Independent Component Analysis (ICA) is a multivariate data analysis process largely sudied these last years in the signal processing community for blind source separation. This paper proposes to show the interest of ICA as a tool for unsupervised analysis of hyperspectral images. The commonly used Principal Component Analysis (PCA) is the mean square optimal projection for gaussian data leadin...

this research was conducted to investigate genotyp×environment interaction for grain yield of ten wheat cultivars in 8 environments (combinations of two nitrogen fertilizer treatment and four irrigation regimes). analysis of additive main effects (analysis of variance) and multiplicative interaction effects (principal component analysis) revealed that the effects of genotype, environment and ge...

This paper introduces a Kolmogorov-type test for the shortfall order (also known in the literature as the right-spread or excess-wealth order) against parametric alternatives. In the case of the null hypothesis corresponding to the Negative Exponential distribution, this provides a test for the new better than used in expectation (NBUE) and for the new worse than used in expectation (NWUE) prop...

The stochastic component of a chemical sensor signal contains valuable information that can be visualized not only by spectral analysis but also by methods of higher-order statistics (HOS). The analysis of HOS enables the extraction of non-conventional features and may lead to significant improvements in selectivity and sensitivity. We pay particular attention to the bispectrum that characteriz...

Because of the nonlinear characteristics of monitoring system in military barracks, the traditional KPCA method either have low sensitivity or unable to detect the fault quickly and accurately. In order to make use of higher-order statistics to get more useful information and meet the requirements of real-time fault diagnosis and sensitivity, a new method of fault detection and diagnosis is pro...

Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are the techniques that deal with extracting the independent components from linear mixtures of Gaussian and non-Gaussian data at the input respectively. PCA is a classical method that deals with the second order statistics of data. It is also known as Karhunen-Loeve Transform or the Hotelling Transform in some applicat...

background: cutaneous leishmaniasis is one of the most important parasitic diseases in humans. in this disease, one of the responsible organisms is leishmania major, which is transmitted by sandfly vector. there are specific differences in biochemical profiles and metabolite pathways in logarithmic and stationary phases of leishmania parasites. in the present study, 1h nmr spectroscopy was used...

Consider a data matrix X of real numbers with n rows and k columns. We view the rows X1, . . . ,Xn as independent samples from a multivariate distribution with mean vector μ and covariance matrix Σ. Can we visualize X in some easy way? One possibility is to use heat plots (also known as heat maps). These are constructed as follows: think of a rectangular matrix in which column (or row) standard...

these days knowledge and information play a vital role in promoting of productivity and sustainability. the main purpose of this descriptive– survey research was to analysis information seeking behavior of carp farmers in sangar’s area of rasht county. information seeking behavior of farmers including definition process and gathering information of usage technology, evaluation, information savi...