluation of time-delay, deep learning and echo state neural networks when used as simulated transhumeral prosthesis controllers (2020)
Conference Proceeding
Day, C. R., Chadwick, E. K., & Blana, D. (2020). A comparative evaluation of time-delay, deep learning and echo state neural networks when used as simulated transhumeral prosthesis controllers. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9206772

Transhumeral amputation has a considerable detrimental effect on the amputee’s quality of life and independence. Previous work has already established the potential for exploiting proximal humerus myoelectric and kinematic signals for the effective c... Read More about A comparative evaluation of time-delay, deep learning and echo state neural networks when used as simulated transhumeral prosthesis controllers.

Digital Twin: Enabling Technologies, Challenges and Open Research (2020)
Journal Article
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 108952 - 108971. https://doi.org/10.1109/ACCESS.2020.2998358

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry... Read More about Digital Twin: Enabling Technologies, Challenges and Open Research.

Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images (2019)
Conference Proceeding
Ibraheem Jabbar, S., Day, C. R., & Chadwick, E. K. (2019). Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2019.8858971

Edge detection in Musculoskeletal Ultrasound Imaging readily allows an ultrasound image to be rendered as a binary image. This facilitates automated measurement of geometric parameters, such as muscle thickness, circumference and cross-sectional area... Read More about Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images.

Fault Detection in Steel-Reinforced Concrete Using Echo State Networks (2018)
Conference Proceeding
Wootton, A. J., Day, C. R., & Haycock, P. W. (2018). Fault Detection in Steel-Reinforced Concrete Using Echo State Networks. In 2018 International Joint Conference on Neural Networks (IJCNN) (1-8). https://doi.org/10.1109/IJCNN.2018.8489761

The cost of maintaining and repairing the world's ageing reinforced concrete infrastructure continues to increase, and is expected to cost the United States economy alone $58 billion by 2020. Consequently, the use of non-destructive testing technolog... Read More about Fault Detection in Steel-Reinforced Concrete Using Echo State Networks.

Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species (2017)
Journal Article
Moore, H. E., Butcher, J. B., Day, C. R., & Drijfhout, F. P. (2017). Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species. Forensic Science International, 280, Article 233-244. https://doi.org/10.1016/j.forsciint.2017.10.001

Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult... Read More about Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species.

Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer (2017)
Conference Proceeding
Butcher, J. B., Rutter, A. V., Wootton, A. J., Day, C. R., & Sulé-Suso, J. (2017). Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer. In Advances in Computational Intelligence Systems (183-190). https://doi.org/10.1007/978-3-319-66939-7_15

Lung cancer is a widespread disease and it is well understood that systematic, non-invasive and early detection of this progressive and life-threatening disorder is of vital importance for patient outcomes. In this work we present a convergence of fa... Read More about Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer.

Structural Health Monitoring of a Footbridge using Echo State Networks and NARMAX (2017)
Journal Article
Wootton, A. J., Butcher, J. B., Kyriacou, T., Day, C. R., & Haycock, P. W. (2017). Structural Health Monitoring of a Footbridge using Echo State Networks and NARMAX. Engineering Applications of Artificial Intelligence, 64, 152-163. https://doi.org/10.1016/j.engappai.2017.05.014

Echo State Networks (ESNs) and a Nonlinear Auto-Regressive Moving Average model with eXogenous inputs (NARMAX) have been applied to multi-sensor time-series data arising from a test footbridge which has been subjected to multiple potentially damaging... Read More about Structural Health Monitoring of a Footbridge using Echo State Networks and NARMAX.

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.

Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images (2016)
Conference Proceeding
Jabbar, S. I., Day, C. R., Heinz, N., & Chadwick, E. K. (2016). Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images. In 2016 International Joint Conference on Neural Networks (IJCNN)

Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging a... Read More about Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images.

An Echo State Network Approach to Structural Health Monitoring (2015)
Conference Proceeding
Wootton, A., Day, C., & Haycock, P. (2015). An Echo State Network Approach to Structural Health Monitoring. In 2015 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2015.7280627

Echo State Networks (ESNs) have been applied to time-series data arising from a structural health monitoring multi-sensor array placed onto a test footbridge which has been subjected to a number of potentially damaging interventions over a three year... Read More about An Echo State Network Approach to Structural Health Monitoring.

