WorldWideScience

Sample records for hierarchical tree network

  1. Prolonging the Lifetime of Wireless Sensor Networks Interconnected to Fixed Network Using Hierarchical Energy Tree Based Routing Algorithm

    Directory of Open Access Journals (Sweden)

    M. Kalpana

    2014-01-01

    Full Text Available This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN. It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP. The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV.

  2. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard;

    2012-01-01

    a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure......Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  3. Hierarchical Network Design

    DEFF Research Database (Denmark)

    Thomadsen, Tommy

    2005-01-01

    of different types of hierarchical networks. This is supplemented by a review of ring network design problems and a presentation of a model allowing for modeling most hierarchical networks. We use methods based on linear programming to design the hierarchical networks. Thus, a brief introduction to the various....... The thesis investigates models for hierarchical network design and methods used to design such networks. In addition, ring network design is considered, since ring networks commonly appear in the design of hierarchical networks. The thesis introduces hierarchical networks, including a classification scheme...... linear programming based methods is included. The thesis is thus suitable as a foundation for study of design of hierarchical networks. The major contribution of the thesis consists of seven papers which are included in the appendix. The papers address hierarchical network design and/or ring network...

  4. Hierarchical Network Design

    DEFF Research Database (Denmark)

    Thomadsen, Tommy

    2005-01-01

    Communication networks are immensely important today, since both companies and individuals use numerous services that rely on them. This thesis considers the design of hierarchical (communication) networks. Hierarchical networks consist of layers of networks and are well-suited for coping...... the clusters. The design of hierarchical networks involves clustering of nodes, hub selection, and network design, i.e. selection of links and routing of ows. Hierarchical networks have been in use for decades, but integrated design of these networks has only been considered for very special types of networks....... The thesis investigates models for hierarchical network design and methods used to design such networks. In addition, ring network design is considered, since ring networks commonly appear in the design of hierarchical networks. The thesis introduces hierarchical networks, including a classification scheme...

  5. Trees and Hierarchical Structures

    CERN Document Server

    Haeseler, Arndt

    1990-01-01

    The "raison d'etre" of hierarchical dustering theory stems from one basic phe­ nomenon: This is the notorious non-transitivity of similarity relations. In spite of the fact that very often two objects may be quite similar to a third without being that similar to each other, one still wants to dassify objects according to their similarity. This should be achieved by grouping them into a hierarchy of non-overlapping dusters such that any two objects in ~ne duster appear to be more related to each other than they are to objects outside this duster. In everyday life, as well as in essentially every field of scientific investigation, there is an urge to reduce complexity by recognizing and establishing reasonable das­ sification schemes. Unfortunately, this is counterbalanced by the experience of seemingly unavoidable deadlocks caused by the existence of sequences of objects, each comparatively similar to the next, but the last rather different from the first.

  6. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Xiaowei Li

    2017-01-01

    Full Text Available A large number of studies demonstrated that major depressive disorder (MDD is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

  7. Hierarchical tree-structured control network for the Antares laser facility

    Energy Technology Data Exchange (ETDEWEB)

    McGirt, F.

    1979-01-01

    The design and implementation of a distributed, computer-based control system for the Antares 100-kJ gas laser fusion facility is presented. Control system requirements and their operational interrelationships that consider both integrated system control and individual subsystem control are described. Several configurations of minicomputers are established to provide direct control of sets of microcomputers and to provide points of operator-laser interaction. Over 100 microcomputers are located very close to the laser device control points or sources of data and perform the real-time functions of the control system, such as data and control signal multiplexing, stepping motor control, and vacuum and gas system control. These microcomputers are designed to be supported as an integral part of the control network and to be software compatible with the larger minicomputers.

  8. Noise enhances information transfer in hierarchical networks.

    Science.gov (United States)

    Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A

    2013-01-01

    We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

  9. APHiD: Hierarchical Task Placement to Enable a Tapered Fat Tree Topology for Lower Power and Cost in HPC Networks

    Energy Technology Data Exchange (ETDEWEB)

    Michelogiannakis, George; Ibrahim, Khaled Z.; Shalf, John; Wilke, Jeremiah J.; Knight, Samuel; Kenny, Joseph P.

    2017-05-14

    The power and procurement cost of bandwidth in system-wide networks has forced a steady drop in the byte/flop ratio. This trend of computation becoming faster relative to the network is expected to hold. In this paper, we explore how cost-oriented task placement enables reducing the cost of system-wide networks by enabling high performance even on tapered topologies where more bandwidth is provisioned at lower levels. We describe APHiD, an efficient hierarchical placement algorithm that uses new techniques to improve the quality of heuristic solutions and reduces the demand on high-level, expensive bandwidth in hierarchical topologies. We apply APHiD to a tapered fat-tree, demonstrating that APHiD maintains application scalability even for severely tapered network configurations. Using simulation, we show that for tapered networks APHiD improves performance by more than 50% over random placement and even 15% in some cases over costlier, state-of-the-art placement algorithms.

  10. Structural changes in the minimal spanning tree and the hierarchical network in the Korean stock market around the global financial crisis

    Science.gov (United States)

    Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

    2015-04-01

    This paper considers stock prices in the Korean stock market during the 2008 global financial crisis by focusing on three time periods: before, during, and after the crisis. Complex networks are extracted from cross-correlation coefficients between the normalized logarithmic return of the stock price time series of firms. The minimal spanning trees (MSTs) and the hierarchical network (HN) are generated from cross-correlation coefficients. Before and after the crisis, securities firms are located at the center of the MST. During the crisis, however, the center of the MST changes to a firm in heavy industry and construction. During the crisis, the MST shrinks in comparison to that before and that after the crisis. This topological change in the MST during the crisis reflects a distinct effect of the global financial crisis. The cophenetic correlation coefficient increases during the crisis, indicating an increase in the hierarchical structure during in this period. When crisis hits the market, firms behave synchronously, and their correlations are higher than those during a normal period.

  11. Onboard hierarchical network

    Science.gov (United States)

    Tunesi, Luca; Armbruster, Philippe

    2004-02-01

    The objective of this paper is to demonstrate a suitable hierarchical networking solution to improve capabilities and performances of space systems, with significant recurrent costs saving and more efficient design & manufacturing flows. Classically, a satellite can be split in two functional sub-systems: the platform and the payload complement. The platform is in charge of providing power, attitude & orbit control and up/down-link services, whereas the payload represents the scientific and/or operational instruments/transponders and embodies the objectives of the mission. One major possibility to improve the performance of payloads, by limiting the data return to pertinent information, is to process data on board thanks to a proper implementation of the payload data system. In this way, it is possible to share non-recurring development costs by exploiting a system that can be adopted by the majority of space missions. It is believed that the Modular and Scalable Payload Data System, under development by ESA, provides a suitable solution to fulfil a large range of future mission requirements. The backbone of the system is the standardised high data rate SpaceWire network http://www.ecss.nl/. As complement, a lower speed command and control bus connecting peripherals is required. For instance, at instrument level, there is a need for a "local" low complexity bus, which gives the possibility to command and control sensors and actuators. Moreover, most of the connections at sub-system level are related to discrete signals management or simple telemetry acquisitions, which can easily and efficiently be handled by a local bus. An on-board hierarchical network can therefore be defined by interconnecting high-speed links and local buses. Additionally, it is worth stressing another important aspect of the design process: Agencies and ESA in particular are frequently confronted with a big consortium of geographically spread companies located in different countries, each one

  12. Image Segmentation Using Hierarchical Merge Tree

    Science.gov (United States)

    Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-10-01

    This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other very recent methods on six public data sets demonstrate that our approach achieves the state-of-the-art region accuracy and is very competitive in image segmentation without semantic priors.

  13. Memory Stacking in Hierarchical Networks.

    Science.gov (United States)

    Westö, Johan; May, Patrick J C; Tiitinen, Hannu

    2016-02-01

    Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.

  14. Tree Representations: Graphics Libraries for Displaying Hierarchical Data

    Directory of Open Access Journals (Sweden)

    Mark Wilhelm

    2009-03-01

    Full Text Available Tree representations can be useful for presenting hierarchical data on the screen. In this article I’ll briefly describe building trees using the Dojo, Yahoo User Interface, Java Server Faces, and Google Web Toolkit libraries.

  15. A hierarchical linear model for tree height prediction.

    Science.gov (United States)

    Vicente J. Monleon

    2003-01-01

    Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...

  16. Hierarchical networks of scientific journals

    CERN Document Server

    Palla, Gergely; Mones, Enys; Pollner, Péter; Vicsek, Tamás

    2015-01-01

    Scientific journals are the repositories of the gradually accumulating knowledge of mankind about the world surrounding us. Just as our knowledge is organised into classes ranging from major disciplines, subjects and fields to increasingly specific topics, journals can also be categorised into groups using various metrics. In addition to the set of topics characteristic for a journal, they can also be ranked regarding their relevance from the point of overall influence. One widespread measure is impact factor, but in the present paper we intend to reconstruct a much more detailed description by studying the hierarchical relations between the journals based on citation data. We use a measure related to the notion of m-reaching centrality and find a network which shows the level of influence of a journal from the point of the direction and efficiency with which information spreads through the network. We can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied ...

  17. Hierarchical Network Design Using Simulated Annealing

    DEFF Research Database (Denmark)

    Thomadsen, Tommy; Clausen, Jens

    2002-01-01

    The hierarchical network problem is the problem of finding the least cost network, with nodes divided into groups, edges connecting nodes in each groups and groups ordered in a hierarchy. The idea of hierarchical networks comes from telecommunication networks where hierarchies exist. Hierarchical...... networks are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the network. The problem is solved heuristically using simulated annealing which as a sub......-algorithm uses a construction algorithm to determine edges and route the demand. Performance for different versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to find solutions of reasonable quality in approximately 1 hour for networks with 100 nodes....

  18. Hierarchical modularity in human brain functional networks

    CERN Document Server

    Meunier, D; Fornito, A; Ersche, K D; Bullmore, E T; 10.3389/neuro.11.037.2009

    2010-01-01

    The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at ...

  19. Analyzing security protocols in hierarchical networks

    DEFF Research Database (Denmark)

    Zhang, Ye; Nielson, Hanne Riis

    2006-01-01

    Validating security protocols is a well-known hard problem even in a simple setting of a single global network. But a real network often consists of, besides the public-accessed part, several sub-networks and thereby forms a hierarchical structure. In this paper we first present a process calculus...... capturing the characteristics of hierarchical networks and describe the behavior of protocols on such networks. We then develop a static analysis to automate the validation. Finally we demonstrate how the technique can benefit the protocol development and the design of network systems by presenting a series...

  20. Modular, Hierarchical Learning By Artificial Neural Networks

    Science.gov (United States)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  1. Universal hierarchical behavior of citation networks

    CERN Document Server

    Mones, Enys; Vicsek, Tamás

    2014-01-01

    Many of the essential features of the evolution of scientific research are imprinted in the structure of citation networks. Connections in these networks imply information about the transfer of knowledge among papers, or in other words, edges describe the impact of papers on other publications. This inherent meaning of the edges infers that citation networks can exhibit hierarchical features, that is typical of networks based on decision-making. In this paper, we investigate the hierarchical structure of citation networks consisting of papers in the same field. We find that the majority of the networks follow a universal trend towards a highly hierarchical state, and i) the various fields display differences only concerning their phase in life (distance from the "birth" of a field) or ii) the characteristic time according to which they are approaching the stationary state. We also show by a simple argument that the alterations in the behavior are related to and can be understood by the degree of specializatio...

  2. Changes of hierarchical network in local and world stock market

    Science.gov (United States)

    Patwary, Enayet Ullah; Lee, Jong Youl; Nobi, Ashadun; Kim, Doo Hwan; Lee, Jae Woo

    2017-10-01

    We consider the cross-correlation coefficients of the daily returns in the local and global stock markets. We generate the minimal spanning tree (MST) using the correlation matrix. We observe that the MSTs change their structure from chain-like networks to star-like networks during periods of market uncertainty. We quantify the measure of the hierarchical network utilizing the value of the hierarchy measured by the hierarchical path. The hierarchy and betweenness centrality characterize the state of the market regarding the impact of crises. During crises, the non-financial company is established as the central node of the MST. However, before the crisis and during stable periods, the financial company is occupying the central node of the MST in the Korean and the U.S. stock markets. The changes in the network structure and the central node are good indicators of an upcoming crisis.

  3. Genetic Algorithm for Hierarchical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sajid Hussain

    2007-09-01

    Full Text Available Large scale wireless sensor networks (WSNs can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.

  4. Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

    CERN Document Server

    Czégel, Dániel

    2015-01-01

    Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications...

  5. Hierarchical social networks and information flow

    Science.gov (United States)

    López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.

    2002-12-01

    Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.

  6. Strategic games on a hierarchical network model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.

  7. Ultrafast Hierarchical OTDM/WDM Network

    Directory of Open Access Journals (Sweden)

    Hideyuki Sotobayashi

    2003-12-01

    Full Text Available Ultrafast hierarchical OTDM/WDM network is proposed for the future core-network. We review its enabling technologies: C- and L-wavelength-band generation, OTDM-WDM mutual multiplexing format conversions, and ultrafast OTDM wavelengthband conversions.

  8. Biased trapping issue on weighted hierarchical networks

    Indian Academy of Sciences (India)

    Meifeng Dai; Jie Liu; Feng Zhu

    2014-10-01

    In this paper, we present trapping issues of weight-dependent walks on weighted hierarchical networks which are based on the classic scale-free hierarchical networks. Assuming that edge’s weight is used as local information by a random walker, we introduce a biased walk. The biased walk is that a walker, at each step, chooses one of its neighbours with a probability proportional to the weight of the edge. We focus on a particular case with the immobile trap positioned at the hub node which has the largest degree in the weighted hierarchical networks. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping issue. Let parameter (0 < < 1) be the weight factor. We show that the efficiency of the trapping process depends on the parameter a; the smaller the value of a, the more efficient is the trapping process.

  9. Maximizing Adaptivity in Hierarchical Topological Models Using Cancellation Trees

    Energy Technology Data Exchange (ETDEWEB)

    Bremer, P; Pascucci, V; Hamann, B

    2008-12-08

    We present a highly adaptive hierarchical representation of the topology of functions defined over two-manifold domains. Guided by the theory of Morse-Smale complexes, we encode dependencies between cancellations of critical points using two independent structures: a traditional mesh hierarchy to store connectivity information and a new structure called cancellation trees to encode the configuration of critical points. Cancellation trees provide a powerful method to increase adaptivity while using a simple, easy-to-implement data structure. The resulting hierarchy is significantly more flexible than the one previously reported. In particular, the resulting hierarchy is guaranteed to be of logarithmic height.

  10. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  11. Non-homogeneous fractal hierarchical weighted networks.

    Science.gov (United States)

    Dong, Yujuan; Dai, Meifeng; Ye, Dandan

    2015-01-01

    A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average degree and the average node strength, taking place on the non-homogeneous hierarchical weighted networks. Moreover the AWSP is scrupulously calculated. We show that the AWSP depends on the number of copies and the sum of all non-homogeneous weight scaling factors in the infinite network order limit.

  12. Hierarchical community structure in complex (social) networks

    CERN Document Server

    Massaro, Emanuele

    2014-01-01

    The investigation of community structure in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science where systems are often represented as graphs. One of the challenges is to find local communities from a local viewpoint in a graph without global information in order to reproduce the subjective hierarchical vision for each vertex. In this paper we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints \\cite{Massaro2012}. In this framework we have introduced two more complex heuristics that allow the algorithm to detect the multi-resolution hierarchical community structure of networks from a source vertex or communities adopting fixed values of model's parameters. Experimental results show that the proposed methods are efficient and well-behaved in both real-world and synthetic networks.

  13. Multi- Layer Tree Hierarchical Architecture Based on Web Service

    Institute of Scientific and Technical Information of China (English)

    TONG Hengjian; LI Deren; ZHU Xinyan; SHAO Zhenfeng

    2006-01-01

    To solve the problem of the information share and services integration in population information system, we propose a multi-layer tree hierarchical architecture. The com mand (Web Service Call) is recursively multicast from top layer of tree to bottom layer of tree and statistical data are gathered from bottom layer to top layer. We implemented the architecture by using Web Services technology. In our implementation, client program is the requestor of Web Services,and all leaf nodes of the last layer are only the provider of Web Services. For those nodes of intermediate layers, every node is not only the provider of Web Services, but also the dispatcher of Web Services. We take population census as an example to describe the working flow of the architecture.

  14. Topology of the correlation networks among major currencies using hierarchical structure methods

    Science.gov (United States)

    Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf

    2011-02-01

    We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.

  15. Multisorted Tree-Algebras for Hierarchical Resources Allocation

    Directory of Open Access Journals (Sweden)

    Erick Patrick Zobo

    2015-01-01

    Full Text Available This paper presents a generic abstract model for the study of disparities between goals and results in hierarchical multiresources allocation systems. In an organization, disparities in resource allocation may occur, when, after comparison of a resource allocation decision with an allocation reference goal or property, some agents have surplus resources to accomplish their tasks, while at the same time other agents have deficits of expected resources. In the real world, these situations are frequently encountered in organizations facing scarcity of resources and/or inefficient management. These disparities can be corrected using allocation decisions, by measuring and reducing gradually such disparities and their related costs, without totally canceling the existing resource distribution. While a lot of research has been carried out in the area of resource allocation, this specific class of problems has not yet been formally studied. The paper exposes the results of an exploratory research study of this class of problems. It identifies the commonalities of the family of hierarchical multiresource allocation systems and proposes the concept of multisorted tree-algebra for the modeling of these problems. The research presented here is not yet an in-depth descriptive research study of the mathematical theory of multisorted tree-algebra, but a formal study on modelling hierarchical multiresource allocation problems.

  16. The Hourglass Effect in Hierarchical Dependency Networks

    CERN Document Server

    Sabrin, Kaeser M

    2016-01-01

    Many hierarchically modular systems are structured in a way that resembles a bow-tie or hourglass. This "hourglass effect" means that the system generates many outputs from many inputs through a relatively small number of intermediate modules that are critical for the operation of the entire system (the waist of the hourglass). We investigate the hourglass effect in general (not necessarily layered) hierarchical dependency networks. Our analysis focuses on the number of source-to-target dependency paths that traverse each vertex, and it identifies the core of a dependency network as the smallest set of vertices that collectively cover almost all dependency paths. We then examine if a given network exhibits the hourglass property or not, comparing its core size with a "flat" (i.e., non-hierarchical) network that preserves the source dependencies of each target in the original network. As a possible explanation for the hourglass effect, we propose the Reuse Preference (RP) model that captures the bias of new mo...

  17. Synchronization patterns: from network motifs to hierarchical networks

    Science.gov (United States)

    Krishnagopal, Sanjukta; Lehnert, Judith; Poel, Winnie; Zakharova, Anna; Schöll, Eckehard

    2017-03-01

    We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks. This article is part of the themed issue 'Horizons of cybernetical physics'.

  18. First-passage phenomena in hierarchical networks

    CERN Document Server

    Tavani, Flavia

    2016-01-01

    In this paper we study Markov processes and related first passage problems on a class of weighted, modular graphs which generalize the Dyson hierarchical model. In these networks, the coupling strength between two nodes depends on their distance and is modulated by a parameter $\\sigma$. We find that, in the thermodynamic limit, ergodicity is lost and the "distant" nodes can not be reached. Moreover, for finite-sized systems, there exists a threshold value for $\\sigma$ such that, when $\\sigma$ is relatively large, the inhomogeneity of the coupling pattern prevails and "distant" nodes are hardly reached. The same analysis is carried on also for generic hierarchical graphs, where interactions are meant to involve $p$-plets ($p>2$) of nodes, finding that ergodicity is still broken in the thermodynamic limit, but no threshold value for $\\sigma$ is evidenced, ultimately due to a slow growth of the network diameter with the size.

  19. Random self-similar trees and a hierarchical branching process

    CERN Document Server

    Kovchegov, Yevgeniy

    2016-01-01

    We study self-similarity in random binary rooted trees. In a well-understood case of Galton-Watson trees, a distribution is called self-similar if it is invariant with respect to the operation of pruning, which cuts the tree leaves. This only happens in the critical case (a constant process progeny), which also exhibits other special symmetries. We extend the prune-invariance set-up to a non-Markov situation and trees with edge lengths. In this general case the class of self-similar processes becomes much richer and covers a variety of practically important situations. The main result is construction of the hierarchical branching processes that satisfy various self-similarity constraints (distributional, mean, in edge-lengths) depending on the process parameters. Taking the limit of averaged stochastic dynamics, as the number of trajectories increases, we obtain a deterministic system of differential equations that describes the process evolution. This system is used to establish a phase transition that separ...

  20. On Tree-Based Phylogenetic Networks.

    Science.gov (United States)

    Zhang, Louxin

    2016-07-01

    A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.

  1. Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

    Science.gov (United States)

    Czégel, Dániel; Palla, Gergely

    2015-01-01

    Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology. PMID:26657012

  2. Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

    Science.gov (United States)

    Czégel, Dániel; Palla, Gergely

    2015-12-10

    Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.

  3. Neural mechanisms underlying the computation of hierarchical tree structures in mathematics.

    Directory of Open Access Journals (Sweden)

    Tomoya Nakai

    Full Text Available Whether mathematical and linguistic processes share the same neural mechanisms has been a matter of controversy. By examining various sentence structures, we recently demonstrated that activations in the left inferior frontal gyrus (L. IFG and left supramarginal gyrus (L. SMG were modulated by the Degree of Merger (DoM, a measure for the complexity of tree structures. In the present study, we hypothesize that the DoM is also critical in mathematical calculations, and clarify whether the DoM in the hierarchical tree structures modulates activations in these regions. We tested an arithmetic task that involved linear and quadratic sequences with recursive computation. Using functional magnetic resonance imaging, we found significant activation in the L. IFG, L. SMG, bilateral intraparietal sulcus (IPS, and precuneus selectively among the tested conditions. We also confirmed that activations in the L. IFG and L. SMG were free from memory-related factors, and that activations in the bilateral IPS and precuneus were independent from other possible factors. Moreover, by fitting parametric models of eight factors, we found that the model of DoM in the hierarchical tree structures was the best to explain the modulation of activations in these five regions. Using dynamic causal modeling, we showed that the model with a modulatory effect for the connection from the L. IPS to the L. IFG, and with driving inputs into the L. IFG, was highly probable. The intrinsic, i.e., task-independent, connection from the L. IFG to the L. IPS, as well as that from the L. IPS to the R. IPS, would provide a feedforward signal, together with negative feedback connections. We indicate that mathematics and language share the network of the L. IFG and L. IPS/SMG for the computation of hierarchical tree structures, and that mathematics recruits the additional network of the L. IPS and R. IPS.

  4. Neural mechanisms underlying the computation of hierarchical tree structures in mathematics.

    Science.gov (United States)

    Nakai, Tomoya; Sakai, Kuniyoshi L

    2014-01-01

    Whether mathematical and linguistic processes share the same neural mechanisms has been a matter of controversy. By examining various sentence structures, we recently demonstrated that activations in the left inferior frontal gyrus (L. IFG) and left supramarginal gyrus (L. SMG) were modulated by the Degree of Merger (DoM), a measure for the complexity of tree structures. In the present study, we hypothesize that the DoM is also critical in mathematical calculations, and clarify whether the DoM in the hierarchical tree structures modulates activations in these regions. We tested an arithmetic task that involved linear and quadratic sequences with recursive computation. Using functional magnetic resonance imaging, we found significant activation in the L. IFG, L. SMG, bilateral intraparietal sulcus (IPS), and precuneus selectively among the tested conditions. We also confirmed that activations in the L. IFG and L. SMG were free from memory-related factors, and that activations in the bilateral IPS and precuneus were independent from other possible factors. Moreover, by fitting parametric models of eight factors, we found that the model of DoM in the hierarchical tree structures was the best to explain the modulation of activations in these five regions. Using dynamic causal modeling, we showed that the model with a modulatory effect for the connection from the L. IPS to the L. IFG, and with driving inputs into the L. IFG, was highly probable. The intrinsic, i.e., task-independent, connection from the L. IFG to the L. IPS, as well as that from the L. IPS to the R. IPS, would provide a feedforward signal, together with negative feedback connections. We indicate that mathematics and language share the network of the L. IFG and L. IPS/SMG for the computation of hierarchical tree structures, and that mathematics recruits the additional network of the L. IPS and R. IPS.

  5. Neural Mechanisms Underlying the Computation of Hierarchical Tree Structures in Mathematics

    Science.gov (United States)

    Nakai, Tomoya; Sakai, Kuniyoshi L.

    2014-01-01

    Whether mathematical and linguistic processes share the same neural mechanisms has been a matter of controversy. By examining various sentence structures, we recently demonstrated that activations in the left inferior frontal gyrus (L. IFG) and left supramarginal gyrus (L. SMG) were modulated by the Degree of Merger (DoM), a measure for the complexity of tree structures. In the present study, we hypothesize that the DoM is also critical in mathematical calculations, and clarify whether the DoM in the hierarchical tree structures modulates activations in these regions. We tested an arithmetic task that involved linear and quadratic sequences with recursive computation. Using functional magnetic resonance imaging, we found significant activation in the L. IFG, L. SMG, bilateral intraparietal sulcus (IPS), and precuneus selectively among the tested conditions. We also confirmed that activations in the L. IFG and L. SMG were free from memory-related factors, and that activations in the bilateral IPS and precuneus were independent from other possible factors. Moreover, by fitting parametric models of eight factors, we found that the model of DoM in the hierarchical tree structures was the best to explain the modulation of activations in these five regions. Using dynamic causal modeling, we showed that the model with a modulatory effect for the connection from the L. IPS to the L. IFG, and with driving inputs into the L. IFG, was highly probable. The intrinsic, i.e., task-independent, connection from the L. IFG to the L. IPS, as well as that from the L. IPS to the R. IPS, would provide a feedforward signal, together with negative feedback connections. We indicate that mathematics and language share the network of the L. IFG and L. IPS/SMG for the computation of hierarchical tree structures, and that mathematics recruits the additional network of the L. IPS and R. IPS. PMID:25379713

  6. HIDEN: Hierarchical decomposition of regulatory networks

    Directory of Open Access Journals (Sweden)

    Gülsoy Günhan

    2012-09-01

    Full Text Available Abstract Background Transcription factors regulate numerous cellular processes by controlling the rate of production of each gene. The regulatory relations are modeled using transcriptional regulatory networks. Recent studies have shown that such networks have an underlying hierarchical organization. We consider the problem of discovering the underlying hierarchy in transcriptional regulatory networks. Results We first transform this problem to a mixed integer programming problem. We then use existing tools to solve the resulting problem. For larger networks this strategy does not work due to rapid increase in running time and space usage. We use divide and conquer strategy for such networks. We use our method to analyze the transcriptional regulatory networks of E. coli, H. sapiens and S. cerevisiae. Conclusions Our experiments demonstrate that: (i Our method gives statistically better results than three existing state of the art methods; (ii Our method is robust against errors in the data and (iii Our method’s performance is not affected by the different topologies in the data.

