Abstract-Providing spontaneously personalized services to users, at anytime, anywhere and through any devices represent the main objective of pervasive computing. Smart home is an intelligent environment that can provide dozens or even hundreds of smart services. In this paper, we propose an approach to present spontaneously and continuously the most relevant services to the user in response to any significant change of his context. Our approach allows, firstly to assist proactively the user in the tasks of his/her daily life and secondly to help him/her to save energy in the smart home environment. The proposed approach is based on the use of context history information together with user profiling and machine learning techniques. Experimental results show that our approach can efficiently provide the most useful services to the user in a smart home environment.
Abstract-In this paper, a color face recognition system is developed to identify human faces using Back propagation neural network. The architecture we adopt is All-Class-in-One-Network, where all the classes are placed in a single network. To accelerate the learning process we propose the use of Bhattacharyya distance as total error to train the network. In the experimental section we compare how the algorithm converge using the mean square error and the Bhattacharyya distance. Experimental results indicated that the image faces can be recognized by the proposed system effectively and swiftly.
Buildings today become complex in terms of structures and advanced technologies. Intelligent buildings are autonomic environment that provide useful services to make occupants lives' more comfortable (e.g. physical security, automatic lighting, thermal comfort, etc…). Smart buildings use IT systems to connect a variety of independent sub-systems, so that these systems can share information to improve total building performance.In this paper, we propose an approach that offers the most relevant services to inhabitants according to the contextual information provided by the physical sensors. The proposed approach considers five parameters to represent the contextual information: the user activity (sleeping, walking, sitting down, …), time(morning, afternoon, night, …) localization ( kitchen, living room,..), temperature(warm, cold, hot, …) , special event (ween end, holidays,..) and activate the most adequate service to be applied in a given context of use.
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