Pub Date : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768550
K. Deva, S. Mohanaselvi
A picture fuzzy set effectively deals with uncertainties present in a given information and it has several uses in decision-making. Aggregation operators are particularly useful in the decision-making process for evaluating provided alternatives, and their goal is to combine all of the discrete evaluation values into a unified form. In this article, a picture fuzzy Einstein weighted geometric aggregate operator and Picture fuzzy Einstein weighted geometric interactive aggregate operator are developed by using Einstein t-norms and t-conorms. The recommended operators' various aspects are investigated in this research. Then, we are using the proposed operators to solve the picture fuzzy multiple attribute decision making problem as well as a comparative study with the existing literature.
{"title":"Picture fuzzy Einstein geometric aggregate Operators and their Application to Multiple Attribute Decision Making","authors":"K. Deva, S. Mohanaselvi","doi":"10.1109/ICEEICT53079.2022.9768550","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768550","url":null,"abstract":"A picture fuzzy set effectively deals with uncertainties present in a given information and it has several uses in decision-making. Aggregation operators are particularly useful in the decision-making process for evaluating provided alternatives, and their goal is to combine all of the discrete evaluation values into a unified form. In this article, a picture fuzzy Einstein weighted geometric aggregate operator and Picture fuzzy Einstein weighted geometric interactive aggregate operator are developed by using Einstein t-norms and t-conorms. The recommended operators' various aspects are investigated in this research. Then, we are using the proposed operators to solve the picture fuzzy multiple attribute decision making problem as well as a comparative study with the existing literature.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128591698","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768483
Shreekant Salotagi, J. Mallapur
In present scenario the world is running to achieve every goal of life through the support of internet and sensor technology. Agriculture is also maintained by sensor devices using IoT network but this network has different sensor devices that will lead to heterogeneity network. The heterogeneity will cause many problems such as routing, resource allocation, latency delayed. The routing of the packet to heterogeneity devices will lead to delay distribution and latency. Therefore we have proposed an algorithm for multicast routing using forward chain mechanism. The simulation results show that we can improve the delay distribution and latency using EPREQLLN algorithm.
{"title":"Optimization of Multicast Routing Using Forward Chain Algorithm for Internet of Things Application (IoT) Agriculture Application","authors":"Shreekant Salotagi, J. Mallapur","doi":"10.1109/ICEEICT53079.2022.9768483","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768483","url":null,"abstract":"In present scenario the world is running to achieve every goal of life through the support of internet and sensor technology. Agriculture is also maintained by sensor devices using IoT network but this network has different sensor devices that will lead to heterogeneity network. The heterogeneity will cause many problems such as routing, resource allocation, latency delayed. The routing of the packet to heterogeneity devices will lead to delay distribution and latency. Therefore we have proposed an algorithm for multicast routing using forward chain mechanism. The simulation results show that we can improve the delay distribution and latency using EPREQLLN algorithm.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128661287","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768579
A. Devaki, C.V. Guru Rao
Brain stroke detection using data-driven approach has economic benefits. Simple approach using Machine Learning (ML) classification algorithms could provide acceptable accuracy for realizing Clinical Decision Support System (CDSS). From the literature, it is ascertained that making ensemble of multiple brain stroke prediction models could improve prediction performance. This is the hypothesis and motivation for the research carried out and presented in this paper. Another important observation from the literature is that most of the ensemble methods found in the literature for brain stroke prediction are not data-driven approaches. This research gap is filled in this paper by focusing on ensemble of data-driven prediction models. Towards this end, we proposed an ensemble framework based on supervised ML techniques for improving brain stroke prediction performance. The framework is named as Brain Stroke Prediction Ensemble (BSPE). We also proposed an algorithm known as Hybrid Ensemble Learning for Brain Stroke Prediction (HEL-BSP). We also reuse our feature engineering algorithm known as Composite Metric based Feature Selection (CMFS). The ensemble is made up of ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), KNeighbours classifier, Gradient Boosting and Stochastic Gradient Descent (SGD). A prototype application is built using Python data science platform to evaluate the proposed framework and the underlying algorithm. The experimental results revealed that the ensemble of the prediction models with majority voting approach could outperform individual prediction models.
