Pub Date : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297483
Ram Kumar Madupu, Chiranjeevi Kothapalli, Vasanthi Yarra, S. Harika, C. Z. Basha
Emotion recognition using facial expression is very much necessary these days. Different kinds of emotions reflect a different definitions. Facial emotion recognition plays a major role in driver warning systems, it can also play an important role in shopping malls to predict unusual activity like terrorist attacks, robbery and much more. Predicting the suicidal tendency of a person also can be done using facial emotion recognition. An automatic facial emotion classification system is proposed in this paper using the Convolution Neural Network (CNN) with the features extracted from the Speeded Up Robust Features (SURF). 91% accuracy is achieved with the proposed model which supports tracking human emotion with facial expressions.
{"title":"Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network","authors":"Ram Kumar Madupu, Chiranjeevi Kothapalli, Vasanthi Yarra, S. Harika, C. Z. Basha","doi":"10.1109/ICECA49313.2020.9297483","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297483","url":null,"abstract":"Emotion recognition using facial expression is very much necessary these days. Different kinds of emotions reflect a different definitions. Facial emotion recognition plays a major role in driver warning systems, it can also play an important role in shopping malls to predict unusual activity like terrorist attacks, robbery and much more. Predicting the suicidal tendency of a person also can be done using facial emotion recognition. An automatic facial emotion classification system is proposed in this paper using the Convolution Neural Network (CNN) with the features extracted from the Speeded Up Robust Features (SURF). 91% accuracy is achieved with the proposed model which supports tracking human emotion with facial expressions.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"682 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126688186","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297645
Arushi Singh, M. Jayakumar
With the evolving communication systems, the need for beamforming to improve the gain of the transmitting or receiving antenna has also increased. Beamforming allows to direct the radiated energy with the intended choice of direction efficiently. The main focus of this work is to develop an effective method for beamforming at the receiver side antennas for deploying Line-of-Sight (LOS) communication in Satellite Communication (Satcom) by using machine learning algorithms to detect signals as accurately as possible and to reduce the time taken to steer the beam as well as complexity of operations if a standard beamforming algorithm was used. To implement this, the antenna array weights are pre-calculated for a number of beam directions and kept as a database which are given to a linear regression machine learning model. The signal weights that are calculated for each array element by using their progressive measured phase difference is due to the arriving signal, that are given as input to a linear regression model and the direction of arrival (DOA) of the signal is predicted. The curve fitted linear regression model can be implemented in real-time geostationary satellite communication systems to accurately intercept the signal of interest.
{"title":"Machine Learning based Digital Beamforming for Line-of-Sight optimization in Satcom on the Move Technology","authors":"Arushi Singh, M. Jayakumar","doi":"10.1109/ICECA49313.2020.9297645","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297645","url":null,"abstract":"With the evolving communication systems, the need for beamforming to improve the gain of the transmitting or receiving antenna has also increased. Beamforming allows to direct the radiated energy with the intended choice of direction efficiently. The main focus of this work is to develop an effective method for beamforming at the receiver side antennas for deploying Line-of-Sight (LOS) communication in Satellite Communication (Satcom) by using machine learning algorithms to detect signals as accurately as possible and to reduce the time taken to steer the beam as well as complexity of operations if a standard beamforming algorithm was used. To implement this, the antenna array weights are pre-calculated for a number of beam directions and kept as a database which are given to a linear regression machine learning model. The signal weights that are calculated for each array element by using their progressive measured phase difference is due to the arriving signal, that are given as input to a linear regression model and the direction of arrival (DOA) of the signal is predicted. The curve fitted linear regression model can be implemented in real-time geostationary satellite communication systems to accurately intercept the signal of interest.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127066255","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297428
B. Dhanalakshmi, R. Ramesh, D. Raguraman, R. Menaka
Due to the increase in vehicle usage, itis a challenging task to monitor, analyze the vehicles by a human for security purposes. There is a need for an automatic vehicle recognition system since various places nowadays have checkpoints for vehicles, to track the stolen vehicles, and to monitor traffic violations. The problem exists when the vehicle number plate is encountered in different formats, different scales, and illumination to number-plates. In the case of an indeterminate situation, identifying vehicle number plates in poor lighting conditions and worse traffic situations can be analyzed using an automatic vehicle number plate recognition system. The vehicle name board edge finding techniques are used to easily identify the vehicle number in the name board. A dataset with 200 license plates has been collected as training datasets for recognition, estimation, and identification, thus improving system accuracy of recognition when compared to existing works. The training input samples include images of vehicle number plates taken from the traffic department. The automated vehicle number recognition system is improvised in terms of accuracy by estimating stability score and using the k-means clustering algorithm.
