Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170195
P. Suhas Reddy, Jayanth Anala, V. Krishnamurthy, B Surendiran, R. Sujithra @ Kanmani
The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a challenge. One approach to optimising the performance of MVC applications is converting lists to dictionaries. This paper discusses the benefits and drawbacks of this approach and presents the findings of recent research on this topic. The main advantage of converting lists to dictionaries is that it can improve the performance of MVC applications by offering faster access times and making code easier to read and maintain. However, there are drawbacks to this approach, such as increased memory usage and slower performance for certain operations. Several studies have been conducted on the performance of MVC applications when using lists versus dictionaries, with varying results. This paper overviews this research and highlights the implications for MVC development.
{"title":"Performance Improvement of Model View Controller based Applications through Linda’s-Key","authors":"P. Suhas Reddy, Jayanth Anala, V. Krishnamurthy, B Surendiran, R. Sujithra @ Kanmani","doi":"10.1109/IConSCEPT57958.2023.10170195","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170195","url":null,"abstract":"The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a challenge. One approach to optimising the performance of MVC applications is converting lists to dictionaries. This paper discusses the benefits and drawbacks of this approach and presents the findings of recent research on this topic. The main advantage of converting lists to dictionaries is that it can improve the performance of MVC applications by offering faster access times and making code easier to read and maintain. However, there are drawbacks to this approach, such as increased memory usage and slower performance for certain operations. Several studies have been conducted on the performance of MVC applications when using lists versus dictionaries, with varying results. This paper overviews this research and highlights the implications for MVC development.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125247930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170238
S. Ajakwe, Dong‐Seong Kim, Jae Min Lee
The importance of a cyber-edge and cognitive artificial intelligence (AI)-based security strategy to boost autonomous underwater navigation and aerial mobility and prevent hetero-geneous reprisal attacks cannot be overemphasized. This paper proposes a split of super-resolution (SR) to reconstruct the channel state information (CSI) through self-supervised learning for a multiple-input-multiple-output (MIMO) system. Unlike existing designs, this study used a split of SR into two disjoint sub-blocks through transfer learning to improve the CSI detailed structures in the reconstruction process. The simulation results show that the proposed system significantly improved the quality of the CSI after reconstruction compared to the existing system in terms of cosine similarity $rho$ of 95.2% and normalized mean square error (NMSE) of −16.33 at different compression rates for both indoor and outdoor environments, which is essential for a MIMO system in improving performance, coverage, reliability, and user experience in 5G and 6G networks.
{"title":"CogNet: Cognitive Super Resolution Network for Persistent End-to-End Mobility Communication in MIMO Systems","authors":"S. Ajakwe, Dong‐Seong Kim, Jae Min Lee","doi":"10.1109/IConSCEPT57958.2023.10170238","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170238","url":null,"abstract":"The importance of a cyber-edge and cognitive artificial intelligence (AI)-based security strategy to boost autonomous underwater navigation and aerial mobility and prevent hetero-geneous reprisal attacks cannot be overemphasized. This paper proposes a split of super-resolution (SR) to reconstruct the channel state information (CSI) through self-supervised learning for a multiple-input-multiple-output (MIMO) system. Unlike existing designs, this study used a split of SR into two disjoint sub-blocks through transfer learning to improve the CSI detailed structures in the reconstruction process. The simulation results show that the proposed system significantly improved the quality of the CSI after reconstruction compared to the existing system in terms of cosine similarity $rho$ of 95.2% and normalized mean square error (NMSE) of −16.33 at different compression rates for both indoor and outdoor environments, which is essential for a MIMO system in improving performance, coverage, reliability, and user experience in 5G and 6G networks.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131243698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170113
Ramanuj Bhattacharjee, K. Suganya Devi, S. Vijaykanth
To improve the chances of survival for a patient with laryngeal cancer, early detection is crucial. Currently, the standard diagnostic method involves an endoscopic examination of the larynx, followed by a biopsy and histological analysis by an oncologist, which can be subject to variability due to subjective evaluation. Therefore, there is a need for a faster and more accurate detection system that can replace the current manual examination. Recent research has shown that Deep Learning technology can assist in identifying laryngeal cancer, including precancerous and cancerous tumors, from endoscopic pictures. However, endoscopic image processing is a challenging task due to the highly dynamic nature of the endoscopic video, spectrum fluctuations, and numerous image interferences. To address this challenge, a Deep Ensemble Learning approach using convolutional neural networks (CNNs) and an effective image segmentation technique has been proposed. The suggested model has an overall accuracy of 98.12%.
