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2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

作者： Piao, Xinglin Hu, Yongli Sun, Yanfeng Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9780769550466

As the energy of nodes in Wireless Sensor Networks (WSNs) is generally constrained, it is urgent to develop an efficient data gathering algorithm. Recently, Low Rank approximation is deeply studied and successfully applied in WSNs data recovery, in which a subset of nodes is randomly selected for sensing the environmental data, such as temperature and humidity. But this random sampling solution generally results in uneven energy consumption of nodes and shortens the life time of networks. In this paper, we propose an efficient data gathering method based on the low rank approximation, in which the spatial distribution of the selected nodes and the networks energy consumption are considered simultaneously. The sensing nodes selection is modeled as a multi-objective 0-1 programming problem which is resolved by the Particle Swarm Optimization (PSO) with high efficiency. In the optimization, the Gini index is adopted to measure the spatial distribution of the selected nodes and energy consumption of the networks. By the presented temporal constraint low rank approximation, the complete sensing data is recovered from the sensing data of the selected node at the sink node. We perform our method both on the real and simulated wireless networks. The experimental results demonstrate our model is practical and efficient. © 2013 IEEE.

关键词： Energy utilization

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An improved particle filter tracking algorithm based on motion and appearance features

An improved particle filter tracking algorithm based on moti...

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2013 5th International Conference on Computational and Information Sciences, ICCIS 2013

作者： Wang, Lichun Li, Lanxiao Kong, Dehui Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9780769550046

Particle filter (PF) has proven successfully for nonlinear and non-Gaussian estimate problems, but its degeneracy will influence the results of tracking. Therefore in the paper, the optical flow algorithm is utilized to generate the proposal distribution of particle filter. With the velocity message which is estimated by optical flow algorithm, the particles could be generated in a right direction. So that, more particles are given a relatively greater weight, which improves the particle degradation. This article proposes the integration of color histogram and wavelet moment into tracking algorithm for improving the veracity of object tracking. An improved particle filter tracking algorithm based on optical flow, color histogram and wavelet moment is proposed in this paper. © 2013 IEEE.

关键词： Optical flows

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Eyebrow recognition based on sparsity preserving projections

Eyebrow recognition based on sparsity preserving projections

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Yang, Xinwu Xu, Xiaojun Liu, Chunnian Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781479916603

In order to study the human eyebrow as a novel biometric feature, we proposed an eyebrow recognition method based on sparsity preserving projections (SPP). SPP has already been successfully applied in face recognition, but it has not been applied in other biometric recognition. In this paper, we use SPP in eyebrow recognition, conduct experiments on an eyebrow image database taken from 32 people to verify the feasibility and validity of eyebrow recognition based on this method. In the meantime we also verified the feasibility of eyebrow used as a unique biometric feature for personal identification. © 2013 IEEE.

关键词： Face recognition

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Distribution aware collaborative spread replication for rare objects in unstructured peer-to-peer networks

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Journal of Networks 2013年第5期8卷 991-998页

作者： Ma, Wenming Zhang, Yujie Meng, Xiangwu School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing China

Existing unstructured peer-to-peer networks have low efficiency to locate the rare objects. Although various algorithms have been provided to address this problem, they do not distinguish whether the objects are rare or not and always incur unnecessary bandwidth consumption. In this paper, we presented a distribution aware replication method for rare objects. Unlike the traditional replication method, we not only replicate the objects but also replicate the peers' requirement, because we believe that the rare objects are also always rarely queried. Distribution aware can control the number of the copy to avoid unnecessary overhead. Our replication methods are actually index replication named collaborative spread index replication. The distribution aware methods include the multi-hop aware and random walk sampling aware. When execute replicating, the algorithm will decide whether replicating the index or not according to the local distribution information obtained. The experimental results show that distribution aware replication can well support to find the rare objects in unstructured peer-to-peer networks with low overhead. © 2013 ACADEMY PUBLISHER.

关键词： Peer to peer networks

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Adaptive weighted prediction for scalable video coding based on HEVC

Adaptive weighted prediction for scalable video coding based...

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14th Pacific-Rim Conference on multimedia, PCM 2013

作者： Wu, Gang Ding, Wenpeng Shi, Yunhui Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology 100124 Beijing China

ISBN: (纸本)9783319037301

Scalable video coding provides an efficient way to serve video contents at different visual quality levels and different resolutions. In this paper, the spatial scalability and quality scalability are evaluated on a base of the new Scalable High-efficiency Video Coding (SHVC) extension of HEVC under development. To further improve the coding efficiency of SHVC, we present a set of coding schemes to exploiting inter-layer redundancies for efficient compression of the enhancement layer. The coding schemes include inter-layer frame prediction, combined weighted prediction and inter-layer adaptive filter, which aim at fully utilizing the reconstructed base pictures in the enhancement layer prediction. Experimental results show the effectiveness of our scheme compared with the simulcast case and other HEVC-based scheme. In average, these schemes can achieve 1.5% and 1.7% BD-rate gain for RA HEVC 2x spatial scalability case and HEVC SNR scalability case, respectively. © Springer International Publishing Switzerland 2013.

关键词： Forecasting

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Hand tracking with mid-level visual cues and ensemble learning

Hand tracking with mid-level visual cues and ensemble learni...

