Alireza Khadivi

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A wireless sensor network with a large number of small sensor nodes can be used as an effective tool for gathering data in various situations. The paradigm of data aggregation enables wireless sensor networks to accurately estimate the different parameters of an environment. The aim of this paper is to introduce a new fault tolerant protocol striving to(More)
The aim of this paper is to present application of higher order statistics for Surface Electromyogram (sEMG) signal pattern classification. The new pattern recognition algorithm exploits a multilayer perceptron (MLP) as the classifier and the feature vector is a combination of cumulants of the second-, thirdand fourthorders and Integral of Absolute (IAV) of(More)
Alzheimer's disease (AD) disrupts functional connectivity in distributed cortical networks. We analyzed changes in the S-estimator, a measure of multivariate intraregional synchronization, in electroencephalogram (EEG) source space in 15 mild AD patients versus 15 age-matched controls to evaluate its potential as a marker of AD progression. All participants(More)
In this paper, we show how proper assignment of weights to the edges of a complex network can enhance the detection of communities and how it can circumvent the resolution limit and the extreme degeneracy problems associated with modularity. Our general weighting scheme takes advantage of graph theoretic measures and it introduces two heuristics for tuning(More)
A Wireless Sensor Network (WSN) is a network of usually a large number of small sensor nodes that are wirelessly connected to each other in order to remotely monitor an environment or phenomena. Sensor nodes use the data aggregation method as an effective tool for estimating the desired parameters accurately and trustfully. In this paper, we have applied a(More)
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