Node Merging Research Articles

Scalable Modeling of Motion and Boundary Geometry With Quad-Tree Node Merging

Quad-tree structures are often used to model motion between frames of a video sequence. However, a fundamental limitation of the quad-tree structure is that it can only capture horizontal and vertical edge discontinuities at dyadically related locations. To address this limitation, recent work has focused on the introduction of geometry information to nodes of tree structured motion representations. In this paper we explore modeling boundary geometry and motion with separate quad-tree structures; thereby enabling each attribute to be refined separately. Recent work into quad-tree representations has also highlighted the benefits of leaf merging. We extend the leaf merging paradigm to incorporate both geometry and motion attributes. We also explore resolution scalability of the merged dual tree representation and present experimental results which demonstrate both rate-distortion and scabalility performance. Theoretical investigations conducted in this paper reveal that to achieve optimal rate-distortion behavior, quad-tree motion models need to incorporate both geometry information and node merging.

FAILURE PREDICTION AND CRACK PROPAGATION WITH STRESS CONCENTRATION USING COHESIVE ZONE FINITE ELEMENT ANALYSIS

In most of the structures, failure occurs due to stress concentrations having notches, sharp corners with different radii’s due to the built in geometry of the structure, thereby tend to weaken the structure and the crack may likely to initiate, followed by crack propagation toward the weakest zone within the material. The aim of this research work is to investigate and predict the structural failure with stress concentration having sharp corners with different corner radii’s, using the finite element modelling. Stress concentration and crack propagation has been considered in detail. In this study, cohesive zone model has been considered in greater details to investigate the stress concentration phenomenon using the finite element modelling approach based on exponential load displacement model for interfacial separation of cohesive surfaces produced by the nodal release approach after node merging mechanism. For each model presented in this research work, a separate user input file has been developed using ANSYS APDL script language, which can later be used for specific change of various variables like crack path, corner radius, meshing, refined meshing, areas and boundary conditions along the specific regions to determine and compare results.

FAILURE PREDICTION AND CRACK PROPAGATION WITH STRESS CONCENTRATION USING COHESIVE ZONE FINITE ELEMENT ANALYSIS

In most of the structures, failure occurs due to stress concentrations having notches, sharp corners with different radii’s due to the built in geometry of the structure, thereby tend to weaken the structure and the crack may likely to initiate, followed by crack propagation toward the weakest zone within the material. The aim of this research work is to investigate and predict the structural failure with stress concentration having sharp corners with different corner radii’s, using the finite element modelling. Stress concentration and crack propagation has been considered in detail. In this study, cohesive zone model has been considered in greater details to investigate the stress concentration phenomenon using the finite element modelling approach based on exponential load displacement model for interfacial separation of cohesive surfaces produced by the nodal release approach after node merging mechanism. For each model presented in this research work, a separate user input file has been developed using ANSYS APDL script language, which can later be used for specific change of various variables like crack path, corner radius, meshing, refined meshing, areas and boundary conditions along the specific regions to determine and compare results.

A biting‐down approach to hierarchical decomposition of object‐oriented systems based on structure analysis

AbstractSystem decomposition has been widely viewed as an effective means to facilitate the comprehension of complex software systems and/or capture potentially reusable components in them. In fact, various approaches to system decomposition have been intensively documented in the literature. However, during the process of system decomposition, only a few of them can also capture the target system's hierarchical organization structure, which is essential when the target system is very complex. In this paper, we present a biting‐down approach to hierarchical decomposition of object‐oriented systems. Compared with the previous hierarchical approaches, the distinct features of this approach are as follows. First, our approach does not rely on agglomeration, and thus can avoid some unnecessary calculations. Second, our approach does not require merging nodes when performing high‐level decomposition, and thus can avoid imprecision induced by the merging. To evaluate our approach, we conducted a case study and an experimental study on our approach. The results of these studies can confirm its effectiveness and its superiority over our previous approach. Copyright © 2009 John Wiley & Sons, Ltd.

