Pub Date : 2018-11-01DOI: 10.1109/ICET.2018.8603608
Sami Ullah, Najmul Hassan, Naeem Bhatti
In this paper, we present human action localization in videos. It is a challenging task to localize actions performed in videos where the foreground areas containing video object as well as the background areas depict motion simultaneously. We set the main objective to identify action related spatio-temporal areas in a video. The proposed approach consists of two main steps. At first, we perform saturation (S plane of HSV space) based background subtraction to obtain foreground in video frames. Secondly, we compute the optical flow of video frames. Performing the superpixels segmentation of each video frame, we classify the superpixels as temporal or stationary using optical flow information and the extracted foreground. The collection of the classified temporal superpixels provide the required action localization in videos. We present qualitative as well as quantitative evaluation of our approach using UCF sports and Weizmann actions dataset. The visual results and quantitative performance measures show that the proposed approach well localizes the action areas in the taken action sequences for evaluation.
{"title":"Temporal Superpixels based Human Action Localization","authors":"Sami Ullah, Najmul Hassan, Naeem Bhatti","doi":"10.1109/ICET.2018.8603608","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603608","url":null,"abstract":"In this paper, we present human action localization in videos. It is a challenging task to localize actions performed in videos where the foreground areas containing video object as well as the background areas depict motion simultaneously. We set the main objective to identify action related spatio-temporal areas in a video. The proposed approach consists of two main steps. At first, we perform saturation (S plane of HSV space) based background subtraction to obtain foreground in video frames. Secondly, we compute the optical flow of video frames. Performing the superpixels segmentation of each video frame, we classify the superpixels as temporal or stationary using optical flow information and the extracted foreground. The collection of the classified temporal superpixels provide the required action localization in videos. We present qualitative as well as quantitative evaluation of our approach using UCF sports and Weizmann actions dataset. The visual results and quantitative performance measures show that the proposed approach well localizes the action areas in the taken action sequences for evaluation.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780428","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603615
Nusrat Shaheen, B. Raza, Ahmad Kamran Malik
For effective workload management and performance tuning in Database Management System (DBMS) the Database Administrators (DBAs) have to deal with many issues. Workload monitoring and controlling can make the things easy for a DBA. Workload type prediction and adaptation can enable monitoring and controlling of workload that helps in DBMS performance tuning. In this study we propose a Case-Based Reasoning (CBR) model for workload type prediction that also has the ability to adapt dynamic workload behavior. To observe the accuracy, effectiveness, significance and adaptiveness of the proposed CBR model, it is compared with existing well-known machine learning approaches, such as, Support Vector Machine (SVM) and Neural Network (NN). For the validation of the proposed CBR model many standard benchmark workloads are experimented using the MySQL DBMS. The standard TPC-C and TPC-H like queries are used for generating training and testing data. In this study various experiments have been performed for Online Transaction Processing (OLTP) and Decision Support System (DSS) workloads. The proposed CBR model characterizes the workload through predicting its types. At the end, for result validation we have performed post-hoc tests which shows that the proposed CBR model produces better results.
