Pub Date : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744236
Pralhad P. Teggi, Harivinod N., Bharathi Malakreddy
Many industries and organizations are moving away from legacy systems towards digital transformation to optimize their business processes. Artificial intelligence for IT operations (AIOps) plays a pivotal role in digital transformation. AIOps platforms utilize a large amount of data coupled with classical machine learning and cutting-edge analytic technologies. This will boost IT operations with proactive dynamic activities. The Micro Focus Operations Bridge (OpsBridge) monitors the health and performance of the systems in the infrastructure and applications across their IT environment and the hundreds of alerts are delivered to respective teams. These huge number of alerts create an alert noise. In this paper, we present an AIOps based automated predictive alerting system using logistic regression to monitor the system environment and reduce the alert noise. This predictive alerting will identify abnormalities in operational data and raise an alert on these abnormalities that could potentially impact an application or service.
{"title":"AIOPs based Predictive Alerting for System Stability in IT Environment","authors":"Pralhad P. Teggi, Harivinod N., Bharathi Malakreddy","doi":"10.1109/ICITIIT54346.2022.9744236","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744236","url":null,"abstract":"Many industries and organizations are moving away from legacy systems towards digital transformation to optimize their business processes. Artificial intelligence for IT operations (AIOps) plays a pivotal role in digital transformation. AIOps platforms utilize a large amount of data coupled with classical machine learning and cutting-edge analytic technologies. This will boost IT operations with proactive dynamic activities. The Micro Focus Operations Bridge (OpsBridge) monitors the health and performance of the systems in the infrastructure and applications across their IT environment and the hundreds of alerts are delivered to respective teams. These huge number of alerts create an alert noise. In this paper, we present an AIOps based automated predictive alerting system using logistic regression to monitor the system environment and reduce the alert noise. This predictive alerting will identify abnormalities in operational data and raise an alert on these abnormalities that could potentially impact an application or service.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126461612","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744232
U. Sankar, Anseel Ameerudheen, Treesa Rose K Sani, Alister Augustine D’Cruz, Sanjna Salim, K. Vinida
Fingerprint recognition is a safe and convenient technology, increasingly being used for biometric identification of the intended beneficiaries of welfare programmes. Kerala’s pubic distribution system, with Ration Shops being its point of contact, also uses fingerprint scanners for identification of beneficiaries. The outbreak of COVID-19 has adversely affected the safety of fingerprint authentication. Touching the sensors by multiple persons can cause the transmission of viruses. Studies have shown that COVID-19 can survive on common surfaces like wood, plastic, metal, and glass for a minimum of 5 days. Despite all the standard operating procedures, it is a common sight to see people crowding at public spaces like Ration Shops that has increased the risk of transmission of the virus. In this context, the present work aims to create a safe and healthy environment for the consumers of Kerala’s ration shops, through a UVC based self-sanitizing system for fingerprint scanners.
{"title":"Fingerprint Scanner Sanitising Module for Kerala Ration Shops","authors":"U. Sankar, Anseel Ameerudheen, Treesa Rose K Sani, Alister Augustine D’Cruz, Sanjna Salim, K. Vinida","doi":"10.1109/ICITIIT54346.2022.9744232","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744232","url":null,"abstract":"Fingerprint recognition is a safe and convenient technology, increasingly being used for biometric identification of the intended beneficiaries of welfare programmes. Kerala’s pubic distribution system, with Ration Shops being its point of contact, also uses fingerprint scanners for identification of beneficiaries. The outbreak of COVID-19 has adversely affected the safety of fingerprint authentication. Touching the sensors by multiple persons can cause the transmission of viruses. Studies have shown that COVID-19 can survive on common surfaces like wood, plastic, metal, and glass for a minimum of 5 days. Despite all the standard operating procedures, it is a common sight to see people crowding at public spaces like Ration Shops that has increased the risk of transmission of the virus. In this context, the present work aims to create a safe and healthy environment for the consumers of Kerala’s ration shops, through a UVC based self-sanitizing system for fingerprint scanners.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132230041","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744238
Ajay Mittur, Aravindh R Shankar, Adithya Narasimhan
The use of multiple languages with different scripts is a common theme in India. There is an emerging need to digitise documents that may be handwritten or available solely as images. This necessitates a system for multilingual classification of different Indic scripts and the subsequent character recognition into digitised standards such as Unicode. However, a learning system for various languages with multiple character combinations can be computationally expensive and prove arduous with a dearth of available data. In this paper, the one-shot learning approach to the optical character recognition of different languages is explored, where there is a need to accurately classify the character given only one example of every additional class introduced. Siamese neural networks are used for learning and to tune a network to work with entirely new, unseen data. Compelling results are attained in the classification of characters in nine different Indian languages using this approach with an accuracy ranging from 77.72 to 91.83 across the Indic languages in the best case.
