One of the most important difficulties which doctors face in diagnosing is the analysis and diagnosis of brain stroke in magnetic resonance imaging (MRI) images. Brain stroke is the interruption of blood flow to parts of the brain that causes cell death. To make the diagnosis easier for doctors, many researchers have treated MRI images with some filters by using Matlab program to improve the images and make them more obvious to facilitate diagnosis by doctors. This paper introduces a digital system using hardware concepts to clarify the brain stroke in MRI image. Field programmable gate arrays (FPGA) is used to implement the system which is divided into four phases: preprocessing, adjust image, median filter, and morphological filters alternately. The entire system has been implemented based on Zynq FPGA evaluation board. The design has been tested on two MRI images and the results are compared with the Matlab to determine the efficiency of the proposed system. The proposed hardware system has achieved an overall good accuracy compared to Matlab where it ranged between 90.00% and 99.48%.

A numerical study is achieved on a new shape of temperature saver solar collector using an artificial neural network. The storage collector is a triangle face and a right triangle pyramid for the volumetric shape. It is obtained by cutting a cube from one upper corner at 45°, down to the opposite hypotenuse of the base of the cube. The numerical study was carried out using the computational fluid dynamics code (ANSYS-Fluent) software with natural convection phenomenon in the pyramid enclosure. Elman backpropagation network is used for his ability to find the nearest solution with the smallest error rate. The network consists of three layers, each of different corresponding weights. The results show that the temperature and velocity distributions throughout the operating period were obtained. The influence of introducing an internal partition inside the triangular storage collector was investigated. Also the optimum geometry and location for this partition were obtained. The enhancement was best at y = 0.25 m, whereas the height of triangular collector was 0.5 m. The hourly system performance was evaluated for all test conditions. The performance of the NN to train a model for this work was 0.000207, while the error of the calculation was 1×10-2 as average.

A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically.