All Outputs (19)

Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks (2022)
Journal Article

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 new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique (2021)
Journal Article

Improving the clarity and visual quality of Musculoskeletal Ultrasound Images (MUI) can help clinicians to detect diseases more easily and accurately. In this work, we described how to enhance the contrast of MUI locally based on a fuzzy inference sy... Read More about A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique.

Automated measurements of morphological parameters of muscles and tendons (2021)
Journal Article

Capturing accurate representations of musculoskeletal system morphology is a core aspect of musculoskeletal modelling of the upper limb. Measurements of important geometric parameters such as the thickness of muscles and tendons are key descriptors o... Read More about Automated measurements of morphological parameters of muscles and tendons.

A comparative evaluation of time-delay, deep learning and echo state neural networks when used as simulated transhumeral prosthesis controllers (2020)
Conference Proceeding

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

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

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.

Enhancement of Panoramic Musculoskeletal Ultrasound Image Based on Fuzzy Technique (2019)
Conference Proceeding

Panoramic Musculoskeletal Ultrasound Images (PMUI) is a developed version of ultrasound images. However, low contrast is a concrete problem which impact negatively on the interpretation of important details of PMUI. Therefore, in this paper a new aut... Read More about Enhancement of Panoramic Musculoskeletal Ultrasound Image Based on Fuzzy Technique.

Fault Detection in Steel-Reinforced Concrete Using Echo State Networks (2018)
Conference Proceeding

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

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

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

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

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

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.

Age estimation of Calliphora (Diptera: Calliphoridae) larvae using cuticular hydrocarbon analysis and Artificial Neural Networks. (2016)
Journal Article

Cuticular hydrocarbons were extracted daily from the larvae of two closely related blowflies Calliphora vicina and Calliphora vomitoria (Diptera: Calliphoridae). The hydrocarbons were then analysed using Gas Chromatography-Mass Spectrometry (GC-MS),... Read More about Age estimation of Calliphora (Diptera: Calliphoridae) larvae using cuticular hydrocarbon analysis and Artificial Neural Networks..

An Echo State Network Approach to Structural Health Monitoring (2015)
Conference Proceeding

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.

Critical mutation rate has an exponential dependence on population size in haploid and diploid populations (2013)
Journal Article

Understanding the effect of population size on the key parameters of evolution is particularly important for populations nearing extinction. There are evolutionary pressures to evolve sequences that are both fit and robust. At high mutation rates, in... Read More about Critical mutation rate has an exponential dependence on population size in haploid and diploid populations.

Artificial neural network analysis of hydrocarbon profiles for the ageing of Lucilia sericata for post mortem interval estimation (2013)
Journal Article

Defect detection in reinforced concrete using random neural architectures (2013)
Journal Article

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.

Reservoir computing and extreme learning machines for non-linear time-series data analysis (2012)
Journal Article