Pub Date : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619398
Anand Krisshna P, Archana R Nair, P. R. Sreenidhi
This paper illustrates the performance assessment and design of CMOS Two Stage OTA under 130nm and 180nm technology nodes focusing on optimization in compensation capacitance, reduction in power dissipation. The designed circuit operates at two different supply voltages of 1.2V and 1.8V and the input relay is dependent on bias current. In this paper, the device parameters such as AC-Gain, Phase margin, Slew rate, CMRR, ICMR, Output offset voltage, Gain bandwidth, Noise and Power dissipation are theoretically calculated and analysed using LT spice software for 130nm and 180nm technology for given specifications. As the power is a major design parameter, the bias current and supply voltage is varied within the range of respective technology nodes to achieve a minimum power dissipation design. For minimum power design, reduction in bandwidth and stability of the system are major trade-offs. The designed circuit uses a specific compensation methodology for implementing the compensation called Miller compensation. It is used for improving the bandwidth and slew rate of the designed system for various capacitive loads.
{"title":"Performance Assessment of Two Stage Operational Transconductance Amplifier in 180nm and 130nm Technology with Optimised Compensation Capacitance","authors":"Anand Krisshna P, Archana R Nair, P. R. Sreenidhi","doi":"10.1109/ETI4.051663.2021.9619398","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619398","url":null,"abstract":"This paper illustrates the performance assessment and design of CMOS Two Stage OTA under 130nm and 180nm technology nodes focusing on optimization in compensation capacitance, reduction in power dissipation. The designed circuit operates at two different supply voltages of 1.2V and 1.8V and the input relay is dependent on bias current. In this paper, the device parameters such as AC-Gain, Phase margin, Slew rate, CMRR, ICMR, Output offset voltage, Gain bandwidth, Noise and Power dissipation are theoretically calculated and analysed using LT spice software for 130nm and 180nm technology for given specifications. As the power is a major design parameter, the bias current and supply voltage is varied within the range of respective technology nodes to achieve a minimum power dissipation design. For minimum power design, reduction in bandwidth and stability of the system are major trade-offs. The designed circuit uses a specific compensation methodology for implementing the compensation called Miller compensation. It is used for improving the bandwidth and slew rate of the designed system for various capacitive loads.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332909","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619207
G. Raviteja, K.S.Rama Praveen, K.Anisha Keerthi, R. Abhishek, V. Sarvari
In this proposed paper, a quad-port C band conformal MIMO antenna is designed. This antenna configuration has four similar CPW-fed elements of size 10x15 mm. It is supported with flexible FR4 epoxy dielectric material with relative permittivity of 4.4 and a loss tangent of 0.02. The proposed antenna achieved an impedance bandwidth in accordance with the -10dB reference from frequency ranges of 4.5 GHz to 7.56 GHz which covers C band satellite applications. Good isolation characteristics are achieved which is less than -15 dB with the help of the orthogonal arrangement of the four MIMO antennas. For the excellent working of MIMO, some of the characteristics like Mean Effective Gain, Total Active Reflection, Envelope Correlation Coefficient are considered as important and they are investigated and found that they are within the standards as MEG < 3dB and ECC < 0.5. The entire work is done with the help of ANSYS High-Frequency Structure Simulator (HFSS) software.
{"title":"A CPW Feed Orthogonal Wideband Quad-Port Conformal MIMO Antenna for Satellite Applications","authors":"G. Raviteja, K.S.Rama Praveen, K.Anisha Keerthi, R. Abhishek, V. Sarvari","doi":"10.1109/ETI4.051663.2021.9619207","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619207","url":null,"abstract":"In this proposed paper, a quad-port C band conformal MIMO antenna is designed. This antenna configuration has four similar CPW-fed elements of size 10x15 mm. It is supported with flexible FR4 epoxy dielectric material with relative permittivity of 4.4 and a loss tangent of 0.02. The proposed antenna achieved an impedance bandwidth in accordance with the -10dB reference from frequency ranges of 4.5 GHz to 7.56 GHz which covers C band satellite applications. Good isolation characteristics are achieved which is less than -15 dB with the help of the orthogonal arrangement of the four MIMO antennas. For the excellent working of MIMO, some of the characteristics like Mean Effective Gain, Total Active Reflection, Envelope Correlation Coefficient are considered as important and they are investigated and found that they are within the standards as MEG < 3dB and ECC < 0.5. The entire work is done with the help of ANSYS High-Frequency Structure Simulator (HFSS) software.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132639677","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619357
Z. Rustam, S. Hartini
Since the first case of COVID-19 appeared in Wuhan city, China, in December 2019, the disease has affected more than millions of people worldwide. Therefore, early detection of COVID-19 is important to prevent transmission to more people. One method widely used to detect COVID-19 through X-ray images is Convolutional Neural Networks (CNN). However, CNN needs large amounts of image data to build models with high accuracy, while the medical image has limited amounts of data. To overcome this problem, transfer learning technique where CNN is used as a feature extraction method is usually be chosen as an alternative. However, most studies use the extraction results of the final layers such as fully connected layer or the last convolutional layer. In this study, all layers will be used by turns to analyze how the extraction results affect the performance of classification method. The CNN models used are pre-trained models VGG16 and VGG19, while the classification method used is Support Vector Machines (SVM). Based on the results of the study, the extraction results by the initial layer gave a better performance on SVM compared to the layers that are deeper in the selected CNN architecture. Several layers in CNN model did not analyze due to limited source capability in doing computation. Therefore, as the future work, the rest layers of CNN in this study can be analyzed as well as the other CNN models and the classification method.