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14th Pacific-Rim Conference on multimedia, PCM 2013

作者： Fang, Yikai Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology 100124 Beijing China

ISBN: (纸本)9783319037301

Hand tracking is a crucial step in vision based gesture interaction. Due to illumination and hand shape variation, tracking of such object is a difficult task. In this paper, an ensemble tracking framework integrated with superpixels for hand tracking is proposed. With treating tracking as a binary classification problem, ensemble tracking utilizes weak classifiers to get strong classifier and get robustness to illumination and appearance change. However, outliers in confidence map caused by irregular object shape bring ill effects to tracking performance. As hand area has similar color and texture, superpixel method can be used to cluster the pixels in hand area and provide solution to exclude outliers. Experiments show that with superpixe integrated, the proposed method get more accurate tracking result. © Springer International Publishing Switzerland 2013.

关键词： Superpixels

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Dynamic feature extraction for facial expression recognition based on optical flow

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Information technology Journal 2013年第23期12卷 7305-7311页

作者： Jia, Xibin Liu, Shuangqiao Powers, David M. W. Beijing Municipal Key Laboratory for Multimedia and Intelligent Software Technology Beijing University of Technology Beijing 100124 China College of Computer Science Beijing University of Technology Beijing 100124 China School of Computer Science Engineering and Mathematics Flinders University of South Australia Adelaide 5042 Australia

Understanding facial expressions is a fundamental problem in affective computing that has the potential to impact both sides of a conversation with a computational agent. Currently static approaches based on techniques such as Gabor transformation represent the state of the art for identifying facial expressions but remain rather slow and unreliable. In this study we introduce and compare a dynamic technique based on optical flow versus this static Gabor baseline, as well as integrating information about static and dynamic facial characteristics into novel fused models. Rather than requiring complex Machine Learning, a simple and fast template model based on K-Means uses a Hidden Markov Model to provide context. The system is trained and evaluated on distinct subsets of the Cohn-Kanade database of adult faces which provides classifications into six basic expressions. Experimental results show that the dynamic expression feature extraction based on the optical flow for facial expression recognition has considerably improved recognition rate. Fusion of the optical flow dynamic expression features with Gabor static expression feature also increases recognition rate somewhat versus the static baseline but the hybrids tested are not competitive with the pure dynamic model. © 2013 Asian Network tor Scientific Information.

关键词： Hidden Markov models

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Dimensionality Reduction Assisted Tensor Clustering

Dimensionality Reduction Assisted Tensor Clustering

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International Joint Conference on Neural Networks

作者： Yanfeng Sun Junbin Gao Xia Hong Yi Guo Chris J. Harris Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing University of Technology School of Computing and Mathematics Charles Sturt University School of Systems Engineering University of Reading CSIRO Mathematics Informatics and Statistics Electronics and Computer Science University of Southampton

ISBN: (纸本)9781479914821

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

关键词： Tensor Tucker Decomposition Tensor Clustering Matrix Factorization Tensor PCA

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Robust H_∞ Consensus Control of Uncertain Multi-Agent Systems with Nonlinear Dynamics and Time-Varying Delays

Robust H_∞ Consensus Control of Uncertain Multi-Agent Syste...

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American Control Conference

作者： Ping Wang Yingmin Jia Junping Du Jun Zhang Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electronic and Information Engineering Beihang University (BUAA) Beijing 100191 China

ISBN: (纸本)9781479932757

This paper is concerned with the H_∞ consensus control problem in undirected networks of autonomous agents with nonlinear dynamics, subject to parameter uncertainties and external disturbances. With the consideration of time-varying delays arising from communication among agents, a distributed protocol is proposed using the local delayed state information. Then, by defining an appropriate controlled output function, the consensus problem under the proposed protocol is converted into an H_∞ control problem. Based on robust H_∞ theory, sufficient conditions are derived to make all agents achieve consensus with desired H_∞ performance. Moreover, the feedback matrix in the proposed protocol is determined by solving two linear matrix inequalities (LMIs) with the same dimensions as a single agent. Finally, a numerical simulation is provided to demonstrate the effectiveness of our theoretical results.

关键词： Multi-agent systems Protocols Nonlinear dynamical systems Uncertain systems Linear matrix inequalities Robustness Delay effects

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Output feedback tracking control for spacecraft relative translation subject to input constraints and partial loss of control effectiveness

Output feedback tracking control for spacecraft relative tra...

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American Control Conference

作者： Lin Zhao Yingmin Jia Junping Du Jun Zhang Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electronic and Information Engineering Beihang University (BUAA) Beijing 100191 China

ISBN: (纸本)9781479932757

In this paper, an adaptive output feedback tracking control scheme is proposed for spacecraft formation flying (SFF) in the presence of external disturbances, uncertain system parameters, input constraints and partial loss of control effectiveness. The proposed controller incorporates a pseudo-velocity filter to account for the unmeasured relative velocity, and the neural network (NN) technique is implemented to approximate the desired nonlinear function and bounded external disturbances. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated nonlinear function, a switch function is employed to generate a switching between the adaptive NN control and the robust controller. Moreover, a fault tolerant part is included in the controller to compensate the partial loss of actuator effectiveness fault. It is shown that the derived controller not only guarantees the tracking error in the closed-loop system to be uniformly ultimately bounded (UUB) but also ensures the control input can rigorously enforce actuator magnitude constraints. Simulation results are provided to demonstrate the effectiveness of the proposed method.

关键词： SFF UUB NN

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