Thin films of cadmium sulphoselenide (CdSSe) have been prepared by a thermal evaporation method on glass substrate, and with pressure of 4x10-5 mbar. The optical constants such as (refractive index n, dielectric constant ?i,r and Extinction coefficient ?) of the deposition films were obtained from the analysis of the experimental recorded transmittance spectral data. The optical band gap of (CdSSe) films is calculate from (?h?)2 vs. photon energy curve. CdSSe films have a direct energy gap, and the values of the energy gap were found to increase when increasing annealing temperature. The band gap of the films varies from 1.68 – 2.39 eV.

This study is dedicated to investigate the effects of initial laser intensity on the nonlinear optical properties of the laser dye DQOCI dissolved in methanol with a concentration of 10 ^-5 M and doped with PMMA film. The properties were studied by using open and closed aperture Z-scan technique, with different levels of initial intensity (I₀), excited by continuous diode solid-state laser at a wavelength of 532 nm. Three lenses of different focal lengths were employed to change the radius of the Gaussian laser beam and then change the initial intensity. For I₀= 6.83 and 27.304 kWatt/cm², the Z-scan curves show a saturation of absorption (SA) known as the negative type of nonlinearity, in which

Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

    In this paper a method  to determine whether an image is forged (spliced) or not is presented. The proposed method is based on  a classification model to determine the authenticity of a tested image. Image splicing causes many sharp edges (high frequencies) and discontinuities to appear in the spliced image. Capturing these high frequencies in the wavelet domain rather than in the spatial domain is investigated in this paper. Correlation between high-frequency sub-bands coefficients of Discrete Wavelet Transform (DWT) is also described using co-occurrence matrix. This matrix was an input feature vector to a classifier. The best accuracy of 92.79% and 94.56% on Casia v1.0 and Casia v2.0 datasets respectively was achieved. This pe

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

Facial identification is one of the biometrical approaches implemented for identifying any facial image with the use of the basic properties of that face. In this paper we proposes a new improved approach for face detection based on coding eyes by using Open CV's Viola-Jones algorithm which removes the falsely detected faces depending on coding eyes. The Haar training module in Open CV is an implementation of the Viola-Jones framework, the training algorithm takes as input a training group of positive and negative images, and generates strong features in the format of an XML file which is capable of subsequently being utilized for detecting the wanted face and eyes in images, the integral image is used to speed up Haar-like features calc