{"title":"A CBR Model for Workload Characterization in Autonomic Database Management System","authors":"Nusrat Shaheen, B. Raza, Ahmad Kamran Malik","doi":"10.1109/ICET.2018.8603615","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603615","url":null,"abstract":"For effective workload management and performance tuning in Database Management System (DBMS) the Database Administrators (DBAs) have to deal with many issues. Workload monitoring and controlling can make the things easy for a DBA. Workload type prediction and adaptation can enable monitoring and controlling of workload that helps in DBMS performance tuning. In this study we propose a Case-Based Reasoning (CBR) model for workload type prediction that also has the ability to adapt dynamic workload behavior. To observe the accuracy, effectiveness, significance and adaptiveness of the proposed CBR model, it is compared with existing well-known machine learning approaches, such as, Support Vector Machine (SVM) and Neural Network (NN). For the validation of the proposed CBR model many standard benchmark workloads are experimented using the MySQL DBMS. The standard TPC-C and TPC-H like queries are used for generating training and testing data. In this study various experiments have been performed for Online Transaction Processing (OLTP) and Decision Support System (DSS) workloads. The proposed CBR model characterizes the workload through predicting its types. At the end, for result validation we have performed post-hoc tests which shows that the proposed CBR model produces better results.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124789391","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603587
Muhammad Shahzaib, S. Shakil
Electromyography (EMG) is the study of electrical activity of muscles signals. This technique can be used for the control of prosthetic for amputees or for medical purposes in muscular disorders. Major challenge faced in this domain is high cost of the devices to control the prosthetic. In addition to the cost of the device, number of parameters used for classification is large for studies in this domain. In this study we propose a low cost circuit for EMG signal extraction. We used 4 channels of proposed EMG circuit to classify 6 different motion that includes individual finger motions and fist motion. Despite being low cost, our circuit provides the signals that can be classified with high accuracies comparable to other studies. For classification, we used artificial neural network with less number of parameters to achieve accuracies comparable to other studies using higher number of parameters. We collected data from 5 healthy subjects using our proposed circuit. Behavior of EMG signal varies from subject to subject depending upon different factors. We used six features from time and frequency domains, gave an accuracy of 98.8% and 96.8% for all combined subjects with two different algorithms and an average accuracy of 99% with standard deviation of 0.6 for all individual subjects.
{"title":"Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network","authors":"Muhammad Shahzaib, S. Shakil","doi":"10.1109/ICET.2018.8603587","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603587","url":null,"abstract":"Electromyography (EMG) is the study of electrical activity of muscles signals. This technique can be used for the control of prosthetic for amputees or for medical purposes in muscular disorders. Major challenge faced in this domain is high cost of the devices to control the prosthetic. In addition to the cost of the device, number of parameters used for classification is large for studies in this domain. In this study we propose a low cost circuit for EMG signal extraction. We used 4 channels of proposed EMG circuit to classify 6 different motion that includes individual finger motions and fist motion. Despite being low cost, our circuit provides the signals that can be classified with high accuracies comparable to other studies. For classification, we used artificial neural network with less number of parameters to achieve accuracies comparable to other studies using higher number of parameters. We collected data from 5 healthy subjects using our proposed circuit. Behavior of EMG signal varies from subject to subject depending upon different factors. We used six features from time and frequency domains, gave an accuracy of 98.8% and 96.8% for all combined subjects with two different algorithms and an average accuracy of 99% with standard deviation of 0.6 for all individual subjects.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126292055","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603581
U. F. Ahmed, S. Rehman, U. Rafique, M. Ahmed
In this paper, a comprehensive study is conducted on AlGaN/GaN FinFETs as a potential candidate for microwave and power applications. It has been described that due to superior material properties associated with GaN and electrical properties exhibited by the tri-gate structure, FinFETs offer superior results for radio frequency applications. The tri-gate structure of FinFET allows full depletion of the channel, which results in low leakage current and dynamic power loss. FinFETs offer higher current density and integration compared to other mainstream CMOS technologies.
{"title":"AlGaN/GaN FinFET: A Comparative Study","authors":"U. F. Ahmed, S. Rehman, U. Rafique, M. Ahmed","doi":"10.1109/ICET.2018.8603581","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603581","url":null,"abstract":"In this paper, a comprehensive study is conducted on AlGaN/GaN FinFETs as a potential candidate for microwave and power applications. It has been described that due to superior material properties associated with GaN and electrical properties exhibited by the tri-gate structure, FinFETs offer superior results for radio frequency applications. The tri-gate structure of FinFET allows full depletion of the channel, which results in low leakage current and dynamic power loss. FinFETs offer higher current density and integration compared to other mainstream CMOS technologies.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131026563","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603598
Saira Jabeen, Gulraiz Khan, Humza Naveed, Zeeshan Khan, Usman Ghani Khan
In recent times, there has been continuous interest in the area of content based information retrieval (CBIR) for images and video sequences. Exponential increase of multimedia data has triggered a cause for managing, storing and retrieving multimedia contents in convenient and efficient ways. Visual features from static images and dynamic videos are extracted to perform retrieval task. Once visual features are extracted, there is a need to search and retrieve relevant videos in efficient amount of time. This paper makes use of seven visual features; human detection, emotion, age, gender, activity, scene and object detection followed by sentence generation. Furthermore, generated sentence is used in multi-class recurrent neural network (RNN) to find genre of a video for retrieval task. Accuracy, precision and recall are used for evaluation of this framework on self generated dataset. Experiments show that our system is able to achieve high accuracy of 88.13%.
