Pub Date : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362391
Venkata A. P. Chavali, A. Deshmukh, A. Ambekar
Detail study of a Star Shape Microstrip Antenna with multiple shorting posts for wideband response with the explanation of resonant modes is presented in this paper. Modal study is performed by observing surface current distributions. Without shorting posts resonant modes excited on star shape microstrip antenna are TM01, TM20 and TM21. With the gradual addition of shorting posts, the impedance of resonance modes decreases enhancing the bandwidth up to 68% with a gain above 5 dBi. Redesign procedure of the similar structure for fundamental mode at 2 GHz on a triple layer substrate is provided. Redesigned antenna realized a bandwidth of 67% with more than 6 dBi gain which is comparable with reported configuration. Antenna exhibited a broadside radiation pattern over entire bandwidth with increased cross polarization in H-plane at higher frequencies.
{"title":"Analysis of Star Shape Microstrip Antenna with Multiple Shorting Posts for Wideband Response","authors":"Venkata A. P. Chavali, A. Deshmukh, A. Ambekar","doi":"10.1109/PuneCon50868.2020.9362391","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362391","url":null,"abstract":"Detail study of a Star Shape Microstrip Antenna with multiple shorting posts for wideband response with the explanation of resonant modes is presented in this paper. Modal study is performed by observing surface current distributions. Without shorting posts resonant modes excited on star shape microstrip antenna are TM01, TM20 and TM21. With the gradual addition of shorting posts, the impedance of resonance modes decreases enhancing the bandwidth up to 68% with a gain above 5 dBi. Redesign procedure of the similar structure for fundamental mode at 2 GHz on a triple layer substrate is provided. Redesigned antenna realized a bandwidth of 67% with more than 6 dBi gain which is comparable with reported configuration. Antenna exhibited a broadside radiation pattern over entire bandwidth with increased cross polarization in H-plane at higher frequencies.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121744633","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362363
Laxmi Shaw, A. Routray
The prime objective of the study is to investigate the effect (effects in the sense of an increase in psychological well-being and decrease in stress & mood disturbances) of specific relaxation technique popularly named as Kriya Yoga (KY) meditation on long-term and short-term practitioners. For comparison, the EEG data for non-meditators or control group has also been recorded. To the best of our knowledge, no such standard EEG datasets are available elsewhere on Kriya practitioners. In this study, two experimental datasets created by us, have been described. The experiments have been carried out at two places, i.e., Hariharananda Gurukulam, Balighai, Puri, Odisha and Hariharananda Balashram, Arua, Kendrapara, India. The detailed protocol and experimental methodology are described. This paper briefly introduces two EEG databases acquired during short Kriya Yoga meditation.
{"title":"An Experimental Design and Data Collection of EEG during Kriya Yoga-An Ancient Indic Meditation Technique","authors":"Laxmi Shaw, A. Routray","doi":"10.1109/PuneCon50868.2020.9362363","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362363","url":null,"abstract":"The prime objective of the study is to investigate the effect (effects in the sense of an increase in psychological well-being and decrease in stress & mood disturbances) of specific relaxation technique popularly named as Kriya Yoga (KY) meditation on long-term and short-term practitioners. For comparison, the EEG data for non-meditators or control group has also been recorded. To the best of our knowledge, no such standard EEG datasets are available elsewhere on Kriya practitioners. In this study, two experimental datasets created by us, have been described. The experiments have been carried out at two places, i.e., Hariharananda Gurukulam, Balighai, Puri, Odisha and Hariharananda Balashram, Arua, Kendrapara, India. The detailed protocol and experimental methodology are described. This paper briefly introduces two EEG databases acquired during short Kriya Yoga meditation.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121323208","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362345
Aniket Shinde, Kapil Mundada
Artificial intelligence is being currently used in many vehicles to self-drive. A variety of sensors are required to give information about components. Health monitoring of vehicles is an area that is dragging the attention of researchers across the world. In this paper, we have dealt with one of the important parameters of vehicle, which is an engine. The engine transmits power using a gear system which generates noise and vibration due to variation in the meshing force. We have used a piezoelectric sensor to collect vibration signals of an engine and convert the time domain signal to frequency domain, based on different frequencies health is predicted using ML Support Vector Classifier. In the proposed mechanism an android application is used to visualize the real-time data.
