INTERACT-2010最新文献_第8页

Navigation aid for main battle tank (MBT) training simulators 主战坦克(MBT)训练模拟器导航辅助设备

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706167

B. Jayaprakash, A. Shenbagamoorthy, V. N. Swamiraj

引用次数: 0

Stock index prediction using regression and neural network models under non normal conditions 非正常条件下回归与神经网络模型的股票指数预测

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706195

K. Sujatha, S. Sundaram

引用次数: 12

Enhancing the pervasive trust management model in MANETs by analyzing the factors affecting performance of various attacks 通过分析影响各种攻击性能的因素，增强了manet中的普遍信任管理模型

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706198

Pandiyan Durairajan, T. Sasikala

{"title":"Enhancing the pervasive trust management model in MANETs by analyzing the factors affecting performance of various attacks","authors":"Pandiyan Durairajan, T. Sasikala","doi":"10.1109/INTERACT.2010.5706198","DOIUrl":"https://doi.org/10.1109/INTERACT.2010.5706198","url":null,"abstract":"Mobile Ad Hoc Network (MANET) consists of a collection of wireless mobile hosts without the required intervention of any existing infrastructure or centralized access point such as base station. The dynamic topology of MANET allows nodes to join and leave the network at any point of time. Wireless MANET is particularly vulnerable due to its fundamental characteristics such as open medium, dynamic topology, distributed cooperation and constrained capability. In Mobile Ad Hoc Network (MANET), network traffics are redirected to a specific node or from the malicious node causing serious damage. And also it causes false identification of a good node as a bad one. This increases false alarms and as a result any further communication with that node or via that node is halted. It occurs due to lack of authentication and it grants provision for any node to corrupt other node's legitimate information. In this paper we compare and analysis the performance of various attacks with proposed attacks on different routing protocol in mobile ad hoc networks. Various types of scenarios are generated and each of the attacks is simulated on each of these, then their parameters like throughput, packet delivery ratio and delay will be compared The performance differentials are analyzed using varying pause time, constant nodes and dynamic topology. Based on the observations, we make valuable conclusions about which attacks induce more false alarm and impact on Mobile Ad Hoc Network.","PeriodicalId":201931,"journal":{"name":"INTERACT-2010","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114954426","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

Evaluation of surface roughness using machine vision 用机器视觉评估表面粗糙度

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706143

G. Babu, K. Babu, B. Gowd

引用次数: 10

Co-operative mobile and static agents for distributed communication systems for data mining applications 用于数据挖掘应用的分布式通信系统的协作移动和静态代理

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706211

P. Kavitha, T. Sasipraba

引用次数: 1

Self-healing robots 自我修复的机器人

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706161

G. Arunalatha, C. Jhoncy

引用次数: 3

Robotic arm showing writing skills by speech recognition 机器人手臂通过语音识别显示写作技能

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706158

M. Balaganesh, C. S. Aadhitya, E. Logashanmugam, R. Manikandan

引用次数: 13

3D object retrieval using silhouette feature vector on shady images 基于阴影图像轮廓特征向量的三维目标检索

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706203

Raju Barskar, Gulfishan Firdose Ahmed

引用次数: 0

A novel energy efficient chain based hierarchical routing protocol for wireless sensor networks 一种新的基于节能链的无线传感器网络分层路由协议

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706176

K. Majumder, Sudhabindu Ray, S. Sarkar

{"title":"A novel energy efficient chain based hierarchical routing protocol for wireless sensor networks","authors":"K. Majumder, Sudhabindu Ray, S. Sarkar","doi":"10.1109/INTERACT.2010.5706176","DOIUrl":"https://doi.org/10.1109/INTERACT.2010.5706176","url":null,"abstract":"The nodes in a sensor network are severely constrained by energy. Reducing the energy consumption of the nodes to prolong the network lifetime is considered a critical challenge while designing a new routing protocol. In this paper we propose a new power-aware, adaptive, hierarchical and chain based protocol — CCPAR (Clustered Chain based Power Aware Routing) that utilizes the periodic assignments of the cluster head role to different nodes based on the highest residual battery capacity for ensuring the even dissipation of power by all the nodes. Transmission from a single cluster head to the base station in each round and the distribution of the data aggregation workload among all the nodes, save the cluster heads from early exhaustion. The use of data aggregation also reduces the amount of information to be transmitted to the base station. By chaining the nodes in each cluster and using a separate chain for the cluster heads, CCPAR offers the advantage of small transmit distances for most of the nodes and thus helps them to be operational for a longer period of time by conserving their limited energy. The simultaneous construction of multiple chains in different clusters reduces the time for chain construction as well as the length of each of the chains. These shorter length chains solve the problem of excessive delay in transmission for the distant nodes. Use of a fresh set of parameter values in each round provides the users the flexibility to change these values in a way to control the power consumption. The introduction of MAX threshold enables CCPAR to be quickly responsive and thus highly suitable for time critical applications. From the performance evaluation we observe that CCPAR outperforms other protocols in terms of energy saving and longevity of the network.","PeriodicalId":201931,"journal":{"name":"INTERACT-2010","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115365541","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: 16

A hybrid approach for acoustic signal segmentation by computing similarity matrix, novelty score and peak detection for vehicular classification in wireless sensor networks 基于相似性矩阵、新颖性评分和峰值检测的混合声信号分割方法在无线传感器网络中用于车辆分类

INTERACT-2010 Pub Date : 2010-12-01 DOI: 10.1109/INTERACT.2010.5706175

G. Padmavathi, D. Shanmugapriya, M. Kalaivani

{"title":"A hybrid approach for acoustic signal segmentation by computing similarity matrix, novelty score and peak detection for vehicular classification in wireless sensor networks","authors":"G. Padmavathi, D. Shanmugapriya, M. Kalaivani","doi":"10.1109/INTERACT.2010.5706175","DOIUrl":"https://doi.org/10.1109/INTERACT.2010.5706175","url":null,"abstract":"Vehicle acoustic signals have long been considered as significant source in sensor networks for classification. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify the type. Vehicle acoustic signal segmentation is important for continuous signal recognition because it reduces the search space effectively in vehicle's signal recognition. However, for Vehicle classification, it is difficult to segment the signal input reliably into useful sub-units because i) vehicle sound units can often be located roughly via intensity changes ii) energy changes in signal spectrum or amplitude help to estimate unit boundaries, but these cues are often unreliable. In this paper the series of steps proposed are signal segmentation is presented. includes decomposition of signals into successive frames of 50 ms without overlap. The computations of the spectrum representation (FFT) of the frames are carried out. The similarity matrix that shows the similarity between the spectrums of different frames is computed. Estimation of the novelty score related to the similarity matrix is done. The detection of the peaks in the novelty score is made and finally segmenting the vehicle acoustic signals using the peaks as position is done. These segmented signals are further used for feature extraction and classification.","PeriodicalId":201931,"journal":{"name":"INTERACT-2010","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116160542","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