{"title":"Video Retrieval System Using Parallel Multi-Class Recurrent Neural Network Based on Video Description","authors":"Saira Jabeen, Gulraiz Khan, Humza Naveed, Zeeshan Khan, Usman Ghani Khan","doi":"10.1109/ICET.2018.8603598","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603598","url":null,"abstract":"In recent times, there has been continuous interest in the area of content based information retrieval (CBIR) for images and video sequences. Exponential increase of multimedia data has triggered a cause for managing, storing and retrieving multimedia contents in convenient and efficient ways. Visual features from static images and dynamic videos are extracted to perform retrieval task. Once visual features are extracted, there is a need to search and retrieve relevant videos in efficient amount of time. This paper makes use of seven visual features; human detection, emotion, age, gender, activity, scene and object detection followed by sentence generation. Furthermore, generated sentence is used in multi-class recurrent neural network (RNN) to find genre of a video for retrieval task. Accuracy, precision and recall are used for evaluation of this framework on self generated dataset. Experiments show that our system is able to achieve high accuracy of 88.13%.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128300928","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603617
Sidra Abbasi, A. Naseem, A. Shamim, M. A. Qureshi
Cyberbullying related with social, emotional and academic harms can be critical and enduring. Cyberbullying is where context has a dynamic impact. Pakistan is one of its late accepters of IT; yet, the IT sector has developed during recent couple of years. By utilizing more extensive proportion of cyberbullying and looking at what cause it to occur, the present investigation plans to inspect (i) motives of the cyber aggressor, by how do individuals get the opportunity to participate in cyberbullying? (ii) Negative influences on cyber victim, (iii) Obscurity of Cyber Aggressor, (iv) Omission in cyberbullying with respect to gender metamorphosis and (v) Sort of social sites used to commit. Data was collected through instrument survey that is grounded on 5-point Likert Scale and effective responses from 600 university students from different provinces and states were utilized, coded and evaluated by using SPSS. Type of electronic media used to commit cyberbullying by respondents were reported as Instant messaging, E-mails, Social Networking Sites, Online games, compromising photos, Videoclips, Phone calls, Raid on personal blogs and websites. Findings demonstrate that the foremost motive amongst respondents to commit electronic bullying is found to be Social Relationship. Main motive for males behind bullying others using cyber medium is "Unevenness of Power" and make themselves socially strong or weaken others. However, Secrecy and Namelessness caused by anonymity offered by cyberbullying is most prominent motive for females. The results showed that females are more affected by cyberbullying as compared to males regarding "Withdrawal and Isolation" and "Indulging in Harmful Habits". However Increased Psychological Distress caused by cyberbullying has no major difference between males and females. Anonymity through secrecy, namelessness and freedom of expression enables cyber bully to feel protected in virtual environments and they may take advantage when contrasted with traditional bullying.
