New Integrated Waveguides Concept and Development of Substrate Integrated Antennas with Controlled Boundary Conditions

The unprecedented development of substrate integrated circuits (SICs) has made a widespread necessity for further studies and development of waveguides and antennas based on this technology. As the operating frequency is on the rise, the conventional designs of the substrate integrated components are becoming more problematic and costly. Therefore, some techniques are proposed to improve the performance of the waveguides and antennas based on the concept of substrate integrated technology. First, the problems of the recently developed ridge gap waveguide (RGW) are resolved by introducing a new configuration of this technology which has considerable advantages over the original version of the RGW regarding its construction technology, propagation mode, characteristic impedance, and insertion loss. Second, the configuration of substrate integrated waveguide (SIW), which has been widely accepted for planar and integrated microwave circuits, is modified to operate with low insertion loss at high frequencies without bearing the anisotropic nature of the dielectric material. The substrate integrated antennas have a strong potential to be used in the compact wireless devices as they can be easily integrated with the baseband circuits. In the horn family, the H-plane horn antenna that can be implemented in the integrated form has received considerable attention in recent years. However, numerous problems are associated with this antenna such as limited bandwidth, tapered aperture distribution, high back radiation, and E-plane asymmetry. Several new techniques are introduced to improve the performance of this antenna, especially at millimeter wave frequencies

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications