<span>It is worth mentioning that the use of wireless systems has been increased in recent years and supposed to highly increase in the few coming years because of the increasing demands of wireless applications such as mobile phones, Internet of Things (IoT), wireless sensors networks (WSNs), mobile applications and tablets. The scarcity of spectrum needs to be into consideration when designing a wireless system specially to answer the two following questions; how to use efficiently the spectrum available for the available networks in sharing process and how to increase the throughput delivered to the serving users. The spectrum sharing between several types of wireless networks where networks are called cognitive networks is used to let networks cooperate with each other by borrowing some spectrum bands between them especially when there is an extra band that is not used. In this project, the simulation of spectrum sensing and sharing in cognitive networks is performed between two cognitive networks. This project discusses the performance of probability of energy detected (Pd) with different values of false alarm (Pf) and Signal-To-Noise Ratio (SNR) values to evaluate the performance of the sensing and sharing process in cognitive networks. The results show that when the request of sharing spectrum increased, the full sharing process occurs for a long time and the error rate decreases for small values of SNR.</span>

With continuous development of hacking technologies, it is necessary to provide users with a protected background that secures their resources against unlawful access by enforcing control mechanisms. To neutralize such these threats, the enhanced one time pad (OTP) technique has been introduced. Accordingly, to encrypt text, the key is combined with the message. The key length has to have a same length of the message.In this paper, a new method to enhance OTP data encryption using AND and XOR functions has been adopted. Firstly, message and original key are combined by logical AND operation, then the new text will be XORed with a key again to produce more complicated cipher.

<p>One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data preprocessing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are; precision of 99.1% for pituitary, sensitivity of 98.7% for glioma, specificity of 99.1%, and accuracy of 99.1% for pituitary. The overall accuracy obtained is 96.1%.</p>

Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.

A solar chimney power plant model, consisting of a solar collector to produce a hot air when the incident solar radiation hit it, a solar chimney and a wind turbine with generator was investigated in this study. The mathematical model as a tool was used to study and analyze the performance of the power plant for electrical production in Baghdad city of Iraq as a result a mathematical equation was obtained for the hot air outlet temperature from the collector as a function of collector’s area and solar radiation. The Manzanares model was used in this study and the results obtained from the proposed model of solar tower, having the height 195 m, diameter of 10 m, and the solar collector diameter of 244 m were compared with the results obtained when the solar tower configuration is changed. The results indicate that the significant impact to improve the output power is by increasing the collector’s diameter from 244 m to 300 m. It is also found that output power is effectively dependent on the chimney’s height, it yields moderate increasing in power output when the height is increased from 195 m to 300 m, and the chimney’s diameter has a lower impact on solar tower output power in comparison with the other configuration of solar tower when it increases from 10 m to 20 m.