Pub Date : 2021-06-03DOI: 10.1109/icoei51242.2021.9453018
{"title":"Index Author","authors":"","doi":"10.1109/icoei51242.2021.9453018","DOIUrl":"https://doi.org/10.1109/icoei51242.2021.9453018","url":null,"abstract":"","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"67 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125843877","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452873
N. E. J. Asha, Ehtesum-Ul-Islam, R. Khan
One of our major organs heart does the pumping process of oxygen-containing blood and its distribution to the body's arteries every minute. Heart rate or pulse indicates the cardiovascular fitness of a human body. The health condition is predicted by measuring the heartbeat rate, which changes with age, physical and mental conditions. The most familiar way of measuring the heart rate or rhythm is by sensing the pulse per minute by various devices. This paper implements a low-cost heart rate monitoring system using sensors and IoT devices. First, the sensor will be placed on the finger, and subsequently, the color variation will be seen. The sensor picks the color variation, and it measures the interval of color variation. An Arduino microcontroller is used to process the signal. These devices use light to track the blood. Next, the measured heart rate data from the Arduino is stored in CSV files. The Geneva affective picture database has been used to record the heart rate and classify it into three classes of positive, negative, and neutral emotions. Finally, a machine learning algorithm, support vector machine, has been implemented to predict the mental stress condition from the obtained heart rate. Experimental results demonstrate that the support vector machine with the polynomial kernel exhibits the best accuracy.
{"title":"Low-Cost Heart Rate Sensor and Mental Stress Detection Using Machine Learning","authors":"N. E. J. Asha, Ehtesum-Ul-Islam, R. Khan","doi":"10.1109/ICOEI51242.2021.9452873","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452873","url":null,"abstract":"One of our major organs heart does the pumping process of oxygen-containing blood and its distribution to the body's arteries every minute. Heart rate or pulse indicates the cardiovascular fitness of a human body. The health condition is predicted by measuring the heartbeat rate, which changes with age, physical and mental conditions. The most familiar way of measuring the heart rate or rhythm is by sensing the pulse per minute by various devices. This paper implements a low-cost heart rate monitoring system using sensors and IoT devices. First, the sensor will be placed on the finger, and subsequently, the color variation will be seen. The sensor picks the color variation, and it measures the interval of color variation. An Arduino microcontroller is used to process the signal. These devices use light to track the blood. Next, the measured heart rate data from the Arduino is stored in CSV files. The Geneva affective picture database has been used to record the heart rate and classify it into three classes of positive, negative, and neutral emotions. Finally, a machine learning algorithm, support vector machine, has been implemented to predict the mental stress condition from the obtained heart rate. Experimental results demonstrate that the support vector machine with the polynomial kernel exhibits the best accuracy.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126134321","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452861
Y. H. Robinson, R. Babu, K. Narayanan, Raikumar Krishnan, R. Krishnan, M. Paramaivaooan
The identification of hot spots while active transmission in Wireless Sensor Networks (WSNs) is a challenging task. Several location discovery techniques have been focused on the device related localization that finds the terminal target devices. This paper proposes an identification of location using ANN methodology. The RSS signal has the parameter within the gathered data within the communication range is computed. The difference within the values is gathered using this method The non-linear functionality through the coordinate location is the identified output. Whenever the output value is in the monitoring range, the matrix index is used to train the nodes using ANN model, finally the coordinates for location identification may be computed. The mobility framework is implemented through the sensor node that the position of the node has been estimated within the communication range. The repeated data transmission is minimized so that the WSN burdens have been reduced using the node density procedure. The performance evaluation has demonstrated that the proposed method is able to achieve good performance without any particular terminals.
