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.

Antennas are a vital component of the wireless body sensor networks devices. A wearable antenna in this system can be used as a communication component or energy harvester. This paper presents a detailed review to recent advances fabrication methods for flexible antennas. Such antennas, for any applications in wireless body sensor networks, have specific considerations such as flexibility, conformability, robustness, and ease of integration, as opposed to conventional antennas. In recent years, intriguing approaches have demonstrated antennas embroidered on fabrics, encapsulated in polymer composites, printed using inkjets on flexible laminates and a 3-D printer and, more interestingly, by injecting liquid metal in microchannels. This article presents an operational perspective of such advanced approaches and beyond, while analyzing the strengths and limitations of each in the microwave as well as millimeter-wave regions. Navigating through recent developments in each area, mechanical and electrical constitutive parameters are reviewed, and finally, some open challenges are presented as well for future research directions.

This research work presents a planar compact electromagnetic bandgap (EBG) structure with the potential to reduce the mutual coupling between the elements of a microstrip antenna array. The proposed structure is investigated at 5.59 GHz, which is the centre frequency of the wireless local area network band. To achieve the highest radiation performance for microstrip antenna arrays, with minimal inter‐element spacing and mutual coupling, different unit cell arrangements were considered along with two adjacent patch elements. The simulations and measurement results for the proposed arrangements indicate that the mutual coupling tends to diminish significantly. For instance, when adjacent patches are spaced by 0.4λ, the mutual coupling improves by ∼25 dB. For the particular spacing of 0.4λ, it is favourably observed that the proposed EBG cells can also improve the antenna gain by ∼2.5 dB. Such improvements can be attributed to the compactness of the cells (∼λ/8 × λ/10) and their remarkable ability to suppress the surface waves.

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In this paper, a new neuro-based approach using a feed-forward neural network is presented to design a Wilkinson power divider. The proposed power divider is composed of symmetrical modified T-shaped resonators, which are a replacement for quarter-wave transmission lines in the conventional structure.The proposed technique reduces the size of the power divider by 45% and suppresses unwanted bands up to the fifth harmonics. To verify the concept, a prototype of the power divider has been fabricated and tested, exhibiting good agreement between the predicted and measured results. The results show that the insertion loss and the isolation at the center frequency are about 3.3 ± 0.1 dB and 23 dB, respectively. K E Y W O R D Sartificial intelligence, couplers, evolutionary optimization, harmonic suppression, lumpedequivalent circuit, microstrip technology, neural network, Wilkinson power divider