{"title":"Performance Analysis of Deep Convolutional Features using Support Vector Machines for COVID-19 Diagnosis on X-ray Images","authors":"Z. Rustam, S. Hartini","doi":"10.1109/ETI4.051663.2021.9619357","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619357","url":null,"abstract":"Since the first case of COVID-19 appeared in Wuhan city, China, in December 2019, the disease has affected more than millions of people worldwide. Therefore, early detection of COVID-19 is important to prevent transmission to more people. One method widely used to detect COVID-19 through X-ray images is Convolutional Neural Networks (CNN). However, CNN needs large amounts of image data to build models with high accuracy, while the medical image has limited amounts of data. To overcome this problem, transfer learning technique where CNN is used as a feature extraction method is usually be chosen as an alternative. However, most studies use the extraction results of the final layers such as fully connected layer or the last convolutional layer. In this study, all layers will be used by turns to analyze how the extraction results affect the performance of classification method. The CNN models used are pre-trained models VGG16 and VGG19, while the classification method used is Support Vector Machines (SVM). Based on the results of the study, the extraction results by the initial layer gave a better performance on SVM compared to the layers that are deeper in the selected CNN architecture. Several layers in CNN model did not analyze due to limited source capability in doing computation. Therefore, as the future work, the rest layers of CNN in this study can be analyzed as well as the other CNN models and the classification method.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134300314","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619353
Lipismita Panigrahi, K. Verma
Reliability and accuracy is the key concern of an automated image classification process. However, the impact of background or surrounding area is very less in compared to object features, which create ambiguity while assigning the appropriate class label and reduce the classification accuracy. This paper presents a new model to address this issue which select the relevant features from the segmented images based on the inner and outer regions. The key idea of this model is that the texture features inside the objects are more relevant than the surrounding or outside region of the objects. The proposed model applying a segmentation method for automated segment the image. These segmented images are further partition into two parts (i.e. inner and outer). The 463 shape and texture features are extracted from the inner, outer parts of the segmented images and also from the whole image. Next, these extracted features are used to train the classifier using support vector machine (SVM). A database of 644 images that consists of 8 classes is used to verify the efficacy of the proposed model. The result proves the efficacy of the proposed model which achieves classification accuracy up to 97.79 % from the inner part of the image. The classification accuracy of inner features is increased by 9.58% from surroundings features.
