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Wireless Sensors and wireless sensor networks have been used broadly in academic communities. That is owing to increasingly development of small scale equipment in engineering that prepares enormous applications. Numerous researches have been done in order to using of these sensors as well as establishing them as a network and large variety of solutions have been suggested. One heuristic method to modeling wireless sensor networks is distributed adaptive modeling. Processing is done in fully distributed manner at this way. An adaptive network contains a number of nodes those are capable to learn and adapt. Every node exchanges its data with neighbor nodes so as to the network could finally solve an estimation or interference problem. In this paper, at first, the position of distributed adaptive processing will be explained, then we review some proposed strategies for adaptive modeling and after that simulation results will be compared.

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In this paper a novel method is introduced for target detection in bistatic passive radars which uses the concept of correntropy to distinguish correct targets from false detections. In proposed method the history of each cell of ambiguity function is modeled as a stochastic process. Then the stochastic processes consist the noise are differentiated from those consisting targets by constructing an FIR adaptive filter. A cost function which is based on correntropy is utilized to update the filter. The performance of the proposed method is evaluated by simulation in presence of rapid and slow moving targets. The obtained results shows the superiority of the proposed method compared to its alternatives in such manner that it detects rapid targets at least 18.7 and 20.1 percent better than HOSCM and PFCM. Furthermore it detects slow targets 19.3 and 21.4 percent better than those alternatives, all in presence of maximum noise (i.e. SCNR=-30dB). 

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Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.