Polycrystalline Cadmium Oxide (CdO) thin films were prepared
using pulsed laser deposition onto glass substrates at room
temperature with different thicknesses of (300, 350 and 400)nm,
these films were irradiated with cesium-137(Cs-137) radiation. The
thickness and irradiation effects on structural and optical properties
were studied. It is observed by XRD results that films are
polycrystalline before and after irradiation, with cubic structure and
show preferential growth along (111) and (200) directions. The
crystallite sizes increases with increasing of thickness, and decreases
with gamma radiation, which are found to be within the range
(23.84-4.52) nm and (41.44-4.974)nm before and after irradiation for
thickness 350nm and 400nm respectively, The dislocation density,
microstrain and number of crystallites per unit surface area,
decreases with increasing of thickness, while they increases with
gamma radiation. From the atomic force microscope (AFM), the
grain size of CdO films decrease from 96.69nm before radiation to
89.49 nm after gamma radiation and RMS roughness increases for
the irradiated sample from 4.26nm to 4.8nm, increase in the surface
roughness is advantages as it increases the efficiency of the CdO
solar cells. The optical properties for thin CdOfilms with different
thickness before and after gamma irradiation have been determined
and reveals direct energy gap. It is decrease with the increase of
thickness, while it is increase after gamma irradiation. These films a
promising candidate for the window layer in solar cells and other
possible optoelectronic application.
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
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