The need to exchange large amounts of real-time data is constantly increasing in wireless communication. While traditional radio transceivers are not cost-effective and their components should be integrated, software-defined radio (SDR) ones have opened up a new class of wireless technologies with high security.  This study aims to design an  SDR  transceiver was built using one type of modulation, which is 16 QAM, and adding a  security subsystem using one type of chaos map, which is a  logistic map, because it is a very simple nonlinear dynamical equations that generate a random key and  EXCLUSIVE  OR with the originally transmitted data to protect data through the transmission. At th

In this paper, compared eight methods for generating the initial value and the impact of these methods to estimate the parameter of a autoregressive model, as was the use of three of the most popular methods to estimate the model and the most commonly used by researchers MLL method, Barg method  and the least squares method and that using the method of simulation model  first order autoregressive through the design of a number of simulation experiments and the different sizes of the samples.

     Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

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

     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

Interested current Research measuring damage currency Swap by converting The ministry of higher Education and scientific Research money The Iraqi dinar To U.S dollar by Trade Bank Of Iraq , And that The damage Generated resulting from Deferent Between the Exchange Rate adopted From Central Bank of Iraq and Market Exchange Rate adopted by The Trade Bank Of Iraq , and Which led to the greet damage ( losses ) in Bearing by the ministry, which led to the reduction of the financial allocations for licensed curriculum outside of Iraq , and this in turn leads  to reduction in the number of students Sender ( scholarships )  outside Iraq.

Where the estimated loss (damage) that suffer by the Ministry of H

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

الملخص ان حق الملكية هو الحق الاوسع نطاقا يمنح صاحبه صلاحية ممارسة السلطات كافة ويكون محل هذه السلطات كل ما يملك الشخص سواء كان شقه او طبقة اسوة بالعقارات الاخرى كدار للسكن او ارض ، ومن اهم هذه السلطات واوسعها نطاقا ( هو سلطة التصرف ). تعد هذه السلطة جوهر حق الملكية وأخطر السلطات الممنوحة للمالك كونه بواسطتها يمكنه الاستغناء عن ملكه بأي تصرف ناقل له كالبيع أو الهبة او الوصية مثلا ، ولأهمية هذه السلط

     Foreign Object Debris (FOD) is defined as one of the major problems in the airline maintenance industry, reducing the levels of safety. A foreign object which may result in causing serious damage to an airplane, including engine problems and personal safety risks. Therefore, it is critical to detect FOD in place to guarantee the safety of airplanes flying. FOD detection systems in the past lacked an effective method for automatic material recognition as well as high speed and accuracy in detecting materials. This paper proposes the FOD model using a variety of feature extraction approaches like Gray-level Co-occurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) to extract features and Deep Learning (DL) for classifi