Pub Date : 2023-05-26DOI: 10.1109/INCET57972.2023.10170452
Dhruv Kolhatkar, Om Jain, Shrikar Patil, Priyanka N Shelke, Riddhi Mirajkar, Pawan Wawage
This project presents a novel blockchain-based disaster management system designed to streamline and optimize the response process to natural disasters. The system’s front-end is built using React, while its back-end runs on Flask. It enables different stakeholders, including aid organizations, government agencies, and relief workers, to access different parts of the system, enhancing collaboration, and increasing efficiency. The system comprises four main categories: missing people log, aid package distribution, critical areas, and refugee-relief camp mapping, each having multiple modules. The blockchain-based system allows for secure and immutable record-keeping of all critical data, enhancing transparency, accountability, and trust. Smart contracts are utilized to automate various aspects disaster management, enabling the rapid and efficient delivery of aid packages to affected areas. The decentralized nature blockchain allows for the efficient coordination of resources and effective management of aid distribution, reducing waste and ensuring that resources are delivered to those in need. The system’s unique features, such as tamper-proof data storage, automated processes, and decentralized coordination, provide an innovative solution to the challenges faced in disaster management and relief efforts.
{"title":"Blockchain Applications in Disaster Management Systems","authors":"Dhruv Kolhatkar, Om Jain, Shrikar Patil, Priyanka N Shelke, Riddhi Mirajkar, Pawan Wawage","doi":"10.1109/INCET57972.2023.10170452","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170452","url":null,"abstract":"This project presents a novel blockchain-based disaster management system designed to streamline and optimize the response process to natural disasters. The system’s front-end is built using React, while its back-end runs on Flask. It enables different stakeholders, including aid organizations, government agencies, and relief workers, to access different parts of the system, enhancing collaboration, and increasing efficiency. The system comprises four main categories: missing people log, aid package distribution, critical areas, and refugee-relief camp mapping, each having multiple modules. The blockchain-based system allows for secure and immutable record-keeping of all critical data, enhancing transparency, accountability, and trust. Smart contracts are utilized to automate various aspects disaster management, enabling the rapid and efficient delivery of aid packages to affected areas. The decentralized nature blockchain allows for the efficient coordination of resources and effective management of aid distribution, reducing waste and ensuring that resources are delivered to those in need. The system’s unique features, such as tamper-proof data storage, automated processes, and decentralized coordination, provide an innovative solution to the challenges faced in disaster management and relief efforts.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115540208","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170613
Babita Sonare, J. Dewan, Sudeep D. Thepade, Vedang Dadape, Tejas Gadge, Aditya Gavali
Sarcasm is the use of sarcastic words to mock or mockingly show disdain for something. Several people frequently use it on social media sites like Reddit and Twitter. This study investigates the effectiveness of deep learning and machine learning algorithms in detecting sarcasm using SARC dataset consisting of 1.3 million Reddit comments with almost equal amounts of sarcastic and neutral comments. We compare several well-known machine learning classification methods, including Logistic Regression, Naïve Bayes, Decision Tree Classifier, and Convolutional Neural Networks (CNN). Our results, with an accuracy of 73.2%, demonstrate that the model designed using a fusion of CNN and Long Short-Term Memory Networks (LSTM) techniques performed better than alternative classification algorithms. Our findings show how machine learning techniques will be used in the future to identify sarcasm on social networking websites.
{"title":"Detecting Sarcasm in Reddit Comments: A Comparative Analysis","authors":"Babita Sonare, J. Dewan, Sudeep D. Thepade, Vedang Dadape, Tejas Gadge, Aditya Gavali","doi":"10.1109/INCET57972.2023.10170613","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170613","url":null,"abstract":"Sarcasm is the use of sarcastic words to mock or mockingly show disdain for something. Several people frequently use it on social media sites like Reddit and Twitter. This study investigates the effectiveness of deep learning and machine learning algorithms in detecting sarcasm using SARC dataset consisting of 1.3 million Reddit comments with almost equal amounts of sarcastic and neutral comments. We compare several well-known machine learning classification methods, including Logistic Regression, Naïve Bayes, Decision Tree Classifier, and Convolutional Neural Networks (CNN). Our results, with an accuracy of 73.2%, demonstrate that the model designed using a fusion of CNN and Long Short-Term Memory Networks (LSTM) techniques performed better than alternative classification algorithms. Our findings show how machine learning techniques will be used in the future to identify sarcasm on social networking websites.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122407209","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170282
Ayush Dodia, Pritesh Shah, R. Aravind Sekhar, M. D.