{"title":"Automated Vehicle Number Plate Recognition System using Stability Score and K-Means Clustering Algorithm","authors":"B. Dhanalakshmi, R. Ramesh, D. Raguraman, R. Menaka","doi":"10.1109/ICECA49313.2020.9297428","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297428","url":null,"abstract":"Due to the increase in vehicle usage, itis a challenging task to monitor, analyze the vehicles by a human for security purposes. There is a need for an automatic vehicle recognition system since various places nowadays have checkpoints for vehicles, to track the stolen vehicles, and to monitor traffic violations. The problem exists when the vehicle number plate is encountered in different formats, different scales, and illumination to number-plates. In the case of an indeterminate situation, identifying vehicle number plates in poor lighting conditions and worse traffic situations can be analyzed using an automatic vehicle number plate recognition system. The vehicle name board edge finding techniques are used to easily identify the vehicle number in the name board. A dataset with 200 license plates has been collected as training datasets for recognition, estimation, and identification, thus improving system accuracy of recognition when compared to existing works. The training input samples include images of vehicle number plates taken from the traffic department. The automated vehicle number recognition system is improvised in terms of accuracy by estimating stability score and using the k-means clustering algorithm.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415646","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297535
M. Ram, Kuda Nageswara Rao, S. J. Basha, S. S. Reddy
Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.
无线传感器网络(WSN)是一个由大量传感器组成的系统，通过将它们放置在特定区域而相互连接。WSN的不同问题包括(但不限于)覆盖范围、网络生命周期和聚合。通过聚类可以提高网络的生命周期，同时降低能耗。集群将相关类型的传感器分组到一个具有头部传感器节点的地方，用于其他节点和基站(BS)之间的消息聚合和传输。在聚合和传输数据时，簇头(CH)消耗更多的能量。通过对CH的适当识别，将大大降低无线传感器网络的能耗，提高无线传感器网络的使用寿命。本文改进了元启发式算法，通过选择合适的簇头和最优路径来提高网络生存时间。为了有效地选择簇头，提出了k -遗传算法(K-GA)。首先，根据传感器的位置采用k-means聚类方法对其进行聚类，并应用遗传算法检测最佳簇头。为了实现安全最优路由，采用了基于信任的萤火虫(Trust based Firefly, T-FA)选路算法。在各种情况下进行了大量的模拟。仿真结果表明，与其他技术相比，K-GA有助于确定最优簇头，T-FA发现最优路径，通过减少端到端延迟，丰富了网络的寿命。
{"title":"Cluster Head and Optimal Path Slection Using K-GA and T-FA Algorithms for Wireless Sensor Networks","authors":"M. Ram, Kuda Nageswara Rao, S. J. Basha, S. S. Reddy","doi":"10.1109/ICECA49313.2020.9297535","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297535","url":null,"abstract":"Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127437727","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297581
M. Rahman, Md Saif Kabir, Md Nazaf Rabbi, Mohammad Hashib Sarker, Ishmam Ahmed Chowdhury, Golam Sarowar
DC-DC and AC-DC converters are often used to obtain the craved voltage level. However, the conventional converters are not suitable for high output voltages without depreciating various parameters like conversion efficiency. In this paper, a new Cascaded Buck-Boost Zeta (BBZ) converter topology is proposed. Also, a closed-loop is implemented to improve THD and power factor. This converter’s DC-DC topology can deliver the output voltage as high as 773V along with high conversion efficiency at an 80% duty cycle. The AC-DC topology gives a maximum efficiency of 98.29%. The efficiency levels of both the topology are also relatively high at different duty cycles.