{"title":"Segmented Region based Feature Extraction for Image Classification","authors":"Lipismita Panigrahi, K. Verma","doi":"10.1109/ETI4.051663.2021.9619353","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619353","url":null,"abstract":"Reliability and accuracy is the key concern of an automated image classification process. However, the impact of background or surrounding area is very less in compared to object features, which create ambiguity while assigning the appropriate class label and reduce the classification accuracy. This paper presents a new model to address this issue which select the relevant features from the segmented images based on the inner and outer regions. The key idea of this model is that the texture features inside the objects are more relevant than the surrounding or outside region of the objects. The proposed model applying a segmentation method for automated segment the image. These segmented images are further partition into two parts (i.e. inner and outer). The 463 shape and texture features are extracted from the inner, outer parts of the segmented images and also from the whole image. Next, these extracted features are used to train the classifier using support vector machine (SVM). A database of 644 images that consists of 8 classes is used to verify the efficacy of the proposed model. The result proves the efficacy of the proposed model which achieves classification accuracy up to 97.79 % from the inner part of the image. The classification accuracy of inner features is increased by 9.58% from surroundings features.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"62 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134543629","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619350
Asha Sara Thomas, E. Sasikala
In the last ten years, Lung Cancer and Chronic Obstructive Pulmonary Disease (COPD) have become two major diseases in the category of Respiratory Diseases which have lead to a large number of death rates in India and also in other countries. The main reason for the increase in these cases is due to the excessive smoking habit among youngsters and adults. Thus, proper diagnosis of both lung cancer and COPD are important in order to save human life. A fast and effective method to do this is to differentiate accurately among both diseases and provide the required treatment. This paper focuses on efficiently differentiating among chest pathologies in chest X-Ray using different artificial neural networks, machine learning, and deep learning approaches. It shows how an artificial neural network can be used in the prediction of diseases based on the image sets. ResNets help in better feature extraction of the image sets that lead to the correct classification of diseases. The model achieves a better performance in evaluating chest radiograph datasets that depicts the changes caused in a person's lungs when compared to normal lung images such as the formation of small lobes (or) the enlarged arteries in lungs and so on..
{"title":"Identifying Lung Cancer and Chronic Obstructive Pulmonary Diseases using Residual Neural Network","authors":"Asha Sara Thomas, E. Sasikala","doi":"10.1109/ETI4.051663.2021.9619350","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619350","url":null,"abstract":"In the last ten years, Lung Cancer and Chronic Obstructive Pulmonary Disease (COPD) have become two major diseases in the category of Respiratory Diseases which have lead to a large number of death rates in India and also in other countries. The main reason for the increase in these cases is due to the excessive smoking habit among youngsters and adults. Thus, proper diagnosis of both lung cancer and COPD are important in order to save human life. A fast and effective method to do this is to differentiate accurately among both diseases and provide the required treatment. This paper focuses on efficiently differentiating among chest pathologies in chest X-Ray using different artificial neural networks, machine learning, and deep learning approaches. It shows how an artificial neural network can be used in the prediction of diseases based on the image sets. ResNets help in better feature extraction of the image sets that lead to the correct classification of diseases. The model achieves a better performance in evaluating chest radiograph datasets that depicts the changes caused in a person's lungs when compared to normal lung images such as the formation of small lobes (or) the enlarged arteries in lungs and so on..","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132189994","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619191
P. M. Joshi, H. Verma
This work proposes weak bus constrained optimal placement of phasor measurement units (PMUs) in standard IEEE systems using binary Equilibrium Optimizer (BEO). The novelty of this work is to place the PMUs on such locations that weak buses are observed with maximum number of PMUs to ascertain full observability and stability of the system. The different combinations for installation of PMUs for other cases are also obtained by BEO. For investigating the effectiveness of the approach, it is tested on different test cases which are IEEE 14 bus, IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems with different conditions that include consideration of zero injection buses, single PMU outage, weak buses in the systems.
{"title":"Binary Equilibrium Optimizer Based Weak bus Constrained PMU Placement","authors":"P. M. Joshi, H. Verma","doi":"10.1109/ETI4.051663.2021.9619191","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619191","url":null,"abstract":"This work proposes weak bus constrained optimal placement of phasor measurement units (PMUs) in standard IEEE systems using binary Equilibrium Optimizer (BEO). The novelty of this work is to place the PMUs on such locations that weak buses are observed with maximum number of PMUs to ascertain full observability and stability of the system. The different combinations for installation of PMUs for other cases are also obtained by BEO. For investigating the effectiveness of the approach, it is tested on different test cases which are IEEE 14 bus, IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems with different conditions that include consideration of zero injection buses, single PMU outage, weak buses in the systems.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846434","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619316
Kshma Trivedi, S. Koley
The present study deals with the hydrodynamic performance of an OWC-WEC in the presence of oblique incoming waves. The effect of chamber length, the draft of the front wall of an OWC-WEC, and the incident angle on the efficiency of an OWC-WEC are provided in details. It is observed that the incident angle significantly influences the performance of an OWC-WEC
{"title":"Hydrodynamic Performance of an OWC Device under the Action of Oblique Incident Waves","authors":"Kshma Trivedi, S. Koley","doi":"10.1109/ETI4.051663.2021.9619316","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619316","url":null,"abstract":"The present study deals with the hydrodynamic performance of an OWC-WEC in the presence of oblique incoming waves. The effect of chamber length, the draft of the front wall of an OWC-WEC, and the incident angle on the efficiency of an OWC-WEC are provided in details. It is observed that the incident angle significantly influences the performance of an OWC-WEC","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116738636","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619208
Nagaraj M. Lutimath, Neha Sharma, B. K. Byregowda
Random Forests are of the vital models in machine learning. They are comprehensive and effective classification paradigms in machine learning. The random forest recognizes the most important attributes of a given problem. The heart disorder is a cardiovascular disease, with a set of conditions affecting the heart. During heart disease there will be heart beat problems with congenital heart disorders and coronary artery defects. Coronary heart defect is a heart disease, which decreases the flow of blood to the heart. When the flow of blood decreases heart attack occurs. It is necessary to analyse the prediction of heart attack based on the symptoms. Available data set instances of the patients with heart defects symptoms is taken and analysed in this paper. Python language is utilized to prediction of the accuracy.