Smart sensors are critical in industry 4.0 because they enable manufacturing processes to be more intelligent. The development and advances in smart sensor technology serves as the backbone of successful industry 4.0 implementation across various sectors. This paper presents various aspects of smart sensors from an industry 4.0 perspective, such as their functions, characteristics, components, working principle, fabrication, types, applications, selection criteria, advantages and limitations. Current and upcoming development technological trends in this field are also highlighted. The paper also briefly discusses some of the latest machine learning based ’smart soft sensor' applications in diverse areas.
{"title":"Smart Sensors in Industry 4.0","authors":"Ayush Dodia, Pritesh Shah, R. Aravind Sekhar, M. D.","doi":"10.1109/INCET57972.2023.10170282","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170282","url":null,"abstract":"Smart sensors are critical in industry 4.0 because they enable manufacturing processes to be more intelligent. The development and advances in smart sensor technology serves as the backbone of successful industry 4.0 implementation across various sectors. This paper presents various aspects of smart sensors from an industry 4.0 perspective, such as their functions, characteristics, components, working principle, fabrication, types, applications, selection criteria, advantages and limitations. Current and upcoming development technological trends in this field are also highlighted. The paper also briefly discusses some of the latest machine learning based ’smart soft sensor' applications in diverse areas.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122556205","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170133
S. Xavier, Eddula Sai Manoj, Bande Rohith, K. Abhilash, Velangini Amith Reddy G
Deep neural networks can effectively be used to predict the type of brain disease caused to the patient and to estimate the age of the patient. This can be used for identifying the age related disorders. So, we are using MobileNet a machine learning algorithm, and Res Net deep convolutional neural network, by using transfer learning. In transfer learning these models are pre trained on larger data sets. These algorithms have been used to train brain MRI scans that have been divided into three classes [1] Normal, which has not been affected by any disease, [2] Mild Cognitive Impairment, and [3] Alzheimer disease. From the categorized photographs the age of the patient is determined easily. We are going to train our model by using MOBILENET and RESNET algorithms by using the dataset of MRI images along with the accompanying disease type.By using this knowledge, the model may identify patterns in the MRI data that point to particular brain illnesses and patient age. The trained model is further used to find the disease type and age of the patients. The outcomes demonstrate that the technique is capable of achieving high accuracy in tasks involving both disease categorization and age estimation. In general, the suggested techniques for categorizing brain disorders and determining age from MRI scans offer a viable answer for enhancing the effectiveness and precision of medical diagnosis and treatment planning for patients with brain diseases. By this model we can predict results in larger scale real time data in short period of time.