  7. The hierarchical brain network for face recognition.

    Science.gov (United States)

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  8. Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

    CERN Document Server

    Perotti, Juan Ignacio; Caldarelli, Guido

    2015-01-01

    The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the {\\it hierarchical mutual information}, which is a generalization of the traditional mutual information, and allows to compare hierarchical partitions and hierarchical community structures. The {\\it normalized} version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here, the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies, and on the hierarchical ...

  9. Optimum Binary Search Trees on the Hierarchical Memory Model

    CERN Document Server

    Thite, Shripad

    2008-01-01

    The Hierarchical Memory Model (HMM) of computation is similar to the standard Random Access Machine (RAM) model except that the HMM has a non-uniform memory organized in a hierarchy of levels numbered 1 through h. The cost of accessing a memory location increases with the level number, and accesses to memory locations belonging to the same level cost the same. Formally, the cost of a single access to the memory location at address a is given by m(a), where m: N -> N is the memory cost function, and the h distinct values of m model the different levels of the memory hierarchy. We study the problem of constructing and storing a binary search tree (BST) of minimum cost, over a set of keys, with probabilities for successful and unsuccessful searches, on the HMM with an arbitrary number of memory levels, and for the special case h=2. While the problem of constructing optimum binary search trees has been well studied for the standard RAM model, the additional parameter m for the HMM increases the combinatorial comp...

  10. Antiferromagnetic Ising Model in Hierarchical Networks

    Science.gov (United States)

    Cheng, Xiang; Boettcher, Stefan

    2015-03-01

    The Ising antiferromagnet is a convenient model of glassy dynamics. It can introduce geometric frustrations and may give rise to a spin glass phase and glassy relaxation at low temperatures [ 1 ] . We apply the antiferromagnetic Ising model to 3 hierarchical networks which share features of both small world networks and regular lattices. Their recursive and fixed structures make them suitable for exact renormalization group analysis as well as numerical simulations. We first explore the dynamical behaviors using simulated annealing and discover an extremely slow relaxation at low temperatures. Then we employ the Wang-Landau algorithm to investigate the energy landscape and the corresponding equilibrium behaviors for different system sizes. Besides the Monte Carlo methods, renormalization group [ 2 ] is used to study the equilibrium properties in the thermodynamic limit and to compare with the results from simulated annealing and Wang-Landau sampling. Supported through NSF Grant DMR-1207431.

  11. Field experiment on a robust hierarchical metropolitan quantum cryptography network

    Institute of Scientific and Technical Information of China (English)

    XU FangXing; CHEN Wei; WANG Shuang; YIN ZhenQiang; ZHANG Yang; LIU Yun; ZHOU Zheng; ZHAO YiBo; LI HongWei; LIU Dong; HAN ZhengFu; GUO GuangCan

    2009-01-01

    these bureaus.The whole implementation including the hierarchical quantum cryptographic communication network links and the corresponding application software shows a big step toward the practical user-oriented network with a high security level.

  12. A fast quad-tree based two dimensional hierarchical clustering.

    Science.gov (United States)

    Rajadurai, Priscilla; Sankaranarayanan, Swamynathan

    2012-01-01

    Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.

  13. A Hierarchical Sensor Network Based on Voronoi Diagram

    Institute of Scientific and Technical Information of China (English)

    SHANG Rui-qiang; ZHAO Jian-li; SUN Qiu-xia; WANG Guang-xing

    2006-01-01

    A hierarchical sensor network is proposed which places the sensing and routing capacity at different layer nodes.It thus simplifies the hardware design and reduces cost. Adopting Voronoi diagram in the partition of backbone network,a mathematical model of data aggregation based on hierarchical architecture is given. Simulation shows that the number of transmission data packages is sharply cut down in the network, thus reducing the needs in the bandwidth and energy resources and is thus well adapted to sensor networks.

  14. Automatic Construction of Hierarchical Road Networks

    Science.gov (United States)

    Yang, Weiping

    2016-06-01

    This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.

  15. Developing Dynamic Virtual Environments Using Hierarchical, Tree-Structured Approach

    Directory of Open Access Journals (Sweden)

    Wan Mohd Rizhan Wan Idris

    2011-05-01

    Full Text Available Virtual reality (VR has been utilized in various applications such as in architecture, medicine, advertisement, business, entertainment, and education. In the world of simulation, VR software allows users to visualize, manipulate and interact with the computers and complex data. However, developing VR environments is costly and expensive. Highly-technical persons are needed to create the virtual objects from scratch. Once a virtual system is created, managing and modifying it creates further problems. There is a need for non-technical users to be able to create and modify their own virtual environments. This paper discusses a systematic technique to develop dynamic virtual environments and to manage virtual objects in their virtual environment. The technique is called hierarchical, tree-structured approach. To implement the technique, object-oriented programming language was used such as Java, Java 3D and Java Swing. For the usability and performance of the technique, a virtual environment has been created to become as case study. The tool has been perceived as an easy tool to use, especially for an environment in education.

  16. DEVELOPING DYNAMIC VIRTUAL ENVIRONMENTS USING HIERARCHICAL, TREE-STRUCTURED APPROACH

    Directory of Open Access Journals (Sweden)

    Wan Mohd Rizhan Wan Idris

    2015-10-01

    Full Text Available Virtual reality (VR has been utilized in various applications such as in architecture, medicine, advertisement, business, entertainment, and education. In the world of simulation, VR software allows users to visualize, manipulate and interact with the computers and complex data. However, developing VR environments is costly and expensive. Highly-technical persons are needed to create the virtual objects from scratch. Once a virtual system is created, managing and modifying it creates further problems. There is a need for non-technical users to be able to create and modify their own virtual environments. This paper discusses a systematic technique to develop dynamic virtual environments and to manage virtual objects in their virtual environment. The technique is called hierarchical, tree-structured approach. To implement the technique, object-oriented programming language was used such as Java, Java 3D and Java Swing. For the usability and performance of the technique, a virtual environment has been created to become as case study. The tool has been perceived as an easy tool to use, especially for an environment in education.

  17. Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

    Science.gov (United States)

    2008-04-01

    REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

  18. Hierarchical Network Models for Education Research: Hierarchical Latent Space Models

    Science.gov (United States)

    Sweet, Tracy M.; Thomas, Andrew C.; Junker, Brian W.

    2013-01-01

    Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another--in short, changing the professional social networks of educators. Current methods of social network analysis are…

  19. Big Data Processing in Complex Hierarchical Network Systems

    CERN Document Server

    Polishchuk, Olexandr; Tyutyunnyk, Maria; Yadzhak, Mykhailo

    2016-01-01

    This article covers the problem of processing of Big Data that describe process of complex networks and network systems operation. It also introduces the notion of hierarchical network systems combination into associations and conglomerates alongside with complex networks combination into multiplexes. The analysis is provided for methods of global network structures study depending on the purpose of the research. Also the main types of information flows in complex hierarchical network systems being the basic components of associations and conglomerates are covered. Approaches are proposed for creation of efficient computing environments, distributed computations organization and information processing methods parallelization at different levels of system hierarchy.

  20. D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

    Science.gov (United States)

    Suhaibah, A.; Uznir, U.; Anton, F.; Mioc, D.; Rahman, A. A.

    2016-06-01

    Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

  1. A hierarchical scheme for geodesic anatomical labeling of airway trees

    DEFF Research Database (Denmark)

    Feragen, Aasa; Petersen, Jens; Owen, Megan;

    2012-01-01

    We present a fast and robust supervised algorithm for label- ing anatomical airway trees, based on geodesic distances in a geometric tree-space. Possible branch label configurations for a given unlabeled air- way tree are evaluated based on the distances to a training set of labeled airway trees....

  2. A hierarchical scheme for geodesic anatomical labeling of airway trees

    DEFF Research Database (Denmark)

    Feragen, Aasa; Petersen, Jens; Owen, Megan

    2012-01-01

    We present a fast and robust supervised algorithm for label- ing anatomical airway trees, based on geodesic distances in a geometric tree-space. Possible branch label configurations for a given unlabeled air- way tree are evaluated based on the distances to a training set of labeled airway trees...

  3. Comparison of tree-child phylogenetic networks.

    Science.gov (United States)

    Cardona, Gabriel; Rosselló, Francesc; Valiente, Gabriel

    2009-01-01

    Phylogenetic networks are a generalization of phylogenetic trees that allow for the representation of nontreelike evolutionary events, like recombination, hybridization, or lateral gene transfer. While much progress has been made to find practical algorithms for reconstructing a phylogenetic network from a set of sequences, all attempts to endorse a class of phylogenetic networks (strictly extending the class of phylogenetic trees) with a well-founded distance measure have, to the best of our knowledge and with the only exception of the bipartition distance on regular networks, failed so far. In this paper, we present and study a new meaningful class of phylogenetic networks, called tree-child phylogenetic networks, and we provide an injective representation of these networks as multisets of vectors of natural numbers, their path multiplicity vectors. We then use this representation to define a distance on this class that extends the well-known Robinson-Foulds distance for phylogenetic trees and to give an alignment method for pairs of networks in this class. Simple polynomial algorithms for reconstructing a tree-child phylogenetic network from its path multiplicity vectors, for computing the distance between two tree-child phylogenetic networks and for aligning a pair of tree-child phylogenetic networks, are provided. They have been implemented as a Perl package and a Java applet, which can be found at http://bioinfo.uib.es/~recerca/phylonetworks/mudistance/.

  4. Object recognition with hierarchical discriminant saliency networks

    Directory of Open Access Journals (Sweden)

    Sunhyoung eHan

    2014-09-01

    Full Text Available The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognitionmodel, the hierarchical discriminant saliency network (HDSN, whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. The HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a neuralnetwork implementation, all layers are convolutional and implement acombination of filtering, rectification, and pooling. The rectificationis performed with a parametric extension of the now popular rectified linearunits (ReLUs, whose parameters can be tuned for the detection of targetobject classes. This enables a number of functional enhancementsover neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation ofsaliency responses by the discriminant power of the underlying features,and the ability to detect both feature presence and absence.In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity totarget object classes and invariance. The resulting performance demonstrates benefits for all the functional enhancements of the HDSN.

  5. Object recognition with hierarchical discriminant saliency networks.

    Science.gov (United States)

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  6. Dynamic load balancing based on restricted multicast tree in triplet-based hierarchical interconnection network%基三分层互连网络中基于受限多播树的动态负载平衡

    Institute of Scientific and Technical Information of China (English)

    刘滨; 石峰; 高玉金; 计卫星; 宋红

    2008-01-01

    To solve the load balancing problem in a triplet-based hierarchical interconnection network(THIN)system,a dynamic load balancing(DLB)algorithm-THINDLBA,which adopts multicast tree(MT)technology to improve the efficiency of interchanging load information,is presented.To support the algorithm,a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed.The load migration request messages from the heavily loaded node(HLN)are spread along an MT whose root is the HLN.And the lightly loaded nodes(LLNs) covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT.So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA,and its load state can be improved as quickly as possible.To avoid wrongly transmitted or redundant DLB messages due to MT overlapping,the MT construction is restricted in the design of the THINDLBA.Through experiments,the effectiveness of four DLB algorithms are compared,and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale compute-intensive tasks more than others.%为了解决基三分层互连网络(THIN)系统中的负载平衡问题,提出一种采用多播树技术提高节点间交换负载信息效率的动态负载平衡(DLB)算法--THINDLBA.设计了一套完整的DLB消息和各节点处的信息维护机制以辅助算法实现.重载节点的负载迁移请求消息沿着一棵以该节点为根的多播树传播,被该树覆盖的轻载节点均成为负载迁移的候选目标节点,可以沿着该树和重载节点交互负载信息,从而使重载节点能够在算法的一次执行中外迁最多的过载进程,尽快改善自身负载状态.算法设计中约束了多播树的构造过程,以避免因树间覆盖造成的消息误传或冗余.通过实验对比了4

  7. Hierarchical Problem Solving with the Linkage Tree Genetic Algorithm

    NARCIS (Netherlands)

    Thierens, D.; Bosman, P.A.N.; Blum, C.; Alba, E.

    2013-01-01

    Hierarchical problems represent an important class of nearly decomposable problems. The concept of near decomposability is central to the study of complex systems. When little a priori information is available, a black box problem solver is needed to optimize these hierarchical problems. The solver

  8. Social network sampling using spanning trees

    Science.gov (United States)

    Jalali, Zeinab S.; Rezvanian, Alireza; Meybodi, Mohammad Reza

    2016-12-01

    Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a sub-graph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on well-known real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of the proposed sampling algorithms in terms of Kolmogorov-Smirnov distance (KSD), skew divergence distance (SDD) and normalized distance (ND).

  9. Road network safety evaluation using Bayesian hierarchical joint model.

    Science.gov (United States)

    Wang, Jie; Huang, Helai

    2016-05-01

    Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.

  10. Road Network Selection Based on Road Hierarchical Structure Control

    Directory of Open Access Journals (Sweden)

    HE Haiwei

    2015-04-01

    Full Text Available A new road network selection method based on hierarchical structure is studied. Firstly, road network is built as strokes which are then classified into hierarchical collections according to the criteria of betweenness centrality value (BC value. Secondly, the hierarchical structure of the strokes is enhanced using structural characteristic identification technique. Thirdly, the importance calculation model was established according to the relationships among the hierarchical structure of the strokes. Finally, the importance values of strokes are got supported with the model's hierarchical calculation, and with which the road network is selected. Tests are done to verify the advantage of this method by comparing it with other common stroke-oriented methods using three kinds of typical road network data. Comparision of the results show that this method had few need to semantic data, and could eliminate the negative influence of edge strokes caused by the criteria of BC value well. So, it is better to maintain the global hierarchical structure of road network, and suitable to meet with the selection of various kinds of road network at the same time.

  11. Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network.

    Science.gov (United States)

    Balaguer, Jan; Spiers, Hugo; Hassabis, Demis; Summerfield, Christopher

    2016-05-18

    Planning allows actions to be structured in pursuit of a future goal. However, in natural environments, planning over multiple possible future states incurs prohibitive computational costs. To represent plans efficiently, states can be clustered hierarchically into "contexts". For example, representing a journey through a subway network as a succession of individual states (stations) is more costly than encoding a sequence of contexts (lines) and context switches (line changes). Here, using functional brain imaging, we asked humans to perform a planning task in a virtual subway network. Behavioral analyses revealed that humans executed a hierarchically organized plan. Brain activity in the dorsomedial prefrontal cortex and premotor cortex scaled with the cost of hierarchical plan representation and unique neural signals in these regions signaled contexts and context switches. These results suggest that humans represent hierarchical plans using a network of caudal prefrontal structures. VIDEO ABSTRACT.

  12. Field Experiment on a Robust Hierarchical Metropolitan Quantum Cryptography Network

    CERN Document Server

    Xu, Fangxing; Wang, Shuang; Yin, Zhenqiang; Zhang, Yang; Liu, Yun; Zhou, Zheng; Zhao, Yibo; Li, Hongwei; Liu, Dong; Han, Zhengfu; Guo, Guangcan

    2009-01-01

    A hierarchical metropolitan quantum cryptography network upon the inner-city commercial telecom fiber cables is reported in this paper. The seven-user network contains a four-node backbone net with one node acting as the subnet gateway, a two-user subnet and a single-fiber access link, which is realized by the Faraday-Michelson Interferometer set-ups. The techniques of the quantum router, optical switch and trusted relay are assembled here to guarantee the feasibility and expandability of the quantum cryptography network. Five nodes of the network are located in the government departments and the secure keys generated by the quantum key distribution network are utilized to encrypt the instant video, sound, text messages and confidential files transmitting between these bureaus. The whole implementation including the hierarchical quantum cryptographic communication network links and corresponding application software shows a big step toward the practical user-oriented network with high security level.

  13. Complex Evaluation of Hierarchically-Network Systems

    CERN Document Server

    Polishchuk, Dmytro; Yadzhak, Mykhailo

    2016-01-01

    Methods of complex evaluation based on local, forecasting, aggregated, and interactive evaluation of the state, function quality, and interaction of complex system's objects on the all hierarchical levels is proposed. Examples of analysis of the structural elements of railway transport system are used for illustration of efficiency of proposed approach.

  14. A method of characterizing network topology based on the breadth-first search tree

    Science.gov (United States)

    Zhou, Bin; He, Zhe; Wang, Nianxin; Wang, Bing-Hong

    2016-05-01

    A method based on the breadth-first search tree is proposed in this paper to characterize the hierarchical structure of network. In this method, a similarity coefficient is defined to quantitatively distinguish networks, and quantitatively measure the topology stability of the network generated by a model. The applications of the method are discussed in ER random network, WS small-world network and BA scale-free network. The method will be helpful for deeply describing network topology and provide a starting point for researching the topology similarity and isomorphism of networks.

  15. Modelling hierarchical and modular complex networks: division and independence

    Science.gov (United States)

    Kim, D.-H.; Rodgers, G. J.; Kahng, B.; Kim, D.

    2005-06-01

    We introduce a growing network model which generates both modular and hierarchical structure in a self-organized way. To this end, we modify the Barabási-Albert model into the one evolving under the principles of division and independence as well as growth and preferential attachment (PA). A newly added vertex chooses one of the modules composed of existing vertices, and attaches edges to vertices belonging to that module following the PA rule. When the module size reaches a proper size, the module is divided into two, and a new module is created. The karate club network studied by Zachary is a simple version of the current model. We find that the model can reproduce both modular and hierarchical properties, characterized by the hierarchical clustering function of a vertex with degree k, C(k), being in good agreement with empirical measurements for real-world networks.

  16. Hierarchical Overlapping Clustering of Network Data Using Cut Metrics

    CERN Document Server

    Gama, Fernando; Ribeiro, Alejandro

    2016-01-01

    A novel method to obtain hierarchical and overlapping clusters from network data -i.e., a set of nodes endowed with pairwise dissimilarities- is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings of the node set depending on the resolution or degree of similarity desired, and it is overlapping since it allows nodes to belong to more than one group. Our construction is rooted on the facts that a hierarchical (non-overlapping) clustering of a network can be equivalently represented by a finite ultrametric space and that a convex combination of ultrametrics results in a cut metric. By applying a hierarchical (non-overlapping) clustering method to multiple dithered versions of a given network and then convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlappi...

  17. Detect overlapping and hierarchical community structure in networks

    CERN Document Server

    Shen, Huawei; Cai, Kai; Hu, Mao-Bin

    2008-01-01

    Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper propose an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results.

  18. Hierarchical Routing over Dynamic Wireless Networks

    CERN Document Server

    Tschopp, Dominique; Grossglauser, Matthias

    2009-01-01

    Wireless network topologies change over time and maintaining routes requires frequent updates. Updates are costly in terms of consuming throughput available for data transmission, which is precious in wireless networks. In this paper, we ask whether there exist low-overhead schemes that produce low-stretch routes. This is studied by using the underlying geometric properties of the connectivity graph in wireless networks.

  19. Hierarchical structures of correlations networks among Turkey’s exports and imports by currencies

    Science.gov (United States)

    Kocakaplan, Yusuf; Deviren, Bayram; Keskin, Mustafa

    2012-12-01

    We have examined the hierarchical structures of correlations networks among Turkey’s exports and imports by currencies for the 1996-2010 periods, using the concept of a minimal spanning tree (MST) and hierarchical tree (HT) which depend on the concept of ultrametricity. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial markets. We derived a hierarchical organization and build the MSTs and HTs during the 1996-2001 and 2002-2010 periods. The reason for studying two different sub-periods, namely 1996-2001 and 2002-2010, is that the Euro (EUR) came into use in 2001, and some countries have made their exports and imports with Turkey via the EUR since 2002, and in order to test various time-windows and observe temporal evolution. We have carried out bootstrap analysis to associate a value of the statistical reliability to the links of the MSTs and HTs. We have also used the average linkage cluster analysis (ALCA) to observe the cluster structure more clearly. Moreover, we have obtained the bidimensional minimal spanning tree (BMST) due to economic trade being a bidimensional problem. From the structural topologies of these trees, we have identified different clusters of currencies according to their proximity and economic ties. Our results show that some currencies are more important within the network, due to a tighter connection with other currencies. We have also found that the obtained currencies play a key role for Turkey’s exports and imports and have important implications for the design of portfolio and investment strategies.

  20. Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

    CERN Document Server

    Hong, Chi-Yao; Duffield, Nick; Wang, Jia

    2012-01-01

    Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents several new challenges, including the volatility and sparseness of data, and the need to perform fast detection (complicating application of schemes that require offline processing or large/stable data sets to converge). To address these challenges, we propose Tiresias, an automated approach to locating anomalous events on hierarchical operational data. Tiresias leverages the hierarchical structure of operational data to identify high-impact aggregates (e.g., locations in the network, failure modes) likely to be associated w...

  1. Hierarchicality of trade flow networks reveals complexity of products.

    Science.gov (United States)

    Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

    2014-01-01

    With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

  2. Hierarchicality of trade flow networks reveals complexity of products.

    Directory of Open Access Journals (Sweden)

    Peiteng Shi

    Full Text Available With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

  3. Cost of Multicast Logical Key Tree Based on Hierarchical Data Processing

    Institute of Scientific and Technical Information of China (English)

    ZHOU Fucai; XU Jian; LI Ting

    2006-01-01

    How to design a multicast key management system with high performance is a hot issue now. This paper will apply the idea of hierarchical data processing to construct a common analytic model based on directed logical key tree and supply two important metrics to this problem: re-keying cost and key storage cost. The paper gives the basic theory to the hierarchical data processing and the analyzing model to multicast key management based on logical key tree. It has been proved that the 4-ray tree has the best performance in using these metrics. The key management problem is also investigated based on user probability model, and gives two evaluating parameters to re-keying and key storage cost.

  4. Trees and networks before and after Darwin

    Directory of Open Access Journals (Sweden)

    Ragan Mark A

    2009-11-01

    Full Text Available Abstract It is well-known that Charles Darwin sketched abstract trees of relationship in his 1837 notebook, and depicted a tree in the Origin of Species (1859. Here I attempt to place Darwin's trees in historical context. By the mid-Eighteenth century the Great Chain of Being was increasingly seen to be an inadequate description of order in nature, and by about 1780 it had been largely abandoned without a satisfactory alternative having been agreed upon. In 1750 Donati described aquatic and terrestrial organisms as forming a network, and a few years later Buffon depicted a network of genealogical relationships among breeds of dogs. In 1764 Bonnet asked whether the Chain might actually branch at certain points, and in 1766 Pallas proposed that the gradations among organisms resemble a tree with a compound trunk, perhaps not unlike the tree of animal life later depicted by Eichwald. Other trees were presented by Augier in 1801 and by Lamarck in 1809 and 1815, the latter two assuming a transmutation of species over time. Elaborate networks of affinities among plants and among animals were depicted in the late Eighteenth and very early Nineteenth centuries. In the two decades immediately prior to 1837, so-called affinities and/or analogies among organisms were represented by diverse geometric figures. Series of plant and animal fossils in successive geological strata were represented as trees in a popular textbook from 1840, while in 1858 Bronn presented a system of animals, as evidenced by the fossil record, in a form of a tree. Darwin's 1859 tree and its subsequent elaborations by Haeckel came to be accepted in many but not all areas of biological sciences, while network diagrams were used in others. Beginning in the early 1960s trees were inferred from protein and nucleic acid sequences, but networks were re-introduced in the mid-1990s to represent lateral genetic transfer, increasingly regarded as a fundamental mode of evolution at least for

  5. Hierarchical document categorization using associative networks

    NARCIS (Netherlands)

    Bloom, Niels; Theune, Mariet; de Jong, Franciska M.G.; Klement, E.P.; Borutzky, W.; Fahringer, T.; Hamza, M.H.; Uskov, V.

    Associative networks are a connectionist language model with the ability to handle dynamic data. We used two associative networks to categorize random sets of related Wikipedia articles with only their raw text as input. We then compared the resulting categorization to a gold standard: the manual

  6. Information Sharing During Crisis Management in Hierarchical vs. Network Teams

    NARCIS (Netherlands)

    Schraagen, J.M.C.; Veld, M.H.I.T.; Koning, L. de

    2010-01-01

    This study examines the differences between hierarchical and network teams in emergency management. A controlled experimental environment was created in which we could study teams that differed in decision rights, availability of information, information sharing, and task division. Thirty-two teams

  7. Self-organized Criticality in Hierarchical Brain Network

    Institute of Scientific and Technical Information of China (English)

    YANG Qiu-Ying; ZHANG Ying-Yue; CHEN Tian-Lun

    2008-01-01

    It is shown that the cortical brain network of the macaque displays a hierarchically clustered organization and the neuron network shows small-world properties. Now the two factors will be considered in our model and the dynamical behavior of the model will be studied. We study the characters of the model and find that the distribution of avalanche size of the model follows power-law behavior.

  8. An Extended Hierarchical Trusted Model for Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    DU Ruiying; XU Mingdi; ZHANG Huanguo

    2006-01-01

    Cryptography and authentication are traditional approach for providing network security. However, they are not sufficient for solving the problems which malicious nodes compromise whole wireless sensor network leading to invalid data transmission and wasting resource by using vicious behaviors. This paper puts forward an extended hierarchical trusted architecture for wireless sensor network, and establishes trusted congregations by three-tier framework. The method combines statistics, economics with encrypt mechanism for developing two trusted models which evaluate cluster head nodes and common sensor nodes respectively. The models form logical trusted-link from command node to common sensor nodes and guarantees the network can run in secure and reliable circumstance.

  9. Hierarchical Ring Network Design Using Branch-and-Price

    DEFF Research Database (Denmark)

    Thomadsen, Tommy; Stidsen, Thomas K.

    2005-01-01

    We consider the problem of designing hierarchical two layer ring networks. The top layer consists of a federal-ring which establishes connection between a number of node disjoint metro-rings in a bottom layer. The objective is to minimize the costs of links in the network, taking both the fixed l...... for jointly solving the clustering problem, the metro ring design problem and the routing problem. Computational results are given for networks with up to 36 nodes.......We consider the problem of designing hierarchical two layer ring networks. The top layer consists of a federal-ring which establishes connection between a number of node disjoint metro-rings in a bottom layer. The objective is to minimize the costs of links in the network, taking both the fixed...... link establishment costs and the link capacity costs into account. Hierarchical ring network design problems combines the following optimization problems: Clustering, hub selection, metro ring design, federal ring design and routing problems. In this paper a branch-and-price algorithm is presented...

