Entropy2015, 17(7), 4664-4683; doi:10.3390/e17074664 (registering DOI) - published 3 July 2015 Show/Hide Abstract
Abstract: The classic principal components analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) feature extraction methods evaluate the importance of components according to their covariance contribution, not considering the entropy contribution, which is important supplementary information for the covariance. To further improve the covariance-based methods such as PCA (or KPCA), this paper firstly proposed an entropy matrix to load the uncertainty information of random variables similar to the covariance matrix loading the variation information in PCA. Then an entropy-difference matrix was used as a weighting matrix for transforming the original training images. This entropy-difference weighting (EW) matrix not only made good use of the local information of the training samples, contrast to the global method of PCA, but also considered the category information similar to LDA idea. Then the EW method was integrated with PCA (or KPCA), to form new feature extracting method. The new method was used for face recognition with the nearest neighbor classifier. The experimental results based on the ORL and Yale databases showed that the proposed method with proper threshold parameters reached higher recognition rates than the usual PCA (or KPCA) methods.
Entropy2015, 17(7), 4654-4663; doi:10.3390/e17074654 (registering DOI) - published 3 July 2015 Show/Hide Abstract
Abstract: Existing experimental implementations of continuous-variable quantum key distribution require shot-noise limited operation, achieved with shot-noise limited lasers. However, loosening this requirement on the laser source would allow for cheaper, potentially integrated systems. Here, we implement a theoretically proposed prepare-and-measure continuous-variable protocol and experimentally demonstrate the robustness of it against preparation noise stemming for instance from technical laser noise. Provided that direct reconciliation techniques are used in the post-processing we show that for small distances large amounts of preparation noise can be tolerated in contrast to reverse reconciliation where the key rate quickly drops to zero. Our experiment thereby demonstrates that quantum key distribution with non-shot-noise limited laser diodes might be feasible.
Entropy2015, 17(7), 4644-4653; doi:10.3390/e17074644 (registering DOI) - published 2 July 2015 Show/Hide Abstract
Abstract: We consider the problem of defining a measure of redundant information that quantifies how much common information two or more random variables specify about a target random variable. We discussed desired properties of such a measure, and propose new measures with some desirable properties.
Entropy2015, 17(7), 4627-4643; doi:10.3390/e17074627 (registering DOI) - published 2 July 2015 Show/Hide Abstract
Abstract: Permutation entropy (PE) has been widely exploited to measure the complexity of the electroencephalogram (EEG), especially when complexity is linked to diagnostic information embedded in the EEG. Recently, the authors proposed a spatial-temporal analysis of the EEG recordings of absence epilepsy patients based on PE. The goal here is to improve the ability of PE in discriminating interictal states from ictal states in absence seizure EEG. For this purpose, a parametrical definition of permutation entropy is introduced here in the field of epileptic EEG analysis: the permutation Rényi entropy (PEr). PEr has been extensively tested against PE by tuning the involved parameters (order, delay time and alpha). The achieved results demonstrate that PEr outperforms PE, as there is a statistically-significant, wider gap between the PEr levels during the interictal states and PEr levels observed in the ictal states compared to PE. PEr also outperformed PE as the input to a classifier aimed at discriminating interictal from ictal states.
Entropy2015, 17(7), 4602-4626; doi:10.3390/e17074602 - published 1 July 2015 Show/Hide Abstract
Abstract: In regression analysis for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. In many situations, the assumptions underlying OLS are not fulfilled, and several other approaches have been proposed. However, most techniques address only part of the shortcomings of OLS. We here discuss a new and more general regression method, which we call geodesic least squares regression (GLS). The method is based on minimization of the Rao geodesic distance on a probabilistic manifold. For the case of a power law, we demonstrate the robustness of the method on synthetic data in the presence of significant uncertainty on both the data and the regression model. We then show good performance of the method in an application to a scaling law in magnetic confinement fusion.
Entropy2015, 17(7), 4582-4601; doi:10.3390/e17074582 - published 1 July 2015 Show/Hide Abstract
Abstract: In this paper, we study the classical Sumudu transform in fuzzy environment, referred to as the fuzzy Sumudu transform (FST). We also propose some results on the properties of the FST, such as linearity, preserving, fuzzy derivative, shifting and convolution theorem. In order to show the capability of the FST, we provide a detailed procedure to solve fuzzy differential equations (FDEs). A numerical example is provided to illustrate the usage of the FST.