Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688246
Burak Taşcı
The number of people dying from brain tumors is increasing day by day. Early diagnosis is very important in the treatment planning and evaluation of the treatment outcome of brain tumors. A patient with a brain tumor may be more likely to survive by applying the right treatment methods if the disease is diagnosed early. Medical imaging methods have an important role in the identification and diagnosis of brain tumors. One of the most popular medical imaging methods is Magnetic Resonance Imaging, MRI. Determining the presence of tumors and tumor characteristics from MRI is done by specialists. In today's technology, computer-assisted detection applications make great contributions to the field of medicine. Computer-Assisted Detection (CAD) software helps radiologists to detect abnormalities in medical images by using advanced pattern recognition and image processing methods. This software not only saves time for radiologists but also minimizes possible errors in the decision-making phase. In this study, deep features were extracted from a total of 942 MRIs with 599 tumor and 343 normal class labels using the AleXNet-based deep learning model, and classification was performed with the K Nearest Neighbor Classifier (KNN) algorithms. In this study, 1000 deep features were extracted from the MRI data with the trained weights of the fully connected layer named “fc8” of the AlexNet model. Then, these features were reduced by Relieff feature selection algorithm, and the performance of the proposed method was increased. A weighted KNN classifier was used in the classification phase. With the proposed method, 87% classification accuracy was achieved.
{"title":"A Classification Method for Brain MRI via AlexNet","authors":"Burak Taşcı","doi":"10.1109/CENTCON52345.2021.9688246","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688246","url":null,"abstract":"The number of people dying from brain tumors is increasing day by day. Early diagnosis is very important in the treatment planning and evaluation of the treatment outcome of brain tumors. A patient with a brain tumor may be more likely to survive by applying the right treatment methods if the disease is diagnosed early. Medical imaging methods have an important role in the identification and diagnosis of brain tumors. One of the most popular medical imaging methods is Magnetic Resonance Imaging, MRI. Determining the presence of tumors and tumor characteristics from MRI is done by specialists. In today's technology, computer-assisted detection applications make great contributions to the field of medicine. Computer-Assisted Detection (CAD) software helps radiologists to detect abnormalities in medical images by using advanced pattern recognition and image processing methods. This software not only saves time for radiologists but also minimizes possible errors in the decision-making phase. In this study, deep features were extracted from a total of 942 MRIs with 599 tumor and 343 normal class labels using the AleXNet-based deep learning model, and classification was performed with the K Nearest Neighbor Classifier (KNN) algorithms. In this study, 1000 deep features were extracted from the MRI data with the trained weights of the fully connected layer named “fc8” of the AlexNet model. Then, these features were reduced by Relieff feature selection algorithm, and the performance of the proposed method was increased. A weighted KNN classifier was used in the classification phase. With the proposed method, 87% classification accuracy was achieved.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131870962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688324
Suraj Ajjampur, Vishnu Shridhar, K. P. Shashikala
The exponential growth of football all around the world has caused the exactness of every single innovation included in the game to be of immense value. Basic circumstances emerge when the referee can't separate a goal or no goal by fine margins because of human visual limitations. In the modern era popular football leagues have adopted the use of hawk-eye technology which is unaffordable by the local football leagues. In this project, we have designed and developed a prototype of an efficient and cost-effective goal-line technology system. The proposed framework utilizes object detection techniques i.e. HSV model and contours. We identify the color of the ball and use line-axis detection to reference the position of the ball with respect to the goal line. If the goal is scored, the updated score line is sent to the referee through an email. The result is also broadcasted to the live audience through a speaker system and LCD screen. This system helps in decision-making, in this manner making the framework quicker to help the referees in quick decision-making and keeping up the momentum of the game.