{"title":"An empirical Investigation of Motives, Nature and online Sources of Cyberbullying","authors":"Sidra Abbasi, A. Naseem, A. Shamim, M. A. Qureshi","doi":"10.1109/ICET.2018.8603617","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603617","url":null,"abstract":"Cyberbullying related with social, emotional and academic harms can be critical and enduring. Cyberbullying is where context has a dynamic impact. Pakistan is one of its late accepters of IT; yet, the IT sector has developed during recent couple of years. By utilizing more extensive proportion of cyberbullying and looking at what cause it to occur, the present investigation plans to inspect (i) motives of the cyber aggressor, by how do individuals get the opportunity to participate in cyberbullying? (ii) Negative influences on cyber victim, (iii) Obscurity of Cyber Aggressor, (iv) Omission in cyberbullying with respect to gender metamorphosis and (v) Sort of social sites used to commit. Data was collected through instrument survey that is grounded on 5-point Likert Scale and effective responses from 600 university students from different provinces and states were utilized, coded and evaluated by using SPSS. Type of electronic media used to commit cyberbullying by respondents were reported as Instant messaging, E-mails, Social Networking Sites, Online games, compromising photos, Videoclips, Phone calls, Raid on personal blogs and websites. Findings demonstrate that the foremost motive amongst respondents to commit electronic bullying is found to be Social Relationship. Main motive for males behind bullying others using cyber medium is \"Unevenness of Power\" and make themselves socially strong or weaken others. However, Secrecy and Namelessness caused by anonymity offered by cyberbullying is most prominent motive for females. The results showed that females are more affected by cyberbullying as compared to males regarding \"Withdrawal and Isolation\" and \"Indulging in Harmful Habits\". However Increased Psychological Distress caused by cyberbullying has no major difference between males and females. Anonymity through secrecy, namelessness and freedom of expression enables cyber bully to feel protected in virtual environments and they may take advantage when contrasted with traditional bullying.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128612479","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603549
Faisal Imran, R. Abbasi, Muddassar Azam Sindhu, Akmal Saeed Khattak, Ali Daud, Tehmina Amjad
Identifying research areas of academicians is a challenging task. Most of the researchers have used supervised machine learning to handle this task which requires labelled data. In this paper, we propose to use clique percolation (an overlapping community detection algorithm) to identify research areas of academicians. We propose a framework to collect data from digital libraries and websites of selected universities in Pakistan. We then identify researchers working in multiple research areas using clique percolation and present results of our analyses.
{"title":"Finding Research Areas of Academicians using Clique Percolation","authors":"Faisal Imran, R. Abbasi, Muddassar Azam Sindhu, Akmal Saeed Khattak, Ali Daud, Tehmina Amjad","doi":"10.1109/ICET.2018.8603549","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603549","url":null,"abstract":"Identifying research areas of academicians is a challenging task. Most of the researchers have used supervised machine learning to handle this task which requires labelled data. In this paper, we propose to use clique percolation (an overlapping community detection algorithm) to identify research areas of academicians. We propose a framework to collect data from digital libraries and websites of selected universities in Pakistan. We then identify researchers working in multiple research areas using clique percolation and present results of our analyses.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125105083","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603553
Imran Ali, Muhammad Asif, Muhammad Riaz ur Rehman, Muhammad Basim, Sung Jin Kim, Kangyoon Lee
In this paper, a comma detection with word alignment controller is proposed for high speed serial interface applications. Before the word alignment, a configurable number of successive comma detection enhances the reliability of the controller. The comma control word is also configurable to make it flexible for numerous applications. In the proposed multi-mode architecture, automatic and manual control are also incorporated. The presented architecture is fully synthesizable. It occupies a very small area of 110 × 100 µm² and it requires only 1.556 K gates for its implementation. The current and power requirements are 681.09 µA and 817.31 µW respectively from 1.2 V power supply. The design is integrated into 3.125 Gbps JESD204B serial interface which is fabricated in 1P6M 130 nm CMOS process. The simulation and measurement result ensures the reliability of the proposed architecture.