{"title":"Design and Analysis of Cascaded Buck-Boost Zeta (BBZ) Converter for Improved Efficiency at High Output Voltage","authors":"M. Rahman, Md Saif Kabir, Md Nazaf Rabbi, Mohammad Hashib Sarker, Ishmam Ahmed Chowdhury, Golam Sarowar","doi":"10.1109/ICECA49313.2020.9297581","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297581","url":null,"abstract":"DC-DC and AC-DC converters are often used to obtain the craved voltage level. However, the conventional converters are not suitable for high output voltages without depreciating various parameters like conversion efficiency. In this paper, a new Cascaded Buck-Boost Zeta (BBZ) converter topology is proposed. Also, a closed-loop is implemented to improve THD and power factor. This converter’s DC-DC topology can deliver the output voltage as high as 773V along with high conversion efficiency at an 80% duty cycle. The AC-DC topology gives a maximum efficiency of 98.29%. The efficiency levels of both the topology are also relatively high at different duty cycles.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645527","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297532
Y. Gupta, Tanusha Mittal
Big data analytics is the one which acquire, organise and analyse the huge volume of data with high velocity to find some patterns and useful information. The data sets are so large that it can’t be handled by traditional databases to manage and process the structure and unstructured data. Hence, big data tools i.e. Hadoop, is required due to its high scalability, availability and cluster environment mechanism for analysing large volume of data. MapReduce is one of the important components of Hadoop which is able to handle the unstructured data. But to use MapReduce, high programming skills are needed. Therefore, due to the reason of programming, users are moving towards some other tools i.e. Apache Pig or Apache Cassandra. In these tools, the data is simply analysed by executing the queries or commands. This paper will discuss about the architectural of Apache Pig and Apache Cassandra and afterwards both the technologies regarding some factors are compared to find out which one is better.
{"title":"Comparative Study of Apache Pig & Apache Cassandra in Hadoop Distributed Environment","authors":"Y. Gupta, Tanusha Mittal","doi":"10.1109/ICECA49313.2020.9297532","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297532","url":null,"abstract":"Big data analytics is the one which acquire, organise and analyse the huge volume of data with high velocity to find some patterns and useful information. The data sets are so large that it can’t be handled by traditional databases to manage and process the structure and unstructured data. Hence, big data tools i.e. Hadoop, is required due to its high scalability, availability and cluster environment mechanism for analysing large volume of data. MapReduce is one of the important components of Hadoop which is able to handle the unstructured data. But to use MapReduce, high programming skills are needed. Therefore, due to the reason of programming, users are moving towards some other tools i.e. Apache Pig or Apache Cassandra. In these tools, the data is simply analysed by executing the queries or commands. This paper will discuss about the architectural of Apache Pig and Apache Cassandra and afterwards both the technologies regarding some factors are compared to find out which one is better.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121628167","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297643
P. Pattnaik, Kalyan Kumar Mohanty
Face recognition is a powerful tool for a biometric system that takes data from both images and videos. The traditional attendance system can be replaced by the automatic attendance system to utilize class time more effectively. In this paper real-time, attendance monitoring uses a web app that can be operated remotely by using a local server and Amazon Web Service (AWS) cloud recognition Application Programming Interface (API). The first approach follows five sections which are face detection, preprocessing, training and, face recognition through which attendance will be recorded and mailed to the respective teacher. The second approach is based on AWS recognition API which processes the data in the cloud.