{"title":"One-Shot Approach for Multilingual Classification of Indic Scripts","authors":"Ajay Mittur, Aravindh R Shankar, Adithya Narasimhan","doi":"10.1109/ICITIIT54346.2022.9744238","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744238","url":null,"abstract":"The use of multiple languages with different scripts is a common theme in India. There is an emerging need to digitise documents that may be handwritten or available solely as images. This necessitates a system for multilingual classification of different Indic scripts and the subsequent character recognition into digitised standards such as Unicode. However, a learning system for various languages with multiple character combinations can be computationally expensive and prove arduous with a dearth of available data. In this paper, the one-shot learning approach to the optical character recognition of different languages is explored, where there is a need to accurately classify the character given only one example of every additional class introduced. Siamese neural networks are used for learning and to tune a network to work with entirely new, unseen data. Compelling results are attained in the classification of characters in nine different Indian languages using this approach with an accuracy ranging from 77.72 to 91.83 across the Indic languages in the best case.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131568356","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744226
A. R, M. Krishnan, Akshay Kekuda
In this paper, we propose a deep reinforcement learning based traffic signal controller. We use the recently developed Distributional Reinforcement Learning with Quantile Regression (QR-DQN) algorithm to design a risk-sensitive approach to traffic signal control. A neural network is used to estimate the value distribution of state-action pairs. A novel control policy that gives variable weightage to the risk of an action depending on the congestion state of the system, effectively minimizes congestion in the network. Our results show that our algorithm outperforms conventional approaches and also classic RL based ones.
{"title":"Intelligent Traffic Control System using Deep Reinforcement Learning","authors":"A. R, M. Krishnan, Akshay Kekuda","doi":"10.1109/ICITIIT54346.2022.9744226","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744226","url":null,"abstract":"In this paper, we propose a deep reinforcement learning based traffic signal controller. We use the recently developed Distributional Reinforcement Learning with Quantile Regression (QR-DQN) algorithm to design a risk-sensitive approach to traffic signal control. A neural network is used to estimate the value distribution of state-action pairs. A novel control policy that gives variable weightage to the risk of an action depending on the congestion state of the system, effectively minimizes congestion in the network. Our results show that our algorithm outperforms conventional approaches and also classic RL based ones.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128288567","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744199
K. Prajwal, Tharun K, N. P, M A.
As the human population increases, so is the chance of getting diseases. There are many illnesses globally, and one of the biggest problems faced by the hospital systems today is the lack of technology to know when the patients are ill. One such illness is Cardiovascular Disease or CVD. It refers to any heart disease, vascular disease, or blood vessel disease. According to WHO, more people die of CVD’s worldwide than any other cause. It affects the low and middle-income countries more. It is very hard for people living alone to contact the hospital when they are sick. Therefore, we have developed a model that can detect when a patient is ill and report back to the hospital. The system currently only identifies patients with heart disease and reports back to the hospital. We decided to go with heart disease identification because it is one of the most deadly diseases, and the risk of patients dying because of heart disease is high. Predicting whether a patient has heart disease or not is very clearly a classification problem. Therefore, we have used five models to classify. We take several factors such as blood sugar level, age, cholesterol level, and many more and give the outcome based on the input.