  10. GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

    CERN Document Server

    Miki, Yohei

    2016-01-01

    The tree method is a widely implemented algorithm for collisionless $N$-body simulations in astrophysics well suited for GPU(s). Adopting hierarchical time stepping can accelerate $N$-body simulations; however, it is infrequently implemented and its potential remains untested in GPU implementations. We have developed a Gravitational Oct-Tree code accelerated by HIerarchical time step Controlling named \\texttt{GOTHIC}, which adopts both the tree method and the hierarchical time step. The code adopts some adaptive optimizations by monitoring the execution time of each function on-the-fly and minimizes the time-to-solution by balancing the measured time of multiple functions. Results of performance measurements with realistic particle distribution performed on NVIDIA Tesla M2090, K20X, and GeForce GTX TITAN X, which are representative GPUs of the Fermi, Kepler, and Maxwell generation of GPUs, show that the hierarchical time step achieves a speedup by a factor of around 3--5 times compared to the shared time step...

  11. GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

    Science.gov (United States)

    Miki, Yohei; Umemura, Masayuki

    2017-04-01

    The tree method is a widely implemented algorithm for collisionless N-body simulations in astrophysics well suited for GPU(s). Adopting hierarchical time stepping can accelerate N-body simulations; however, it is infrequently implemented and its potential remains untested in GPU implementations. We have developed a Gravitational Oct-Tree code accelerated by HIerarchical time step Controlling named GOTHIC, which adopts both the tree method and the hierarchical time step. The code adopts some adaptive optimizations by monitoring the execution time of each function on-the-fly and minimizes the time-to-solution by balancing the measured time of multiple functions. Results of performance measurements with realistic particle distribution performed on NVIDIA Tesla M2090, K20X, and GeForce GTX TITAN X, which are representative GPUs of the Fermi, Kepler, and Maxwell generation of GPUs, show that the hierarchical time step achieves a speedup by a factor of around 3-5 times compared to the shared time step. The measured elapsed time per step of GOTHIC is 0.30 s or 0.44 s on GTX TITAN X when the particle distribution represents the Andromeda galaxy or the NFW sphere, respectively, with 224 = 16,777,216 particles. The averaged performance of the code corresponds to 10-30% of the theoretical single precision peak performance of the GPU.

  12. Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks

    Science.gov (United States)

    Zuo, Zhen; Shuai, Bing; Wang, Gang; Liu, Xiao; Wang, Xingxing; Wang, Bing; Chen, Yushi

    2016-07-01

    Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information among sequential data, and they only require a limited number of network parameters. General RNNs can hardly be directly applied on non-sequential data. Thus, we proposed the hierarchical RNNs (HRNNs). In HRNNs, each RNN layer focuses on modeling spatial dependencies among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two recurrent neural network models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost; and 2) hierarchical long-short term memory recurrent network (HLSTM), which performs better than HSRN with the price of more computational cost. In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs). C-HRNNs not only make use of the representation power of CNNs, but also efficiently encodes spatial and scale dependencies among different image regions. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve state-of-the-art results on Places 205, SUN 397, MIT indoor, and competitive results on ILSVRC 2012.

  13. Brain rhythms reveal a hierarchical network organization.

    Directory of Open Access Journals (Sweden)

    G Karl Steinke

    2011-10-01

    Full Text Available Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic, in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states

  14. Design of Hierarchical Ring Networks Using Branch-and-Price

    DEFF Research Database (Denmark)

    Thomadsen, Tommy; Stidsen, Thomas K.

    2004-01-01

    We consider the problem of designing hierarchical two layer ring networks. The top layer consists of a federal-ring which establishes connection between a number of node disjoint metro-rings in a bottom layer. The objective is to minimize the costs of links in the network, taking both the fixed...... link establishment costs and the link capacity costs into account. The hierarchical two layer ring network design problem is solved in two stages: First the bottom layer, i.e. the metro-rings are designed, implicitly taking into account the capacity cost of the federal-ring. Then the federal......-ring is designed connecting the metro-rings, minimizing fixed link establishment costs of the federal-ring. A branch-and-price algorithm is presented for the design of the bottom layer and it is suggested that existing methods are used for the design of the federal-ring. Computational results are given...

  15. Strongly Resilient Non-Interactive Key Predistribution For Hierarchical Networks

    CERN Document Server

    Chen, Hao

    2010-01-01

    Key establishment is the basic necessary tool in the network security, by which pairs in the network can establish shared keys for protecting their pairwise communications. There have been some key agreement or predistribution schemes with the property that the key can be established without the interaction (\\cite{Blom84,BSHKY92,S97}). Recently the hierarchical cryptography and the key management for hierarchical networks have been active topics(see \\cite{BBG05,GHKRRW08,GS02,HNZI02,HL02,Matt04}. ). Key agreement schemes for hierarchical networks were presented in \\cite{Matt04,GHKRRW08} which is based on the Blom key predistribution scheme(Blom KPS, [1]) and pairing. In this paper we introduce generalized Blom-Blundo et al key predistribution schemes. These generalized Blom-Blundo et al key predistribution schemes have the same security functionality as the Blom-Blundo et al KPS. However different and random these KPSs can be used for various parts of the networks for enhancing the resilience. We also presentk...

  16. Hierarchical Compressed Sensing for Cluster Based Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Vishal Krishna Singh

    2016-02-01

    Full Text Available Data transmission consumes significant amount of energy in large scale wireless sensor networks (WSNs. In such an environment, reducing the in-network communication and distributing the load evenly over the network can reduce the overall energy consumption and maximize the network lifetime significantly. In this work, the aforementioned problem of network lifetime and uneven energy consumption in large scale wireless sensor networks is addressed. This work proposes a hierarchical compressed sensing (HCS scheme to reduce the in-network communication during the data gathering process. Co-related sensor readings are collected via a hierarchical clustering scheme. A compressed sensing (CS based data processing scheme is devised to transmit the data from the source to the sink. The proposed HCS is able to identify the optimal position for the application of CS to achieve reduced and similar number of transmissions on all the nodes in the network. An activity map is generated to validate the reduced and uniformly distributed communication load of the WSN. Based on the number of transmissions per data gathering round, the bit-hop metric model is used to analyse the overall energy consumption. Simulation results validate the efficiency of the proposed method over the existing CS based approaches.

  17. Time Synchronization in Hierarchical TESLA Wireless Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Jason L. Wright; Milos Manic

    2009-08-01

    Time synchronization and event time correlation are important in wireless sensor networks. In particular, time is used to create a sequence events or time line to answer questions of cause and effect. Time is also used as a basis for determining the freshness of received packets and the validity of cryptographic certificates. This paper presents secure method of time synchronization and event time correlation for TESLA-based hierarchical wireless sensor networks. The method demonstrates that events in a TESLA network can be accurately timestamped by adding only a few pieces of data to the existing protocol.

  18. Predicting gene function using hierarchical multi-label decision tree ensembles

    Directory of Open Access Journals (Sweden)

    Kocev Dragi

    2010-01-01

    Full Text Available Abstract Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO. We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Conclusions Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.

  19. Hierarchical Tree Algorithm for Collisional N-body Simulations on GRAPE

    CERN Document Server

    Fukushige, Toshiyuki

    2016-01-01

    We present an implementation of the hierarchical tree algorithm on the individual timestep algorithm (the Hermite scheme) for collisional $N$-body simulations, running on GRAPE-9 system, a special-purpose hardware accelerator for gravitational many-body simulations. Such combination of the tree algorithm and the individual timestep algorithm was not easy on the previous GRAPE system mainly because its memory addressing scheme was limited only to sequential access to a full set of particle data. The present GRAPE-9 system has an indirect memory addressing unit and a particle memory large enough to store all particles data and also tree nodes data. The indirect memory addressing unit stores interaction lists for the tree algorithm, which is constructed on host computer, and, according to the interaction lists, force pipelines calculate only the interactions necessary. In our implementation, the interaction calculations are significantly reduced compared to direct $N^2$ summation in the original Hermite scheme. ...

  20. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

    OpenAIRE

    2014-01-01

    Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs). Self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the...

  1. Impact of informal networks on opinion dynamics in hierarchically formal organization

    Science.gov (United States)

    Song, Xiao; Shi, Wen; Ma, Yaofei; Yang, Chen

    2015-10-01

    Traditional opinion dynamics model focused mainly on the conditions under which a group of agents would reach a consensus. Conclusion has been gained that continuous opinion dynamics are subject to the constraint that convergent opinion adjustment only proceeds when opinion difference is below a given tolerance. This conclusion is useful but neglected the fact that an organization often consists of overlapped networks including formally hierarchical network and small-world/scale-free informal networks. To study the impact of different types of informal networks on converging speed or the number of opinion clusters, four typical types of informal networks (small-world, scale-free, tree and fully connected) are modeled and proposed as complements to formal communications. Experiments to compare formal network and hybrid networks are then carried out. It is observed that opinion dynamics with supplemented communications of informal networks can benefit convergence speed and reduce opinion clusters. More importantly, it is revealed that three key factors of informal networks affect their impact on formal network. These factors of informal network in descending orders are: agents' tolerances, scale and number of links.

  2. Extending stability through hierarchical clusters in Echo State Networks

    Directory of Open Access Journals (Sweden)

    Sarah Jarvis

    2010-07-01

    Full Text Available Echo State Networks (ESN are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analysed the impact of reservoir substructures on stability in hierarchically clustered ESNs (HESN, as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius.

  3. A Hybrid P2P Overlay Network for Non-strictly Hierarchically Categorized Content

    Science.gov (United States)

    Wan, Yi; Asaka, Takuya; Takahashi, Tatsuro

    In P2P content distribution systems, there are many cases in which the content can be classified into hierarchically organized categories. In this paper, we propose a hybrid overlay network design suitable for such content called Pastry/NSHCC (Pastry for Non-Strictly Hierarchically Categorized Content). The semantic information of classification hierarchies of the content can be utilized regardless of whether they are in a strict tree structure or not. By doing so, the search scope can be restrained to any granularity, and the number of query messages also decreases while maintaining keyword searching availability. Through simulation, we showed that the proposed method provides better performance and lower overhead than unstructured overlays exploiting the same semantic information.

  4. Hierarchical rendering of trees from precomputed multi-layer z-buffers

    Energy Technology Data Exchange (ETDEWEB)

    Max, N. [California Univ., Davis, CA (United States)

    1996-02-01

    Chen and Williams show how precomputed z-buffer images from different fixed viewing positions can be reprojected to produce an image for a new viewpoint. Here images are precomputed for twigs and branches at various levels in the hierarchical structure of a tree, and adaptively combined, depending on the position of the new viewpoint. The precomputed images contain multiple z levels to avoid missing pixels in the reconstruction, subpixel masks for anti-aliasing, and colors and normals for shading after reprojection.

  5. Hierarchical Resource Allocation in Femtocell Networks using Graph Algorithms

    CERN Document Server

    Sadr, Sanam

    2012-01-01

    This paper presents a hierarchical approach to resource allocation in open-access femtocell networks. The major challenge in femtocell networks is interference management which in our system, based on the Long Term Evolution (LTE) standard, translates to which user should be allocated which physical resource block (or fraction thereof) from which femtocell access point (FAP). The globally optimal solution requires integer programming and is mathematically intractable. We propose a hierarchical three-stage solution: first, the load of each FAP is estimated considering the number of users connected to the FAP, their average channel gain and required data rates. Second, based on each FAP's load, the physical resource blocks (PRBs) are allocated to FAPs in a manner that minimizes the interference by coloring the modified interference graph. Finally, the resource allocation is performed at each FAP considering users' instantaneous channel gain. The two major advantages of this suboptimal approach are the significa...

  6. Replanning Using Hierarchical Task Network and Operator-Based Planning

    Science.gov (United States)

    Wang, X.; Chien, S.

    1997-01-01

    In order to scale-up to real-world problems, planning systems must be able to replan in order to deal with changes in problem context. In this paper we describe hierarchical task network and operatorbased re-planning techniques which allow adaptation of a previous plan to account for problems associated with executing plans in real-world domains with uncertainty, concurrency, changing objectives.

  7. Multi-mode clustering model for hierarchical wireless sensor networks

    Science.gov (United States)

    Hu, Xiangdong; Li, Yongfu; Xu, Huifen

    2017-03-01

    The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

  8. Hierarchical Communication Network Architectures for Offshore Wind Power Farms

    Directory of Open Access Journals (Sweden)

    Mohamed A. Ahmed

    2014-05-01

    Full Text Available Nowadays, large-scale wind power farms (WPFs bring new challenges for both electric systems and communication networks. Communication networks are an essential part of WPFs because they provide real-time control and monitoring of wind turbines from a remote location (local control center. However, different wind turbine applications have different requirements in terms of data volume, latency, bandwidth, QoS, etc. This paper proposes a hierarchical communication network architecture that consist of a turbine area network (TAN, farm area network (FAN, and control area network (CAN for offshore WPFs. The two types of offshore WPFs studied are small-scale WPFs close to the grid and medium-scale WPFs far from the grid. The wind turbines are modelled based on the logical nodes (LN concepts of the IEC 61400-25 standard. To keep pace with current developments in wind turbine technology, the network design takes into account the extension of the LNs for both the wind turbine foundation and meteorological measurements. The proposed hierarchical communication network is based on Switched Ethernet. Servers at the control center are used to store and process the data received from the WPF. The network architecture is modelled and evaluated via OPNET. We investigated the end-to-end (ETE delay for different WPF applications. The results are validated by comparing the amount of generated sensing data with that of received traffic at servers. The network performance is evaluated, analyzed and discussed in view of end-to-end (ETE delay for different link bandwidths.

  9. Routing and wavelength assignment in hierarchical WDM networks

    Institute of Scientific and Technical Information of China (English)

    Yiyi LU; Ruxiang JIN; Chen HE

    2008-01-01

    A new routing and wavelength assignment method applied in hierarchical wavelength division multiplexing(WDM)networks is proposed.The algorithm is called offiine band priority algorithm(offiine BPA).The offline BPA targets to maximize the number of waveband paths under the condition of minimum number of wavelengths,and solve the routing and wavelength assignment(RWA)problem with waveband grooming to reduce cost.Based on the circle construction algorithm,waveband priority function is introduced to calculate the RWA problem.Simulation results demonstrate that the proposed algorithm achieves significant cost reduction in WDM network construction.

  10. Hierarchical control based on Hopfield network for nonseparable optimization problems

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The nonseparable optimization control problem is considered, where the overall objective function is not of an additive form with respect to subsystems. Since there exists the problem that computation is very slow when using iterative algorithms in multiobjective optimization, Hopfield optimization hierarchical network based on IPM is presented to overcome such slow computation difficulty. Asymptotic stability of this Hopfield network is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale nonseparable steady state systems.

  11. HCsnip: An R Package for Semi-supervised Snipping of the Hierarchical Clustering Tree.

    Science.gov (United States)

    Obulkasim, Askar; van de Wiel, Mark A

    2015-01-01

    Hierarchical clustering (HC) is one of the most frequently used methods in computational biology in the analysis of high-dimensional genomics data. Given a data set, HC outputs a binary tree leaves of which are the data points and internal nodes represent clusters of various sizes. Normally, a fixed-height cut on the HC tree is chosen, and each contiguous branch of data points below that height is considered as a separate cluster. However, the fixed-height branch cut may not be ideal in situations where one expects a complicated tree structure with nested clusters. Furthermore, due to lack of utilization of related background information in selecting the cutoff, induced clusters are often difficult to interpret. This paper describes a novel procedure that aims to automatically extract meaningful clusters from the HC tree in a semi-supervised way. The procedure is implemented in the R package HCsnip available from Bioconductor. Rather than cutting the HC tree at a fixed-height, HCsnip probes the various way of snipping, possibly at variable heights, to tease out hidden clusters ensconced deep down in the tree. The cluster extraction process utilizes, along with the data set from which the HC tree is derived, commonly available background information. Consequently, the extracted clusters are highly reproducible and robust against various sources of variations that "haunted" high-dimensional genomics data. Since the clustering process is guided by the background information, clusters are easy to interpret. Unlike existing packages, no constraint is placed on the data type on which clustering is desired. Particularly, the package accepts patient follow-up data for guiding the cluster extraction process. To our knowledge, HCsnip is the first package that is able to decomposes the HC tree into clusters with piecewise snipping under the guidance of patient time-to-event information. Our implementation of the semi-supervised HC tree snipping framework is generic, and can

  12. Hierarchical organization of brain functional network during visual task

    CERN Document Server

    Zhuo, Zhao; Fu, Zhong-Qian; Zhang, Jie

    2011-01-01

    In this paper, the brain functional networks derived from high-resolution synchronous EEG time series during visual task are generated by calculating the phase synchronization among the time series. The hierarchical modular organizations of these networks are systematically investigated by the fast Girvan-Newman algorithm. At the same time, the spatially adjacent electrodes (corresponding to EEG channels) are clustered into functional groups based on anatomical parcellation of brain cortex, and this clustering information are compared to that of the functional network. The results show that the modular architectures of brain functional network are in coincidence with that from the anatomical structures over different levels of hierarchy, which suggests that population of neurons performing the same function excite and inhibit in identical rhythms. The structure-function relationship further reveals that the correlations among EEG time series in the same functional group are much stronger than those in differe...

  13. Complex networks with scale-free nature and hierarchical modularity

    Science.gov (United States)

    Shekatkar, Snehal M.; Ambika, G.

    2015-09-01

    Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

  14. Dynamics of Gas Exchange through the Fractal Architecture of the Human Lung, Modeled as an Exactly Solvable Hierarchical Tree

    Science.gov (United States)

    Mayo, Michael; Pfeifer, Peter; Gheorghiu, Stefan

    2008-03-01

    The acinar airways lie at the periphery of the human lung and are responsible for the transfer of oxygen from air to the blood during respiration. This transfer occurs by the diffusion-reaction of oxygen over the irregular surface of the alveolar membranes lining the acinar airways. We present an exactly solvable diffusion-reaction model on a hierarchically branched tree, allowing a quantitative prediction of the oxygen current over the entire system of acinar airways responsible for the gas exchange. We discuss the effect of diffusional screening, which is strongly coupled to oxygen transport in the human lung. We show that the oxygen current is insensitive to a loss of permeability of the alveolar membranes over a wide range of permeabilities, similar to a ``constant-current source'' in an electric network. Such fault tolerance has been observed in other treatments of the gas exchange in the lung and is obtained here as a fully analytical result.

  15. Global tree network for computing structures enabling global processing operations

    Science.gov (United States)

    Blumrich; Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

    2010-01-19

    A system and method for enabling high-speed, low-latency global tree network communications among processing nodes interconnected according to a tree network structure. The global tree network enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the tree via links to facilitate performance of low-latency global processing operations at nodes of the virtual tree and sub-tree structures. The global operations performed include one or more of: broadcast operations downstream from a root node to leaf nodes of a virtual tree, reduction operations upstream from leaf nodes to the root node in the virtual tree, and point-to-point message passing from any node to the root node. The global tree network is configurable to provide global barrier and interrupt functionality in asynchronous or synchronized manner, and, is physically and logically partitionable.

  16. Motion Tree Delineates Hierarchical Structure of Protein Dynamics Observed in Molecular Dynamics Simulation.

    Directory of Open Access Journals (Sweden)

    Kei Moritsugu

    Full Text Available Molecular dynamics (MD simulations of proteins provide important information to understand their functional mechanisms, which are, however, likely to be hidden behind their complicated motions with a wide range of spatial and temporal scales. A straightforward and intuitive analysis of protein dynamics observed in MD simulation trajectories is therefore of growing significance with the large increase in both the simulation time and system size. In this study, we propose a novel description of protein motions based on the hierarchical clustering of fluctuations in the inter-atomic distances calculated from an MD trajectory, which constructs a single tree diagram, named a "Motion Tree", to determine a set of rigid-domain pairs hierarchically along with associated inter-domain fluctuations. The method was first applied to the MD trajectory of substrate-free adenylate kinase to clarify the usefulness of the Motion Tree, which illustrated a clear-cut dynamics picture of the inter-domain motions involving the ATP/AMP lid and the core domain together with the associated amplitudes and correlations. The comparison of two Motion Trees calculated from MD simulations of ligand-free and -bound glutamine binding proteins clarified changes in inherent dynamics upon ligand binding appeared in both large domains and a small loop that stabilized ligand molecule. Another application to a huge protein, a multidrug ATP binding cassette (ABC transporter, captured significant increases of fluctuations upon binding a drug molecule observed in both large scale inter-subunit motions and a motion localized at a transmembrane helix, which may be a trigger to the subsequent structural change from inward-open to outward-open states to transport the drug molecule. These applications demonstrated the capabilities of Motion Trees to provide an at-a-glance view of various sizes of functional motions inherent in the complicated MD trajectory.

  17. Doubly Optimal Secure Multicasting: Hierarchical Hybrid Communication Network : Disaster Relief

    CERN Document Server

    Garimella, Rama Murthy; Singhal, Deepti

    2011-01-01

    Recently, the world has witnessed the increasing occurrence of disasters, some of natural origin and others caused by man. The intensity of the phenomenon that cause such disasters, the frequency in which they occur, the number of people affected and the material damage caused by them have been growing substantially. Disasters are defined as natural, technological, and human-initiated events that disrupt the normal functioning of the economy and society on a large scale. Areas where disasters have occurred bring many dangers to rescue teams and the communication network infrastructure is usually destroyed. To manage these hazards, different wireless technologies can be launched in the area of disaster. This paper discusses the innovative wireless technologies for Disaster Management. Specifically, issues related to the design of Hierarchical Hybrid Communication Network (arising in the communication network for disaster relief) are discussed.

  18. Hierarchical modular granular neural networks with fuzzy aggregation

    CERN Document Server

    Sanchez, Daniela

    2016-01-01

    In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

  19. Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

    Science.gov (United States)

    Tipton, John; Hooten, Mevin B.; Pederson, Neil; Tingley, Martin; Bishop, Daniel

    2016-01-01

    Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

  20. INDUCTION OF DECISION TREES BASED ON A FUZZY NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Tang Bin; Hu Guangrui; Mao Xiaoquan

    2002-01-01

    Based on a fuzzy neural network, the letter presents an approach for the induction of decision trees. The approach makes use of the weights of fuzzy mappings in the fuzzy neural network which has been trained. It can realize the optimization of fuzzy decision trees by branch cutting, and improve the ratio of correctness and efficiency of the induction of decision trees.

  1. Retrieval capabilities of hierarchical networks: from Dyson to Hopfield.

    Science.gov (United States)

    Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Tantari, Daniele; Tavani, Flavia

    2015-01-16

    We consider statistical-mechanics models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer than their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of metastabilities, beyond the ordered state, which become stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform single pattern retrieval as well as multiple-pattern retrieval, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, Markov chain theory, signal-to-noise ratio technique, and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.

  2. Category theoretic analysis of hierarchical protein materials and social networks.

    Directory of Open Access Journals (Sweden)

    David I Spivak

    Full Text Available Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.

  3. Constructal tree networks for heat transfer

    Energy Technology Data Exchange (ETDEWEB)

    Ledezma, G.A.; Bejan, A.; Errera, M.R. [Department of Mechanical Engineering and Materials Science, Box 90300, Duke University, Durham, North Carolina 27708-0300 (United States)

    1997-07-01

    This paper addresses the fundamental problem of how to connect a heat generating volume to a point heat sink by using a finite amount of high-conductivity material that can be distributed through the volume. The problem is one of optimizing the access (or minimizing the thermal resistance) between a finite-size volume and one point. The solution is constructed by covering the volume with a sequence of building blocks, which proceeds toward larger sizes (assemblies), hence, the {open_quotes}constructal{close_quotes} name for this approach. Optimized numerically at each stage are geometric features such as the overall shape of the building block, its number of constituents, and the internal distribution of high-conductivity inserts. It is shown that in the optimal design, the high-conductivity material has a distribution with the shape of a tree. Every aspect of the tree architecture is deterministic: the shapes of the largest assembly and all its constituents, the number of branches at each level of assembly, the relative position of building blocks in each assembly, and the relative thicknesses of successive branches. The finer, innermost details of the tree architecture (e.g., the branching angle) have a negligible effect on the overall thermal resistance. The main conclusion is that the structure, working mechanism, and minimal resistance of the tree network can be obtained deterministically, and that the constrained optimization of access routes accounts for the macroscopic structure in nature. {copyright} {ital 1997 American Institute of Physics.}

  4. Genomic analysis of the hierarchical structure of regulatory networks

    Science.gov (United States)

    Yu, Haiyuan; Gerstein, Mark

    2006-01-01

    A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

  5. A HIERARCHICAL INTRUSION DETECTION ARCHITECTURE FOR WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Hossein Jadidoleslamy

    2011-10-01

    Full Text Available Networks protection against different types of attacks is one of most important posed issue into the network andinformation security application domains. This problem on Wireless Sensor Networks (WSNs, in attention to theirspecial properties, has more importance. Now, there are some of proposed architectures and guide lines to protectWireless Sensor Networks (WSNs against different types of intrusions; but any one of them do not has acomprehensive view to this problem and they are usually designed and implemented in single-purpose; but, theproposed design in this paper tries to has been a comprehensive view to this issue by presenting a complete andcomprehensive Intrusion Detection Architecture (IDA. The main contribution of this architecture is its hierarchicalstructure; i.e., it is designed and applicable, in one or two levels, consistent to the application domain and itsrequired security level. Focus of this paper is on the clustering WSNs, designing and deploying Cluster-basedIntrusion Detection System (CIDS on cluster-heads and Wireless Sensor Network wide level Intrusion DetectionSystem (WSNIDS on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA are:static and heterogeneous network, hierarchical and clustering structure, clusters' overlapping and using hierarchicalrouting protocol such as LEACH, but along with minor changes. Finally, the proposed idea has been verified bydesigning a questionnaire, representing it to some (about 50 people experts and then, analyzing and evaluating itsacquired results.

  6. Macroscopic Models of Clique Tree Growth for Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to...

  7. [A medical image semantic modeling based on hierarchical Bayesian networks].

    Science.gov (United States)

    Lin, Chunyi; Ma, Lihong; Yin, Junxun; Chen, Jianyu

    2009-04-01

    A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in allusion to characters of medical images. It used GMM (Gaussian mixture models) to map low-level image features into object semantics with probabilities, then it captured high-level semantics through fusing these object semantics using a Bayesian network, so that it built a multi-layer medical image semantic model, aiming to enable automatic image annotation and semantic retrieval by using various keywords at different semantic levels. As for the validity of this method, we have built a multi-level semantic model from a small set of astrocytoma MRI (magnetic resonance imaging) samples, in order to extract semantics of astrocytoma in malignant degree. Experiment results show that this is a superior approach.