Fast Node Merging With Don't Cares Using Logic Implications

Node merging is a popular and effective logic restructuring technique that has recently been applied to minimize logic circuits. However, in the previous satisfiability (SAT)-based methods, the search for node mergers required trial-and-error validity checking of a potentially large set of candidate mergers. Here, we propose a new method, which directly identifies node mergers using logic implications without any SAT solving calls. Although the efficiency benefits of the method come at the expense of quality, we further engage the redundancy removal and the wire replacement techniques to enhance its quality. The experimental results show that the proposed optimization method achieves approximately 46 times the speedup while possessing a competitive capability of circuit minimization compared to the state-of-the-art method.

Quad-Tree Motion Modeling With Leaf Merging

In this paper, we are concerned with the modeling of motion between frames of a video sequence. Typically, it is not possible to represent the motion between frames by a single model and therefore a quad-tree structure is often employed where smaller, variable size regions or blocks are allowed to take on separate motion models. Previous work into quad-tree representations has demonstrated the sub-optimal performance of quad-trees where the dependency between neighboring leaf nodes with different parents is not exploited. Leaf merging has been proposed to rectify this performance loss as it allows joint coding and optimization of related nodes. In this paper, we describe how the merging step can be incorporated into quad-tree motion representations for a range of motion modeling contexts. In particular, we study the impact of rate-distortion optimized merging for two motion coding schemes, these being spatially predictive coding, as used in H.264, and hierarchical coding. We present experimental results which demonstrate that node merging can provide significant gains for both the hierarchical and spatial prediction schemes. Interestingly, experimental results also show that in the presence of merging, the rate-distortion performance of hierarchical coding is comparable to that of spatial prediction. We pursue the case of hierarchical coding further in this paper, introducing polynomial motion models to the quad-tree representation and exploring resolution scalability of the merged quad-tree structure. We also present a theoretical study of the impact of leaf merging in modeling motion, identifying the inherent advantages of merging which give rise to a more efficient description of frame motion.

A graphical method for reducing and relating models in systems biology

Motivation: In Systems Biology, an increasing collection of models of various biological processes is currently developed and made available in publicly accessible repositories, such as biomodels.net for instance, through common exchange formats such as SBML. To date, however, there is no general method to relate different models to each other by abstraction or reduction relationships, and this task is left to the modeler for re-using and coupling models. In mathematical biology, model reduction techniques have been studied for a long time, mainly in the case where a model exhibits different time scales, or different spatial phases, which can be analyzed separately. These techniques are however far too restrictive to be applied on a large scale in systems biology, and do not take into account abstractions other than time or phase decompositions. Our purpose here is to propose a general computational method for relating models together, by considering primarily the structure of the interactions and abstracting from their dynamics in a first step.Results: We present a graph-theoretic formalism with node merge and delete operations, in which model reductions can be studied as graph matching problems. From this setting, we derive an algorithm for deciding whether there exists a reduction from one model to another, and evaluate it on the computation of the reduction relations between all SBML models of the biomodels.net repository. In particular, in the case of the numerous models of MAPK signalling, and of the circadian clock, biologically meaningful mappings between models of each class are automatically inferred from the structure of the interactions. We conclude on the generality of our graphical method, on its limits with respect to the representation of the structure of the interactions in SBML, and on some perspectives for dealing with the dynamics.Availability: The algorithms described in this article are implemented in the open-source software modeling platform BIOCHAM available at http://contraintes.inria.fr/biocham The models used in the experiments are available from http://www.biomodels.net/Contact: francois.fages@inria.fr

A Novel Texture-Preceded Segmentation Algorithm for High-Resolution Imagery

Image segmentation is crucial to object-oriented remote sensing imagery analysis. In this paper, a novel texture-preceded segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as a loose constraint for later segmentation. The algorithm is based on the graph models of region adjacency graph and nearest neighbor graph, which can achieve fast node merging, depending on the global optimum. Here, a combined distance, composed of texture, spectral, and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. Then, the combined distance is applied to graph models, and the final segmentation result can be obtained iteratively by fast merging. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogeneous texture segments with spectral variability easily but can also detect the real object boundaries well. The experiments on high-resolution imagery show that, in terms of the same number of segments, the proposed algorithm can improve segmentation accuracy by 10%-20% compared to the results obtained by pure spectral features with Definiens Developer software.

Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy

Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorithm construct FP-tree, then compress orderly FP-tree by merging nodes which has equal minsup in same branch, also uses subset mix pruning technique, avoid superset checking. The experimental results show the algorithm has higher time, space efficiency and good scalability.

New Policy of Maximal Frequent Itemsets in Data Stream Mining

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FP-tree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

Distributed address auto configuration protocol for Manet networks

Mobile ad hoc networks are wireless networks characterized by: limited bandwidth, shared transmission channel and the lack of central processing unit of administration and configuration. Most research related to Manet assures that host IP address is configured as the node joins the network. However, it is impossible to ensure that because there is no infrastructure. Therefore, an auto configuration scheme is needed to perform network management and to attribute connection parameters. Hence, we present a new distributed IP address configuration approach for Manet networks. Our solution provides a unique address allocation for each node in the network. The address assignment is assured under different network conditions including node failures, network partitioning and merging.

An improved recursive decomposition algorithm for reliability evaluation of lifeline networks

The seismic reliability evaluation of lifeline networks has received considerable attention and been widely studied. In this paper, on the basis of an original recursive decomposition algorithm, an improved analytical approach to evaluate the seismic reliability of large lifeline systems is presented. The proposed algorithm takes the shortest path from the source to the sink of a network as decomposition policy. Using the Boolean laws of set operation and the probabilistic operation principal, a recursive decomposition process is constructed in which the disjoint minimal path set and the disjoint minimal cut set are simultaneously enumerated. As the result, a probabilistic inequality can be used to provide results that satisfy a prescribed error bound. During the decomposition process, different from the original recursive decomposition algorithm which only removes edges to simplify the network, the proposed algorithm simplifies the network by merging nodes into sources and removing edges. As a result, the proposed algorithm can obtain simpler networks. Moreover, for a network owning s-independent components in its component set, two network reduction techniques are introduced to speed up the proposed algorithm. A series of case studies, including an actual water distribution network and a large urban gas system, are calculated using the proposed algorithm. The results indicate that the proposed algorithm provides a useful probabilistic analysis method for the seismic reliability evaluation of lifeline networks.

Towards Learning Morphology for Under-Resourced Fusional and Agglutinating Languages

In this paper, we describe a novel and effective approach for automatically decomposing a word into stem and suffixes. Russian and Turkish are used as exemplars of fusional and agglutinating languages. Rather than relying on corpus counts, we use a small number of word-pairs as training data, that can be particularly suited for under-resourced languages. For fusional languages, we initially learn a tree of aligned suffix rules (TASR) from word-pairs. The tree is built top-down, from general to specific rules, using suffix rule frequency and rule subsumption, and is executed bottom-up, i.e., the most specific rule that fires is chosen. TASR is used to segment a word form into a stem and suffix sequence. For fusional languages learning through generation (using TASR) is essential for proper stem extraction. Subsequently, an unsupervised segmentation algorithm graph-based unsupervised suffix segmentation (GBUSS) is used to segment the suffix sequence. GBUSS employs a suffix graph where node merging, guided by an information-theoretic measure, generates suffix sequences. The approach, experimentally validated on Russian, is shown to be highly effective. For agglutinating languages only the GBUSS is needed for word decomposition. Promising experimental results for Turkish are obtained.

Analysis of fMRI Data Using Improved Self-Organizing Mapping and Spatio-Temporal Metric Hierarchical Clustering

The self-organizing mapping (SOM) and hierarchical clustering (HC) methods are integrated to detect brain functional activation; functional magnetic resonance imaging (fMRI) data are first processed by SOM to obtain a primary merged neural nodes image, and then by HC to obtain further brain activation patterns. The conventional Euclidean distance metric was replaced by the correlation distance metric in SOM to improve clustering and merging of neural nodes. To improve the use of spatial and temporal information in fMRI data, a new spatial distance (node coordinates in the 2-D lattice) and temporal correlation (correlation degree of each time course in the exemplar matrix) are introduced in HC to merge the primary SOM results. Two simulation studies and two in vivo fMRI data that both contained block-design and event-related experiments revealed that brain functional activation can be effectively detected and that different response patterns can be distinguished using these methods. Our results demonstrate that the improved SOM and HC methods are clearly superior to the statistical parametric mapping (SPM), independent component analysis (ICA), and conventional SOM methods in the block-design, especially in the event-related experiment, as revealed by their performance measured by receiver operating characteristic (ROC) analysis. Our results also suggest that the proposed new integrated approach could be useful in detecting block-design and event-related fMRI data.

