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distributed computing method based on mathematical morphology

2nd International Conference on Green Communications and Networks, GCN 2012

作者： Wang, Yanbo Modern Education Technique Center Changchun University of Chinese Medicine Changchun 130117 China

ISBN: (纸本)9783642354694

The mathematical basis of mathematical morphology is set theory, which is widely used in the field of image processing, and distributed computing methods will require significant computing resources case that is broken down into small pieces, it has a plurality of computer parallel collaborative, and finally gives the results. Based on the heterogeneous network workstation as research platform, the mapping hyperspectral image processing technology is studied as a case that discusses the mathematical morphology and distributed computing method in the platform applications surveying and mapping hyperspectral image processing heterogeneous. Hyperspectral imaging is a new remote sensing technology. Its effective response time is the key of space earth science and planetary exploration. In response to this problem, the distributed calculation method is realized based on mathematical morphology to establish performance evaluation indicators. Through comparative analysis, the method is verified that using the morphological algorithm of distributed algorithm has a time advantage, which will contribute to the high spectrum image to select the appropriate distributed computing technology and to adopt a particular application strategy. © 2013 Springer-Verlag.

关键词： distributed computer systems

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Soft-Constrained Nonnegative Matrix Factorization via Normalization

Soft-Constrained Nonnegative Matrix Factorization via Normal...

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International Joint Conference on Neural Networks

作者： Long Lan Naiyang Guan Xiang Zhang Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Semi-supervised clustering aims at boosting the clustering performance on unlabeled samples by using labels from a few labeled samples. Constrained NMF (CNMF) is one of the most significant semi-supervised clustering methods, and it factorizes the whole dataset by NMF and constrains those labeled samples from the same class to have identical encodings. In this paper, we propose a novel soft-constrained NMF (SCNMF) method by softening the hard constraint in CNMF. Particularly, SCNMF factorizes the whole dataset into two lower-dimensional factor matrices by using multiplicative update rule (MUR). To utilize the labels of labeled samples, SCNMF iteratively normalizes both factor matrices after updating them with MURs to make encodings of labeled samples close to their label vectors. It is therefore reasonable to believe that encodings of unlabeled samples are also close to their corresponding label vectors. Such strategy significantly boosts the clustering performance even when the labeled samples are rather limited, e.g., each class owns only a single labeled sample. Since the normalization procedure never increases the computational complexity of MUR, SCNMF is quite efficient and effective in practices. Experimental results on face image datasets illustrate both efficiency and effectiveness of SCNMF compared with both NMF and CNMF.

关键词： Matrix decomposition Encoding Vectors Clustering methods Clustering algorithms Time complexity Face

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Box-constrained Projective Nonnegative Matrix Factorization via Augmented Lagrangian Method

Box-constrained Projective Nonnegative Matrix Factorization ...

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International Joint Conference on Neural Networks

作者： Xiang Zhang Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Projective non-negative matrix factorization (PNMF) projects a set of examples onto a subspace spanned by a non-negative basis whose transpose is regarded as the projection matrix. Since PNMF learns a natural parts-based representation, it has been successfully used in text mining and pattern recognition. However, it is non-trivial to analyze the convergence of the optimization algorithms for PNMF because its objective function is non-convex. In this paper, we propose a Box-constrained PNMF (BPNMF) method to overcome this deficiency of PNMF. In particular, BPNMF introduces an auxiliary variable, i.e., the coefficients of examples, and incorporates the following two types of constraints: 1) each entry of the basis is non-negative and upper-bounded, i.e., box-constrained, and 2) the coefficients equal to the projected points of the examples. The first box constraint makes the basis to be bound and the second equality constraint keeps its equivalence to PNMF. Similar to PNMF, BPNMF is difficult because the objective function is non-convex. To solve BPNMF, we developed an efficient algorithm in the frame of augmented Lagrangian multiplier (ALM) method and proved that the ALM-based algorithm converges to local minima. Experimental results on two face image datasets demonstrate the effectiveness of BPNMF compared with the representative methods.