  8. Spatially Resolved Monitoring of Drying of Hierarchical Porous Organic Networks.

    Science.gov (United States)

    Velasco, Manuel Isaac; Silletta, Emilia V; Gomez, Cesar G; Strumia, Miriam C; Stapf, Siegfried; Monti, Gustavo Alberto; Mattea, Carlos; Acosta, Rodolfo H

    2016-03-01

    Evaporation kinetics of water confined in hierarchal polymeric porous media is studied by low field nuclear magnetic resonance (NMR). Systems synthesized with various degrees of cross-linker density render networks with similar pore sizes but different response when soaked with water. Polymeric networks with low percentage of cross-linker can undergo swelling, which affects the porosity as well as the drying kinetics. The drying process is monitored macroscopically by single-sided NMR, with spatial resolution of 100 μm, while microscopic information is obtained by measurements of spin-spin relaxation times (T2). Transition from a funicular to a pendular regime, where hydraulic connectivity is lost and the capillary flow cannot compensate for the surface evaporation, can be observed from inspection of the water content in different sample layers. Relaxation measurements indicate that even when the larger pore structures are depleted of water, capillary flow occurs through smaller voids.

  9. Architecture of the parallel hierarchical network for fast image recognition

    Science.gov (United States)

    Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

    2016-09-01

    Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

  10. Reliable Point to Multipoint Hierarchical Routing in Scatternet Sensor Network

    Directory of Open Access Journals (Sweden)

    R.Dhaya

    2011-01-01

    Full Text Available In the recent development of communication, Bluetooth Scatternet wireless is a technology developed for wideband local accesses. Bluetooth technology is very popular because of its low cost and easy deployment which is based on IEEE 802.11standards. On the other hand Wireless Sensor Network (WSN consists of large number of sensor nodes distributed to monitor an environment and each node in a WSN consists of a small CPU, a sensing device and battery. Mostly, the sensor networks are distributed in an inconvenient location and it is difficult to recharge often. So routing in WSN is an important issue to consume energy and as well as to increase the life of the network, since a routing protocol finds the path between sources and sink. Moreover it is a challenging task to schedule the data between nodes in a scatternet in a congestive environment. Here this paper presents a new scheduling method for point to multi- point routing in Scatternet sensor network and the new dynamic routing method designed is cluster-based with hierarchical routing. The efficiency of this method is also compared in terms of energy consumption and the results show that the proposed routing is an energy efficient one which simultaneously increases the lifetime of the network.

  11. GSMNet: A Hierarchical Graph Model for Moving Objects in Networks

    Directory of Open Access Journals (Sweden)

    Hengcai Zhang

    2017-03-01

    Full Text Available Existing data models for moving objects in networks are often limited by flexibly controlling the granularity of representing networks and the cost of location updates and do not encompass semantic information, such as traffic states, traffic restrictions and social relationships. In this paper, we aim to fill the gap of traditional network-constrained models and propose a hierarchical graph model called the Geo-Social-Moving model for moving objects in Networks (GSMNet that adopts four graph structures, RouteGraph, SegmentGraph, ObjectGraph and MoveGraph, to represent the underlying networks, trajectories and semantic information in an integrated manner. The bulk of user-defined data types and corresponding operators is proposed to handle moving objects and answer a new class of queries supporting three kinds of conditions: spatial, temporal and semantic information. Then, we develop a prototype system with the native graph database system Neo4Jto implement the proposed GSMNet model. In the experiment, we conduct the performance evaluation using simulated trajectories generated from the BerlinMOD (Berlin Moving Objects Database benchmark and compare with the mature MOD system Secondo. The results of 17 benchmark queries demonstrate that our proposed GSMNet model has strong potential to reduce time-consuming table join operations an d shows remarkable advantages with regard to representing semantic information and controlling the cost of location updates.

  12. Inferring gene regression networks with model trees

    Directory of Open Access Journals (Sweden)

    Aguilar-Ruiz Jesus S

    2010-10-01

    Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear

  13. Image segmentation using neural tree networks

    Science.gov (United States)

    Samaddar, Sumitro; Mammone, Richard J.

    1993-06-01

    We present a technique for Image Segmentation using Neural Tree Networks (NTN). We also modify the NTN architecture to let is solve multi-class classification problems with only binary fan-out. We have used a realistic case study of segmenting the pole, coil and painted coil regions of light bulb filaments (LBF). The input to the network is a set of maximum, minimum and average of intensities in radial slices of a circular window around a pixel, taken from a front-lit and a back-lit image of an LBF. Training is done with a composite image drawn from images of many LBFs. Each node of the NTN is a multi-layer perceptron and has one output for each segment class. These outputs are treated as probabilities to compute a confidence value for the segmentation of that pixel. Segmentation results with high confidence values are deemed to be correct and not processed further, while those with moderate and low confidence values are deemed to be outliers by this node and passed down the tree to children nodes. These tend to be pixels in boundary of different regions. The results are favorably compared with a traditional segmentation technique applied to the LBF test case.

  14. Neural tree network method for image segmentation

    Science.gov (United States)

    Samaddar, Sumitro; Mammone, Richard J.

    1994-02-01

    We present an extension of the neural tree network (NTN) architecture to let it solve multi- class classification problems with only binary fan-out. We then demonstrate it's effectiveness by applying it in a method for image segmentation. Each node of the NTN is a multi-layer perceptron and has one output for each segment class. These outputs are treated as probabilities to compute a confidence value for the segmentation of that pixel. Segmentation results with high confidence values are deemed to be correct and not processed further, while those with moderate and low confidence values are deemed to be outliers by this node and passed down the tree to children nodes. These tend to be pixels in boundary of different regions. We have used a realistic case study of segmenting the pole, coil and painted coil regions of light bulb filaments (LBF). The input to the network is a set of maximum, minimum and average of intensities in radial slices of a circular window around a pixel, taken from a front-lit and a back-lit image of an LBF. Training is done with a composite image drawn from images of many LBFs. The results are favorably compared with a traditional segmentation technique applied to the LBF test case.

  15. A hierarchical network modeling method for railway tunnels safety assessment

    Science.gov (United States)

    Zhou, Jin; Xu, Weixiang; Guo, Xin; Liu, Xumin

    2017-02-01

    Using network theory to model risk-related knowledge on accidents is regarded as potential very helpful in risk management. A large amount of defects detection data for railway tunnels is collected in autumn every year in China. It is extremely important to discover the regularities knowledge in database. In this paper, based on network theories and by using data mining techniques, a new method is proposed for mining risk-related regularities to support risk management in railway tunnel projects. A hierarchical network (HN) model which takes into account the tunnel structures, tunnel defects, potential failures and accidents is established. An improved Apriori algorithm is designed to rapidly and effectively mine correlations between tunnel structures and tunnel defects. Then an algorithm is presented in order to mine the risk-related regularities table (RRT) from the frequent patterns. At last, a safety assessment method is proposed by consideration of actual defects and possible risks of defects gained from the RRT. This method cannot only generate the quantitative risk results but also reveal the key defects and critical risks of defects. This paper is further development on accident causation network modeling methods which can provide guidance for specific maintenance measure.

  16. Hierarchical Interference Mitigation for Massive MIMO Cellular Networks

    Science.gov (United States)

    Liu, An; Lau, Vincent

    2014-09-01

    We propose a hierarchical interference mitigation scheme for massive MIMO cellular networks. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each BS (CSIT). The outer precoder controls the inter-cell interference and is adaptive to channel statistics. Such hierarchical precoding structure reduces the number of pilot symbols required for CSI estimation in massive MIMO downlink and is robust to the backhaul latency. We study joint optimization of the outer precoders, the user selection, and the power allocation to maximize a general concave utility which has no closed-form expression. We first apply random matrix theory to obtain an approximated problem with closed-form objective. We show that the solution of the approximated problem is asymptotically optimal with respect to the original problem as the number of antennas per BS grows large. Then using the hidden convexity of the problem, we propose an iterative algorithm to find the optimal solution for the approximated problem. We also obtain a low complexity algorithm with provable convergence. Simulations show that the proposed design has significant gain over various state-of-the-art baselines.

  17. Distributed Plume Source Localization Using Hierarchical Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    KUANG Xing-hong; LIU Yu-qing; WU Yan-xiang; SHAO Hui-he

    2009-01-01

    A hierarchical wireless sensor networks (WSN) was proposed to estimate the plume source location. Such WSN can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding to an accident. The entire surveillant field is divided into several small sub-regions. In each sub-region, the localization algorithm based on the improved particle filter (IPF) was performed to estimate the location. Some improved methods such as weighted centroid, residual resampling were introduced to the IPF algorithm to increase the localization performance. This distributed estimation method elirninates many drawbacks inherent with the traditional centralized optimization method. Simulation results show that localization algorithm is efficient far estimating the plume source location.

  18. Hierarchical self-organization of cytoskeletal active networks

    CERN Document Server

    Gordon, Daniel; Keasar, Chen; Farago, Oded

    2012-01-01

    The structural reorganization of the actin cytoskeleton is facilitated through the action of motor proteins that crosslink the actin filaments and transport them relative to each other. Here, we present a combined experimental-computational study that probes the dynamic evolution of mixtures of actin filaments and clusters of myosin motors. While on small spatial and temporal scales the system behaves in a very noisy manner, on larger scales it evolves into several well distinct patterns such as bundles, asters, and networks. These patterns are characterized by junctions with high connectivity, whose formation is possible due to the organization of the motors in "oligoclusters" (intermediate-size aggregates). The simulations reveal that the self-organization process proceeds through a series of hierarchical steps, starting from local microscopic moves and ranging up to the macroscopic large scales where the steady-state structures are formed. Our results shed light into the mechanisms involved in processes li...

  19. Learning Probabilistic Hierarchical Task Networks to Capture User Preferences

    CERN Document Server

    Li, Nan; Kambhampati, Subbarao; Yoon, Sungwook

    2010-01-01

    We propose automatically learning probabilistic Hierarchical Task Networks (pHTNs) in order to capture a user's preferences on plans, by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are (a) learning structure and (b) representing preferences. In contrast, prior work employing HTNs considers learning method preconditions (instead of structure) and representing domain physics or search control knowledge (rather than preferences). Initially we will assume that the observed distribution of plans is an accurate representation of user preference, and then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. In order to learn a distribution on plans we adapt an Expectation-Maximization (EM) technique from the discipline of (probabilistic) grammar induction, taking the perspective of task reductions as productions in a context-free...

  20. 3D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

    DEFF Research Database (Denmark)

    Suhaibah, A.; Uznir, U.; Antón Castro, Francesc/François

    2016-01-01

    , with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our...... findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results...... of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However...

  1. Hierarchical Place Trees: A Portable Abstraction for Task Parallelism and Data Movement

    Science.gov (United States)

    Yan, Yonghong; Zhao, Jisheng; Guo, Yi; Sarkar, Vivek

    Modern computer systems feature multiple homogeneous or heterogeneous computing units with deep memory hierarchies, and expect a high degree of thread-level parallelism from the software. Exploitation of data locality is critical to achieving scalable parallelism, but adds a significant dimension of complexity to performance optimization of parallel programs. This is especially true for programming models where locality is implicit and opaque to programmers. In this paper, we introduce the hierarchical place tree (HPT) model as a portable abstraction for task parallelism and data movement. The HPT model supports co-allocation of data and computation at multiple levels of a memory hierarchy. It can be viewed as a generalization of concepts from the Sequoia and X10 programming models, resulting in capabilities that are not supported by either. Compared to Sequoia, HPT supports three kinds of data movement in a memory hierarchy rather than just explicit data transfer between adjacent levels, as well as dynamic task scheduling rather than static task assignment. Compared to X10, HPT provides a hierarchical notion of places for both computation and data mapping. We describe our work-in-progress on implementing the HPT model in the Habanero-Java (HJ) compiler and runtime system. Preliminary results on general-purpose multicore processors and GPU accelerators indicate that the HPT model can be a promising portable abstraction for future multicore processors.

  2. Registration Cost Performance Analysis of a Hierarchical Mobile Internet Protocol Network

    Institute of Scientific and Technical Information of China (English)

    XU Kai; JI Hong; YUE Guang-xin

    2004-01-01

    On the basis of introducing principles for hierarchical mobile Internet protocol networks, the registration cost performance in this network model is analyzed in detail. Furthermore, the functional relationship is also established in the paper among registration cost, hierarchical level number and the maximum handover time for gateway foreign agent regional registration. At last, the registration cost of the hierarchical mobile Internet protocol network is compared with that of the traditional mobile Internet protocol. Theoretic analysis and computer simulation results show that the hierarchical level number and the maximum handover times can both affect the registration cost importantly, when suitable values of which are chosen, the hierarchical network can significantly improve the registration performance compared with the traditional mobile IP.

  3. Hierarchical Real-time Network Traffic Classification Based on ECOC

    Directory of Open Access Journals (Sweden)

    Yaou Zhao

    2013-09-01

    Full Text Available Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classification methods were diminished.An alternative method based on statistics and machine learning hadattracted researchers' attention in recent years. However, most ofthe proposed algorithms were off-line and usually used a single classifier.In this paper a new hierarchical real-time model was proposed which comprised of a three tuple (source ip, destination ip and destination portlook up table(TT-LUT part and layered milestone part. TT-LUT was used to quickly classify short flows whichneed not to pass the layered milestone part, and milestones in layered milestone partcould classify the other flows in real-time with the real-time feature selection and statistics.Every milestone was a ECOC(Error-Correcting Output Codes based model which was usedto improve classification performance. Experiments showed that the proposedmodel can improve the efficiency of real-time to 80%, and themulti-class classification accuracy encouragingly to 91.4% on the datasets which had been captured from the backbone router in our campus through a week.

  4. Category theoretic analysis of hierarchical protein materials and social networks

    CERN Document Server

    Spivak, David I; Buehler, Markus J

    2011-01-01

    Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we review an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other ologs. We consider a simple example of an alpha-helical and an amyloid-like protein filament subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog f...

  5. Vulnerability of complex networks under three-level-tree attacks

    Science.gov (United States)

    Hao, Yao-hui; Han, Ji-hong; Lin, Yi; Liu, Lin

    2016-11-01

    We investigate vulnerability of complex networks including model networks and real world networks subject to three-level-tree attack. Specifically, we remove three different three-level-tree structures: RRN (Random Root Node), MaxDRN (Max Degree Root Node) and MinDRN (Min Degree Root Node) from a network iteratively until there is no three-level-tree left. Results demonstrate that random network is more robust than scale-free network against three tree attacks, and the robustness of random network decreases as the increases. And scale-free network shows different characteristics in different tree attack modes. The robustness of scale-free is not affected by the parameters for RRN, but increases as the increases for MinDRN. The important thing is that MaxDRN is the most effective in the three tree attack modes, especially for scale-free network. These findings supplement and extend the previous attack results on nodes and edges, and can thus help us better explain the vulnerability of different networks, and provide an insight into more tolerant real complex systems design.

  6. Complex Networks and Minimal Spanning Trees in International Trade Network

    Science.gov (United States)

    Maeng, Seong Eun; Choi, Hyung Wooc; Lee, Jae Woo

    The wealth of a nation is changed by the internal economic growth of a nation and by the international trade among countries. Trade between countries are one of their most important interactions and thus expects to affect crucially the wealth distribution over countries. We reviewed the network properties of the international trade networks (ITN). We analyzed data sets of world trade. The data set include a total number of 190 countries from 1950 to 2000. We observed that the world trade network showed the uneven trading relationships which are measured by the disparity. The effective disparity followed a power law, tδ, for the import and export network. We also construct the minimal spanning tree(MST) of international trade network, where each node is a country and directed links connecting them represent money flow from a source node to a target one. The topology of the MST shows the flow patterns of the international trades. From the MST we can identify the sub-economic zone if we delete the hub node. We observed that the cumulative degree distribution functions follow the power law, P>(k) k-α, with the average exponent α = 1.1(1)). We also calculated the betweenness centrality(BC) of the MST. The cumulative probability distribution of the betweenness centrality follows the power law, P>(BC) BC-β, with the average exponent β = 1.09(7).

  7. Artificial photosynthesis on tree trunk derived alkaline tantalates with hierarchical anatomy: towards CO2 photo-fixation into CO and CH4.

    Science.gov (United States)

    Zhou, Han; Li, Peng; Guo, Jianjun; Yan, Runyu; Fan, Tongxiang; Zhang, Di; Ye, Jinhua

    2015-01-07

    Artificial photosynthesis, the photochemical fixation and recycling of CO2 back to hydrocarbon fuels using sunlight and water, is both a significant challenge and an opportunity that, if realized, could have a revolutionary impact on our energy system. Herein, we demonstrate one of the first examples using biomass derived hierarchical porous photocatalysts for CO2 photo-fixation into sustainable hydrocarbon fuels. A generic method is proposed to build a series of alkaline tantalates MTaO3 (M = Li, Na, K) with hierarchical anatomy from macro- to nanoscales using activated carbonized tree trunks as templates. Artificial photosynthesis is carried out on MTaO3 series using only artificial sunlight, water, and carbon dioxide as inputs to produce carbon monoxide and methane as the main outputs. The CO2 photo-fixation performance can be enhanced by introducing a macropore network, which mainly enhances light transfer and accelerates gas diffusion. The research provides prototype models that integrate individual nanoscale components into higher level macroscopic artificial photosynthetic systems for better solar-to-fuel conversion efficiencies. This work would have potential significance for the ultimate construction of "artificial trees" and provide envisions creating "forests" of these CO2-capturing artificial trees to remove carbon dioxide from the atmosphere and convert it into sustainable fuels.

  8. Sensor Networks Hierarchical Optimization Model for Security Monitoring in High-Speed Railway Transport Hub

    Directory of Open Access Journals (Sweden)

    Zhengyu Xie

    2015-01-01

    Full Text Available We consider the sensor networks hierarchical optimization problem in high-speed railway transport hub (HRTH. The sensor networks are optimized from three hierarchies which are key area sensors optimization, passenger line sensors optimization, and whole area sensors optimization. Case study on a specific HRTH in China showed that the hierarchical optimization method is effective to optimize the sensor networks for security monitoring in HRTH.

  9. Spectral characterization of hierarchical network modularity and limits of modularity detection.

    Directory of Open Access Journals (Sweden)

    Somwrita Sarkar

    Full Text Available Many real world networks are reported to have hierarchically modular organization. However, there exists no algorithm-independent metric to characterize hierarchical modularity in a complex system. The main results of the paper are a set of methods to address this problem. First, classical results from random matrix theory are used to derive the spectrum of a typical stochastic block model hierarchical modular network form. Second, it is shown that hierarchical modularity can be fingerprinted using the spectrum of its largest eigenvalues and gaps between clusters of closely spaced eigenvalues that are well separated from the bulk distribution of eigenvalues around the origin. Third, some well-known results on fingerprinting non-hierarchical modularity in networks automatically follow as special cases, threreby unifying these previously fragmented results. Finally, using these spectral results, it is found that the limits of detection of modularity can be empirically established by studying the mean values of the largest eigenvalues and the limits of the bulk distribution of eigenvalues for an ensemble of networks. It is shown that even when modularity and hierarchical modularity are present in a weak form in the network, they are impossible to detect, because some of the leading eigenvalues fall within the bulk distribution. This provides a threshold for the detection of modularity. Eigenvalue distributions of some technological, social, and biological networks are studied, and the implications of detecting hierarchical modularity in real world networks are discussed.

  10. Hierarchical Structure, Disassortativity and Information Measures of the US Flight Network

    Institute of Scientific and Technical Information of China (English)

    WANG Ru; CAI Xu

    2005-01-01

    @@ We investigate the mixing structure of directed and evolutionary US flight network. It is shown that such a network is a hierarchical network, with average assortativity coefficient -0.37. Application of the informationbased method that can give the same result provides a way to explore the structure of complex networks.

  11. Hierarchical approximate policy iteration with binary-tree state space decomposition.

    Science.gov (United States)

    Xu, Xin; Liu, Chunming; Yang, Simon X; Hu, Dewen

    2011-12-01

    In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.

  12. Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

    Science.gov (United States)

    Nitta, Tohru

    2016-06-30

    We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

  13. Systemic risk and hierarchical transitions of financial networks

    Science.gov (United States)

    Nobi, Ashadun; Lee, Jae Woo

    2017-06-01

    In this paper, the change in topological hierarchy, which is measured by the minimum spanning tree constructed from the cross-correlations between the stock indices from the S & P 500 for 1998-2012 in a one year moving time window, was used to analyze a financial crisis. The hierarchy increased in all minor crises in the observation time window except for the sharp crisis of 2007-2008 when the global financial crisis occurred. The sudden increase in hierarchy just before the global financial crisis can be used for the early detection of an upcoming crisis. Clearly, the higher the hierarchy, the higher the threats to financial stability. The scaling relations were developed to observe the changes in hierarchy with the network topology. These scaling relations can also identify and quantify the financial crisis periods, and appear to contain the predictive power of an upcoming crisis.

  14. A Tool for Fast Development of Modular and Hierarchic Neural Network-based Systems

    Directory of Open Access Journals (Sweden)

    Francisco Reinaldo

    2006-08-01

    Full Text Available This paper presents PyramidNet tool as a fast and easy way to develop Modular and Hierarchic Neural Network-based Systems. This tool facilitates the fast emergence of autonomous behaviors in agents because it uses a hierarchic and modular control methodology of heterogeneous learning modules: the pyramid. Using the graphical resources of PyramidNet the user is able to specify a behavior system even having little understanding of artificial neural networks. Experimental tests have shown that a very significant speedup is attained in the development of modular and hierarchic neural network-based systems by using this tool.

  15. Maximum Leaf Spanning Trees of Growing Sierpinski Networks Models

    CERN Document Server

    Yao, Bing; Xu, Jin

    2016-01-01

    The dynamical phenomena of complex networks are very difficult to predict from local information due to the rich microstructures and corresponding complex dynamics. On the other hands, it is a horrible job to compute some stochastic parameters of a large network having thousand and thousand nodes. We design several recursive algorithms for finding spanning trees having maximal leaves (MLS-trees) in investigation of topological structures of Sierpinski growing network models, and use MLS-trees to determine the kernels, dominating and balanced sets of the models. We propose a new stochastic method for the models, called the edge-cumulative distribution, and show that it obeys a power law distribution.

  16. SO2 Emissions in China - Their Network and Hierarchical Structures

    Science.gov (United States)

    Yan, Shaomin; Wu, Guang

    2017-04-01

    SO2 emissions lead to various harmful effects on environment and human health. The SO2 emission in China has significant contribution to the global SO2 emission, so it is necessary to employ various methods to study SO2 emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO2 emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO2 emissions from power generation sector were highly individualized as small-sized clusters, the SO2 emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO2 emissions from residential sector was not impacted by time, and the SO2 emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO2 emissions from all four sectors and is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measure to combat SO2 emissions.

  17. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  18. Parameter estimation in tree graph metabolic networks

    Directory of Open Access Journals (Sweden)

    Laura Astola

    2016-09-01

    Full Text Available We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

  19. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems

    CERN Document Server

    Rosvall, M

    2010-01-01

    To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation that reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network, the optimal number of levels and modular partition at each level, with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines:...

  20. Natural forest conservation hierarchical program with neural network

    Institute of Scientific and Technical Information of China (English)

    LUO Chuanwen; LI Jihong

    2006-01-01

    In this paper,the implementing steps of a natural forest protection program grading (NFPPG) with neural network (NN) were summarized and the concepts of program illustration,patch sign unification and regression,and inclining factor were set forth.Employing Arc/Info GIS,the tree species diversity and rarity,disturbance degree,protection of channel system,and classification management in the Maoershan National Forest Park were described,and used as the input factors of NN.The relationships between NFPPG and above factors were also analyzed.By artificially determining training samples,the NFPPG of Moershan National Forest Park was created.Tested with all patches in the park,the generalization of NFPPG was satisfied.NFPPG took both the classification management and the protection of forest community types into account,as well as the ecological environment.The excitation function of NFPPG was not seriously saturated,indicating the leading effect of the inclining factor on the network optimization.

  1. Multiple dynamical time-scales in networks with hierarchically nested modular organization

    Indian Academy of Sciences (India)

    Sitabhra Sinha; Swarup Poria

    2011-11-01

    Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters defined at one level appear as elementary entities at the next higher level. Using a simple model of a hierarchical modular network, we show that such a topological structure gives rise to characteristic time-scale separation between dynamics occurring at different levels of the hierarchy. This generalizes our earlier result for simple modular networks, where fast intramodular and slow intermodular processes were clearly distinguished. Investigating the process of synchronization of oscillators in a hierarchical modular network, we show the existence of as many distinct time-scales as there are hierarchical levels in the system. This suggests a possible functional role of such mesoscopic organization principle in natural systems, viz., in the dynamical separation of events occurring at different spatial scales.

  2. Loss Performance Modeling for Hierarchical Heterogeneous Wireless Networks With Speed-Sensitive Call Admission Control

    DEFF Research Database (Denmark)

    Huang, Qian; Huang, Yue-Cai; Ko, King-Tim;

    2011-01-01

    dimensioning and planning. This paper investigates the computationally efficient loss performance modeling for multiservice in hierarchical heterogeneous wireless networks. A speed-sensitive call admission control (CAC) scheme is considered in our model to assign overflowed calls to appropriate tiers...

  3. BulkTree: an overlay network architecture for live media streaming

    Institute of Scientific and Technical Information of China (English)

    GONG An; DING Gui-guang; DAI Qiong-hai; LIN Chuang

    2006-01-01

    Peer-to-peer (P2P) systems are now very popular. Current P2P systems are broadly of two kinds, structured and unstructured. The tree structured P2P systems used technologies such as distributed hash tables (DHT) and hierarchical clustering can search the required target quickly, however, in a tree, the internal node has a higher load and its leave or crash often causes a large population of its offspring's problems, so that in the highly dynamic Internet environment the tree structure may still suffer frequent breaks. On the other hand, most widely used unstructured P2P networks rely on central directory servers or massive message flooding, clearly not scalable. So, we consider both of the above systems' advantages and disadvantages and realize that in the P2P systems one node may fail easily, but that when a number of nodes organized as a set, which we call "super node", the set is robust. Super nodes can be created and updated aware of topology-aware, and used with simple protocol such as flooding or "servers" to exchange information. Furthermore the entire robust super node can be organized into exquisite tree structure. By using this overlay network architecture, P2P systems are robust, efficient, scalable and secure. The simulation results demonstrated that our architecture greatly reduces the alteration time of the structure while decreasing the average delay time, compared to the common tree structure.

  4. Spanning Trees and bootstrap reliability estimation in correlation based networks

    CERN Document Server

    Tumminello, M; Lillo, F; Micciché, S; Mantegna, R N

    2006-01-01

    We introduce a new technique to associate a spanning tree to the average linkage cluster analysis. We term this tree as the Average Linkage Minimum Spanning Tree. We also introduce a technique to associate a value of reliability to links of correlation based graphs by using bootstrap replicas of data. Both techniques are applied to the portfolio of the 300 most capitalized stocks traded at New York Stock Exchange during the time period 2001-2003. We show that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimum Spanning Tree does. We also show that the average reliability of links in the Minimum Spanning Tree is slightly greater than the average reliability of links in the Average Linkage Minimum Spanning Tree.