{"title":"Goal Line Technology Using Line axis detection","authors":"Suraj Ajjampur, Vishnu Shridhar, K. P. Shashikala","doi":"10.1109/CENTCON52345.2021.9688324","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688324","url":null,"abstract":"The exponential growth of football all around the world has caused the exactness of every single innovation included in the game to be of immense value. Basic circumstances emerge when the referee can't separate a goal or no goal by fine margins because of human visual limitations. In the modern era popular football leagues have adopted the use of hawk-eye technology which is unaffordable by the local football leagues. In this project, we have designed and developed a prototype of an efficient and cost-effective goal-line technology system. The proposed framework utilizes object detection techniques i.e. HSV model and contours. We identify the color of the ball and use line-axis detection to reference the position of the ball with respect to the goal line. If the goal is scored, the updated score line is sent to the referee through an email. The result is also broadcasted to the live audience through a speaker system and LCD screen. This system helps in decision-making, in this manner making the framework quicker to help the referees in quick decision-making and keeping up the momentum of the game.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133251996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687881
P. S, A. R., Chaithra A, S. S
Vedic Mathematics, an ancient system of Indian mathematics discovered in the early twentieth century, is based on the sixteen formulas called as sutras. These methods have the capability to speed up the processor performance. The proposed work is a design for Vedic multiplier using the Vedic sutra to speed up the operation of the processor to calculate the roots of the quadratic equation. The third sutra “Urdhva Tiragbhyam” is adopted for the solving quadratic equations. The design is implemented using Xilinx Tool Suite and the performance is analysed.
{"title":"A Newer Vedic Module to Solve Quadratic Equations","authors":"P. S, A. R., Chaithra A, S. S","doi":"10.1109/CENTCON52345.2021.9687881","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687881","url":null,"abstract":"Vedic Mathematics, an ancient system of Indian mathematics discovered in the early twentieth century, is based on the sixteen formulas called as sutras. These methods have the capability to speed up the processor performance. The proposed work is a design for Vedic multiplier using the Vedic sutra to speed up the operation of the processor to calculate the roots of the quadratic equation. The third sutra “Urdhva Tiragbhyam” is adopted for the solving quadratic equations. The design is implemented using Xilinx Tool Suite and the performance is analysed.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123313061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688192
B. Sowmiya, E. Poovammal
In recent years, securely sharing personal information between two parties involves high risk. Most of the medical health records and financial transactions includes huge uncertainty while storing and retrieving from cloud for query processing. Blockchain is an open platform where each transaction is tampered proof. Various methods like zero knowledge proof or hashing methods used to hide the sensitive information from the real world. When associating blockchain with cloud this uncertainty is reduced. The user information is segregated into two categories sensitive and non-sensitive using linear regression method before processing in cloud. To improve security and increase privacy from various attacks, the sensitive part of data is encrypted using ECC and non- sensitive part of data is encrypted using RSA algorithm. Using Ethereum blockchain the policy of the user is verified and query processing is done. The performance of the model is compared with the existing techniques and results are evaluated using the classification error rate and performance of security against manual attacks.
{"title":"Improving Cloud Security and Privacy Using Blockchain","authors":"B. Sowmiya, E. Poovammal","doi":"10.1109/CENTCON52345.2021.9688192","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688192","url":null,"abstract":"In recent years, securely sharing personal information between two parties involves high risk. Most of the medical health records and financial transactions includes huge uncertainty while storing and retrieving from cloud for query processing. Blockchain is an open platform where each transaction is tampered proof. Various methods like zero knowledge proof or hashing methods used to hide the sensitive information from the real world. When associating blockchain with cloud this uncertainty is reduced. The user information is segregated into two categories sensitive and non-sensitive using linear regression method before processing in cloud. To improve security and increase privacy from various attacks, the sensitive part of data is encrypted using ECC and non- sensitive part of data is encrypted using RSA algorithm. Using Ethereum blockchain the policy of the user is verified and query processing is done. The performance of the model is compared with the existing techniques and results are evaluated using the classification error rate and performance of security against manual attacks.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115820414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687919
K. P. B. Madavi, P. Karthick
Data security is a key concern for organizations considering a transfer of their on-premises applications to the cloud. Organizations must shift their security controls from historical perimeter and detection-based technologies to a focus on establishing enhanced protection at the application and data levels to ensure the confidentiality, integrity, and availability of these various systems and datasets. Data integrity is a critical component of cloud data security, preventing unauthorized alteration or removal and guaranteeing that data stays as it was when it was initially uploaded. This article presents a compacting with steganography technique which is used to hide data with substantial security and also perfect invisibility while utilizing a combination of DES, AES, and RC4 encryption methods. The objective of this study is to provide data security using steganography with the Least Significant Bit (LSB) Algorithm and Hybrid Encryption that encrypts user input and conceals it in an image file to provide the highest level of security for messages sent and received.