{"title":"An Ensemble Framework for Improving Brain Stroke Prediction Performance","authors":"A. Devaki, C.V. Guru Rao","doi":"10.1109/ICEEICT53079.2022.9768579","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768579","url":null,"abstract":"Brain stroke detection using data-driven approach has economic benefits. Simple approach using Machine Learning (ML) classification algorithms could provide acceptable accuracy for realizing Clinical Decision Support System (CDSS). From the literature, it is ascertained that making ensemble of multiple brain stroke prediction models could improve prediction performance. This is the hypothesis and motivation for the research carried out and presented in this paper. Another important observation from the literature is that most of the ensemble methods found in the literature for brain stroke prediction are not data-driven approaches. This research gap is filled in this paper by focusing on ensemble of data-driven prediction models. Towards this end, we proposed an ensemble framework based on supervised ML techniques for improving brain stroke prediction performance. The framework is named as Brain Stroke Prediction Ensemble (BSPE). We also proposed an algorithm known as Hybrid Ensemble Learning for Brain Stroke Prediction (HEL-BSP). We also reuse our feature engineering algorithm known as Composite Metric based Feature Selection (CMFS). The ensemble is made up of ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), KNeighbours classifier, Gradient Boosting and Stochastic Gradient Descent (SGD). A prototype application is built using Python data science platform to evaluate the proposed framework and the underlying algorithm. The experimental results revealed that the ensemble of the prediction models with majority voting approach could outperform individual prediction models.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124581710","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768444
J. Sharma, Salauddin Ansari, O. Gupta
To optimize the power system operation, different regional grids are usually interconnected with the help of long transmission lines (TLs). Sometimes, depending upon the geographical position, renewable energy sources (like hydro energy plants) have been integrated with the utility grid using long TLs. However, transmitting power over a long distance is not preferred as it creates operational and economic issues. A half-wave transmission line (HTL) is proposed in 1940 to cope with such problems. HTL is very long compared to conventionally used TLs, making the dynamics of HTL completely different during normal and abnormal conditions. The conventional protection schemes find limitations in the case of HTL with these abrupt dynamics. This paper proposes a DC component-based differential pilot relaying system capable of fault discrimination and classification. Moreover, the performance of the presented method is scrutinized under different scenarios, such as variation of fault location and inception angle/time, CT saturation error, evolving faults, cross-country faults, and found to be reliable and precise. All the results have been simulated and validated using PSCAD/EMTDC software.
{"title":"DC Component-based Differential Pilot Relaying Scheme for Half-wave Transmission Lines","authors":"J. Sharma, Salauddin Ansari, O. Gupta","doi":"10.1109/ICEEICT53079.2022.9768444","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768444","url":null,"abstract":"To optimize the power system operation, different regional grids are usually interconnected with the help of long transmission lines (TLs). Sometimes, depending upon the geographical position, renewable energy sources (like hydro energy plants) have been integrated with the utility grid using long TLs. However, transmitting power over a long distance is not preferred as it creates operational and economic issues. A half-wave transmission line (HTL) is proposed in 1940 to cope with such problems. HTL is very long compared to conventionally used TLs, making the dynamics of HTL completely different during normal and abnormal conditions. The conventional protection schemes find limitations in the case of HTL with these abrupt dynamics. This paper proposes a DC component-based differential pilot relaying system capable of fault discrimination and classification. Moreover, the performance of the presented method is scrutinized under different scenarios, such as variation of fault location and inception angle/time, CT saturation error, evolving faults, cross-country faults, and found to be reliable and precise. All the results have been simulated and validated using PSCAD/EMTDC software.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614074","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768653
Rahul Kejriwal, Ritika H J, Arpit Arora, Mohana
Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.
车辆计数是估算道路交通密度以评估智能交通系统交通状况的过程。由于高端摄像机和技术的可用性，实时交通管理系统最近变得流行起来。目前的交通管理系统主要集中在速度检测、信号跳变、斑马线等方面，但对交通密度的估计还不够。提出了一种基于视频的车辆计数和跟踪方法，使用闭路电视和手持移动摄像机捕获的视频。该系统可用于智能城市创建智能交通灯信号，其中每个信号的持续时间取决于特定车道上的实时车辆密度。车辆计数分两步进行:将捕获的视频发送到基于YOLO (You Only Look Once)的深度学习框架，对车辆进行检测、计数和分类。采用深度排序算法对视频帧中的车辆进行多车跟踪。使用谷歌协作实验室对模型进行了六个不同类别的训练。考虑实施与印度道路环境有关的车辆数据集。对模型的性能进行了分析，提出的模型经过测试，平均计数准确率为86.56%，平均精度为93.85%。该模型可用于确定道路上的交通密度，并为当局的基础设施发展提供知识。开发智能化、智能化的交通监控系统也可以作为智慧城市项目的组成部分。
{"title":"Vehicle Detection and Counting using Deep Learning basedYOLO and Deep SORT Algorithm for Urban Traffic Management System","authors":"Rahul Kejriwal, Ritika H J, Arpit Arora, Mohana","doi":"10.1109/ICEEICT53079.2022.9768653","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768653","url":null,"abstract":"Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124150256","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768553
P. Venkatesh
This paper presents the installation procedure of 2 KW Grid connected solar roof top PV panel in home. The selection of rating of 2 KW Grid connected solar roof top PV panel is done by the calculation with the electricity bill of the consumer. The bimonthly electricity bill is given in the paper. The site survey, PV module arrangements, IV and PV characteristics of solar panel have been made in the case study. The electrical connection of 2 KW grid connected solar roof top PV panel is depicted with suitable figures. The case study shows a reduction of energy consumption approximately INR. 1500 from the home after installation of 2 KW Grid connected solar roof top PV panel which is reflected in the bimonthly Electricity bill of the consumer.
