Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their sur...Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard.展开更多
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ...Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.展开更多
In the modern world,the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems.This study aims to enhance road safety by propos...In the modern world,the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems.This study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness,which combines heart rate monitoring and face recognition technologies.The research objectives include developing a non-contact method for detecting driver drowsiness,training and assessing the proposed system using pre-trained machine learning models,and implementing a real-time alert feature to trigger warnings when drowsiness is detected.Deep learning models based on convolutional neural networks(CNNs),including ResNet and DenseNet,were trained and evaluated.The CNN model emerged as the top performer compared to ResNet50,ResNet152v2,and DenseNet.Laboratory tests,employing different camera angles using Logitech BRIO 4K Ultra HD Pro Stream webcam produces accurate face recognition and heart rate monitoring.Real-world vehicle tests involved six participants and showcased the system’s stability in calculating heart rates and its ability to correlate lower heart rates with drowsiness.The incorporation of heart rate and face recognition technologies underscores the effectiveness of the proposed system in enhancing road safety and mitigating the risks associated with drowsy driving.展开更多
文摘Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard.
基金funded by Multimedia University(Ref:MMU/RMC/PostDoc/NEW/2024/9804).
文摘Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.
文摘In the modern world,the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems.This study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness,which combines heart rate monitoring and face recognition technologies.The research objectives include developing a non-contact method for detecting driver drowsiness,training and assessing the proposed system using pre-trained machine learning models,and implementing a real-time alert feature to trigger warnings when drowsiness is detected.Deep learning models based on convolutional neural networks(CNNs),including ResNet and DenseNet,were trained and evaluated.The CNN model emerged as the top performer compared to ResNet50,ResNet152v2,and DenseNet.Laboratory tests,employing different camera angles using Logitech BRIO 4K Ultra HD Pro Stream webcam produces accurate face recognition and heart rate monitoring.Real-world vehicle tests involved six participants and showcased the system’s stability in calculating heart rates and its ability to correlate lower heart rates with drowsiness.The incorporation of heart rate and face recognition technologies underscores the effectiveness of the proposed system in enhancing road safety and mitigating the risks associated with drowsy driving.
