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12th International Symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Kokulan, N. Lai, C. -H. Univ Greenwich Sch Comp & Math Sci London SE18 6PF England

ISBN: (纸本)9780769550602

There are many image processing techniques based on partial differential equations that perform well, but they consume much computational time. It's vital that rapid and efficient ways of solving these equations are developed. Use of the Laplace transforms permits solution to the time dependent problems in a parallel environment. The solution procedure requires numerical computation of an inverse Laplace transform of which the Stehfest method was examined in the tests. We investigated the performance and efficiency of using the Laplace transform technique for the solution of a mathematical model related to image in-painting and compared the results with temporal integration.

关键词： CDD in-painting temporal parallel methods time dependent problems

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Overlapping community detection via link partition of asymmetric weighted graph

Overlapping community detection via link partition of asymme...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Wenju Zhang Naiyang Guan Xuhui Huang Zhigang Luo Jianwu Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China 410073 National University of Defense Technology Changsha Hunan CN Beijing Institute of Technology Beijing Beijing CN

Link partition clusters edges of a complex network to discover its overlapping communities. Due to Its effectiveness, link partition has attracted much attentions from the network science community. However, since link partition assigns each edge of a network to unique community, it cannot detect the disjoint communities. To overcome this deficiency, this paper proposes a link partition on asymmetric weighted graph (LPAWG) method for detecting overlapping communities. Particularly, LPAWG divides each edge into two parts to distinguish the roles of connected nodes. This strategy biases edges to a specific node and helps assigning each node to its affiliated community. Since LPAWG introduces more edges than those in the original network, it cannot efficiently detect communities from some networks with relative large amount of edges. We therefore aggregate the line graph of LPAWG to shrink its scale. Experimental results of community detection on both synthetic datasets and the realworld networks show the effectiveness of LPAWG comparing with the representative methods.

关键词： Communities image edge detection Partitioning algorithms Benchmark testing Acceleration Educational institutions Complex networks

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distributed computing method based on mathematical morphology

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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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Oscillatory Neural Networks : In Problems of parallel Information processing

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2013年

作者： Kuzmina, Margarita G. Manykin, Eduard A. De Gruyter, Inc.

Understanding of the human brain functioning currently represents a challenging problem. In contrast to usual serial computers and complicated hierarchically organized artificial man-made systems, decentralized, parallel and distributed information processing principles are inherent to the brain. Besides adaptation and learning, which play a crucial role in brain functioning, oscillatory neural activity, synchronization and resonance accompany the brain work. Neural-like oscillatory network models, designed by the authors for image processing, allow to elucidate the capabilities of dynamical, synchronization-based types of image processing, presumably exploited by the brain. The oscillatory network models, studied by means of computer modeling and qualitative analysis, are presented and discussed in the book. Some other problems of parallel distributed information processing are also considered, such as a recall process from network memory for large-scale recurrent associative memory neural networks, performance of oscillatory networks of associative memory, dynamical oscillatory network methods of image processing with synchronization-based performance, optical parallel information processing based on the nonlinear optical phenomenon of photon echo, and modeling random electric fields of quasi-monochromatic polarized light beams using systems of superposed stochastic oscillators. This makes the book highly interesting to researchers dealing with various aspects of parallel information processing.

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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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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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Do You See What I See?

Do You See What I See?

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IEEE Applied imagery Pattern Recognition (AIPR) Workshop on Sensing for Control and Augmentation

作者： Cerkez, P. S. DCS Corp Lexington Pk MD 20653 USA

ISBN: (纸本)9781479925407

Semagrams are a subset of steganography. When a message is transmitted in a non-textual format, (i.e., in the visual content of an image), it is referred to as a semagram. While semagrams are relatively easy to create (as shown in published papers covering hiding techniques), detecting a hidden message in or embedded as an image-based semagram is a greater magnitude of difficultly than typical digital steganography. US Patents issued based on semagram technology show that this feature has been exploited in the copyright/watermarking world to increase protection. In a semagram, the image is the message and they work well for simple messages and dead drops. Attacks on semagrams are primarily visual examinations of artifacts. In the counter-espionage world, the rule of the thumb is that there is always a message hidden in an image or graphic, it is simply up to the steganalyst to find it. In short, detecting semagrams is a matter of recognizing patterns of patterns that represent a hidden message within an image. This presentation provides a brief summary of the technology underlying semagrams, present a short non-technical discussion of the technology used in the attack on semagrams, followed by a discussion on current work and planned future implementations of the proven semagram detection ANN. It will focus on extending the ANN to other domains (e. g., non-visual spectrums, multi/cross spectrum correlation, scene identification, image classification) and efforts to improve the processing speed and throughput via parallel/distributed methods.

关键词： images imagery (Psychotherapy) Steganography Messages image processing SPEED Spectra Thumb Artificial neural networks Throughput

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