Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587716
A. Gautam, V. Laxmi
PCB (Printed Circuit Board) designing has been an integral part of almost all hardware projects since ages. Personalizing a PCB layout for an electrical circuit is the ultimate aim of an electrical and electronics engineer working on a project application in any practical scenario. PCB also has a significance while commercializing our design as a product in the market. This paper works through the KiCAD’s PCB design of the gate drive circuits used in power electronic converters.
{"title":"Gate Drive for Power Electronic Converters : An Insight into KiCAD’s PCB design !","authors":"A. Gautam, V. Laxmi","doi":"10.1109/GCAT52182.2021.9587716","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587716","url":null,"abstract":"PCB (Printed Circuit Board) designing has been an integral part of almost all hardware projects since ages. Personalizing a PCB layout for an electrical circuit is the ultimate aim of an electrical and electronics engineer working on a project application in any practical scenario. PCB also has a significance while commercializing our design as a product in the market. This paper works through the KiCAD’s PCB design of the gate drive circuits used in power electronic converters.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131087006","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587630
Ramya Patibandla, Yukthi Sravani Tummalapalli, Sneha Lingamaneni, Kumari A Prasanna, K. P. Kumar
Recommender System’s main idea is to decide the most suitable products for the customers. It enhances the relation between the users and the products. There are various applications of Recommender Systems. One such application that is most needed as well as helpful for users is the Movie Recommender System. A Movie Recommender System helps the users to find the movies those are more appropriate for them and which they may like the recommender system considers the user preferences to recommend movies to the users. There are various factors that can be considered to recommend a movie to the users. They are actors, genre and language of the movies. It will also consider the history of the movies watched by a particular user to recommend movies to them. The dataset that will be used for this project is Netflix prize dataset and hotstar dataset. Two models will be developed in this project namely Collaborative Filtering Algorithm and Pearson’s R Correlation algorithm. The outcome of this recommender system will be a customized list of top-rated movies from Netflix and Hotstar respectively. The future scope of this system is to recommend top rated movies from various OTT platforms which will help the user to identify his/her favorites in single application.
{"title":"Efficient Recommender System for Over-the-Top Media Service","authors":"Ramya Patibandla, Yukthi Sravani Tummalapalli, Sneha Lingamaneni, Kumari A Prasanna, K. P. Kumar","doi":"10.1109/GCAT52182.2021.9587630","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587630","url":null,"abstract":"Recommender System’s main idea is to decide the most suitable products for the customers. It enhances the relation between the users and the products. There are various applications of Recommender Systems. One such application that is most needed as well as helpful for users is the Movie Recommender System. A Movie Recommender System helps the users to find the movies those are more appropriate for them and which they may like the recommender system considers the user preferences to recommend movies to the users. There are various factors that can be considered to recommend a movie to the users. They are actors, genre and language of the movies. It will also consider the history of the movies watched by a particular user to recommend movies to them. The dataset that will be used for this project is Netflix prize dataset and hotstar dataset. Two models will be developed in this project namely Collaborative Filtering Algorithm and Pearson’s R Correlation algorithm. The outcome of this recommender system will be a customized list of top-rated movies from Netflix and Hotstar respectively. The future scope of this system is to recommend top rated movies from various OTT platforms which will help the user to identify his/her favorites in single application.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125418169","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587588
Mahesh C. Pawaskar, G. Vijay
Digital video storing, video streaming and video conferencing are globally important industry which will continue to spread across the networks, businesses, and homes. Internet multimedia data like videos and images are the most frequently used digital format for information transmission. Video compression is the way to deliver more and higher quality video in a cost-effective way. Video coding process plays vital role to compress and decompress digital video. High Efficiency Video Coding (HEVC) is latest video compression standard which has potential to give better performance than any other standards like H.264. This paper proposes a novel video compression strategy to improve the quality of video without degradation. The newly devised RD tradeoff ensure mode decision effectively and the selection of the blocks optimally for motion estimation. Performance of proposed method is evaluated with the help of PSNR as well as SSIM, and it is compared with other two methods. Compression is done without affecting quality.