Exploitation of Interframe Redundancy for Real-Time Volumetric Reconstruction of Arbitrary Shapes

Scalable systems reconstructing arbitrary shapes in real time need a cluster of computers and an efficient strategy to share information. Optimizing the performances of the system requires considering how to spread the complexity over the nodes of the cluster. It means taking into account the kind of information to share between nodes, how to transmit it, and how to merge it given the available bandwidth and the impact of those steps on performances. We present a distributed and scalable volumetric architecture based on an efficient exploitation of interframe redundancy and on an efficient merging of partial models. The architecture is composed of acquisition nodes reconstructing partial models from multiple views and of a master node merging partial models. The master node updates local copies of the partial models with nonredundant information from the acquisition nodes. Then it merges the partial models to produce the volumetric description of the scene. The test of this architecture with a single feature coding visibility, occupancy, and subdivision of space proves its efficiency. The chosen feature allows each camera to see only part of the volume of interest. We show real-time results on a cluster of usual PC platforms.

CanTree: a canonical-order tree for incremental frequent-pattern mining

Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree-based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree in the incremental mining of frequent patterns. Moreover, the applicability of CanTrees is not confined to incremental mining; CanTrees can also be applicable to other frequent-pattern mining tasks including constrained mining and interactive mining.

Rule-Based Learning Systems for Support Vector Machines

In this article, we propose some methods for deriving symbolic interpretation of data in the form of rule based learning systems by using Support Vector Machines (SVM). First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for performing rule extraction. By using support vectors from a learned SVM it is possible in our approach to use any standard Radial Basis Function (RBF) learning technique for the rule extraction, whilst avoiding the overlapping between classes problem. We will show that merging node centers and support vectors explanation rules can be obtained in the form of ellipsoids and hyper-rectangles. Next, in a dual form, following the framework developed for RBFNN, we construct an algorithm for SVM. Taking SVM as the main paradigm, geometry in the input space is defined from a combination of support vectors and prototype vectors obtained from any clustering algorithm. Finally, randomness associated with clustering algorithms or RBF learning is avoided by using only a learned SVM to define the geometry of the studied region. The results obtained from a certain number of experiments on benchmarks in different domains are also given, leading to a conclusion on the viability of our proposal.

Legitimate Skew Clock Routing with Buffer Insertion

In this paper, we propose a new quick and effective legitimate skew clock routing with buffer insertion algorithm. We analyze the optimal buffer position in the clock path, and conclude the sufficient condition and heuristic condition for buffer insertion in clock net. During the routing process, this algorithm integrates buffer insertion and node merging together, and performs them in parallel. Compared with the method of buffer insertion after zero skew clock routing, our method improves the maximal clock delay by at least 48%. Compared with legitimate skew clock routing algorithm with no buffer, this algorithm further decreases the total wire length and gets reductions from 42 to 82% in maximal clock delay. The experimental results show that our algorithm is quick and effective.

Evolving networks by merging cliques

We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra, and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erdös-Rényi or regular graphs. Our model becomes the same model as the Barabási-Albert model under a specific condition.

Mappings between probabilistic Boolean networks

Probabilistic Boolean Networks (PBNs) comprise a graphical model based on uncertain rule-based dependencies between nodes and have been proposed as a model for genetic regulatory networks. As with any algebraic structure, the characterization of important mappings between PBNs is critical for both theory and application. This paper treats the construction of mappings to alter PBN structure while at the same time maintaining consistency with the original probability structure. It considers projections onto sub-networks, adjunctions of new nodes, resolution reduction mappings formed by merging nodes, and morphological mappings on the graph structure of the PBN. It places PBNs in the framework of many-sorted algebras and in that context defines homomorphisms between PBNs.