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Content available remote Probabilistic deformable models for weld defect contour estimation in radiography
EN
This paper describes a novel method for segmentation of weld defect in radiographic images. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. Our approach can be described as a region-based maximum likelihood formulation of parametric deformable contours. This formulation provides robustness against the poor image quality, and allows simultaneous estimation of the contour parameters together with other parameters of the model. Implementation is performed by a deterministic iterative algorithm with minimal user intervention. Results testify very good performance of such contour estimation approach.
EN
It is essential to have deep understanding of the mobile radio channel in particular for radio communication modeling and advanced technology system design. Models for the mobile radio channel are vital for the study of smart antenna systems, both for the design of algorithms, system-testing purposes, and for network planning. This paper provides an intensive study of the spatial characteristics of the mobile channel for low antenna height cellular environments, i.e., picocells and microcells, assuming Gaussian distributed scatterers. We investigate previous work on the angle of arrival (AoA) statistics for Gaussian distributed scatterers and make appropriate comments. Further, we employ the recently proposed eccentro-scattering physical channel model, as a generalized model, to derive the probability density function (pdf) of AoA of the multipaths at base station (BS) assuming Gaussian distributed scatterers around both BS and mobile station (MS). We found that the pdf of AoA at BS is directly affected by the standard deviation of the scatterers' density and the size of the scattering disc. The derived formulas, in closed form, can be used further for performance assessment of smart antennas and beamwidth design purposes.
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