Removing noise from hyperspectral images is an inevitable step to improve the quality of these types of images. Many methods have been proposed by researchers in this field. Most of these methods do not address simultaneous spatial-spectral similarities. When the noise removal method applies data globally without regard to spatial-spectral similarities, it usually has a negative effect on low-level pixels; when in the spectral data, a large number of pixels have little noise and a small number of pixels are destroyed by the high level of noise. In this paper, we first extract spatial-spectral similarities in images by defining cluster-based latent variables. In the following, a low-rank matrix factorization method based on these latent variables is proposed to eliminate the noise of hyperspectral images and to improve the resistance to noise (as compared to other methods). The performance of the proposed method is compared visually with six new methods on real noise-contaminated images. For quantitative comparison, the same experiments are done on clean images combined with six types of simulated noise. The simulation results show that by applying latent variables in the Bayesian Inference framework, the performance of the noise removal method is improved and the proposed method performs better than the other methods.

In this paper we prove that if X is a Banach space, then for every lower semi-continuous bounded below function f; there exists a ('1; '2)-convex function g; with arbitrarily small norm, such that f + g attains its strong minimum on X: This result extends some of the well-known varitional principles as that of Ekeland [On the Variational principle, J. Math. Anal. Appl. 47 (1974) 323{ 353], that of Borwein-Preiss [A smooth Variational principle with applications to subdi erentiability and to di erentiability of convex functions, Trans. Amer. Math. Soc. 303 (1987) 517{527] and that of Deville-Godefroy-Zizler [Un principe variationel utilisant des fonctions bosses, C. R. Acad. Sci. (Paris). Ser. I 312 (1991) 281{286] and [A smooth Variational principle with applications to Hamilton-Jacobi equations in in nite dimensions, J. Funct. Anal. 111 (1993) 197{212].

In this paper, we consider different types of generalized vector Variational-like inequalities and study the relationships between their  olutions . We study the general forms of Stampacchia and Minty type vector Variational inequalities for bifunctions and establish the existence of their solutions in the setting of topological vector spaces. We extend these vector Variational inequalities for the Clarke’s sub differential of non-differentiable locally Lipschitz functions and prove the existence of their solutions. As applications, we establish some existence results for a solution of the vector optimization problem by using Stampacchia and Minty type vector Variational inequalities.

In this paper, we examine the perturbed absolute value Variational inequalities (PAVVI), a new class of Variational inequalities. For the (PAVVI), some new merit functions are established. We develop the error bounds for (PAVVI) using these merit functions. The results presented here recapture a number of previously established findings in the relevant fields because (PAVVI) include Variational inequalities, the absolute value complementarity problem, and systems of absolute value equations as special cases.

In this paper, we propose a split nonconvex Variational inequality problem which is a natural extension of split convex Variational inequality problem in two different Hilbert spaces. Relying on the prox-regularity notion, we introduce and establish the convergence of an iterative method for the new split nonconvex Variational inequality problem. Further, we also establish the convergence of an iterative method for the split convex Variational inequality problem. The results presented in this paper are new and different form the previously known results for nonconvex (convex) Variational inequality problems. These results also generalize, unify, and improve the previously known results of this area.

In this study the possibility of using fuzzy Inference system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, seven models for estimating daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbagh et al. (R2/t) criteria. An experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data, 75 percent was used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the fuzzy models with different input parameters as compared with AQUACROP model showed that fuzzy systems were very well able to estimate the daily Yield. Fuzzy model so that the highest correlation with the 9 input variables (r=0. 98) had in mind and evaluate other parameters, the model with 2 parameters, match very well with the AQUACROP model had stage training. In the test phase, training phase was very similar results and the model with the second phase of harvest index and canopy cover will get the best match. According to the results of this study it can be concluded that fuzzy model approach is an appropriate method to estimate the daily yield.

It is well known that the quasi Variational inequalities are equivalent to the fixed point problems. We this equivalent alternative formulation to discuss the existence of a solution of quasi Variational inequalities under some mild conditions Since the quasi Variational inequalities include Variational inequalities, implicit complementarity problems and optimization problems as special cases, results proved in this paper continue to hold these problems. This shows that results proved in this paper can be viewed as an important and significant improvement and refinement of the previous results.