By Helena Lindgren, Juan F. De Paz, Paulo Novais, Antonio Fernández-Caballero, Hyun Yoe, Andres Jiménez Ramírez, Gabriel Villarrubia
Ambient Intelligence (AmI) is a contemporary paradigm rising from synthetic Intelligence, the place desktops are used as proactive instruments supporting individuals with their day by day actions, making everyone’s existence extra comfortable.
Another major challenge of AmI originates from the human laptop interplay area and makes a speciality of supplying how you can have interaction with platforms in a extra common means by means of ability consumer pleasant interfaces. This box is evolving fast as will be witnessed via the rising ordinary language and gesture dependent varieties of interaction.
The inclusion of computational energy and conversation applied sciences in daily gadgets is growing to be and their embedding into our environments might be as invisible as attainable. to ensure that AmI to achieve success, human interplay with computing energy and embedded platforms within the atmosphere could be gentle and take place with out humans truly noticing it. the one know-how humans must have arises from AmI: extra protection, convenience and well being, rising in a average and inherent way.
ISAmI is the overseas Symposium on Ambient Intelligence and aiming to collect researchers from a number of disciplines that represent the medical box of Ambient Intelligence to offer and speak about the newest effects, new principles, tasks and classes discovered, specifically by way of software program and functions, and goals to collect researchers from quite a few disciplines which are drawn to all points of this zone.
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Additional info for Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016)
In recent years, it has proven its importance also in mobile social networks (MSN), where ties are used to detect communities of users that, in turn, are exploited to improve the strategies for the diffusion of information and services in such networks . A. Álvarez-García() · S. Chessa · L. Fortunati · M. es S. Chessa · L. Fortunati · M. Girolami Italian National Council of Research (CNR-ISTI), Via G. A. A. es © Springer International Publishing Switzerland 2016 H. Lindgren et al. A. Álvarez-García et al.
The solution is naturally prone to false positives, since speech can be detected from external conversations not involving the user. Activity: the physical activity of the device’s owner by exploiting the Google Activity Recognition APIs is gathered. The list of detected activities is filtered by three means of activities: in vehicle, on bicycle and on foot. If the sum of their confidence is over 50%, then the activity detected is moving, otherwise it is still. Location: information about the WiFi access points detected is gathered.
Pbest: This is thhe best position on the current particle (speed that min nimizes the heart rrate and improves the driving style). C. Magaña et al. ● ● Gbest: It is the best position among all particles. Speed: Is calculated using equation 1. It determines which will be the next speed of the particle. vi(t) = w*vi(t-1) + c1*r1*(Pbest(t-1) - xi(t-1)) + c2*r2*(Gbest-xi(t-1)) where vi(t) is the particle’s velocity at time t, w is the inertia weight, xi(t) is the particle’s position at time t, Pbest is the particle’s individual best solution as of time t, and gBest(t) is the swarm’s best solution as of time t, c1 and c2 are two positive constants, and r1 and r2 are random values in the range [0–1] The particles “fly” or “swarm” evolve through the search space to find the minimum value.
Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016) by Helena Lindgren, Juan F. De Paz, Paulo Novais, Antonio Fernández-Caballero, Hyun Yoe, Andres Jiménez Ramírez, Gabriel Villarrubia