Abstract-In this paper, we present a new ultra wideband antenna design with band rejection for UWB applications. A CPW-fed circular patch radiates through a circular aperture, which ensures wideband impedance matching and stable omnidirectional pattern over an UWB frequency range, from 3 GHz to 10.6 GHz. In order to avoid interference with WLAN applications, at 5.8 GHz, the antenna is slightly modified to reject undesired band. A semi-circular slot ring is etched on the circular patch at the notch frequency, which creates an open circuit and avoids impedance matching and current propagation. A prototype was fabricated and measured, and the obtained experimental results agree with simulations and show an omnidirectional azimuth pattern over the entire bandwidth.

Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, Kappa coefficient. Proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, Computational cost of 59%, Kappa coefficient of 78%.

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The amount of particles and organic matter in wash-waters and effluent from the processing of fruits and vegetables determines whether they need to be treated to fulfil regulatory standards for their intended use. This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques. Here, the saline water oxidation is carried out based on photovoltaic cell-based renewable and saline water analysis is carried out using Markov fuzzy-based Q-radial function neural networks (MFQRFNN). The plan is entirely web-oriented to enable better control and effective monitoring of water consumption. This monitoring makes use of a communication system that collects data in the form of irregularly spaced time series. Experimental analysis has been carried out based on water salinity data in terms of accuracy, precision, recall, specificity, computational cost, and kappa coefficient.

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