{"title":"Enhanced location identification technique for Wireless Sensor Networks","authors":"Y. H. Robinson, R. Babu, K. Narayanan, Raikumar Krishnan, R. Krishnan, M. Paramaivaooan","doi":"10.1109/ICOEI51242.2021.9452861","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452861","url":null,"abstract":"The identification of hot spots while active transmission in Wireless Sensor Networks (WSNs) is a challenging task. Several location discovery techniques have been focused on the device related localization that finds the terminal target devices. This paper proposes an identification of location using ANN methodology. The RSS signal has the parameter within the gathered data within the communication range is computed. The difference within the values is gathered using this method The non-linear functionality through the coordinate location is the identified output. Whenever the output value is in the monitoring range, the matrix index is used to train the nodes using ANN model, finally the coordinates for location identification may be computed. The mobility framework is implemented through the sensor node that the position of the node has been estimated within the communication range. The repeated data transmission is minimized so that the WSN burdens have been reduced using the node density procedure. The performance evaluation has demonstrated that the proposed method is able to achieve good performance without any particular terminals.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330107","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453080
M. Guo
As an important part of English teaching, oral English evaluation plays an important role in promoting students to learn English. The establishment of a diversified oral college English evaluation system is conducive to changing the traditional summative evaluation model, promoting the smooth progress of college English reform, and facilitating the in-depth development of the overall education reform. Fuzzy measure theory abandons the requirement of additivity in classical measure theory. On the basis of the concept of generalized additivity, the condition of additivity is weakened to make it additive in the new sense. With the development of deep learning, speech recognition technology has undergone tremendous technological changes, in which the acoustic model has gradually developed from the traditional Gaussian mixture model to the neural network model. In this paper, the speech recognition technology and fuzzy measure rules are analyzed, and the evaluation system of spoken English is constructed.
{"title":"Oral English Evaluation Algorithm Based on Fuzzy Measures and Speech Recognition Technology","authors":"M. Guo","doi":"10.1109/ICOEI51242.2021.9453080","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453080","url":null,"abstract":"As an important part of English teaching, oral English evaluation plays an important role in promoting students to learn English. The establishment of a diversified oral college English evaluation system is conducive to changing the traditional summative evaluation model, promoting the smooth progress of college English reform, and facilitating the in-depth development of the overall education reform. Fuzzy measure theory abandons the requirement of additivity in classical measure theory. On the basis of the concept of generalized additivity, the condition of additivity is weakened to make it additive in the new sense. With the development of deep learning, speech recognition technology has undergone tremendous technological changes, in which the acoustic model has gradually developed from the traditional Gaussian mixture model to the neural network model. In this paper, the speech recognition technology and fuzzy measure rules are analyzed, and the evaluation system of spoken English is constructed.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126221013","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452896
F M Javed Mehedi Shamrat, Md. Al Jubair, M. Billah, Sovon Chakraborty, M. Alauddin, Rumesh Ranjan
Deep learning is a trendy term these days, and it refers to a modern age in machine learning in which algorithms are taught to identify patterns in vast amounts of data. It mostly refers to studying various layers of representation, which assists in the understanding of data that includes text, sound, and pictures. To interact with the objects in a video series, many researchers use a form of deep learning called a CNN. Face detection involves several face-related technologies, such as face authentication, facial recognition, and face clustering. For identification and understanding, effective preparation must be carried out. The standard technique did not produce a positive outcome in terms of face recognition precision. The objectives of this research are by using a deep learning model to enhance the accuracy of face detection. For recognizing faces from datasets, the proposed model utilizes a deep learning technique named convolutional neural networks. The proposed work is applied using Max Pooling, a well-known deep learning process. Our model is trained and validated using the LFW dataset, which includes 13000 photos collected from Kaggle. The training accuracy of the model was 95.72% percent, and the validation accuracy was 96.27%.