{"title":"Detecting Laryngeal Cancer Lesions From Endoscopy Images Using Deep Ensemble Model","authors":"Ramanuj Bhattacharjee, K. Suganya Devi, S. Vijaykanth","doi":"10.1109/IConSCEPT57958.2023.10170113","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170113","url":null,"abstract":"To improve the chances of survival for a patient with laryngeal cancer, early detection is crucial. Currently, the standard diagnostic method involves an endoscopic examination of the larynx, followed by a biopsy and histological analysis by an oncologist, which can be subject to variability due to subjective evaluation. Therefore, there is a need for a faster and more accurate detection system that can replace the current manual examination. Recent research has shown that Deep Learning technology can assist in identifying laryngeal cancer, including precancerous and cancerous tumors, from endoscopic pictures. However, endoscopic image processing is a challenging task due to the highly dynamic nature of the endoscopic video, spectrum fluctuations, and numerous image interferences. To address this challenge, a Deep Ensemble Learning approach using convolutional neural networks (CNNs) and an effective image segmentation technique has been proposed. The suggested model has an overall accuracy of 98.12%.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131560302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170474
P. Aruna, V. Vasan Prabhu, V. Krishnakumar
In this paper, modeling and estimating the parameters of the Enhanced Self-Correcting (ESC) model of a lithium-ion cell is presented so that the behaviour of the cell can be better understood with high fidelity. When the lithium-ion cell is used as battery pack in Electric Vehicle (EV), it is critical to have reliable temperaturedependent parameters to forecast aging and to determine how the cell responds to different operating scenarios of EV. This study is significant because it takes into account the voltage hysteresis effect, which is necessary for precise estimation of State of Charge (SOC) and State of Health (SOH) in order to forecast EV range. Open circuit voltage testing and dynamic testing at various temperatures are used in this paper to determine the parameters of the ESC model. The simulations are done using MATLAB and the results are obtained with high accuracy.
{"title":"Modeling and Estimation of Enhanced Self Correcting Model Parameters of Lithium Ion Cell","authors":"P. Aruna, V. Vasan Prabhu, V. Krishnakumar","doi":"10.1109/IConSCEPT57958.2023.10170474","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170474","url":null,"abstract":"In this paper, modeling and estimating the parameters of the Enhanced Self-Correcting (ESC) model of a lithium-ion cell is presented so that the behaviour of the cell can be better understood with high fidelity. When the lithium-ion cell is used as battery pack in Electric Vehicle (EV), it is critical to have reliable temperaturedependent parameters to forecast aging and to determine how the cell responds to different operating scenarios of EV. This study is significant because it takes into account the voltage hysteresis effect, which is necessary for precise estimation of State of Charge (SOC) and State of Health (SOH) in order to forecast EV range. Open circuit voltage testing and dynamic testing at various temperatures are used in this paper to determine the parameters of the ESC model. The simulations are done using MATLAB and the results are obtained with high accuracy.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170546
N. Usha Bhanu, C. Saravanakumar
The growing demand of high-resolution video on portable devices, the applications require higher coding efficiency, high throughput and low power for handling heterogenous types of video signals. This paper presents a survey on possibility of applying Machine Learning (ML) models in H.265/ HEVC video encoder unit. Higher computational complexity with respect to motion estimation, coding, and parallel processing architectures are required for HEVC. The existing HEVC algorithms are based on spatial temporal relationship which requires dynamic video sequences handling for fast changes in scenes. This paper focuses on the possible realization of machine learning algorithms for Rate Control (RC) in video sequences, Coding Unit (CU) depth decision, Neural network-based Motion Estimation and Compensation, adaptive de-blocking filter for reducing blocking artifacts and task driven semantic coding for real time video applications. The algorithms are surveyed with respect to the learning process used in various units of HEVC encoders and summarized in terms of parameters achieved and datasets used in the existing literature.
