A Novel Compact Highly Isolated UWB MIMO Antenna with WLAN Notch

This paper presents a compact Multiple Input Multiple Output(MIMO)antenna with WLAN band notch for Ultra-Wideband(UWB)applications.The antenna is designed on 0.8mmthick low-cost FR-4 substrate having a compact size of 22mm×30 mm.The proposed antenna comprises of two monopole patches on the top layer of substrate while having a shared ground on its bottom layer.The mutual coupling between adjacent patches has been reduced by using a novel stub with shared ground structure.The stub consists of complementary rectangular slots that disturb the surface current direction and thus result in reducing mutual coupling between two ports.A slot is etched in the radiating patch for WLAN band notch.The slot is used to suppress frequencies ranging from 5.1 to 5.9 GHz.The results show that the proposed antenna has a very good impedance bandwidth of|S11|<−10 dB within the frequency band from 3.1–14 GHz.A low mutual coupling of less than−23 dB is achieved within the entire UWB band.Furthermore,the antenna has a peak gain of 5.8 dB,low ECC<0.002 and high Diversity Gain(DG>9.98).

Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks

For efficient and robust information exchange in the vehicular adhoc network,a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel.In addition,we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards.Unfortunately,there may be some misbehaving nodes and due to their selfish and greedy approach,these nodes may not help others on the network.To deal with this challenge,trust-based misbehavior avoidance schemes are generally reflected as the capable resolution.In this paper,we employed a fair incentive mechanism for cooperation aware vehicular communication systems.In order to deploy a comprehensive credit based rewarding scheme,the proposed rewardbased scheme fully depends on secure and reliable cryptographic procedures.In order to achieve the security goals,we used the cryptographic scheme to generate a certified public key for the authenticity of every message exchange over the network.We evaluated the friction of misbehaving vehicles and the effect of rewarding schemes in context with honest messages dissemination over the network.

Fractional order adaptive robust formation control of multiple quad-rotor UAVs with parametric uncertainties and wind disturbances

In recent times,multiple Unmanned Aerial Vehicles(UAVs)are being widely utilized in several areas of applications such as agriculture,surveillance,disaster management,search and rescue operations.Degree of robustness of applied control schemes determines how accurate a swarm of UAVs accomplish group tasks.Formation and trajectory tracking controllers are required for the swarm of multiple UAVs.Factors like external environmental effects,parametric uncertainties and wind gusts make the controller design process as a challenging task.This article proposes fractional order formation and trajectory tacking controllers for multiple quad-rotors using Super Twisting Sliding Mode Control(STSMC)technique.To compensate the effects of the disturbances due to parametric uncertainties and wind gusts,Lyapunov function based adaptive controllers are formulated.Moreover,Lyapunov theorem is used to guarantee the stability of the proposed controllers.Three types of controllers,namely fixed gain STSMC and fractional order Adaptive Super Twisting Sliding Mode Control(ASTSMC)methods are tested for the swarm of UAVs by performing the numerical simulations in MATLAB/Simulink environment.From the presented results,it is verified that in presence of wind disturbances and parametric uncertainties,the proposed fractional order ASTSMC technique showed improved robustness as compared to the fixed gain STSMC and integer order ASTSMC.

Short-and long-term predictions of novel corona virus using mathematical modeling and artificial intelligence methods

This paper proposes spread prediction of novel corona virus outbreak using different compartmental models and artificial intelligence(AI)methods.Real data for several months is collected from the Ministry of Health(MOH)website,Kingdom of Saudi Arabia and two compartmental models,namely SIR(susceptible,infectious,recovered)and SEIRD(susceptible,exposed,infectious,recovered,dead)are utilized to best fit the data.AI methods are well suited for short-and long-term stochastic forecasts.Keeping in view the inherent advantages of AI methods,adaptive neuro-fuzzy inference system(ANFIS)models are trained using the collected data to replicate the dynamic behavior of the COVID-19 spread in Kingdom of Saudi Arabia.The prediction comparison for COVID-19 spread is made between the compartmental and ANFIS models for both short-and long-term forecasts of the experimental data.From the presented results,ANFIS-based models show superior performance as compared to compartmental models.