{"title":"A Deep Learning Approach for Face Detection using Max Pooling","authors":"F M Javed Mehedi Shamrat, Md. Al Jubair, M. Billah, Sovon Chakraborty, M. Alauddin, Rumesh Ranjan","doi":"10.1109/ICOEI51242.2021.9452896","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452896","url":null,"abstract":"Deep learning is a trendy term these days, and it refers to a modern age in machine learning in which algorithms are taught to identify patterns in vast amounts of data. It mostly refers to studying various layers of representation, which assists in the understanding of data that includes text, sound, and pictures. To interact with the objects in a video series, many researchers use a form of deep learning called a CNN. Face detection involves several face-related technologies, such as face authentication, facial recognition, and face clustering. For identification and understanding, effective preparation must be carried out. The standard technique did not produce a positive outcome in terms of face recognition precision. The objectives of this research are by using a deep learning model to enhance the accuracy of face detection. For recognizing faces from datasets, the proposed model utilizes a deep learning technique named convolutional neural networks. The proposed work is applied using Max Pooling, a well-known deep learning process. Our model is trained and validated using the LFW dataset, which includes 13000 photos collected from Kaggle. The training accuracy of the model was 95.72% percent, and the validation accuracy was 96.27%.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126556184","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452980
Sree Lakshmi K, Theertha Jayarajan N, Nitha L
Data flows from various sources in structured, semistructured or unstructured form and this type of data flow is referred as big data. Due to their large scale, rapid growth and diverse formats, these datasets are difficult to manage using conventional tools and techniques. Big Data analysis is a daunting activity as it requires large decentralized file systems that should be adaptive, resilient and responsive to fault. For the effective analysis of big data, Map Reduce is commonly used. Big data analysis helps researchers, scholars, and business users to extract the value and knowledge. Huge amounts of data have become accessible to decision makers in the information age. Due to the rapid increase of such data, strategies to manage and obtain value and knowledge from these datasets must be studied and delivered. Moreover, decision-makers must be able to extract useful information from such a dynamic and rapidly changing set of data, which includes everything from daily transactions to customer contact and social media data. In this paper, we explore Hadoop's parallel processing power in two application areas. The first scenario is calculation of minimum and maximum temperature with huge amount of weather data, which has been collected from an open source. The application analyses the entire weather station data set and the minimum and maximum temperatures (in Fahrenheit) of the respective weather stations will be displayed. The second scenario is to find the word count from huge datasets and checks the frequency of each word in a given data set irrespective of the data volume.
{"title":"Ascendancy of MapReduce with Hadoop for Weather Data and Word Count Analytics","authors":"Sree Lakshmi K, Theertha Jayarajan N, Nitha L","doi":"10.1109/ICOEI51242.2021.9452980","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452980","url":null,"abstract":"Data flows from various sources in structured, semistructured or unstructured form and this type of data flow is referred as big data. Due to their large scale, rapid growth and diverse formats, these datasets are difficult to manage using conventional tools and techniques. Big Data analysis is a daunting activity as it requires large decentralized file systems that should be adaptive, resilient and responsive to fault. For the effective analysis of big data, Map Reduce is commonly used. Big data analysis helps researchers, scholars, and business users to extract the value and knowledge. Huge amounts of data have become accessible to decision makers in the information age. Due to the rapid increase of such data, strategies to manage and obtain value and knowledge from these datasets must be studied and delivered. Moreover, decision-makers must be able to extract useful information from such a dynamic and rapidly changing set of data, which includes everything from daily transactions to customer contact and social media data. In this paper, we explore Hadoop's parallel processing power in two application areas. The first scenario is calculation of minimum and maximum temperature with huge amount of weather data, which has been collected from an open source. The application analyses the entire weather station data set and the minimum and maximum temperatures (in Fahrenheit) of the respective weather stations will be displayed. The second scenario is to find the word count from huge datasets and checks the frequency of each word in a given data set irrespective of the data volume.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793025","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452933
Biqin Huang
In the initial stage of learning Japanese, the most common problem is a variety of mistakes in pronunciation. The main reason for these errors is the difference between Chinese and Japanese in pronunciation position and language system. Language learning should combine theory with practice and spend more time on practice with students. Practice has proved that language mastery requires a lot of practical practice, and too much theory may hinder students' flexible mastery of the language. Some media data, such as audio data, can be converted into time series for research. The similarity measurement (pattern matching) of the converted speech time series can find the similar speech signals, which can also be called speech recognition technology. With the rapid development of intelligent control technology, speech signal processing has attracted extensive attention and high attention of researchers.