{"title":"Investigations of Machine Learning Algorithms for High Efficiency Video Coding (HEVC)","authors":"N. Usha Bhanu, C. Saravanakumar","doi":"10.1109/IConSCEPT57958.2023.10170546","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170546","url":null,"abstract":"The growing demand of high-resolution video on portable devices, the applications require higher coding efficiency, high throughput and low power for handling heterogenous types of video signals. This paper presents a survey on possibility of applying Machine Learning (ML) models in H.265/ HEVC video encoder unit. Higher computational complexity with respect to motion estimation, coding, and parallel processing architectures are required for HEVC. The existing HEVC algorithms are based on spatial temporal relationship which requires dynamic video sequences handling for fast changes in scenes. This paper focuses on the possible realization of machine learning algorithms for Rate Control (RC) in video sequences, Coding Unit (CU) depth decision, Neural network-based Motion Estimation and Compensation, adaptive de-blocking filter for reducing blocking artifacts and task driven semantic coding for real time video applications. The algorithms are surveyed with respect to the learning process used in various units of HEVC encoders and summarized in terms of parameters achieved and datasets used in the existing literature.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170410
Sumanth Reddy Poluri, Venkata Krishna Reddy Tiyyagura, K. S. Sri
An accurate model for DBSCAN (Outlier detection and removal). And implementing KNN by predicting the suitable k value. While SMOTE-ENN is used to balance the training dataset. Gradient boosting is a technique where new models are made and used to forecast the residuals or error, then the scores are added to find the presence or absence of disease. And implementing KNN by predicting the suitable k value. The model was built using few publicly accessible datasets, Statlog, heart failure clinical records datasets and Cleveland. These respective models output was compared to Each other respectively.
{"title":"Heart Disease Prediction Based On Machine Learning","authors":"Sumanth Reddy Poluri, Venkata Krishna Reddy Tiyyagura, K. S. Sri","doi":"10.1109/IConSCEPT57958.2023.10170410","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170410","url":null,"abstract":"An accurate model for DBSCAN (Outlier detection and removal). And implementing KNN by predicting the suitable k value. While SMOTE-ENN is used to balance the training dataset. Gradient boosting is a technique where new models are made and used to forecast the residuals or error, then the scores are added to find the presence or absence of disease. And implementing KNN by predicting the suitable k value. The model was built using few publicly accessible datasets, Statlog, heart failure clinical records datasets and Cleveland. These respective models output was compared to Each other respectively.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170710
S. Karthika Shree, Vaishali Vijayarajan, B. Sathya Bama, S. Mohammed Mansoor Roomi
Milk has been an essential part of our food culture as it contains important micronutrients and macronutrients. Milk is contaminated by the addition of water and preservatives. Traditionally, screening of milk quality was performed using human-based methods which have limitations such as being labor-intensive, time-consuming, and expensive. Therefore, non-destructive testing of milk quality using Hyperspectral imaging (HSI) is implemented. Compared to manual milk quality tests, HSI (Hyperspectral image) is faster and does not involve destructive methods. Pasteurized milk and vendor milk are used for sample preparation whereas water, Ammonium sulphate, and Ammonium chloride are chosen as adulterants. Therefore, the database is generated by capturing the images of milk samples with three different types of adulterants that are mixed with milk (Water, Ammonium Sulphate, and Ammonium Chloride) using the Resonon Hyperspectral camera (pika L, 400–1000 nm). Further, they are classified into three class classifications depending on the level of adulterants added. The problem of feature redundancy and noise is solved by using PCA-based Explained variance. On choosing ROI, the mean spectral curve is obtained and the optimal wavelength is chosen for extracting features and trained through machine learning classifiers like Ensemble, K-nearest neighbor, and Support Vector Machine for the three-class classification problem out of which the K-nearest neighbor, classifier reported the highest accuracy of 87%, 85%, 88% for vendor milk adulterant level classification and 84%, 87%, 85% for pasteurized milk adulterant level classification.
