Mobile sensor networks rely heavily on inter-sensor connectivity for collection of data. Nodes in these networks monitor different regions of an area of interest and collectively present a global overview of some monitored activities or phenomena. A failure of a sensor leads to loss of connectivity and may cause partitioning of the network into disjoint segments. A number of approaches have be...

Introduction: Many theories have been proposed about the etiology of autism. One is related to brain connectivity in patients with autism. Several studies have reported brain connectivity changes in autism disease. This study was performed on Electroencephalogram (EEG) studies that evaluated patients with autism, using functional brain connectivity, and compared them with typically-developing i...

Recently, brain connectivity networks have been used for classification of Alzheimer's disease and mild cognitive impairment (MCI) from normal controls (NC). In typical connectivity-networks-based classification approaches, local measures of connectivity networks are first extracted from each region-of-interest as network features, which are then concatenated into a vector for subsequent featur...

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain local descriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. T...

Path vector routing protocols such as BGP are known to suffer from slow convergence. In response a number of convergence enhancements have been proposed, but convergence dynamics have yet to be well understood and there has been no general framework to assess and compare the various improvement algorithms. In this paper we present a general framework to analyze the convergence delay bounds of p...

We propose a mixed-effects vector auto-regressive (ME-VAR) model for studying brain effective connectivity. One common approach to investigating inter-regional associations in brain activity is the multivariate auto-regressive (VAR) model. The standard VAR model unrealistically assumes the connectivity structure to be identical across all participants in a study and therefore, could yield misle...