41663574
Mining Frequent Tree-Like Patterns in Large Datasets
In this paper, we propose a novel data mining scheme to explore the frequent hierarchical structure patterns, named tree-like patterns, with the relationship of each item on a sequence. By tree-like patterns, we are clear to find out the relation of items between the cause and effect. Finally, we discuss the different characteristics to our mined patterns with others. As a consequence, we can find out that our addressed tree-like patterns can be widely used to explore a variety of different applications.
29824463
Task migration in 2D wormhole-routed mesh multicomputers
In this paper, two simple task migration schemes are first proposed for 2D mesh, multicomputers with supporting X-Y wormhole routing in the one-port communication model. We next propose a hybrid task migration scheme which attempts to minimize the total transmission latency. Finally, we compare all of our proposed task migration schemes using performance analysis.
17694458
Optimal broadcasting on incomplete star graph interconnection networks
Broadcasting is an important collective communication operation in many applications which use parallel computing. In this paper, we focus on designing broadcasting algorithms for general incomplete star graphs. We propose two optimal one-to-all broadcasting algorithms for incomplete star graphs with a single-port communication model. An incomplete star graph with N nodes, where (n - 1)! < N < n!, is a subgraph of an n-dimensional star graph. The first scheme is single-message one-to-all broadcasting that takes O(nlogn) steps. The second one is multi-message one-to-all broadcasting that takes O(nlogn + m) steps.
206854715
Task Migration in 2D Wormhole-Routed Mesh Multicomputers
In this paper, two simple task migration schemes are first proposed for 2D mesh, multicomputers with supporting X-Y wormhole routing in the one-port communication model. We next propose a hybrid task migration scheme which attempts to minimize the total transmission latency. Finally, we compare all of our proposed task migration schemes using performance analysis.
6997568
Gathering-Load-Balanced Tree Protocol for Wireless Sensor Networks
Wireless communications and digital electronics have enabled the development of low-power, low-cost, multifunctional sensors. The functionality of sensor network mainly is the measurement collection. The previous proposed protocols mainly focus on broadcasting tree structure, but it may not be efficient in data gathering. In this paper, we propose novel tree architecture that is a dynamic, adjustable, load-balanced tree in sensor networks to decrease the energy utilization and to prolong the lifetime of sensors. A load-balanced tree can share the forwarding loads between non-leaf nodes. Due to the limit of tree topology, we try to adjust forming a tree that has similar amount of children between non-leaf nodes. When the energy of non-leaf node is lower than a designated threshold, the tree structure can be readjusted to prolong the lifetime of tree. In simulation, we compare our protocol with different adjustment phases and an existed protocol. From the result, our protocol can have better performance than others
45004012
Dual-Hamiltonian-path-based multicasting on wormhole-routed star graph interconnection networks
The star graph interconnection network has been recognized as an attractive alternative to the popular hypercube network. In this paper we first address a dual-Hamiltonian-path-based (DHPB) routing model with two virtual channels based on two Hamiltonian paths and a network partitioning strategy for wormhole-routed star graph networks. Then, we propose three efficient multicast routing schemes on basis of such a model. The proposed schemes are network-selection-based (NSB), heuristic-network-selection-based (HNSB), and two-phase heuristic-network-selection-based (TP-HNSB) dual-path routing. All of the three proposed schemes are deadlock-free. Finally, experimental results are given to show our proposed three routing schemes outperform the unicast-based, the Hamiltonian-path, and the single-Hamiltonian-path-based (SHPB) dual-path routing schemes significantly.
5876317
Framework of performance evaluation for mobile process based on mobile ambient
Formal methodology for distributed and concurrent system, especially computation system with mobility, is increasingly important both in the theory and the practice. Based on the calculus of mobile ambient (MA), a widely studied formal mechanism for mobile computation, this paper focuses on the quantitative analysis of mobile computation system and provides a framework of performance evaluation for it. In details, this paper introduces an enhanced labelled transition system as the system model, and assigns rate to each label through so-called cost function. Based on it, the labelled transition system can be mapped to continuous time Markov chains and thus performance evaluation can be carried out by standard numerical techniques and tools. In some sense the main work of this paper can be regarded as integrating behavior and performance analysis in a compositional formal framework, which provides some basis of unified formal methodology for the development of mobile system.
1080835
Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation
In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms. Keywords—Finite Gaussian mixture model, Hidden Markov random field model, image segmentation, MRI.
34739655
On the Bisimulation Congruence in chi-Calculus
In this paper, we study weak bisimulation congruences for the χ-calculus, a symmetric variant of the π-calculus. We distinguish two styles of such bisimulation definitions, i.e. “open” and “closed” bisimulation, the difference between which lies in that in open style the equivalence is closed under context in every bisimulation step whereas in closed style the equivalence is closed under context only at the very beginning. As a result, we show that both in labelled and barbed congruence, the open and closed style definitions coincide. Thus all bisimulation congruences collapse into two equivalences, that is, the well-known open congruence and open barbed congruence, which are the same in the strong case, while in the weak case their difference can be reflected by one axiom. The results of this paper close some conjectures in the literatures and shed light on the algebraic theory of a large class of mobile process calculi.
15282622
Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.