{"title":"Enhanced Cloud Security using Cryptography and Steganography Techniques","authors":"K. P. B. Madavi, P. Karthick","doi":"10.1109/CENTCON52345.2021.9687919","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687919","url":null,"abstract":"Data security is a key concern for organizations considering a transfer of their on-premises applications to the cloud. Organizations must shift their security controls from historical perimeter and detection-based technologies to a focus on establishing enhanced protection at the application and data levels to ensure the confidentiality, integrity, and availability of these various systems and datasets. Data integrity is a critical component of cloud data security, preventing unauthorized alteration or removal and guaranteeing that data stays as it was when it was initially uploaded. This article presents a compacting with steganography technique which is used to hide data with substantial security and also perfect invisibility while utilizing a combination of DES, AES, and RC4 encryption methods. The objective of this study is to provide data security using steganography with the Least Significant Bit (LSB) Algorithm and Hybrid Encryption that encrypts user input and conceals it in an image file to provide the highest level of security for messages sent and received.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117095560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688115
Priyesh Kumar, K. Varalakshmi
Due to the obvious exponential growth in the usage of the internet by individuals of all ethnicities and educational backgrounds, dangerous internet media has become a serious concern in today's society. In the automated identification of hazardous text material, distinguishing between offensive speech and offensive language is a major problem. Most of the current approaches revolve around TF-IDF feature extraction, followed by the traditional classification techniques like Support Vector Machines (SVM), Decision Trees etc., As a result, there is a scope of improvement in the Accuracy of Emotion Detection and long training times. Most of the works considered only tweet data only. But in this work, we would like to include image characters and image components also. We propose a technique in this study for automatically classifying tweets on Twitter into two categories: Hate speech, Offensive speech and non-hate speech. A training and testing step are included in the suggested technique. Traditional Tweet preparation procedures such as removing Twitter handles, URLs, punctuation, stop words, and stemming were used. In both testing and training, we pad each tweet to its maximum length based on the vocabulary. This padding can have an impact on how the network works and can have a significant impact on performance and accuracy. The normalized characteristics are supplied into Bi-directional Long Short-Term Memory, which learns bidirectional long-term relationships between time steps in a time series or sequential twitter data. In comparison research, we compare the models utilizing each of these approaches. We used the Kaggle data set to predict Hate, offensive and Neutral Messages. After conducting many tests, we discovered that the suggested technique outperforms state-of-the-art algorithms by more than 90 percent.
{"title":"Hate Speech Detection using Text and Image Tweets Based On Bi-directional Long Short-Term Memory","authors":"Priyesh Kumar, K. Varalakshmi","doi":"10.1109/CENTCON52345.2021.9688115","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688115","url":null,"abstract":"Due to the obvious exponential growth in the usage of the internet by individuals of all ethnicities and educational backgrounds, dangerous internet media has become a serious concern in today's society. In the automated identification of hazardous text material, distinguishing between offensive speech and offensive language is a major problem. Most of the current approaches revolve around TF-IDF feature extraction, followed by the traditional classification techniques like Support Vector Machines (SVM), Decision Trees etc., As a result, there is a scope of improvement in the Accuracy of Emotion Detection and long training times. Most of the works considered only tweet data only. But in this work, we would like to include image characters and image components also. We propose a technique in this study for automatically classifying tweets on Twitter into two categories: Hate speech, Offensive speech and non-hate speech. A training and testing step are included in the suggested technique. Traditional Tweet preparation procedures such as removing Twitter handles, URLs, punctuation, stop words, and stemming were used. In both testing and training, we pad each tweet to its maximum length based on the vocabulary. This padding can have an impact on how the network works and can have a significant impact on performance and accuracy. The normalized characteristics are supplied into Bi-directional Long Short-Term Memory, which learns bidirectional long-term relationships between time steps in a time series or sequential twitter data. In comparison research, we compare the models utilizing each of these approaches. We used the Kaggle data set to predict Hate, offensive and Neutral Messages. After conducting many tests, we discovered that the suggested technique outperforms state-of-the-art algorithms by more than 90 percent.