Abstract. We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists an adaptive length-scale function that allows non-stationary prior and such enables recovery smooth narrow layers in profiles. estimation d...

introduction: one of the most important parameters in x-ray ct imaging is the noise induced by detected scattered radiation. the detected scattered radiation is completely dependent on the scanner geometry as well as size, shape and material of the scanned object. the magnitude and spatial distribution of the scattered radiation in x-ray ct should be quantified for development of robust scatter...

Introduction: One of the most important parameters in x-ray CT imaging is the noise induced by detected scattered radiation. The detected scattered radiation is completely dependent on the scanner geometry as well as size, shape and material of the scanned object. The magnitude and spatial distribution of the scattered radiation in x-ray CT should be quantified for development of robust scatter...

First and second order Rayleigh and Raman scatter is a common problem when fitting Parallel Factor Analysis (PARAFAC) to fluorescence excitationemission data (EEM). The scatter does not contain any relevant chemical information and does not conform to the low-rank trilinear model. The scatter complicates the analysis instead and contributes to model inadequacy. As such, scatter can be considere...

Clustering is rapidly becoming a powerful data mining technique, and has been broadly applied to many domains such as bioinformatics and text mining. However, the existing methods can only deal with a data matrix of scalars. In this paper, we introduce a hierarchical clustering procedure that can handle a data matrix of scatter plots. To more accurately reflect the nature of data, we introduce ...