关键词： Accuracy Linear programming Training Convergence Optimization Face Educational institutions

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Weighted sum-rate maximization for multi-user SIMO multiple access channels in cognitive radio networks

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EURASIP JOURNAL ON ADVANCES IN SIGNAL processing 2013年第1期2013卷 1页

作者： He, Peter Zhao, Lian Lu, Jianhua Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In this article, an efficient distributed and parallel algorithm is proposed to maximize the sum-rate and optimize the input distribution policy for the multi-user single input multiple output multiple access channel (MU-SIMO MAC) system with concurrent access within a cognitive radio (CR) network. The single input means that every user has a single antenna and multiple output means that base station(s) has multiple antennas. The main features are: (i) the power distribution for the users is updated by using variable scale factors which effectively and efficiently maximize the objective function at each iteration;(ii) distributed and parallel computation is employed to expedite convergence of the proposed distributed algorithm;and (iii) a novel water-filling with mixed constraints is investigated, and used as a fundamental block of the proposed algorithm. Due to sufficiently exploiting the structure of the proposed model, the proposed algorithm owns fast convergence. Numerical results verify that the proposed algorithm is effective and fast convergent. Using the proposed approach, for the simulated range, the required number of iterations for convergence is two and this number is not sensitive to the increase of the number of users. This feature is quite desirable for large scale systems with dense active users. In addition, it is also worth noting that the proposed algorithm is a monotonic feasible operator to the iteration. Thus, the stop criterion for computation could be easily set up.

关键词： Channel capacity Multi-user MIMO (MU-MIMO) Multi-access Channels (MAC) Cognitive Radio (CR) Multiple-antenna Broadcast systems Maximum sum-rate Optimal power distribution Optimization methods Water-filling Algorithm with mixed constraints

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distributed and parallel Subarray Beamforming for Near-Field 3D Sonar Imaging 3

Distributed and Parallel Subarray Beamforming for Near-Field...

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3rd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Han, Yeqiang Fu, Jiabao Zhou, Fan Tian, Xiang Chen, Yaowu Zhejiang Univ Inst Adv Digital Technol & Instrumentat Hangzhou Zhejiang Peoples R China Zhejiang Prov Key Lab Network Multimedia Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479905614

The computational load is prohibitive in 3D sonar systems for real-time image generation. In the literature, an efficient frequency beamforming method based on the distributed and parallel subarray (DPS) has been introduced for planar arrays working in the far field. This paper extends the DPS beamforming algorithm to process the signals acquired from a scene in the near field. The alogrithm divides the full populated transducer array into two stage distributed subarrays. It performs parallel beamforming on the first-stage subarrays and applies the approximation for time delays in the near-field beam patterns of the second-stage subarray. The computational efficiency is significantly improved as compared with the traditional methods, while achieving the similar resolutions. In addition, a multi-FPGA signal processor is proposed for implementing the underwater 3-D imaging.

关键词： underwater 3D imaging sonar sensor array system sonar signal processing distributed and parallel subarray beamforming

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Multi-label Classification with Clustering for image and Text Categorization

Multi-label Classification with Clustering for Image and Tex...

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6th International Congress on image and Signal processing (CISP)

作者： Nasierding, Gulisong Sajjanhar, Atul Xinjiang Normal Univ Sch Comp Sci & Technol 102 Xin Yi Rd Urumqi 830001 Peoples R China Deakin Univ Sch Informat Technol Parallel & Distributed Comp Lab Burwood Vic 3125 Australia

ISBN: (纸本)9781479927630

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.

关键词： multi-label classification clustering image and text categorization

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The best-so-far ABC with multiple patrilines for clustering problems

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NEUROCOMPUTING 2013年 116卷 355-366页

作者： Banharnsakun, Anan Sirinaovakul, Booncharoen Achalakul, Tiranee King Mongkuts Univ Technol Thonburi Dept Comp Engn Bangkok Thailand