{"title":"Cardiovascular Disease Prediction Using Machine Learning","authors":"K. Prajwal, Tharun K, N. P, M A.","doi":"10.1109/ICITIIT54346.2022.9744199","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744199","url":null,"abstract":"As the human population increases, so is the chance of getting diseases. There are many illnesses globally, and one of the biggest problems faced by the hospital systems today is the lack of technology to know when the patients are ill. One such illness is Cardiovascular Disease or CVD. It refers to any heart disease, vascular disease, or blood vessel disease. According to WHO, more people die of CVD’s worldwide than any other cause. It affects the low and middle-income countries more. It is very hard for people living alone to contact the hospital when they are sick. Therefore, we have developed a model that can detect when a patient is ill and report back to the hospital. The system currently only identifies patients with heart disease and reports back to the hospital. We decided to go with heart disease identification because it is one of the most deadly diseases, and the risk of patients dying because of heart disease is high. Predicting whether a patient has heart disease or not is very clearly a classification problem. Therefore, we have used five models to classify. We take several factors such as blood sugar level, age, cholesterol level, and many more and give the outcome based on the input.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133022267","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744225
Deepa V, B. Sivakumar
Software-defined networking (SDN) is a good approach, framework for virtually designing and building hardware network components. In the traditional network domain, fixed automation is made, and it is not possible to change the network connections. SDN has dynamic automation but is still exposed to DDoS attacks. With rising detection accuracy, IDS (Intrusion Detection System) against DDoS still faces provocation in detecting the intrusions and reducing the false alarm rate. In the network, the most efficient way of spotting intrusions is through the deployment of machine Learning (ML) - IDS and deep Learning (DL) - IDS systems. In this paper, our method based on DL proposes an efficient unsupervised level of shallow and deep multiple kernel level algorithms (MKL). To detect the malicious traffic, carry out experiments on DDoS attack databases with the MKL algorithm and correlate the end results with developed methods. Our test outcome reveal that the proposed method provides better accuracy and detection rate.
{"title":"Detection of DDoS Attack using Multiple Kernel Level (MKL) Algorithm","authors":"Deepa V, B. Sivakumar","doi":"10.1109/ICITIIT54346.2022.9744225","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744225","url":null,"abstract":"Software-defined networking (SDN) is a good approach, framework for virtually designing and building hardware network components. In the traditional network domain, fixed automation is made, and it is not possible to change the network connections. SDN has dynamic automation but is still exposed to DDoS attacks. With rising detection accuracy, IDS (Intrusion Detection System) against DDoS still faces provocation in detecting the intrusions and reducing the false alarm rate. In the network, the most efficient way of spotting intrusions is through the deployment of machine Learning (ML) - IDS and deep Learning (DL) - IDS systems. In this paper, our method based on DL proposes an efficient unsupervised level of shallow and deep multiple kernel level algorithms (MKL). To detect the malicious traffic, carry out experiments on DDoS attack databases with the MKL algorithm and correlate the end results with developed methods. Our test outcome reveal that the proposed method provides better accuracy and detection rate.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133115671","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744148
Tapas Si, D. Patra, Sukumar Mondal, Prakash Mukherjee
Breast cancer causes the highest death among all types of cancers in women. Early detection and diagnosis leading to early treatment can save the life. The computer-assisted methodologies for breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) segmentation can help the radiologists/doctors in the diagnosis of the disease as well as further treatment planning. In this article, we propose a breast DCE-MRI segmentation method using a hard-clustering technique with a Non-dominated Sorting Genetic Algorithm (NSGA-II). The well-known cluster validity metrics namely DB-index and Dunn-index are utilized as objective functions in NSGA-II algorithm. The noise and intensity inhomogeneities in MRI are removed from MRI in the preprocessing step as these artifacts affect the segmentation process. After segmentation, the lesions are separated and finally, localized in the MRI. The devised method is applied to segment 10 Sagittal T2-Weighted fat-suppressed DCE-MRI of the breast. A comparative study has been conducted with the K-means algorithm and the devised method outperforms K-means both quantitatively and qualitatively.
