Pub Date : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666718
Hosam Alamleh, A. A. AlQahtani, Baker Al Smadi
Underwater navigation is essential and widely deployed in underwater machines such as submarines and un-manned underwater vehicles. Such machines are used for many purposes including fish farms management, defense, research, and exploration. In the last decade, there has been an advancement in underwater sensing and energy harvesting technologies. This resulted in improvements in underwater positioning and navigation systems and also in novel underwater positioning and navigation systems. This paper surveys and categorizes underwater positioning and navigation systems. Then, it analyzes and compares each category’s characteristics and performance. Moreover, it reviews the advantages and disadvantages of the different categories of underwater positioning and navigation systems. Also, it suggests the ecosystems that fit each category the most. In this paper, navigation and positioning systems are categorized into the following categories: acoustic, multi-sensory, GPS buoys, vision-based, SLAM, and cooperative.
{"title":"Comparative Analysis of Underwater Positioning and Navigation Systems","authors":"Hosam Alamleh, A. A. AlQahtani, Baker Al Smadi","doi":"10.1109/uemcon53757.2021.9666718","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666718","url":null,"abstract":"Underwater navigation is essential and widely deployed in underwater machines such as submarines and un-manned underwater vehicles. Such machines are used for many purposes including fish farms management, defense, research, and exploration. In the last decade, there has been an advancement in underwater sensing and energy harvesting technologies. This resulted in improvements in underwater positioning and navigation systems and also in novel underwater positioning and navigation systems. This paper surveys and categorizes underwater positioning and navigation systems. Then, it analyzes and compares each category’s characteristics and performance. Moreover, it reviews the advantages and disadvantages of the different categories of underwater positioning and navigation systems. Also, it suggests the ecosystems that fit each category the most. In this paper, navigation and positioning systems are categorized into the following categories: acoustic, multi-sensory, GPS buoys, vision-based, SLAM, and cooperative.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131445778","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666557
Yusheng Bi, Lihua Tian, Chen Li
Existing Named Entity Recognition (NER) models have achieved good performance, but they have low accuracy in Chinese address element recognition tasks. After analysis, we believe that the boundary information of the address text is more sensitive than the general text, and the sentences are independent of each other, unlike the general paragraph-style text with contextual connections. On the other hand, the previous NER models rarely consider the use of interaction between subtasks to enhance the performance of the NER task.This paper proposes an Interactive Attention Network (IAN) model, which uses boundary-based information and type-based information to improve NER task performance and introduces an interaction mechanism to share information between each subtask. In addition, a boundary auxiliary module is added to obtain explicit boundary information. The experimental results show that the proposed IAN model can solve the address element recognition task more effectively.
{"title":"Interactive Attention Network for Chinese Address Element Recognition","authors":"Yusheng Bi, Lihua Tian, Chen Li","doi":"10.1109/UEMCON53757.2021.9666557","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666557","url":null,"abstract":"Existing Named Entity Recognition (NER) models have achieved good performance, but they have low accuracy in Chinese address element recognition tasks. After analysis, we believe that the boundary information of the address text is more sensitive than the general text, and the sentences are independent of each other, unlike the general paragraph-style text with contextual connections. On the other hand, the previous NER models rarely consider the use of interaction between subtasks to enhance the performance of the NER task.This paper proposes an Interactive Attention Network (IAN) model, which uses boundary-based information and type-based information to improve NER task performance and introduces an interaction mechanism to share information between each subtask. In addition, a boundary auxiliary module is added to obtain explicit boundary information. The experimental results show that the proposed IAN model can solve the address element recognition task more effectively.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829813","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666555
Md. Rafid Kaysar Shagor, Al Jaber Mahmud, M. M. Nishat, Fahim Faisal, Mehedi Hasan Mithun, Md. Ashik Khan
Firefly Algorithm (FA) refers to a swarm intelligence-based technique which appears to be one of the most influential optimization algorithms in designing optimized controllers for power converters in recent years. Hence, an optimized PID controller is designed based on this algorithm, and stability analysis and performance enhancement of the closed-loop Single-Ended Primary Inductor Converter (SEPIC) are demonstrated comprehensively. Using the state space average technique, the SEPIC converter is mathematically modeled, and the transfer function for the closed-loop system is obtained. The performance parameters noted in the analysis of the system's stability are the percentage of overshoot (%OS), rise time (Tr), settling time (Ts), and peak amplitude (PA). However, fitness functions like integral absolute error (IAE), integral squared error (ISE), integral time squared error (ITSE), integral time absolute error (ITAE) are considered in this optimization process. Step responses based on the gain parameters are attained, and a comparative study with performance evaluation is shown between the optimized and conventional PID controller. MATLAB and Simulink are utilized for simulation.
