Face-to-face media sharing using wireless mobile devices

Abstract Advanced personal wireless mobile devices, such as today 's emerging smart-phones, ar

Traffic integration in personal, local and geograhical wireless networks

Currently, users identify wireless networks with the first and second generation of cellular-telephony networks. Although voice and short messaging have driven the success of these networks so far, data and more sophisticated applications are emerging as the future driving forces for the extensive deployment of new wireless technologies. In this chapter we will consider future wireless technologies that will provide support to different types of traffic including legacy voice applications, Internet data traffic, and sophisticated multimedia applications. In the near future, wireless technologies will span from broadband wide-area technologies (such as satellite-based network and cellular networks) to local and personal area networks. Hereafter, for each class of networks, we will present the emerging wireless technologies for supporting service integration. Our overview will start by analyzing the Bluetooth technology that is the de-facto standard for Wireless Personal Area Networks (WPANs), i.e. networks that connect devices placed inside a circle with radius of 10 meters. Two main standards exist for Wireless Local Area Networks (WLANs): IEEE 802. and HiperLAN. In this chapter we focus on the IEEE 802.11 technology, as it is the technology currently available on the market. In this chapter, after a brief description of the IEEE 802.11 architecture, we will focus on the mechanisms that have been specifically designed to support delay sensitive traffics

Integrated Architecture for Configuration and Service Management in MANET Environments

Esta tesis nos ha permitido trasladar algunos conceptos teóricos de la computación ubicua a escenarios reales, identificando las necesidades específicas de diferentes tipos de aplicaciones. Con el fin de alcanzar este objetivo, proponemos dos prototipos que proporcionan servicios sensibles al contexto en diferentes entornos, tales como conferencias o salas de recuperación en hospitales. Estos prototipos experimentales explotan la tecnología Bluetooth para ofrecer información basada en las preferencias del usuario. En ambos casos, hemos llevado a cabo algunos experimentos con el fin de evaluar el comportamiento de los sistemas y su rendimento. También abordamos en esta tesis el problema de la autoconfiguración de redes MANET basadas en el estándar 802.11 a través de dos soluciones novedosas. La primera es una solución centralizada que se basa en la tecnología Bluetooth, mientras la segunda es una solución distribuida que no necesita recurrir a ninguna tecnología adicional, ya que se basa en el uso del parámetro SSID. Ambos métodos se han diseñado para permitir que usuarios no expertos puedan unirse a una red MANET de forma transparente, proporcionando una configuración automática, rápida, y fiable de los terminales. Los resultados experimentales en implementaciones reales nos han permitido evaluar el rendimiento de las soluciones propuestas y demostrar que las estaciones cercanas se pueden configurar en pocos segundos. Además, hemos comparado ambas soluciones entre sí para poner de manifiesto las diferentes ventajas y desventajas en cuanto a rendimento. La principal contribución de esta tesis es EasyMANET, una plataforma ampliable y configurable cuyo objetivo es automatizar lo máximo posible las tareas que afectan a la configuración y puesta en marcha de redes MANET, de modo que su uso sea más simple y accesible.Cano Reyes, J. (2012). Integrated Architecture for Configuration and Service Management in MANET Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14675Palanci

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth