5 research outputs found

    Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection

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    Sleep is not just a passive process, but rather a highly dynamic process that is terminated by waking up. Throughout the night a specific number of sleep stages that are repeatedly changing in various periods of time take place. These specific time intervals and specific sleep stages are very important for the wake up event. It is far more difficult to wake up during the deep NREM (2–4) stage of sleep because the rest of the body is still sleeping. On the other hand if we wake up during the mild (REM, NREM1) sleep stage it is a much more pleasant experience for us and for our bodies. This problem led the authors to undertake this study and develop a Windows Mobile-based device application called wakeNsmile. The wakeNsmile application records and monitors the sleep stages for specific amounts of time before a desired alarm time set by users. It uses a built-in microphone and determines the optimal time to wake the user up. Hence, if the user sets an alarm in wakeNsmile to 7:00 and wakeNsmile detects that a more appropriate time to wake up (REM stage) is at 6:50, the alarm will start at 6:50. The current availability and low price of mobile devices is yet another reason to use and develop such an application that will hopefully help someone to wakeNsmile in the morning. So far, the wakeNsmile application has been tested on four individuals introduced in the final section

    White Paper for Research Beyond 5G

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    The documents considers both research in the scope of evolutions of the 5G systems (for the period around 2025) and some alternative/longer term views (with later outcomes, or leading to substantial different design choices). This document reflects on four main system areas: fundamental theory and technology, radio and spectrum management; system design; and alternative concepts. The result of this exercise can be broken in two different strands: one focused in the evolution of technologies that are already ongoing development for 5G systems, but that will remain research areas in the future (with “more challenging” requirements and specifications); the other, highlighting technologies that are not really considered for deployment today, or that will be essential for addressing problems that are currently non-existing, but will become apparent when 5G systems begin their widespread deployment

    Analyse intelligente de la qualité d'expérience (QoE) dans les réseaux de diffusion de contenu web et mutimédia

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    Today user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end to end system functioning. Moreover, to compete for a prominent market share, different network operators and service providers should retain and increase the customers’ subscription. To fulfil these requirements they require an efficient Quality of Experience (QoE) monitoring and estimation. However, QoE is a subjective metric and its evaluation is expensive and time consuming since it requires human participation. Therefore, there is a need for an objective tool that can measure the QoE objectively with reasonable accuracy in real-Time. As a first contribution, we analyzed the impact of network conditions on Video on Demand (VoD) services. We also proposed an objective QoE estimation tool that uses fuzzy expert system to estimate QoE from network layer QoS parameters. As a second contribution, we analyzed the impact of MAC layer QoS parameters on VoD services over IEEE 802.11n wireless networks. We also proposed an objective QoE estimation tool that uses random neural network to estimate QoE from the MAC layer perspective. As our third contribution, we analyzed the effect of different adaption scenarios on QoE of adaptive bit rate streaming. We also developed a web based subjective test platform that can be easily integrated in a crowdsourcing platform for performing subjective tests. As our fourth contribution, we analyzed the impact of different web QoS parameters on web service QoE. We also proposed a novel machine learning algorithm i.e. fuzzy rough hybrid expert system for estimating web service QoE objectivelyDe nos jours, l’expĂ©rience de l'utilisateur appelĂ© en anglais « User Experience » est devenue l’un des indicateurs les plus pertinents pour les fournisseurs de services ainsi que pour les opĂ©rateurs de tĂ©lĂ©communication pour analyser le fonctionnement de bout en bout de leurs systĂšmes (du terminal client, en passant par le rĂ©seaux jusqu’à l’infrastructure des services etc.). De plus, afin d’entretenir leur part de marchĂ© et rester compĂ©titif, les diffĂ©rents opĂ©rateurs de tĂ©lĂ©communication et les fournisseurs de services doivent constamment conserver et accroĂźtre le nombre de souscription des clients. Pour rĂ©pondre Ă  ces exigences, ils doivent disposer de solutions efficaces de monitoring et d’estimation de la qualitĂ© d'expĂ©rience (QoE) afin d’évaluer la satisfaction de leur clients. Cependant, la QoE est une mesure qui reste subjective et son Ă©valuation est coĂ»teuse et fastidieuse car elle nĂ©cessite une forte participation humaine (appelĂ© panel de d’évaluation). Par consĂ©quent, la conception d’un outil qui peut mesurer objectivement cette qualitĂ© d'expĂ©rience avec une prĂ©cision raisonnable et en temps rĂ©el est devenue un besoin primordial qui constitue un challenge intĂ©ressant Ă  rĂ©soudre. Comme une premiĂšre contribution, nous avons analysĂ© l'impact du comportement d’un rĂ©seau sur la qualitĂ© des services de vidĂ©o Ă  la demande (VOD). Nous avons Ă©galement proposĂ© un outil d'estimation objective de la QoE qui utilise le systĂšme expert basĂ© sur la logique floue pour Ă©valuer la QoE Ă  partir des paramĂštres de qualitĂ© de service de la couche rĂ©seau. Dans une deuxiĂšme contribution, nous avons analysĂ© l'impact des paramĂštres QoS de couche MAC sur les services de VoD dans le cadre des rĂ©seaux sans fil IEEE 802.11n. Nous avons Ă©galement proposĂ© un outil d'estimation objective de la QoE qui utilise le rĂ©seau alĂ©atoire de neurones pour estimer la QoE dans la perspective de la couche MAC. Pour notre troisiĂšme contribution, nous avons analysĂ© l'effet de diffĂ©rents scĂ©narios d'adaptation sur la QoE dans le cadre du streaming adaptatif au dĂ©bit. Nous avons Ă©galement dĂ©veloppĂ© une plate-Forme Web de test subjectif qui peut ĂȘtre facilement intĂ©grĂ© dans une plate-Forme de crowd-Sourcing pour effectuer des tests subjectifs. Finalement, pour notre quatriĂšme contribution, nous avons analysĂ© l'impact des diffĂ©rents paramĂštres de qualitĂ© de service Web sur leur QoE. Nous avons Ă©galement proposĂ© un algorithme d'apprentissage automatique i.e. un systĂšme expert hybride rugueux basĂ© sur la logique floue pour estimer objectivement la QoE des Web service

    Improved online services by personalized recommendations and optimal quality of experience parameters

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