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Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

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Power source replacement of the sensor node which are once deployed in the network area, is generally difficult. So energy saving is one of the most important issues for object tracking in wireless sensor networks. To reduce the consumed energy and prolong the network lifetime the nodes surrounding the mobile object should be responsible for sensing the target. The number of participant nodes in target tracking can be reduced by an accurate prediction of the object location. In this paper we present fast energy efficient with high accuracy target tracking scheme which is based on location prediction. The missing rate of proposed predictor is very low in comparison with other predictors especially in random way point mobility model in which after pause time the three main parameters: direction, velocity and acceleration would be changed. The accuracy of predictor has direct effect on missing rate and so strongly reduces the consumed energy. Additionally a new node selection criterion is proposed in which minimum nodes surrounding the object are wakened and track the object. Simulation results show that our proposed predictor has low consumed energy and complexity in comparison with EKF and UKF predictors.