The present work describes a unique planar wideband circularly polarized MIMO antenna for 4G and Sub-6 5G band (1.35-2.75 GHz), with pattern diversity over the entire axial-ratio bandwidth. The design consists of two tri-branch planar inverted-F antenna (PIFA) antennas with a ground T-stub between the antennas, which is used to realize circular polarization and high isolation. The third antenna is an integrated Sub-6 5G (4.45-4.7 GHz) and LTE band (786.7-807.7 MHz) antenna, which is folded above the ground and placed vertically around the side. It also provides circular polarization at LTE band. The 3 dB axial ratio bandwidth (ARBW) of the MIMO antenna is 1.08 GHz (1.47-2.55 GHz); impedance matching bandwidth (IMBW) is 1.4 GHz (1.35-2.75 GHz); and its isolation is better than 13.4 dB in the whole band. It is fabricated on an FR-4 substrate and is suitable for mobile handset.
The project is basically based upon the determination of parameters of Hydraulic Dynamometer using LabVIEW. LabVIEW is the programming tool that is used for automation purposes mainly in industries. Herein, we will be judging the various parameters like Load Cell, Temperature Sensors etc, by interfacing the digital set-up with our manual set-up. The Load Cell that will be used is the S-shaped Load Cell and the various temperature sensors are used to determine temperatures like water inlet temperature, water outlet temperature, air temperature etc. After measuring these parameters we will be interfacing these devices with the manual dynamometer system. After interfacing, there will be separate meters installed wherein we will be view parameters measured by the system.
The study that was conducted resulted in following results that are mentioned in the graphs and tables below. We have tried to mention more and more tables and graphs related to our study so that it can be possible to figure out the exact motive behind the research conducted.
In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.
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