The proceedings contain 23 papers. The topics discussed include: challenges for formal verification in industrial setting;an easy-to-use, efficient tool-chain to analyze the availability of telecommunication equipment...
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(纸本)3540709517
The proceedings contain 23 papers. The topics discussed include: challenges for formal verification in industrial setting;an easy-to-use, efficient tool-chain to analyze the availability of telecommunication equipment;discovering symmetries;on combining partial order reduction with fairness assumptions;test coverage for loose timing annotations;heuristics for ioco-based test-based modelling;verified design of an automated parking garage;evaluating quality of service for service level agreements;simulation-based performance analysis of a medical image-processing architecture;a finite state modeling of AFDX frame management using spin;automated incremental synthesis of timed automata;SAT-based verification of LTL formulas;parallel SAT solving in bounded model checking;and parallel algorithms for finding SCCs in implicitly given graphs.
Convergence of computer systems and communication technologies are moving to switched high-performance modular system architectures on the basis of high-speed switched interconnections. Multi-core processors become mo...
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Convergence of computer systems and communication technologies are moving to switched high-performance modular system architectures on the basis of high-speed switched interconnections. Multi-core processors become more perspective way to high-performance system, and traditional parallel bus system architectures (VME/VXI, cPCI/PXI) are moving to new higher speed serial switched interconnections. Fundamentals in system architecture development are compact modular component strategy, low-power processor, new serial high-speed interface chips on the board, and high-speed switched fabric for SAN architectures. Overview of advanced modular concepts and new international standards for development high-performance embedded and compact modular systems for real-time applications are described. (c) 2006 Elsevier B.V. All rights reserved.
Large-scale numerical simulation produces datasets with ever-growing size and complexity. In particular, unstructured meshes are encountered in many applications. Volume rendering provides a way to efficiently analyze...
A difference scheme for noise removal based on four-order partial differential equations is suggested. It can approximate actual image while preserving edges and avoiding blocky effects in imageprocessing. Numerical ...
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A difference scheme for noise removal based on four-order partial differential equations is suggested. It can approximate actual image while preserving edges and avoiding blocky effects in imageprocessing. Numerical results are demonstrated its efficiency and the better choice of parameters.
The following topics were dealt with: information assurance; cryptography and network security; component-based software engineering; software specification and architecture; software testing; requirements engineering...
The following topics were dealt with: information assurance; cryptography and network security; component-based software engineering; software specification and architecture; software testing; requirements engineering; user-centered design methods; embedded systems; operating systems; image, speech, and signal processing; data mining and knowledge discovery; Internet technology and applications; artificial intelligence; natural language processing; neural networks and genetic algorithms; parallel and distributed computing; voice-over-IP; communication systems and networks; mobile/wireless/ad-hoc networks; collaborative computing; algorithms; visual and multimedia computing; web-based applications; E-commerce and its applications.
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fields such as computer vision, image pro...
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Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fields such as computer vision, imageprocessing, and distributed sensor networks. In this the- sis we study two central signal processing problems involving Gaussian graphical models, namely modeling and estimation. The modeling problem involves learning a sparse graphi- cal model approximation to a specified distribution. The estimation problem in turn exploits this graph structure to solve high-dimensional estimation problems very efficiently. We propose a new approach for learning a thin graphical model approximation to a specified multivariate probability distribution (e. g., the empirical distribution from sample data). The selection of sparse graph structure arises naturally in our approach through the solution of a convex optimization problem, which differentiates our procedure from stan- dard combinatorial methods. In our approach, we seek the maximum entropy relaxation (MER) within an exponential family, which maximizes entropy subject to constraints that marginal distributions on small subsets of variables are close to the prescribed marginals in relative entropy. We also present a primal-dual interior point method that is scalable and tractable provided the level of relaxation is sufficient to obtain a thin graph. A crucial ele- ment of this algorithm is that we exploit sparsity of the Fisher information matrix in models defined on chordal graphs. The merits of this approach are investigated by recovering the graphical structure of some simple graphical models from sample data. Next, we present a general class of algorithms for estimation in Gaussian graphical models with arbitrary structure. These algorithms involve a sequence of inference prob- lems on tractable subgraphs over subsets of variables. This framework includes parallel iterations such as Embedded Trees, serial iterati
The Katsevich image reconstruction algorithm is the first theoretically exact cone beam image reconstruction algorithm for a helical scanning path in computed tomography (CT). However, it requires much more computatio...
