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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Ji-An Luo Zhi Wang Yu-Hen Hu Hangzhou Dianzi University Institute of Information and Control China Zhejiang University The State Key Laboratory of Industrial Control Technology China Dept. Electrical and Computer Engineering University of Wisconsin-Madison WI U. S. A

A novel sparse representation based multi-source localization method is presented in this work. We envision a wireless network infrastructure containing multiple phase arrays of acoustic sensors. With multiple arrays, direct estimation of a set of source locations is achieved using a new joint sparse representation of array covariance matrices (JSRACM). This representation transforms the source location estimation problem into a spatial sparse signal representation (SSSR) optimization problem. To mitigate the high computation complexity of JSRACM, a novel binary sparse indicative vector (SIV) is introduced to represent the support of joint SSSR of array covariance matrices. As such, the multiple source locations may be estimated by solving an unconstrained optimization problem of the SIV vector using existing FOCUSS-like algorithms. The resulting SIVR-JSRACM algorithm does not require prior information of the number of sources nor initial source location estimates. It promises super-resolution, robustness to noise, and low computing complexity which is independent of the number of sensor phase arrays. Simulation results demonstrate superior performance of the proposed algorithm.

关键词： Arrays Vectors Covariance matrices Noise Signal processing algorithms Joints Dictionaries

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Sparse signal analysis using Ramanujan sums 1

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9th International Conference on Intelligent Computing, ICIC 2013

作者： Chen, Guangyi Krishnan, Sridhar Liu, Weihua Xie, Wenfang Department of Computer Science and Software Engineering Concordia University Montreal QC H3G 1M8 Canada Department of Computer and Electrical Engineering Ryerson University Toronto ON Canada State Key Lab. of Virtual Reality Technology and Systems Beihang University No 37 Xueyuan Rd. Haidian District 100191 Beijing China Department of Mechanical and Industrial Engineering Concordia University Montreal QC H3G 1M8 Canada

ISBN: (数字)9783642394829

ISBN: (纸本)9783642394812

In this paper, we perform sparse signal analysis by using the Ramanujan Sums (RS). The RS are orthogonal in nature and therefore offer excellent energy conservation. Our analysis shows that the RS can compress the energy of a periodic impulse chain signal into fewer number of RS coefficients than the Fourier transform (FT). In addition, the RS are faster than the FT in computation time because we can calculate the RS basis functions only once and save them to a file. We can retrieve these RS basis functions for our calculation instead of computing them online. To process a signal of 128 samples, we spend 1.0 millisecond for the RS and 5.82 milliseconds for the FT by using our unoptimized Matlab code. © 2013 Springer-Verlag.

关键词： Signal analysis

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Canonical correlation analysis based on Hilbert-Schmidt independence criterion and centered Kernel target alignment

Canonical correlation analysis based on Hilbert-Schmidt inde...

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30th International Conference on Machine Learning, ICML 2013

作者： Chang, Billy Kruger, Uwe Kustra, Rafal Zhang, Junping Dalla Lana School of Public Health University of Toronto Toronto ON M5T 3M7 Canada Dept. Mechanical and Industrial Engineering Sultan Qaboos University Al Khoud Oman Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China

ISBN: (纸本)9781629933061

Canonical correlation analysis (CCA) is a well established technique for identifying linear relationships among two variable sets. Kernel CCA (KCCA) is the most notable nonlinear extension but it lacks interpretability and robustness against irrelevant features. The aim of this article is to introduce two nonlinear CCA extensions that rely on the recently proposed Hilbert-Schmidt independence criterion and the centered kernel target alignment. These extensions determine linear projections that provide maximally dependent projected data pairs. The paper demonstrates that the use of linear projections allows removing irrelevant features, whilst extracting combinations of strongly associated features. This is exemplified through a simulation and the analysis of recorded data that are available in the literature. Copyright 2013 by the author(s).

关键词： Correlation methods

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Cooperative and Active Sensing in Mobile Sensor Networks for Scalar Field Mapping

Cooperative and Active Sensing in Mobile Sensor Networks for...

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IEEE International Conference on Automation Science and engineering

作者： Hung Manh La Weihua Sheng Jiming Chen Center for Advanced Infrastructure and Transportation (CAIT) Rutgers University Piscataway NJ 08854 USA School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 USA State Key Lab. of Industrial Control Technology Department of Control Zhejiang University Hangzhou P.R.China 310027

ISBN: (纸本)9781479915132

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each location. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are taken and fused with the corresponding confidences using distributed consensus filters. As a result an online map of the scalar field is built with a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms

关键词： Active sensing Sensor fusion Sensor networks

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Undo the codebook bias by linear transformation for visual applications 13

Undo the codebook bias by linear transformation for visual a...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Zhang, Yifan Wang, Shuhui Pang, Junbiao Liang, Chao Huang, Qingming Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Computer Science and Technology Beijing University of Technology 100124 Beijing China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Department of Computer Sciences University of Texas at San Antonio San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the corresponding codebook, the elements of a codebook are viewed as visual words. The local features within each image are then encoded to get the final histogram representation. However, the codebook is dataset dependent and has to be generated for each image dataset. This costs a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by codebook linear transformation. To represent every points within the local feature space using Euclidean distance, the number of bases should be no less than the space dimensions. Hence, each codebook can be viewed as a linear transformation of these bases. In this way, we can transform the pre-learned codebooks for a new dataset. However, not all of the visual words are equally important for the new dataset, it would be more effective if we can make some selection using sparsity constraints and choose the most discriminative visual words for transformation. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. Image classification experimental results on several image datasets show the effectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Linear transformations

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Online and Remote Machine Condition Monitoring and Fault Diagnosis System Using Wireless Sensor Networks

Online and Remote Machine Condition Monitoring and Fault Dia...

