The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of...The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.展开更多
Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locatio...Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locations.To ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in WSNs.Moreover,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of WSNs.Here,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and authentication.By examining state information,SLA projects a trust model to enhance efficacy of data delivery.Indeed,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational costs.Specifically,this model acts as a verification strategy to accelerate?attackers and to handle isolation.This strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic routing.Simulation is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other approaches.The anticipated model shows better trade off in comparison to the prevailing ones.展开更多
Electric vehicles(EVs)have the potential to mitigate the severity of significant concerns including environmental pollution and reliance on fossil fuels;however,despite strong governmental promotional efforts,their ma...Electric vehicles(EVs)have the potential to mitigate the severity of significant concerns including environmental pollution and reliance on fossil fuels;however,despite strong governmental promotional efforts,their market penetration is still at the nascent stage.This paper empirically investigates the factors that affect the consumers'intention to adopt EVs by conducting an exhaustive literature review.The initial search resulted in 1,690 publications,but after a thorough exclusion process,537 articles were deemed relevant and were sorted by source,publication year,country of origin,data collection method,and research domain.The results revealed the influential factors over individuals’desire to adopt an EV were categorized into four main types(contextual,situational,demographic,and psychological);situational factors,that can act as both barriers and motivators,had the most influencing components.The most cited barriers to adoption of EVs were found to be the lack of charging stations availability and their limited driving range.The most cited motivators to EV adoption were found to be reduction in air pollution and the availability of policy incentives.The findings of this study may guide policymakers in formulating effective transportation and energy policies,as well as provide guidance to those who are responsible for designing EVs that fit the needs and demands of potential consumers.展开更多
文摘The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.
文摘Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locations.To ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in WSNs.Moreover,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of WSNs.Here,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and authentication.By examining state information,SLA projects a trust model to enhance efficacy of data delivery.Indeed,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational costs.Specifically,this model acts as a verification strategy to accelerate?attackers and to handle isolation.This strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic routing.Simulation is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other approaches.The anticipated model shows better trade off in comparison to the prevailing ones.
基金The authors gratefully acknowledge the support and generosity of the Center for Transportation Equity,Decisions and Dollars(CTEDD),without which the present study could not have been completed.
文摘Electric vehicles(EVs)have the potential to mitigate the severity of significant concerns including environmental pollution and reliance on fossil fuels;however,despite strong governmental promotional efforts,their market penetration is still at the nascent stage.This paper empirically investigates the factors that affect the consumers'intention to adopt EVs by conducting an exhaustive literature review.The initial search resulted in 1,690 publications,but after a thorough exclusion process,537 articles were deemed relevant and were sorted by source,publication year,country of origin,data collection method,and research domain.The results revealed the influential factors over individuals’desire to adopt an EV were categorized into four main types(contextual,situational,demographic,and psychological);situational factors,that can act as both barriers and motivators,had the most influencing components.The most cited barriers to adoption of EVs were found to be the lack of charging stations availability and their limited driving range.The most cited motivators to EV adoption were found to be reduction in air pollution and the availability of policy incentives.The findings of this study may guide policymakers in formulating effective transportation and energy policies,as well as provide guidance to those who are responsible for designing EVs that fit the needs and demands of potential consumers.