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The Katsevich image reconstruction algorithm is the first theoretically exact cone beam image reconstruction algorithm for a helical scanning path in computed tomography (CT). However, it requires much more computation and memory than other CT algorithms. Fortunately, there are many opportunities for coarse-grained parallelism using multiple threads and fine-grained parallelism using SIMD units that can be exploited by emerging multi- core processors. In this paper, we implemented and optimized Katsevich image reconstruction based on the previously proposed pi- interval method and cone beam cover method and parallelized them using OpenMP API and SIMD instructions. We also exploited symmetry in the backprojection stage. Our results show that reconstructing a 1024 times 1024 times 1024 image using 5120 512 times 128 projections on a dual-socket quad-core system took 23,798 seconds on our baseline and 642 seconds on our final version, a more than 37 times speedup. Furthermore, by parallelizing the code with more threads we found that the scalability is eventually hinged by the limited front-side bus bandwidth.
In message-passing parallel applications, messages are not delivered in a strict order. In most applications, the computation results and the set of messages produced during the execution should be the same for all di...
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In message-passing parallel applications, messages are not delivered in a strict order. In most applications, the computation results and the set of messages produced during the execution should be the same for all distinct orderings of messages delivery. Finding an ordering that produces a different outcome then reveals a message race. Assuming that the partial order execution graph (POEG) capturing the causality between events is known for a reference execution, the present paper describes techniques for identifying independent sets of messages and within each set equivalent message orderings. Orderings of messages belonging to different sets may then be re-executed independently from each other, thereby reducing the number of orderings that must be tested to detect message races. We integrated the presented techniques into the dynamic parallel schedules parallelization framework, and applied our approach on an imageprocessing, a linear algebra, and a neighborhood-dependent parallel computation. In all cases, the number of possible orderings is reduced by several orders of magnitudes. In order to further reduce this number, we describe an algorithm that generates a subset of orderings that are likely to reveal existing message races.
A tristate approach (TA) for image denoising processing is presented; the noise is aimed at the presence of pepper-and-salt noise. The newness of this method is that it develops a new route in the field of image resto...
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A tristate approach (TA) for image denoising processing is presented; the noise is aimed at the presence of pepper-and-salt noise. The newness of this method is that it develops a new route in the field of image restoration. The tristate approach algorithm focuses on the removal and restoration of the noisy speckles and avoids blurring and averaging edges and non-noise pixels in a way different from other known algorithms. Any noisy pixel is replaced by an estimated value. This value is the weighted mean of the pixels neighboring to the noisy pixel or the four iteration pixels got before it. This paper describes, analyzes and compares several methods and results of removing noise from an image. We have performed the experiments by adding Salt-and-Pepper in an original image.
image segment is one of the most fundamental and difficult problems in image analysis and computer vision. The image thresholding problem is treated as an important issue in imageprocessing, and it can not only reduc...
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image segment is one of the most fundamental and difficult problems in image analysis and computer vision. The image thresholding problem is treated as an important issue in imageprocessing, and it can not only reduce the image data, but also lay a good foundation for succedent target recognition and image understanding. Character of global thresholding segmentation and local thresholding was analyzed in image segmentation. A new thresholding statistic iterative arithmetic is presented to overcome the direct worth method in thresholding, aiming at some lighting asymmetry and the abrupt a blemish for, or bigger arithmetic figure in ratio in a variety in gray of background image. Statistics iterative thresholding segmentation, based on image gray histogram and Gauss statistics distributing, obtain the theory expression of statistics iterative method and the best worth thresholding method and steps. Aviation image was thresholding segmentation using statistic iterative arithmetic, histogram technique and adaptive method respectively. Compared three thresholding results, it shows that statistic iterative method greatly improved the anti-noise capability of image segmentation and had the good result to the image of the worth and not easy to segment in full value thresholding method.
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