{"title":"A Configurable, Multi-Mode Comma Detection and Word Alignment Controller for High Speed Serial Interface in 130 nm CMOS Technology","authors":"Imran Ali, Muhammad Asif, Muhammad Riaz ur Rehman, Muhammad Basim, Sung Jin Kim, Kangyoon Lee","doi":"10.1109/ICET.2018.8603553","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603553","url":null,"abstract":"In this paper, a comma detection with word alignment controller is proposed for high speed serial interface applications. Before the word alignment, a configurable number of successive comma detection enhances the reliability of the controller. The comma control word is also configurable to make it flexible for numerous applications. In the proposed multi-mode architecture, automatic and manual control are also incorporated. The presented architecture is fully synthesizable. It occupies a very small area of 110 × 100 µm² and it requires only 1.556 K gates for its implementation. The current and power requirements are 681.09 µA and 817.31 µW respectively from 1.2 V power supply. The design is integrated into 3.125 Gbps JESD204B serial interface which is fabricated in 1P6M 130 nm CMOS process. The simulation and measurement result ensures the reliability of the proposed architecture.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946838","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603653
Muhammad Zeeshan Khan, Muhammad A. Hassan, S. U. Hassan, Muhammad Usman Ghanni Khan
Video data analysis is a fascinating field since the last few decades. It assists user to find the genre of a video without watching it. In this paper, an application for analysing Pakistani news data based on the scene classification using deep convolution neural network has been presented. For this purpose the dataset has been collected on our own, which consists of 200 videos of different news channels, and covers almost all categories which we possess in our methodology. The results have been evaluated using the 2D convolution neural network on the fine-tuned inception model. The methodology achieved the 92.2% accuracy on the proposed architecture, such a high accuracy on locally prepared dataset of the news for the video data analysis shows the novelty in literature.
{"title":"Semantic Analysis of News Based on the Deep Convolution Neural Network","authors":"Muhammad Zeeshan Khan, Muhammad A. Hassan, S. U. Hassan, Muhammad Usman Ghanni Khan","doi":"10.1109/ICET.2018.8603653","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603653","url":null,"abstract":"Video data analysis is a fascinating field since the last few decades. It assists user to find the genre of a video without watching it. In this paper, an application for analysing Pakistani news data based on the scene classification using deep convolution neural network has been presented. For this purpose the dataset has been collected on our own, which consists of 200 videos of different news channels, and covers almost all categories which we possess in our methodology. The results have been evaluated using the 2D convolution neural network on the fine-tuned inception model. The methodology achieved the 92.2% accuracy on the proposed architecture, such a high accuracy on locally prepared dataset of the news for the video data analysis shows the novelty in literature.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114623110","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 : 2018-11-01DOI: 10.1109/ICET.2018.8603662
M. Rasheed, Rimsha Tanveer, M. Akhtar, Z. Khan
This paper describes a low-cost design for flocking control of mobile robots. The robots are in Master-Slave configuration with full duplex communication mode. Two important factors included in the present research are the co-design of coordination and communication. To provide coordination between master and slave, a wireless communication network is built between the master and the slave units. For practical implementation of coordination between robots we selected two basic problems in mobile robotics. One of which is line following technique and second one is collision avoidance using swarm based control. Both techniques comprise of one master and one slave unit. The master robot acts as a controller for the slave unit in order to control all the position, orientation and speed of slave.
{"title":"ROBO-FLOCK: Development of a low-cost Leader-follower Swarm of Mobile Robots","authors":"M. Rasheed, Rimsha Tanveer, M. Akhtar, Z. Khan","doi":"10.1109/ICET.2018.8603662","DOIUrl":"https://doi.org/10.1109/ICET.2018.8603662","url":null,"abstract":"This paper describes a low-cost design for flocking control of mobile robots. The robots are in Master-Slave configuration with full duplex communication mode. Two important factors included in the present research are the co-design of coordination and communication. To provide coordination between master and slave, a wireless communication network is built between the master and the slave units. For practical implementation of coordination between robots we selected two basic problems in mobile robotics. One of which is line following technique and second one is collision avoidance using swarm based control. Both techniques comprise of one master and one slave unit. The master robot acts as a controller for the slave unit in order to control all the position, orientation and speed of slave.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121590431","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：