  5. An empirical, hierarchical typology of tree species assemblages for assessing forest dynamics under global change scenarios.

    Science.gov (United States)

    Costanza, Jennifer K; Coulston, John W; Wear, David N

    2017-01-01

    The composition of tree species occurring in a forest is important and can be affected by global change drivers such as climate change. To inform assessment and projection of global change impacts at broad extents, we used hierarchical cluster analysis and over 120,000 recent forest inventory plots to empirically define forest tree assemblages across the U.S., and identified the indicator and dominant species associated with each. Cluster typologies in two levels of a hierarchy of forest assemblages, with 29 and 147 groups respectively, were supported by diagnostic criteria. Groups in these two levels of the hierarchy were labeled based on the top indicator species in each, and ranged widely in size. For example, in the 29-cluster typology, the sugar maple-red maple assemblage contained the largest number of plots (30,068), while the butternut-sweet birch and sourwood-scarlet oak assemblages were both smallest (6 plots each). We provide a case-study demonstration of the utility of the typology for informing forest climate change impact assessment. For five assemblages in the 29-cluster typology, we used existing projections of changes in importance value (IV) for the dominant species under one low and one high climate change scenario to assess impacts to the assemblages. Results ranged widely for each scenario by the end of the century, with each showing an average decrease in IV for dominant species in some assemblages, including the balsam fir-quaking aspen assemblage, and an average increase for others, like the green ash-American elm assemblage. Future work should assess adaptive capacity of these forest assemblages and investigate local population- and community-level dynamics in places where dominant species may be impacted. This typology will be ideal for monitoring, assessing, and projecting changes to forest communities within the emerging framework of macrosystems ecology, which emphasizes hierarchies and broad extents.

  6. A Distributed Network Mobility Management Scheme for Hierarchical Mobile IPv6 Networks

    Science.gov (United States)

    Kawano, Keita; Kinoshita, Kazuhiko; Yamai, Nariyoshi

    Route optimization for network mobility is a key technique for providing a node in a mobile network (Mobile Network Node or MNN) with high quality broadband communications. Many schemes adding route optimization function to Network Mobility (NEMO) Basic Support protocol, the standardized network mobility management protocol from the IETF nemo working group, have already been proposed in recent years. One such scheme, a scheme using Hierarchical Mobile IPv6 (HMIPv6) aims to overcome micromobility management issues as well by applying a mechanism based on HMIPv6. The traditional scheme, however, suffers from a significant number of signaling messages as the number of MNNs and/or the number of their Correspondent Nodes (CNs) increase, because many messages notifying the MNNs' Home Agents (HAMNNs) and the CNs of the mobile network's movement are generated simultaneously each time the mobile network moves to the domain of another micromobility management router (Mobility Anchor Point or MAP). This paper proposes a scheme to overcome this problem. Our scheme reduces the number of signaling messages generated at the same time by managing the mobility of MNNs using multiple MAPs distributed within a network for load sharing. The results of simulation experiments show that our scheme works efficiently compared to the traditional scheme when a mobile network has many MNNs and/or these MNNs communicate with many CNs.

  7. IPTV traffic management using topology-based hierarchical scheduling in Carrier Ethernet transport networks

    DEFF Research Database (Denmark)

    Yu, Hao; Yan, Ying; Berger, Michael Stubert

    2009-01-01

    of Service (QoS) provisioning abilities, which guarantee end-to-end performances of voice, video and data traffic delivered over networks. This paper introduces a topology-based hierarchical scheduler scheme, which controls the incoming traffic at the edge of the network based on the network topology...

  8. Traffic Accident Analysis Using Decision Trees and Neural Networks

    OpenAIRE

    Chong, Miao M.; Abraham, Ajith; Paprzycki, Marcin

    2004-01-01

    The costs of fatalities and injuries due to traffic accident have a great impact on society. This paper presents our research to model the severity of injury resulting from traffic accidents using artificial neural networks and decision trees. We have applied them to an actual data set obtained from the National Automotive Sampling System (NASS) General Estimates System (GES). Experiment results reveal that in all the cases the decision tree outperforms the neural network. Our research analys...

  9. Workload-Aware Tree Construction Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kayiram Kavitha

    2012-04-01

    Full Text Available Wireless Sensor Networks play a vital role in applications like disaster management and human relief, habitat monitoring, studying the weather and eco systems, etc. Since the location of deployment of these WSNs is usually remote, the source of energy is restricted to battery. A Significant amount of work has been done by researchers in the past to achieve energy efficiency in WSNs. In this paper we propose a scheme to optimize the power utilization in a WSN. Many of the WSN applications form a tree topology for communication. In a WSN, that adopts tree topology, it is observed that the nodes at higher levels of the tree tend to consume more power when compared to those at lowerlevels. In our proposed workload-aware query/result routing tree construction scheme, we construct thefinal routing tree by keeping in mind the workload of nodes at various levels of the tree. This way, the tree construction takes place with workload at each level being evenly distributed among the nodes at that level. The proposed approach not only increases the lifetime of the network, but also utilizes the battery power optimally. Simulation results show a considerable increase in the lifetime, and effectiveness of the wireless senor network, as a result of applying our proposed tree construction technique.

  10. Hierarchical Route Optimization By Using Memetic Algorithm In A Mobile Networks

    Directory of Open Access Journals (Sweden)

    K .K. Gautam

    2011-02-01

    Full Text Available The networks Mobility (NEMO Protocol is a way of managing the mobility of an entire network, and mobile internet protocol is the basic solution for networks Mobility. A hierarchical route optimization system for mobile network is proposed to solve management of hierarchical route optimization problems. In present paper we study hierarchical Route Optimization scheme using memetic algorithm(HROSMA The concept of optimization- finding the extreme of a function that maps candidate ‘solution’ to scalar values of ‘quality’ – is an extremely general and useful idea. For solving this problem, we use a few salient adaptations, and we also extend HROSMA perform routing between the mobile networks.

  11. Improving cloud network security using tree-rule firewall

    NARCIS (Netherlands)

    He, Xiangjian; Chomsiri, Thawatchai; Nanda, Priyadarsi; Tan, Zhiyuan

    2014-01-01

    This study proposes a new model of firewall called the ‘Tree-Rule Firewall’, which offers various benefits and is applicable for large networks such as ‘cloud’ networks. The recently available firewalls (i.e., Listed-Rule firewalls) have their limitations in performing the tasks and are inapplicable

  12. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems.

    Directory of Open Access Journals (Sweden)

    Martin Rosvall

    Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.

  13. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

    Science.gov (United States)

    Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

    2017-01-01

    The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

  14. 3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE

    Directory of Open Access Journals (Sweden)

    A. Suhaibah

    2016-06-01

    Full Text Available Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

  15. Enhancing the Color Set Partitioning in Hierarchical Tree (SPIHT Algorithm Using Correlation Theory

    Directory of Open Access Journals (Sweden)

    a a a

    2011-01-01

    Full Text Available Problem statement: Efficient color image compression algorithm is essential for mass storage and the transmission of the image. The compression efficiency of the Set Partitioning in Hierarchical Tree (SPIHT coding algorithm for color images is improved by using correlation theory. Approach: In this study the correlation between the color channels are used to propose the new algorithm. The correlation between the color channels are analyzed in various color spaces and the color space CIE-UVW in which the color channels are highly correlated is taken. The most correlated U channel is considered as base color and compressed by using the wavelet filter and the SPIHT algorithm. The linear approximation of the two of the color components (V and W based on the primary color component U is used to code subordinate color components. The image is divided into N*N blocks in each color channels. The linear approximation coefficients are calculated for each block of the subordinate colors V and W as functions of the base color. Only these coefficients of each block are coded and send to the receiver along with the SPIHT coding of the base color. Results: By using this algorithm, a significant (4 dB mean value Peak Signal to Noise Ratio (PSNR improvement is obtained compared to the traditional coding scheme for the same compression rate and reduces the coding and decoding time. Also the proposed compression algorithm reduces the complexity in coding and decoding algorithms. Conclusion: This algorithm allows the reduction of complexity for both coding and decoding of color images. It is concluded that a significant PSNR gain and visual quality improvement is obtained. It is found that in color image coding, this algorithm is superior to the traditional de-correlation based methods and reduces the coding and decoding time.

  16. Small Worlds in the Tree Topologies of Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Qiao, Li; Lingguo, Cui; Baihai, Zhang

    2010-01-01

    In this study, the characteristics of small worlds are investigated in the context of the tree topologies of wireless sensor networks. Tree topologies, which construct spatial graphs with larger characteristic path lengths than random graphs and small clustering coefficients, are ubiquitous...... in wireless sensor networks. Suffering from the link rewiring or the link addition, the characteristic path length of the tree topology reduces rapidly and the clustering coefficient increases greatly. The variety of characteristic path length influences the time synchronization characteristics of wireless...... sensor networks greatly. With the increase of the link rewiring or the link addition probability, the time synchronization error decreases drastically. Two novel protocols named LEACH-SW and TREEPSI-SW are proposed to improve the performances of the sensor networks, in which the small world...

  17. Artificial photosynthesis on tree trunk derived alkaline tantalates with hierarchical anatomy: towards CO2 photo-fixation into CO and CH4

    Science.gov (United States)

    Zhou, Han; Li, Peng; Guo, Jianjun; Yan, Runyu; Fan, Tongxiang; Zhang, Di; Ye, Jinhua

    2014-11-01

    Artificial photosynthesis, the photochemical fixation and recycling of CO2 back to hydrocarbon fuels using sunlight and water, is both a significant challenge and an opportunity that, if realized, could have a revolutionary impact on our energy system. Herein, we demonstrate one of the first examples using biomass derived hierarchical porous photocatalysts for CO2 photo-fixation into sustainable hydrocarbon fuels. A generic method is proposed to build a series of alkaline tantalates MTaO3 (M = Li, Na, K) with hierarchical anatomy from macro- to nanoscales using activated carbonized tree trunks as templates. Artificial photosynthesis is carried out on MTaO3 series using only artificial sunlight, water, and carbon dioxide as inputs to produce carbon monoxide and methane as the main outputs. The CO2 photo-fixation performance can be enhanced by introducing a macropore network, which mainly enhances light transfer and accelerates gas diffusion. The research provides prototype models that integrate individual nanoscale components into higher level macroscopic artificial photosynthetic systems for better solar-to-fuel conversion efficiencies. This work would have potential significance for the ultimate construction of ``artificial trees'' and provide envisions creating ``forests'' of these CO2-capturing artificial trees to remove carbon dioxide from the atmosphere and convert it into sustainable fuels.Artificial photosynthesis, the photochemical fixation and recycling of CO2 back to hydrocarbon fuels using sunlight and water, is both a significant challenge and an opportunity that, if realized, could have a revolutionary impact on our energy system. Herein, we demonstrate one of the first examples using biomass derived hierarchical porous photocatalysts for CO2 photo-fixation into sustainable hydrocarbon fuels. A generic method is proposed to build a series of alkaline tantalates MTaO3 (M = Li, Na, K) with hierarchical anatomy from macro- to nanoscales using activated

  18. Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks

    CERN Document Server

    Gorski, Piotr J; Holyst, Janusz A

    2015-01-01

    Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.

  19. Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

    Science.gov (United States)

    Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

    2010-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  20. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

    Directory of Open Access Journals (Sweden)

    Sheng-Jun Wang

    2011-06-01

    Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

  1. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations.

    Science.gov (United States)

    Wang, Sheng-Jun; Hilgetag, Claus C; Zhou, Changsong

    2011-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  2. Minimum spanning trees and random resistor networks in d dimensions.

    Science.gov (United States)

    Read, N

    2005-09-01

    We consider minimum-cost spanning trees, both in lattice and Euclidean models, in d dimensions. For the cost of the optimum tree in a box of size L , we show that there is a correction of order L(theta) , where theta or =1 . The arguments all rely on the close relation of Kruskal's greedy algorithm for the minimum spanning tree, percolation, and (for some arguments) random resistor networks. The scaling of the entropy and free energy at small nonzero T , and hence of the number of near-optimal solutions, is also discussed. We suggest that the Steiner tree problem is in the same universality class as the minimum spanning tree in all dimensions, as is the traveling salesman problem in two dimensions. Hence all will have the same value of theta=-3/4 in two dimensions.

  3. Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chih-Yu Wen

    2009-05-01

    Full Text Available This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

  4. On Computing Compression Trees for Data Collection in Sensor Networks

    CERN Document Server

    Li, Jian; Khuller, Samir

    2009-01-01

    We address the problem of efficiently gathering correlated data from a wired or a wireless sensor network, with the aim of designing algorithms with provable optimality guarantees, and understanding how close we can get to the known theoretical lower bounds. Our proposed approach is based on finding an optimal or a near-optimal {\\em compression tree} for a given sensor network: a compression tree is a directed tree over the sensor network nodes such that the value of a node is compressed using the value of its parent. We consider this problem under different communication models, including the {\\em broadcast communication} model that enables many new opportunities for energy-efficient data collection. We draw connections between the data collection problem and a previously studied graph concept, called {\\em weakly connected dominating sets}, and we use this to develop novel approximation algorithms for the problem. We present comparative results on several synthetic and real-world datasets showing that our al...

  5. Shortest Tree Routing With Security In Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    P.T Arshad

    2014-12-01

    Full Text Available We propose STR to resolve the main reasons of overall network performance degradation of ZTR, which are the detour path problem and the traffic concentration problem. Second, we prove that the 1-hop neighbor nodes used by STR improve the routing path efficiency and alleviate the traffic load concentrated on tree links in ZTR. Third, we analyze the performance of ZTR, STR, and AODV by differentiating the network conditions such as network density, ZigBee network constraints, traffic types, and the network traffic. For modification security purpose we are also encrypting the data packets during transmission. So that the intermediate nodes are not able to view the data during transmission. For Encryption process, we are using RC4 Algorithm. Short cut tree routing is used for minimizing the routing path from source to destination.

  6. An Isolation Intrusion Detection System for Hierarchical Wireless Sensor Networks

    OpenAIRE

    Rung-Ching Chen; Chia-Fen Hsieh; Yung-Fa Huang

    2010-01-01

    A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor environmental conditions, such as battlefield data and personal health information, and some environment limited resources. To avoid malicious damage is important while information is transmitted in wireless network. Thus, Wireless Intrusion Detection Systems are crucial to safe operation in wireless sensor networks. Wireless networks are subject ...

  7. Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts

    CERN Document Server

    Russell, Chris; Kohli, Pushmeet; Torr, Philip H S

    2012-01-01

    Markov Networks are widely used through out computer vision and machine learning. An important subclass are the Associative Markov Networks which are used in a wide variety of applications. For these networks a good approximate minimum cost solution can be found efficiently using graph cut based move making algorithms such as alpha- expansion. Recently a related model has been proposed, the associative hierarchical net- work, which provides a natural generalisation of the Associative Markov Network for higher order cliques (i.e. clique size greater than two). This method provides a good model for object class segmentation problem in com- puter vision. Within this paper we briefly describe the associative hierarchical network and provide a computationally efficient method for ap- proximate inference based on graph cuts. Our method performs well for networks con- taining hundreds of thousand of variables, and higher order potentials are defined over cliques containing tens of thousands of vari- ables. Due to th...

  8. The Griffiths Phase on Hierarchical Modular Networks with Small-world Edges

    CERN Document Server

    Li, Shanshan

    2016-01-01

    The Griffiths phase has been proposed to induce a stretched critical regime that facilitates self organizing of brain networks for optimal function. This phase stems from the intrinsic structural heterogeneity of brain networks, such as the hierarchical modular structure. In this work, we extend this concept to modified hierarchical networks with small-world connections based on Hanoi networks [1]. Through extensive simulations, we identify the essential role played by the exponential distribution of the inter-moduli connectivity probability across hierarchies on the emergence of the Griffiths phase in this network. Additionally, the spectral analysis on the adjacency matrix of the relevant networks [2] shows that a localized principle eigenvector is not necessarily the fingerprint of the Griffiths phase.

  9. NEW METHOD TO ESTIMATE SCALING OF POWER-LAW DEGREE DISTRIBUTION AND HIERARCHICAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    YANG Bo; DUAN Wen-qi; CHEN Zhong

    2006-01-01

    A new method and corresponding numerical procedure are introduced to estimate scaling exponents of power-law degree distribution and hierarchical clustering func tion for complex networks. This method can overcome the biased and inaccurate faults of graphical linear fitting methods commonly used in current network research. Furthermore, it is verified to have higher goodness-of-fit than graphical methods by comparing the KS (Kolmogorov-Smirnov) test statistics for 10 CNN (Connecting Nearest-Neighbor)networks.

  10. Extracting a common high frequency signal from northern Quebec black spruce tree-rings with a Bayesian hierarchical model

    Directory of Open Access Journals (Sweden)

    J.-J. Boreux

    2009-03-01

    Full Text Available Dendrochronology, the scientific dating method based on the analysis of tree-ring growth patterns, has been frequently applied in climatology. The basic premise of dendroclimatology is that tree rings can be viewed as climate proxies, i.e. rings are assumed to contain some hidden information about past climate. From a statistical perspective, this extraction problem can be understood as the search of a hidden variable which represents the common signal within a collection of tree-ring width series. Classical average-based techniques used in dendrochronology have been, with different degrees of success (depending on tree species, regional factors and statistical methods, applied to estimate the mean behavior of this latent variable. Still, a precise quantification of uncertainties associated to the hidden variable distribution is difficult to assess. To model the error propagation throughout the extraction procedure, we propose and study a Bayesian hierarchical model that focuses on extracting an inter-annual high frequency signal. Our method is applied to black spruce (Picea mariana tree-rings recorded in northern Quebec and compared to a classical average-based techniques used by dendrochronologists.

  11. Modular networks with hierarchical organization: The dynamical implications of complex structure

    Indian Academy of Sciences (India)

    Raj Kumar Pan; Sitabhra Sinha

    2008-08-01

    Several networks occurring in real life have modular structures that are arranged in a hierarchical fashion. In this paper, we have proposed a model for such networks, using a stochastic generation method. Using this model we show that, the scaling relation between the clustering and degree of the nodes is not a necessary property of hierarchical modular networks, as had previously been suggested on the basis of a deterministically constructed model. We also look at dynamics on such networks, in particular, the stability of equilibria of network dynamics and of synchronized activity in the network. For both of these, we find that, increasing modularity or the number of hierarchical levels tends to increase the probability of instability. As both hierarchy and modularity are seen in natural systems, which necessarily have to be robust against environmental fluctuations, we conclude that additional constraints are necessary for the emergence of hierarchical structure, similar to the occurrence of modularity through multi-constraint optimization as shown by us previously.

  12. Inference of Transmission Network Structure from HIV Phylogenetic Trees

    Science.gov (United States)

    Britton, Tom; Leitner, Thomas

    2017-01-01

    Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic. PMID:28085876

  13. Combination of Fault Tree and Neural Networks in Excavator Diagnosis

    OpenAIRE

    Li Guoping; Zhang Qingwei; Ma Xiao

    2013-01-01

    By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA) and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree...

  14. Application of growing hierarchical SOM for visualisation of network forensics traffic data.

    Science.gov (United States)

    Palomo, E J; North, J; Elizondo, D; Luque, R M; Watson, T

    2012-08-01

    Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of these attacks and large amount of data. Therefore, software tools that can help with these digital investigations are in great demand. In this paper, a novel approach to analysing and visualising network traffic data based on growing hierarchical self-organising maps (GHSOM) is presented. The self-organising map (SOM) has been shown to be successful for the analysis of highly-dimensional input data in data mining applications as well as for data visualisation in a more intuitive and understandable manner. However, the SOM has some problems related to its static topology and its inability to represent hierarchical relationships in the input data. The GHSOM tries to overcome these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relationships among them. Moreover, the proposed GHSOM has been modified to correctly treat the qualitative features that are present in the traffic data in addition to the quantitative features. Experimental results show that this approach can be very useful for a better understanding of network traffic data, making it easier to search for evidence of attacks or anomalous behaviour in a network environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Nerual Networks with Decision Trees for Diagnosis Issues

    Directory of Open Access Journals (Sweden)

    Yahia Kourd

    2013-05-01

    Full Text Available This paper presents a new idea for fault detection and isolation (FDI technique which is applied to industrial system. This technique is bas ed on Neural Networks fault-free and Faulty behaviours Models (NNFMs. NNFMs are used for resid ual generation, while decision tree architecture is used for residual evaluation. The d ecision tree is realized with data collected from the NNFM’s outputs and is used to isolate dete ctable faults depending on computed threshold. Each part of the tree corresponds to spe cific residual. With the decision tree, it becomes possible to take the appropriate decision r egarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less com putational effort and can be used for on line diagnosis. An application example is presented to i llustrate and confirm the effectiveness and the accuracy of the proposed approach.

  16. Combination of Fault Tree and Neural Networks in Excavator Diagnosis

    Directory of Open Access Journals (Sweden)

    Li Guoping

    2013-04-01

    Full Text Available By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.

  17. A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2014-12-01

    Full Text Available This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data.

  18. Extracting a common high frequency signal from Northern Quebec black spruce tree-rings with a Bayesian hierarchical model

    Directory of Open Access Journals (Sweden)

    J.-J. Boreux

    2009-10-01

    Full Text Available One basic premise of dendroclimatology is that tree rings can be viewed as climate proxies, i.e. rings are assumed to contain some hidden information about past climate. From a statistical perspective, this extraction problem can be understood as the search of a hidden variable which represents the common signal within a collection of tree-ring width series. Classical average-based techniques used in dendrochronology have been applied to estimate the mean behavior of this latent variable. Still, depending on tree species, regional factors and statistical methods, a precise quantification of uncertainties associated to the hidden variable distribution is difficult to assess. To model the error propagation throughout the extraction procedure, we propose and study a Bayesian hierarchical model that focuses on extracting an inter-annual high frequency signal. Our method is applied to black spruce (Picea mariana tree-rings recorded in Northern Quebec and compared to a classical average-based techniques used by dendrochronologists (Cook and Kairiukstis, 1992.

  19. Dynamics in small worlds of tree topologies of wireless sensor networks

    DEFF Research Database (Denmark)

    Li, Qiao; Zhang, Baihai; Fan, Zhun

    2012-01-01

    Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients, are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link additions, characteristic path lengths reduce...

  20. Network Intrusion Detection Using FP Tree Rules

    Directory of Open Access Journals (Sweden)

    P Srinivasulu

    2009-07-01

    Full Text Available In the faceless world of the Internet, online fraud is one of the greatest reasons of loss for web merchants. Advanced solutions are needed to protect e-businesses from the constant problems of fraud. Many popular fraud detection algorithms require supervised training, which needs human intervention to prepare training cases. Since it is quite often for an online transaction database to have Terabyte-level storage, human investigation to identify fraudulent transactions is very costly. This paper describes the automatic design of user profiling method for the purpose of fraud detection. We use a FP (Frequent Pattern Tree rule-learning algorithm to adaptively profile legitimate customer behavior in a transaction database. Then the incoming transactions are compared against the user profile to uncover the anomalies. The anomaly outputs are used as input to an accumulation system for combining evidence to generate high-confidence fraud alert value. Favorable experimental results are presented.

  1. Network Intrusion Detection Using FP Tree Rules

    CERN Document Server

    Srinivasulu, P; Babu, I Ramesh

    2010-01-01

    In the faceless world of the Internet,online fraud is one of the greatest reasons of loss for web merchants.Advanced solutions are needed to protect e businesses from the constant problems of fraud.Many popular fraud detection algorithms require supervised training,which needs human intervention to prepare training cases.Since it is quite often for an online transaction database to ha e Terabyte level storage,human investigation to identify fraudulent transactions is very costly.This paper describes the automatic design of user profiling method for the purpose of fraud detection.We use a FP (Frequent Pattern) Tree rule learning algorithm to adaptively profile legitimate customer behavior in a transaction database.Then the incoming transactions are compared against the user profile to uncover the anomalies The anomaly outputs are used as input to an accumulation system for combining evidence to generate high confidence fraud alert value. Favorable experimental results are presented.

  2. Arithmetic functions in torus and tree networks

    Science.gov (United States)

    Bhanot, Gyan; Blumrich, Matthias A.; Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard D.; Vranas, Pavlos M.

    2007-12-25

    Methods and systems for performing arithmetic functions. In accordance with a first aspect of the invention, methods and apparatus are provided, working in conjunction of software algorithms and hardware implementation of class network routing, to achieve a very significant reduction in the time required for global arithmetic operation on the torus. Therefore, it leads to greater scalability of applications running on large parallel machines. The invention involves three steps in improving the efficiency and accuracy of global operations: (1) Ensuring, when necessary, that all the nodes do the global operation on the data in the same order and so obtain a unique answer, independent of roundoff error; (2) Using the topology of the torus to minimize the number of hops and the bidirectional capabilities of the network to reduce the number of time steps in the data transfer operation to an absolute minimum; and (3) Using class function routing to reduce latency in the data transfer. With the method of this invention, every single element is injected into the network only once and it will be stored and forwarded without any further software overhead. In accordance with a second aspect of the invention, methods and systems are provided to efficiently implement global arithmetic operations on a network that supports the global combining operations. The latency of doing such global operations are greatly reduced by using these methods.

  3. HIERARCHICAL ACCESS CONTROL IN DYNAMIC PEER GROUPS USING SYMMETRIC POLYNOMIAL AND TREE BASED GROUP ELLIPTIC CURVE DIFFIE HELLMAN SCHEME

    Directory of Open Access Journals (Sweden)

    Nafeesa Begum Jeddy

    2014-01-01

    Full Text Available Hierarchical Access Control in group communication is an active area of research which is difficult to achieve it. Its primary objective is to allow users of a higher authority group to access information or resource held by lower group users and preventing the lower group users to access information held by higher class users. Large collection of collaborative applications in organizations inherently has hierarchical structures for functioning, where providing security by efficient group key management is a big challenging issue. While preserving centralized methods for hierarchical access control, it is difficult to achieve efficiency as a single membership change will result in lot of changes which are difficult to maintain. So, using distributed key agreement techniques is more appropriate for this scenario. This study explore on novel group key agreement approach, which combines both the symmetric polynomial scheme and Tree Based Group elliptic Curve key exchange. Also, it yields a secure protocol suite that is good in fault-tolerant and simple. The efficiency of SP-TGECDH is better than many other schemes. Using TGECDH makes the scheme suitable small Low powered devices.

