2011 Third International Conference on Advanced Computing最新文献_第2页

Comparative analysis of contrast enhancement techniques between histogram equalization and CNN 直方图均衡化与CNN对比度增强技术的对比分析

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165157

R. Vaddi, L. Boggavarapu, H. D. Vankayalapati, K. R. Anne

引用次数: 4

Improving energy efficiency using request tracing as a tool in a multi-tier virtualized environment 在多层虚拟化环境中使用请求跟踪作为工具来提高能源效率

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165179

V. M. Raj, R. Shriram

{"title":"Improving energy efficiency using request tracing as a tool in a multi-tier virtualized environment","authors":"V. M. Raj, R. Shriram","doi":"10.1109/ICOAC.2011.6165179","DOIUrl":"https://doi.org/10.1109/ICOAC.2011.6165179","url":null,"abstract":"Cloud computing and Virtualization has become the focus of tremendous amount of research in recent years. The advances in network bandwidth, the need for services that can be accessed anytime from anywhere and the change in ownership models are some of the factors responsible. As cloud services are intended to be ‘always on’, the energy costs to provision services are already significant with increase in both cloud services user-base and rise in global energy prices. Increase in energy consumption has a direct impact on provider's total cost of ownership and inflates the subscriber's price point. Identifying areas within datacenter hosting cloud infrastructure and services for energy inefficiencies or bottlenecks is one approach to save energy. In this paper, we propose a simulation approach to track service/application request level flow path in a virtualized multi-tier environment from initiation to completion; analyze the energy consumption of the request at every tier, identify bottlenecks that would impact the QOS criteria; and take necessary action to recover. Our simulation results show a 16% improvement in energy consumption using proposed approach when compared with unconstraint/default approach.","PeriodicalId":369712,"journal":{"name":"2011 Third International Conference on Advanced Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122629001","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 combined hierarchical model for automatic image annotation and retrieval 一种用于图像自动标注和检索的组合层次模型

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165162

T. Sumathi, M. Hemalatha

{"title":"A combined hierarchical model for automatic image annotation and retrieval","authors":"T. Sumathi, M. Hemalatha","doi":"10.1109/ICOAC.2011.6165162","DOIUrl":"https://doi.org/10.1109/ICOAC.2011.6165162","url":null,"abstract":"Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision technique is used in image retrieval system to organize and locate images of interest from a database. Many techniques have been proposed for image annotation in the last decade that gives reasonable performance on standard datasets. In this work, we introduce an innovative hybrid model for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low level image features and a simple combination of basic distances using JEC to find the nearest neighbors of a given image; the keywords are then assigned using SVM approach which aims to explore the combination of three different methods. First, the initial annotation of the data using two known methods, and that takes the hierarchy into consideration by classifying consecutively its instances; finally, we make use of pair wise majority voting between methods by simply summing strings in order to produce a final annotation. The proposed technique results show that this method outperforms the current state of art methods on the standard datasets.","PeriodicalId":369712,"journal":{"name":"2011 Third International Conference on Advanced Computing","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133640289","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: 6

Spectrum opportunity in UHF — ISM band of 902–928 MHz for cognitive radio 902 - 928mhz的UHF - ISM频段中用于认知无线电的频谱机会

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165189

Dhananjay Kumar, G. Kalaichelvi, D. Saravanan, T. K. Loheswari

引用次数: 3

An efficient method for color image segmentation using adaptive mean shift and normalized cuts 一种利用自适应均值移位和归一化分割的彩色图像分割方法

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165194

V. Shibu, Philomina Simon

引用次数: 3

Detection of small moving objects based on motion vector processing using BDD method 基于BDD方法的运动矢量处理的小运动目标检测

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165180

V. Raj, M. Srinivasan, B. Sathiya

引用次数: 0

Genetically optimized ANFIS based Intelligent Navigation System 基于遗传优化ANFIS的智能导航系统

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165207

M. Malleswaran, V. Vaidehi, R. A. Joseph

引用次数: 2

Randomized multipath routing protocol for secure data transmission in wireless IP-over-WDM networks 无线IP-over-WDM网络中安全数据传输的随机多径路由协议

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165176

R. Mariappan, K. Shriram

引用次数: 0

Unsupervised feature selection based on the measures of degree of dependency using rough set theory in digital mammogram image classification 基于粗糙集理论的依赖度度量的无监督特征选择在数字乳房x光图像分类中的应用

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165167

C. Velayutham, K. Thangavel

{"title":"Unsupervised feature selection based on the measures of degree of dependency using rough set theory in digital mammogram image classification","authors":"C. Velayutham, K. Thangavel","doi":"10.1109/ICOAC.2011.6165167","DOIUrl":"https://doi.org/10.1109/ICOAC.2011.6165167","url":null,"abstract":"Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised feature selection in mammogram image, using rough set based measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, preprocessing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with existing rough set based supervised feature selection methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.","PeriodicalId":369712,"journal":{"name":"2011 Third International Conference on Advanced Computing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124075995","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: 9

A hybrid approach to content based image retrieval using visual features and textual queries 使用视觉特征和文本查询的基于内容的图像检索的混合方法

2011 Third International Conference on Advanced Computing Pub Date : 2011-12-01 DOI: 10.1109/ICOAC.2011.6165182

R. Sudhakar, K. Krishnan, S. Muthukrishnan

引用次数: 5