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115447373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless Sensor network (WSN) is widely used in many applications such as Defense, Agriculture, Road Transport and Highways, Healthcare, Shopping Malls. The usage of IoT devices has risen rapidly with the advancement of WSN techniques. WSN is the dominant techniques over low-cost, easy to use, scalability and portability. The major concern that arise with WSN are battery lifetime, energy consumption and short lifetime of sensor nodes. Hence, different routing network sensor protocols improve the ways of data aggregation and transmission to Base Station in the wireless sensor network. The Low Energy Adaptive Clustering Hierarchy (LEACH) is well used routing protocol based on hierarchical network flow. Researchers improves the traditional LEACH over years. In this work, traditional and integrated LEACH protocol are considered which integrated LEACH improves the threshold equation of sensor nodes for selecting cluster head. A* search algorithm is used with integrated LEACH protocol to form the tree of the sensor nodes that searches the shortest path for selecting the cluster head. This minimizes dead sensor nodes and improve the average energy residual of the sensor nodes. Using MATLAB application, simulation of the protocol is observed that finds 30 percent reduce the dead sensor nodes over integrated LEACH and improve average energy residual.
{"title":"Improved Energy Lifetime of Integrated LEACH Protocol for Wireless Sensor Network","authors":"Chongtham Pankaj, Gurumayum Nirmal Sharma, Khoirom Rajib Singh","doi":"10.1109/CENTCON52345.2021.9688050","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688050","url":null,"abstract":"Wireless Sensor network (WSN) is widely used in many applications such as Defense, Agriculture, Road Transport and Highways, Healthcare, Shopping Malls. The usage of IoT devices has risen rapidly with the advancement of WSN techniques. WSN is the dominant techniques over low-cost, easy to use, scalability and portability. The major concern that arise with WSN are battery lifetime, energy consumption and short lifetime of sensor nodes. Hence, different routing network sensor protocols improve the ways of data aggregation and transmission to Base Station in the wireless sensor network. The Low Energy Adaptive Clustering Hierarchy (LEACH) is well used routing protocol based on hierarchical network flow. Researchers improves the traditional LEACH over years. In this work, traditional and integrated LEACH protocol are considered which integrated LEACH improves the threshold equation of sensor nodes for selecting cluster head. A* search algorithm is used with integrated LEACH protocol to form the tree of the sensor nodes that searches the shortest path for selecting the cluster head. This minimizes dead sensor nodes and improve the average energy residual of the sensor nodes. Using MATLAB application, simulation of the protocol is observed that finds 30 percent reduce the dead sensor nodes over integrated LEACH and improve average energy residual.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116856154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687860
Ganesh A Siva Raja, Maddi Siddart, S. Kashyap, P. Ramadevi
Content Based Image Retrieval (CBIR) systems are used to retrieve similar images to the query image from a large database. This paper represents a CBIR model which has been tested with multiple feature descriptors such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and rotated BRIEF (ORB) and combinations of them. Multiple linguistic processing techniques such as Bag of Words and Topic modelling have been used for optimizing the image retrieval and making them meaningful based on human semantics. Using a combination of descriptors and Latent Dirichlet Allocation, our model has proven to yield high precision when tested against standard image retrieval data set.
{"title":"Comprehensive Analysis of Fused Descriptors for Image Retrieval","authors":"Ganesh A Siva Raja, Maddi Siddart, S. Kashyap, P. Ramadevi","doi":"10.1109/CENTCON52345.2021.9687860","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687860","url":null,"abstract":"Content Based Image Retrieval (CBIR) systems are used to retrieve similar images to the query image from a large database. This paper represents a CBIR model which has been tested with multiple feature descriptors such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and rotated BRIEF (ORB) and combinations of them. Multiple linguistic processing techniques such as Bag of Words and Topic modelling have been used for optimizing the image retrieval and making them meaningful based on human semantics. Using a combination of descriptors and Latent Dirichlet Allocation, our model has proven to yield high precision when tested against standard image retrieval data set.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126957547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688241
Hari Chandan T., S. Nagaraju, Bharath Nandan Varma, K. M., M. S, Mukund K. V.
