Pub Date : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076185
J. Sodha
A single code trellis decoding algorithm is pro-posed to decode a polar code. Key features of a polar tree are exploited to identify critical states into which branches must enter. For a rate 1/2 polar code with K = 4, there are two states which need to be first established. Thereafter, the rest of the symbols are corrected making use of this constraint. Only Euclidean distance calculations were used within the algorithm to create a simple sub-optimal decoder. Its performance is the same as the equivalent standard SC-polar decoder if only Euclidean distance calculations are used. The single code trellis interpretation of the polar decoder provides the insight to a simple method to accurately predict decoding errors of the standard polar decoder over low SNRs. Specifically, the predicted FER closely matches that of a Genie algorithm that has knowledge of the transmitted symbols.
{"title":"Polar Code Trellis Decoder","authors":"J. Sodha","doi":"10.1109/ICCT56969.2023.10076185","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076185","url":null,"abstract":"A single code trellis decoding algorithm is pro-posed to decode a polar code. Key features of a polar tree are exploited to identify critical states into which branches must enter. For a rate 1/2 polar code with K = 4, there are two states which need to be first established. Thereafter, the rest of the symbols are corrected making use of this constraint. Only Euclidean distance calculations were used within the algorithm to create a simple sub-optimal decoder. Its performance is the same as the equivalent standard SC-polar decoder if only Euclidean distance calculations are used. The single code trellis interpretation of the polar decoder provides the insight to a simple method to accurately predict decoding errors of the standard polar decoder over low SNRs. Specifically, the predicted FER closely matches that of a Genie algorithm that has knowledge of the transmitted symbols.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121518984","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075972
N. N. Das, Amrita Rai, Anaam Singh, Krishanu Kundu
The recent development in worldwide technology of renewable energy and different resource of electricity generation open a new research on electricity utilization and development. India is developing good progress in field of energy efficiency and boosting access to electricity. However, millions of people around the world still lack access to clean cooking fuels and technologies, and progress in this area is too slow. The pandemic has underlined the necessity for health clinics to have reliable and affordable electricity. Furthermore, a survey undertaken in a few developing nations indicated that one-fourth of the health facilities surveyed were not electrified, and another quarter experienced unplanned outages, limiting their ability to provide important health services. Such flaws jeopardize the health-care system's ability to respond to the current health crisis. Our civilization is always looking for new and better ways to generate energy that is both sustainable and renewable. People frequently associate these energy sources with solar cells or wind turbines. This paper proposed that using sludge to create electricity could potentially be a viable option. Sludge-to-electricity is a bio-electrochemical system that uses bacteria’ natural metabolic processes to generate electricity. Microbes in the sludge consume nutrients from their surroundings and release a portion of the energy contained in the meal in the form of electricity.
{"title":"Systems & Methods for Generation Of Electrical Power From A Sludge","authors":"N. N. Das, Amrita Rai, Anaam Singh, Krishanu Kundu","doi":"10.1109/ICCT56969.2023.10075972","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075972","url":null,"abstract":"The recent development in worldwide technology of renewable energy and different resource of electricity generation open a new research on electricity utilization and development. India is developing good progress in field of energy efficiency and boosting access to electricity. However, millions of people around the world still lack access to clean cooking fuels and technologies, and progress in this area is too slow. The pandemic has underlined the necessity for health clinics to have reliable and affordable electricity. Furthermore, a survey undertaken in a few developing nations indicated that one-fourth of the health facilities surveyed were not electrified, and another quarter experienced unplanned outages, limiting their ability to provide important health services. Such flaws jeopardize the health-care system's ability to respond to the current health crisis. Our civilization is always looking for new and better ways to generate energy that is both sustainable and renewable. People frequently associate these energy sources with solar cells or wind turbines. This paper proposed that using sludge to create electricity could potentially be a viable option. Sludge-to-electricity is a bio-electrochemical system that uses bacteria’ natural metabolic processes to generate electricity. Microbes in the sludge consume nutrients from their surroundings and release a portion of the energy contained in the meal in the form of electricity.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122677786","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075744
Rishabh Goel, Satish C J
Wearable sensors and social networking sites help gather participant information for healthcare monitoring. Wearable sensors generate a lot of healthcare data for continuous patient monitoring. Social networking sites' user-generated healthcare data is huge and unstructured. Existing healthcare monitoring systems have trouble gathering and evaluating sensor and social network data, and traditional machine learning methods are insufficient to anticipate abnormalities in big healthcare data. A novel cloud-based healthcare monitoring architecture is presented to properly save and evaluate healthcare data and increase classification results. A Wireless Sensor Network and Big Data Analytics based Intelligent Health Monitoring System (IHMS) is suggested in this paper. The suggested large data analytics engine uses data mining, ontologies, and Bidirectional Long Short-Term Memory (Bi-LSTM). Data processing approaches preprocess information and minimize dimensionality. Proposed ontologies give semantic information about diabetes and blood pressure entities, aspects, and relationships (BP). Bi-LSTM categorizes information properly to forecast medication side effects and patient abnormalities. The suggested approach defines patients' health using diabetes, BP, mental health, and medicine reviews, and this model uses Java and Protégé Web Ontology Language. The findings demonstrate that the suggested system accurately manages healthcare information and predicts pharmacological side effects.