{"title":"Bike Engine Health Monitoring using Vibration","authors":"Aniket Shinde, Kapil Mundada","doi":"10.1109/PuneCon50868.2020.9362345","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362345","url":null,"abstract":"Artificial intelligence is being currently used in many vehicles to self-drive. A variety of sensors are required to give information about components. Health monitoring of vehicles is an area that is dragging the attention of researchers across the world. In this paper, we have dealt with one of the important parameters of vehicle, which is an engine. The engine transmits power using a gear system which generates noise and vibration due to variation in the meshing force. We have used a piezoelectric sensor to collect vibration signals of an engine and convert the time domain signal to frequency domain, based on different frequencies health is predicted using ML Support Vector Classifier. In the proposed mechanism an android application is used to visualize the real-time data.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130632056","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362395
P. Kandpal, Yash Wadkar, Harsh Attri, Siddharth Bhorge
In today’s day & age Text Summarization and Sentiment Analysis add lot of value to businesses and other organizations. Sentiment Analysis can help a business get an idea about their product and gather meaningful feedback from customers. And auto-text summarization helps in articulating the important points from a large data-set, doing so can make the viewers/readers get a quicker idea about that data-set, this data-set can be a large document, a blog or an article. This paper presents a new method of combining the concepts of Sentiment Analysis and Auto-Text Summarization so that content-writers can enhance the quality of their manuscript. In this research work, certain observations have been made which can help in analyzing the polarity and subjectivity of the summarized text using various summarizers. Businesses and other organizations can use this technique to enhance their online content and intrigue viewers or readers by creating a better digital content ecosystem.
{"title":"Comparison of Sentiment Analysis on Auto-Summarized Text & Original Text using various Summarization Techniques","authors":"P. Kandpal, Yash Wadkar, Harsh Attri, Siddharth Bhorge","doi":"10.1109/PuneCon50868.2020.9362395","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362395","url":null,"abstract":"In today’s day & age Text Summarization and Sentiment Analysis add lot of value to businesses and other organizations. Sentiment Analysis can help a business get an idea about their product and gather meaningful feedback from customers. And auto-text summarization helps in articulating the important points from a large data-set, doing so can make the viewers/readers get a quicker idea about that data-set, this data-set can be a large document, a blog or an article. This paper presents a new method of combining the concepts of Sentiment Analysis and Auto-Text Summarization so that content-writers can enhance the quality of their manuscript. In this research work, certain observations have been made which can help in analyzing the polarity and subjectivity of the summarized text using various summarizers. Businesses and other organizations can use this technique to enhance their online content and intrigue viewers or readers by creating a better digital content ecosystem.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126943884","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362433
M. Rane, U. Bhadade
A multimodal fusion biometric verification system for face and palmprint modalities is proposed. The goal is to achieve a higher Accuracy for standard Databases. Fusion is done at score level using feature extraction algorithms such as, Radon transform, Ridgelet transform, TPLBP, FPLBP HOG, Gabor filter and DCT. Experiments are conducted on face94, face95, face96, FRGC IITD and PolyU databases. Only 1 image is given as a training set for each subject in respective databases. Matching Algorithm is used so as to achieve maximum GAR (Genuine acceptance rate). The results are discussed further in the paper. The accuracy achieved is 99.6% for FAR (False Acceptance rate) of 0.1%. Experimental results indicate that this approach although simple yet can achieve a greater accuracy.
{"title":"Face and Palmprint Biometric Recognition by using Weighted Score Fusion Technique","authors":"M. Rane, U. Bhadade","doi":"10.1109/PuneCon50868.2020.9362433","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362433","url":null,"abstract":"A multimodal fusion biometric verification system for face and palmprint modalities is proposed. The goal is to achieve a higher Accuracy for standard Databases. Fusion is done at score level using feature extraction algorithms such as, Radon transform, Ridgelet transform, TPLBP, FPLBP HOG, Gabor filter and DCT. Experiments are conducted on face94, face95, face96, FRGC IITD and PolyU databases. Only 1 image is given as a training set for each subject in respective databases. Matching Algorithm is used so as to achieve maximum GAR (Genuine acceptance rate). The results are discussed further in the paper. The accuracy achieved is 99.6% for FAR (False Acceptance rate) of 0.1%. Experimental results indicate that this approach although simple yet can achieve a greater accuracy.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995219","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362347
D. Phalke, Sunita Jahirabadkar
The field of machine learning is going through its golden era. Deep Learning, the subfield of Machine Learning has seen amazing applications in various areas. The perception of information is extracted by using different layers of Deep Learning. Numerous deep learning algorithms like Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN) have completely changed the viewpoint of researchers of data science and big data. However, still there is huge scope of learning in this extremely quick-paced domain. The use of deep learning for Near Duplicate Video Retrieval (NDVR) shows the popularity of various algorithms of deep learning amongst researchers. This survey provides an overview of Near Duplicate Video Retrieval (NDVR) using deep learning and trends in development and usage of revolutionary Deep Learning frameworks, tools and their applications in recent years.
