After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.

Abstract

Hexapod robot is a flexible mechanical robot with six legs. It has the ability to walk over terrain. The hexapod robot look likes the insect so it has the same gaits. These gaits are tripod, wave and ripple gaits. Hexapod robot needs to stay statically stable at all the times during each gait in order not to fall with three or more legs continuously contacts with the ground. The safety static stability walking is called (the stability margin). In this paper, the forward and inverse kinematics are derived for each hexapod’s leg in order to simulate the hexapod robot model walking using MATLAB R2010a for all gaits and the geometry in order to derive the equations of the sub-constraint workspaces for each

In this paper a decoder of binary BCH code is implemented using a PIC microcontroller for code length n=127 bits with multiple error correction capability, the results are presented for correcting errors up to 13 errors. The Berkelam-Massey decoding algorithm was chosen for its efficiency. The microcontroller PIC18f45k22 was chosen for the implementation and programmed using assembly language to achieve highest performance. This makes the BCH decoder implementable as a low cost module that can be used as a part of larger systems. The performance evaluation is presented in terms of total number of instructions and the bit rate.

     Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes.  The data augmentation techniques have been used. In addition to dropout and weight reg

The origin of this technique lies in the analysis of François Kenai (1694-1774), the leader of the School of Naturalists, presented in Tableau Economique. This method was developed by Karl Marx in his analysis of the Departmental Relationships and the nature of these relations in the models of " "He said. The current picture of this type of economic analysis is credited to the Russian economist Vasily Leontif. This analytical model is commonly used in developing economic plans in developing countries (p. 1, p. 86). There are several types of input and output models, such as static model, mobile model, regional models, and so on. However, this research will be confined to the open-ended model, which found areas in practical application.

Image compression has become one of the most important applications of the image processing field because of the rapid growth in computer power. The corresponding growth in the multimedia market, and the advent of the World Wide Web, which makes the internet easily accessible for everyone. Since the early 1980, digital image sequence processing has been an attractive research area because an image sequence, as acollection of images, may provide much compression than a single image frame. The increased computational complexity and memory space required for image sequence processing, has in fact, becoming more attainable. this research absolute Moment Block Truncation compression technique which is depend on adopting the good points of oth

A description of the implementation of integrated practical work in a remote laboratory was presented in this paper. The student, in real time, can access an online web page in order to manipulate a practical work of digital electronics. This work is based on the use of an embedded system PcDuino. The hardware architecture and software solutions are described, as well as the supervision tool that allows the student to follow changes in the output states of the Practical Work remotely.

As s widely use of exchanging private information in various communication applications, the issue to secure it became top urgent. In this research, a new approach to encrypt text message based on genetic algorithm operators has been proposed. The proposed approach follows a new algorithm of generating 8 bit chromosome to encrypt plain text after selecting randomly crossover point. The resulted child code is flipped by one bit using mutation operation. Two simulations are conducted to evaluate the performance of the proposed approach including execution time of encryption/decryption and throughput computations. Simulations results prove the robustness of the proposed approach to produce better performance for all evaluation metrics with res

The integration of decision-making will lead to the robust of its decisions, and then determination optimum inventory level to the required materials to produce and reduce the total cost by the cooperation of purchasing department with inventory department and also with other company^,s departments. Two models are suggested to determine Optimum Inventory Level (OIL), the first model (OIL-model 1) assumed that the inventory level for materials quantities equal to the required materials, while the second model (OIL-model 2) assumed that the inventory level for materials quantities more than the required materials for the next period.             &nb