In this paper, we address the problem of simultaneous classification and estimation ofhidden parameters in a sensor network with communications constraints. In particular, we considera network of noisy sensors which measure a common scalar unknown parameter. We assume thata fraction of the nodes represent faulty sensors, whose measurements are poorly reliable. The goalfor each n...

We consider three distinct and well studied problems concerning network structure: community detection by modularity maximization, community detection by statistical inference, and normalized-cut graph partitioning. Each of these problems can be tackled using spectral algorithms that make use of the eigenvectors of matrix representations of the network. We show that with certain choices of the ...

Modeling consistency of style in isogenous fields of patterns (such as character patterns in a word from the same font or writer) can improve classification accuracy. Since such patterns are interdependent, the Bayes classifier requires maximization of a probability score over all fieldlabels, which are exponentially more numerous with increasing field length. The iterative field classification...

In this paper we present an empirical Bayes method for flexible and efficient Independent Component Analysis (ICA). The method is flexible with respect to choice of source prior, dimensionality and positivity of the mixing matrix, and structure of the noise covariance matrix. The efficiency is ensured using parameter optimizers which are more advanced than the expectation maximization (EM) algo...

Local probabilistic sensitivity of input variable X with respect to output variable Z is proportional to the derivative of the conditional expectation E(X|z). This paper reports on experience in computing this conditional expectation. Linearized estimates are found to give acceptable performance, but are not generally applicable. A new method of linearization based on re-weighting a Monte Carlo...

In diverse engineering problems including wireless communications, the estimate of the signal-to-noise ratio (SNR) is required. In this study, the authors develop a shrinkage-based SNR estimator in the data-aided and non-dataaided schemes for higher M-ary phase-shift-keying (M ≥ 8) and quadrature amplitude modulations. The observed Cramér-Rao lower bound is used as the variance of the expectati...

In this paper, we discuss how to approximate the conditional expectation of a random variable Y given a random variable X, i.e. E(Y|X). We propose and compare two different non parametric methodologies to approximate E(Y|X). The first approach (namely the OLP method) is based on a suitable approximation of the σ-algebra generated by X. A second procedure is based on the well known kernel non-pa...

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closedform expectation-maximiz...

background following the implementation of family physician plan in rural areas, the quantity of provided services has been increased, but what leads on the next topic is the improvement in expected quality of service, as well. the present study aims at determining the gap between patients’ expectation and perception from the quality of services provided by family physicians during the spring a...

This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for threshold segmentation techniques image. These techniques applied on three satellite ima...