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Compromised nodes in wireless sensor network

APWeb 2006 International Workshops: XRA, IWSN, MEGA, and ICSE

作者： Lin, Zhi-Ting Qu, Yu-Gui Jing, Li Zhao, Bao-Hua University of Science and Technology of China MOE-Microsoft Key Laboratory of Multimedia Computing and Communication Department of Electronic Engineering and Information Science Hefei Anhui 230027 China

ISBN: (纸本)3540311580

Sensor webs consisting of nodes with limited battery power and wireless communication are deployed to collect useful information from a variety of environments. A new challenge in the wireless sensor networks (WSN) is the compromised nodes problem. Compromised nodes may exhibit arbitrary behavior and may collude with other compromised nodes. In this paper, we propose a novel security strategy with assistant cluster heads, SSACH, which focuses on limiting the attack from compromised nodes. It adds the Assistant Cluster Heads (ACH) so as to monitor Cluster Head (CH) and take precautions against the inside attack. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Wireless telecommunication systems

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Zone routing protocol based on energy consumption

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Jisuanji Gongcheng/Computer Engineering 2006年第12期32卷 117-118+135页

作者： Zhai, Weifeng Gu, Naijie Zhang, Jianming Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Computing and Communication Software Hefei 230027 China

A zone routing protocol based on the energy consumption speed of nodes in mobile Adhoc networks is presented. A route select parameter computed by residual lifetime of nodes and hop count of route is used to be selected the best route, the nodes that have more energy have more probability of chosen. The mechanism can balance the energy consumption of node and prolong network's life. Simulation result comparing with zone routing protocol in NS-2 shows EZRP has less number of death nodes, lower routing overhead, and longer lifetime of network.

关键词： Energy consumption EZRP Hybrid routing protocol MANET

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A Robust Facial Feature Points Location Algorithm under Variable Illumination

A Robust Facial Feature Points Location Algorithm under Vari...

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IEEE International Conference on Mechatronics and Automation

作者： Liansheng Zhuang Yong Wang Bin Li Zhenquan Zhuang Hao Zhang MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei Anhui China

In this paper, a novel algorithm is proposed to locate facial feature points robustly and accurately. It uses the coarse-to-fine strategy to search feature points. Firstly, it used phase congruency to model the feature points locally and locate facial feature points roughly. Secondly, it used reflectance, which is intrinsic texture of object, to drive the search of fine location. Experiments showed that our algorithm could locate the feature points more accurately and robustly, and work efficiently even under a wide range of illumination and poor initialization

关键词： Robustness Facial features Lighting Reflectivity Active shape model Face recognition Laboratories Multimedia computing Change detection algorithms Shadow mapping

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Kurtosis Normalization after Short-Time Gaussianization for Robust Speaker Verification

Kurtosis Normalization after Short-Time Gaussianization for ...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Yanlu Xie Beiqian Dai Jun Sun MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei Anhui China

Statistical normalization has been popularly used to transform the distribution of the speech feature to decrease the mismatch between the training and test environment in robust speaker verification. This paper presents a method to normalization kurtosis which can measure the non-Gaussianity in quantity. The kurtosis normalization method proposed here is based on the cumulative distribution function (CDF) of a sub-Gaussian function and used after the short-time Gaussianization normalization method in feature space. Thus the distribution of speech parameters becomes more approximate the normal one. Experimental results based on the 2001 NIST SRE database show that when the short-time non-Gaussianity and kurtosis are both normalized, significant improvement could be achieved in not only equal error rate but also minimum detection cost compared to baseline system

关键词： Gaussian processes Robustness Speech Testing Distribution functions Gaussian distribution NIST Spatial databases Error analysis Costs

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Precise Eye Localization with AdaBoost and Fast Radial Symmetry

Precise Eye Localization with AdaBoost and Fast Radial Symme...

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International Conference on Computational Intelligence and Security

作者： Zhang Wencong Chen Hong Yao Peng Li Bin Zhuang Zhenquan MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei Anhui China

ISBN: (纸本)1424406048

The accuracy of face alignment affects greatly the performance of a face recognition system. Since the face alignment is usually conducted using eye positions, the algorithm for accurate eye localization is therefore essential for the accurate face recognition. In this paper, we propose a novel algorithm for eye localization. Based on the special gray distribution in the eye region, proper AdaBoost detection is adaptively trained to segment the eye region. After getting the region of eyes, a fast radial symmetry operator is used to precisely locate the center of eyes. Experimental results show that the method can accurately locate the eyes, and it is robust to the variations of face poses, expressions, accessions and illuminations

关键词： Eyes Face recognition Face detection Lighting Robustness Laboratories Multimedia computing Object detection Detectors Layout

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An Evolutionary Support Vector Machines Classifier for Pedestrian Detection

An Evolutionary Support Vector Machines Classifier for Pedes...