{"title":"Phonetic Feature Extraction and Recognition Model in Japanese Pronunciation Practice","authors":"Biqin Huang","doi":"10.1109/ICOEI51242.2021.9452933","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452933","url":null,"abstract":"In the initial stage of learning Japanese, the most common problem is a variety of mistakes in pronunciation. The main reason for these errors is the difference between Chinese and Japanese in pronunciation position and language system. Language learning should combine theory with practice and spend more time on practice with students. Practice has proved that language mastery requires a lot of practical practice, and too much theory may hinder students' flexible mastery of the language. Some media data, such as audio data, can be converted into time series for research. The similarity measurement (pattern matching) of the converted speech time series can find the similar speech signals, which can also be called speech recognition technology. With the rapid development of intelligent control technology, speech signal processing has attracted extensive attention and high attention of researchers.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126955127","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453024
Monal Nagar, M.Bhuvaneshwar Reddy, Usha Nandini, Albert Mayan, Sathyabama Krishna, S. Mary
India is starting to become digital in every sector. In this developing environment people need more and more improvised versions and easy way to tackle various problems. This project focuses on providing all necessary information about a district/city. Also, not only for providing information about different sectors of a district it also enables the user to report any social or environmental issues. Many people addresses their surrounding problems but fails to complain against them. The biggest reason is the lack of proper systematic approach and the attitude of the existing department. There is no such accurate field to file a complaint online, therefore we aim towards solving this problem by creating a user-friendly web application where people can easily lodge a complaint simply sitting at home. Previously people were required to write an official letter and post it, later they have to wait for an response which takes a lot of time and resources without any confirmation that whether their query raised will be solved or not. Another benefit of this application will be that it will make the user to have access to all major and minor sector details like transportation, tourism, schools, hospitals etc. The focus will a web application for a particular district only.
{"title":"Smart District Analysis and Complaint Website","authors":"Monal Nagar, M.Bhuvaneshwar Reddy, Usha Nandini, Albert Mayan, Sathyabama Krishna, S. Mary","doi":"10.1109/ICOEI51242.2021.9453024","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453024","url":null,"abstract":"India is starting to become digital in every sector. In this developing environment people need more and more improvised versions and easy way to tackle various problems. This project focuses on providing all necessary information about a district/city. Also, not only for providing information about different sectors of a district it also enables the user to report any social or environmental issues. Many people addresses their surrounding problems but fails to complain against them. The biggest reason is the lack of proper systematic approach and the attitude of the existing department. There is no such accurate field to file a complaint online, therefore we aim towards solving this problem by creating a user-friendly web application where people can easily lodge a complaint simply sitting at home. Previously people were required to write an official letter and post it, later they have to wait for an response which takes a lot of time and resources without any confirmation that whether their query raised will be solved or not. Another benefit of this application will be that it will make the user to have access to all major and minor sector details like transportation, tourism, schools, hospitals etc. The focus will a web application for a particular district only.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129537666","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452948
Senathipathi K, G. S, Gokul G, Hari Priya M J
Smart phone use has been gradually growing in recent years, as has the number of Android device users. As the number of Android app users grows, malicious Android apps are being developed as a tool to steal sensitive data and commit identity theft / fraud on mobile banks and wallets. There are a plethora of malware identification tools and apps on the market. However, new complex malicious apps generated by intruders or hackers need powerful and efficient malicious application detection tools. To begin, we must collect a dataset of prior malicious apps as a training set, and then compare the training dataset to the trained dataset using the CNN algorithm and the RNN algorithm. Open source datasets, such as Kaggle datasets, were used to build the datasets. We use a pre-processing and attribute extraction technique before running the algorithm. Preprocessing of data that is related to independent variables or data features. It ultimately assists in the normalisation of data within a specified boundary. Standard scalar data is usually distributed within each function, and will scale them to the point where the distribution is zero and the root mean square deviation is one, feature extraction techniques such as the tf-idf transform and data pruning are used. It also aids in the acceleration of algorithmic calculations. Using this algorithm, we can detect threatful Mobile applications.