{"title":"Brain Disease Classification along with Age Estimation from MRI","authors":"S. Xavier, Eddula Sai Manoj, Bande Rohith, K. Abhilash, Velangini Amith Reddy G","doi":"10.1109/INCET57972.2023.10170133","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170133","url":null,"abstract":"Deep neural networks can effectively be used to predict the type of brain disease caused to the patient and to estimate the age of the patient. This can be used for identifying the age related disorders. So, we are using MobileNet a machine learning algorithm, and Res Net deep convolutional neural network, by using transfer learning. In transfer learning these models are pre trained on larger data sets. These algorithms have been used to train brain MRI scans that have been divided into three classes [1] Normal, which has not been affected by any disease, [2] Mild Cognitive Impairment, and [3] Alzheimer disease. From the categorized photographs the age of the patient is determined easily. We are going to train our model by using MOBILENET and RESNET algorithms by using the dataset of MRI images along with the accompanying disease type.By using this knowledge, the model may identify patterns in the MRI data that point to particular brain illnesses and patient age. The trained model is further used to find the disease type and age of the patients. The outcomes demonstrate that the technique is capable of achieving high accuracy in tasks involving both disease categorization and age estimation. In general, the suggested techniques for categorizing brain disorders and determining age from MRI scans offer a viable answer for enhancing the effectiveness and precision of medical diagnosis and treatment planning for patients with brain diseases. By this model we can predict results in larger scale real time data in short period of time.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122836348","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10169912
Deeplakshmi Sachin Zingade, Rajesh Keshavrao Deshmukh, D. Kadam
Polarity categorization affects sentiment analysis. Categorization is a fundamental knowledge discovery challenge. Categorization accuracy depends on data quality. Hence, data must be preprocessed to obtain meaningful information. In real-world applications, data is enormous, and many properties are duplicates or useless. The classification depends on feature selection. It finds the best data representation qualities. Feature selection reduces model training time. When unnecessary attributes are eliminated, models learn better. Combinatorial optimization makes feature selection harder. Feature selection balances decreasing features and enhancing classification performance. We propose two multi-objective optimization techniques for the feature selection. The particle swarm optimization (PSO) and Krill Herd Algorithm (KHA) are applied for optimal feature selection. The proposed model consists of key steps such as review pre-processing, multi-objective optimization-based feature selection, and supervised classification. The performance of both PSO-based and KHA-based models is evaluated using the two sentiment analysis datasets. The results show the efficiency of both models in terms of precision, recall, accuracy, and F1-score parameters.
{"title":"Sentiment Analysis using Multi-objective Optimization-based Feature Selection Approach","authors":"Deeplakshmi Sachin Zingade, Rajesh Keshavrao Deshmukh, D. Kadam","doi":"10.1109/INCET57972.2023.10169912","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10169912","url":null,"abstract":"Polarity categorization affects sentiment analysis. Categorization is a fundamental knowledge discovery challenge. Categorization accuracy depends on data quality. Hence, data must be preprocessed to obtain meaningful information. In real-world applications, data is enormous, and many properties are duplicates or useless. The classification depends on feature selection. It finds the best data representation qualities. Feature selection reduces model training time. When unnecessary attributes are eliminated, models learn better. Combinatorial optimization makes feature selection harder. Feature selection balances decreasing features and enhancing classification performance. We propose two multi-objective optimization techniques for the feature selection. The particle swarm optimization (PSO) and Krill Herd Algorithm (KHA) are applied for optimal feature selection. The proposed model consists of key steps such as review pre-processing, multi-objective optimization-based feature selection, and supervised classification. The performance of both PSO-based and KHA-based models is evaluated using the two sentiment analysis datasets. The results show the efficiency of both models in terms of precision, recall, accuracy, and F1-score parameters.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"143 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114058986","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170321
Balaji M, P. N., G. P., Saif Ali Shaik, S. P, Sai Geetesh R
The primary driving force behind the creation of this work was to provide the design and implementation of a 4-tap, 8-tap, 16-tap, 32-tap, and 64-tap RNS (Residue Number System) based on efficient and excessive-overall performance FIR filter. RNS mathematics is a prized tool for theoretical investigation of the speed limitations of rapid mathematics. Some suggested solutions also include a few addition operations; however, using conventional adders will slow down operation and add to the amount of logic gates. So, to address the aforementioned concerns, Kogge-Stone Adder and Brent Kung Adder are being used to reduce delay and area and enhance performance as a whole. First, the multiplier is created using the RNS methodology. In which the Vedic multiplier's power dissipation is also minimized while the latency is shortened from 70% to 90%. In order to assess the findings, we are also using a simple adder and a simple multiplier. Using the Quartus 9.0 Simulation Tool, the combination of those methods results in a completely new structure with an excessively high speed and a small implementation area for the FIR filter.