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2013 15th IEEE International Conference on Communication Technology

作者： Yanyu Zhang Peng Zeng Guang Yang Jinying Li Key Lab.of Networked Control System Shenyang Institute of AutomationChinese Academy of Sciences School of Computer and Information Engineering Henan University University of Chinese Academy of Sciences

ISBN: (纸本)9781479900763

Motor systems are critical equipment in modern *** make the equipment maintenance more efficient,this paper proposes an online and remote machine condition monitoring and fault diagnosis system for motor system based on WIA-PA(Wireless network for Industry Automation Process Automation).The system continuously monitors the status of the target motor systems using wireless vibration *** extracting the feature of vibration signals,the wireless vibration transmitters send feature data to host computer through WIA-PA wireless sensor *** data are processed further in host computer to make diagnosis for the motor system using the customized expert *** paper describes the structure of the system,vibration transmitter,WIA-PA wireless network and host computer system in *** proposed system was tested in field,and the results are presented.

关键词： Condition-based maintenance Fault diagnosis industrial wireless sensor network WIA-PA Vibration transmitter

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A Linear control Approach to Distributed Multi-Agent Formations in d-Dimensional Space

A Linear Control Approach to Distributed Multi-Agent Formati...

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IEEE Annual Conference on Decision and control

作者： Zhiyun Lin Zhiyong Chen Minyue Fu State Key laboratory of Industrial Control Technology College of Electrical Engineering Zhejiang University 38 Zheda Road Hangzhou 310027 P.R. China School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9781467357159

The paper presents a linear approach for formation control of multiple autonomous agents in d-dimensional space. The generalized notion of graph Laplacian, associated to a graph with possibly negative weights on the edges, is introduced aiming to solve the formation problem of controlling a network of point agents to form a given pattern (including rotation, translation, and scaling). By assuming the number of agents is larger than d + 1, we derive that a linear distributed control law exists for this purpose if and only if certain algebraic conditions hold (or equivalently, the graph is globally rigid). Next, it is shown that the generalized graph Laplacian used to stabilize the formations can be obtained by solving a convex optimization problem. Further results are also provided to reveal the conditions under which the attained formations are congruent to or just translations of the desired one.

关键词： Laplace equations Aerospace electronics Decentralized control Vectors Symmetric matrices Asymptotic stability Stress

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Beyond bag of words: Image representation in sub-semantic space 13

Beyond bag of words: Image representation in sub-semantic sp...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Wang, Shuhui Liang, Chao Liu, Jing Huang, Qingming Li, Haojie Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Software Dalian University of Technology Liaoning China Department of Computer Sciences University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

Due to the semantic gap, the low-level features are not able to semantically represent images well. Besides, traditional semantic related image representation may not be able to cope with large inter class variations and are not very ro- bust to noise. To solve these problems, in this paper, we propose a novel image representation method in the sub- semantic space. First, examplar classifiers are trained by separating each training image from the others and serve as the weak semantic similarity measurement. Then a graph is constructed by combining the visual similarity and weak se- mantic similarity of these training images. We partition this graph into visually and semantically similar sub-sets. Each sub-set of images are then used to train classifiers in order to separate this sub-set from the others. The learned sub-set classifiers are then used to construct a sub-semantic space based representation of images. This sub-semantic space is not only more semantically meaningful but also more reli- Able and resistant to noise. Finally, we make categorization of images using this sub-semantic space based representation on several public datasets to demonstrate the e®ectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Image representation

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A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression

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Journal of Iron and Steel Research International 2012年第3期19卷 25-30页

作者： ZHANG Xiu-ling, ZHANG Shao-yu, TAN Guang-zhong, ZHAO Wen-bao (key laboratory of industrial computer control engineering of Hebei Province, National engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao 066004, Hebei, China) Key Laboratory of Industrial Computer Control Engineering of Hebei Province National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University Qinhuangdao 066004 Hebei China

To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to over- come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regres- sion （SVR） theory, a novel flatness pattern recognition method based on least squares support vector regression （LS-SVR） was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan- cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out （LOO） error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.

关键词： flatness pattern recognition least squares support vector regression cross-validation

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Fine-grained image classification using color exemplar classifiers

Fine-grained image classification using color exemplar class...

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14th Pacific-Rim Conference on Multimedia, PCM 2013

作者： Zhang, Chunjie Xiong, Wei Liu, Jing Zhang, Yifan Liang, Chao Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing China National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783319037301

The use of local features has demonstrated its effectiveness for many visual applications. However, local features are often extracted with gray images. This ignores the useful information within different color channels which eventually hinders the final performance, especially for fine-grained image classification. Besides, the semantic information of local features is too weak to be applied for high-level visual applications. To cope with these problems, in this paper, we propose a novel fine-grained image classification method by using color exemplar classifiers. For each image, we first decompose it into multiple color channels to take advantage of the color information. For each color channel, we represent each image with a response histogram which is generated by exemplar classifiers. Experiments on several public image datasets demonstrate the effectiveness of the proposed color exemplar classifier based image classification method. © Springer International Publishing Switzerland 2013.

关键词： Image classification

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