2016 3rd International Conference on Recent Advances in Information Technology (RAIT)最新文献_第6页

A real-time heartbeat detection technique using TMS320C6713 processor and Multi-Rate Signal Processing 基于TMS320C6713处理器和多速率信号处理的实时心跳检测技术

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507892

D. Mandal, Madhabi Ganguly

{"title":"A real-time heartbeat detection technique using TMS320C6713 processor and Multi-Rate Signal Processing","authors":"D. Mandal, Madhabi Ganguly","doi":"10.1109/RAIT.2016.7507892","DOIUrl":"https://doi.org/10.1109/RAIT.2016.7507892","url":null,"abstract":"A lot of works have been done on Electrocardiogram and Phonocardiogram (ECG and PCG). But in most of the cases data for ECG or PCG have been taken from standard database like MIT-BIH Arrhythmia or any other sources. In this work a real-time signal processing task (RTST) on heart sound is presented. In the first part it is shown that the phonocardiogram signal (PCG) could be detected using an electronic circuit along with the DSP (TMS320C6713) processor from human heart. In the second part reduction of PCG signal and analysis of PCG signal have been shown using different digital signal processing techniques like Decimation, Interpolation, Windowing, Discrete Fourier Transform, Fast Fourier Transforms etc. In this work we have applied the Multi-rate Signal Processing Technique to reduce the size of the heart sound signal. The MSPT deals with different sampling frequencies. The challenging part of this work is to apply the DSP KIT to achieve the heart sound data and to apply the different signal processing techniques to reach the goal. An electronic circuit is designed and implemented along with a stethoscope to receive the heart sound signal (PCG signal) from the human heart. The DSP KIT is incorporated to proceed to the main work. The task has been completed through taking near about 20 person's real time heart sound samples (PCG samples).","PeriodicalId":289216,"journal":{"name":"2016 3rd International Conference on Recent Advances in Information Technology (RAIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123005936","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

Computer aided automatic water pipe network analysis by simultaneous loop equation solution method 同时循环方程解法的计算机辅助自动管网分析

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507984

Kailash Jha, M. Mishra

引用次数: 1

Algorithm of 2D-Voronoi diagram construction with rectangular block 矩形块2D-Voronoi图构建算法

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507963

A. Sinharay, Tanusree Roy, Antara Saha, Vaswar Pande

引用次数: 0

Analysis and QoS measurement for voluminous data flow in Wireless Sensor Network 无线传感器网络中海量数据流的分析与QoS测量

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507876

P. K. Swain, M. Rath

引用次数: 4

A survey on Wireless Sensor Network for Community Intrusion Detection Systems 面向社区入侵检测系统的无线传感器网络研究

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507884

N. K. Mittal

引用次数: 9

Voice and touch control home automation 语音和触摸控制家庭自动化

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507951

Sushant Kumar, S. Solanki

引用次数: 12

Active power factor correction for rectifier using micro-controller 用单片机对整流器进行有源功率因数校正

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507926

Kartikesh Kumar Jha, B. Mahato, Prem Prakash

引用次数: 6

A modified NCO based LSPF-DPLL Phase Resolver for wireless communication 一种改进的基于NCO的无线通信LSPF-DPLL相位解析器

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507877

Sabyasachi Bhattacharyya, Aradhana Misra, K. K. Sarma

引用次数: 4

A smart phone based app for automated segmentation and counting of platelets 一个基于智能手机的应用程序，用于自动分割和计数血小板

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507941

K. Roy, Ratnadeep Dey, D. Bhattacharjee, M. Nasipuri, Pramit Ghosh

引用次数: 3

Offline signature verification based on geometric feature extraction using artificial neural network 基于几何特征提取的人工神经网络离线签名验证

2016 3rd International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2016-03-03 DOI: 10.1109/RAIT.2016.7507937

Subhash Chandra, Sushila Maheskar

{"title":"Offline signature verification based on geometric feature extraction using artificial neural network","authors":"Subhash Chandra, Sushila Maheskar","doi":"10.1109/RAIT.2016.7507937","DOIUrl":"https://doi.org/10.1109/RAIT.2016.7507937","url":null,"abstract":"Signature verification is widely used for personal verification. But, it has an inevitable problem of getting exploited for forgery therefore an automatic signature recognition and verification system is required. Verification can be accomplished either Online or Offline based application. Offline systems work on the scanned image of a signature. Online systems use dynamic information like pressure, speed etc. of a signature during the time when the signature is made. In this paper, we present an offline signature verification technique based on geometric features. We have used six geometric features namely Area, Centroid, Standard deviation, Even pixels, Kurtosis and Skewness. In our technique first the preprocessing of a scanned signature image is done to isolate the signature and to remove noise. The system is trained using a database of signatures obtained from authenticated users. Then artificial neural network (ANN) is used in recognition and verification of signatures: genuine or forged, and efficiency is about 89.24% having threshold of 80%. Simulation results shows that the technique is robust and clearly differentiates between genuine and forgery signatures.","PeriodicalId":289216,"journal":{"name":"2016 3rd International Conference on Recent Advances in Information Technology (RAIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117008408","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: 29