{"title":"Precision Monitoring of Health-Care Using Big Data and Java from Social Networking and Wearable Devices","authors":"Rishabh Goel, Satish C J","doi":"10.1109/ICCT56969.2023.10075744","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075744","url":null,"abstract":"Wearable sensors and social networking sites help gather participant information for healthcare monitoring. Wearable sensors generate a lot of healthcare data for continuous patient monitoring. Social networking sites' user-generated healthcare data is huge and unstructured. Existing healthcare monitoring systems have trouble gathering and evaluating sensor and social network data, and traditional machine learning methods are insufficient to anticipate abnormalities in big healthcare data. A novel cloud-based healthcare monitoring architecture is presented to properly save and evaluate healthcare data and increase classification results. A Wireless Sensor Network and Big Data Analytics based Intelligent Health Monitoring System (IHMS) is suggested in this paper. The suggested large data analytics engine uses data mining, ontologies, and Bidirectional Long Short-Term Memory (Bi-LSTM). Data processing approaches preprocess information and minimize dimensionality. Proposed ontologies give semantic information about diabetes and blood pressure entities, aspects, and relationships (BP). Bi-LSTM categorizes information properly to forecast medication side effects and patient abnormalities. The suggested approach defines patients' health using diabetes, BP, mental health, and medicine reviews, and this model uses Java and Protégé Web Ontology Language. The findings demonstrate that the suggested system accurately manages healthcare information and predicts pharmacological side effects.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124835365","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075814
Haleema Essa Solayman, Rawaa Qasha
The Internet of Things contains smart devices and tools that collect and communicate sensing data, with different processing capabilities. Due to the heterogeneous nature of IoT ecosystems, an IoT application should be distributed, portable, reusable, and managed automatically in heterogeneous environments. The integration of automated provisioning and orchestration is needed to achieve these features effectively, in order to reduce the response time and enhance the system performance. In this research, we propose and develop a new system to automate the provisioning and orchestration of IoT system components at various infrastructures such as Edge and Cloud. To achieve our goals, we present a seamless integration of DevOps orchestration tools for provisioning and orchestrating a container-based IoT application, which requires achieving two key milestones. The first step is to build CI, or quick, dependable, and systematic integrations. The second is to enable CD, automate deployment, and make it simple to test new code in environments similar to production.
