Chapters authored
New Radiation Pattern-Reconfigurable 60-GHz Antenna for 5G Communications
Reconfigurable beam steering using circular disc microstrip patch antenna with a ring slot is proposed. The overall dimension of the antenna is 5.4 × 5.4 mm2 printed on 0.504 mm thick, RT5870 substrate with relative permittivity 2.3 and loss tangent 0.0012. The designed antenna operates at the expected 60 GHz 5G frequency band with a central coaxial probe feed. Two NMOS switches are utilized to generate three different beam patterns. Activating each switch individually results in a 70° shift in the main beam direction with constant frequency characteristics. The power gain is 3.9–4.8 dB in the three states of switch configurations. Simulated results in terms of return loss, peak gains and radiation pattern are presented and show good performance at the expected 60 GHz band for 5G applications.
Part of the book: Modern Printed-Circuit Antennas
Wavelet Neural Networks for Speed Control of BLDC Motor
In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.
Part of the book: Automation and Control