  4. Hierarchical Task Network Prototyping In Unity3d

    Science.gov (United States)

    2016-06-01

    in a video game application. Each application has an agent which can be programmed using one of several major pro - gramming languages. The user can...visually debug. Here we present a solution for prototyping HTNs by extending an existing commercial implementation of Behavior Trees within the Unity3D game ...HTN, dynamic behaviors, behavior prototyping, agent-based simulation, entity-level combat model, game engine, discrete event simulation, virtual

  5. Detecting Hidden Hierarchy of Non Hierarchical Terrorist Networks

    DEFF Research Database (Denmark)

    Memon, Nasrullah

    to analyze terrorist networks and prioritize their targets. Applying recently introduced mathematical methods for constructing the hidden hierarchy of "nonhierarchical" terrorist networks; we present case studies of the terrorist attacks occurred / planned in the past, in order to identify hidden hierarchy...

  6. A Hierarchical Multiobjective Routing Model for MPLS Networks with Two Service Classes

    Science.gov (United States)

    Craveirinha, José; Girão-Silva, Rita; Clímaco, João; Martins, Lúcia

    This work presents a model for multiobjective routing in MPLS networks formulated within a hierarchical network-wide optimization framework, with two classes of services, namely QoS and Best Effort (BE) services. The routing model uses alternative routing and hierarchical optimization with two optimization levels, including fairness objectives. Another feature of the model is the use of an approximate stochastic representation of the traffic flows in the network, based on the concept of effective bandwidth. The theoretical foundations of a heuristic strategy for finding “good” compromise solutions to the very complex bi-level routing optimization problem, based on a conjecture concerning the definition of marginal implied costs for QoS flows and BE flows, will be described. The main features of a first version of this heuristic based on a bi-objective shortest path model and some preliminary results for a benchmark network will also be revealed.

  7. An Advanced Survey on Secure Energy-Efficient Hierarchical Routing Protocols in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Abdoulaye Diop

    2013-01-01

    Full Text Available Wireless Sensor Networks (WSNs are often deployed in hostile environments, which make such networks highly vulnerable and increase the risk of attacks against this type of network. WSN comprise of large number of sensor nodes with different hardware abilities and functions. Due to the limited memory resources and energy constraints, complex security algorithms cannot be used in sensor networks. Therefore, it is necessary to balance between the security level and the associated energy consumption overhead to mitigate the security risks. Hierarchical routing protocol is more energy-efficient than other routing protocols in WSNs. Many secure cluster-based routing protocols have been proposed in the literature to overcome these constraints. In this paper, we discuss Secure Energy-Efficient Hierarchical Routing Protocols in WSNs and compare them in terms of security, performance and efficiency. Security issues for WSNs and their solutions are also discussed.

  8. Effective Hierarchical Routing Algorithm for Large-scale Wireless Mobile Networks

    Directory of Open Access Journals (Sweden)

    Guofeng Yan

    2014-02-01

    Full Text Available The growing interest in wireless mobile network techniques has resulted in many routing protocol proposals. The unpredictable motion and the unreliable behavior of mobile nodes is one of the key issues in wireless mobile network. Virtual mobile node (VMN consists of robust virtual nodes that are both predictable and reliable. Based on VMN, in this paper, we present a hierarchical routing algorithm, i.e., EHRA-WAVE, for large-scale wireless mobile networks. By using mobile WAVE technology, a routing path can be found rapidly between VMNs without accurate topology information. We introduce the routing algorithm and the implementation issues of the proposed EHRA-WAVE routing algorithm. Finally, we evaluate the performance of EHRA-WAVE through experiments, and compare the performance on VMN failure and message delivery ratio using hierarchical and non-hierarchical routing methods. However, due to the large amounts WAVE flooding, EHRAWAVE results in too large load which would impede the application of the EHRA-WAVE algorithm. Therefore, the further routing protocol focuses on minimizing the number of WAVE using hierarchical structures in large-scale wireless mobile networks

  9. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Science.gov (United States)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  10. A Novel Hierarchical Semi-centralized Telemedicine Network Architecture Proposition for Bangladesh

    DEFF Research Database (Denmark)

    Choudhury, Samiul; Peterson, Carrie Beth; Kyriazakos, Sofoklis

    2011-01-01

    where there are extreme paucities of efficient healthcare professionals and equipments, specifically in the rural areas. In this paper a novel, hierarchical and semi-centralized telemedicine network architecture has been proposed holisti-cally focusing on the rural underdeveloped areas of Bangladesh...... of Bangladesh. Finally, some features and services associated with the model have also been proposed which are pragmatic and easily implementable....

  11. The exact Laplacian spectrum for the Dyson hierarchical network

    CERN Document Server

    Agliari, Elena

    2016-01-01

    We consider the Dyson hierarchical graph $\\mathcal{G}$, that is a weighted fully-connected graph, where the pattern of weights is ruled by the parameter $\\sigma \\in (1/2, 1]$. Exploiting the deterministic recursivity through which $\\mathcal{G}$ is built, we are able to derive explicitly the whole set of the eigenvalues and the eigenvectors for its Laplacian matrix. Given that the Laplacian operator is intrinsically implied in the analysis of dynamic processes (e.g., random walks) occurring on the graph, as well as in the investigation of the dynamical properties of connected structures themselves (e.g., vibrational structures and the relaxation modes), this result allows addressing analytically a large class of problems. In particular, as examples of applications, we study the random walk and the continuous-time quantum walk embedded in $\\mathcal{G}$, and the relaxation times of a polymer whose structure is described by $\\mathcal{G}$.

  12. The exact Laplacian spectrum for the Dyson hierarchical network

    Science.gov (United States)

    Agliari, Elena; Tavani, Flavia

    2017-01-01

    We consider the Dyson hierarchical graph , that is a weighted fully-connected graph, where the pattern of weights is ruled by the parameter σ ∈ (1/2, 1]. Exploiting the deterministic recursivity through which is built, we are able to derive explicitly the whole set of the eigenvalues and the eigenvectors for its Laplacian matrix. Given that the Laplacian operator is intrinsically implied in the analysis of dynamic processes (e.g., random walks) occurring on the graph, as well as in the investigation of the dynamical properties of connected structures themselves (e.g., vibrational structures and relaxation modes), this result allows addressing analytically a large class of problems. In particular, as examples of applications, we study the random walk and the continuous-time quantum walk embedded in , the relaxation times of a polymer whose structure is described by , and the community structure of in terms of modularity measures.

  13. Cluster based hierarchical resource searching model in P2P network

    Institute of Scientific and Technical Information of China (English)

    Yang Ruijuan; Liu Jian; Tian Jingwen

    2007-01-01

    For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer (P2P) network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network,auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

  14. Avalanche transmission and critical behaviour in load-bearing hierarchical networks

    Indian Academy of Sciences (India)

    Ajay Deep Kachhvah; Neelima Gupte

    2011-11-01

    The strength and stability properties of hierarchical load-bearing networks and their strengthened variants have been discussed in a recent work. Here, we study the avalanche time distributions on these load-bearing networks. The avalanche time distributions of the V-lattice, a unique realization of the networks, show power-law behaviour when tested with certain fractions of its trunk weights. All other avalanche distributions show Gaussian peaked behaviour. Thus the V-lattice is the critical case of the network. We discuss the implications of this result.

  15. Internal representation of hierarchical sequences involves the default network

    Directory of Open Access Journals (Sweden)

    Rogers Baxter P

    2010-04-01

    Full Text Available Abstract Background The default network is a set of brain regions that exhibit a reduction in BOLD response during attention-demanding cognitive tasks, and distinctive patterns of functional connectivity that typically include anti-correlations with a fronto-parietal network involved in attention, working memory, and executive control. The function of the default network regions has been attributed to introspection, self-awareness, and theory of mind judgments, and some of its regions are involved in episodic memory processes. Results Using the method of psycho-physiological interactions, we studied the functional connectivity of several regions in a fronto-parietal network involved in a paired image discrimination task involving transitive inference. Some image pairs were derived from an implicit underlying sequence A>B>C>D>E, and some were independent (F>G, H>J, etc. Functional connectivity between the fronto-parietal regions and the default network regions depended on the presence of the underlying sequence relating the images. When subjects viewed learned and novel pairs from the sequence, connectivity between these two networks was higher than when subjects viewed learned and novel pairs from the independent sets. Conclusions These results suggest that default network regions were involved in maintaining the internal model that subserved discrimination of image pairs derived from the implicit sequence, and contributed to introspective access of an internal sequence model built during training. The default network may not be a unified entity with a specific function, but rather may interact with other functional networks in task-dependent ways.

  16. Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks.

    Directory of Open Access Journals (Sweden)

    Raghvendra Mall

    Full Text Available Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks.

  17. Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

    Science.gov (United States)

    Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

    2017-07-19

    Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

  18. Delay-induced driven patterns in coupled Cayley tree networks

    CERN Document Server

    Singh, Aradhana

    2013-01-01

    We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibits sensitiveness for value of delay, and leads to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths are very robust against change in delay values, and leads to stable driven clusters comprising only nodes from last generation of the Calaye tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values, hence demonstrating richness of the model. To the end we relate the results with social conflicts and cooperation observed in families.

  19. Hierarchical Synchrony of Phase Oscillators in Modular Networks

    CERN Document Server

    Skardal, Per Sebastian

    2011-01-01

    We study synchronization of sinusoidally coupled phase oscillators on networks with modular structure and a large number of oscillators in each community. Of particular interest is the hierarchy of local and global synchrony, i.e., synchrony within and between communities, respectively. Using the recent ansatz of Ott and Antonsen, we find that the degree of local synchrony can be determined from a set of coupled low-dimensional equations. If the number of communities in the network is large, a low-dimensional description of global synchrony can be also found. Using these results, we study bifurcations between different types of synchrony. We find that, depending on the relative strength of local and global coupling, the transition to synchrony in the network can be mediated by local or global effects.

  20. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    Science.gov (United States)

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  1. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    Directory of Open Access Journals (Sweden)

    Frank Havemann

    Full Text Available The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  2. Hierarchical network architectures of carbon fiber paper supported cobalt oxide nanonet for high-capacity pseudocapacitors.

    Science.gov (United States)

    Yang, Lei; Cheng, Shuang; Ding, Yong; Zhu, Xingbao; Wang, Zhong Lin; Liu, Meilin

    2012-01-11

    We present a high-capacity pseudocapacitor based on a hierarchical network architecture consisting of Co(3)O(4) nanowire network (nanonet) coated on a carbon fiber paper. With this tailored architecture, the electrode shows ideal capacitive behavior (rectangular shape of cyclic voltammograms) and large specific capacitance (1124 F/g) at high charge/discharge rate (25.34 A/g), still retaining ~94% of the capacitance at a much lower rate of 0.25 A/g. The much-improved capacity, rate capability, and cycling stability may be attributed to the unique hierarchical network structures, which improves electron/ion transport, enhances the kinetics of redox reactions, and facilitates facile stress relaxation during cycling.

  3. A CDMA Based Scalable Hierarchical Architecture for Network-On-Chip

    Directory of Open Access Journals (Sweden)

    Mohamed A. Abd El Ghany

    2012-09-01

    Full Text Available A Scalable hierarchical architecture based Code-Division Multiple Access (CDMA is proposed for high performance Network-on-Chip (NoC. This hierarchical architecture provides the integration of a large number of IPs in a single on-chip system. The network encoding and decoding schemes for CDMA transmission are provided. The proposed CDMA NoC architecture is compared to the conventional architecture in terms of latency, area and power dissipation. The overall area required to implement the proposed CDMA NoC design is reduced by 24.2%. The design decreases the latency of the network by 40%. The total power consumption required to achieve the proposed design is also decreased by 25%.

  4. Scaling of the Average Receiving Time on a Family of Weighted Hierarchical Networks

    Science.gov (United States)

    Sun, Yu; Dai, Meifeng; Sun, Yanqiu; Shao, Shuxiang

    2016-08-01

    In this paper, based on the un-weight hierarchical networks, a family of weighted hierarchical networks are introduced, the weight factor is denoted by r. The weighted hierarchical networks depend on the number of nodes in complete bipartite graph, denoted by n1, n2 and n = n1 + n2. Assume that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the weight of edge linking them. We deduce the analytical expression of the average receiving time (ART). The obtained remarkable results display two conditions. In the large network, when nr > n1n2, the ART grows as a power-law function of the network size |V (Gk)| with the exponent, represented by θ =logn( nr n1n2 ), 0 < θ < 1. This means that the smaller the value of θ, the more efficient the process of receiving information. When nr ≤ n1n2, the ART grows with increasing order |V (Gk)| as logn|V (Gk)| or (logn|V (Gk)|)2.

  5. Evidential Networks for Fault Tree Analysis with Imprecise Knowledge

    Science.gov (United States)

    Yang, Jianping; Huang, Hong-Zhong; Liu, Yu; Li, Yan-Feng

    2012-06-01

    Fault tree analysis (FTA), as one of the powerful tools in reliability engineering, has been widely used to enhance system quality attributes. In most fault tree analyses, precise values are adopted to represent the probabilities of occurrence of those events. Due to the lack of sufficient data or imprecision of existing data at the early stage of product design, it is often difficult to accurately estimate the failure rates of individual events or the probabilities of occurrence of the events. Therefore, such imprecision and uncertainty need to be taken into account in reliability analysis. In this paper, the evidential networks (EN) are employed to quantify and propagate the aforementioned uncertainty and imprecision in fault tree analysis. The detailed conversion processes of some logic gates to EN are described in fault tree (FT). The figures of the logic gates and the converted equivalent EN, together with the associated truth tables and the conditional belief mass tables, are also presented in this work. The new epistemic importance is proposed to describe the effect of ignorance degree of event. The fault tree of an aircraft engine damaged by oil filter plugs is presented to demonstrate the proposed method.

  6. Synchronization in heterogeneous FitzHugh-Nagumo networks with hierarchical architecture

    Science.gov (United States)

    Plotnikov, S. A.; Lehnert, J.; Fradkov, A. L.; Schöll, E.

    2016-07-01

    We study synchronization in heterogeneous FitzHugh-Nagumo networks. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. Here we develop a controller to counteract the impact of these heterogeneities. We first analyze the stability of the equilibrium point in a ring network of heterogeneous nodes. We then derive a sufficient condition for synchronization in the absence of control. Based on these results we derive the controller providing synchronization for parameter values where synchronization without control is absent. We demonstrate our results in networks with different topologies. Particular attention is given to hierarchical (fractal) topologies, which are relevant for the architecture of the brain.

  7. Hierarchical micro-mobility management in high-speed multihop access networks

    Institute of Scientific and Technical Information of China (English)

    TANG Bi-hua; MA Xiao-lei; LIU Yuan-an; GAO Jin-chun

    2006-01-01

    This article integrates the hierarchical micro-mobility management and the high-speed multihop access networks (HMAN), to accomplish the smooth handover between different access routers. The proposed soft handover scheme in the high-speed HMAN can solve the micro-mobility management problem in the access network. This article also proposes the hybrid access router (AR) advertisement scheme and AR selection algorithm, which uses the time delay and stable route to the AR as the gateway selection parameters. By simulation, the proposed micro-mobility management scheme can achieve high packet delivery fraction and improve the lifetime of network.

  8. Combining regression trees and radial basis function networks.

    Science.gov (United States)

    Orr, M; Hallam, J; Takezawa, K; Murra, A; Ninomiya, S; Oide, M; Leonard, T

    2000-12-01

    We describe a method for non-parametric regression which combines regression trees with radial basis function networks. The method is similar to that of Kubat, who was first to suggest such a combination, but has some significant improvements. We demonstrate the features of the new method, compare its performance with other methods on DELVE data sets and apply it to a real world problem involving the classification of soybean plants from digital images.

  9. Recapitulating phylogenies using k-mers: from trees to networks.

    Science.gov (United States)

    Bernard, Guillaume; Ragan, Mark A; Chan, Cheong Xin

    2016-01-01

    Ernst Haeckel based his landmark Tree of Life on the supposed ontogenic recapitulation of phylogeny, i.e. that successive embryonic stages during the development of an organism re-trace the morphological forms of its ancestors over the course of evolution. Much of this idea has since been discredited. Today, phylogenies are often based on families of molecular sequences. The standard approach starts with a multiple sequence alignment, in which the sequences are arranged relative to each other in a way that maximises a measure of similarity position-by-position along their entire length. A tree (or sometimes a network) is then inferred. Rigorous multiple sequence alignment is computationally demanding, and evolutionary processes that shape the genomes of many microbes (bacteria, archaea and some morphologically simple eukaryotes) can add further complications. In particular, recombination, genome rearrangement and lateral genetic transfer undermine the assumptions that underlie multiple sequence alignment, and imply that a tree-like structure may be too simplistic. Here, using genome sequences of 143 bacterial and archaeal genomes, we construct a network of phylogenetic relatedness based on the number of shared k-mers (subsequences at fixed length k). Our findings suggest that the network captures not only key aspects of microbial genome evolution as inferred from a tree, but also features that are not treelike. The method is highly scalable, allowing for investigation of genome evolution across a large number of genomes. Instead of using specific regions or sequences from genome sequences, or indeed Haeckel's idea of ontogeny, we argue that genome phylogenies can be inferred using k-mers from whole-genome sequences. Representing these networks dynamically allows biological questions of interest to be formulated and addressed quickly and in a visually intuitive manner.

  10. Robustness of a Tree-like Network of Interdependent Networks

    CERN Document Server

    Gao, Jianxi; Havlin, S; Stanley, H E

    2011-01-01

    In reality, many real-world networks interact with and depend on other networks. We develop an analytical framework for studying interacting networks and present an exact percolation law for a network of $n$ interdependent networks (NON). We present a general framework to study the dynamics of the cascading failures process at each step caused by an initial failure occurring in the NON system. We study and compare both $n$ coupled Erd\\H{o}s-R\\'{e}nyi (ER) graphs and $n$ coupled random regular (RR) graphs. We found recently [Gao et. al. arXive:1010.5829] that for an NON composed of $n$ ER networks each of average degree $k$, the giant component, $P_{\\infty}$, is given by $P_{\\infty}=p[1-\\exp(-kP_{\\infty})]^n$ where $1-p$ is the initial fraction of removed nodes. Our general result coincides for $n=1$ with the known Erd\\H{o}s-R\\'{e}nyi second-order phase transition at a threshold, $p=p_c$, for a single network. For $n=2$ the general result for $P_{\\infty}$ corresponds to the $n=2$ result [Buldyrev et. al., Natu...

  11. Mapping the hierarchical layout of the structural network of the macaque prefrontal cortex.

    Science.gov (United States)

    Goulas, Alexandros; Uylings, Harry B M; Stiers, Peter

    2014-05-01

    A consensus on the prefrontal cortex (PFC) holds that it is pivotal for flexible behavior and the integration of the cognitive, affective, and motivational domains. Certain models have been put forth and a dominant model postulates a hierarchical anterior-posterior gradient. The structural connectivity principles of this model dictate that increasingly anterior PFC regions exhibit more efferent connections toward posterior ones than vice versa. Such hierarchical asymmetry principles are thought to pertain to the macaque PFC. Additionally, the laminar patterns of the connectivity of PFC regions can be used for defining hierarchies. In the current study, we formally tested the asymmetry-based hierarchical principles of the anterior-posterior model by employing an exhaustive dataset on macaque PFC connectivity and tools from network science. On the one hand, the asymmetry-based principles and predictions of the hierarchical anterior-posterior model were not confirmed. The wiring of the macaque PFC does not fully correspond to the principles of the model, and its asymmetry-based hierarchical layout does not follow a strict anterior-posterior gradient. On the other hand, our results suggest that the laminar-based hierarchy seems a more tenable working hypothesis for models advocating an anterior-posterior gradient. Our results can inform models of the human PFC.

  12. Sleeping of a Complex Brain Networks with Hierarchical Organization

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ying-Yue; YANG Qiu-Ying; CHEN Tian-Lun

    2009-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

  13. Hierarchical brain networks active in approach and avoidance goal pursuit

    Directory of Open Access Journals (Sweden)

    Jeffrey Martin Spielberg

    2013-06-01

    Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  14. Virtual private network design: a proof of the tree routing conjecture on ring networks

    NARCIS (Netherlands)

    Hurkens, C.A.J.; Keijsper, J.C.M.; Stougie, L.

    2005-01-01

    A basic question in Virtual Private Network (VPN) design is if the symmetric version of the problem always has an optimal solution which is a tree network. An affirmative answer would imply that the symmetric VPN problem is solvable in polynomial time. We give an affirmative answer in case the commu

  15. Frontoparietal Connectivity and Hierarchical Structure of the Brain’s Functional Network during Sleep

    Directory of Open Access Journals (Sweden)

    Victor I Spoormaker

    2012-05-01

    Full Text Available Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging (fMRI data acquired in polysomnographically validated wakefulness, light sleep and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between inferior parietal lobules and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a crucial role, possibly in combination with the thalamus.

  16. HIERARCHICAL DESIGN BASED INTRUSION DETECTION SYSTEM FOR WIRELESS AD HOC SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    Mohammad Saiful Islam Mamun

    2010-07-01

    Full Text Available In recent years, wireless ad hoc sensor network becomes popular both in civil and military jobs.However, security is one of the significant challenges for sensor network because of their deploymentin open and unprotected environment. As cryptographic mechanism is not enough to protect sensornetwork from external attacks, intrusion detection system needs to be introduced. Though intrusionprevention mechanism is one of the major and efficient methods against attacks, but there might besome attacks for which prevention method is not known. Besides preventing the system from someknown attacks, intrusion detection system gather necessary information related to attack technique andhelp in the development of intrusion prevention system. In addition to reviewing the present attacksavailable in wireless sensor network this paper examines the current efforts to intrusion detectionsystem against wireless sensor network. In this paper we propose a hierarchical architectural designbased intrusion detection system that fits the current demands and restrictions of wireless ad hocsensor network. In this proposed intrusion detection system architecture we followed clusteringmechanism to build a four level hierarchical network which enhances network scalability to largegeographical area and use both anomaly and misuse detection techniques for intrusion detection. Weintroduce policy based detection mechanism as well as intrusion response together with GSM cellconcept for intrusion detection architecture.

  17. Predicting Hierarchical Structure in Small World Social Networks

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar

    2009-01-01

    Typisk analytisk foranstaltninger i grafteori gerne grad centralitet, betweenness og nærhed centralities er meget almindelige og har lang tradition for deres vellykkede brug. Men modellering af skjult, terrorister eller kriminelle netværk gennem sociale grafer ikke rigtig give den hierarkiske str...... udnyttet til at forudsige den kommandostruktur af nettet. Nøgleord: Social Networks Analyse, Bayes Teorem, entropi, hierarkisk struktur.......Typisk analytisk foranstaltninger i grafteori gerne grad centralitet, betweenness og nærhed centralities er meget almindelige og har lang tradition for deres vellykkede brug. Men modellering af skjult, terrorister eller kriminelle netværk gennem sociale grafer ikke rigtig give den hierarkiske...

  18. Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?. -based on different credit correlations using hierarchical methods

    Science.gov (United States)

    He, Fang; Chen, Xi

    2016-11-01

    The accelerating accumulation and risk concentration of Chinese local financing platforms debts have attracted wide attention throughout the world. Due to the network of financial exposures among institutions, the failure of several platforms or regions of systemic importance will probably trigger systemic risk and destabilize the financial system. However, the complex network of credit relationships in Chinese local financing platforms at the state level remains unknown. To fill this gap, we presented the first complex networks and hierarchical cluster analysis of the credit market of Chinese local financing platforms using the ;bottom up; method from firm-level data. Based on balance-sheet channel, we analyzed the topology and taxonomy by applying the analysis paradigm of subdominant ultra-metric space to an empirical data in 2013. It is remarked that we chose to extract the network of co-financed financing platforms in order to evaluate the effect of risk contagion from platforms to bank system. We used the new credit similarity measure by combining the factor of connectivity and size, to extract minimal spanning trees (MSTs) and hierarchical trees (HTs). We found that: (1) the degree distributions of credit correlation backbone structure of Chinese local financing platforms are fat tailed, and the structure is unstable with respect to targeted failures; (2) the backbone is highly hierarchical, and largely explained by the geographic region; (3) the credit correlation backbone structure based on connectivity and size is significantly heterogeneous; (4) key platforms and regions of systemic importance, and contagion path of systemic risk are obtained, which are contributed to preventing systemic risk and regional risk of Chinese local financing platforms and preserving financial stability under the framework of macro prudential supervision. Our approach of credit similarity measure provides a means of recognizing ;systemically important; institutions and regions

  19. Data acquisition in modeling using neural networks and decision trees

    Directory of Open Access Journals (Sweden)

    R. Sika

    2011-04-01

    Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too

  20. NHRPA: a novel hierarchical routing protocol algorithm for wireless sensor networks

    Institute of Scientific and Technical Information of China (English)

    CHENG Hong-bing; YANG Geng; HU Su-jun

    2008-01-01

    Considering severe resources constraints and security threat of wireless sensor networks (WSN), the article proposed a novel hierarchical routing protocol algorithm. The proposed routing protocol algorithm can adopt suitable routing technology for the nodes according to the distance of nodes to the base station, density of nodes distribution, and residual energy of nodes. Comparing the proposed routing protocol algorithm with simple direction diffusion routing technology, cluster-based routing mechanisms, and simple hierarchical routing protocol algorithm through comprehensive analysis and simulation in terms of the energy usage, packet latency, and security in the presence of node compromise attacks, the results show that the proposed routing protocol algorithm is more efficient for wireless sensor networks.

  1. Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

    CERN Document Server

    Havemann, Frank; Heinz, Michael; Struck, Alexander

    2011-01-01

    We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  2. Communication Theories and Protocols for Smart Grid Hierarchical Network

    Directory of Open Access Journals (Sweden)

    CHHAYA Lipi

    2017-05-01

    Full Text Available Smart grid technology is a revolutionary approach for improvisation in existing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid infrastructure is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Different levels of smart grid deployment require diverse set of communication protocols. Application of information theory and optimization of various communication technologies is essential for layered architecture of smart grid technology. This paper is anticipated to serve as a comprehensive survey and analysis of communication theories and wireless communication protocols for optimization and design of energy efficient smart grid communication infrastructure.

  3. Sensor Network Data Fault Detection using Hierarchical Bayesian Space-Time Modeling

    OpenAIRE

    Ni, Kevin; Pottie, G J

    2009-01-01

    We present a new application of hierarchical Bayesian space-time (HBST) modeling: data fault detection in sensor networks primarily used in environmental monitoring situations. To show the effectiveness of HBST modeling, we develop a rudimentary tagging system to mark data that does not fit with given models. Using this, we compare HBST modeling against first order linear autoregressive (AR) modeling, which is a commonly used alternative due to its simplicity. We show that while HBST is mo...

  4. Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation.

    Directory of Open Access Journals (Sweden)

    Salvador Dura-Bernal

    Full Text Available Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance. Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom

  5. An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

    Science.gov (United States)

    Nasim, Mehwish; Qaisar, Saad; Lee, Sungyoung

    2012-01-01

    In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.