{"title":"Taylor Series based RD trade-off and Laplace Correction based coding for HEVC encoder","authors":"Mahesh C. Pawaskar, G. Vijay","doi":"10.1109/GCAT52182.2021.9587588","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587588","url":null,"abstract":"Digital video storing, video streaming and video conferencing are globally important industry which will continue to spread across the networks, businesses, and homes. Internet multimedia data like videos and images are the most frequently used digital format for information transmission. Video compression is the way to deliver more and higher quality video in a cost-effective way. Video coding process plays vital role to compress and decompress digital video. High Efficiency Video Coding (HEVC) is latest video compression standard which has potential to give better performance than any other standards like H.264. This paper proposes a novel video compression strategy to improve the quality of video without degradation. The newly devised RD tradeoff ensure mode decision effectively and the selection of the blocks optimally for motion estimation. Performance of proposed method is evaluated with the help of PSNR as well as SSIM, and it is compared with other two methods. Compression is done without affecting quality.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125513670","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587733
Aswathi Cd, N. Mathew, K. S. Riyas, R. Jose
Drowsy driving due to insufficient sleep has led to many serious traffic accidents. Measuring the drowsiness of the driver and taking timely actions can avoid such accidents. Earlier, conventional methods such as eye states and facial expressions were used to detect drowsiness. Nowadays new techniques have been developed for the same purpose, which uses bio-electric signals like an Electro Cardio Gram(ECG). Heart Rate Variability (HRV) can be used to assess drivers’ drowsiness, fatigue, and stress levels. HRV is determined by the interval of RR measured by an Electro Cardiogram. Twelve features are monitored, including both time and frequency domains, in order to determine the HRV changes. HRV monitoring is used to actually predict epileptic seizures. The proposed work uses Heart Rate Variability (HRV) analysis with a Machine Learning and Deep Learning to detect drowsiness. A comparison is also made between the performance of four different Machine Learning(ML) algorithms while using one-dimensional convolutional neural networks (1D CNNs). Convolutional neural networks (CNN) are used increasingly in Computer Vision and Machine Learning operations. 2D CNNs consist of millions of parameters and many hidden layers, and it has Interpreting complex patterns and objects. Two-dimensional signals, such as images and video frames, are used as inputs for 2D CNNs. However, this may not be the ideal choice in many applications, especially those involving One-Dimensional signals such as biomedical signals. To solve the problem, 1D CNNs were introduced with the highest level of performance. Specifically, the 1D CNN has four layers: a Convolutional Layer, Batch Normalization Layer, Maxpooling Layer, and Fully Connected Layer. The proposed strategy has the potential to help avoid accidents caused by drowsy driving.
