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This paper addresses the problem of exploring an unknown environment using multiple robots and maintaining the communication among the team of robots. The existing exploration methods assume that the communication is maintained among the team of robots throughout the exploration process. However, this is not possible when the area to be explored is very large and when there is limited communication range. The presented exploration methods provide communication maintenance among the team of robots and maintain coordination. Three different approaches are presented for exploration. They are centralized approach, leader-follower approach, and ad hoc approach. The concept of centralized approach is developed where the base station is connected to all the robots in the team. The concept of leader-follower approach is developed, where among the team of robots, a leader will be selected. The concept of ad hoc approach is developed in which even though the robots are not within the communication range of one another, communication will occur through ad hoc network. The performance evaluation is done for the above three approaches based on the path length, exploration time, and total number of cycles required to explore the complete area. Systematic approach is used to examine the effect of influence of the exploration parameters like the number of robots, communication range of a robot, and sensor range of a robot on the performance metrics. It is investigated that when there is increase in the exploration parameters, then the exploration time is reduced.

Wireless body area networks (WBAN) are becoming a promising solution for health care applications. WBAN allows monitoring of patients continuously in their own comfort zone. These devices use the industrial, scientific, and medical band (ISM) for communication. This band is overcrowded due to the increasing number of wireless medical devices and other wireless devices occupying this band. This causes interference, which can be damaging and could result in a change in received power. However, WBAN also needs minimum and reliable energy communication for a longer lifetime and improved quality of service. This work addresses both problems and proposes solutions for the same. A cognitive radio controller is employed as a centralized controller with dynamic spectrum allocation properties to mitigate the interference. The sensing of the spectrum is based on compressed sensing with a nonreconstruction model to save energy. To quantify interference measurement, the interference mitigation factor is introduced. Further, to increase energy efficiency, the K-means algorithm is used to cluster WBANs. However, critical emergency data and normal data are categorized as priority data and normal data, respectively, by the proposed priority scheduling algorithm. The performance of this cognitive radio-based system for telemedicine applications is analyzed through simulations. The simulations are performed using MATLAB 2019.

Hard turning has replaced conventional grinding in production processes in recent years as an emerging technique. Nowadays, coated carbide tools are replacing expensive CBN inserts in turning. Wear is a significant concern when turning with coated carbide; it immediately affects the acceptability of the machined surface, which causes machine downtime and loss due to wastage in machined parts. Online tool condition monitoring (TCM) is required to prevent such critical conditions. Hard turning differs from conventional turning in energy balance during metal cutting, resulting in greater thrust force; hence, the TCM model presented for conventional turning may not be suitable for hard turning. Hence, tool wear prediction for turning is projected based on thrust force using an artificial neural network (ANN). All of the tests were done using a design of experiments called full factorial design (FFD). The specimens were made of AISI 4140 steel that had been hardened to 47 HRC, and the inserts were made of coated carbide. The most impactful input features for wear, selected based on experimental outputs, were given to the neural network and trained. Tool wear is an estimated output from the training set that has been validated with satisfactory results for random conditions. The 5–10–1 network structure with the Levenberg–Marquardt (LM) learning algorithm, R2 values of 0.996602 and 0.969437 for the training and testing data, and mean square error values of 0.000133152 and 0.004443 for the training and testing data, respectively, gave the best results. The MEP values of 0.575407 and 2.977617 are very low (5%). The LM learning algorithm-based ANN is good at predicting tool wear based on how well it predicts tool wear for both the testing set and the training set.