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Dynamic clustering algorithm in mobile wireless sensor network

Journal of information and Computational Science 2015年第13期12卷 4897-4907页

作者： Wang, Bingting Liang, Hui Lin, Qibin Zhang, Yangyi Wang, Yujie Hong, Peilin Hu, Yanjun Department of Electronic and Electrical Engineering Chuzhou University Chuzhou China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we analyzed the blindness of traditional clustering algorithms, which select cluster head based on residual energy. Then we proposed the Dynamic Clustering Algorithm (DCA) in Mobile Wireless Sensor Network, which takes into consideration residual buffer level when selecting cluster heads and allots time slots dynamically base on the occupied buffer level. We analyzed the energy model of the proposed DCA algorithm and calculated the optimal distance between cluster heads. Simulation results show that our DCA clustering algorithm has good performances in terms of residual energy, data transmission and network lifetime by comparing it with LEACH and LEACH-CI. ©, 2015, Journal of information and Computational Science. All right reserved.

关键词： Clustering algorithms

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An Environment Visual Awareness Approach in Cognitive Model ABGP

An Environment Visual Awareness Approach in Cognitive Model ...

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International Conference on Tools with Artificial Intelligence

作者： Gang Ma Xi Yang Bo Zhang Baoyuan Qi Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781509001644

ABGP is a special cognitive model, which consists of awareness, beliefs, goals and plans. As most agent architectures, ABGP agents obtain knowledge from the natural scenes only through single preestablished rules as well, don't directly capture the natural scenes information like human visual. Inspired by the biological visual cortex (V1) and the higher brain areas perceiving visual features, we propose a novel deep network model convolutional generative stochastic model (CGSM) used to visual feature representation, and firstly introduce it into the awareness module of the cognitive model ABGP to construct a state-of-the-art cognitive model ABGP-CGSM. For the novel cognitive model ABGP-CGSM, we construct a rat-robot maze search simulation platform to show the validity recognizing natural scenes. According to the simulation results on the noise and noiseless natural scenes, the rat-robot implemented by ABGP-CGSM has an excellent success rate when passing through the maze. The simulation shows that the ABGP-CGSM model proposed in our work can directly enhance the capability of communication between agent and natural scenes, improve the ability to cognize the real world as human being and conduct the agent to plan independently its path in terms of the visual information from the natural scenes.

关键词： ABGP V1 CGSM ABGP-CGSM Awareness Visual pathway

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Video saliency prediction through machine learning with semantic information

Video saliency prediction through machine learning with sema...

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IEEE China Summit & International Conference on Signal and information processing (ChinaSIP)

作者： Xiaohui Fu Li Su Lei Qin University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Lab on Big Data Mining and Knowledge Management Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479919499

Saliency prediction is valuable in many video applications, such as intelligent retrieval, advertisement design and delivering, video coding and video summarization generating. Although image saliency is well explored, less works have been done on videos. Compared to images, the semantic orientation is more obvious for video saliency. In this paper, we propose a method to predict video saliency by introducing semantic information. Different from existing approaches, we simultaneously consider the bottom-up and top-down factors in a machine learning framework and utilize a semantic object learning model to compute the semantic related saliency map. The proposed method is tested on two datasets. The experiment results show that the proposed method keeps higher consistent with human's gaze tracks data on various video contents. Furthermore, the computation efficiency is also improved as we don't need to process every pixel of each frame during prediction features extraction.

关键词： Decision support systems Feature extraction Semantics Training Videos Indexes Training data

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MR-COF:A Genetic MapReduce Configuration Optimization Framework

MR-COF:A Genetic MapReduce Configuration Optimization Framew...

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The 7th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP2015(2015年第七届国际并行体系结构,算法和编程研讨会)

作者： Chao Liu Deze Zeng Hong Yao Chengyu Hu Xuesong Yan Yuanyuan Fan China Services Computing Technology and System Lab and Cluster and GridComputing Lab Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China

Hadoop/MapReduce has emerged as a de facto programming framework to explore cloud-computing *** has many configuration parameters,some of which are crucial to the performance of MapReduce *** practice,these parameters... 详细信息

ISBN: (纸本)9783319271392

关键词： Mapreduce Massive data processing Parameter configuration Performance optimization Search algorithm

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Whole-proteome based phylogenetic tree construction with inter-amino-acid distances and the conditional geometric distribution profiles

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Molecular phylogenetics and evolution 2015年 89卷 37-45页

作者： Xian-Hua Xie Zu-Guo Yu Guo-Sheng Han Wei-Feng Yang Vo Anh 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 Hunan 411105 PR China School of Mathematics and Computer Science Gannan Normal University Jiangxi 341000 PR China. Electronic address: xxianhua@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: yuzuguo@***. 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 Hunan 411105 PR China. Electronic address: korea10282003@***. 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 Hunan 411105 PR China. Electronic address: yangweifeng09@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: v.anh@qut.edu.au.

