2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)最新文献_第3页

Performance Improvement of Model View Controller based Applications through Linda’s-Key 基于Linda - key的模型-视图-控制器应用程序的性能改进

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170195

P. Suhas Reddy, Jayanth Anala, V. Krishnamurthy, B Surendiran, R. Sujithra @ Kanmani

引用次数: 0

CogNet: Cognitive Super Resolution Network for Persistent End-to-End Mobility Communication in MIMO Systems 在MIMO系统中持续端到端移动通信的认知超分辨率网络

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170238

S. Ajakwe, Dong‐Seong Kim, Jae Min Lee

{"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}

引用次数: 0

Detecting Laryngeal Cancer Lesions From Endoscopy Images Using Deep Ensemble Model 基于深度集合模型的喉癌内窥镜图像检测

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170113

Ramanuj Bhattacharjee, K. Suganya Devi, S. Vijaykanth

引用次数: 2

Modeling and Estimation of Enhanced Self Correcting Model Parameters of Lithium Ion Cell 锂离子电池增强自校正模型参数的建模与估计

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170474

P. Aruna, V. Vasan Prabhu, V. Krishnakumar

引用次数: 0

Investigations of Machine Learning Algorithms for High Efficiency Video Coding (HEVC) 高效视频编码(HEVC)的机器学习算法研究

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170546

N. Usha Bhanu, C. Saravanakumar

{"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}

引用次数: 0

Heart Disease Prediction Based On Machine Learning 基于机器学习的心脏病预测

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170410

Sumanth Reddy Poluri, Venkata Krishna Reddy Tiyyagura, K. S. Sri

引用次数: 0

Milk Quality Inspection Using Hyperspectral Imaging 利用高光谱成像技术检测牛奶质量

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170710

S. Karthika Shree, Vaishali Vijayarajan, B. Sathya Bama, S. Mohammed Mansoor Roomi

{"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}

引用次数: 0

A Two-level authentication for Attendance Management System using deep learning techniques 基于深度学习技术的考勤管理系统的两级认证

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170617

Akhil Nair, R. Charan, Hari Krishna S, G. Rohith

{"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}

引用次数: 0

Multiple Linear Regression Model for Prediction of Roughness of Grind Surface 磨削表面粗糙度预测的多元线性回归模型

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170549

N. K. Sahu, Ruchi Patel, A. Verma

{"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}

引用次数: 0

Design of MIMO Antenna for 5G Base Station Design 5G基站MIMO天线设计

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) Pub Date : 2023-05-25 DOI: 10.1109/IConSCEPT57958.2023.10170724

Gunasekaran Thangavel, Ahmed Jabal Salman Bait Jamil, Fazilaton Nisha, Malak Mubarak Mohamed Al Masharfi, Hanna Juman Saaiyed Al Habsi

引用次数: 0