Image Classification by Neural Networks for the Quality Control of Watches

A method is presented for the automatic time detection of watches, where the hands are classified by a neural network. In order to reduce the overall cost of data collection, strict limits were imposed on the data collection time. This constraint severely limits the available amount of images, and poses the challenge to solve the hand recognition problem with a minimum amount of training and test data. Two neural network approaches are presented together with their performance results, which show an excellent final recognition rate

Software for Wearable Devices: Challenges and Opportunities

Wearable devices are a new form of mobile computer system that provides exclusive and user-personalized services. Wearable devices bring new issues and challenges to computer science and technology. This paper summarizes the development process and the categories of wearable devices. In addition, we present new key issues arising in aspects of wearable devices, including operating systems, database management system, network communication protocol, application development platform, privacy and security, energy consumption, human-computer interaction, software engineering, and big data.Comment: 6 pages, 1 figure, for Compsac 201

A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class islabeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selectionproblems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates thehistogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to the Journa

Quality competition, Pricing-To-Market and Non-Tariff measures: A Unified Framework For the Analysis of Bilateral Unit Values

This paper presents a unified framework for analyzing several factors that have been independently studied as determinants of unit values in international trade: product differentiation by quality (which suggests that unit values should be positively correlated with exporters' per capita income), pricing-to-market (which suggests they should be positively correlated with importers' per capita income), and non-tariff measures (which suggests that remaining residuals may contain evidence of trade barriers). On a large sample of bilateral unit values for 2005, we find that about 58 percent of all HS-6 products demonstrate both significant quality-ladder effects and pricing-to-market effects, with quality-ladder effects predominating in importance. Distance-related effects appearing directly in prices appear significantly larger than one would expect as a result of shipping margins. We also rank importers by the remaining unexplained variation in import prices, and examine whether these variations are plausibly related to non-tariff measures.

An Application of pre-Trained CNN for Image Classification

Image Classification is a branch of computer vision where images are classified into categories. This is a very important topic in today’s context as large databases of images are becoming very common. Images can be classified as supervised or unsupervised techniques. This paper investigates supervised classification and evaluates performances of two classifiers as well as two feature extraction techniques. The classifiers used are Linear Support Vector Machine (SVM) and Quadratic SVM. The classifiers are trained and tested with features extracted using Bag of Words and pre-trained Convolution Neural Network (CNN), namely AlexNet. It has been observed that the classifiers are able to classify images with very high accuracy when trained with features from CNN. The image categories consisted of Binocular, Motorbikes, Watches, Airplanes, and Faces, which are taken from Caltech 265 image archive

Dementia, music and biometric gaming: Rising to the Dementia Challenge

In 2012, the U.K. government launched its Dementia Challenge, authorizing additional funding for dementia research and health care. The search for curative medicines is ongoing, but scientific research reveals evidence that music can play a positive role in general health, and in dementia and Alzheimer’s disease in particular. This article considers whether some of the challenges that dementia presents could be addressed through music therapy and proposes that biometric gaming might offer one means of channeling such associated health benefits to sufferers of dementia, even in the final stages of the disease