Vision and Reading Difficulties Part 1: Specific learning difficulties and vision

This article is the first in a series of five about vision and reading difficulties, and provides an introduction and an overview of learning disabilities and specific learning difficulties. It outlines the role of the optometrist in helping people with such problems; it describes the symptoms that optometrists should look for and it provides an introduction of the evidence-based approach. The second article in this series will cover the optometric and orthoptic correlates of reading difficulties. Articles three and four will describe the use of coloured filters, including background, techniques, evidence, and mechanism. The final article will draw together the themes in the series of articles and discuss the clinical protocol and the role of the eye care practitioner in managing visual factors associated with reading difficulties

Componential coding in the condition monitoring of electrical machines Part 2: application to a conventional machine and a novel machine

This paper (Part 2) presents the practical application of componential coding, the principles of which were described in the accompanying Part 1 paper. Four major issues are addressed, including optimization of the neural network, assessment of the anomaly detection results, development of diagnostic approaches (based on the reconstruction error) and also benchmarking of componential coding with other techniques (including waveform measures, Fourier-based signal reconstruction and principal component analysis). This is achieved by applying componential coding to the data monitored from both a conventional induction motor and from a novel transverse flux motor. The results reveal that machine condition monitoring using componential coding is not only capable of detecting and then diagnosing anomalies but it also outperforms other conventional techniques in that it is able to separate very small and localized anomalies

Image Captioning and Classification of Dangerous Situations

Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 % and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted