2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第5页

Derivative-free distributed filtering for integrity monitoring of AGV navigation sensors AGV导航传感器完整性监测的无导数分布式滤波

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343072

G. Rigatos

引用次数: 0

Multiple active speaker localization based on audio-visual fusion in two stages 基于视听融合的多主动式说话人定位分两个阶段

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343015

Z. Li, T. Herfet, Martin P. Grochulla, Thorsten Thormählen

引用次数: 9

Robust source localization using decision-directed algorithm and confidence weights in Unattended Ground Sensors system 基于决策导向算法和置信度加权的无人值守地面传感器系统鲁棒源定位

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343077

Uri Levy, Evyatar Hemo

{"title":"Robust source localization using decision-directed algorithm and confidence weights in Unattended Ground Sensors system","authors":"Uri Levy, Evyatar Hemo","doi":"10.1109/MFI.2012.6343077","DOIUrl":"https://doi.org/10.1109/MFI.2012.6343077","url":null,"abstract":"Seismic Unattended Ground Sensors (UGS) systems have a major role in the developing area of seismic signal processing, with applications mainly in security and surveillance systems. Identifying and localizing a potential threat is a preliminary requirement in such systems. Array processing based on measured time of arrivals or gain-ratio values is widely used for solving the localization problem. However, for real world seismic data, estimating time differences and gain-ratios of arrival is a difficult task, due to both the nature of sensors networks and of seismic signals. Sensors synchronization is a common difficulty in networks and the demand for low power consumption and transmission rates prevents solving it by cross-correlating the signals. High variations in sound velocity and background noise among different types of ground, which characterize the underground environment, are additional factors for these difficulties. Hence, applying direct localization algorithms on seismic data often proves ineffective. In this paper, a novel approach toward seismic source localization using UGS system is presented. Given an event of recurring nature, the proposed algorithm is based on two principles which increase its robustness. First, it utilizes both time differences and gain-ratios measurements in a decision directed process. In addition, confidence weights are assigned for each recurrence of the event thus further performance improvement is achieved. Results for applying the proposed algorithm on real-world seismic data are presented and the advantages of the proposed algorithm are demonstrated.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116556330","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

Robust multi-algorithm object recognition using Machine Learning methods 基于机器学习方法的鲁棒多算法目标识别

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343014

Tobias Fromm, B. Staehle, W. Ertel

引用次数: 4

3D point cloud registration based on planar surfaces 基于平面的三维点云配准

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343035

Junhao Xiao, B. Adler, Houxiang Zhang

引用次数: 46

A congestion will based constraints mechanism of mobile sensor network 一种基于拥塞的移动传感器网络约束机制

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343069

Ping Song, Yiping Wang, Xiaoyue Wang, Zhiqiang Pan

引用次数: 1

Dexterous robotic hand grasp modeling using piecewise linear dynamic model 基于分段线性动力学模型的灵巧机械手抓取建模

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343076

Wei Xiao, F. Sun, Huaping Liu, Heyu Liu, Chao He

引用次数: 7

Robot3D — A simulator for mobile modular self-reconfigurable robots Robot3D -移动模块化自重构机器人模拟器

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343016

L. Winkler, Vojtěch Vonásek, H. Wörn, L. Preucil

引用次数: 18

Tactile image based contact shape recognition using neural network 基于触觉图像的神经网络接触形状识别

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343036

Hongbin Liu, Juan Greco, Xiaojing Song, João Bimbo, L. Seneviratne, K. Althoefer

{"title":"Tactile image based contact shape recognition using neural network","authors":"Hongbin Liu, Juan Greco, Xiaojing Song, João Bimbo, L. Seneviratne, K. Althoefer","doi":"10.1109/MFI.2012.6343036","DOIUrl":"https://doi.org/10.1109/MFI.2012.6343036","url":null,"abstract":"This paper proposes a novel algorithm for recognizing the shape of object which in contact with a robotic finger through the tactile pressure sensing. The developed algorithm is capable of distinguishing the contact shapes between a set of low-resolution pressure map. Within this algorithm, a novel feature extraction technique is developed which transforms a pressure map into a 512-feature vector. The extracted feature of the pressure map is invariant to scale, positioning and partial occlusion, and is independent of the sensor's resolution or image size. To recognize different contact shape from a pressure map, a neural network classifier is developed and uses the feature vector as inputs. It has proven from tests of using four different contact shapes that, the trained neural network can achieve a high success rate of over 90%. Contact sensory information plays a crucial role in robotic hand gestures. The algorithm introduced in this paper has the potential to provide valuable feedback information to automate and improve robotic hand grasping and manipulation.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124497594","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: 59

Self-map building in wireless sensor network based on TDOA measurements 基于TDOA测量的无线传感器网络自映射构建

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343041

Shuanglong Liu, Yuchao Tang, Chun Zhang, Shigang Yue

引用次数: 5