{"title":"Milk Quality Inspection Using Hyperspectral Imaging","authors":"S. Karthika Shree, Vaishali Vijayarajan, B. Sathya Bama, S. Mohammed Mansoor Roomi","doi":"10.1109/IConSCEPT57958.2023.10170710","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170710","url":null,"abstract":"Milk has been an essential part of our food culture as it contains important micronutrients and macronutrients. Milk is contaminated by the addition of water and preservatives. Traditionally, screening of milk quality was performed using human-based methods which have limitations such as being labor-intensive, time-consuming, and expensive. Therefore, non-destructive testing of milk quality using Hyperspectral imaging (HSI) is implemented. Compared to manual milk quality tests, HSI (Hyperspectral image) is faster and does not involve destructive methods. Pasteurized milk and vendor milk are used for sample preparation whereas water, Ammonium sulphate, and Ammonium chloride are chosen as adulterants. Therefore, the database is generated by capturing the images of milk samples with three different types of adulterants that are mixed with milk (Water, Ammonium Sulphate, and Ammonium Chloride) using the Resonon Hyperspectral camera (pika L, 400–1000 nm). Further, they are classified into three class classifications depending on the level of adulterants added. The problem of feature redundancy and noise is solved by using PCA-based Explained variance. On choosing ROI, the mean spectral curve is obtained and the optimal wavelength is chosen for extracting features and trained through machine learning classifiers like Ensemble, K-nearest neighbor, and Support Vector Machine for the three-class classification problem out of which the K-nearest neighbor, classifier reported the highest accuracy of 87%, 85%, 88% for vendor milk adulterant level classification and 84%, 87%, 85% for pasteurized milk adulterant level classification.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170617
Akhil Nair, R. Charan, Hari Krishna S, G. Rohith
Monitoring attendance is an essential administrative function in all educational institutions and organizations. A well-structured framework will facilitate the expansion of institutions. It reduces the instructors’ time and effort by assisting both students and teachers in improving attendance. The existing conventional physical classroom system is insecure, disruptive to teaching, and time-consuming to gather and store student attendance, which hampers the educational activities. The proposed system is a hybridized framework of face detection and recognition, and ID card detection and card text verification that adds to the two level authentication system. At the first level, the proposed system recognizes the individual, authenticates it with database data, and detects the ID card using deep Hog based ResNet feature extraction syttem. At the second level, YoloV7 based Easy OCR reads the details and marks the concerned individual as present. This hybridized framework is accurate in identifying the persons irrespective of the illumination conditions and an efficient attendance system.
{"title":"A Two-level authentication for Attendance Management System using deep learning techniques","authors":"Akhil Nair, R. Charan, Hari Krishna S, G. Rohith","doi":"10.1109/IConSCEPT57958.2023.10170617","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170617","url":null,"abstract":"Monitoring attendance is an essential administrative function in all educational institutions and organizations. A well-structured framework will facilitate the expansion of institutions. It reduces the instructors’ time and effort by assisting both students and teachers in improving attendance. The existing conventional physical classroom system is insecure, disruptive to teaching, and time-consuming to gather and store student attendance, which hampers the educational activities. The proposed system is a hybridized framework of face detection and recognition, and ID card detection and card text verification that adds to the two level authentication system. At the first level, the proposed system recognizes the individual, authenticates it with database data, and detects the ID card using deep Hog based ResNet feature extraction syttem. At the second level, YoloV7 based Easy OCR reads the details and marks the concerned individual as present. This hybridized framework is accurate in identifying the persons irrespective of the illumination conditions and an efficient attendance system.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131490880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170549
N. K. Sahu, Ruchi Patel, A. Verma
Multiple linear regression is process of attempting linear relation between response and a set of variables. In the present work, the roughness of grind surface was considered as a regressed variable during cylindrical grinding operation performed on lathe machine. The data was generated after performing experiments with varying regressor variables i.e. grinding wheel rotation (RPM), feed motion (mm/rev), and grinding depth cut (mm). These independent variables are varied in sequential manner using central composite design (CCD) under Response surface methodology (RSM). Regression coefficients are estimated to develop linear regression model. Later on, inference of regressor variables on regressed variable is done to interpret the regression model. The value of R2 and Adjusted R2 are found to be 95% and 94% respectively which suggests that model can be correlated with experimental data. Multicollinearity among regressor variables is done to check the correlations for assurance of interpretation of individual regressor variable over regressed variable. A hypothesis testing was done for predicting roughness of grind surface for 95 % confidence interval and found acceptable. Regression model is validated with additional experimental values of roughness of grind surface and found within acceptable range (max. 10% absolute error). Regression model can be interpreted as reduction in roughness of grind surface with increase in grinding wheel (RPM) whereas it increases with increase in grinding depth (mm) and feed motion (mm/rev).
