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Congestion control strategy based on probability selection in delay tolerant mobile sensor network

Journal of information and Computational Science 2014年第14期11卷 5121-5130页

作者： Wang, Bingting Hu, Yanjun Liang, Hui Lin, Qibin Shi, Yonghua Wang, Xianbing School of Electronic and Electrical Engineering Chuzhou University Chuzhou China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China School of Information Science & Technology Southwest Jiaotong University Chengdu China

We analyzed the relationship between packet validity and encounter probability as well as the relationship between packet validity and remaining lifetime. Then we proposed the congestion control strategy based on probability selection, which implements the operations of congestion detection and congestion avoidance. When node buffer is fully occupied and needs to store a new packet, it will look for the packets whose remaining lifetime is lower than the threshold T, and then drop the packet with minimum encounter probability. If there is no packet whose remaining lifetime is lower than T, the packet with the shortest remaining lifetime will be dropped. Simulation results show that this congestion control strategy improves the performances of delivery probability, average delay and overhead ratio compared with four kinds of congestion control strategies. 1548-7741/Copyright © 2014 Binary information Press

关键词： Probability

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Current Bioinformatics 2014年第3期9卷 218-227页

作者： Chen, Xiao-Su Yu, Zu-Guo Zheng, Juan Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 BrisbaneQ 4001 Australia School of Information and Engineering Xiangtan University Hunan411105 China

Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignments could not be directly applied to the whole-genome comparison and phylogenomic studies. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. The "distances" used in these alignment-free methods are not proper distance metrics in the strict mathematical sense. In this study, we first review them in a more general frame — dissimilarity. Then we propose some new dissimilarities for phylogenetic analysis. Last three genome datasets are employed to evaluate these dissimilarities from a biological point of view. © 2014 Bentham Science Publishers.

关键词： Alignment

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An adaptive haze removal based on dark channel prior and image decoding acceleration method

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ICIC Express Letters 2015年第3期9卷 775-781页

作者： Sun, Hong-Guang Fang, Chao Yang, Xi-Quan Yang, Feng-Qin Sun, Tie-Li Hung, Chih-Cheng School of Computer Science and Information Technology No. 2555 Jingyue Ave. Changchun China College of Humanities and Sciences Northeast Normal University No. 2555 Jingyue Ave. Changchun China Key Laboratory of Intelligent Information Processing of Jilin Universities No. 2555 Jingyue Ave. Changchun China School of Computing and Software Engineering Southern Polytechnic State University MariettaGA United States

In this paper, we propose an adaptive haze removal method based on dark channel prior. The dark channel value is compensated for estimating the transmission;the atmospheric light will be re-estimated through the analysis of the characteristics of images. Images are usually degraded with haze due to the object absorption and atmospheric scattering. The experiments show that the images recovered by the proposed method are better than other existing methods and recovered images are closer to real images. Meanwhile, the Mean Square Error (MSE) is used to verify the proposed approach for the images recovered which are taken from the FRIDA database. Through the comparison of Inverse Discrete Cosine Transforms (IDCT) of haze images and haze-free images, the statistics of the comparison shows that the haze images consume more computation time. To improve the time complexity, the Graphics processing Unit (GPU) was used to increase the computing efficiency. © 2015, ICIC International.

关键词： Graphics processing unit

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Transfer learning with multiple sources via consensus regularized autoencoders

Transfer learning with multiple sources via consensus regula...

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European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014

作者： Zhuang, Fuzhen Cheng, Xiaohu Pan, Sinno Jialin Yu, Wenchao He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Institute for Infocomm Research Singapore Singapore

ISBN: (纸本)9783662448441

Knowledge transfer from multiple source domains to a target domain is crucial in transfer learning. Most existing methods are focused on learning weights for different domains based on the similarities between each source domain and the target domain or learning more precise classifiers from the source domain data jointly by maximizing their consensus of predictions on the target domain data. However, these methods only consider measuring similarities or building classifiers on the original data space, and fail to discover a more powerful feature representation of the data when transferring knowledge from multiple source domains to the target domain. In this paper, we propose a new framework for transfer learning with multiple source domains. Specifically, in the proposed framework, we adopt autoencoders to construct a feature mapping from an original instance to a hidden representation, and train multiple classifiers from the source domain data jointly by performing an entropy-based consensus regularizer on the predictions on the target domain. Based on the framework, a particular solution is proposed to learn the hidden representation and classifiers simultaneously. Experimental results on image and text real-world datasets demonstrate the effectiveness of our proposed method compared with state-of-the-art methods. © 2014 Springer-Verlag.

关键词： Learning systems

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Hierarchical Extreme Learning Machine for Unsupervised Representation Learning

Hierarchical Extreme Learning Machine for Unsupervised Repre...

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International Joint Conference on Neural Networks

作者： Wentao Zhu Jun Miao Laiyun Qing Guang-Bin Huang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Electrical & Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798

ISBN: (纸本)9781479919611

Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.