{"title":"Design of FIR filter with Fast Adders and Fast Multipliers using RNS Algorithm","authors":"Balaji M, P. N., G. P., Saif Ali Shaik, S. P, Sai Geetesh R","doi":"10.1109/INCET57972.2023.10170321","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170321","url":null,"abstract":"The primary driving force behind the creation of this work was to provide the design and implementation of a 4-tap, 8-tap, 16-tap, 32-tap, and 64-tap RNS (Residue Number System) based on efficient and excessive-overall performance FIR filter. RNS mathematics is a prized tool for theoretical investigation of the speed limitations of rapid mathematics. Some suggested solutions also include a few addition operations; however, using conventional adders will slow down operation and add to the amount of logic gates. So, to address the aforementioned concerns, Kogge-Stone Adder and Brent Kung Adder are being used to reduce delay and area and enhance performance as a whole. First, the multiplier is created using the RNS methodology. In which the Vedic multiplier's power dissipation is also minimized while the latency is shortened from 70% to 90%. In order to assess the findings, we are also using a simple adder and a simple multiplier. Using the Quartus 9.0 Simulation Tool, the combination of those methods results in a completely new structure with an excessively high speed and a small implementation area for the FIR filter.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114159231","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170477
Hiteshi Aglawe, P. Bhore, Supriya Kelkar, Radhika Mahajan, Pooja Gambhir
Fault detection in a distributed computing system is a challenge that needs to be adaptive and efficient. Here, a new algorithm, namely "Event Driven Fault Diagnosis and Partition Detection (ED-FDPD) Algorithm for distributed systems" is proposed. ED-FDPD is applicable to any arbitrary topology. This is a sequential algorithm which gets executed periodically on each system to detect faulty nodes with minimum number of tests. The t-diagnosibility of this algorithm is (N-1) where, ‘N’ denotes the number of nodes in the network. ED-FDPD allows new nodes to be recognized in a diagnostic cycle. Also, repaired faulty nodes can reenter during a diagnostic cycle. There may be vulnerable nodes in the system which when becoming faulty can create a partition in the network. Detection of such vulnerable nodes is also incorporated in every diagnostic cycle. The diagnostic cycle does not stop even after the partitioning of the network. Administrator is informed about faulty, fault-free and vulnerable nodes in the entire network after the end of every diagnostic cycle.
{"title":"Event Driven Fault Diagnosis and Partition Detection (ED-FDPD) Algorithm","authors":"Hiteshi Aglawe, P. Bhore, Supriya Kelkar, Radhika Mahajan, Pooja Gambhir","doi":"10.1109/INCET57972.2023.10170477","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170477","url":null,"abstract":"Fault detection in a distributed computing system is a challenge that needs to be adaptive and efficient. Here, a new algorithm, namely \"Event Driven Fault Diagnosis and Partition Detection (ED-FDPD) Algorithm for distributed systems\" is proposed. ED-FDPD is applicable to any arbitrary topology. This is a sequential algorithm which gets executed periodically on each system to detect faulty nodes with minimum number of tests. The t-diagnosibility of this algorithm is (N-1) where, ‘N’ denotes the number of nodes in the network. ED-FDPD allows new nodes to be recognized in a diagnostic cycle. Also, repaired faulty nodes can reenter during a diagnostic cycle. There may be vulnerable nodes in the system which when becoming faulty can create a partition in the network. Detection of such vulnerable nodes is also incorporated in every diagnostic cycle. The diagnostic cycle does not stop even after the partitioning of the network. Administrator is informed about faulty, fault-free and vulnerable nodes in the entire network after the end of every diagnostic cycle.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114485549","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170352
Saahithi S, Raghu C N, Sai Shashank K, Jyotheswara Reddy K, Ritesh Dash, V. Subburaj
This paper focuses on the switched-capacitor converter configurations for wide ranges of applications, which have numerous stages which are designed for different voltage levels. A single-phase multilevel boost inverter topology configuration with fifteen levels and nineteen levels are discussed with same switching circuit. The capacitors are connected in series-parallel configuration to generate an output voltage more than input voltage. The same topology can be used for dual conversion configurations like DC-DC and DC-AC by adding a ground terminal at load point.