{"title":"Firefly Algorithm Based Optimized PID Controller for Stability Analysis of DC-DC SEPIC Converter","authors":"Md. Rafid Kaysar Shagor, Al Jaber Mahmud, M. M. Nishat, Fahim Faisal, Mehedi Hasan Mithun, Md. Ashik Khan","doi":"10.1109/uemcon53757.2021.9666555","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666555","url":null,"abstract":"Firefly Algorithm (FA) refers to a swarm intelligence-based technique which appears to be one of the most influential optimization algorithms in designing optimized controllers for power converters in recent years. Hence, an optimized PID controller is designed based on this algorithm, and stability analysis and performance enhancement of the closed-loop Single-Ended Primary Inductor Converter (SEPIC) are demonstrated comprehensively. Using the state space average technique, the SEPIC converter is mathematically modeled, and the transfer function for the closed-loop system is obtained. The performance parameters noted in the analysis of the system's stability are the percentage of overshoot (%OS), rise time (Tr), settling time (Ts), and peak amplitude (PA). However, fitness functions like integral absolute error (IAE), integral squared error (ISE), integral time squared error (ITSE), integral time absolute error (ITAE) are considered in this optimization process. Step responses based on the gain parameters are attained, and a comparative study with performance evaluation is shown between the optimized and conventional PID controller. MATLAB and Simulink are utilized for simulation.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128889903","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666738
Md. Abu Obaidah, Mahmudunnabi, Mohammad Monirujjaman Khan
People like to listen music often. They use music as a therapy for a sound sleep nowadays. But still they are facing some problem with the traditional musical device. An automated mind-controlled music system is going to be designed in this paper for people of all ages especially who have a deep affection with music are the targeted people of this system. This smart system targets to give the user a new level of experience with music system which will allow them to have the pleasure of music without causing any kind of power wastage and unwanted risk of having the musical device nearby them.
{"title":"Brain Wave and Head Motion Controlled Music System","authors":"Md. Abu Obaidah, Mahmudunnabi, Mohammad Monirujjaman Khan","doi":"10.1109/UEMCON53757.2021.9666738","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666738","url":null,"abstract":"People like to listen music often. They use music as a therapy for a sound sleep nowadays. But still they are facing some problem with the traditional musical device. An automated mind-controlled music system is going to be designed in this paper for people of all ages especially who have a deep affection with music are the targeted people of this system. This smart system targets to give the user a new level of experience with music system which will allow them to have the pleasure of music without causing any kind of power wastage and unwanted risk of having the musical device nearby them.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"28 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004203","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666662
M. Samir, Zeinab Y. A. Mohamed, M. A. A. Hussein, Ayman Atia
Blood glucose monitoring is a wide area of research as it plays a huge part in controlling diabetes and many of its symptoms. A common human disease ‘Diabetes Mellitus’ (DM), which is characterized by hyperglycemia, has a number of harmful complications. In addition, the low glucose level in blood caused by hypoglycemia is correlated to fatal brain failure and death. In this paper, we explore a variety of related research to have a grasp on some of the systems and concepts that can assist in forming an autonomous system for glucose monitoring, including deep learning techniques. The proposed system in this paper utilizes non-intrusive Continuous Glucose Monitoring (CGM) devices for tracking glucose levels, combined with food classification and Human Activity Recognition (HAR) using deep learning. We relate the preprandial and peak postprandial glucose levels extracted from CGM with the Glycimc Load (GL) present in food, which makes it possible to form an estimation of blood sugar increase as well as predict hyperglycemia. The system also relates human activity with decrease in blood glucose to warn against possible signs of hypoglycemia before it occurs. We have conducted 3 different experiments; two of which are comparison between deep learning models for food classification and HAR with good results achieved, as well as an experimental result that we obtained by testing hyperglycemia prediction on real data of diabetic patients. The system was able to predict hyperglycemia with an accuracy percentage of 93.2%.
