Abstract

In this work, the plasma parameters (electron temperature (T_e), electron density( n_e), plasma frequency (f_p) and Debye length (λ_D)) have been studied by using the spectrometer that collect the spectrum of Laser produce CdTe_(X):S_(1-X) plasma at X=0.5 with different energies. The results of electron temperature for CdTe range 0.758-0.768 eV also the electron density 3.648 10¹⁸ – 4.560 10¹⁸ cm^-3  have been measured under vacuum reaching 2.5 10^-2 mbar .Optical properties of CdTe:S were determined through the optical transmission method using ultraviolet visible spectrophotometer within the r

In this work, the optical emission spectrum technique was used to analyze the spectrum resulting from the CdO:Sn plasma produced by laser Nd:YAG with a wavelength of (1064) nm, duration of (9) ns, and a focal length of (10) cm in the range of energy of 500-800 mJ. The electron temperature (T_e) was calculated using the in ratio line intensities method, while the electron density (n_e) was calculated using Saha-Boltzmann equation. Also, other plasma parameters were calculated, such as plasma (f_p), Debye length (λ_D) and Debye number (N_D). At mixing ratios of X=0.1, 0.3 and 0.5, the CdO_1-X :Sn_X plasma spectrum was recorded for different energies. The change

     A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN),

Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model  in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling  the Symmetric gray scale texture image and estimating by using

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

     In this study, we propose a suitable solution for a non-linear system of ordinary differential equations (ODE) of the first order with the initial value problems (IVP) that contains multi variables and multi-parameters with missing real data. To solve the mentioned system, a new modified numerical simulation method is created for the first time which is called Mean Latin Hypercube Runge-Kutta (MLHRK). This method can be obtained by combining the Runge-Kutta (RK) method with the statistical simulation procedure which is the Latin Hypercube Sampling (LHS) method. The present work is applied to the influenza epidemic model in Australia in 1919  for a previous study. The comparison between the numerical and numerical simulation res

      In this study, the plasma formed by the preparation of Se and Tin (Sn) using a Nd: YAG laser with a wavelength of 1064 nm in air, which was then studied using the technique of optical emission spectrum, was presented (OES).The laser-induced plasma parameters such an electron temperature (Te) were identified using two-ratio methods, using Stark broadening methods to determine the density of electrons (n_e). According to the findings, there is a correlation between the amount of laser energy that is applied and the increase in the emission intensity of the spectral lines. In the case of Se plasma, an increase in laser energy causes a rise in the temperature of the electrons. While increasing the temperature of the elec

          In this work, the optical emission characteristics of the ZnO plasma were presented. The plasma parameters: electron temperature (T_e), electron density( n_e), plasma frequency (f_p) and Debye length (λ_D) were  studied with  a spectrometer that collects the spectrum ZnO plasma in air produced by Nd:YAG laser,(λ=1064 nm) at ratio X=0.5 in the range of energy of (700-1000 mJ), duration (10 ns). The Boltzmann plot methodwas employed to calculate the electron temperature (T_e), while the Stark broadening  was used to determine the electron density (n_e), Debye duration (λ_D), and plasma frequency (f_p).  T_e, ne, and f_p

 In this work, plasma parameters such as (electron temperature (Te), electron density (ne), plasma frequency (fp) and Debye length (λD)) were studied using spectral analysis techniques. The spectrum of the plasma was recorded with different energy values, SnO2 and ZnO anesthetized at a different ratio (X = 0.2, 0.4 and 0.6) were recorded. Spectral study of this mixing in the air. The results showed electron density and electron temperature increase in zinc oxide: tin oxide alloy targets. It was located  that  The intensity of the lines increases in different laser peak powers when the laser peak power increases and then decreases when the force continues to increase.