Clustering is an important process in many application domains such as machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics. The main objective of clustering is to search for hidden patterns that may exist in datasets. Since the clustering problem is considered to be NP-hard, previous research has applied bio-inspired heuristic methods to solve such problems. In this paper we propose an effective method for clustering using an algorithm inspired by the decision making processes of bee swarms. The algorithm is called the Best-so-far Artificial Bee Colony with multiple patrilines. In the Best-so-far method, the solution direction is biased toward the Best-so-far solution rather than a neighboring solution proposed in the original Artificial Bee Colony algorithm. We introduce another bee-inspired concept called multiple patrilines to further improve the diversity of solutions and allow the calculations to be distributed among multiple computing units. We empirically assess the performance of our proposed method on several standard datasets taken from the UCI Machine Learning Repository. The results show that the proposed method produces solutions that are as good as or better than the current state-of-the-art clustering techniques reported in the literature. Furthermore, to demonstrate the computing performance and scalability of the algorithm, we assess the algorithm on a large disk drive manufacturing dataset. The results indicate that our distributed Best-so-far approach is scalable and produces good solutions while significantly improving the processing time. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Clustering Best-so-far ABC Multiple patrilines Swarm intelligence Optimization distributed environments parallel computing

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Multimodal inspection in power engineering and building industries: challenges and solutions

Multimodal inspection in power engineering and building indu...

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Conference on Dimensional Optical Metrology and Inspection for Practical Applications ii

作者： Kujawinska, Malgorzata Malesa, Marcin Malowany, Krzysztof Warsaw Univ Technol Inst Micromech & Photon PL-02525 Warsaw Poland

ISBN: (纸本)9780819496898

Recently the demand and number of applications of full-field, optical measurement methods based on noncoherent light sources increased significantly. They include traditional image processing, thermovision, digital image correlation (DIC) and structured light methods. However, there are still numerous challenges connected with implementation of these methods to in-situ, long-term monitoring in industrial, civil engineering and cultural heritage applications, multimodal measurements of a variety of object features or simply adopting instruments to work in hard environmental conditions. In this paper we focus on 3D DIC method and present its enhancements concerning software modifications (new visualization methods and a method for automatic merging of data distributed in time) and hardware improvements. The modified 3D DIC system combined with infrared camera system is applied in many interesting cases: measurements of boiler drum during annealing and of pipelines in heat power stations and monitoring of different building steel struts at construction site and validation of numerical models of large building structures constructed of graded metal plate arches.

关键词： optical inspection methods digital image correlation method thermovision displacement and strain measurements industrial measurements power plants civil engineering

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Multiview image compression and transmission techniques in wireless multimedia sensor networks: A survey

Multiview image compression and transmission techniques in w...

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2013 7th International Conference on distributed Smart Cameras, ICDSC 2013

作者： Wang, Xiaoqin Ahmet Sekercioĝlu, Y. Drummond, Tom Department of Electrical and Computer System Engineering Monash University Melbourne VIC Australia

ISBN: (纸本)9781479921645

We present a survey of recent research works on multiview image compression and transmission techniques developed for Wireless Multimedia Sensor Networks (WMSNs). We classify them into two categories with respect to the coding methods adopted: (i) in-network processing with joint coding schemes, and (ii) distributed source coding schemes. The survey also includes a comprehensive evaluation of the limitations of each approach. Based on the review results, we discuss future research directions, and identify the ways of more efficient transmission of the spatially correlated and redundant data in WMSNs. © 2013 IEEE.

关键词： Surveys

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Feature Selection parallel Technique for Remotely Sensed imagery Classification

Feature Selection Parallel Technique for Remotely Sensed Ima...

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13th International Conference on Computational Science and Its Applications (ICCSA)

作者： LeKhac, NhienAn Wu, Bo Chen, ChongCheng Kechadi, M-Tahar Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland Fuzhou Univ Key Lab Spatial Data Mining & Informat Sharing Fuzhou Peoples R China

ISBN: (纸本)9783642396434

Remote sensing research focusing on feature selection has long attracted the attention of the remote sensing community because feature selection is a prerequisite for image processing and various applications. Different feature selection methods have been proposed to improve the classification accuracy. They vary from basic search techniques to clonal selections, and various optimal criteria have been investigated. Recently, methods using dependence-based measures have attracted much attention due to their ability to deal with very high dimensional datasets. However, these methods are based on Cramer's V test, which has performance issues with large datasets. In this paper, we propose a parallel approach to improve their performance. We evaluate our approach on hyper-spectral and high spatial resolution images and compare it to the proposed methods with a centralized version as preliminary results. The results are very promising.

关键词： feature selection parallel algorithm Cramer's V Test min-max association image classification

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