Microstrip array antennas are essentially for radar and communications systems. They are used to get a needed pattern that cannot be realized with a single element. This paper aims to design and simulate of rectangular microstrip patch array antenna 1 patch (1×1), 2 patches (1×2), and 4 patches (1×4) and improve the performance results. The proposed antenna is simulated by using electromagnetic simulation, computer software technology Microwave studio (CST) printed on Rogers RT5880 (lossy) substrate with dielectric constant 2.2, 0.0009 loss tangent, and thickness 0.1 mm. The simulation results show that the small patch antenna size (1.57 mm × 2 mm) for three designs works at dual bandwidth. The major target of this work is to accomplish an unusual directivity with improved gain for three antenna array designs.

Respiratory diseases indicate severe medical problems. They cause death for more than three million people annually according to the world health organization (WHO). Recently, with corona virus disease 19 (COVID-19) spreading the situation has become extremely serious. Thus, early detection of infected people is very vital in limiting the spread of respiratory diseases and COVID-19. In this paper, we have examined two different models using convolution neural networks. Firstly, we proposed and build a convolution neural network (CNN) model from scratch for classification the lung breath sounds. Secondly, we employed transfer learning using the pre-trained network AlexNet applying on the similar dataset. Our proposed model achieved an accuracy of 0.91 whereas the transfer learning model performing much better with an accuracy of 0.94.

No abstract