2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)最新文献

Research challenges for Internet of Things: A review 物联网的研究挑战:综述

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284461

Vibha Lohan, R. Singh

引用次数: 8

An improved lexicon using logistic regression for sentiment analysis 使用逻辑回归进行情感分析的改进词典

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284501

Kunal Bhargava, R. Katarya

引用次数: 7

Evaluation of spectrum sensing process through energy detection technique 利用能量检测技术评价频谱传感过程

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284503

Ritu Rani, Krishan Kumar, Rajender Kumar, Seema Jangra

引用次数: 1

A review on basics of QSD number addition & subtraction QSD数加减法基础述评

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/ic3tsn.2017.8284442

Simranjeet Singh Sudan, Manish Singhal

引用次数: 0

MATLAB based encoder designing of 5.90 kbps narrow-band AMR codec 基于MATLAB的5.90 kbps窄带AMR编解码器的编码器设计

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/ic3tsn.2017.8284450

Pameet Kaur, V. Chouhan, Hemant Purohit

引用次数: 0

A study on new biometric approaches 生物识别新方法的研究

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284496

Shally Gupta, Lakshmi Singh

引用次数: 5

Time frequency analysis of non-stationary motor imagery EEG signals 非平稳运动意象脑电信号的时频分析

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284448

Aruna Tyagi, Vijay Nehra

{"title":"Time frequency analysis of non-stationary motor imagery EEG signals","authors":"Aruna Tyagi, Vijay Nehra","doi":"10.1109/IC3TSN.2017.8284448","DOIUrl":"https://doi.org/10.1109/IC3TSN.2017.8284448","url":null,"abstract":"Brain Computer Interface (BCI) offers an effective medium of communication for physically disabled people who are partially or completely locked in their body. It also improves the performance level of healthy people by providing them a new input modality. Recently, BCI systems based on motor imagery electroencephalogram (EEG) signals have been focussed intensively. Extraction and classification of motor imagery features is the most crucial part of motor imagery based BCI system. The biggest challenge in processing motor imagery EEG signals is that these signals are non stationary. In the present investigation, left hand and foot motor imagery tasks have been classified. Time-frequency features have been extracted using discrete wavelet transform (DWT) to deal with the non stationarity of motor imagery EEG signals. Linear discriminant analysis (LDA) is used to reduce the dimensionality of data. The efficacy of DWT and LDA has been tested using state vector machine (SVM), k-nearest neighbour (k-NN) and artificial neural network (ANN) classifiers. Overall, combining DWT with LDA and ANN achieved a promising average accuracy of 90% in 4.453 secs. The present study may prove to be helpful in developing intelligent devices to help people in locked-in state for controlling some application by imagination of the movement of their body parts.","PeriodicalId":322178,"journal":{"name":"2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131935526","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: 1

Performance comparision of IEEE 802.11g WLANs with respect to increasing number of workstation using OPNET modeler 使用OPNET建模器增加工作站数量的IEEE 802.11g无线局域网性能比较

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284463

A. Shrestha, Manish Karki, V. Singh

{"title":"Performance comparision of IEEE 802.11g WLANs with respect to increasing number of workstation using OPNET modeler","authors":"A. Shrestha, Manish Karki, V. Singh","doi":"10.1109/IC3TSN.2017.8284463","DOIUrl":"https://doi.org/10.1109/IC3TSN.2017.8284463","url":null,"abstract":"With the ever increasing pace of use of internet as well as transfer of various data over it, there is a great demand of faster internet browsing and faster data transfer. In the present scenario wireless LAN plays a vital role. Wireless LAN mobility allows the users to access information any time and from any location. The 802.11 standard of wireless LAN is currently the most widely used technique to browse internet and data transfer. The focus is high speed wireless internet access from homes and business, bypassing the telephone system. Wireless LAN is often called as the future of data transfer. So, there is greater need of the study of data transfer rate, internet browsing speed over wireless LAN. This paper presents set up of various different wireless networks in wireless simulation tool OPNET modeler 14.5. Each network is given an access point (AP), server, application configuration, profile configuration and workstations under IEEE 802.11g protocol. All the wireless networks made are given a set of rules like heavy file transfer, calling, etc. The networks are made with increasing number of workstations and the graphs we get from the network are compared with respect to delay, throughput, received power and the result is obtained.","PeriodicalId":322178,"journal":{"name":"2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116974410","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: 2

Multilevel security framework for cloud data 云数据的多级安全框架

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284478

Akshat Kumar Dixit, Charu

引用次数: 2

Multi-modal hand-based biometric system using energy compaction of various transforms and wavelets 多模态手部生物识别系统，利用各种变换和小波的能量压缩

2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) Pub Date : 2017-10-01 DOI: 10.1109/IC3TSN.2017.8284510

R. Vig, Navika Iyer, Twinkle Arora

{"title":"Multi-modal hand-based biometric system using energy compaction of various transforms and wavelets","authors":"R. Vig, Navika Iyer, Twinkle Arora","doi":"10.1109/IC3TSN.2017.8284510","DOIUrl":"https://doi.org/10.1109/IC3TSN.2017.8284510","url":null,"abstract":"The need for stringent security and accurate identification is to a large extent accomplished by multi-modal biometric systems. These systems counter-verify the identity using more than one biometrics. In this paper we have used three hand-based biometric viz. fingerprint, palmprint and finger-knuckle print. The main advantage of this system is ease of acquiring biometric and hence high user's acceptability. Individual transforms like DCT, Walsh, Kekre and Haar and their combinations resulting in hybrid transforms, hybrid two-resolution wavelets and hybrid multi-resolution wavelets have been applied to each biometric. Energy compaction of these transforms and wavelets has been used to generate feature vectors. Both open set and closed set experiments have been performed on partial fingerprint database and palmprint and finger-knuckle print databases from Hong Kong polyu and plots of efficiency of closed set and receiver operating characteristics of open set have been generated. With 100% efficiency for hybrid multi-resolution wavelet with combination of Walsh-Kekre and 0% Equal Error Rate (EER) and 100% Security Parameter Index (SPI) for hybrid multi-resolution wavelet with combination of Walsh-DCT and Haar-DCT, this method is best suitable for applications with relatively smaller database.","PeriodicalId":322178,"journal":{"name":"2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)","volume":"22 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120928834","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