{"title":"Comparison Of Machine Learning Algorithms For Heart Rate Variability Based Driver Drowsiness Detection","authors":"Aswathi Cd, N. Mathew, K. S. Riyas, R. Jose","doi":"10.1109/GCAT52182.2021.9587733","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587733","url":null,"abstract":"Drowsy driving due to insufficient sleep has led to many serious traffic accidents. Measuring the drowsiness of the driver and taking timely actions can avoid such accidents. Earlier, conventional methods such as eye states and facial expressions were used to detect drowsiness. Nowadays new techniques have been developed for the same purpose, which uses bio-electric signals like an Electro Cardio Gram(ECG). Heart Rate Variability (HRV) can be used to assess drivers’ drowsiness, fatigue, and stress levels. HRV is determined by the interval of RR measured by an Electro Cardiogram. Twelve features are monitored, including both time and frequency domains, in order to determine the HRV changes. HRV monitoring is used to actually predict epileptic seizures. The proposed work uses Heart Rate Variability (HRV) analysis with a Machine Learning and Deep Learning to detect drowsiness. A comparison is also made between the performance of four different Machine Learning(ML) algorithms while using one-dimensional convolutional neural networks (1D CNNs). Convolutional neural networks (CNN) are used increasingly in Computer Vision and Machine Learning operations. 2D CNNs consist of millions of parameters and many hidden layers, and it has Interpreting complex patterns and objects. Two-dimensional signals, such as images and video frames, are used as inputs for 2D CNNs. However, this may not be the ideal choice in many applications, especially those involving One-Dimensional signals such as biomedical signals. To solve the problem, 1D CNNs were introduced with the highest level of performance. Specifically, the 1D CNN has four layers: a Convolutional Layer, Batch Normalization Layer, Maxpooling Layer, and Fully Connected Layer. The proposed strategy has the potential to help avoid accidents caused by drowsy driving.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116204316","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 : 2021-10-01DOI: 10.1109/gcat52182.2021.9587646
{"title":"[Copyright notice]","authors":"","doi":"10.1109/gcat52182.2021.9587646","DOIUrl":"https://doi.org/10.1109/gcat52182.2021.9587646","url":null,"abstract":"","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116233493","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587866
Kanchi Tank
Mathematics plays a predominant role in each of our lives. When it comes to solving a mathematical expression, we are highly dependent on the calculators that are available in almost every electronic gadget. Since all these gadgets are touchscreen-based nowadays, building a system that recognizes and solves online handwritten mathematical expressions is the potential area of this research. Recognition of online handwritten mathematical expressions is a complicated task. In this paper, an Artificial Neural Network model is built for the recognition of handwritten digits, operators, and symbols. Tkinter GUI interface is built for the users to type in their expressions and image processing is done by capturing an image from the canvas and converting it into a NumPy array and then applying the thresholding technique to convert it into a binary array. Connected component labeling is done to separate every number and symbol on the canvas. These numbers and symbols are then sent to the artificial neural network for predictions. The model gave a training accuracy of 98.97% and a test accuracy of 98.95%. Finally, the expression is evaluated, and the translated expression and output are shown on the Tkinter GUI interface.
{"title":"Online Handwritten Mathematical Expression Solver Using Artificial Neural Network","authors":"Kanchi Tank","doi":"10.1109/GCAT52182.2021.9587866","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587866","url":null,"abstract":"Mathematics plays a predominant role in each of our lives. When it comes to solving a mathematical expression, we are highly dependent on the calculators that are available in almost every electronic gadget. Since all these gadgets are touchscreen-based nowadays, building a system that recognizes and solves online handwritten mathematical expressions is the potential area of this research. Recognition of online handwritten mathematical expressions is a complicated task. In this paper, an Artificial Neural Network model is built for the recognition of handwritten digits, operators, and symbols. Tkinter GUI interface is built for the users to type in their expressions and image processing is done by capturing an image from the canvas and converting it into a NumPy array and then applying the thresholding technique to convert it into a binary array. Connected component labeling is done to separate every number and symbol on the canvas. These numbers and symbols are then sent to the artificial neural network for predictions. The model gave a training accuracy of 98.97% and a test accuracy of 98.95%. Finally, the expression is evaluated, and the translated expression and output are shown on the Tkinter GUI interface.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"40 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121009853","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587703
Mayank Singhal, R. Agarwal
Cartoon to Human Translation transforms a 2D vector cartoon face to a Real Human Face. The mapping is based on semantic similarity of both the input domains. This is an image$rightarrow$mage translation problem that finds its applications in the entertainment and animation industry. Cartoon movies evolved from 2D animations in 1930 and became more lifelike with timeline. In image synthesis, audio, and other sorts of data, Generative Adversarial Networks have demonstrated promising outcomes. They also produce excellent results when translating images to images. In this research, a CycleGAN based methodology for generating target Human Faces from source Cartoon Faces is proposed, preserving the facial characteristics i.e. face shape, eyebrow alignment and hair style. In order to improve the mapping we have used contour loss along with cycle consistency loss in our model and patch discriminator is used with L2 norm.
