SiO2 nanostructure is synthesized by the Sol-Gel method and thin films are prepared using dip coating technique. The effect of laser densification is studied. X-ray Diffraction (XRD), Fourier Transformation Infrared Spectrometer (FTIR), and Field Emission Scanning Electron Microscopy (FESEM) are used to analyze the samples. The results show that the silica nanoparticles are successfully synthesized by the sol-gel method after laser densification. XRD patterns show that cristobalite structure is observed from diode laser (410 nm) rather than diode laser (532 nm). FESEM images showed that the shape of nano silica is spherical and the particles size is in nano range (? 100 nm). It is concluded that the spherical nanocrystal structure of silica

ZnO thin films have been prepared by pulse laser deposition technique at room temperatures (RT). These films were deposited on GaAs substrate to form the ZnO/GaAs heterojunction solar cell. The effect of annealing temperatures at ( RT,100, 200)K on structural and optical properties of ZnO thin films has been investigated. The X-ray diffraction analysis indicated that all films have hexagonal polycrystalline structure. AFM shows that the grains uniformly distributed with homogeneous structure. The optical absorption spectra showed that all films have direct energy gap. The band gap energy of these films decreased with increasing annealing temperatures.  From the electrical properties, the carriers have n-type conductivity.  From

Thin films of ZnO nano crystalline doped with different concentrations (0, 6, 9, 12, and 18 )wt. % of copper were deposited on a glass substrate via pulsed laser deposition method (PLD). The properties of ZnO: Cu thin-nanofilms have been studied by absorbing UV-VIS, X-ray diffraction (XRD) and atomic force microscopes (AFM). UV-VIS spectroscopy was used to determine the type and value of the optical energy gap, while X-ray diffraction was used to examine the structure and determine the size of the crystals.  Atomic force microscopes were used to study the surface formation of precipitated materials. The UV-VIS spectroscopy was used to determine the type and value of the optical energy gap.

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl