The nanocomposite on the base of synthesis Copper iodide
nanoparticles and polyvinyl alcohol (PVA/CuI) with different
concentration of CuI were obtained using casting technique.
PVA/CuI polymer composite samples have been prepared and
subjected to characterizations using FTIR spectroscopy, The FTIR
spectral analysis shows remarkable variation of the absorption peak
positions with increasing CuI concentration. The obtained results by
X-ray diffraction indicated the formation of cubic CuI particles. The
effects of CuI concentrations on the optical properties of the PVA
films were studied in the region of wavelength, (190-1100) nm.
From the derivation of Tauc's relation it was found that the direct
allowed t

Chalcopyrite thin films ternary Silver Indium Diselenide AgInSe2 (AIS) pure and Aluminum Al doped with ratio 0.03 was prepared using thermal evaporation with a vacuum of 7*10-6 torr on glass with (400) nm thickness for study the structural and optical properties. X-ray diffraction was used to show the inflance of Al ratio dopant on structural properties. X-ray diffraction show that thin films AIS pure, Al doped at RT and annealing at 573 K are polycrystalline with tetragonal structure with preferential orientation (112). raise the crystallinity degree. AFM used to study the effect of Al on surfaces roughness and Grain Size Optical properties such as the optical band gap, absorption coefficient, Extinction coefficient, refractive ind

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Thrust blocks and restraint joints are the two most popular methods of counteracting the thrust force that generated at pipe fittings (bends, Tee, wye, reducers, dead ends, etc…). Both systems perform the same function, which is to prevent the joints from separating from the pipes. The aim of the study is to review previous studies and scientific theories related to the study and design of thrust blocks and restraint joints to study the behavior of both systems under thrust force and to study the factors and variables that affect the behavior of these systems. The behavior of both systems must be studied because they cannot be abandoned, as each system has conditions whose use is more feasible, scientific, and economic

Iraq suffers from serious pollution with harmful particles that have important direct and indirect effects on human activities and human health. In this research, a system for detecting pollutants in the air was designed and manufactured using infrared laser technology. This system was used to detect the presence of pollutants in the dust storms that swept the city of Baghdad which could have a negative impact on human health and living organisms.

The designed detection system based on the use of infrared laser (IR) with a wavelength of 1064 nm was used for the purposes of detecting pollutants based on the scattering of the laser beam from these pollutants. The system was aligned to obtain the best signal for the scattered rays, w

Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less numb