{"title":"Applying Deep Learning to Track Food Consumption and Human Activity for Non-intrusive Blood Glucose Monitoring","authors":"M. Samir, Zeinab Y. A. Mohamed, M. A. A. Hussein, Ayman Atia","doi":"10.1109/uemcon53757.2021.9666662","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666662","url":null,"abstract":"Blood glucose monitoring is a wide area of research as it plays a huge part in controlling diabetes and many of its symptoms. A common human disease ‘Diabetes Mellitus’ (DM), which is characterized by hyperglycemia, has a number of harmful complications. In addition, the low glucose level in blood caused by hypoglycemia is correlated to fatal brain failure and death. In this paper, we explore a variety of related research to have a grasp on some of the systems and concepts that can assist in forming an autonomous system for glucose monitoring, including deep learning techniques. The proposed system in this paper utilizes non-intrusive Continuous Glucose Monitoring (CGM) devices for tracking glucose levels, combined with food classification and Human Activity Recognition (HAR) using deep learning. We relate the preprandial and peak postprandial glucose levels extracted from CGM with the Glycimc Load (GL) present in food, which makes it possible to form an estimation of blood sugar increase as well as predict hyperglycemia. The system also relates human activity with decrease in blood glucose to warn against possible signs of hypoglycemia before it occurs. We have conducted 3 different experiments; two of which are comparison between deep learning models for food classification and HAR with good results achieved, as well as an experimental result that we obtained by testing hyperglycemia prediction on real data of diabetic patients. The system was able to predict hyperglycemia with an accuracy percentage of 93.2%.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117011108","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666523
Josh Wong, Natalie Doan, Michal Aibin
A gradual transition from traditional fixed frequency networks towards Spectrally-Spatially Flexible Optical Networks (SS-FONs) will ensure that networks continue to meet increasing Internet bandwidth demands. The DeepRMCSA algorithm, proposed in this paper, uses deep reinforcement learning to determine the optimal policies for solving the Routing, Modulation, Core and Spectrum Assignment problem in SS-FONs. We evaluate the performance of our algorithm by comparing it with other approaches used in the literature.
{"title":"Deep RMCSA for Resource Allocation in Spectrally-Spatially Flexible Optical Networks","authors":"Josh Wong, Natalie Doan, Michal Aibin","doi":"10.1109/UEMCON53757.2021.9666523","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666523","url":null,"abstract":"A gradual transition from traditional fixed frequency networks towards Spectrally-Spatially Flexible Optical Networks (SS-FONs) will ensure that networks continue to meet increasing Internet bandwidth demands. The DeepRMCSA algorithm, proposed in this paper, uses deep reinforcement learning to determine the optimal policies for solving the Routing, Modulation, Core and Spectrum Assignment problem in SS-FONs. We evaluate the performance of our algorithm by comparing it with other approaches used in the literature.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117334065","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666559
N. Madathil, F. Dankar
Many real-life scenarios require the analysis of large amounts of data from multiple sources. Often, the data contain highly sensitive information and may be subject to privacy laws preventing its aggregation and sharing. Privacy-preserving data mining has emerged as a solution to this problem. It enables data scientists to analyze the distributed data without having to place it in a central location and while guaranteeing its privacy. Decision tree classification is a popular and widely studied machine learning technique for which many privacy-preserving versions exist. In this paper, we review recent privacy preserving implementations of the ID3 classification technique in a distributed environment and compare them in terms of efficiency and privacy. We consider cases where data is split horizontally over multiple parties.
{"title":"Privacy-Preserving ID3 Algorithms: A Comparison","authors":"N. Madathil, F. Dankar","doi":"10.1109/uemcon53757.2021.9666559","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666559","url":null,"abstract":"Many real-life scenarios require the analysis of large amounts of data from multiple sources. Often, the data contain highly sensitive information and may be subject to privacy laws preventing its aggregation and sharing. Privacy-preserving data mining has emerged as a solution to this problem. It enables data scientists to analyze the distributed data without having to place it in a central location and while guaranteeing its privacy. Decision tree classification is a popular and widely studied machine learning technique for which many privacy-preserving versions exist. In this paper, we review recent privacy preserving implementations of the ID3 classification technique in a distributed environment and compare them in terms of efficiency and privacy. We consider cases where data is split horizontally over multiple parties.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116810358","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666714
Mobasshir Mahbub, B. Barua, R. Shubair
Heterogeneous cell deployment technique in 5G wireless networks has evolved as a promising solution for wireless and mobile communication to ensure proper network coverage and meet bandwidth requirements. The off-loading of users or subscribers from macro cell base stations to small cell base stations enhances the entire system throughput. The densely distributed small cells deployed under the conventional macro cell network (heterogeneous network or HetNet) is a potential option to meet the higher data rate requirements of the 5G wireless communications. The paper, therefore, analyzed the optimal deployment of small cells in HetNets to maximize and enhance the downlink and uplink throughput under the SIR (signal to interference ratio) constraints in terms of both macro cell and small cell densities. The work in this context presented mathematical formulas for measurements, optimization procedures, and MATLAB-based simulation results to establish the research.