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2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： D. Chen X.b. Cao Y.w. Xu H. Qiao Department of Computer Science and Technology University of Science and Technology Hefei China Anhui Province Key Laboratory Software Computing and Communication Hefei China Institute of Automation Chinese Academy and Sciences Beijing China

In a pedestrian detection system, a classifier is usually designed to recognize whether a candidate is a pedestrian. Support vector machines (SVM) has become a primary technique to train a classifier for pedestrian detection. However, it is hard to give the best training model which has a tremendous effect to the performance of a SVM classifier. In this paper, we design special code/decode scheme and evaluation function for a training model firstly; and then use genetic algorithm to optimize key parameters which represent the SVM training model. Therefore a most suitable SVM classifier can be obtained for pedestrian detection. Experiments have been carried out in a single camera based pedestrian detection system. The results show that the evolutionary SVM classifier has a better detection rate; moreover, RBF kernel is more suitable than polynomial kernel when chosen in an evolutionary SVM classifier for pedestrian detection

关键词： Support vector machines Support vector machine classification Kernel Genetic algorithms Decoding Design optimization Cameras Intelligent robots Computer science Laboratories

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Rank-one projections with adaptive margins for face recognition

Rank-one projections with adaptive margins for face recognit...

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2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006

作者： Xu, Dong Lin, Stephen Yan, Shuicheng Tang, Xiaoou MOE-Microsoft Key Laboratory of Multimedia Computing and Communication Department of EEIS University of Science and Technology of China Hefei Anhui China Microsoft Research Asia Beijing China Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong Department of Electrical Engineering Columbia University United States Beckman Institute University of Illinois Urbana-Champaign United States

ISBN: (纸本)0769525970

In supervised dimensionality reduction, tensor representations of images have recently been employed to enhance classification of high-dimensional data with small training sets. To handle tensor data, this approach has been formulated with tight restrictions on projection directions that, along with convergence issues and the assumption of Gaussian distributed class data, limits its face recognition performance. To overcome these problems, we propose a method of rank-one projections with adaptive margins (RPAM) that gives a provably convergent solution for tensor data over a more general class of projections, while accounting for margins between samples of different classes. In contrast to previous margin based works which determine margin sample pairs within the original high dimensional space, RPAM instead aims to maximize the margins defined in the expected lower dimensional feature subspace by progressive margin refinement after each rank-one projection. In addition to handling tensor data, vector-based variants of RPAM are presented for linear mappings and for nonlinear mappings using kernel tricks. Comprehensive experimental results demonstrate that RPAM brings significant improvement in face recognition over previous subspace learning techniques. © 2006 IEEE.

关键词： Adaptive algorithms

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Rank-one Projections with Adaptive Margins for Face Recognition

Rank-one Projections with Adaptive Margins for Face Recognit...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dong Xu S. Lin Shuicheng Yan Xiaoou Tang Department of Electrical Engineering Columbia University USA MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China Department of Information Engineering Chinese University of Hong Kong Hong Kong China Beckman Institute University of Illinois Urbana-Champaign Champaign USA

关键词： Face recognition Tensile stress Linear discriminant analysis Asia Computer vision Pattern recognition Scattering Laboratories Multimedia computing Data engineering

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Implicit curves and surfaces based on BP neural network

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Journal of Information and Computational Science 2005年第2期2卷 259-271页

作者： Li, Daolun Lu, Detang Kong, Xiangyang Wu, Gang Laboratory of Software in Computing and Communication of Anhui Province University of Science and Technology of China Hefei 230026 China Institute of Engineering and Science Software University of Science and Technology of China Hefei 230026 China Nanjing University of Finance and Economics Nanjing 210012 China

Neural networks, combined with implicit polynomials, can be employed to represent 2D curves as well as 3D surfaces. The zero-set of a neural network can be used to describe data points constituting a 2D curve or 3D surface. The neural network fitting algorithms developed in this paper aim to reduce the learning time of neural network by selecting the exterior and interior constraint points simultaneously and improve the fitting performance by using the linear transfer function at the output layers. The neural network adopted so far is a multilayer feed-forward type. Simulation results show that the proposed algorithms provide a significant reduction in learning time, and good fitting results. This paper lays the bases of the neural network modeling approach.

关键词： Computer graphics

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Concurrent subspaces analysis

Concurrent subspaces analysis

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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

作者： Xu, Dong Yan, Shuicheng Zhang, Lei Zhang, Hong-Jiang Liu, Zhengkai Shum, Heung-Yeung MOE-Microsoft Key Laboratory of Multimedia Computing and Communication Department of EEIS University of Science and Technology of China Hefei Anhui China Department of Information Engineering Chinese University of Hong Kong Shatin Hong Kong Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called Concurrent Subspaces Analysis (CSA), to derive representative subspaces by encoding image objects as 2 nd or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3rd order tensor data, simulated video sequences and Gabor filtered digital number image database show that CSA outper-forms Principal Component Analysis in terms of both reconstruction and classification capability. © 2005 IEEE.

关键词： Image analysis

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