Conditions for the local manipulation of Gaussian states.

Giant Goos-Häenchen Shift of a Gaussian Beam Reflected from One-Dimensional Photonic Crystals Containing Left-Handed Lossy Metamaterials

We perform a theoretical investigation on the Goos-Häenchen shift (the lateral shift) in one-dimensional photonic crystals (1DPCs) containing left-handed (LH) metamaterials. The effect was studied by use of a Gaussian beam. We show that the giant lateral displacement is due to the localization of the electromagnetic wave which can be both positive and negative depending on the incidence angle o...

Expected Number of Local Maxima of Some Gaussian Random Polnomials

Recognizing the Emotional State Changes in Human Utterance by a Learning Statistical Method based on Gaussian Mixture Model

Speech is one of the most opulent and instant methods to express emotional characteristics of human beings, which conveys the cognitive and semantic concepts among humans. In this study, a statistical-based method for emotional recognition of speech signals is proposed, and a learning approach is introduced, which is based on the statistical model to classify internal feelings of the utterance....

Minimal conditions for local pure-state entanglement manipulation

We find a minimal set of necessary and sufficient conditions for the existence of a local procedure that converts a finite pure state into one of a set of possible final states. This result provides a powerful method for obtaining optimal local entanglement manipulation protocols for pure initial states. As an example, we determine analytically the optimal distillable entanglement for arbitrary...

Novel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection

In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...