2019 2nd International Conference on Signal Processing and Communication (ICSPC)最新文献

Analysis on Fingerprint Extraction Using Edge detection and Minutiae Extraction 基于边缘检测和细节提取的指纹提取方法分析

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976803

P. Mohan, S. Anand, Rohan Benjamin Varghese, P. Aravinth, D. J. Dolly

{"title":"Analysis on Fingerprint Extraction Using Edge detection and Minutiae Extraction","authors":"P. Mohan, S. Anand, Rohan Benjamin Varghese, P. Aravinth, D. J. Dolly","doi":"10.1109/ICSPC46172.2019.8976803","DOIUrl":"https://doi.org/10.1109/ICSPC46172.2019.8976803","url":null,"abstract":"Biometrics authentication is performed as a method of unique identification and selective restrictions to access. Fingerprints are unique and permanent. Hence amidst the various authentication methods, fingerprints are considered to be the utmost level of reliability and are extensively used by forensic experts. The minutiae present in the fingerprints of a human provide adequate information. These practical details can be used as a mark of identification to verify the fingerprints. Minutiae matching is the widely used method for fingerprint detection and verification. This paper analyses the various advantages of the widely used minutiae-based matching in fingerprint recognition systems. Earlier methods focus only on regional detection but this process involves edge detection and minutiae extraction. Firstly, the fingerprint image is taken as the input and is converted to binary image for further processing. Further, the image is thinned and minutiae details are extracted. Finally, the matching score is obtained. This process helps to identify the correspondence between input minutiae and the custom minutiae without vigorous investigation.","PeriodicalId":321652,"journal":{"name":"2019 2nd International Conference on Signal Processing and Communication (ICSPC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123316879","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

A Survey on IntelligentTraffic Control System Using Image Processing 基于图像处理的智能交通控制系统研究

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976520

Dakshayani Ijeri, Amar Gulaganji, Abhishek A. Mandewali, Pavitra C. Bannad, Jyoti A. Aiholli

{"title":"A Survey on IntelligentTraffic Control System Using Image Processing","authors":"Dakshayani Ijeri, Amar Gulaganji, Abhishek A. Mandewali, Pavitra C. Bannad, Jyoti A. Aiholli","doi":"10.1109/ICSPC46172.2019.8976520","DOIUrl":"https://doi.org/10.1109/ICSPC46172.2019.8976520","url":null,"abstract":"The present traffic control systems depends upon the manual operation which leads to unnecessary wastage of fuel and time, which again leads to increase in the traffic congestion. More time is wasted at the signals even if there are no vehicles on the lane because the signals have fixed timing for all roads which is not dependent of traffic queue. Some nonuniform illuminations that come from artificial light sources affect the night-time surveillance. Artificial light sources result in glow that the objects or the vehicles present near the light sources will not be visible. Web camera is fixed and the images of all the four lanes will be captured. The method used in this paper removes the effect of glow through image gradient decomposition. The enhanced image is obtained through Poisson solver. The real time vehicle density is measured to control the traffic system using canny edge detection algorithm and the vehicle count is calculated for each path or road and comparison is made with the other roads. The green signal and red signal are received by the roads with the maximum vehicles and minimum vehicles respectively.","PeriodicalId":321652,"journal":{"name":"2019 2nd International Conference on Signal Processing and Communication (ICSPC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124587898","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

Novel Optimized Pyramidal Horn Antenna for UWB Applications 用于超宽带应用的新型优化锥形喇叭天线

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976646

Harish Ravichandran, V. Jayakrishnan, Sethuraman N. Rao

{"title":"Novel Optimized Pyramidal Horn Antenna for UWB Applications","authors":"Harish Ravichandran, V. Jayakrishnan, Sethuraman N. Rao","doi":"10.1109/ICSPC46172.2019.8976646","DOIUrl":"https://doi.org/10.1109/ICSPC46172.2019.8976646","url":null,"abstract":"In this paper, a novel Pyramidal Horn Antenna for Ultra Wideband(UWB) applications is proposed. The designed horn antenna is fed with a co-axial cable and is backed by the back cavity containing absorber. The proposed structure consists of three main parts: waveguide section, pyramidal flared section and the feeding section. In this, the flaring section is exponentially tapered for a particular rate parameter of 0.050. This type of UWB horn antenna with curved flaring gives a proper and smooth guidance to the incoming travelling waves. This indeed helps in better impedance matching when the wave wholly leaves the antenna. This paper also aims to reduce the spurious back reflections due to the sharp corners of the horn's aperture segment. As a follow up, the sharp corners are replaced by curvy profiles that minimizes the point edge diffraction problem. The effect of having a curvy profile at the end corners of the horn's aperture on the reflection coefficient, gain, directivity, voltage standing wave ratio, etc. is investigated and studied. The designed antenna operates in the allocated ultrawideband spectrum of 3.1 GHz to 10.6 GHz. The design is carried out using CST Microwave Studio Suite 17.0 software which follows FDTD method for computation. The simulated results show the gain, VSWR (less than 2) performances maintained without much deviation over the entire spectrum and makes the antenna suitable for UWB applications.","PeriodicalId":321652,"journal":{"name":"2019 2nd International Conference on Signal Processing and Communication (ICSPC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124005525","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

Design of All-Digital Phase Locked Loop for Improved Frequency Lock Range 提高锁频范围的全数字锁相环设计

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976793

Anjeleena Erm, Nidhi Toppo, D. Sugumar, P. Vanathi

引用次数: 1

A Four Grade Brain Tumor Classification System Using Deep Neural Network 基于深度神经网络的脑肿瘤四级分类系统

