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The present study developed a fast MEG source imaging technique based on Fast Vector-based Spatio-Temporal Analysis using a L1-minimum-norm (Fast-VESTAL) and then used the method to obtain the source amplitude images of resting-state magnetoencephalography (MEG) signals for different frequency bands. The Fast-VESTAL technique consists of two steps. First,(More)
Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild (and some moderate) TBI can be difficult to diagnose because the injuries are often not detectable on conventional MRI or CT. Injured brain tissues in TBI patients generate abnormal low-frequency magnetic activity (ALFMA, peaked at 1-4(More)
As mobile ad hoc networks (MANETs) are increasingly deployed in critical environments, security becomes a paramount issue. The dynamic and decentralized nature of MANETs makes their protocols very vulnerable to attacks, for example, by malicious insiders, who can cause packets to be misrouted or cause other nodes to have improper configuration. This paper(More)
The unique characteristics of mobile ad hoc networks, such as shared wireless channels, dynamic topologies and a reliance on cooperative behavior, makes routing protocols employed by these networks more vulnerable to attacks than routing protocols employed within traditional wired networks. We propose a specification-based intrusion-detection model for ad(More)
The "Dual-Core Beamformer" (DCBF) is a new lead-field based MEG inverse-modeling technique designed for localizing highly correlated networks from noisy MEG data. Conventional beamformer techniques are successful in localizing neuronal sources that are uncorrelated under poor signal-to-noise ratio (SNR) conditions. However, they fail to reconstruct multiple(More)
A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network(More)
Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. Asynchronous SN P systems are non-synchronized systems, where the use of spik-ing rules (even if they are enabled by the contents of neurons) is not obligatory. In this paper, with a(More)
Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In(More)
Propofol is one of the most commonly used intravenous anesthetic agents during cancer resection surgery. It can influence proliferation, motility, and invasiveness of cancer cells in vitro and in vivo. However, the role of the propofol in the lung cancer cells remains unclear. In this study, we demonstrated the effects of propofol on the proliferation and(More)