No abstract

Electrical study of Ni and Ti metals of Schottky contacts on n-6H-SiC epitaxial layers is performed, by current-voltage (I-V) characterization. Ni/6H-SiC shows inhomogeneous barrier height behavior. Thermionic emission model is coupled with the Lambert function to obtain an explicit form of the Schottky equation as well as to specify the number of branches necessary for modeling the abnormal behavior. The inhomogeneous barrier height for the investigated Ni/6H-SiC junction can be reproduced by a model that includes two Schottky branches, which give a low (L) and a high (H) Schottky barriers ( L bn   0.92 eV, H bn   1.56 eV), as well as give a low and a high ideality factors (n L  1.93, n H  1.23).

In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimator uses the weight (w) and the length (F) of the code word, the number of active users (Ν) and the signal to noise ratio as inputs to estimate the required optimal threshold. We have evaluated the proposed approach on a data set of 46,200 samples. We have found that it gives accurate results: 0.029 for the root mean square error, 0.37% for the relative root mean square error and 99.984% for the correlation coefficient (R), which reflects the efficiency of the proposed optimal threshold estimator.

No abstract

Daily solar radiation forecasting has recently become critical in developing solar energy and its integration into grid systems. Despite the huge number of proposed forecasting techniques, an accurate estimation remains a significant challenge because of the non-stationary variation of solar radiation components due to the continuously changing climatic conditions. Usually, several input data predictors are used for the forecasting process, which can cause redundancy and correlation between data features. This work assesses a set of feature selection techniques to check their ability to select the relevant predictors and reduce redundant and irrelevant information. An Artificial Neural Network is used to fit the measured solar radiation based on the selected features. The developed model is evaluated through various objective evaluation metrics using historical data of three years measured at the Ghardaiaregion inAlgeria. Results show the effectiveness of the proposed method, where values of 0,0189, 0.0286, 5.4387, and 98.28% have been found as MABE, RMSE, nRMSE and r, respectively.