  6. A hierarchical P2P overlay network for interest-based media contents lookup

    Science.gov (United States)

    Lee, HyunRyong; Kim, JongWon

    2006-10-01

    We propose a P2P (peer-to-peer) overlay architecture, called IGN (interest grouping network), for contents lookup in the DHC (digital home community), which aims to provide a formalized home-network-extended construction of current P2P file sharing community. The IGN utilizes the Chord and de Bruijn graph for its hierarchical overlay network construction. By combining two schemes and by inheriting its features, the IGN efficiently supports contents lookup. More specifically, by introducing metadata-based lookup keyword, the IGN offers detailed contents lookup that can reflect the user interests. Moreover, the IGN tries to reflect home network environments of DHC by utilizing HG (home gateway) of each home network as a participating node of the IGN. Through experimental and analysis results, we show that the IGN is more efficient than Chord, a well-known DHT (distributed hash table)-based lookup protocol.

  7. Identifying failure in a tree network of a parallel computer

    Science.gov (United States)

    Archer, Charles J.; Pinnow, Kurt W.; Wallenfelt, Brian P.

    2010-08-24

    Methods, parallel computers, and products are provided for identifying failure in a tree network of a parallel computer. The parallel computer includes one or more processing sets including an I/O node and a plurality of compute nodes. For each processing set embodiments include selecting a set of test compute nodes, the test compute nodes being a subset of the compute nodes of the processing set; measuring the performance of the I/O node of the processing set; measuring the performance of the selected set of test compute nodes; calculating a current test value in dependence upon the measured performance of the I/O node of the processing set, the measured performance of the set of test compute nodes, and a predetermined value for I/O node performance; and comparing the current test value with a predetermined tree performance threshold. If the current test value is below the predetermined tree performance threshold, embodiments include selecting another set of test compute nodes. If the current test value is not below the predetermined tree performance threshold, embodiments include selecting from the test compute nodes one or more potential problem nodes and testing individually potential problem nodes and links to potential problem nodes.

  8. PERFORMANCE ANALYSIS OF SET PARTITIONING IN HIERARCHICAL TREES (SPIHT ALGORITHM FOR A FAMILY OF WAVELETS USED IN COLOR IMAGE COMPRESSION

    Directory of Open Access Journals (Sweden)

    A. Sreenivasa Murthy

    2014-11-01

    Full Text Available With the spurt in the amount of data (Image, video, audio, speech, & text available on the net, there is a huge demand for memory & bandwidth savings. One has to achieve this, by maintaining the quality & fidelity of the data acceptable to the end user. Wavelet transform is an important and practical tool for data compression. Set partitioning in hierarchal trees (SPIHT is a widely used compression algorithm for wavelet transformed images. Among all wavelet transform and zero-tree quantization based image compression algorithms SPIHT has become the benchmark state-of-the-art algorithm because it is simple to implement & yields good results. In this paper we present a comparative study of various wavelet families for image compression with SPIHT algorithm. We have conducted experiments with Daubechies, Coiflet, Symlet, Bi-orthogonal, Reverse Bi-orthogonal and Demeyer wavelet types. The resulting image quality is measured objectively, using peak signal-to-noise ratio (PSNR, and subjectively, using perceived image quality (human visual perception, HVP for short. The resulting reduction in the image size is quantified by compression ratio (CR.

  9. A Cayley Tree Immune Network Model with Antibody Dynamics

    CERN Document Server

    Anderson, R W; Perelson, A S; Anderson, Russell W.; Neumann, Avidan U.; Perelson, Alan S.

    1993-01-01

    Abstract: A Cayley tree model of idiotypic networks that includes both B cell and antibody dynamics is formulated and analyzed. As in models with B cells only, localized states exist in the network with limited numbers of activated clones surrounded by virgin or near-virgin clones. The existence and stability of these localized network states are explored as a function of model parameters. As in previous models that have included antibody, the stability of immune and tolerant localized states are shown to depend on the ratio of antibody to B cell lifetimes as well as the rate of antibody complex removal. As model parameters are varied, localized steady-states can break down via two routes: dynamically, into chaotic attractors, or structurally into percolation attractors. For a given set of parameters, percolation and chaotic attractors can coexist with localized attractors, and thus there do not exist clear cut boundaries in parameter space that separate regions of localized attractors from regions of percola...

  10. Encoding nondeterministic fuzzy tree automata into recursive neural networks.

    Science.gov (United States)

    Gori, Marco; Petrosino, Alfredo

    2004-11-01

    Fuzzy neural systems have been a subject of great interest in the last few years, due to their abilities to facilitate the exchange of information between symbolic and subsymbolic domains. However, the models in the literature are not able to deal with structured organization of information, that is typically required by symbolic processing. In many application domains, the patterns are not only structured, but a fuzziness degree is attached to each subsymbolic pattern primitive. The purpose of this paper is to show how recursive neural networks, properly conceived for dealing with structured information, can represent nondeterministic fuzzy frontier-to-root tree automata. Whereas available prior knowledge expressed in terms of fuzzy state transition rules are injected into a recursive network, unknown rules are supposed to be filled in by data-driven learning. We also prove the stability of the encoding algorithm, extending previous results on the injection of fuzzy finite-state dynamics in high-order recurrent networks.

  11. An Alternative to Optimize the Indonesian’s Airport Network Design: An Application of Minimum Spanning Tree (MST Technique

    Directory of Open Access Journals (Sweden)

    Luluk Lusiantoro

    2012-09-01

    Full Text Available Using minimum spanning tree technique (MST, this exploratory research was done to optimize the interrelation and hierarchical network design of Indonesian’s airports. This research also identifies the position of the Indonesian’s airports regionally based on the ASEAN Open Sky Policy 2015. The secondary data containing distance between airports (both in Indonesia and in ASEAN, flight frequency, and correlation of Gross Domestic Regional Product (GDRP for each region in Indonesia are used as inputs to form MST networks. The result analysis is done by comparing the MST networks with the existing network in Indonesia. This research found that the existing airport network in Indonesia does not depict the optimal network connecting all airports with the shortest distance and maximizing the correlation of regional economic potential in the country. This research then suggests the optimal networks and identifies the airports and regions as hubs and spokes formed by the networks. Lastly, this research indicates that the Indonesian airports have no strategic position in the ASEAN Open Sky network, but they have an opportunity to get strategic positions if 33 airports in 33 regions in Indonesia are included in the network.

  12. Bayesian Models for Streamflow and River Network Reconstruction using Tree Rings

    Science.gov (United States)

    Ravindranath, A.; Devineni, N.

    2016-12-01

    Water systems face non-stationary, dynamically shifting risks due to shifting societal conditions and systematic long-term variations in climate manifesting as quasi-periodic behavior on multi-decadal time scales. Water systems are thus vulnerable to long periods of wet or dry hydroclimatic conditions. Streamflow is a major component of water systems and a primary means by which water is transported to serve ecosystems' and human needs. Thus, our concern is in understanding streamflow variability. Climate variability and impacts on water resources are crucial factors affecting streamflow, and multi-scale variability increases risk to water sustainability and systems. Dam operations are necessary for collecting water brought by streamflow while maintaining downstream ecological health. Rules governing dam operations are based on streamflow records that are woefully short compared to periods of systematic variation present in the climatic factors driving streamflow variability and non-stationarity. We use hierarchical Bayesian regression methods in order to reconstruct paleo-streamflow records for dams within a basin using paleoclimate proxies (e.g. tree rings) to guide the reconstructions. The riverine flow network for the entire basin is subsequently modeled hierarchically using feeder stream and tributary flows. This is a starting point in analyzing streamflow variability and risks to water systems, and developing a scientifically-informed dynamic risk management framework for formulating dam operations and water policies to best hedge such risks. We will apply this work to the Missouri and Delaware River Basins (DRB). Preliminary results of streamflow reconstructions for eight dams in the upper DRB using standard Gaussian regression with regional tree ring chronologies give streamflow records that now span two to two and a half centuries, and modestly smoothed versions of these reconstructed flows indicate physically-justifiable trends in the time series.

  13. A new Hierarchical Group Key Management based on Clustering Scheme for Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Ayman EL-SAYED

    2014-05-01

    Full Text Available The migration from wired network to wireless network has been a global trend in the past few decades because they provide anytime-anywhere networking services. The wireless networks are rapidly deployed in the future, secure wireless environment will be mandatory. As well, The mobility and scalability brought by wireless network made it possible in many applications. Among all the contemporary wireless networks,Mobile Ad hoc Networks (MANET is one of the most important and unique applications. MANET is a collection of autonomous nodes or terminals which communicate with each other by forming a multihop radio network and maintaining connectivity in a decentralized manner. Due to the nature of unreliable wireless medium data transfer is a major problem in MANET and it lacks security and reliability of data. The most suitable solution to provide the expected level of security to these services is the provision of a key management protocol. A Key management is vital part of security. This issue is even bigger in wireless network compared to wired network. The distribution of keys in an authenticated manner is a difficult task in MANET. When a member leaves or joins the group, it needs to generate a new key to maintain forward and backward secrecy. In this paper, we propose a new group key management schemes namely a Hierarchical, Simple, Efficient and Scalable Group Key (HSESGK based on clustering management scheme for MANETs and different other schemes are classified. Group members deduce the group key in a distributed manner.

  14. Hierarchically Coordinated Power Management for Target Tracking in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Feng Juan

    2013-10-01

    Full Text Available Energy efficiency is very important for wireless sensor networks (WSNs since sensor nodes have a limited energy supply from a battery. So far, a lot research has focused on this issue, while less emphasis has been placed on the adaptive sleep time for each node with a consideration for the application constraints. In this paper, we propose a hierarchically coordinated power management (HCPM approach, which both addresses the energy conservation problem and reduces the packet forwarding delay for target tracking WSNs based on a virtual‐grid‐based network structure. We extend the network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM and the adaptive coordinate PM strategies to schedule the activities of the sensor nodes at the surveillance stage. Furthermore, we propose a hierarchical structure for the tracking stage. Experimental results show that the proposed approach has a greater capability of extending the network lifetime while maintaining a short transmission delay when compared with the protocol which does not consider the application constraints in target tracking sensor networks.

  15. Optimal computation of symmetric Boolean functions in Tree networks

    CERN Document Server

    Kowshik, Hemant

    2010-01-01

    In this paper, we address the scenario where nodes with sensor data are connected in a tree network, and every node wants to compute a given symmetric Boolean function of the sensor data. We first consider the problem of computing a function of two nodes with integer measurements. We allow for block computation to enhance data fusion efficiency, and determine the minimum worst-case total number of bits to be exchanged to perform the desired computation. We establish lower bounds using fooling sets, and provide a novel scheme which attains the lower bounds, using information theoretic tools. For a class of functions called sum-threshold functions, this scheme is shown to be optimal. We then turn to tree networks and derive a lower bound for the number of bits exchanged on each link by viewing it as a two node problem. We show that the protocol of recursive innetwork aggregation achieves this lower bound in the case of sumthreshold functions. Thus we have provided a communication and in-network computation stra...

  16. Capacity of a Class of Multicast Tree Networks

    CERN Document Server

    Lee, Si-Hyeon

    2010-01-01

    In this paper, a class of single-source multicast discrete memoryless relay networks having a tree topology is considered in which the root node is the source, each parent node in the graph has at most one noisy child node and any number of noiseless child nodes, and subsets of leaf nodes are destinations. For this class of multicast tree networks, lower and upper bounds on the capacity are presented and these two bounds are shown to meet when each set of nodes forming a destination is included in a disjoint subtree, which includes the single-destination case. For achievablity, a coding scheme is constructed where each noisy relay employs a combination of decode-and-forward (DF) and compress-and-forward (CF) and each noiseless relay performs random binning. In our coding scheme, codebook constructions and relay operations are independent for each node and do not depend on the network topology, which is a key ingredient of our achievability proof. We note that our result is the first to show that the combinati...

  17. Analysis of thermal conductivity in tree-like branched networks

    Institute of Scientific and Technical Information of China (English)

    Kou Jian-Long; Lu Hang-Jun; Wu Feng-Min; Xu You-Sheng

    2009-01-01

    Asymmetric tree-like branched networks are explored by geometric algorithms.Based on the network,an analysis of the thermal conductivity is presented.The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique.In all studied cases,a clear behaviour is observed,where angle(δ,θ)among parent branching extended lines,branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity.When the angle δ is fixed,the optical diameter ratio β* is dependent on angle θ.Moreover,γ and m are not related to β*.The longer the branch is,the smaller the effective thermal conductivity will be.It is also found that when the angle θ<δ/2,the higher the iteration m is,the lower the thermal conductivity will be and it tends to zero,otherwise,it is bigger than zero.When the diameter ratio β1<0.707 and angle δ is bigger,the optimal k of the perfect ratio increases with the increase of the angle δ;when β1>0.707,the optimal k decreases.In addition,the effective thermal conductivity is always less than that of single channel material.The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray's law.

  18. Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network.

    Science.gov (United States)

    Krumsiek, Jan; Marr, Carsten; Schroeder, Timm; Theis, Fabian J

    2011-01-01

    Hematopoiesis is an ideal model system for stem cell biology with advanced experimental access. A systems view on the interactions of core transcription factors is important for understanding differentiation mechanisms and dynamics. In this manuscript, we construct a Boolean network to model myeloid differentiation, specifically from common myeloid progenitors to megakaryocytes, erythrocytes, granulocytes and monocytes. By interpreting the hematopoietic literature and translating experimental evidence into Boolean rules, we implement binary dynamics on the resulting 11-factor regulatory network. Our network contains interesting functional modules and a concatenation of mutual antagonistic pairs. The state space of our model is a hierarchical, acyclic graph, typifying the principles of myeloid differentiation. We observe excellent agreement between the steady states of our model and microarray expression profiles of two different studies. Moreover, perturbations of the network topology correctly reproduce reported knockout phenotypes in silico. We predict previously uncharacterized regulatory interactions and alterations of the differentiation process, and line out reprogramming strategies.

  19. Spectral dimensions of hierarchical scale-free networks with weighted shortcuts

    Science.gov (United States)

    Hwang, S.; Yun, C.-K.; Lee, D.-S.; Kahng, B.; Kim, D.

    2010-11-01

    Spectral dimensions have been widely used to understand transport properties on regular and fractal lattices. However, they have received little attention with regard to complex networks such as scale-free and small-world networks. Here, we study the spectral dimension and the return-to-origin probability of random walks on hierarchical scale-free networks, which can be either fractal or nonfractal depending on the weight of the shortcuts. Applying the renormalization-group (RG) approach to a Gaussian model, we obtain the exact spectral dimension. While the spectral dimension varies between 1 and 2 for the fractal case, it remains at 2, independent of the variation in the network structure, for the nonfractal case. The crossover behavior between the two cases is studied by carrying out the RG flow analysis. The analytical results are confirmed by simulation results and their implications for the architecture of complex systems are discussed.

  20. Hierarchical Location Service with Prediction in Mobile Ad-Hoc Networks

    CERN Document Server

    Amar, Ebtisam; Renault, Éric; 10.5121/ijcnc.2010.2204

    2010-01-01

    Position-based routing protocols take advantage of location information to perform a stateless and efficient routing. To enable position-based routing, a node must be able to discover the location of the messages' destination node. This task is typically accomplished by a location service. Recently, several location service protocols have been developed for ad hoc networks. In this paper we propose a novel location service called PHLS: Predictive Hierarchical Location Service. In PHLS, the entire network is partitioned into a hierarchy of smaller and smaller regions. For each node, one node in each-level region of the hierarchy is chosen as its local location server. When the network initializes or when a node attaches the network, nodes contact their local location server with their current location information (ie. position and velocity). Then, they only need to update their location server when they move away from their current region. Finally, nodes query their location servers and get the exact or predic...

  1. Interaction network of vascular epiphytes and trees in a subtropical forest

    Science.gov (United States)

    Ceballos, Sergio Javier; Chacoff, Natacha Paola; Malizia, Agustina

    2016-11-01

    The commensalistic interaction between vascular epiphytes and host trees is a type of biotic interaction that has been recently analysed with a network approach. This approach is useful to describe the network structure with metrics such as nestedness, specialization and interaction evenness, which can be compared with other vascular epiphyte-host tree networks from different forests of the world. However, in several cases these comparisons showed different and inconsistent patterns between these networks, and their possible ecological and evolutionary determinants have been scarcely studied. In this study, the interactions between vascular epiphytes and host trees of a subtropical forest of sierra de San Javier (Tucuman, Argentina) were analysed with a network approach. We calculated metrics to characterize the network and we analysed factors such as the abundance of species, tree size, tree bark texture, and tree wood density in order to predict interaction frequencies and network structure. The interaction network analysed exhibited a nested structure, an even distribution of interactions, and low specialization, properties shared with other obligated vascular epiphyte-host tree networks with a different assemblage structure. Interaction frequencies were predicted by the abundance of species, tree size and tree bark texture. Species abundance and tree size also predicted nestedness. Abundance indicated that abundant species interact more frequently; and tree size was an important predictor, since larger-diameter trees hosted more vascular epiphyte species than small-diameter trees. This is one of the first studies analyzing interactions between vascular epiphytes and host trees using a network approach in a subtropical forest, and taking the whole vascular epiphyte assemblage of the sampled community into account.

  2. Tifinagh Character Recognition Using Geodesic Distances, Decision Trees & Neural Networks

    Directory of Open Access Journals (Sweden)

    O.BENCHAREF

    2011-09-01

    Full Text Available The recognition of Tifinagh characters cannot be perfectly carried out using the conventional methods which are based on the invariance, this is due to the similarity that exists between some characters which differ from each other only by size or rotation, hence the need to come up with new methods to remedy this shortage. In this paper we propose a direct method based on the calculation of what is called Geodesic Descriptors which have shown significant reliability vis-à-vis the change of scale, noise presence and geometric distortions. For classification, we have opted for a method based on the hybridization of decision trees and neural networks.

  3. TWO-LEVEL HIERARCHICAL COORDINATION QUEUING METHOD FOR TELECOMMUNICATION NETWORK NODES

    Directory of Open Access Journals (Sweden)

    M. V. Semenyaka

    2014-07-01

    Full Text Available The paper presents hierarchical coordination queuing method. Within the proposed method a queuing problem has been reduced to optimization problem solving that was presented as two-level hierarchical structure. The required distribution of flows and bandwidth allocation was calculated at the first level independently for each macro-queue; at the second level solutions obtained on lower level for each queue were coordinated in order to prevent probable network link overload. The method of goal coordination has been determined for multilevel structure managing, which makes it possible to define the order for consideration of queue cooperation restrictions and calculation tasks distribution between levels of hierarchy. Decisions coordination was performed by the method of Lagrange multipliers. The study of method convergence has been carried out by analytical modeling.

  4. Secure Session Mobility using Hierarchical Authentication Key Management in Next Generation Networks

    Directory of Open Access Journals (Sweden)

    Muhammad Zubair

    2014-05-01

    Full Text Available In this paper we propose a novel authentication mechanism for session mobility in Next Generation Networks named as Hierarchical Authentication Key Management (HAKM. The design objectives of HAKM are twofold: i to minimize the authentication latency in NGNs; ii to provide protection against an assortment of attacks such as denial-of-service attacks, man-in-the-middle attacks, guessing attacks, and capturing node attacks. In order to achieve these objectives, we combine Session Initiation Protocol (SIP with Hierarchical Mobile IPv6 (HMIPv6 to perform local authentication for session mobility. The concept of group keys and pairwise keys with one way hash function is employed to make HAKM vigorous against the aforesaid attacks. The performance analysis and numerical results demonstrate that HAKM outperforms the existing approaches in terms of latency and protection against the abovementioned attacks

  5. Coevolution of information processing and topology in hierarchical adaptive random Boolean networks

    Science.gov (United States)

    Górski, Piotr J.; Czaplicka, Agnieszka; Hołyst, Janusz A.

    2016-02-01

    Random Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) - subnetworks - connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.

  6. Integration of relational and hierarchical network information for protein function prediction

    Directory of Open Access Journals (Sweden)

    Jiang Xiaoyu

    2008-08-01

    Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

  7. A Hierarchical Energy Efficient Reliable Transport Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Prabhudutta Mohanty

    2014-12-01

    Full Text Available The two important requirements for many Wireless Senor Networks (WSNs are prolonged network lifetime and end-to-end reliability. The sensor nodes consume more energy during data transmission than the data sensing. In WSN, the redundant data increase the energy consumption, latency and reduce reliability during data transmission. Therefore, it is important to support energy efficient reliable data transport in WSNs. In this paper, we present a Hierarchical Energy Efficient Reliable Transport Protocol (HEERTP for the data transmission within the WSN. This protocol maximises the network lifetime by controlling the redundant data transmission with the co-ordination of Base Station (BS. The proposed protocol also achieves end-to-end reliability using a hop-by-hop acknowledgement scheme. We evaluate the performance of the proposed protocol through simulation. The simulation results reveal that our proposed protocol achieves better performance in terms of energy efficiency, latency and reliability than the existing protocols.

  8. A Hierarchical Approach to Real-time Activity Recognition in Body Sensor Networks

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Tao, Xianping

    2012-01-01

    algorithm to detect gestures at the sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good......Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we rst use a fast and lightweight...... performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also signicantly reduces the network communication cost by 60.2%....

  9. Toughening mystery of natural rubber deciphered by double network incorporating hierarchical structures.

    Science.gov (United States)

    Zhou, Weiming; Li, Xiangyang; Lu, Jie; Huang, Ningdong; Chen, Liang; Qi, Zeming; Li, Liangbin; Liang, Haiyi

    2014-12-16

    As an indispensible material for modern society, natural rubber possesses peerless mechanical properties such as strength and toughness over its artificial analogues, which remains a mystery. Intensive experimental and theoretical investigations have revealed the self-enhancement of natural rubber due to strain-induced crystallization. However a rigorous model on the self-enhancement, elucidating natural rubber's extraordinary mechanical properties, is obscured by deficient understanding of the local hierarchical structure under strain. With spatially resolved synchrotron radiation micro-beam scanning X-ray diffraction we discover weak oscillation in distributions of strain-induced crystallinity around crack tip for stretched natural rubber film, demonstrating a soft-hard double network structure. The fracture energy enhancement factor obtained by utilizing the double network model indicates an enhancement of toughness by 3 orders. It's proposed that upon stretching spontaneously developed double network structures integrating hierarchy at multi length-scale in natural rubber play an essential role in its remarkable mechanical performance.

  10. Decentralized Cooperative TOA/AOA Target Tracking for Hierarchical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chih-Yu Wen

    2012-11-01

    Full Text Available This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processingis conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for thelocalization task. The proposed energy-efficient tracking algorithm allows each sub-clustermember to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for objectposition estimation. 

  11. Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

    Science.gov (United States)

    Chen, Ying-Chih; Wen, Chih-Yu

    2012-11-08

    This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

  12. Structure function relationship in complex brain networks expressed by hierarchical synchronization

    Science.gov (United States)

    Zhou, Changsong; Zemanová, Lucia; Zamora-López, Gorka; Hilgetag, Claus C.; Kurths, Jürgen

    2007-06-01

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

  13. A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-06-01

    Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

  14. Control of Boolean networks: hardness results and algorithms for tree structured networks.

    Science.gov (United States)

    Akutsu, Tatsuya; Hayashida, Morihiro; Ching, Wai-Ki; Ng, Michael K

    2007-02-21

    Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed.

  15. Using Medical History Embedded in Biometrics Medical Card for User Identity Authentication: Data Representation by AVT Hierarchical Data Tree

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2012-01-01

    Full Text Available User authentication has been widely used by biometric applications that work on unique bodily features, such as fingerprints, retina scan, and palm vessels recognition. This paper proposes a novel concept of biometric authentication by exploiting a user’s medical history. Although medical history may not be absolutely unique to every individual person, the chances of having two persons who share an exactly identical trail of medical and prognosis history are slim. Therefore, in addition to common biometric identification methods, medical history can be used as ingredients for generating Q&A challenges upon user authentication. This concept is motivated by a recent advancement on smart-card technology that future identity cards are able to carry patents’ medical history like a mobile database. Privacy, however, may be a concern when medical history is used for authentication. Therefore in this paper, a new method is proposed for abstracting the medical data by using attribute value taxonomies, into a hierarchical data tree (h-Data. Questions can be abstracted to various level of resolution (hence sensitivity of private data for use in the authentication process. The method is described and a case study is given in this paper.

  16. Using medical history embedded in biometrics medical card for user identity authentication: data representation by AVT hierarchical data tree.

    Science.gov (United States)

    Fong, Simon; Zhuang, Yan

    2012-01-01

    User authentication has been widely used by biometric applications that work on unique bodily features, such as fingerprints, retina scan, and palm vessels recognition. This paper proposes a novel concept of biometric authentication by exploiting a user's medical history. Although medical history may not be absolutely unique to every individual person, the chances of having two persons who share an exactly identical trail of medical and prognosis history are slim. Therefore, in addition to common biometric identification methods, medical history can be used as ingredients for generating Q&A challenges upon user authentication. This concept is motivated by a recent advancement on smart-card technology that future identity cards are able to carry patents' medical history like a mobile database. Privacy, however, may be a concern when medical history is used for authentication. Therefore in this paper, a new method is proposed for abstracting the medical data by using attribute value taxonomies, into a hierarchical data tree (h-Data). Questions can be abstracted to various level of resolution (hence sensitivity of private data) for use in the authentication process. The method is described and a case study is given in this paper.

  17. Hierarchical neural network model of the visual system determining figure/ground relation

    Science.gov (United States)

    Kikuchi, Masayuki

    2017-07-01

    One of the most important functions of the visual perception in the brain is figure/ground interpretation from input images. Figural region in 2D image corresponding to object in 3D space are distinguished from background region extended behind the object. Previously the author proposed a neural network model of figure/ground separation constructed on the standpoint that local geometric features such as curvatures and outer angles at corners are extracted and propagated along input contour in a single layer network (Kikuchi & Akashi, 2001). However, such a processing principle has the defect that signal propagation requires manyiterations despite the fact that actual visual system determines figure/ground relation within the short period (Zhou et al., 2000). In order to attain speed-up for determining figure/ground, this study incorporates hierarchical architecture into the previous model. This study confirmed the effect of the hierarchization as for the computation time by simulation. As the number of layers increased, the required computation time reduced. However, such speed-up effect was saturatedas the layers increased to some extent. This study attempted to explain this saturation effect by the notion of average distance between vertices in the area of complex network, and succeeded to mimic the saturation effect by computer simulation.