{"title":"Solar Roof top PV panel in home - A case study","authors":"P. Venkatesh","doi":"10.1109/ICEEICT53079.2022.9768553","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768553","url":null,"abstract":"This paper presents the installation procedure of 2 KW Grid connected solar roof top PV panel in home. The selection of rating of 2 KW Grid connected solar roof top PV panel is done by the calculation with the electricity bill of the consumer. The bimonthly electricity bill is given in the paper. The site survey, PV module arrangements, IV and PV characteristics of solar panel have been made in the case study. The electrical connection of 2 KW grid connected solar roof top PV panel is depicted with suitable figures. The case study shows a reduction of energy consumption approximately INR. 1500 from the home after installation of 2 KW Grid connected solar roof top PV panel which is reflected in the bimonthly Electricity bill of the consumer.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127895478","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768435
Priyanka kumari Bhansali, Dilendra Hiran
The Internet of Things (IoT) is a model that allows objects to monitor and collect data from their surroundings and then transmit that data over the Internet to be evaluated and used for various purposes. Healthcare is one of the IoT application fields that has attracted much attention from industry, academia, and government. The rise of IoT, fog and cloud computing in the medical sector enhances patient safety, staff happiness, and operational efficiency. A three-layer integrated framework for a secure, intelligent healthcare system is proposed in this research. The first layer is the IoT layer, which acquires and transmits healthcare data. The IoT layer uses healthcare embedded sensors and wearable's to communicate to exchange sensitive data with an aggregating node, which can then share data with the Fog server. The second layer is the Fog layer, which retrieves the measured value from the IoT layer and saves them in a local repository. Ensemble learning is used in the Fog layer to forecast diabetes and stroke problems. The third layer is the Cloud layer which provides secure data storage and access control for a smart healthcare system. For fine-grained data access, security, authentication, and user privacy of medical data, the cloud layer use hash-based ciphertext policy attribute-based encryption with signature. The suggested work's performance is tested for each layer using a different set of parameters, and the combination of Io'T, Fog, and Cloud improves the healthcare system's efficiency.
{"title":"IFC_Health: Three-Layer Integrated Framework for Secure Smart Healthcare System","authors":"Priyanka kumari Bhansali, Dilendra Hiran","doi":"10.1109/ICEEICT53079.2022.9768435","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768435","url":null,"abstract":"The Internet of Things (IoT) is a model that allows objects to monitor and collect data from their surroundings and then transmit that data over the Internet to be evaluated and used for various purposes. Healthcare is one of the IoT application fields that has attracted much attention from industry, academia, and government. The rise of IoT, fog and cloud computing in the medical sector enhances patient safety, staff happiness, and operational efficiency. A three-layer integrated framework for a secure, intelligent healthcare system is proposed in this research. The first layer is the IoT layer, which acquires and transmits healthcare data. The IoT layer uses healthcare embedded sensors and wearable's to communicate to exchange sensitive data with an aggregating node, which can then share data with the Fog server. The second layer is the Fog layer, which retrieves the measured value from the IoT layer and saves them in a local repository. Ensemble learning is used in the Fog layer to forecast diabetes and stroke problems. The third layer is the Cloud layer which provides secure data storage and access control for a smart healthcare system. For fine-grained data access, security, authentication, and user privacy of medical data, the cloud layer use hash-based ciphertext policy attribute-based encryption with signature. The suggested work's performance is tested for each layer using a different set of parameters, and the combination of Io'T, Fog, and Cloud improves the healthcare system's efficiency.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130144529","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768648
Gowri Sai Priya, G. Sai, Gowthami, I. Singh, Shubham Tayal, Mohd Javed Khan
Intrinsic advantages of direct sequence spread spectrum (DSSS) are interference free, multiple access, and low probability of intercept (LPI), as well as the ease with which it may be deployed, make it a suitable transmission system for both defence and commercial applications. DSSS is a standard mechanism used by the majority of current remote control devices to transfer command and control data. Only DSSS technology might not be sufficient to convey numerous accesses as soon as the multiple users of aircraft to control cultivate. To attain an efficient multiple access, a hybrid technique should be used in combination of DSSS with time division multiple accesses (TDMA) as an alternative multiple access strategy. The Bit Error Rate (BER) performance for unmanned aerial vehicles (UAV) communication has been analysed over adaptive white Gaussian noise (AWGN) channel and Rayleigh faded channel by varying number of UAVs.
