Research on Recognition and Evaluation of Traffic Guide Sign

Traffic guide signs are effective only when they are clearly recognized by drivers. Three experiments were conducted in this study. In the first, the influence factors of guide sign recognition were studied. This study investigated 11 main factors with a convenience sample of drivers from Nanjing city in China. Weights of different influence factors were obtained through analytic hierarchy process (AHP). The results showed that the setting position, occlusion degree, and character size of guide sign had the most significant influence on the guide sign recognition, while other factors were less important. In the second stage, an evaluation model of guide sign recognition was developed based on weights of different factors. Four equations were presented to calculate the comprehensive score of guide sign, and the level of recognition was divided into five grades according to the comprehensive score. At last, a typical case in Nanjing was studied to verify the rationality and reliability of the evaluation model. Results from the real application indicate that the method had good applicability and can be used by traffic engineers

Traffic sign detection based on simple XOR and discriminative features

Traffic Sign Detection (TSD) is an important application in computer vision. It plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. In this paper, in addition to detecting Traffic Signs (TSs), the proposed technique also recognizes the shape of the TS. The proposed technique consist of two stages. The first stage is an image segmentation technique that is based on Learning Vector Quantization (LVQ), which divides the image into six different color regions. The second stage is based on discriminative features (area, color, and aspect ratio) and the exclusive OR logical operator (XOR). The output is the location and shape of the TS. The proposed technique is applied on the German Traffic Sign Detection Benchmark (GTSDB), and achieves overall detection and shape matching of around 97% and 100% respectively. The testing speed is around 0.8 seconds per image on a mainstream PC, and the technique is coded using the Matlab toolbox

SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework

Wrist-bands such as smartwatches have become an unobtrusive interface for collecting physiological and contextual data from users. Smartwatches are being used for smart healthcare, telecare, and wellness monitoring. In this paper, we used data collected from the AnEAR framework leveraging smartwatches to gather and store physiological data from patients in naturalistic settings. This data included temperature, galvanic skin response (GSR), acceleration, and heart rate (HR). In particular, we focused on HR and acceleration, as these two modalities are often correlated. Since the data was unlabeled we relied on unsupervised learning for multi-modal signal analysis. We propose using k-means clustering, GMM clustering, and Self-Organizing maps based on Neural Networks for group the multi-modal data into homogeneous clusters. This strategy helped in discovering latent structures in our data

Parrot poo on the windscreen: Metaphor in academic skills learning

Metaphor can be a powerful tool in communicating the purposes and processes involved in learning as the use of metaphor enables new and complex ideas to be presented through more familiar forms. A considerable range of literature recognises the role of metaphor in learning and teaching both as an analytical tool and as a medium for conveying meaning. However, little has been written about the use of metaphor in the context of academic skills learning. This research was prompted by the authors’ personal experience in using metaphor and students’ positive feedback. It explores the use of metaphor both among academic skills advisers and in academic skills texts. It was found that it was not uncommon for academic skills practitioners to use metaphor in learning and teaching situations and the research revealed a rich assortment of metaphors. Similarly texts in this field use metaphors, albeit more tentatively and sparingly. Empirical research into student understanding and perceived benefits of the use of metaphors would further contribute to this initial discussion

Mental maps and the use of sensory information by blind and partially sighted people

This article aims to fill an important gap in the literature by reporting on blind and partially sighted people's use of spatial representations (mental maps) from their perspective and when travelling on real routes. The results presented here were obtained from semi-structured interviews with 100 blind and partially sighted people in five different countries. They are intended to answer three questions about the representation of space by blind and partially sighted people, how these representations are used to support travel, and the implications for the design of travel aids and orientation and mobility training. They show that blind and partially sighted people do have spatial representations and that a number of them explicitly use the term mental map. This article discusses the variety of approaches to spatial representations, including the sensory modalities used, the use of global or local representations, and the applications to support travel. The conclusions summarize the answers to the three questions and include a two-level preliminary classification of the spatial representations of blind and partially sighted people

Unsupervised machine learning clustering and data exploration of radio-astronomical images

In this thesis, I demonstrate a novel and efficient unsupervised clustering and data exploration method with the combination of a Self-Organising Map (SOM) and a Convolutional Autoencoder, applied to radio-astronomical images from the Radio Galaxy Zoo (RGZ) dataset. The rapidly increasing volume and complexity of radio-astronomical data have ushered in a new era of big-data astronomy which has increased the demand for Machine Learning (ML) solutions. In this era, the sheer amount of image data produced with modern instruments and has resulted in a significant data deluge. Furthermore, the morphologies of objects captured in these radio-astronomical images are highly complex and challenging to classify conclusively due to their intricate and indiscrete nature. Additionally, major radio-astronomical discoveries are unplanned and found in the unexpected, making unsupervised ML highly desirable by operating with few assumptions and without labelled training data. In this thesis, I developed a novel unsupervised ML approach as a practical solution to these astronomy challenges. Using this system, I demonstrated the use of convolutional autoencoders and SOM’s as a dimensionality reduction method to delineate the complexity and volume of astronomical data. My optimised system shows that the coupling of these methods is a powerful method of data exploration and unsupervised clustering of radio-astronomical images. The results of this thesis show this approach is capable of accurately separating features by complexity on a SOM manifold and unified distance matrix with neighbourhood similarity and hierarchical clustering of the mapped astronomical features. This method provides an effective means to explore the high-level topological relationships of image features and morphology in large datasets automatically with minimal processing time and computational resources. I achieved these capabilities with a new and innovative method of SOM training using the autoencoder compressed latent feature vector representations of radio-astronomical data, rather than raw images. Using this system, I successfully investigated SOM affine transformation invariance and analysed the true nature of rotational effects on this manifold using autoencoder random rotation training augmentations. Throughout this thesis, I present my method as a powerful new approach to data exploration technique and contribution to the field. The speed and effectiveness of this method indicates excellent scalability and holds implications for use on large future surveys, large-scale instruments such as the Square Kilometre Array and in other big-data and complexity analysis applications

Usage of convolutional neural network ensemble for traffic sign recognition

Предлагается для распознавания дорожных знаков использовать ансамбль сверточных нейронных сетей, который является модификацией робастного метода распознавания на основе нейронных сетей глубокого обучения. Данный ансамбль повышает скорость работы робастного метода распознавания, а также позволяет увеличить быстродействие с сохранением высокой точности распознавания за счет удаления из набора данных значений, которые не представляют полезной нагрузки