This paper proposes a dual band reconfigurable microstrip slotted antenna for supporting the wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications, providing coverage where both directive and omni-directive radiations are needed. The design consists of a feedline, a ground plane with two slots and two gaps between them to provide the switching capability and a 1.6 mm thick flame retardant 4 (FR4) substrate (dielectric constant Ɛ=4.3, loss tangent δ=0.019), modeling an antenna size of 30x35x1.6 mm3. The EM simulation, which was carried out using the connected speech test (CST) studio suite 2017, generated dual wide bands of 40% (2-3 GHz) with -55 dB of S11 and 24% (5.2-6.6 GHz) higher than its predecessors with lower complexity and -60 dB of S11 in addition to the radiation pattern versatility while maintaining lower power consumption. Moreover, the antenna produced omnidirectional radiation patterns with over than 40% bandwith at 2.4 GHz and directional radiation patterns with 24% bandwith at the 5.8 GHz band. Furthermore, a comprehensive review of previously proposed designs has also been made and compared with current work.

<span>Handwritten digits recognition has attracted the attention of researchers in pattern recognition fields, due to its importance in many applications in public real life, such as read bank checks and formal documents which is a continuous challenge in the last years. For this motivation, the researchers created several algorithms in recognition of different human languages, but the problem of the Arabic language is still widespread. Concerning its importance in many Arab and Islamic countries, because the people of these countries speak this language, However, there is still a little work to recognize patterns of letters and digits. In this paper, a new method is proposed that used pre-trained convolutional neural networks with resnet-34 model what is known as transfer learning for recognizing digits in the arabic language that provides us a high accuracy when this type of network is applied. This work uses a famous arabic handwritten digits dataset that called MADBase that contains 60000 training and 1000 testing samples that in later steps was converted to grayscale samples for convenient handling during the training process. This proposed method recorded the highest accuracy compared to previous methods, which is 99.6%.</span>

<p><span>Animation and virtual reality movie-making technologies are still witnessing significant progress to this day. Building and stimulating virtual characters inside these applications is a goal. Build a 3D face via using some special tools inside the virtual world is the most important part of identifying a 3D animation. Keen Tools Face Builder add-on for Blender. Interested in creating a 3D face of a famous figure, artist or the general public by adopting several 2D images added to the virtual blinder software environment. The main problem facing these tools is that they deal with high-resolution and sharpness pictures because some images that contain blurring, the result is to build a 3D face model that contains design distortions and non- clearly. in this proposed paper, build a data set for 2D pictures of a specific character (actor), at a resolution of 1920 x 1080 pixels. These images were caught by the camera, different in sharpness and blurring (four types of blurry). Using the “Laplacian Filter algorithm” and OpenCV library with Python language, to isolate blurry from sharpness 2D images. Sharpness images used to build a 3D face model that gave real and similar results to the character in the pictures. </span></p>