A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study

In the wear and tear process of synovial joints, wear particles generated and released from articular cartilage within the joints have surface topography and mechanical property which can be used to reveal wear conditions. Three-dimensional (3D) particle images acquired using laser scanning confocal microscopy (LSCM) contain appropriate surface information for quantitatively characterizing the surface morphology and changes to seek a further understanding of the wear process and wear features. This paper presents a new attempt on the 3D numerical characterisation of wear particle surfaces using the field and feature parameter sets which are defined in ISO/FDIS 25178-2. Based on the innovative pattern recognition capability, the feature parameters are, for the first time, employed for quantitative analysis of wear debris surface textures. Through performing parameter classification, ANOVA analysis and correlation analysis, typical changing trends of the surface transformation of the wear particles along with the severity of wear conditions and osteoarthritis (OA) have been observed. Moreover, the feature parameters have shown a significant sensitivity with the wear particle surfaces texture evolution under OA development. A correlation analysis of the numerical analysis results of cartilage surface texture variations and that of their wear particles has been conducted in this study. Key surface descriptors have been determined. Further research is needed to verify the above outcomes using clinic samples

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

Ship Wake Detection in SAR Images via Sparse Regularization

In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page

The investigation of the characterisation of flotation froths and design of a machine vision system for monitoring the operation of a flotation cell ore concentration

Electrical and Electronic EngineeringThis dissertation investigates the application of digital image processing techniques in the development of a machine vision system that is capable of characterising the froth structures prevalent on the surface of industrial flotation cells. At present, there is no instrument available that has the ability to measure the size and shape of the bubbles that constitute the surface froth. For this reason, research into a vision based system for surface froth characterisation has been undertaken. Being able to measure bubble size and shape would have far reaching consequences, not only in enhancing the understanding of the flotation process but also in the control and optimization of flotation cells

Automated methods for tuberculosis detection/diagnosis : a literature review

Funding: Welcome Trust Institutional Strategic Support fund of the University of St Andrews, grant code 204821/Z/16/Z.Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.Publisher PDFPeer reviewe