In this paper a low profile patch antenna has been designed for 5G communication application. The resonating frequency has been chosen as 3.5 GHz for 5G application. FR4 epoxy material with permittivity of 4.3 has been chosen for the substrate material which has fire redundant property. The size of the substrate is 25.2×48 mm². The main radiating patch is in a shape of ellipse and the line feed technique is used. The simulation is done by CST microwave studio software. Different parameters like S-Parameter, Antenna Gain, Directivity, Efficiency has been observed. The antenna has a gain of more than 5 dB which is very useful for communication applications. The antenna is designed for 5G communication applications.

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.

Clustering evaluation techniques are important to check the clustering algorithm quality. High cluster similarity help to reduce the distance between a node to node within the cluster, also good separation was more important to avoid overlapping clusters. The network performance will increase and the signal will be high. Many researchers proposed different validation indexes such as Davies–Bouldin, Dunn, and Silhouette indexes. These cluster validation indexes focus on the internal or external cluster similarity, and some of them deal with both cases. The employing of graph-based distance to non-spherical clusters and selection of reference points will not be effective all the time because the average distance between reference points and all nodes will be changed dynamically such as in the VANET application. To solve this problem a dynamic sample node should be selected or the similarity of all nodes should be checked. This paper proposes a new Minimum intra-distance and Maximum inter-distance Index (M2I) to improve these indexes. The proposed index checks the internal similarity and the external distance among all nodes from cluster to cluster to ensure that high separation will occur. M2I checks the similarity from node to node within the cluster and cluster to cluster. The proposed index will be an improvement of all high-rank indexes. The proposed index was applied in different scenarios (VANET and real datasets scenarios) and compared with other indexes. The index result shows that the proposed M2I outperforms the others. The M2I accuracy is 100% in the VANET scenario and 89% in the real datasets scenario.

Renewable solar energy is the key target to reduce fossil fuel consumption, minimize global warming issues, and indirectly minimizes erratic weather patterns. Herein, the authors synthesized an ultrathin reduced graphene oxide (rGO) nanosheet with ~47 nm via an improved Hummer’s method. The TiO₂ was deposited by RF sputtering onto an rGO nanosheet with a variation of temperature to enhance the photogenerated electron or charge carrier mobility transport for the photoanode component. The morphology, topologies, element composition, crystallinity as well as dye-sensitized solar cells’ (DSSCs) performance were determined accordingly. Based on the results, FTIR spectra revealed presence of Ti-O-C bonds in every rGO-TiO₂ nanocomposite samples at 800 cm^–1. Besides, XRD revealed that a broad peak of anatase TiO₂ was detected at ~25.4° after incorporation with the rGO. Furthermore, it was discovered that sputtering temperature of 120 °C created a desired power conversion energy (PCE) of 7.27% based on the J-V plot. Further increase of the sputtering temperature to 160 °C and 200 °C led to excessive TiO₂ growth on the rGO nanosheet, thus resulting in undesirable charge recombination formed at the photoanode in the DSSC device.

With the goal of providing stable electric quality and safety, it is important for power utilities to maintain their substation equipment by predicting and diagnosing their failures. The common failure is dielectric break downs due to insulator wear and tear. The goal is to develop a non destructive, non contact testing method to predict the location of these failures for preventive maintenance work. Partial discharges (PD) are emitted in the case of insulator deterioration. This paper focuses on the development of methods/algorithm to accurately locate and detect these PD signals within the 3D space of a medium voltage (MV) switch gear. Along with this, the methods described in this paper also allow for the PD to be detected while the switch gear is still live, omitting the need to disrupt power while detecting the PD location. The core principles chosen to detect PD in these conditions are radiometric signal detection. Results are presented showing the location ability both in simulation and live field conditions.

This paper describes a method to determine the dielectric constant of FR4 substrate material at the operating frequency of 2.3 GHz. The method make used of computer aided engineering (CAE) tool and microstrip bandstop resonator for investigating the dielectric value of a material. In accordance with the data sheet provided by manufacturer which is 4.5 at 10 MHz, a set of preliminary dielectric values are selected in order to explain the proposed technique. Microstrip bandstop resonators with different dimension are designed and analyzed with the electromagnetic simulator. They are fabricated using conventional wet-etching process which is relatively economical compared to other method. After that, the actual responses of the square ring resonators are measured with the vector network analyzer. By matching up the simulation results to the measured results, dielectric values selected earlier are tuned accordingly. The obtained values are used to validate the accuracy of the proposed method. The method is simple to practice. It provides the beginners a fast way and relatively straightforward method in investigating FR4 materials for their research and learning.