{"title":"Analysis of Bit Error Rate for Multi-user TDMA-based Communication System","authors":"Gowri Sai Priya, G. Sai, Gowthami, I. Singh, Shubham Tayal, Mohd Javed Khan","doi":"10.1109/ICEEICT53079.2022.9768648","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768648","url":null,"abstract":"Intrinsic advantages of direct sequence spread spectrum (DSSS) are interference free, multiple access, and low probability of intercept (LPI), as well as the ease with which it may be deployed, make it a suitable transmission system for both defence and commercial applications. DSSS is a standard mechanism used by the majority of current remote control devices to transfer command and control data. Only DSSS technology might not be sufficient to convey numerous accesses as soon as the multiple users of aircraft to control cultivate. To attain an efficient multiple access, a hybrid technique should be used in combination of DSSS with time division multiple accesses (TDMA) as an alternative multiple access strategy. The Bit Error Rate (BER) performance for unmanned aerial vehicles (UAV) communication has been analysed over adaptive white Gaussian noise (AWGN) channel and Rayleigh faded channel by varying number of UAVs.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131534681","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768455
M. K. Rasheed, T. Padma, C. Kumari, N. Rao
The biomedical data signals captured from the patient is sent for compression where the size of the digital data is 24 bits. Compression is executed for two consecutive data elements. One is current data where data is shared for compression and previous data stores old values. In the starting, previous data storage contains no data. Compression is accomplished using log2 sub-band methodology that involves the identification of changes by making use of XOR logic. The XOR-generated data are further calculated using OR gates that are required to generate flags. Using the Chinese remainder theorem that involves the division method the 24-bit data is compressed into 12 bits. According to the theorem, the dividend should be divided with divisor where the degree of divisor must be less than that of dividend. In our work, the compressed data generated before the division process can be up to 26 bits which is divided only with a prime number as per the theorem rule. The division process produces 12-bit data which is given to the decoder along with the quotient and remainder obtained after division for decompression. It should satisfy the division rule where the decompressed result should be the same as the data to be compressed then the overall compression & decompression is correct and is satisfied in our project.
{"title":"Compression and Decompression of Biomedical Signals Using Chinese Remainder Theorem","authors":"M. K. Rasheed, T. Padma, C. Kumari, N. Rao","doi":"10.1109/ICEEICT53079.2022.9768455","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768455","url":null,"abstract":"The biomedical data signals captured from the patient is sent for compression where the size of the digital data is 24 bits. Compression is executed for two consecutive data elements. One is current data where data is shared for compression and previous data stores old values. In the starting, previous data storage contains no data. Compression is accomplished using log2 sub-band methodology that involves the identification of changes by making use of XOR logic. The XOR-generated data are further calculated using OR gates that are required to generate flags. Using the Chinese remainder theorem that involves the division method the 24-bit data is compressed into 12 bits. According to the theorem, the dividend should be divided with divisor where the degree of divisor must be less than that of dividend. In our work, the compressed data generated before the division process can be up to 26 bits which is divided only with a prime number as per the theorem rule. The division process produces 12-bit data which is given to the decoder along with the quotient and remainder obtained after division for decompression. It should satisfy the division rule where the decompressed result should be the same as the data to be compressed then the overall compression & decompression is correct and is satisfied in our project.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130716295","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768490
K. R. Prabha, B. Nataraj, M. Jagadeeswari
This paper presents the design and analysis of microstrip patch antennas for wireless communication applications under sub-6GHzfrequency band. The designed patch antenna is suitable for WiMAX applications operated at 3. 55GHz aimed to provide high speed data rates and internet access for a wide coverage range. The patch antenna was designed on a FR4 substrate with dielectric permittivity of 4.4 and 1.6mm thickness using simple feed line. The square patch design presented in this paper exhibited better performance in terms of minimization in area, improvement in gain and directivity with respect to the rectangular patch design.
{"title":"Design and Analysis of Microstrip Patch Antenna for Sub-6GHz Applications","authors":"K. R. Prabha, B. Nataraj, M. Jagadeeswari","doi":"10.1109/ICEEICT53079.2022.9768490","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768490","url":null,"abstract":"This paper presents the design and analysis of microstrip patch antennas for wireless communication applications under sub-6GHzfrequency band. The designed patch antenna is suitable for WiMAX applications operated at 3. 55GHz aimed to provide high speed data rates and internet access for a wide coverage range. The patch antenna was designed on a FR4 substrate with dielectric permittivity of 4.4 and 1.6mm thickness using simple feed line. The square patch design presented in this paper exhibited better performance in terms of minimization in area, improvement in gain and directivity with respect to the rectangular patch design.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700437","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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