{"title":"Breast Lesion Segmentation in DCE-MRI using Multi-Objective Clustering with NSGA-II","authors":"Tapas Si, D. Patra, Sukumar Mondal, Prakash Mukherjee","doi":"10.1109/ICITIIT54346.2022.9744148","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744148","url":null,"abstract":"Breast cancer causes the highest death among all types of cancers in women. Early detection and diagnosis leading to early treatment can save the life. The computer-assisted methodologies for breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) segmentation can help the radiologists/doctors in the diagnosis of the disease as well as further treatment planning. In this article, we propose a breast DCE-MRI segmentation method using a hard-clustering technique with a Non-dominated Sorting Genetic Algorithm (NSGA-II). The well-known cluster validity metrics namely DB-index and Dunn-index are utilized as objective functions in NSGA-II algorithm. The noise and intensity inhomogeneities in MRI are removed from MRI in the preprocessing step as these artifacts affect the segmentation process. After segmentation, the lesions are separated and finally, localized in the MRI. The devised method is applied to segment 10 Sagittal T2-Weighted fat-suppressed DCE-MRI of the breast. A comparative study has been conducted with the K-means algorithm and the devised method outperforms K-means both quantitatively and qualitatively.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116820611","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744152
Sudha Ellison Mathe, Mamatha Bandaru, Hari Kishan Kondaveeti, Suseela Vappangi, G. Sanjiv Rao
Raspberry Pi is one of the most popular electronic prototyping boards used for prototyping the applications such as Home, Industry, Research, Agriculture etc. This paper provides a summary of Raspberry Pi adoption in agriculture to aid researchers in their work for remote sensing, controlling and automation. Soil quality testing, crop selection, soil fertility and productivity detection, weather monitoring, crop yield detection, plant growth monitoring and automatic spraying of fertilizers and pesticides are some of the Raspberry Pi applications which range from simple solutions to dedicated custom-built devices. The focus was mainly on different farming applications in which information is collected and processed to provide advice to farmers to make right decisions in right time with optimal expenditure. Research challenges, limitations and future trends associated with automated application development using Raspberry Pi are also presented.
{"title":"A Survey of Agriculture Applications Utilizing Raspberry Pi","authors":"Sudha Ellison Mathe, Mamatha Bandaru, Hari Kishan Kondaveeti, Suseela Vappangi, G. Sanjiv Rao","doi":"10.1109/ICITIIT54346.2022.9744152","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744152","url":null,"abstract":"Raspberry Pi is one of the most popular electronic prototyping boards used for prototyping the applications such as Home, Industry, Research, Agriculture etc. This paper provides a summary of Raspberry Pi adoption in agriculture to aid researchers in their work for remote sensing, controlling and automation. Soil quality testing, crop selection, soil fertility and productivity detection, weather monitoring, crop yield detection, plant growth monitoring and automatic spraying of fertilizers and pesticides are some of the Raspberry Pi applications which range from simple solutions to dedicated custom-built devices. The focus was mainly on different farming applications in which information is collected and processed to provide advice to farmers to make right decisions in right time with optimal expenditure. Research challenges, limitations and future trends associated with automated application development using Raspberry Pi are also presented.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128234412","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744241
A. N, V. M
Blockchain is secure, decentralized, and immune to attackers because of digital encryption without any trusted third party. In blockchain, list of transactions are recorded into a block, which is added to the blockchain ledger is known as mining. In mining process miners are involved in block validation and add new block to the existing blockchain. Miners crypto currencies stored in a mining pool, in public blockchain, mining pool is access by all the users in the network, which makes mining pool vulnerable to block with holding attack. This attack is done by malicious miner in order to either earning higher amount of incentive or waste honest miner computation power. To solve this issue, we propose Reputation-based Incentive Mechanism (RIM) based on Proof-of-Improved (PoIP) consensus process, which is implemented using blockchain technology. RIM provides a high incentive for legitimate miner and punishes the withholding-based irrelevant miner. The simulation results showed that proposed approach can discover the optimal option for distributing incentive and computing power for each mining pool, as well as punishing block withholding attackers.
