remotely sensed imagery is proving to be a useful tool to estimate water depths in coastalzones. bathymetric algorithms attempt to isolate water attenuation and hence depth from other factors byusing different combinations of spectral bands. in this research, images of absolute bathymetry using twodifferent but related methods in a region in the southern caspian sea coasts has been produced. th...

frost is one of the atmospheric phenomena which seriously threaten crop production. it also causes numerousaccidents in mountainous roads. in this research the spatial synoptic classification ssc method was employed toclassify the type of air masses. for the classification, such meteorological data as: temperature, dew point, mean sealevel pressure, cloudiness, direction and speed of wind were ...

the analysis of geochemical data in frequency domain, as indicated in this research study, can provide new exploratory informationthat may not be exposed in spatial domain. to identify deep geochemical anomalies, sulfide zone and geochemical noises in dalli cu–au porphyry deposit, a new approach based on coupling fourier transform (ft) and principal component analysis (pca) has beenused. the re...

We consider the dimensionality-reduction problem for a contaminated data set in a very high dimensional space, i.e., the problem of finding a subspace approximation of observed data, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some outlying observations. We propose a High-dimension Robust Principal Component...

Due to being so effective to mitigate the fading effect of wireless channels relay networks have received so much attention recently. Especially because relays are typically small, power limited and low cost and also can remove the problem of attenuation of signal due to propagation loss. Moreover increasing the number of relays improves the system performance and also using more power. The sys...

Usually, principal components analysis is carried out by calculating the eigenvalues and eigenvectors of the correlation matrix. With N cases and P variables, if we write X for the N × P matrix which has been standardised so that columns have zero mean and unit standard deviation, we find the eigenvalues and eigenvectors of the P × P matrix X.X (which is N or (N − 1) times the correlation matri...

multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of veli-akkulam lake and compared with a regional reference lake vellayani of south india. seasonal variations of 14 differen...

We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove optimal, non-asymptotic lower and upper bounds on the minimax estimation error for the leading eigenvector when it belongs to an lq ball for q ∈ [0, 1]. Our bounds are sharp in p and n for all q ∈ [0, 1] over a wide cla...

this paper presents a combination of data envelopment analysis (dea) and principal component analysis (pca) to reduce the dimensionality of data set. dea is known as effective tool for assessment and benchmarking. the weak point of dea, it is that the number of efficient dmus relies on the number of variables (inputs and outputs). for solving this, first, we do principal component analysis (pca...