The single particle phase function and the linear polarization for large stochastically deformed spheres have been calculated by Monte Carlo simulation using the geometrical optics approximation. The radius vector of a particle is assumed to obey a bivariate lognormal distribution with three free parameters: mean radius, its standard deviation and the coherence length of the autocorrelation f...

Similarity learning has received a large amount of interest and is an important tool for many scientific and industrial applications. In this framework, we wish to infer the distance (similarity) between points with respect to an arbitrary distance function d. Here, we formulate the problem as a regression from a feature space X to an arbitrary vector space Y, where the Euclidean distance is pr...

Image compression operator based on discrete cosine transform was brought up. A securer scrambling locational operator was put forward based on the concept of anti-tamper radius. The basic idea of the algorithm is that it first combined image block compressed data with eigenvalue of image block and its offset block, then scrambled or encrypted and embeded them into least significant bit of corr...

To set the values of the hyperparameters of a support vector machine (SVM), one can use cross-validation. Its leave-one-out variant produces an estimator of the generalization error which is almost unbiased. Its major drawback rests in its time requirement. To overcome this difficulty, several upper bounds on the leave-one-out error of the pattern recognition SVM have been derived. The most pop...

The residual dependence index of bivariate Gaussian distributions is determined by the correlation coefficient. This tail index is of certain statistical importance when extremes and related rare events of bivariate samples with asymptotic independent components are being modeled. In this paper we calculate the partial residual dependence indices of a multivariate elliptical random vector assum...

Maximum Likelihood (ML) detection is the optimum method for decoding the received signal vector in communication systems. Its complexity increases with the number of antennas and the constellation size. Sphere Decoding (SD) is an alternate for ML detection. Although SD significantly reduces the complexity of MIMO-ML decoding, its complexity remains too high to apply it for practical systems. As...

Here H = div (n) is the mean curvature, n is the outward unit normal, x is the position vector and 〈, 〉 denotes the Euclidean inner product. One reason that selfshrinking solutions to the mean curvature flow are particularly interesting is that they provide singularity models of the flow; see [20, 21], [24] and [46]. Throughout, O is the origin of R; BR denotes the open ball in R n+1 centered a...

To set the values of the hyperparameters of a support vector machine (SVM), the method of choice is cross-validation. Several upper bounds on the leave-one-out error of the pattern recognition SVM have been derived. One of the most popular is the radius–margin bound. It applies to the hard margin machine, and, by extension, to the 2-norm SVM. In this article, we introduce the first quadratic lo...

In digital communication systems, Maximum likelihood (ML) detection is the optimum method to decode a received signal vector. In wireless communication systems, the complexity increases with the constellation size and the number of antennas. It becomes infeasible to apply ML decoding to the practical systems as it searches through all the lattice points in the constellation. Sphere decoding is ...