Vehicle over-speed detection and accident-avoidance system is an Internet of Things (IoT) based system which collects data via sensors such as ultrasonic sensors and alerts the driver. The sensor is mounted upon a microcontroller i.e. Arduino which is responsible for the sensors to work. This system consists of an Ultrasonic Sensor, Arduino UNO, Potentiometer, CAN Controller, DC Motor, GSM, LCD display and a buzzer. The ultrasonic sensor detects the object/vehicle ahead of the vehicle and sends the data to Arduino UNO, if a particular vehicle is in close proximity to the front vehicle, the proposed system automatically controls the vehicle speed. This system also consists of an over-speed detection, which detects the speed and alerts the driver if the vehicle reaches a specific speed limit. Also, in the proposed system in case if driver overspeeds, an SMS alert would be sent to cab company or car rental agency concerned person's cellphone.
{"title":"IoT based Vehicle Over-Speed Detection and Accident Avoidance System","authors":"Hari Chandan T., S. Nagaraju, Bharath Nandan Varma, K. M., M. S, Mukund K. V.","doi":"10.1109/CENTCON52345.2021.9688241","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688241","url":null,"abstract":"Vehicle over-speed detection and accident-avoidance system is an Internet of Things (IoT) based system which collects data via sensors such as ultrasonic sensors and alerts the driver. The sensor is mounted upon a microcontroller i.e. Arduino which is responsible for the sensors to work. This system consists of an Ultrasonic Sensor, Arduino UNO, Potentiometer, CAN Controller, DC Motor, GSM, LCD display and a buzzer. The ultrasonic sensor detects the object/vehicle ahead of the vehicle and sends the data to Arduino UNO, if a particular vehicle is in close proximity to the front vehicle, the proposed system automatically controls the vehicle speed. This system also consists of an over-speed detection, which detects the speed and alerts the driver if the vehicle reaches a specific speed limit. Also, in the proposed system in case if driver overspeeds, an SMS alert would be sent to cab company or car rental agency concerned person's cellphone.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687937
Chandran Nandkumar, Pranshu Shukla, Viren Varma
This paper proposes a method for indoor localization and navigation of Turtlebot 3 using Real Time Object Detection (RTOD). The robot is capable of recognizing the room it is placed inside based on the knowledge of positions of certain fixed arbitrary objects. The robot then proceeds to understand its position inside the room and is capable of moving to other locations. The robot is simulated using the ROS and Gazebo framework. The RTOD is trained to identify certain distinct objects like a rover, bowl, quadcopter and wheel based on which the robot is able to ascertain its location.
提出了一种基于实时目标检测(Real Time Object Detection, RTOD)的Turtlebot 3室内定位与导航方法。机器人能够基于某些固定任意物体的位置知识来识别它所在的房间。然后，机器人继续了解自己在房间内的位置，并能够移动到其他位置。利用ROS和Gazebo框架对机器人进行仿真。RTOD经过训练，可以识别某些不同的物体，如漫游者、碗、四轴飞行器和轮子，机器人可以根据这些物体确定自己的位置。
{"title":"Simulation of Indoor Localization and Navigation of Turtlebot 3 using Real Time Object Detection","authors":"Chandran Nandkumar, Pranshu Shukla, Viren Varma","doi":"10.1109/CENTCON52345.2021.9687937","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687937","url":null,"abstract":"This paper proposes a method for indoor localization and navigation of Turtlebot 3 using Real Time Object Detection (RTOD). The robot is capable of recognizing the room it is placed inside based on the knowledge of positions of certain fixed arbitrary objects. The robot then proceeds to understand its position inside the room and is capable of moving to other locations. The robot is simulated using the ROS and Gazebo framework. The RTOD is trained to identify certain distinct objects like a rover, bowl, quadcopter and wheel based on which the robot is able to ascertain its location.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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