{"title":"Prediction of Heart Disease using Random Forest","authors":"Nagaraj M. Lutimath, Neha Sharma, B. K. Byregowda","doi":"10.1109/ETI4.051663.2021.9619208","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619208","url":null,"abstract":"Random Forests are of the vital models in machine learning. They are comprehensive and effective classification paradigms in machine learning. The random forest recognizes the most important attributes of a given problem. The heart disorder is a cardiovascular disease, with a set of conditions affecting the heart. During heart disease there will be heart beat problems with congenital heart disorders and coronary artery defects. Coronary heart defect is a heart disease, which decreases the flow of blood to the heart. When the flow of blood decreases heart attack occurs. It is necessary to analyse the prediction of heart attack based on the symptoms. Available data set instances of the patients with heart defects symptoms is taken and analysed in this paper. Python language is utilized to prediction of the accuracy.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127528210","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619393
Darshana A. Naik, V. Sangeetha, G. Sandhya
Since the word picture was coined, resolution has always been a challenge. Many studies have been conducted to generate high-resolution photographs, but none have been able to develop a process that is both time and quality effective. As a result, the super resolution issue is discussed in this paper using single-processing techniques. Deep learning methods are used to solve the same problem. The method suggested here will transform a low-resolution image into a high-resolution image of a pleasant and satisfactory quality. This can be accomplished using GANs (Generative Adversarial Networks) with significant up scaling factors.
{"title":"Generative Adversarial Networks based method for Generating Photo-Realistic Super Resolution Images","authors":"Darshana A. Naik, V. Sangeetha, G. Sandhya","doi":"10.1109/ETI4.051663.2021.9619393","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619393","url":null,"abstract":"Since the word picture was coined, resolution has always been a challenge. Many studies have been conducted to generate high-resolution photographs, but none have been able to develop a process that is both time and quality effective. As a result, the super resolution issue is discussed in this paper using single-processing techniques. Deep learning methods are used to solve the same problem. The method suggested here will transform a low-resolution image into a high-resolution image of a pleasant and satisfactory quality. This can be accomplished using GANs (Generative Adversarial Networks) with significant up scaling factors.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115103717","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619268
Sunakshi Sharma, V. Sharma
For electronic circuits, one of the most promising technology in modern days is Quantum Cellular Automata (QCA). It provides high speed, low power consumption and higher density as compared to CMOS technology. Quantumdot cell is a basic device which can be used to implement logic gates and various other digital circuits. In QCA, reversible computing approach helps in mitigating the power dissipation, hence providing a reliable solution. This paper presents a novel design for a reversible circuit which act as full adder, even parity as well as odd parity generator. Our proposed design is simple in structure with no garbage output. The design consists minimum number of clock zones and can be used for implementing various other logic gates. Simulation results are verified using software QCADesigner2.0.3.
{"title":"Design of Full Adder and Parity Generator Based on Reversible Logic","authors":"Sunakshi Sharma, V. Sharma","doi":"10.1109/ETI4.051663.2021.9619268","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619268","url":null,"abstract":"For electronic circuits, one of the most promising technology in modern days is Quantum Cellular Automata (QCA). It provides high speed, low power consumption and higher density as compared to CMOS technology. Quantumdot cell is a basic device which can be used to implement logic gates and various other digital circuits. In QCA, reversible computing approach helps in mitigating the power dissipation, hence providing a reliable solution. This paper presents a novel design for a reversible circuit which act as full adder, even parity as well as odd parity generator. Our proposed design is simple in structure with no garbage output. The design consists minimum number of clock zones and can be used for implementing various other logic gates. Simulation results are verified using software QCADesigner2.0.3.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293818","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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