In this paper, a new algorithm based on ESPRIT is proposed for the estimation of central angle and angular extension of Incoherently Distributed (ID) sources. The central angles are estimated using TLS-ESPRIT. The covariance matrix is approximated using a finite Taylor series expansion which leads to the formulation of covariance matrix in terms of central moments of the angular power distribution. The extension widths are estimated using the central moments of distribution. The algorithm can be used for sources with different angular distributions and has low computational cost.

Introduction: Myxomas are benign mesenchymal neoplasms which arise mainly in the heart. The laryngeal LOCALIZATION is very rare. We aim to describe the clinical, histological and therapeutic features of this condition. Case Report: We report two cases of laryngeal myxomas occurred in male and female patients, presenting with a history of prolonged hoarseness. Laryngoscopy revealed a polypoid mass on the true vocal folds. The lesions were excised with cold instruments. One patient presented a recurrence 4 years after the first surgery. Conclusions: Laryngeal myxoma should be considered in case of a benign looking vocal fold lesion, especially a vocal cord polyp. Histologic exam is the only tool to confirm the diagnosis. It is treated by surgical resection. In the literature, recurrence is rare in laryngeal site, but patients need to be kept on close follow-up.

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In this paper, at first the philosophical concept of objectivity in scientific studies and analyses is described. Then, it's meaning in damage analysis and, especially, "strain LOCALIZATION analyses" is represented. After that, the most important solution for making an analysis objective i.e. using the nonlocal values of relevant variables is discussed. Then, the current idea on objective analysis based on the secant modulus, is represented and the authors suggestion for objective analysis, based on the tangent modulus, is represented. Finally, a sample problem is solved.

In this paper, we study the estimation of the spatially sparse radio emitter locations via the proposed Quad-tree variational Bayesian expectation maximization (QVBEM) algorithm. Firstly, we assume that the emitters are approximately lie on a uniform grid points in the region under surveillance. The VBEM algorithm is applied and the points exceeding the threshold level are considered as potential targets. Then, the grids are refined around the potential targets via the Quad-tree algorithm and the process is iterated. It allows us to find the location of sparse emitters with much less computational complexity due to the use of fewer grid points. Simulation results show the superiority of the QVBEM to existing methods. The impact of threshold value on the performance of QVBEM is also studied.

Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. LOCALIZATION is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of LOCALIZATION error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system.Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z=0.4597.