{"title":"Seamless Integration of DevOps Tools for Provisioning Automation of the IoT Application on Multi-Infrastructures","authors":"Haleema Essa Solayman, Rawaa Qasha","doi":"10.1109/ICCT56969.2023.10075814","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075814","url":null,"abstract":"The Internet of Things contains smart devices and tools that collect and communicate sensing data, with different processing capabilities. Due to the heterogeneous nature of IoT ecosystems, an IoT application should be distributed, portable, reusable, and managed automatically in heterogeneous environments. The integration of automated provisioning and orchestration is needed to achieve these features effectively, in order to reduce the response time and enhance the system performance. In this research, we propose and develop a new system to automate the provisioning and orchestration of IoT system components at various infrastructures such as Edge and Cloud. To achieve our goals, we present a seamless integration of DevOps orchestration tools for provisioning and orchestrating a container-based IoT application, which requires achieving two key milestones. The first step is to build CI, or quick, dependable, and systematic integrations. The second is to enable CD, automate deployment, and make it simple to test new code in environments similar to production.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124867838","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075871
S. Lakhanpal, Ajay K. Gupta
In this paper, insights and data analyses are presented for tracking the use of phrases in the representation of key domain areas in scientific publications, over time. A domain refers to a particular branch of scientific knowledge and hence largely defines the main topic or theme of any scientific research paper. These domains can be extracted from scientific publications over a fixed time period. Varied phrases can then be analyzed whether they are representative of the same domain. The representative phrases are then analyzed as to whether they are trending in usage over time or are being phased out. Thus, insights are proposed for a domain to be qualified by its most trending representative phrase. These insights are supported by rigorous data analyses.
{"title":"Trends in Research Topic Representation","authors":"S. Lakhanpal, Ajay K. Gupta","doi":"10.1109/ICCT56969.2023.10075871","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075871","url":null,"abstract":"In this paper, insights and data analyses are presented for tracking the use of phrases in the representation of key domain areas in scientific publications, over time. A domain refers to a particular branch of scientific knowledge and hence largely defines the main topic or theme of any scientific research paper. These domains can be extracted from scientific publications over a fixed time period. Varied phrases can then be analyzed whether they are representative of the same domain. The representative phrases are then analyzed as to whether they are trending in usage over time or are being phased out. Thus, insights are proposed for a domain to be qualified by its most trending representative phrase. These insights are supported by rigorous data analyses.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128496528","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075968
Salma Sultana, Anik Pramanik, Md. Sadekur Rahman
In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early.
{"title":"Lung Disease Classification Using Deep Learning Models from Chest X-ray Images","authors":"Salma Sultana, Anik Pramanik, Md. Sadekur Rahman","doi":"10.1109/ICCT56969.2023.10075968","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075968","url":null,"abstract":"In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125270715","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075774
Ashwin Muthuraman A., Balaaditya M., Snofy D. Dunston, M. V
Medical image diagnosis is a time-consuming process when done manually, where the predictions are subjected to human error. Various Deep Learning models have brought about an efficient and reliable automated system for medical image analysis. However, these models are highly vulnerable to attacks, upon exposure of which the models lose their reliability and misclassify the input images. Adversarial attack is one such technique which fools the deep learning models with deceptive data. DeepFool is an adversarial attack that efficiently computes perturbations that fool deep networks. With the help of two different datasets, we studied the impact of DeepFool attack on EfficientNet-B0 model in this research. There are several defense mechanisms to protect the model against various attacks. Adversarial training is one such defense method, which trains the model towards a particular attack. In this study, we have also analysed how effectively adversarial training would defend a model and make it robust.
{"title":"Analysis of the Effect of Adversarial Training in Defending EfficientNet-B0 Model from DeepFool Attack","authors":"Ashwin Muthuraman A., Balaaditya M., Snofy D. Dunston, M. V","doi":"10.1109/ICCT56969.2023.10075774","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075774","url":null,"abstract":"Medical image diagnosis is a time-consuming process when done manually, where the predictions are subjected to human error. Various Deep Learning models have brought about an efficient and reliable automated system for medical image analysis. However, these models are highly vulnerable to attacks, upon exposure of which the models lose their reliability and misclassify the input images. Adversarial attack is one such technique which fools the deep learning models with deceptive data. DeepFool is an adversarial attack that efficiently computes perturbations that fool deep networks. With the help of two different datasets, we studied the impact of DeepFool attack on EfficientNet-B0 model in this research. There are several defense mechanisms to protect the model against various attacks. Adversarial training is one such defense method, which trains the model towards a particular attack. In this study, we have also analysed how effectively adversarial training would defend a model and make it robust.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121582773","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076090
Nandini Babbar, Ashish Kumar, Vivek Kuma Verma
Early-season Crop Yield Prediction can assist farmers in India's leading economic sector of agriculture by assisting them in formulating their decision-making strategies. Deep Learning approaches have surpassed conventional statistical methods for yield prediction and crop forecasting as the artificial intelligence field has grown. The goal of the current work is to employ a LSTM model to estimate wheat crop yields in India. The dataset in this paper consist of soil and the metrological parameters. On the basis of consideration of individual factor one at a time, soil parameters such as temperature, humidity, moisture, soil type, crop, nitrogen, potassium, phosphorous in addition to nourishment used with consideration of metrological data, it contains minimum and maximum temperature as well as rainfall. At the end, we are able to get the accuracy and mean absolute error with R2 value for both the parameters. Later, we can merge these two parameters and get more efficient results for accurate prediction.