{"title":"Cartoon Face to Human Face Translation using Contour Loss based CycleGAN","authors":"Mayank Singhal, R. Agarwal","doi":"10.1109/GCAT52182.2021.9587703","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587703","url":null,"abstract":"Cartoon to Human Translation transforms a 2D vector cartoon face to a Real Human Face. The mapping is based on semantic similarity of both the input domains. This is an image$rightarrow$mage translation problem that finds its applications in the entertainment and animation industry. Cartoon movies evolved from 2D animations in 1930 and became more lifelike with timeline. In image synthesis, audio, and other sorts of data, Generative Adversarial Networks have demonstrated promising outcomes. They also produce excellent results when translating images to images. In this research, a CycleGAN based methodology for generating target Human Faces from source Cartoon Faces is proposed, preserving the facial characteristics i.e. face shape, eyebrow alignment and hair style. In order to improve the mapping we have used contour loss along with cycle consistency loss in our model and patch discriminator is used with L2 norm.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131286885","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587471
Imran Ali Shah, Nitika Kapoor
Mobile Ad hoc Networks ‘MANETs’ are still defenseless against peripheral threats due to the fact that this network has vulnerable access and also the absence of significant fact of administration. The black hole attack is a kind of some routing attack, in this type of attack the attacker node answers to the Route Requests (RREQs) thru faking and playing itself as an adjacent node of the destination node in order to get through the data packets transported from the source node. To counter this situation, we propose to deploy some nodes (exhibiting some distinctive functionality) in the network called DPS (Detection and Prevention System) nodes that uninterruptedly monitor the RREQs advertised by all other nodes in the networks. DPS nodes target to satisfy the set objectives in which it has to sense the mischievous nodes by detecting the activities of their immediate neighbor. In the case, when a node demonstrates some peculiar manners, which estimates according to the experimental data, DPS node states that particular distrustful node as black hole node by propagation of a threat message to all the remaining nodes in the network. A protocol with a clustering approach in AODV routing protocol is used to sense and avert the black hole attack in the mentioned network. Consequently, empirical evaluation shows that the black hole node is secluded and prohibited from the whole system and is not allowed any data transfer from any node thereafter.
{"title":"To Detect and Prevent Black Hole Attack in Mobile Ad Hoc Network","authors":"Imran Ali Shah, Nitika Kapoor","doi":"10.1109/GCAT52182.2021.9587471","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587471","url":null,"abstract":"Mobile Ad hoc Networks ‘MANETs’ are still defenseless against peripheral threats due to the fact that this network has vulnerable access and also the absence of significant fact of administration. The black hole attack is a kind of some routing attack, in this type of attack the attacker node answers to the Route Requests (RREQs) thru faking and playing itself as an adjacent node of the destination node in order to get through the data packets transported from the source node. To counter this situation, we propose to deploy some nodes (exhibiting some distinctive functionality) in the network called DPS (Detection and Prevention System) nodes that uninterruptedly monitor the RREQs advertised by all other nodes in the networks. DPS nodes target to satisfy the set objectives in which it has to sense the mischievous nodes by detecting the activities of their immediate neighbor. In the case, when a node demonstrates some peculiar manners, which estimates according to the experimental data, DPS node states that particular distrustful node as black hole node by propagation of a threat message to all the remaining nodes in the network. A protocol with a clustering approach in AODV routing protocol is used to sense and avert the black hole attack in the mentioned network. Consequently, empirical evaluation shows that the black hole node is secluded and prohibited from the whole system and is not allowed any data transfer from any node thereafter.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116497864","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}
In this paper, we have discussed the use of augmented reality for data visualization. As we know that data is one of the most valued resources of today’s world, analysis of this data is a task of great importance. Multiple technologies have come up with data visualization for enabling accurate data analysis. Clearer the visualization the easier it is to perform accurate data analysis. Another technology that has grown vastly in various domains is Augmented Reality. It aims to bridge the gap between the physical and the digital world by introducing virtual elements into our environment. Our Implementation is a use case of AR technology in data visualization and analytics. Our proposed system uses mobile AR to perform data visualization giving more engagement and immersion to the users which can be used to gain more human insights over data such as trends and patterns that would otherwise get missed out in a depthless 2D visualization. We have developed an application as a prototype of our proposed system demonstrating different visualization techniques.