{"title":"Investigation of Switched Capacitor Multi Level Inverter Topology for Different Voltage Levels","authors":"Saahithi S, Raghu C N, Sai Shashank K, Jyotheswara Reddy K, Ritesh Dash, V. Subburaj","doi":"10.1109/INCET57972.2023.10170352","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170352","url":null,"abstract":"This paper focuses on the switched-capacitor converter configurations for wide ranges of applications, which have numerous stages which are designed for different voltage levels. A single-phase multilevel boost inverter topology configuration with fifteen levels and nineteen levels are discussed with same switching circuit. The capacitors are connected in series-parallel configuration to generate an output voltage more than input voltage. The same topology can be used for dual conversion configurations like DC-DC and DC-AC by adding a ground terminal at load point.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041713","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170640
R. Kumaraswamy, Madhushree H K, Manushree S, Nischitha C, Priyanka C
New technologies and techniques in the field of wireless communication have been developed in response to the growing demand for high-speed and reliable wireless communication services. The sixth generation (6G) of communication networks uses multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) to fulfil the high data rate need. Two cutting-edge methods that have attracted a lot of interest recently are MIMO and NOMA. In comparison to conventional single-antenna systems, MIMO, which employs multiple antennas at the transmitter and receiver, offers higher capacity and improved spectral efficiency. On the other side, NOMA can significantly boost system capacity and enhance user fairness since it uses power domain multiplexing to let multiple users share the same frequency band.This study compares MIMO with NOMA in-depth using a variety of performance criteria, including spectrum efficiency, channel prediction, and detection. The findings of this study suggest that both MIMO and NOMA have certain benefits and drawbacks, and that the decision between the two depends on the particular needs of the application situation.
{"title":"Study on performance of MIMO and NOMA system","authors":"R. Kumaraswamy, Madhushree H K, Manushree S, Nischitha C, Priyanka C","doi":"10.1109/INCET57972.2023.10170640","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170640","url":null,"abstract":"New technologies and techniques in the field of wireless communication have been developed in response to the growing demand for high-speed and reliable wireless communication services. The sixth generation (6G) of communication networks uses multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) to fulfil the high data rate need. Two cutting-edge methods that have attracted a lot of interest recently are MIMO and NOMA. In comparison to conventional single-antenna systems, MIMO, which employs multiple antennas at the transmitter and receiver, offers higher capacity and improved spectral efficiency. On the other side, NOMA can significantly boost system capacity and enhance user fairness since it uses power domain multiplexing to let multiple users share the same frequency band.This study compares MIMO with NOMA in-depth using a variety of performance criteria, including spectrum efficiency, channel prediction, and detection. The findings of this study suggest that both MIMO and NOMA have certain benefits and drawbacks, and that the decision between the two depends on the particular needs of the application situation.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129586511","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170134
M. Sindhuja, M. Marimuthu, S. Vijayalakshmi
This study suggests a (MBC) multi level boost converter with a doubling frequency. The DC-DC multilevel boost converter uses two driven switches, two inductors, two 2X-1 diodes, and a 2X-1 capacitor for a XN DFMBC. It is a PWM (Pulse Width Modulation) based converter that also accomplishes boost conversion and switched capacitor function to provide various output voltages and self equalized voltage. Power electronics is concerned with the DC-DC converter's efficiency, static and dynamic properties. This converter includes two boost cells, each of which functions at a high frequency rate and the another at a low frequency rate. Normal step up converters cannot retain high gain under high voltage stress, and low frequency converters have low efficiency. High frequency converters are also less efficient due to poor performance. As a result of using DF multilevel converter it, enhance performance, efficiency, gain with different output voltage level. MATLAB/Simulink is used to model the suggested converter.
{"title":"Double-Frequency Multilevel Boost Converter","authors":"M. Sindhuja, M. Marimuthu, S. Vijayalakshmi","doi":"10.1109/INCET57972.2023.10170134","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170134","url":null,"abstract":"This study suggests a (MBC) multi level boost converter with a doubling frequency. The DC-DC multilevel boost converter uses two driven switches, two inductors, two 2X-1 diodes, and a 2X-1 capacitor for a XN DFMBC. It is a PWM (Pulse Width Modulation) based converter that also accomplishes boost conversion and switched capacitor function to provide various output voltages and self equalized voltage. Power electronics is concerned with the DC-DC converter's efficiency, static and dynamic properties. This converter includes two boost cells, each of which functions at a high frequency rate and the another at a low frequency rate. Normal step up converters cannot retain high gain under high voltage stress, and low frequency converters have low efficiency. High frequency converters are also less efficient due to poor performance. As a result of using DF multilevel converter it, enhance performance, efficiency, gain with different output voltage level. MATLAB/Simulink is used to model the suggested converter.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129874921","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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