{"title":"Forecasting Wheat Yield Using Long Short- Term Memory Considering Soil and Metrological Parameters","authors":"Nandini Babbar, Ashish Kumar, Vivek Kuma Verma","doi":"10.1109/ICCT56969.2023.10076090","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076090","url":null,"abstract":"Early-season Crop Yield Prediction can assist farmers in India's leading economic sector of agriculture by assisting them in formulating their decision-making strategies. Deep Learning approaches have surpassed conventional statistical methods for yield prediction and crop forecasting as the artificial intelligence field has grown. The goal of the current work is to employ a LSTM model to estimate wheat crop yields in India. The dataset in this paper consist of soil and the metrological parameters. On the basis of consideration of individual factor one at a time, soil parameters such as temperature, humidity, moisture, soil type, crop, nitrogen, potassium, phosphorous in addition to nourishment used with consideration of metrological data, it contains minimum and maximum temperature as well as rainfall. At the end, we are able to get the accuracy and mean absolute error with R2 value for both the parameters. Later, we can merge these two parameters and get more efficient results for accurate prediction.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131064279","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 : 2023-01-19DOI: 10.1109/icct56969.2023.10075951
{"title":"About Manipal University Jaipur","authors":"","doi":"10.1109/icct56969.2023.10075951","DOIUrl":"https://doi.org/10.1109/icct56969.2023.10075951","url":null,"abstract":"","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128235552","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075877
Sujit Goswami, S. K. Mandal, S. Banerjee
A tri-band microstrip antenna using two symmetrically inverted slotted T-shaped patch is presented. By introducing rectangular slots, the patches are fed through two rectangular strips and the ground plane is modified so that the tri-band characteristics are achieved which are applicable for radars, satellites, and WLAN (wireless local area network) communications. The designed compact antenna structure offers three operating bands by fulfilling the bandwidth requirements with impedance bandwidth of 250 MHz (4.6-4.85 GHz), 500 MHz (6.7-7.2 GHz) and 800 MHz (7.75-8.55 GHz), having the respective resonant frequencies of 4.7 GHz, 6.9 GHz, and 8.05 GHz. The designed antenna provides good and stable radiation characteristics at all the desired operating bands with gain of 5.1 dBi, 6.5 dBi, and 4.6 dBi, respectively. The measured results of the fabricated antenna prototype reflect a justified compliance with the simulated results.
{"title":"A Compact Symmetrically Inverted Slotted T-Shaped Patch Antenna for Tri-band Communications","authors":"Sujit Goswami, S. K. Mandal, S. Banerjee","doi":"10.1109/ICCT56969.2023.10075877","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075877","url":null,"abstract":"A tri-band microstrip antenna using two symmetrically inverted slotted T-shaped patch is presented. By introducing rectangular slots, the patches are fed through two rectangular strips and the ground plane is modified so that the tri-band characteristics are achieved which are applicable for radars, satellites, and WLAN (wireless local area network) communications. The designed compact antenna structure offers three operating bands by fulfilling the bandwidth requirements with impedance bandwidth of 250 MHz (4.6-4.85 GHz), 500 MHz (6.7-7.2 GHz) and 800 MHz (7.75-8.55 GHz), having the respective resonant frequencies of 4.7 GHz, 6.9 GHz, and 8.05 GHz. The designed antenna provides good and stable radiation characteristics at all the desired operating bands with gain of 5.1 dBi, 6.5 dBi, and 4.6 dBi, respectively. The measured results of the fabricated antenna prototype reflect a justified compliance with the simulated results.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127824181","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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