There has been a growing interest in alignment-free methods for whole genome comparison and phylogenomic studies. In this study, we propose an alignment-free method for phylogenetic tree construction using whole-proteome sequences. Based on the inter-amino-acid distances, we first convert the whole-proteome sequences into inter-amino-acid distance vectors, which are called observed inter-amino-acid distance profiles. Then, we propose to use conditional geometric distribution profiles (the distributions of sequences where the amino acids are placed randomly and independently) as the reference distribution profiles. Last the relative deviation between the observed and reference distribution profiles is used to define a simple metric that reflects the phylogenetic relationships between whole-proteome sequences of different organisms. We name our method inter-amino-acid distances and conditional geometric distribution profiles (IAGDP). We evaluate our method on two data sets: the benchmark dataset including 29 genomes used in previous published papers, and another one including 67 mammal genomes. Our results demonstrate that the new method is useful and efficient.

关键词： Alignment-free whole-proteome comparison Conditional geometric distribution profile Inter-amino-acid distances Phylogenetic tree

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Research on BP imaging algorithm parallelization using C6678 DSPS

Research on BP imaging algorithm parallelization using C6678...

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IET International Radar Conference 2015

作者： Jiayun Zhao Yuan Yuan Jinghua Wang Ye Jin Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China North China Institute of Computing Technology Beijing China Beijing institute of spacecraft system engineering China academy of space technology Beijing China

ISBN: (纸本)9781510822023

Compared to the traditional SAR imaging algorithm, Back Projection(BP) algorithm is an accurate point-by-point imaging radar algorithm based on time-domain, with simple principle and without any approximation error in the imaging process. However, because of intensive computation and low efficiency, it's a new challenge to storage to capacity, throughput and processing ability of DSPs, a single DSP is not enough to meet these demands. So a parallel implementation method of BP algorithm based on TMS320C6678 DSP is proposed in this *** put forward a large point FFT multi-core parallel processing method on 2/4/8 cores what is frequently used in BP algorithm, and a multi-core synchronization method based on distributed memory. Finally using the measured data, we verify the parallel method can greatly enhance the multi-core parallelism, and the real-time performance of BP algorithm has been significantly improved.

关键词： BP imaging algorithm large point FFT multi-core parallel processing multi-core synchronization

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Research and development of advanced computing technologies

Scientific World Journal

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Scientific World Journal 2015年第1期2015卷 239723页

作者： Ding, Shifei Shi, Zhongzhi Azar, Ahmad Taher School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Faculty of Computers and Information Benha University Benha 13511 Egypt

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An efficient co-evolutionary particle swarm optimizer for solving multi-objective optimization problems

An efficient co-evolutionary particle swarm optimizer for so...

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第27届中国控制与决策会议

作者： Daqing Wu Li Liu Xiang Jian Gong Li Deng Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Computer Science and Technology Institute University of South China Glorious Sun School of Business and Management Dong Hua University

An efficient co-evolutionary multi-objective particle swarm optimizer named ECMPSO was *** uses dynamic multiple swarms to deal with multiple objectives,taking one objective is optimized by each swarm into account,and maintains diversity of new found non-dominated solutions via adopts a three-level particle swarm optimization(PSO) updating rule wherein the particles learn their experiences based on personal,neighborhood,and external *** prove the validity of the ECMPSO algorithm for solving multi-objective problems,some benchmark problems and one real-life problem are selected to validate the performance of the ECMPSO *** experiment results show that the ECMPSO algorithm is better in terms of search precision and convergence performance than other three algorithms from the literature.

关键词： Multi-objective Optimization,Particle Swarm Optimizer,Neighborhood Best Particle,Dynamic Swarms,Economic Environmental Dispatch

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Adaptive weighted label propagation for local matching based face recognition

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8833卷 78-85页

作者： Guo, Yan Li, Xiaohui Yi, Yugen Wei, Yunyan Wang, Jianzhong Changchun Vocational Institute Of Technology Changchun China College of Computer Science and Information Technology Key Laboratory of Intelligent Information Processing of Jilin Universities Northeast Normal University Changchun China

In this paper, a new algorithm named Adaptive Weighted Label Propagation (AWLP) which explores the complementary property among subpatterns from the same face image is proposed for local matching based face recognition. The proposed AWLP first partitions the face images into several smaller sub-images. Then, multiple similarity graphs are constructed for different sub-pattern sets. At last, in order to take correlation among different subpatterns into account, the graphs obtained by various sub-pattern sets are combined and the procedures of label prediction and graph weight learning are integrated into a unified framework to propagate the class information of the labeled samples to unlabeled ones. Moreover, a simple yet efficient iterative update algorithm is also proposed to solve our AWLP. Extensive experiments on three face benchmark databases show that AWLP has very competitive performance with the state-of-the-art algorithms. © Springer International Publishing Switzerland 2014.

关键词： Face recognition

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