Optimized Neural Network-Based Micro Strip Patch Antenna Design for Radar Application

Microstrip antennas are low-profile antennas that are utilized in wireless communication systems.In recent years,communication engineers have been increasingly interested in it.Because of downsizing,novelty,and cost reduction,the number of wireless standards has expanded in recent years.Wideband tech-nologies have evolved in addition to analog and digital services.Radars necessi-tate antenna subsystems that are low-profile and lightweight.Microstrip antennas have these qualities and are suited for radars as an alternative to the bulky and heavyweight reflector/slotted waveguide array antennas.A perforated corner single-line fed microstrip antenna is designed here.When compared to the basic square microstrip antenna,this antenna has better specifications.Because key issue is determining the best values for various antenna parameters when devel-oping the patch antenna.Optimized Neural Network(ONN)is one potential tech-nique utilized to solve this issue,and this work also uses Particle Swarm Optimization(PSO)to enhance the antenna performance.Return loss(S11)and Voltage Standing Wave Ratio(VSWR)parameters are considered in all situations,developed with Advanced Design System(ADS)applications.The transmitters are made to emit in the Ku-band,which covers a wide range of wavelengths.From 5–15 GHz,it is used in most current radars.The ADS suite is used to create the simulation design.

Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Huge data over the cloud computing and big data are processed over the network.The data may be stored,send,altered and communicated over the network between the source and destination.Once data send by source to destina-tion,before reaching the destination data may be attacked by any intruders over the network.The network has numerous routers and devices to connect to inter-net.Intruders may attack any were in the network and breaks the original data,secrets.Detection of attack in the network became interesting task for many researchers.There are many intrusion detection feature selection algorithm has been suggested which lags on performance and accuracy.In our article we pro-pose new IDS feature selection algorithm with higher accuracy and performance in detecting the intruders.The combination of wrapperfiltering method using Pearson correlation with recursion function is used to eliminate the unwanted fea-tures.This feature extraction process clearly extracts the attacked data.Then the deep neural network is used for detecting intruders attack over the data in the net-work.This hybrid machine learning algorithm in feature extraction process helps tofind attacked information using recursive function.Performance of proposed method is compared with existing solution.The traditional feature selection in IDS such as differential equation(DE),Gain ratio(GR),symmetrical uncertainty(SU)and artificial bee colony(ABC)has less accuracy than proposed PCRFE.The experimented results are shown that our proposed PCRFE-CDNN gives 99%of accuracy in IDS feature selection process and 98%in sensitivity.

Ground-Based In Situ Measurements of Near-Surface Aerosol Mass Concentration over Anantapur:Heterogeneity in Source Impacts

Surface measurements of aerosol physical properties were made at Anantapur （14.62°N, 77.65°E, 331 m a.s.l）, a semiarid rural site in India, during August 2008-July 2009. Measurements included the segregated sizes of aerosolsas as well as total mass concentration and size distributions of aerosols measured at low relative humidity （RH〈75%） using a Quartz Crystal Microbalance （QCM） in the 25-0.05 um aerodynamic diameter range. The hourly average total surface aerosol mass concentration in a day varied from 15 to 70 ug m-3, with a mean value of 34.02±9.05 ug m-3 for the entire study period. A clear diurnal pattern appeared in coarse, accumulation and nucleation-mode particle concentrations, with two local maxima occurring in early morning and late evening hours. The concentration of coarse-mode particles was high during the summer season, with a maximum concentration of 11.81±0.98 ug m-3 in the month of April, whereas accumulationmode concentration was observed to be high in the winter period contributed 〉68% to the total aerosol mass concentration. Accumulation aerosol mass fraction, Af （= Ma/Mt） was highest during winter （mean value of Af -0.80） and lowest （Af - 0.64） during the monsoon season. The regression analysis shows that both Reff and Rm are dependent on coarse-mode aerosols. The relationship between the simultaneous measurements of daily mean aerosol optical depth at 500 nm （AOD500） and PM2.5 mass concentration （[PM2.5]） shows that surface-level aerosol mass concentration increases with the increase in columnar aerosol optical depth over the observation period.