Defect detection in reinforced concrete using random neural architectures (2013)
Journal Article
Butcher, J., Day, C., Austin, J., Haycock, P., Verstraeten, D., & Schrauwen, B. (2014). Defect detection in reinforced concrete using random neural architectures. Computer-Aided Civil and Infrastructure Engineering, 29(3), 191-207. https://doi.org/10.1111/mice.12039

Detecting defects within reinforced concrete is vital to the safety and durability of our built infrastructure upon which we heavily rely. In this work a non-invasive technique, ElectroMagnetic Anomaly Detection (EMAD), is used which provides informa... Read More about Defect detection in reinforced concrete using random neural architectures.

Comparison method to differentiate between painted objects using polychromatic X-rays (2010)
Journal Article
Austin, J. C., Day, C. R., Kearon, A. T., Evans, D. L., & Haycock, P. W. (2010). Comparison method to differentiate between painted objects using polychromatic X-rays. Insight - Non-Destructive Testing & Condition Monitoring, 52(3), 140-143. https://doi.org/10.1784/insi.2010.52.3.140

X-radiography using the bremsstrahlung of a commercial broad spectrum X-ray source was used to quantify the differences and relationships between complex samples of unknown composition. The samples examined were painted and glazed ceramic mugs. The r... Read More about Comparison method to differentiate between painted objects using polychromatic X-rays.

Single element mapping in radiography (2009)
Journal Article
Austin, J. C., Day, C. R., Kearon, A. T., & Haycock, P. W. (2009). Single element mapping in radiography. X-Ray Spectrometry, 38(6), 492-504. https://doi.org/10.1002/xrs.1204

This article reports on the application of element-specific mapping using the Bremsstrahlung of a commercial broad spectrum X-ray source to map lighter elements (zirconium to tin) and heavier elements (tantalum to bismuth) individually within an imag... Read More about Single element mapping in radiography.

Element-specific determination of X-ray transmission signatures using neural networks (2009)
Journal Article
Day, C. R., Austin, J. C., Butcher, J. B., Haycock, P. W., & Kearon, A. T. (2009). Element-specific determination of X-ray transmission signatures using neural networks. NDT and E International, 42(5), 446-451. https://doi.org/10.1016/j.ndteint.2009.02.005

In this article, we report on the application of neural networks to the problem of making an element-specific determination of unknown metal targets based on the characteristics of their transmitted X-ray signatures. Our method was applied to two gro... Read More about Element-specific determination of X-ray transmission signatures using neural networks.

Using polychromatic X-radiography to examine realistic imitation firearms (2008)
Journal Article
Austin, J., Day, C., Kearon, A., Valussi, S., & Haycock, P. (2008). Using polychromatic X-radiography to examine realistic imitation firearms. Forensic Science International, 191(1-3), 26-31. https://doi.org/10.1016/j.forsciint.2008.08.007

Sections 36–41 of the Violent Crimes Reduction Act (2006), which came into force in England and Wales on 1st October 2007, have placed significant restrictions on the sale and possession of ‘realistic imitation firearms’. This legislation attempts to... Read More about Using polychromatic X-radiography to examine realistic imitation firearms.

Characterisation of metallic powder impregnated pastes using polychromatic X-radiography (2008)
Journal Article
Austin, J. C., Day, C. R., Kearon, A. T., Valussi, S., & Haycock, P. W. (2008). Characterisation of metallic powder impregnated pastes using polychromatic X-radiography. Insight - Non-Destructive Testing & Condition Monitoring, 50(10), 550-553. https://doi.org/10.1784/insi.2008.50.10.550

This article reports on the employment of X-radiography using the bremsstrahlung of a commercial broad-spectrum X-ray source to quantify the differences and relationships between complex materials of unknown composition. The materials examined were m... Read More about Characterisation of metallic powder impregnated pastes using polychromatic X-radiography.

A coarse-grained spectral signature generator (2007)
Conference Proceeding
Lam, K., Austin, J., & Day, C. (2007). A coarse-grained spectral signature generator. . https://doi.org/10.1117/12.736723

This paper investigates the method for object fingerprinting in the context of element specific x-ray imaging. In particular, the use of spectral descriptors that are illumination invariant and viewpoint independent for pattern identification was exa... Read More about A coarse-grained spectral signature generator.