Metamaterial is the arrangement of "artificial" elements in a periodic manner providing unusual electromagnetic properties. This unusual property has made it an area of interest for last few decades. It has wide applications in antennas. Gain, directivity, bandwidth, efficiency, and many other parameters of microstrip patch antenna can be improved using metamaterials. In this review paper, we first overview the metamaterials, its types and then the application of metamaterials in Microstrip patch antennas over the last 13-15 years.Here, the metamaterials are classified on the basis of permittivity and permeability as shown in Figure 2.In this review paper, the metamaterial and its types on the basis of permittivity and permeability have been studied. Metamaterials has many applications in patch antennas. It can improve the gain, bandwidth, directivity, and the efficiency of the antenna. It can reduce the size, sidelobes, and the backlobes of the antenna. The applications of the metamaterial to improve gain, directivity, size, bandwidth, and efficiency of the patch antenna has also been studied.
Mobility prediction and fault tolerance are extremely difficult due to underwater characteristics. Energy drain is one of the major causes for node faults.Hence, in this research article, a hybrid optimization algorithm is developed for fault-tolerant and accurate localization in UWSN. In this technique, Artificial Butterfly Optimization (ABO) algorithm is applied for finding the distance between the anchors and the sensors. Each non-localized node runs ABO algorithm for finding the distance amid the anchor or beacon nodes. Then, applying Quaternion-based Backtracking Search Optimization (QBSA) algorithm, non-localized sensor nodes are localized and to decrease the localization error based on the Received Signal Strength Indicator (RSSI), battery energy, and distance parameters. Aqua-Sim a tool kit of NS2 is an open-source software developed for Underwater Wireless Sensor Network research, and this proposed algorithm will be implemented using this software. By simulation results, it is shown that the proposed optimization algorithm reduces the localization error, latency, and cost and energy consumption.
The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.
This paper presents a method for the study of the influence of stability of a power transformer on the power system based on the vibration principle. Traditionally, the EMD and EEMD algorithms are employed to test the box vibration signal data of the power transformer under three working conditions. The proposed method utilizes a partial EMD screening along with MPEEMD method for the online monitoring of power transformer. A complete online monitoring system is designed by using the STM32 processor and LabVIEW system. The proposed system is compared with EMD and EEMD algorithms in terms of the number of IMFs obtained by decomposition, maximum correlation coefficient, and mean square error. The inherent mode correlation, when compared with the mean square error of the reconstructed signal, shows that the reconstruction error of MPEEMD algorithm is 4.762×10−15 which is better than the traditional EMD algorithm. It is observed from the results that the proposed method outperforms both EMD and EEMD algorithms.
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