Láminas de Revolucion

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Apuntes de Láminas de Revolución. Estructuras 4 de la ETSAC, curso 2004-05: http://www.udc.es/dep/dtcon/estructuras/ETSAC/Asignaturas/Estructuras-IV/ApuntesLaminas/Apuntes.htm

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One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.

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Context-aware computing refers to a general class of mobile systems that can sense their physical environment, and adapt their behavior accordingly. In this paper we seek to develop a systematic understanding of context-aware computing by constructing a formal model and notation for expressing context-aware computations. This discussion is followed by a description and comparison of current context modeling and reasoning techniques.

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Mobile Commerce, also known as M-Commerce or mCommerce, is the ability to conduct commerce using a mobile device. Research is done by Mining and Prediction of Mobile Users’ Commerce Behaviors such as their purchase transactions. The problem of PMCP-Mine algorithm has been overcome by the efficient framework based graph diffusion method. The main objective is to construct the graph based diffusion method. Graph is constructed for the items purchased by the Mobile users and then finding the frequently purchased item. By using ranking method, we are ranking the items based on the transactions. Then, by analyzing the mobile users behavior and recommending the ranked items. This framework produces more efficient and accurate item recommendation than the MCE framework.

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In this paper channel state information is exploited for improving system performance. The performance parameters of the Multiple Input Multiple Output system is better and are even achieved using additional RF modules that are required as multiple antennas are employed. To reduce the cost associated with the multiple RF modules, antenna selection techniques can be used to employ a smaller number of RF modules than the number of transmit antennas. The exploiting of information for complexity reduced antenna selection is performed for achieving high channel capacity. Simulation results show that the channel capacity increases in proportion to the number of the selected antennas.

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