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Current 4G - the fourth-generation wireless communication, which exists in most countries represents an advance of the previous 3 generation wireless communication. However, there are some challenges and limitations, associated with an explosion of wireless devices, which cannot be accommodated by 4G. Increasing the proliferation of smart devices, the development of new multimedia applications and growing demand for high data rates are among the main problems of the existing 4G system. As a solution, the wireless system designers have started research on the fifth-generation wireless systems. 5G will be the paradigm shift that could provide with ultra-high data rate, low latency, increase of the base station capacity and improved quality of services. This paper will be the review of the changes through the evolution of existing cellular networks toward 5G.  There will be represented a comprehensive study associated with 5G, requirements for 5G, its advantages, and challenges. We will explain the architecture changes – radio access network (RAN), air interfaces, smart antennas, cloud RAN and HetNet. Furthermore, there will be discussed about physical layer technologies, which include new channel modes estimation, new antenna design, and MIMO technologies. Also, we will discuss the MAC layer protocols. We will look at three kinds of technologies: heterogeneous networks, massive multiple-input, and output, millimeter-wave. Finally, there will be explained the applications, supported by 5G, new features, various possibilities, and predictions.

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The Deep Residual Network (ResNet) learning model is known to achieve better accuracy and requiring shorter training time compared to other pre-trained learning models for image classifications and recognition. In this paper, the use of ResNet networks for semantic segmentation of coral reefs images was explored. Three ResNet networks (ResNet-18, ResNet-50, and ResNet-101) were evaluated and compared using 900 images as training dataset and 38 images as test dataset. The last three layers of the pre-trained ResNets were replaced with a set of layers that classified each pixel of the images into four classes: 'dead', 'alive', 'sand' and 'unknown'. A Softmax layer was introduced to reduce the imbalanced defects. Then, DeepLabv3+ employed the Atrous convolution to extract the features computed by applied CNN and segment the pixels of the object. ResNet-101 was shown to achieve better results compared to ResNet-18 and ResNet-50. Further analysis of the results implied that the class weightage assignment needs to be improved and more a larger training dataset should be acquired.

This paper presents a design of dual resonance high frequency bandpass filter using Sonnet EM Simulator. Filter prototype is enhanced by adding the short stub in the center of the design. A parametric study shows the impact of choosing appropriate configuration characteristics. This is a dual resonance bandpass filter design, with center frequencies  and . Performed input match  at first resonance is  and insertion loss  is . The obtained results at second resonance are  and . Due to its characteristics, this is a high-Q medium bandwidth dual resonance bandpass filter. The design is simulated and planning to be fabricated with 0.36 mm thickness and dielectric constant . This paper benefits from its compact size and simple geometry over the similar filters found in literature.