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The purpose of this paper is to build a neural network (NN) inverse model for the multi-band unequal-power Wilkinson power divider (WPD). Because closed-form expressions of the inverse input–output relationship do not exist, the NN becomes an appropriate choice, because it can be trained to learn from the data in inverse modeling. The design parameters of WPD are the characteristic impedances, lengths of the transmission line sections and the isolation resistors. The design equations used to train the NN inverse model are based on the even–odd mode analysis.

An inverse model of a multi-band unequal WPD using NNs is presented. In inverse modeling of a microwave component, the inputs to the model are the required electrical parameters such as reflection coefficients, and the outputs of the model are the geometrical or the physical parameters.

For verification purposes, a quad-band WPD and a penta-band WPD are designed. The results of the full-wave simulations verify the validity of the design procedure. The resulting NN model outperforms traditional time-consuming optimization procedures in terms of computation time with acceptable accuracy. The designed WPDs using NN are implemented by microstrip lines and verified by using full-wave analysis based on high-frequency structure simulator (HFSS). The results of the microstrip WPDs have good agreements with the corresponding results obtained by using ideal transmission line sections.

The associated time-consuming procedure and computational burden in realizing WPD through optimization are major disadvantages; needless to mention the substantial increase in optimization time because of the multi-band design. NNs are one of the best candidates in addressing the abovementioned challenges, owing to their ability to process the interrelation between electrical and geometrical/physical characteristics of the WPD in a superfast manner.

The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.

The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.

To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods.

Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

This study aims to investigate the nature of the relationship between the state and civil society after the 2011 uprising.

The study adopted Mygdal’s approach to analyze the relationship between the state and civil society and identify their ability to control the rules of the political game. The study also draws on the theoretical framework of the hypotheses introduced by a number of scholars on the forms of potential relations between the state and civil society, and the impact of these forms on advancing the process of democratization.

This study argues that despite some important changes in favor of civil society vis-à-vis the state, it is too early to conclude that a dramatic change has occurred in this relationship, due to a discernable unbalanced power in favor of the state. The state revealed after 2011 that these organizations acted against the state’s stability and against its fundamentals.

To the best of the author’s knowledge, this research is the first to study the relationship between the state and the civil society in Egypt after 2011 events.