{"title":"DVAR: Data Visualization using Augmented Reality","authors":"Pareena Padwal, Yashpreet Singh, Jeetesh Singh, Suvarna Pansambal","doi":"10.1109/GCAT52182.2021.9587831","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587831","url":null,"abstract":"In this paper, we have discussed the use of augmented reality for data visualization. As we know that data is one of the most valued resources of today’s world, analysis of this data is a task of great importance. Multiple technologies have come up with data visualization for enabling accurate data analysis. Clearer the visualization the easier it is to perform accurate data analysis. Another technology that has grown vastly in various domains is Augmented Reality. It aims to bridge the gap between the physical and the digital world by introducing virtual elements into our environment. Our Implementation is a use case of AR technology in data visualization and analytics. Our proposed system uses mobile AR to perform data visualization giving more engagement and immersion to the users which can be used to gain more human insights over data such as trends and patterns that would otherwise get missed out in a depthless 2D visualization. We have developed an application as a prototype of our proposed system demonstrating different visualization techniques.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127563018","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587742
Asif Ikbal Mondal, B. Mandal, Amitava Choudhury
In this paper, the whole system starts with a real time picture of the sender and the receiver. The pairing is done utilizing the image of the sender and the receiver at the place of the pin used for pairing. The key generation for the encryption also utilizes the facial characteristics of the parties involved. This method will always generate a key of variable length. As the same person taking the snap of his/her face will change the coordinates every time making it is hard to predict the exact key. The text converted into ciphertext is also padded from both sides with variable bits to make it hard to identify the starting point of the ciphertext to start the actual decryption. The overall aim of this research is to develop a cryptosystem that can be used for achieving good encryption even if the length of the plain text is very small(one or two words). It is an encryption system with associated authentication. An electronic device cannot distinguish between its real users and fake one just by looking at the number which is used for the authentication process but if trained to identify the picture of the used the device up to a certain point is secure. Next thing if someone has requested a pairing merely by looking at the numbers one cannot predict that a legal connection is made but by looking at the real-time image of the person the same can be done with satisfaction. Previously used encryption used key and ciphertext of fixed length but here, a variable ciphertext and key are created in every session of transmission as the image of every sender and the receiver is different.
{"title":"Cryptosystem using Facial Landmark for Authentication Pairing and Key Generation in Bluetooth Security","authors":"Asif Ikbal Mondal, B. Mandal, Amitava Choudhury","doi":"10.1109/GCAT52182.2021.9587742","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587742","url":null,"abstract":"In this paper, the whole system starts with a real time picture of the sender and the receiver. The pairing is done utilizing the image of the sender and the receiver at the place of the pin used for pairing. The key generation for the encryption also utilizes the facial characteristics of the parties involved. This method will always generate a key of variable length. As the same person taking the snap of his/her face will change the coordinates every time making it is hard to predict the exact key. The text converted into ciphertext is also padded from both sides with variable bits to make it hard to identify the starting point of the ciphertext to start the actual decryption. The overall aim of this research is to develop a cryptosystem that can be used for achieving good encryption even if the length of the plain text is very small(one or two words). It is an encryption system with associated authentication. An electronic device cannot distinguish between its real users and fake one just by looking at the number which is used for the authentication process but if trained to identify the picture of the used the device up to a certain point is secure. Next thing if someone has requested a pairing merely by looking at the numbers one cannot predict that a legal connection is made but by looking at the real-time image of the person the same can be done with satisfaction. Previously used encryption used key and ciphertext of fixed length but here, a variable ciphertext and key are created in every session of transmission as the image of every sender and the receiver is different.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128027600","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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