{"title":"A Survey on Near Duplicate Video Retrieval Using Deep Learning Techniques and Framework","authors":"D. Phalke, Sunita Jahirabadkar","doi":"10.1109/PuneCon50868.2020.9362347","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362347","url":null,"abstract":"The field of machine learning is going through its golden era. Deep Learning, the subfield of Machine Learning has seen amazing applications in various areas. The perception of information is extracted by using different layers of Deep Learning. Numerous deep learning algorithms like Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN) have completely changed the viewpoint of researchers of data science and big data. However, still there is huge scope of learning in this extremely quick-paced domain. The use of deep learning for Near Duplicate Video Retrieval (NDVR) shows the popularity of various algorithms of deep learning amongst researchers. This survey provides an overview of Near Duplicate Video Retrieval (NDVR) using deep learning and trends in development and usage of revolutionary Deep Learning frameworks, tools and their applications in recent years.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122363070","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 : 2020-12-16DOI: 10.1109/punecon50868.2020.9362385
{"title":"PuneCon 2020 Address by Guests","authors":"","doi":"10.1109/punecon50868.2020.9362385","DOIUrl":"https://doi.org/10.1109/punecon50868.2020.9362385","url":null,"abstract":"","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198158","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362389
Bhushan Bhagwan Gawde
With its lethal spread to more than 200 countries, COVID-19 has brought a global crisis, affecting more than 3 crore people across the world. Viruses don’t have a cure, and this makes the population vulnerable and heavily rely on preventing the infection. Hence, following the rules of social distancing and wearing a face mask are two very essential approaches to fight against this pandemic. Motivated by this notion, this work proposes a deep learning-based framework for automating the detection of risk due to COVID19. The proposed framework utilizes YOLOv3 object detector to detect whether a person has worn a mask. In case of absence of mask, to categorize the level of risk, the person’s age category is estimated, and the result of the risk detector is displayed on the image with a bounding box. In case of multiple boxes, the framework also calculates the distance between them to check whether the rules of social distancing are being followed. The result of the YOLOv3 model is compared with popular state-of-the-art model, Faster Regionbased Convolutional Neural Network. From the experimental analysis, it is concluded that YOLOv3 object detector displays best results with respect to the trade-off between speed and accuracy.
随着COVID-19在200多个国家的致命传播，它带来了一场全球危机，影响了全球300多万人。病毒无法治愈，这使得人们变得脆弱，严重依赖于预防感染。因此，遵守社交距离规则和佩戴口罩是抗击新冠肺炎疫情的两个非常重要的方法。在这一概念的推动下，这项工作提出了一个基于深度学习的框架，用于自动检测covid - 19风险。提出的框架利用YOLOv3对象检测器来检测一个人是否戴过面具。在没有口罩的情况下，对风险级别进行分类，估计人的年龄类别，并将风险检测器的结果显示在带有边界框的图像上。如果有多个盒子，该框架还会计算它们之间的距离，以检查是否遵守社交距离规则。YOLOv3模型的结果与目前流行的最先进的模型Faster region - based Convolutional Neural Network进行了比较。通过实验分析，得出YOLOv3目标检测器在速度和精度之间取得最佳效果的结论。
{"title":"A Fast, Automatic Risk Detector for COVID-19","authors":"Bhushan Bhagwan Gawde","doi":"10.1109/PuneCon50868.2020.9362389","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362389","url":null,"abstract":"With its lethal spread to more than 200 countries, COVID-19 has brought a global crisis, affecting more than 3 crore people across the world. Viruses don’t have a cure, and this makes the population vulnerable and heavily rely on preventing the infection. Hence, following the rules of social distancing and wearing a face mask are two very essential approaches to fight against this pandemic. Motivated by this notion, this work proposes a deep learning-based framework for automating the detection of risk due to COVID19. The proposed framework utilizes YOLOv3 object detector to detect whether a person has worn a mask. In case of absence of mask, to categorize the level of risk, the person’s age category is estimated, and the result of the risk detector is displayed on the image with a bounding box. In case of multiple boxes, the framework also calculates the distance between them to check whether the rules of social distancing are being followed. The result of the YOLOv3 model is compared with popular state-of-the-art model, Faster Regionbased Convolutional Neural Network. From the experimental analysis, it is concluded that YOLOv3 object detector displays best results with respect to the trade-off between speed and accuracy.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124586972","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362382
A. Wadhwani, Priyank Jain
In our day to day life, we rely on information that is provided by product makers to make rightful choices such as the nutritional content of food, warnings in medications, strength parameters of a constructed road, etc but when it comes to AI there’s has not been any such provided information. The machine learning models are very often distributed without a proper clear understanding of how it functions, i.e. under what conditions would it perform the best and most consistently, whether or not it has blind spots, and, if so, then where are they.Model cards are a very recent and hot topic of research. In Machine Learning (ML), transparency with model cards is relevant as it affects a wide range of domains, from health care to finance and jobs, etc. This research paper presents the importance of model cards and transparency issues.
