Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems

Abstract— Nowadays careful measurement applications are handed over to Wired and Wireless Sensor Network. Taking the scenario of train location as an example, this would lead to an increase in uncertainty about position related to sensors with long acquisition times like Balises, RFID and Transponders along the track. We take into account the data without any synchronization protocols, for increase the accuracy and reduce the uncertainty after the data fusion algorithms. The case studies, we have analysed, derived from the needs of the project partners: train localization, head of an auger in the drilling sector localization and the location of containers of radioactive material waste in a reprocessing nuclear plant. They have the necessity to plan the maintenance operations of their infrastructure basing through architecture that taking input from the sensors, which are localization and diagnosis, maps and cost, to optimize the cost effectiveness and reduce the time of operation

Fusing drug enforcement: a study of the El Paso Intelligence Center

This article examines the evolution of the El Paso Intelligence Center (EPIC), a key intelligence component of the Drug Enforcement Administration, to shed light on fusion efforts in drug enforcement. Since 1974, EPIC has strived to fuse the resources and capabilities of multiple government agencies to counter drug trafficking and related threats along the Southwest US border. While undergoing a steady growth, the Center has confronted a host of challenges that illuminate the uses and limits of multi-agency endeavors in drug enforcement. An evaluative study of the Center shows that it is well aligned with the federal government priorities in the realm of drug enforcement; however the extent to which the Center’s activities support the government’s efforts in this domain is not so clear. The Center needs to improve the way it reviews its own performance to better adapt and serve its customers

Web based system architecture for long pulse remote experimentation

Remote experimentation (RE) methods will be essential in next generation fusion devices. Requirements for long pulse RE will be: on-line data visualization, on-line data acquisition processes monitoring and on-line data acquisition systems interactions (start, stop or set-up modifications). Note that these methods are not oriented to real-time control of fusion plant devices. INDRA Sistemas S.A., CIEMAT (Centro de Investigaciones Energéticas Medioambientales y Tecnológicas) and UPM (Universidad Politécnica de Madrid) have designed a specific software architecture for these purposes. The architecture can be supported on the BeansNet platform, whose integration with an application server provides an adequate solution to the requirements. BeansNet is a JINI based framework developed by INDRA, which makes easy the implementation of a remote experimentation model based on a Service Oriented Architecture. The new software architecture has been designed on the basis of the experience acquired in the development of an upgrade of the TJ-II remote experimentation system