{"title":"Optimal Small/Macro-Cell Densities in 5G Heterogeneous Networks: Maximizing Small-Cell Throughput Under SIR Constraints","authors":"Mobasshir Mahbub, B. Barua, R. Shubair","doi":"10.1109/uemcon53757.2021.9666714","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666714","url":null,"abstract":"Heterogeneous cell deployment technique in 5G wireless networks has evolved as a promising solution for wireless and mobile communication to ensure proper network coverage and meet bandwidth requirements. The off-loading of users or subscribers from macro cell base stations to small cell base stations enhances the entire system throughput. The densely distributed small cells deployed under the conventional macro cell network (heterogeneous network or HetNet) is a potential option to meet the higher data rate requirements of the 5G wireless communications. The paper, therefore, analyzed the optimal deployment of small cells in HetNets to maximize and enhance the downlink and uplink throughput under the SIR (signal to interference ratio) constraints in terms of both macro cell and small cell densities. The work in this context presented mathematical formulas for measurements, optimization procedures, and MATLAB-based simulation results to establish the research.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124607098","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666724
M. I. Nashiruddin, Putri Rahmawati, M. Nugraha
An essential resource for the deployment of 5G technology is the frequency spectrum. A high spectrum provides high data rates and a large bandwidth to support many new devices, applications, and services that meet the needs across multiple domains. However, a high spectrum has a smaller range (10–100 m2) with a sub-6 GHz spectrum. So, it takes careful design for 5G implementation using high frequencies (mmWave). As a result, this research will plan a 5G New Radio (NR) network implementation employing a 28 GHz mmWave frequency. The dense urban scenario research design was carried out by selecting Central Jakarta as the research object. Central Jakarta was chosen as a pilot project because it is feasible in market potential and infrastructure support to pre-pare 5G implementation in Indonesia. The capacity approach considers the data rate and users, while the coverage approach considers Maximum Allowable Path Loss (MAPL) parameters and path loss propagation. A propagation model based on 3GPP TS 38.901 UMi Street. The results of this study indicate that Central Jakarta requires a traffic demand of 4.72 Gbps/km2. In addition, the deployment of a 5G NR network with mmWave frequency based on the capacity planning approach requires 33 uplink and 12 downlink gNobeB. As a result, the coverage area necessitates 738 uplink gNodeB with a coverage area of 69 m2 and 130 downlink gNodeB with a coverage area of 370 m2. Based on these results, the gNodeB needed for Central Jakarta was selected based on downlink coverage with 738 gNodeB.
{"title":"Network Planning Analysis of 5G Millimeter-Wave Deployment in Indonesia’s Dense Urban Area","authors":"M. I. Nashiruddin, Putri Rahmawati, M. Nugraha","doi":"10.1109/uemcon53757.2021.9666724","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666724","url":null,"abstract":"An essential resource for the deployment of 5G technology is the frequency spectrum. A high spectrum provides high data rates and a large bandwidth to support many new devices, applications, and services that meet the needs across multiple domains. However, a high spectrum has a smaller range (10–100 m2) with a sub-6 GHz spectrum. So, it takes careful design for 5G implementation using high frequencies (mmWave). As a result, this research will plan a 5G New Radio (NR) network implementation employing a 28 GHz mmWave frequency. The dense urban scenario research design was carried out by selecting Central Jakarta as the research object. Central Jakarta was chosen as a pilot project because it is feasible in market potential and infrastructure support to pre-pare 5G implementation in Indonesia. The capacity approach considers the data rate and users, while the coverage approach considers Maximum Allowable Path Loss (MAPL) parameters and path loss propagation. A propagation model based on 3GPP TS 38.901 UMi Street. The results of this study indicate that Central Jakarta requires a traffic demand of 4.72 Gbps/km2. In addition, the deployment of a 5G NR network with mmWave frequency based on the capacity planning approach requires 33 uplink and 12 downlink gNobeB. As a result, the coverage area necessitates 738 uplink gNodeB with a coverage area of 69 m2 and 130 downlink gNodeB with a coverage area of 370 m2. Based on these results, the gNodeB needed for Central Jakarta was selected based on downlink coverage with 738 gNodeB.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128415621","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666667
Sean Hicks, Christian Huang, Eunseo Lee, Kushal Patel, Gregg Vesonder
Smart spaces aim to collect data using sensors installed in an area to provide insights of various aspects of its environment. Our goal for this project was to give students a tool to find quiet, comfortable places to study. We did this by utilizing a Raspberry Pi 4 and a suite of sensors to collect data on environmental factors and internet speeds in popular study spaces throughout the campus and making that data available to students through a web application.
{"title":"Smart Campus for Better Study Spaces","authors":"Sean Hicks, Christian Huang, Eunseo Lee, Kushal Patel, Gregg Vesonder","doi":"10.1109/UEMCON53757.2021.9666667","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666667","url":null,"abstract":"Smart spaces aim to collect data using sensors installed in an area to provide insights of various aspects of its environment. Our goal for this project was to give students a tool to find quiet, comfortable places to study. We did this by utilizing a Raspberry Pi 4 and a suite of sensors to collect data on environmental factors and internet speeds in popular study spaces throughout the campus and making that data available to students through a web application.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129953731","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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