in this paper, we shall define and study the concept of  -statistical convergence and  -statistical cauchy inrandom 2-normed space. we also introduce the concept of  -statistical completeness which would provide amore general frame work to study the completeness in random 2-normed space. furthermore, we also prove some new results.

A method is proposed to experimentally determine the intrinsic time scale or a decay rate of turbulent fluctuations. The method is based on the assumption that the Breit-Wigner spectrum model with a non-Gaussian frequency broadening is valid in the data analysis. The decay rate estimate is applied to the multi-spacecraft magnetic field data in interplanetary space, yielding that the result that...

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert–Peano scan of the image, constitute a fast ...

This paper describes a simple novel compound random field model capable of realistic modelling the most advanced recent representation of visual properties of surface materials the bidirectional texture function. The presented compound random field model combines a non-parametric control random field with local multispectral models for single regions and thus allows to avoid demanding iterative...

In this paper, we develop a method of performing the one-point statistics of a perturbed density field with a multiresolutional decomposition based on the discrete wavelet transform (DWT). We establish the algorithm of the one-point variable and its moments in considering the effects of Poisson sampling and selection function. We also establish the mapping between the DWT one-point statistics i...

In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations.Spatiotemporal models arise whenever data are collected across bothtime and space. Therefore such models have to be analyzed in termsof thei...

An improved MRF algorithm–hierarchical Gauss Markov Random Field model in the wavelet domain is presented for fabric image segmentation in this paper, which obtains the relation of inter-scale dependency from the feature field modeling and label field modeling. The Gauss-Markov random field modeling is usually adopted to feature field modeling. The label field modeling employs the inter-scale c...