By Emile Aarts, Boris de Ruyter, Panos Markopoulos, Evert van Loenen, Reiner Wichert, Ben Schouten, Jacques Terken, Rob Van Kranenburg, Elke Den Ouden, Gregory O'Hare
This ebook constitutes the refereed court cases of the fifth overseas Joint convention of Ambient Intelligence, AmI 2014, held in Eindhoven, The Netherlands, in November 2014. The 21 revised complete papers provided including five brief papers and four workshop papers have been rigorously reviewed and chosen from fifty nine submissions. The papers are prepared alongside a suite of thematic tracks: ambient assisted residing; web of items; ambient play and studying; clever structures and towns; clever using; information technology; shrewdpermanent healthcare and therapeutic environments; ambient persuasion; and new and rising themes.
Read Online or Download Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers PDF
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Extra resources for Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers
The overall SALT fusion system Figure 1 shows the overall SALT technique. For each sample provided by the localization subsystem (xL , yL , ti ) representing its output coordinates at time ti , we built a geometric model of the trajectory that keeps track of the current position and of the velocity v m calculated on previous samples received using a Simple Moving Average (SMA). When a context data (xC , yC , ti ) is received from the binary sensor network, the SALT overlay gives as output the coordinates of the corresponding activated sensor, since it represents a checkpoint of the user path.
Simple binary sensors in a Bayesian framework are also used in  to provide room-level location estimation and rudimentary activity recognition. In  a HMM (Hidden Markov Model) is used to stabilize a Bayesian-based location inference output in a WiFi-based localization system. In the domain of mobile robotics, RFID and Bayesian inference is used to perform obstacle detection, mitigating multipath eﬀects [16,17]. A recursive Bayesian estimator, integrating WSN-based location data and kinematic information, is presented in .
E. binary sensor network) that we will leverage in this work will be given. The fundamental studies of target tracking often focus on networks composed of sensor nodes with the most elementary sensing capabilities that provide just binary information about the target, indicating whether it is present or absent in the sensing range of a node. These so-called binary sensor networks constitute the simplest type of sensor networks that can be used for target tracking. es/ 24 F. Palumbo and P. Barsocchi H í HYHQWLQIR í VWDWXVLQIR Fig.
Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers by Emile Aarts, Boris de Ruyter, Panos Markopoulos, Evert van Loenen, Reiner Wichert, Ben Schouten, Jacques Terken, Rob Van Kranenburg, Elke Den Ouden, Gregory O'Hare