{"title":"Machine Learning Model Cards Transparency Review : Using model card toolkit","authors":"A. Wadhwani, Priyank Jain","doi":"10.1109/PuneCon50868.2020.9362382","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362382","url":null,"abstract":"In our day to day life, we rely on information that is provided by product makers to make rightful choices such as the nutritional content of food, warnings in medications, strength parameters of a constructed road, etc but when it comes to AI there’s has not been any such provided information. The machine learning models are very often distributed without a proper clear understanding of how it functions, i.e. under what conditions would it perform the best and most consistently, whether or not it has blind spots, and, if so, then where are they.Model cards are a very recent and hot topic of research. In Machine Learning (ML), transparency with model cards is relevant as it affects a wide range of domains, from health care to finance and jobs, etc. This research paper presents the importance of model cards and transparency issues.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"195 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132798378","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362474
Md Nasful Huda Prince, M. Faisal
The contribution of fiber dispersion in the deterioration of bit error rate (BER) performance for an optical time division multiplexed (TDM) link is illustrated in this paper. Signal to noise ratio (SNR) and BER is calculated according to the conventional way i.e. by only considering receiver noise. An analytical model is developed to compute the signal to noise and crosstalk ratio (SCNR) where the additional crosstalk is the contribution of dispersion. Such crosstalk occurs due to the overlapping of pulses with the neighboring pulses which is called inter-symbol-interference (ISI). Dispersion causes pulses to get broadened for which ISI occurs. By comparing the results obtained from this analytical model with the conventional process the crosstalk due to dispersion is determined. The model which is validated by MATLAB shows that the more the dispersion increases the more the crosstalk occurs and as a consequence, the more the signal suffers. The power penalty due to dispersion is also computed comparing the conventional computational method where the result indicates a non-linear incremental pattern of such penalty for increasing the degree of dispersion. The penalty becomes higher than 1 dB for the value of dispersion index from 0.08 to above.
{"title":"Evaluation of the Contribution of Fiber Dispersion in SNR and BER Performance for an Optical TDM Link","authors":"Md Nasful Huda Prince, M. Faisal","doi":"10.1109/PuneCon50868.2020.9362474","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362474","url":null,"abstract":"The contribution of fiber dispersion in the deterioration of bit error rate (BER) performance for an optical time division multiplexed (TDM) link is illustrated in this paper. Signal to noise ratio (SNR) and BER is calculated according to the conventional way i.e. by only considering receiver noise. An analytical model is developed to compute the signal to noise and crosstalk ratio (SCNR) where the additional crosstalk is the contribution of dispersion. Such crosstalk occurs due to the overlapping of pulses with the neighboring pulses which is called inter-symbol-interference (ISI). Dispersion causes pulses to get broadened for which ISI occurs. By comparing the results obtained from this analytical model with the conventional process the crosstalk due to dispersion is determined. The model which is validated by MATLAB shows that the more the dispersion increases the more the crosstalk occurs and as a consequence, the more the signal suffers. The power penalty due to dispersion is also computed comparing the conventional computational method where the result indicates a non-linear incremental pattern of such penalty for increasing the degree of dispersion. The penalty becomes higher than 1 dB for the value of dispersion index from 0.08 to above.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130136151","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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