The coronavirus,formerly known as COVID-19,has caused massive global disasters.As a precaution,most governments imposed quarantine periods ranging from months to years and postponed significantfinancial obligations.Furt...The coronavirus,formerly known as COVID-19,has caused massive global disasters.As a precaution,most governments imposed quarantine periods ranging from months to years and postponed significantfinancial obligations.Furthermore,governments around the world have used cutting-edge technologies to track citizens’activity.Thousands of sensors were connected to IoT(Internet of Things)devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps,privacy and security issues regarding data transmission and memory space abound.In this study,we suggest a block-chain-based methodology for safeguarding data in the billions of devices and sen-sors connected over the internet.Various trial secrecy and safety qualities are based on cutting-edge cryptography.To evaluate the proposed model,we recom-mend using an application of the system,a Raspberry Pi single-board computer in an IoT system,a laptop,a computer,cell phones and the Ethereum smart contract platform.The models ability to ensure safety,effectiveness and a suitable budget is proved by the Gowalla dataset results.展开更多
With the development of artificial intelligence technology,various sectors of industry have developed.Among them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems ...With the development of artificial intelligence technology,various sectors of industry have developed.Among them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively conducted.Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed.In this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions.The robot should be able to follow the trajectory with speed control.Two controllers were used simultaneously to achieve this.Convolutional neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed control.This study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022TR140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The coronavirus,formerly known as COVID-19,has caused massive global disasters.As a precaution,most governments imposed quarantine periods ranging from months to years and postponed significantfinancial obligations.Furthermore,governments around the world have used cutting-edge technologies to track citizens’activity.Thousands of sensors were connected to IoT(Internet of Things)devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps,privacy and security issues regarding data transmission and memory space abound.In this study,we suggest a block-chain-based methodology for safeguarding data in the billions of devices and sen-sors connected over the internet.Various trial secrecy and safety qualities are based on cutting-edge cryptography.To evaluate the proposed model,we recom-mend using an application of the system,a Raspberry Pi single-board computer in an IoT system,a laptop,a computer,cell phones and the Ethereum smart contract platform.The models ability to ensure safety,effectiveness and a suitable budget is proved by the Gowalla dataset results.
文摘With the development of artificial intelligence technology,various sectors of industry have developed.Among them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively conducted.Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed.In this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions.The robot should be able to follow the trajectory with speed control.Two controllers were used simultaneously to achieve this.Convolutional neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed control.This study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.
