2010 Sixth International Conference on Intelligent Environments最新文献_第7页

Deployment of Adaptive Workflows in Intelligent Environments 智能环境中自适应工作流的部署

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.43

C. Goumopoulos, Ioannis Calemis, A. Kameas

引用次数: 2

Decloaking Big Brother: Demonstrating Intelligent Environments 遮蔽老大哥:展示智能环境

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.66

James Dooley, Marc Davies, Matthew Ball, V. Callaghan, H. Hagras, M. Colley, M. Gardner

引用次数: 6

A Data Mining Framework for Activity Recognition in Smart Environments 智能环境中活动识别的数据挖掘框架

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.22

Chao Chen, Barnan Das, D. Cook

引用次数: 67

Inter-labeler Agreement for Anger Detection in Interactive Voice Response Systems 交互式语音应答系统中愤怒检测的标签间协议

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.28

Alexander Schmitt, Ulrich Tschaffon, W. Minker

引用次数: 4

Occupancy Pattern Extraction and Prediction in an Inhabited Intelligent Environment Using NARX Networks 基于NARX网络的智能居住环境占用模式提取与预测

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.18

Sawsan M. Mahmoud, Ahmad Lotfi, C. Langensiepen

{"title":"Occupancy Pattern Extraction and Prediction in an Inhabited Intelligent Environment Using NARX Networks","authors":"Sawsan M. Mahmoud, Ahmad Lotfi, C. Langensiepen","doi":"10.1109/IE.2010.18","DOIUrl":"https://doi.org/10.1109/IE.2010.18","url":null,"abstract":"In this paper, occupancy pattern extraction and prediction in an intelligent inhabited environment is addressed. The results of this research will help elderly people to live independently in their own home longer and help them in case of an emergency. Using a wireless sensor network system, daily behavioral patterns of the occupant are extracted. This information is then used to build a behavioral model of the occupant which ultimately is used to predict the future values representing the expected occupancy and other activities. The occupancy signal is represented by a long sequence of binary series indicating presence or absence of the occupant in a specific area. It is essential to convert this series of binary data into a more flexible and efficient format before it is applied for any further analysis and prediction. After converting the occupancy binary signals, the prediction model is built through a recurrent dynamic network, with feedback connections enclosing several layers of a Nonlinear Autoregressive netwoRk with eXogenous inputs (NARX) network. The results reported here shows that NARX provide better prediction results than conventional recurrent neural networks such as Elman networks. The case study reported here is based on a one bedroom flat with a single occupant.","PeriodicalId":180375,"journal":{"name":"2010 Sixth International Conference on Intelligent Environments","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124246097","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: 7

PerCraft: Towards Live Deployment of Pervasive Applications PerCraft:面向普适应用程序的实时部署

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.42

G. Vanderhulst, K. Luyten, K. Coninx

引用次数: 2

Viewpoint Invariant Reconstruction of 3D Human Skeleton from Monocular Images 基于单目图像的三维人体骨骼视点不变性重建

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.75

Z. Htike, S. Egerton, Y. Kuang

引用次数: 0

Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification 使用智能手机的加速度计进行分层分类的活动监测

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.36

Shumei Zhang, P. Mccullagh, C. Nugent, Huiru Zheng

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引用次数: 107

Sensor-Aware Adaptive Push-Pull Query Processing in Wireless Sensor Networks 无线传感器网络中感知传感器的自适应推拉查询处理

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.51

R. Bose, A. Helal

{"title":"Sensor-Aware Adaptive Push-Pull Query Processing in Wireless Sensor Networks","authors":"R. Bose, A. Helal","doi":"10.1109/IE.2010.51","DOIUrl":"https://doi.org/10.1109/IE.2010.51","url":null,"abstract":"Till date, sensor network research has assumed that the cost of transmitting a sensor reading over the network is much higher than the cost of sampling a sensor. However, this assumption is no longer always valid, due to availability of new generation sensor platform hardware, which utilizes industry standard mesh-networking protocols such as ZigBee on top of relatively high-speed, yet low-power wireless radios. In fact, we have experimentally verified that the energy consumed for acquiring a sample from a sensor can be significantly higher than the energy consumed for transmitting its reading over the network. Hence, new querying strategies need to be formulated, which optimize the order of sampling sensors across the network in such a manner that sensors with expensive acquisition costs are not sampled unless absolutely required. We propose distributed pull-push querying mechanisms, which optimize the query plan by adapting to variable costs of acquiring readings from different sensors across the network. The goal of these mechanisms is to minimize the energy consumption of nodes executing a query while ensuring that the latency of query response does not exceed user-specified bounds. To validate our approach, we also describe experimental results, which analyze the performance of various plan options in terms of energy consumption and latency under the effect of various parameters such as selectivity of data and number of sensors participating in the query.","PeriodicalId":180375,"journal":{"name":"2010 Sixth International Conference on Intelligent Environments","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127743228","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

The Intelligent Campus (iCampus): End-to-End Learning Lifecycle of a Knowledge Ecosystem 智慧校园:知识生态系统的端到端学习生命周期

2010 Sixth International Conference on Intelligent Environments Pub Date : 2010-07-18 DOI: 10.1109/IE.2010.68

Jason W. P. Ng, N. Azarmi, M. Leida, F. Saffre, Ali Afzal, Paul Yoo

引用次数: 51