2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)最新文献_第6页

Transfer Learning with L2 Norm Regularization for classifying static Two Hand Hindi Sign Language Gestures 基于L2范数正则化的印度语静态双手手势迁移学习

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115767

Mohita Jaiswal, Vaidehi Sharmay, Abhishek Sharmaz, Raghuvir Tomar

引用次数: 2

A Survey on Hardware Implementation of Cryptographic Algorithms Using Field Programmable Gate Array 基于现场可编程门阵列的加密算法硬件实现综述

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115742

K. Kumar, K. Ramkumar, Amanpreet Kaur, Somanshu Choudhary

{"title":"A Survey on Hardware Implementation of Cryptographic Algorithms Using Field Programmable Gate Array","authors":"K. Kumar, K. Ramkumar, Amanpreet Kaur, Somanshu Choudhary","doi":"10.1109/CSNT48778.2020.9115742","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115742","url":null,"abstract":"In the past recent years the idea towards secure data communication is increasing day by day. The secure communication is being achieved by applying various cryptographic algorithms on the data which is to be transferred over wireless networks. The different cryptographic algorithms that are generally practiced in the current cyber world are Advanced Encryption Standard (AES), Data Encryption Standard (DES), RSA algorithm, Message Digest 5 (MD5), Secure Hash Algorithm (SHA). All these algorithms are highly secured with sound and complex mathematical computations that makes the hacker tedious to breach the data which is protected by these algorithm. The hardware implementation of algorithms enhances the speed, efficiency and reliability of security standards. In this work the Field Programmable Gate Array (FPGA) implementation of various cryptographic algorithms is discussed in details. The main motivation behind the FPGA implementations of Security algorithms is to increase the speed and decrease delays of software implementations. There are millions of logic gates that are clustered in FPGA, this brings new innovations to existing algorithms. This paper surveyss the parameters such as throughput, operating frequency, number of slice registers used and number of clock cycles of FPGA that have the major role in execution process of cryptographic algorithms. Comparative analysis on hardware implementation of security algorithms on different FPGA’s is also done.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208091","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: 4

Microwave Imaging based Automatic Crack Detection System using Machine Learning for Columns 基于微波成像的圆柱裂纹自动检测系统

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115763

Prashanth Kannadaguli, Vidya Bhat

{"title":"Microwave Imaging based Automatic Crack Detection System using Machine Learning for Columns","authors":"Prashanth Kannadaguli, Vidya Bhat","doi":"10.1109/CSNT48778.2020.9115763","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115763","url":null,"abstract":"Buildings are exposed to damage and deterioration during their life cycle. So, damage assessment plays an important role in Structural stability. Cracks in the structures are of common occurrence, hence early detection of cracks is necessary. Damages like cracks can be detected using Microwave Imaging of the columns. Damages like Horizontal and vertical cracks are determined by training the Bayesian classifier and the Artificial Neural Networks. Both these approaches are required as Structural health to be monitored for predicting damages in columns. Crack detection system is built in columns of civil structures based on Artificial Neural Network and Bayesian Classifiers, which are constructed upon probabilistic pattern recognition and data modelling. The frequency data was collected from 12 microwave sensors for 30 positions of column and is required to train and test the mathematical models. Since, mean and covariance of the statistical data are well known features used in feature extraction. Finally, performance analysis of the models has been provided in terms of Crack Error Rate (CER) justifies that dynamic modelling using ANN yields better results than Bayesian Classifiers and this can also be used in developing Automatic Crack detection systems of civil structures.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130280267","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: 3

A Comprehensive Survey on Various Machine Learning Methods used for Intrusion Detection System 入侵检测系统中各种机器学习方法综述

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115764

A. Gupta, Jitendra Agrawal

引用次数: 12

Welcome from CSNT 2020 General Chair 欢迎来自CSNT 2020的总主席

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/csnt48778.2020.9115743

引用次数: 0

Demystifying and Anticipating Graduate School Admissions using Machine Learning Algorithms 使用机器学习算法揭开和预测研究生入学的神秘面纱

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115788

Mohd Aijaj Khan, M. Dixit, Aaradhya Dixit

引用次数: 2

Case for Dynamic Parallelisation using Learning Techniques 使用学习技术实现动态并行化的案例

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115757

Karthik Gurunathan, K. Kartikey, T. Sudarshan, KN Divyaprabha

{"title":"Case for Dynamic Parallelisation using Learning Techniques","authors":"Karthik Gurunathan, K. Kartikey, T. Sudarshan, KN Divyaprabha","doi":"10.1109/CSNT48778.2020.9115757","DOIUrl":"https://doi.org/10.1109/CSNT48778.2020.9115757","url":null,"abstract":"Parallelisation involves dividing computational tasks statically or dynamically. Static analyses and studies on evolution of compilation approaches show how different techniques are employed to distribute the computational load from the main CPU (Central Processing Unit) to associated GPUs (Graphical Processing Units), and other pre-defined set of accelerators. This load sharing is often done before deployment of hardware for its core computational task. Several learning techniques have evolved to optimise such load sharing. The purpose of this paper is to provide an insight into how dynamic parallelisation can be accomplished. This work takes inspiration from current learning techniques in static systems, which continue to grow more scalable, more efficient and offer better memory access and extends these in the field of dynamic load sharing, which is a fledgling field that has not used learning techniques in its fullest, yet. As a precursor, existing static parallelisation techniques are surveyed to provide a compelling case for the above. Learning techniques help evolve a robust data parallelism scheme, that allows any parallelising tool to learn incrementally.","PeriodicalId":131745,"journal":{"name":"2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"343 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122757922","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 Survey on Underwater Images Enhancement Techniques 水下图像增强技术综述

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/CSNT48778.2020.9115732

Om Kumari Soni, J. S. Kumare

引用次数: 10

CSNT 2020 Author Index CSNT 2020作者索引

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/csnt48778.2020.9115747

引用次数: 0

CSNT 2020 Committees CSNT 2020委员会

2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) Pub Date : 2020-04-01 DOI: 10.1109/csnt48778.2020.9115756

引用次数: 0