Through the ages the importance of the swarm intelligence-based algorithm has gained popularity in the field of optimization. The Whale Optimization Algorithm (WOA) is one such popular meta-heuristics nature inspired swarm intelligence-based algorithm which is well accepted for solving optimization problems, as it is simple and capable to obtain optimal results efficiently. In this paper, few benchmark functions along with radiation patterns of concentric circular antenna array are presented and their performance is optimized using WOA. Simulation results are shown by taking various benchmark functions, namely: Three hump & Six hump camel function, Ackley, Egg Crate Function, and Egg Holder Function. The capability of the WOA algorithm is also tested through pattern synthesis with respect to the thinned array as well as non-uniformly excited array for four diverse sets of CCAA geometries. When comparison is being done with respect to renowned and most popular optimization approaches like PSO, the WOA technique proves to be excellent in terms of maximum SLL suppression. Optimization results show the better capability of WOA in the field of optimization.

Microstrip antennas are high gain aerials for low-profile wireless applications working with frequencies over 100 MHz. This paper presents a study and design of a low cost slotted-type microstrip patch antenna that can be used in 5G millimeter wave applications. This research focuses on the effect of ground slots and patch slots which, in turn, affect different antenna parameters, such as return loss, VSWR, gain, radiation pattern, and axial ratio. The working frequency range varies from 24 to 28 GHz, thus falling within 5G specifications. A subset of artificial intelligence (AI) known as particle swarm optimization (PSO) is used to approximatively solve issues involving maximization and minimization of numerical values, being highly challenging or even impossible to solve in a precise manner. Here, we have designed and analyzed a low-profile printed microstrip antenna for 5G applications using the AI-based PSO approach. The novelty of the research is mainly in the design approach, compactness of size and antenna applicability. The antenna was simulated with the use of HFSS simulation software.

The main objective of this paper is to design and develop a virtual doctor robot (VDR) that will operate on the command of the actual doctor available far away from the patient through new technology AI and IoT. It is not possible for doctors to be present everywhere and every time, especially in remote areas of India during COVID-19. As a result, in an emergency, many patients lose their lives because they couldn’t reach the hospital on time or the doctor couldn’t be available on time. In such a situation, virtual doctor robots play a vital role in healthcare with real-time data processing with machine learning algorithms. In this paper, technological innovation has created new opportunities to enhance doctor services without contact using robots that can assist patients in dealing with their illnesses and providing solutions. Virtual doctor robots can benefit healthcare by satisfying patients with more precise solutions. The virtual doctor robot design presented in this paper can rescue patients in remote areas during any pandemic like COVID-19. Through this virtual doctor robot, the doctors can observe and communicate with the patients through video calls and prescribe necessary medicines through scanner. The proposed VDR will help the doctors check the patient's pulse, body temperature, heart rate, etc. through different sensors mounted in the robot structure and send that data using the Wi-Fi network. The doctors will use an IoT-based panel to control and monitor the robot and patients. The control commands that are sent by the doctor to the VDR are sent online and the robot controller then receives these commands. The VDR also showcases other functions like alerting the battery status to remind one that the battery needs to be charged. It also stores the data related to diagnosed patients through cloud networking. The framework of VDR is based on an automated vehicle with a four-wheel drive for its movement and communication between the doctor and VDR is in real-time through the internet. This paper also discusses the future trends of Virtual Doctor robots for the health sector, humanoid robots for surgical help, and other activities of the healthcare sector.

Background: Antennas serve a vital aspect in modern wireless communication. Designing antennas with very high directivity is very important to solve the long-distance communication problem. Though regularly excited and evenly spaced linear antenna arrays delivers good directivity but also leads to problem related to higher side lobe. For diminishing the level of side lobe, the array can be constructed either by amending the excitation amplitudes non-uniformly with all physical spaces of the antenna elements keeping consistent or vice versa. Methods: In this work, a novel mathematical objective function has been formulated. The objective function has been solved using a recently developed evolutionary optimization technique, i.e., Binary cat swarm optimization. So for better efficiency, the cat swarm optimization technique has been modified. Results: The results have been compared with the popular algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) in terms of Side Lobe Level (SLL), achieved fitness and execution time. The proposed algorithm achieves 0.5dB, 1.7 dB and 3dB smaller SLL as compared to CSO, PSO and GA respectively. In addition to SLL, achieved fitness using BCSO is in the range of 0.001 which is smallest among the compared algorithms. Conclusion: It was found that the modified version namely binary cat swarm optimization algorithm outperform other well-known evolutionary optimization algorithms.