{"title":"AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study","authors":"P. Pattnaik, Kalyan Kumar Mohanty","doi":"10.1109/ICECA49313.2020.9297643","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297643","url":null,"abstract":"Face recognition is a powerful tool for a biometric system that takes data from both images and videos. The traditional attendance system can be replaced by the automatic attendance system to utilize class time more effectively. In this paper real-time, attendance monitoring uses a web app that can be operated remotely by using a local server and Amazon Web Service (AWS) cloud recognition Application Programming Interface (API). The first approach follows five sections which are face detection, preprocessing, training and, face recognition through which attendance will be recorded and mailed to the respective teacher. The second approach is based on AWS recognition API which processes the data in the cloud.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121804702","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297404
Sining Hua
City brand image building strategy based on the interactive data mining and visual saliency is discussed in this paper. In the big data environment, through the general interface and interaction design, the operation and management and also scheduling capabilities of big data can be improved. By using this feature, this paper proposes the listed novelties. (1) The GSSL method mainly relies on the Euclidean distance between point pairs to construct a graph model composed of multiple overlapping local blocks. This feature is used to estimate the distance of the visual information. (2) The simplest form of the region matching is to divide the whole image into many sub-regions, and then measure the similarity of photometric information. This has been used to construct the analytic framework. The verifications have proven the better performance.
{"title":"City Brand Image Building Strategy Based on Interactive Data Mining and Visual Saliency","authors":"Sining Hua","doi":"10.1109/ICECA49313.2020.9297404","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297404","url":null,"abstract":"City brand image building strategy based on the interactive data mining and visual saliency is discussed in this paper. In the big data environment, through the general interface and interaction design, the operation and management and also scheduling capabilities of big data can be improved. By using this feature, this paper proposes the listed novelties. (1) The GSSL method mainly relies on the Euclidean distance between point pairs to construct a graph model composed of multiple overlapping local blocks. This feature is used to estimate the distance of the visual information. (2) The simplest form of the region matching is to divide the whole image into many sub-regions, and then measure the similarity of photometric information. This has been used to construct the analytic framework. The verifications have proven the better performance.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164113","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297499
L. Rao, Coneri Harshitha, C. Z. Basha, Nazia Parveen
Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.
{"title":"Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN","authors":"L. Rao, Coneri Harshitha, C. Z. Basha, Nazia Parveen","doi":"10.1109/ICECA49313.2020.9297499","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297499","url":null,"abstract":"Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133139793","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297548
R. Prasad, T. Jaya
Cognitive Radio (CR) is a mode of wireless communication, where a transceiver has been used to automatically detect the communication channel that are in use and not used, where it will switch immediately into the vacant space. To avoid the causing interference with primary user, CR needs to change the transmission and receiver parameter. The adaptive framework used for range handoff is driven by a decision technique i.e. Additive weighting method (AW), Technique for Order Preference (ETOP) etc. Decision Method (DM) technique utilize video, voice and data organizations by depending on CR tendencies. The reenactment shows that, ETOP strategy is incredible than AW technique for picking the ideal system for range handoff to significantly increase the play administration.
{"title":"Decision Making Method ETOP for Handoff in Cognitive Radio Network","authors":"R. Prasad, T. Jaya","doi":"10.1109/ICECA49313.2020.9297548","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297548","url":null,"abstract":"Cognitive Radio (CR) is a mode of wireless communication, where a transceiver has been used to automatically detect the communication channel that are in use and not used, where it will switch immediately into the vacant space. To avoid the causing interference with primary user, CR needs to change the transmission and receiver parameter. The adaptive framework used for range handoff is driven by a decision technique i.e. Additive weighting method (AW), Technique for Order Preference (ETOP) etc. Decision Method (DM) technique utilize video, voice and data organizations by depending on CR tendencies. The reenactment shows that, ETOP strategy is incredible than AW technique for picking the ideal system for range handoff to significantly increase the play administration.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131431304","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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