Miguel Jimeno

Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare

This article makes a literature review of applications developed in the health industry which are focused on patient care from home and implement a service-oriented (SOA) design in architecture. Throughout this work, the applicability of the concept of Internet of Things (IoT) in the field of telemedicine and health care in general is evaluated. It also performs an introduction to the concept of SOA and its main features, making a small emphasis on safety aspects. As a central theme, the description of different solutions that can be found in the health industry is developed, especially those whose goal is health care at home; the main component of these solutions are body sensor networks. Finally, an analysis of the literature from the perspectives of functionalities, security implementation and semantic interoperability is made to have a better understanding of what has been done and which are probable research paths to be studied in the future.

Empirical approach to network sizing for connectivity in wireless sensor networks with realistic radio propagation models

Choosing the appropriate network size to guarantee connectivity in a WSN deployment is a challenging and important question. Classic techniques to answer this question are not up to the challenge because they rarely consider realistic radio models. This work proposes a methodology to evaluate the performance of network size estimation techniques in terms of connectivity efficiency under realistic radio scenarios. This study is carried out using Atarraya, a simulation tool for wireless sensor networks, considering three classical estimation techniques and a radio model based on the specifications of the ZigBee radio from off-the-shelf WaspMote nodes from Libelium. The results show that the hexagon-based optimal grid technique provides the most efficient estimate, offering a high connectivity level with the lowest estimated number of nodes for a given proximity radius parameter, followed by the circle packing and the triangle-based grid distribution. In addition, the results show that packet error rates of 10% could still produce highly connected topologies.

A Service-Oriented Architecture for Bioinformatics: An Application in Cell Image Analysis

The advance technology in microscopy and computing has allowed the development of cell image analysis. Cloud Computing offers services, software and computing infrastructure to manage cell images’ big data. However the usability of these platforms is adequate to expert users only. Many software tools are oriented to expert users in image processing, likewise the use of bioinformatics require a basic knowledge in programming. In this paper we present a framework to develop a software solution with a Service-Oriented Architecture (SOA) applied to the analysis of cell images using cloud computing.

A pattern recognition procedure for the identification of digital numbers in electrical meters using Neural Networks

This paper presents an approach for number pattern recognition process applied to a practical example: identification of digital numbers in electrical meters using Neural Networks. Results of the implementation were confronted with real measurements for validation purposes.

NEURAL NETWORKS AND HAUSDORFF DISTANCE APPLIED TO NUMBER RECOGNITION IN ELECTRICAL METERS

Pattern recognition is the area of research dedicated to recognizing objects, images, faces, letters, numbers, and so forth. Number recognition processes have an important role in remotely monitoring data for electrical meter readings, and monitoring data from these devices can help to reduce energy consumption. Research in the area of number recognition is vast and there are many different methods have been developed; some of these approaches follow characteristics extraction methods and others, such as the Hausdorff Distance, use the calculation of the distance between two finite sets. In this article, some of these approaches and a comparison among them are presented. Results showed that for recognizing complete digits, characteristic extraction methods offer a better result in terms of recognition time than Hausdorff Distance methods; however, both are similar when considering recognition percentage.