关键词： Hierarchical Extreme Learning Machine Unsupervised Representation Learning deep learning models Learning machines Dataset representation method Recognition (Psychology) Receptive field Learning representations Religious Missions Hierarchical

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Artifact reduction of compressed video via three-dimensional adaptive estimation of transform coefficients

Artifact reduction of compressed video via three-dimensional...

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作者： Zhang, Xinfeng Xiong, Ruiqin Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Digital Media Peking University Beijing100871 China

ISBN: (纸本)9781479957514

Block transform compressed videos usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. The inter prediction utilized in video coding also induces block boundary artifacts when the neighboring blocks using different motion vectors from previous decoded frames. In this paper, a three-dimensional adaptive estimation method for video transform coefficients is proposed to reduce the artifact in compressed video. In the proposed method, transform coefficients of each block are estimated by adaptively fusing three prediction sources based on their reliabilities. One prediction source is the transform coefficients directly acquired from the decoded video, whose reliability is determined by the distribution of the quantization noise. The other prediction source is derived by a temporal autoregressive model of transform-blocks along motion trajectory among neighboring reference frames. Its reliability is estimated from prediction variance of local blocks. The last prediction source is derived from the nonlocal transform-blocks, whose reliability is estimated based on the distribution of the nonlocal coefficients and their similarity with the estimated block. Experimental results for compressed video sequences by HEVC show that, the proposed method can reduce the compression artifacts and improve both the objective and subjective quality. © 2014 IEEE.

关键词： Forecasting

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3D Reconstruction by Kinect Sensor:A Brief Review

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Computer Aided Drafting,Design and Manufacturing 2014年第1期24卷 1-11页

作者： LI Shi-rui TAO Ke-lu WANG Si-yuan LI Hai-yang CAO Wei-guo LI Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

While Kinect was originally designed as a motion sensing input device of the gaming console Microsoft Xbox 360 for gaming purposes, it＇s easy-to-use, low-cost, reliability, speed of the depth measurement and relatively high quality of depth measurement make it can be used for 3D reconstruction. It could make 3D scanning technology more accessible to everyday users and turn 3D reconstruction models into much widely used asset for many applications. In this paper, we focus on Kinect 3D reconstruction.

关键词： Kinect 3D reconstruction RGBD

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Extracting part-whole relations from online encyclopedia 8

Extracting part-whole relations from online encyclopedia

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8th IFIP International Conference on intelligent information processing, IIP 2014

作者： Xia, Fei Cao, Cungen Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783662449790

Automatic discovery of part-whole relations is a fundamental problem in the area of information extraction. In this paper, we present an unsupervised approach to learning lexical patterns from online encyclopedia for extracting part-whole relations. The only input is a few part-whole instances. To tackle the term recognition problem, terms from the domain of the seeds are extracted taking use of the semantic information contained in the online encyclopedia. Instead of collecting sentences that contain relation instances from the seeds, we introduce a novel process to select sentences that may indicate part-whole relations. Patterns are produced from these sentences with terms replaced by Part and Whole tags. A similarity measurement based on a new edit distance is used and an algorithm is described to cluster similar patterns. We rank the pattern clusters according to their frequencies, and patterns from the top-k clusters are chosen to be applied to identify the new part-whole relations. Experimental results show that our method can extract abundant part-whole relations and achieve a preferable precision compared to the other state-of-the-art approaches. © IFIP International Federation for information processing 2014.

关键词： Semantics

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Signal processing for the 5G Revolution

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IEEE SIGNAL processing MAGAZINE 2014年第6期31卷 12-13页

作者： Heath, Robert W., Jr. Leus, Geert Quek, Tony Q. S. Talwar, Shilpa Zhou, Peiying Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Department of Computer Systems University of São Paulo - USP São Carlos Brazil IRISA Research Institute University of South Brittany - IRISA/UBS Vannes France

The articles in this special section provide an overview of recent advances in signal processing for communication with an emphasis on signal processing techniques that will be relevant for 5G cellular systems. It covers a wide range of topics including modulation, beamforming, cross-layer optimization based on different performance metrics, location-aware communication, cloud computing, and cloud radio access networks. The articles provide a diverse perspective on the potential challenges in 5G cellular systems.

关键词： Special issues and sections 5G mobile communication Next generation networking Wireless communication Cellular networks Wireless cellular networks

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Latency-aware routing with bandwidth assignment for Software Defined Networks

Latency-aware routing with bandwidth assignment for Software...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Qiongyu Zhang Liehuang Zhu Meng Shen Mingzhong Wang Fan Li Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education P. R. China Faculty of Arts and Business University of the Sunshine Coast Queensland Australia

Reducing the flow latency is of great importance in traffic management, which benefits both service providers and end users. Routing design and flow scheduling are typical ways to improve the flow transmission efficiency. However, existing studies usually consider them separately, due to the complexity of joint consideration. Here, we propose a latency-aware routing scheme with bandwidth assignment in the Software Defined Networks, which can efficiently reduce the flow latency with a moderate complexity, to combine the routing and scheduling together.

关键词： Routing Bandwidth Processor scheduling Complexity theory Bismuth Software Mathematical model

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