International Conference & Workshop on Emerging Trends in Technology最新文献_第7页

Soft output Viterbi decoder using hybrid register exchange 采用混合寄存器交换的软输出维特比解码器

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980223

D. M. Khatri, S. Haridas

{"title":"Soft output Viterbi decoder using hybrid register exchange","authors":"D. M. Khatri, S. Haridas","doi":"10.1145/1980022.1980223","DOIUrl":"https://doi.org/10.1145/1980022.1980223","url":null,"abstract":"There are two types of decoding techniques, trace back method (TBM) and register exchange method (REM). In trace back method, memory requirement is high. TBM is the preferred method used in Viterbi decoders (VD) having large constraint length and high performance. However, the TBM has drawbacks, which requires last-in-first-out (LIFO) buffer and has to use multiple read operations for high speed operation. This multiple operation results in complex control logic In register exchange method, the no. of switching activities is high. So total power dissipation is high. Using Hybrid register exchange, no. of switching activities is reduced and so the total power dissipation is reduced. So we concentrate more on REM. There are two types of decision outputs, hard output and soft output. In hard decision output, error correcting capability is less as compared to soft output decision. So to increase the accuracy as compared with hard decision outputs, soft output viterbi algorithm is used.. In this paper, we propose a new approache hybrid register exchange method (HREM). The simulation model is ready. The main advantage of the proposed method is that it reduces the number of decoding operation and memory read operations, less switching activity and fewer requirements on additional control logic.","PeriodicalId":197580,"journal":{"name":"International Conference & Workshop on Emerging Trends in Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123483751","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

Design and implementation of a universal DMA controller 通用DMA控制器的设计与实现

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980281

A. Wadekar, S. Swapnil, R. Lohani

引用次数: 1

Neural network based sensor drift compensation of induction motor 基于神经网络的感应电机传感器漂移补偿

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980148

R. Uthra, N. Kalaiarasi, A. Rathinam

引用次数: 1

e-business adoption and implications in the banking industry in India 电子商务在印度银行业的应用及其影响

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980414

P. Mudholkar, M. Shanker, S. Maitra

引用次数: 1

Resistive analysis of mixed carbon nanotube bundle interconnect and its comparison with copper interconnect 混合碳纳米管束互连的电阻分析及其与铜互连的比较

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980271

T. Alam, R. Dhiman, R. Chandel

引用次数: 0

Motion sensing in mobile phone 移动电话的运动感应

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980366

S. U. Pacharaney, K. Reddy

引用次数: 0

Escaping difference image histogram steganalysis 转义差分图像直方图隐写分析

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980121

H. B. Kekre, A. Athawale, S. Maheshwari

引用次数: 0

Overview of visual analytics 视觉分析概述

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980329

A. Chopra, R. Samant

{"title":"Overview of visual analytics","authors":"A. Chopra, R. Samant","doi":"10.1145/1980022.1980329","DOIUrl":"https://doi.org/10.1145/1980022.1980329","url":null,"abstract":"Visual analytics is \"the science of analytical reasoning facilitated by visual interactive interfaces.\" It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Visual Analytics is a relatively new multidisciplinary field that combines various research areas including knowledge discovery, data analysis, visualization, human-computer interaction, data management, geo-spatial and temporal data processing and statistics. Visual Analytics has seen unprecedented growth in the past five years and is expected to grow ten folds in the coming future seeing the amount of massive, dynamic and heterogeneous data which concludes nothing unless put to use using the visual analytical techniques. The basic goal of visual analytics includes deriving an insight from the large amount of dynamic data which is from different sources and to analyze them to discover the unexpected and provide timely, defensible, and understandable assessments which can be then communicated effectively for action. An integration of the increasing processing power of computers with the efficient pattern recognition abilities and domain knowledge of human analysts is a challenging and promising road in dealing with large amounts of complex data. It will be also a major driving force for solutions for information overload in many research and commercial areas.","PeriodicalId":197580,"journal":{"name":"International Conference & Workshop on Emerging Trends in Technology","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123752174","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

Obtaining patterns for identification of power quality disturbances using continuous wavelet transform 利用连续小波变换获取电能质量扰动识别模式

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980106

R. A. Gupta, R. Kumar, Manoj Gupta

引用次数: 2

Outlier detection and visualization of large datasets 大型数据集的异常值检测和可视化

International Conference & Workshop on Emerging Trends in Technology Pub Date : 2011-02-25 DOI: 10.1145/1980022.1980134

L. Gunisetti

{"title":"Outlier detection and visualization of large datasets","authors":"L. Gunisetti","doi":"10.1145/1980022.1980134","DOIUrl":"https://doi.org/10.1145/1980022.1980134","url":null,"abstract":"Outliers are special observations or extraordinary cases in the available data which deviate so much from other observations so as to arouse suspicions that they were generated by a different mechanism. Outliers detected can be used to identify special or extraordinary or fraudulent cases in day to day transactions. Outlier detection can be used to identify the noise in the data and these detected outliers have to be removed to improve data quality. Outlier Detection can be used for Traffic Analysis, Credit Card Fraud Detection. We applied Outlier Detection to Traffic data set for identifying the outlier stations on the highway. Detected outlier stations represent abnormalities in the traffic sensors data. This information is used by us to identify the faulty traffic sensors located at the highway stations. We have provided two dimensional visualization of the outliers which can be used for analyzing the data in an efficient manner. Traffic Management becomes easier when the abnormal traffic sensors identified at the corresponding outlier stations are identified. The method used here is a Statistic Approach. This technique compares every location to its neighbors using the Statistic. The Statistic is calculated to identify whether the data generated at a highway traffic station sensor is abnormal or not. This technique can be used efficiently to identify the outliers. This method can be easily applied to very large datasets as compared to existing conventional approaches.","PeriodicalId":197580,"journal":{"name":"International Conference & Workshop on Emerging Trends in Technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505641","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