{"title":"Multiple Linear Regression Model for Prediction of Roughness of Grind Surface","authors":"N. K. Sahu, Ruchi Patel, A. Verma","doi":"10.1109/IConSCEPT57958.2023.10170549","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170549","url":null,"abstract":"Multiple linear regression is process of attempting linear relation between response and a set of variables. In the present work, the roughness of grind surface was considered as a regressed variable during cylindrical grinding operation performed on lathe machine. The data was generated after performing experiments with varying regressor variables i.e. grinding wheel rotation (RPM), feed motion (mm/rev), and grinding depth cut (mm). These independent variables are varied in sequential manner using central composite design (CCD) under Response surface methodology (RSM). Regression coefficients are estimated to develop linear regression model. Later on, inference of regressor variables on regressed variable is done to interpret the regression model. The value of R2 and Adjusted R2 are found to be 95% and 94% respectively which suggests that model can be correlated with experimental data. Multicollinearity among regressor variables is done to check the correlations for assurance of interpretation of individual regressor variable over regressed variable. A hypothesis testing was done for predicting roughness of grind surface for 95 % confidence interval and found acceptable. Regression model is validated with additional experimental values of roughness of grind surface and found within acceptable range (max. 10% absolute error). Regression model can be interpreted as reduction in roughness of grind surface with increase in grinding wheel (RPM) whereas it increases with increase in grinding depth (mm) and feed motion (mm/rev).","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"74 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120822805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170724
Gunasekaran Thangavel, Ahmed Jabal Salman Bait Jamil, Fazilaton Nisha, Malak Mubarak Mohamed Al Masharfi, Hanna Juman Saaiyed Al Habsi
Due to the excessive use of digital platforms and the quickly expanding user base in the wireless domain, communication systems are necessary to provide information at high data rates with great dependability and quality. Wireless systems with a single element cannot meet the demands. As a result, wireless MIMO (Multiple-Input-Multiple-Output) technology is getting a lot of attention in contemporary high-speed communication. Even while these MIMO systems can considerably enhance channel capacity, it is still difficult to achieve an ideal isolation in 5G terminals that are small in size. Mobile devices, electronic devices, smart phones, RFIDs, wireless sensors, cars, etc. are some of the uses of MIMO systems. The foundations of MIMO antennas, performance characteristics, a design strategy, and techniques have all been covered in this research.
{"title":"Design of MIMO Antenna for 5G Base Station Design","authors":"Gunasekaran Thangavel, Ahmed Jabal Salman Bait Jamil, Fazilaton Nisha, Malak Mubarak Mohamed Al Masharfi, Hanna Juman Saaiyed Al Habsi","doi":"10.1109/IConSCEPT57958.2023.10170724","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170724","url":null,"abstract":"Due to the excessive use of digital platforms and the quickly expanding user base in the wireless domain, communication systems are necessary to provide information at high data rates with great dependability and quality. Wireless systems with a single element cannot meet the demands. As a result, wireless MIMO (Multiple-Input-Multiple-Output) technology is getting a lot of attention in contemporary high-speed communication. Even while these MIMO systems can considerably enhance channel capacity, it is still difficult to achieve an ideal isolation in 5G terminals that are small in size. Mobile devices, electronic devices, smart phones, RFIDs, wireless sensors, cars, etc. are some of the uses of MIMO systems. The foundations of MIMO antennas, performance characteristics, a design strategy, and techniques have all been covered in this research.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125991935","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：