{"title":"RIM: A Reputation-Based Incentive Mechanism using Blockchain","authors":"A. N, V. M","doi":"10.1109/ICITIIT54346.2022.9744241","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744241","url":null,"abstract":"Blockchain is secure, decentralized, and immune to attackers because of digital encryption without any trusted third party. In blockchain, list of transactions are recorded into a block, which is added to the blockchain ledger is known as mining. In mining process miners are involved in block validation and add new block to the existing blockchain. Miners crypto currencies stored in a mining pool, in public blockchain, mining pool is access by all the users in the network, which makes mining pool vulnerable to block with holding attack. This attack is done by malicious miner in order to either earning higher amount of incentive or waste honest miner computation power. To solve this issue, we propose Reputation-based Incentive Mechanism (RIM) based on Proof-of-Improved (PoIP) consensus process, which is implemented using blockchain technology. RIM provides a high incentive for legitimate miner and punishes the withholding-based irrelevant miner. The simulation results showed that proposed approach can discover the optimal option for distributing incentive and computing power for each mining pool, as well as punishing block withholding attackers.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133503138","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744179
K. Annarose, Debarshiya Chandra, A. Ravi Sankar, S. Umadevi
Speed is an integral part of circuit designing. Conventional CMOS (C-CMOS) is one of the widely used logic style; however, it has the disadvantage of producing greater delay. Several alternatives have been proposed. One such alternative is the hybrid adders that provide better performance in terms of delay. Various hybrid adders have been proposed, for instance Transmission gate full adders (TGA) and Hybrid pass logic with static CMOS output drive (New HPSC), that provide different delays. In this research work, the performance comparison analysis of different adders is presented by observing its propagation delay and transistor count. The C-CMOS, TGA and New HPSC full adders were considered for the performance comparison. The circuits have been implemented in FINFET model with 32nm technology node and in MOSFET model with 180nm technology node. The circuit implementation and analysis are performed using Cadence® Virtuoso tool. Simulation results reveal that TGA is relatively faster and requires minimum hardware than the other adders
{"title":"Delay Estimation of MOSFET- and FINFET-based Hybrid Adders","authors":"K. Annarose, Debarshiya Chandra, A. Ravi Sankar, S. Umadevi","doi":"10.1109/ICITIIT54346.2022.9744179","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744179","url":null,"abstract":"Speed is an integral part of circuit designing. Conventional CMOS (C-CMOS) is one of the widely used logic style; however, it has the disadvantage of producing greater delay. Several alternatives have been proposed. One such alternative is the hybrid adders that provide better performance in terms of delay. Various hybrid adders have been proposed, for instance Transmission gate full adders (TGA) and Hybrid pass logic with static CMOS output drive (New HPSC), that provide different delays. In this research work, the performance comparison analysis of different adders is presented by observing its propagation delay and transistor count. The C-CMOS, TGA and New HPSC full adders were considered for the performance comparison. The circuits have been implemented in FINFET model with 32nm technology node and in MOSFET model with 180nm technology node. The circuit implementation and analysis are performed using Cadence® Virtuoso tool. Simulation results reveal that TGA is relatively faster and requires minimum hardware than the other adders","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"272 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132444355","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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