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976495

Nimmy George, Manju Manuel

引用次数: 2

A New Methodology for Vehicle Collision Avoidance using FMCW Radar and Critical Distance Estimations using K-Means Clustering Algorithm 基于FMCW雷达和基于K-Means聚类算法的临界距离估计的车辆避碰新方法

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976775

A. Joshi, Christopher Ebenezer, S. Raj

{"title":"A New Methodology for Vehicle Collision Avoidance using FMCW Radar and Critical Distance Estimations using K-Means Clustering Algorithm","authors":"A. Joshi, Christopher Ebenezer, S. Raj","doi":"10.1109/ICSPC46172.2019.8976775","DOIUrl":"https://doi.org/10.1109/ICSPC46172.2019.8976775","url":null,"abstract":"In this paper we propose novel techniques to avoid vehicle collisions using a collision avoidance system in highway scenarios. A personalized time delay close to 2 seconds is maintained between the host and target vehicle. Compared to the conventional laser and radar system we use FMCW radar to track the speed parameters and position, with which a virtual boundary is created for two purposes. To maintain headway distance and provide braking when the target vehicle comes very close to the host vehicle. The system calculates the reaction time of the driver and applies K-means clustering algorithm to obtain a specific reaction time for different ranges of velocity, personalized for an individual driver. Unlike certain collision avoidance systems which take relative velocity as a major factor in determining the braking distance, we take into account of host vehicle velocity as a major parameter. This will provide a more comfortable distance between the host vehicle and the target vehicle. A graduated light display indicates the proximity of the target vehicle from the host vehicle enabling the driver to maintain an apt and comfortable distance.","PeriodicalId":321652,"journal":{"name":"2019 2nd International Conference on Signal Processing and Communication (ICSPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129372595","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

State of art of Network on Chip 片上网络技术现状

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976501

T. P. E. Fizardo, Royston Zico Dias

引用次数: 1

Classification of Lung Images Using Deep Convolutional Neural Network 基于深度卷积神经网络的肺部图像分类

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976494

Jessica Sharon Christopher, P. Bruntha, S. Suresh, Sakhina Crosslin, Ansia Liji

{"title":"Classification of Lung Images Using Deep Convolutional Neural Network","authors":"Jessica Sharon Christopher, P. Bruntha, S. Suresh, Sakhina Crosslin, Ansia Liji","doi":"10.1109/ICSPC46172.2019.8976494","DOIUrl":"https://doi.org/10.1109/ICSPC46172.2019.8976494","url":null,"abstract":"The history of medical imaging clearly portrays numerous computer aided diagnosis system (CAD) which was successfully used and implemented to assist radiologists about their patients. Medical image analysis had taken great hike over two decades using Artificial Neural Network for its task but since recent past it is being taken over by the Convolutional Neural Network and has also gained high popularity in medical imaging. CNN has mainly been developed as medical images possess high semantic features. In this paper, the tasks proposed ideology is on novel deep convolution neural network (DCNN) based method for lung normality classification. The extracted deep features from computer tomography (CT) images of the lungs are widely further used to classify the lungs abnormality i.e either as malignant or benign. Suitable modifications are performed to produce an acceptably high accuracy rate, thereby reducing the computational complexity rate. The proposed methodology involves the role of fully connected layer. While nearing to the outcome before which this layer plays a vital role in acquiring the desired classified images as per the requirement once the convolution process is finished. Therefore, this methodology is likely to be found only supportive to the system formed and thus improvising the accuracy level.","PeriodicalId":321652,"journal":{"name":"2019 2nd International Conference on Signal Processing and Communication (ICSPC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126140511","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

Automated detection of epileptic seizures using DWT based features and SVM classifier 基于DWT特征和SVM分类器的癫痫发作自动检测

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976611

N. Sriraam, S. Raghu, Y. Temel, Shyam Vasudevarao, A. S. Hedge, JV Mahendra, P. Kubben

{"title":"Automated detection of epileptic seizures using DWT based features and SVM classifier","authors":"N. Sriraam, S. Raghu, Y. Temel, Shyam Vasudevarao, A. S. Hedge, JV Mahendra, P. Kubben","doi":"10.1109/ICSPC46172.2019.8976611","DOIUrl":"https://doi.org/10.1109/ICSPC46172.2019.8976611","url":null,"abstract":"Automated detection of epileptic seizures has gained significant attention in the recent decades. This is due to the fact that it helps neurologist to take timely decision and reduces the manual intervention of assessing electroencephalogram (EEG) recordings. Therefore, in this study, the discrete wavelet transform (DWT) features based automated detection of epileptic seizures has been proposed. EEG signal was decomposed using DWT with Haar wavelet and eleven feature were extracted from each sub-band. The extracted features in each sub-band were classified using support vector machine classifier with 10-fold cross-validation. Classification results showed the highest sensitivity, specificity, accuracy and F measure of 97.37%, 98.88%, 98.06%, and 97.84 % respectively using the Ramaiah Memorial College and Hospitals database. Similarly, the highest sensitivity, specificity, accuracy and F measure of 98.90%, 99.62%, 99.18%, 99.17% were achieved respectively using University of Bonn database. The experimental results show that the proposed algorithm is well suited for real-time detection of epileptic seizures.","PeriodicalId":321652,"journal":{"name":"2019 2nd International Conference on Signal Processing and Communication (ICSPC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128606003","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

Parkinson's Disease Detection using Gray Level Spatial Dependance Matrix (GLSDM) 基于灰度空间依赖矩阵(GLSDM)的帕金森病检测

2019 2nd International Conference on Signal Processing and Communication (ICSPC) Pub Date : 2019-03-01 DOI: 10.1109/ICSPC46172.2019.8976693

M. S. Blessyee, A. Sumitha, R. Nivethaa, R.S. Vincy Ananthi, Raveena Judie Dolly

引用次数: 0