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Wireless sensor networks are widely used in many application domains. One of the well-known applications of such networks is tracking of moving objects. In this paper we propose a novel algorithm in which head and members of next clusters are chosen and woken up dynamically by the current active cluster head in a grid network of wireless sensors. This dynamic algorithm is designed for target tracking applications. The proposed solution is a distributed clustering scheme, in which, head and cluster members of the next cluster are selected cooperatively. Based on the distributed solution, a semi-localized clustering scheme is also presented. Moreover, a distributed target movement history recording algorithm is proposed to reduce target misses. Simulation results show that by using the proposed method, there are significant reductions in the average number of received messages, averaged number of sent messages, and average number of samplings of the sensing subsystem. In addition, a meaningful reduction in target miss rate is achieved by using distributed target movement recording algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Bruise damage is a major cause of fruit quality loss. Bruises occur under dynamic and static loading when stress induced in the fruit exceeds the failure stress of the fruit tissue. In this article the potential of an artificial neural network (ANN) technique has evaluated as an alternative method for the prediction of apple bruise volume. Neural bruise estimation models were constructed to calculate Golden Delicious apple bruise volume with respect to fruit properties. The neural models were built based upon impact force and impact energy as the main input parameters including fruit curvature radius, temperature and acoustical stiffness. Optimal parameters for the network were selected via a trial and error procedure on the available data. In this paper, the performance of Basic Backpropagation (BB) training algorithm was also compared with Backpropagation with Declining Learning Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of apple bruise volume. It is concluded that ANN represents a promising tool for predicting apple bruise volume in comparison to regression model. [Copyright &y& Elsevier]