{"title":"SIGPID For Machine Learning based Android Malware Detection","authors":"Senathipathi K, G. S, Gokul G, Hari Priya M J","doi":"10.1109/ICOEI51242.2021.9452948","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452948","url":null,"abstract":"Smart phone use has been gradually growing in recent years, as has the number of Android device users. As the number of Android app users grows, malicious Android apps are being developed as a tool to steal sensitive data and commit identity theft / fraud on mobile banks and wallets. There are a plethora of malware identification tools and apps on the market. However, new complex malicious apps generated by intruders or hackers need powerful and efficient malicious application detection tools. To begin, we must collect a dataset of prior malicious apps as a training set, and then compare the training dataset to the trained dataset using the CNN algorithm and the RNN algorithm. Open source datasets, such as Kaggle datasets, were used to build the datasets. We use a pre-processing and attribute extraction technique before running the algorithm. Preprocessing of data that is related to independent variables or data features. It ultimately assists in the normalisation of data within a specified boundary. Standard scalar data is usually distributed within each function, and will scale them to the point where the distribution is zero and the root mean square deviation is one, feature extraction techniques such as the tf-idf transform and data pruning are used. It also aids in the acceleration of algorithmic calculations. Using this algorithm, we can detect threatful Mobile applications.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132830082","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452747
Amit Abhishek, P. Suraj
In the proposed paper, we presented a dual-band applicative antenna for satellite communication. According to WARC-92, a band with 13.75-14.0 GHz has been allocated for FSS considering a primary basis. This band covers by our proposed antenna and at the same time, another band which is also a part of satellite communication term as 3-centimeter wave i.e. operating frequency is 10GHz. This band is allocated to amateur radio and satellite use as far as a secondary basis is considered. The antenna size is 35.5x35x0.8mm3and the substrate is Rogers RT-Duroid with relative permittivity (€r) of 2.2. The reflection coefficient| S11| observe at the 10 GHz is -31.43 dB, at the 13.75 GHz is - 18.14dB and at 14.0GHz is -13.8 dB. The bandwidth covered by the first band i.e. 10 GHz is 100 MHz. The second band resonating from 13.2 GHz (lower Ku band) to 14.4 GHz (upper Ku band) under which our required band is easily covered with an ample amount of return loss. The peak gain of 7.642 dB and 6.81 dB is observed at respective bands. The simulation is done with HFSS software.
{"title":"Dual-Band Antenna For Fixed Satellite Service With Amateur Radio","authors":"Amit Abhishek, P. Suraj","doi":"10.1109/ICOEI51242.2021.9452747","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452747","url":null,"abstract":"In the proposed paper, we presented a dual-band applicative antenna for satellite communication. According to WARC-92, a band with 13.75-14.0 GHz has been allocated for FSS considering a primary basis. This band covers by our proposed antenna and at the same time, another band which is also a part of satellite communication term as 3-centimeter wave i.e. operating frequency is 10GHz. This band is allocated to amateur radio and satellite use as far as a secondary basis is considered. The antenna size is 35.5x35x0.8mm3and the substrate is Rogers RT-Duroid with relative permittivity (€r) of 2.2. The reflection coefficient| S11| observe at the 10 GHz is -31.43 dB, at the 13.75 GHz is - 18.14dB and at 14.0GHz is -13.8 dB. The bandwidth covered by the first band i.e. 10 GHz is 100 MHz. The second band resonating from 13.2 GHz (lower Ku band) to 14.4 GHz (upper Ku band) under which our required band is easily covered with an ample amount of return loss. The peak gain of 7.642 dB and 6.81 dB is observed at respective bands. The simulation is done with HFSS software.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470839","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：