  18. Network dynamics of eukaryotic LTR retroelements beyond phylogenetic trees

    Directory of Open Access Journals (Sweden)

    Bernad Lucia

    2009-11-01

    Full Text Available Abstract Background Sequencing projects have allowed diverse retroviruses and LTR retrotransposons from different eukaryotic organisms to be characterized. It is known that retroviruses and other retro-transcribing viruses evolve from LTR retrotransposons and that this whole system clusters into five families: Ty3/Gypsy, Retroviridae, Ty1/Copia, Bel/Pao and Caulimoviridae. Phylogenetic analyses usually show that these split into multiple distinct lineages but what is yet to be understood is how deep evolution occurred in this system. Results We combined phylogenetic and graph analyses to investigate the history of LTR retroelements both as a tree and as a network. We used 268 non-redundant LTR retroelements, many of them introduced for the first time in this work, to elucidate all possible LTR retroelement phylogenetic patterns. These were superimposed over the tree of eukaryotes to investigate the dynamics of the system, at distinct evolutionary times. Next, we investigated phenotypic features such as duplication and variability of amino acid motifs, and several differences in genomic ORF organization. Using this information we characterized eight reticulate evolution markers to construct phenotypic network models. Conclusion The evolutionary history of LTR retroelements can be traced as a time-evolving network that depends on phylogenetic patterns, epigenetic host-factors and phenotypic plasticity. The Ty1/Copia and the Ty3/Gypsy families represent the oldest patterns in this network that we found mimics eukaryotic macroevolution. The emergence of the Bel/Pao, Retroviridae and Caulimoviridae families in this network can be related with distinct inflations of the Ty3/Gypsy family, at distinct evolutionary times. This suggests that Ty3/Gypsy ancestors diversified much more than their Ty1/Copia counterparts, at distinct geological eras. Consistent with the principle of preferential attachment, the connectivities among phenotypic markers, taken as

  19. Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

    Science.gov (United States)

    Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

    2016-03-01

    The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

  20. Hierarchical self-assembly of a striped gyroid formed by threaded chiral mesoscale networks

    DEFF Research Database (Denmark)

    Kirkensgaard, Jacob Judas Kain; Evans, Myfanwy; de Campo, Lilliana;

    2014-01-01

    the gyroid film are densely packed and contain either graphitic hcb nets (chicken wire) or srs nets, forming convoluted intergrowths of multiple nets. Furthermore, each net is ideally a single chiral enantiomer, induced by the gyroid architecture. However, the numerical simulations result in defect......Numerical simulations reveal a family of hierarchical and chiral multicontinuous network structures self-assembled from a melt blend of Y-shaped ABC and ABD three-miktoarm star terpolymers, constrained to have equal-sized A/B and C/D chains, respectively. The C and D majority domains within...

  1. A hierarchical virtual backbone construction protocol for mobile ad hoc networks

    Directory of Open Access Journals (Sweden)

    Bharti Sharma

    2016-07-01

    Full Text Available We propose a hierarchical backbone construction protocol for mobile ad hoc networks. Our protocol is based on the idea of using an efficient extrema finding method to create clusters comprising the nodes that are within certain prespecified wireless hop distance. Afterward, we apply our ‘diameter’ algorithm among clusters to identify the dominating nodes that are, finally, connected via multi-hop virtual links to construct the backbone. We present the analytic as well as simulation study of our algorithm and also a method for the dynamic maintenance of constructed backbone. In the end, we illustrate the use of the virtual backbone with the help of an interesting application.

  2. On Hierarchical Extensions of Large-Scale 4-regular Grid Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Patel, A.; Knudsen, Thomas Phillip

    It is studied how the introduction of ordered hierarchies in 4-regular grid network structures decreses distances remarkably, while at the same time allowing for simple topological routing schemes. Both meshes and tori are considered; in both cases non-hierarchical structures have power law......, and it is shown that while they allow for more flexibility in design and construction of structures supporting topological routing, their performances are comparable to the performance of the perfect square mesh. Finally suggestions for further research within the field are given....

  3. On Hierarchical Extensions of Large-Scale 4-regular Grid Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Patel, A.; Knudsen, Thomas Phillip

    2004-01-01

    It is studied how the introduction of ordered hierarchies in 4-regular grid network structures decreases distances remarkably, while at the same time allowing for simple topological routing schemes. Both meshes and tori are considered; in both cases non-hierarchical structures have power law......, and it is shown that while they allow for more flexibility in design and construction of structures supporting topological routing, their performances are comparable to the performance of the perfect square mesh. Finally suggestions for further research within the field are given....

  4. Hierarchical Self-healing Key Distribution for Heterogeneous Wireless Sensor Networks

    Science.gov (United States)

    Yang, Yanjiang; Zhou, Jianying; Deng, Robert H.; Bao, Feng

    Self-healing group key distribution aims to achieve robust key distribution over lossy channels in wireless sensor networks (WSNs). However, all existing self-healing group key distribution schemes in the literature consider homogenous WSNs which are known to be unscalable. Heterogeneous WSNs have better scalability and performance than homogenous ones. We are thus motivated to study hierarchial self-healing group key distribution, tailored to the heterogeneous WSN architecture. In particular, we revisit and adapt Dutta et al.’s model to the setting of hierarchical self-healing group key distribution, and propose a concrete scheme that achieves computational security and high efficiency.

  5. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

    Science.gov (United States)

    Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

    2016-08-01

    This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

  6. Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

    Science.gov (United States)

    Susan L. King

    2003-01-01

    The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

  7. Random sequential renormalization of networks: application to critical trees.

    Science.gov (United States)

    Bizhani, Golnoosh; Sood, Vishal; Paczuski, Maya; Grassberger, Peter

    2011-03-01

    We introduce the concept of random sequential renormalization (RSR) for arbitrary networks. RSR is a graph renormalization procedure that locally aggregates nodes to produce a coarse grained network. It is analogous to the (quasi)parallel renormalization schemes introduced by C. Song et al. [C. Song et al., Nature (London) 433, 392 (2005)] and studied by F. Radicchi et al. [F. Radicchi et al., Phys. Rev. Lett. 101, 148701 (2008)], but much simpler and easier to implement. Here we apply RSR to critical trees and derive analytical results consistent with numerical simulations. Critical trees exhibit three regimes in their evolution under RSR. (i) For N₀{ν}≲Nrenormalization and N₀ is the initial size of the tree, RSR is described by a mean-field theory, and fluctuations from one realization to another are small. The exponent ν=1/2 is derived using random walk and other arguments. The degree distribution becomes broader under successive steps, reaching a power law p{k}~1/k{γ} with γ=2 and a variance that diverges as N₀¹/² at the end of this regime. Both of these latter results are obtained from a scaling theory. (ii) For N₀{ν{star}}≲N ≲ N₀¹/², with ν_{star}≈1/4 hubs develop, and fluctuations between different realizations of the RSR are large. Trees are short and fat with an average radius that is O(1). Crossover functions exhibiting finite-size scaling in the critical region N~N₀¹/²→∞ connect the behaviors in the first two regimes. (iii) For N ≲ N₀{ν{star}}, star configurations appear with a central hub surrounded by many leaves. The distribution of stars is broadly distributed over this range. The scaling behaviors found under RSR are identified with a continuous transition in a process called "agglomerative percolation" (AP), with the coarse-grained nodes in RSR corresponding to clusters in AP that grow by simultaneously attaching to all their neighboring clusters.

  8. Evaluating the Performance of Fast Handover for Hierarchical MIPv6 in Cellular Networks

    Directory of Open Access Journals (Sweden)

    Li Jun Zhang

    2008-06-01

    Full Text Available Next-Generation Wireless Networks (NGWNs present an all-IP-based architecture integrating existing cellular networks with Wireless Local Area Networks (WLANs, Wireless Metropolitan Area Networks (WMANs, ad hoc networks, Bluetooth, etc. This makes mobility management an important issue for users roaming among these networks/systems. On one hand, intelligent schemes need to be devised to empower mobile users to benefit from the IP-based technology. On the other hand, new solutions are required to take into account global roaming among various radio access technologies and support of real-time multimedia services. This paper presents a comprehensive performance analysis of Fast handover for Hierarchical Mobile IPv6 (F-HMIPv6 using the fluid-flow and randomwalk mobility models. Location update cost, packet delivery cost and total cost functions are formulated based on the proposed analytical models. We investigate the impact of several wireless system factors such as user velocity, user density, mobility domain size, session-to-mobility ratio on these costs, and present some numerical results.

  9. Mobile Agent Based Hierarchical Intrusion Detection System in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Surraya Khanum

    2012-01-01

    Full Text Available Security mechanism is a fundamental requirement of wireless networks in general and Wireless Sensor Networks (WSN in particular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. WSN needs strong security mechanism as it is usually deployed in a critical, hostile and sensitive environment where human labour is usually not involved. However, due to inbuilt resource and computing restriction, security in WSN needs a special consideration. Traditional security techniques such as encryption, VPN, authentication and firewalls cannot be directly applied to WSN as it provides defence only against external threats. The existing literature shows that there seems an inverse relationship between strong security mechanism and efficient network resource utilization. In this research article, we have proposed a Mobile Agent Based Hierarchical Intrusion Detection System (MABHIDS for WSN. The Proposed scheme performs two levels of intrusion detection by utilizing minimum possible network resources. Our proposed idea enhance network lifetime by reducing the work load on Cluster Head (CH and it also provide enhanced level of security in WSN.

  10. Hierarchical Supervisor and Agent Routing Algorithm in LEO/MEO Double-layered Optical Satellite Network

    Science.gov (United States)

    Li, Yongjun; Zhao, Shanghong

    2016-09-01

    A novel routing algorithm (Hierarchical Supervisor and Agent Routing Algorithm, HSARA) for LEO/MEO (low earth orbit/medium earth orbit) double-layered optical satellite network is brought forward. The so-called supervisor (MEO satellite) is designed for failure recovery and network management. LEO satellites are grouped according to the virtual managed field of MEO which is different from coverage area of MEO satellite in RF satellite network. In each LEO group, one LEO satellite which has maximal persistent link with its supervisor is called the agent. A LEO group is updated when this optical inter-orbit links between agent LEO satellite and the corresponding MEO satellite supervisor cuts off. In this way, computations of topology changes and LEO group updating can be decreased. Expense of routing is integration of delay and wavelength utilization. HSARA algorithm simulations are implemented and the results are as follows: average network delay of HSARA can reduce 21 ms and 31.2 ms compared with traditional multilayered satellite routing and single-layer LEO satellite respectively; LEO/MEO double-layered optical satellite network can cover polar region which cannot be covered by single-layered LEO satellite and throughput is 1% more than that of single-layered LEO satellite averagely. Therefore, exact global coverage can be achieved with this double-layered optical satellite network.

  11. Hierarchical alteration of brain structural and functional networks in female migraine sufferers.

    Directory of Open Access Journals (Sweden)

    Jixin Liu

    Full Text Available BACKGROUND: Little is known about the changes of brain structural and functional connectivity networks underlying the pathophysiology in migraine. We aimed to investigate how the cortical network reorganization is altered by frequent cortical overstimulation associated with migraine. METHODOLOGY/PRINCIPAL FINDINGS: Gray matter volumes and resting-state functional magnetic resonance imaging signal correlations were employed to construct structural and functional networks between brain regions in 43 female patients with migraine (PM and 43 gender-matched healthy controls (HC by using graph theory-based approaches. Compared with the HC group, the patients showed abnormal global topology in both structural and functional networks, characterized by higher mean clustering coefficients without significant change in the shortest absolute path length, which indicated that the PM lost optimal topological organization in their cortical networks. Brain hubs related to pain-processing revealed abnormal nodal centrality in both structural and functional networks, including the precentral gyrus, orbital part of the inferior frontal gyrus, parahippocampal gyrus, anterior cingulate gyrus, thalamus, temporal pole of the middle temporal gyrus and the inferior parietal gyrus. Negative correlations were found between migraine duration and regions with abnormal centrality. Furthermore, the dysfunctional connections in patients' cortical networks formed into a connected component and three dysregulated modules were identified involving pain-related information processing and motion-processing visual networks. CONCLUSIONS: Our results may reflect brain alteration dynamics resulting from migraine and suggest that long-term and high-frequency headache attacks may cause both structural and functional connectivity network reorganization. The disrupted information exchange between brain areas in migraine may be reshaped into a hierarchical modular structure progressively.

  12. Analysis of Hierarchical Diff-EDF Schedulability over Heterogeneous Real-Time Packet Networks

    Directory of Open Access Journals (Sweden)

    M. Saleh

    2007-01-01

    Full Text Available Packet networks are currently enabling the integration of traffic with a wide range of characteristics that extend from video traffic with stringent QoS requirements to the best-effort traffic requiring no guarantees. QoS guarantees can be provided in conventional packet networks by the use of proper packet scheduling algorithms. As a computer revolution, many scheduling algorithms have been proposed to provide different schemes of QoS guarantees with Earliest Deadline First (EDF as the most popular one. With EDF scheduling, all flows receive the same miss rate regardless of their traffic characteristics and deadlines. This makes the standard EDF algorithm unsuitable for situations in which the different flows have different miss rate requirements since in order to meet all miss rate requirements it is necessary to limit admissions so as to satisfy the flow with the most stringent miss rate requirements. In this paper, we propose a new priority assignment scheduling algorithm, Hierarchal Diff-EDF (Differentiate Earliest Deadline First, which can meet the real-time needs of these applications while continuing to provide best effort service to non-real time traffic. The Hierarchal Diff-EDF features a feedback control mechanism that detects overload conditions and modifies packet priority assignments accordingly. To examine our proposed scheduler model, we introduced our attempt to provide an exact analytical solution. The attempt showed that the solution was apparently very complicated due to the high interdependence between the system queues' service. Hence, the use of simulation techniques seems inevitable. The simulation results showed that the Hierarchical Diff-EDF achieved the minimum packet average delay when compared with both EDF and Diff-EDF schedulers.

  13. Gel-based composite polymer electrolytes with novel hierarchical mesoporous silica network for lithium batteries

    Energy Technology Data Exchange (ETDEWEB)

    Wang Xiaoliang; Cai Qiang [Department of Materials Science and Engineering, and State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University, Beijing 100084 (China); Fan Lizhen [School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083 (China); Hua Tao; Lin Yuanhua [Department of Materials Science and Engineering, and State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University, Beijing 100084 (China); Nan Cewen [Department of Materials Science and Engineering, and State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University, Beijing 100084 (China)], E-mail: cwnan@tsinghua.edu.cn

    2008-11-15

    In the present work, novel gel-based composite polymer electrolytes for lithium batteries were prepared by introducing a hierarchical mesoporous silica network to the poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP)-based gel electrolytes. As compared with the PVDF-HFP-based gel electrolytes with/without conventional nano-sized silica fillers, the novel electrolytes have shown more homogeneous microstructure, higher ionic conductivity and better mechanical stability, which could be caused by the strong silica network and the effective interactions among the polymer, the liquid electrolytes and the silica. Moreover, the cell with this kind of electrolytes could achieve a discharge capacity as much as 150 mAh g{sup -1} at room temperature (LiCoO{sub 2} as the cathode active material), with high Coulomb efficiency.

  14. Gel-based composite polymer electrolytes with novel hierarchical mesoporous silica network for lithium batteries

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Xiao-Liang; Cai, Qiang; Hua, Tao; Lin, Yuan-Hua; Nan, Ce-Wen [Department of Materials Science and Engineering, and State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University, Beijing 100084 (China); Fan, Li-Zhen [School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083 (China)

    2008-11-15

    In the present work, novel gel-based composite polymer electrolytes for lithium batteries were prepared by introducing a hierarchical mesoporous silica network to the poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP)-based gel electrolytes. As compared with the PVDF-HFP-based gel electrolytes with/without conventional nano-sized silica fillers, the novel electrolytes have shown more homogeneous microstructure, higher ionic conductivity and better mechanical stability, which could be caused by the strong silica network and the effective interactions among the polymer, the liquid electrolytes and the silica. Moreover, the cell with this kind of electrolytes could achieve a discharge capacity as much as 150 mAh g{sup -1} at room temperature (LiCoO{sub 2} as the cathode active material), with high Coulomb efficiency. (author)

  15. A New Enhanced Fast Handover Algorithm in Hierarchical Mobile IPv6 Network

    Institute of Scientific and Technical Information of China (English)

    XU Kai; JI Hong; YUE Guang-xin

    2004-01-01

    Hierarchical Mobile IPv6 (HMIPv6) can reduce the delay and the amount of signaling during handover compared with the basic mobile IPv6. However, the protocol still cannot meet the requirement for traffic that is delay sensitive, such as voice, especially in macro mobility handover. Duplicate address detection and the transmission time for the handover operation could cause high handover delay. This paper proposes a new mechanism to improve the fast handover algorithms efficiency in HMIPv6 network. And we present and analyze the performance testing for our proposal by comparing it with the traditional HMIPv6 fast handover algorithm. The results of simulation show that our scheme can reduce the handover delay much more than the traditional fast handover method for HMIPv6 network.

  16. A special hierarchical fuzzy neural-networks based reinforcement learning for multi-variables system

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wen-zhi; LU Tian-sheng

    2005-01-01

    Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN) for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.

  17. Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    TAN Yanghong; HE Yigang; FANG Gefeng

    2007-01-01

    A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

  18. Robustness Results for Hierarchical Diff-EDF Scheduling upon Heterogeneous Real-Time Packet Networks

    Directory of Open Access Journals (Sweden)

    Moutaz Saleh

    2007-01-01

    Full Text Available Packet networks are currently enabling the integration of traffic with a wide range of characteristics that extend from video traffic with stringent QoS requirements to the best-effort traffic requiring no guarantees. QoS guarantees can be provided in conventional packet networks by the use of proper packet scheduling algorithms. As a computer revolution, many scheduling algorithms have been proposed to provide different schemes of QoS guarantees with Earliest Deadline First (EDF as the most popular one. With EDF scheduling, all flows receive the same miss rate regardless of their traffic characteristics and deadlines. This makes the standard EDF algorithm unsuitable for situations in which the different flows have different miss rate requirements since in order to meet all miss rate requirements it is necessary to limit admissions so as to satisfy the flow with the most stringent miss rate requirements. In this study, we propose a new priority assignment scheduling algorithm, Hierarchal Diff-EDF (Differentiate Earliest Deadline First, which can meet the real-time needs of these applications while continuing to provide best effort service to non-real time traffic. The Hierarchal Diff-EDF features a feedback control mechanism that detects overload conditions and modifies packet priority assignments accordingly.

  19. SMR-Based Adaptive Mobility Management Scheme in Hierarchical SIP Networks

    Directory of Open Access Journals (Sweden)

    KwangHee Choi

    2014-10-01

    Full Text Available In hierarchical SIP networks, paging is performed to reduce location update signaling cost for mobility management. However, the cost efficiency largely depends on each mobile node’s session-to-mobility-ratio (SMR, which is defined as a ratio of the session arrival rate to the movement rate. In this paper, we propose the adaptive mobility management scheme that can determine the policy regarding to each mobile node’s SMR. Each mobile node determines whether the paging is applied or not after comparing its SMR with the threshold. In other words, the paging is applied to a mobile node when a mobile node’s SMR is less than the threshold. Therefore, the proposed scheme provides a way to minimize signaling costs according to each mobile node’s SMR. We find out the optimal threshold through performance analysis, and show that the proposed scheme can reduce signaling cost than the existing SIP and paging schemes in hierarchical SIP networks.

  20. Fastest Distributed Consensus Averaging Problem on Perfect and Complete n-ary Tree networks

    CERN Document Server

    Jafarizadeh, Saber

    2010-01-01

    Solving fastest distributed consensus averaging problem (i.e., finding weights on the edges to minimize the second-largest eigenvalue modulus of the weight matrix) over networks with different topologies is one of the primary areas of research in the field of sensor networks and one of the well known networks in this issue is tree network. Here in this work we present analytical solution for the problem of fastest distributed consensus averaging algorithm by means of stratification and semidefinite programming, for two particular types of tree networks, namely perfect and complete n-ary tree networks. Our method in this paper is based on convexity of fastest distributed consensus averaging problem, and inductive comparing of the characteristic polynomials initiated by slackness conditions in order to find the optimal weights. Also the optimal weights for the edges of certain types of branches such as perfect and complete n-ary tree branches are determined independently of rest of the network.

  1. Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.

    Science.gov (United States)

    Tang, Binhua; Hsu, Hang-Kai; Hsu, Pei-Yin; Bonneville, Russell; Chen, Su-Shing; Huang, Tim H-M; Jin, Victor X

    2012-01-01

    Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

  2. A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

    Directory of Open Access Journals (Sweden)

    Nabil Sabor

    2017-01-01

    Full Text Available Introducing mobility to Wireless Sensor Networks (WSNs puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs. Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

  3. Communication Network Community Detection Algorithm Orienting Hierarchical Structure Analysis%面向层次结构分析的通信网络社区检测算法

    Institute of Scientific and Technical Information of China (English)

    陈鸿昶; 李印海; 刘力雄

    2011-01-01

    In order to detect communication community and analyze its hierarchical structure of a communication network, this paper presents a community detection algorithm based on reachable communication distance ordering. By building multi-resolution embedded tree of communication density, it can display the hierarchical structure and key members of a community. Via pruning the embedded tree, computation complexity can be reduced in the process of community detection and hierarchical structure analysis. Experimental results on artificially synthesized network data and real network data approve that the algorithm is effective.%针对通信网络社区发现及其层次结构分析问题,提出一种基于可达通信距离排序的通信社区检测算法,通过建立通信密度的多分辨率嵌套树,展示社区的层次关系和核心成员,并对嵌套树进行修剪,从而在实现社区发现与层次结构分析的同时降低计算复杂度.对人工合成网络和真实网络数据进行测试,结果表明该算法有效.

  4. A Time Tree Medium Access Control for Energy Efficiency and Collision Avoidance in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kilhung Lee

    2010-03-01

    Full Text Available This paper presents a medium access control and scheduling scheme for wireless sensor networks. It uses time trees for sending data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, time trees are built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possibly different activation rates. Through the simulation, the proposed scheme that uses time trees shows better characteristics toward burst traffic than the previous energy and data arrival rate scheme.

  5. A Time Tree Medium Access Control for Energy Efficiency and Collision Avoidance in Wireless Sensor Networks

    Science.gov (United States)

    Lee, Kilhung

    2010-01-01

    This paper presents a medium access control and scheduling scheme for wireless sensor networks. It uses time trees for sending data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, time trees are built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possibly different activation rates. Through the simulation, the proposed scheme that uses time trees shows better characteristics toward burst traffic than the previous energy and data arrival rate scheme. PMID:22319270

  6. A time tree medium access control for energy efficiency and collision avoidance in wireless sensor networks.

    Science.gov (United States)

    Lee, Kilhung

    2010-01-01

    This paper presents a medium access control and scheduling scheme for wireless sensor networks. It uses time trees for sending data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, time trees are built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possibly different activation rates. Through the simulation, the proposed scheme that uses time trees shows better characteristics toward burst traffic than the previous energy and data arrival rate scheme.

  7. Scalable Hierarchical Network Management System for Displaying Network Information in Three Dimensions

    Science.gov (United States)

    George, Jude (Inventor); Schlecht, Leslie (Inventor); McCabe, James D. (Inventor); LeKashman, John Jr. (Inventor)

    1998-01-01

    A network management system has SNMP agents distributed at one or more sites, an input output module at each site, and a server module located at a selected site for communicating with input output modules, each of which is configured for both SNMP and HNMP communications. The server module is configured exclusively for HNMP communications, and it communicates with each input output module according to the HNMP. Non-iconified, informationally complete views are provided of network elements to aid in network management.

  8. AN OPTIMUM VEHICULAR PATH ALGORITHM FOR TRAFFIC NETWORK BASED ON HIERARCHICAL SPATIAL REASONING

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Human beings' intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers' choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large-scale traffic networks.

  9. Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

    Directory of Open Access Journals (Sweden)

    D. Hämäläinen

    2008-03-01

    Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

  10. Efficient tree tensor network states (TTNS) for quantum chemistry: generalizations of the density matrix renormalization group algorithm.

    Science.gov (United States)

    Nakatani, Naoki; Chan, Garnet Kin-Lic

    2013-04-07

    We investigate tree tensor network states for quantum chemistry. Tree tensor network states represent one of the simplest generalizations of matrix product states and the density matrix renormalization group. While matrix product states encode a one-dimensional entanglement structure, tree tensor network states encode a tree entanglement structure, allowing for a more flexible description of general molecules. We describe an optimal tree tensor network state algorithm for quantum chemistry. We introduce the concept of half-renormalization which greatly improves the efficiency of the calculations. Using our efficient formulation we demonstrate the strengths and weaknesses of tree tensor network states versus matrix product states. We carry out benchmark calculations both on tree systems (hydrogen trees and π-conjugated dendrimers) as well as non-tree molecules (hydrogen chains, nitrogen dimer, and chromium dimer). In general, tree tensor network states require much fewer renormalized states to achieve the same accuracy as matrix product states. In non-tree molecules, whether this translates into a computational savings is system dependent, due to the higher prefactor and computational scaling associated with tree algorithms. In tree like molecules, tree network states are easily superior to matrix product states. As an illustration, our largest dendrimer calculation with tree tensor network states correlates 110 electrons in 110 active orbitals.

  11. Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

    Science.gov (United States)

    Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

    2017-09-01

    Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

  12. A Time Tree Medium Access Control for Energy Efficiency and Collision Avoidance in Wireless Sensor Networks

    OpenAIRE

    Kilhung Lee

    2010-01-01

    This paper presents a medium access control and scheduling scheme for wireless sensor networks. It uses time trees for sending data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, time trees are built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is eit...

  13. Hierarchical transport networks optimizing dynamic response of permeable energy-storage materials.

    Science.gov (United States)

    Nilson, Robert H; Griffiths, Stewart K

    2009-07-01

    Channel widths and spacing in latticelike hierarchical transport networks are optimized to achieve maximum extraction of gas or electrical charge from nanoporous energy-storage materials during charge and discharge cycles of specified duration. To address a range of physics, the effective transport diffusivity is taken to vary as a power, m , of channel width. Optimal channel widths and spacing in all levels of the hierarchy are found to increase in a power-law manner with normalized system size, facilitating the derivation of closed-form approximations for the optimal dimensions. Characteristic response times and ratios of channel width to spacing are both shown to vary by the factor 2/m between successive levels of any optimal hierarchy. This leads to fractal-like self-similar geometry, but only for m=2 . For this case of quadratic dependence of diffusivity on channel width, the introduction of transport channels permits increases in system size on the order of 10;{4} , 10;{8} , and 10;{10} , without any reduction in extraction efficiency, for hierarchies having 1, 2 and, 8 levels, respectively. However, we also find that for a given system size there is an optimum number of hierarchical levels that maximizes extraction efficiency.

  14. Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network.

    Science.gov (United States)

    Hinoshita, Wataru; Arie, Hiroaki; Tani, Jun; Okuno, Hiroshi G; Ogata, Tetsuya

    2011-05-01

    We show that a Multiple Timescale Recurrent Neural Network (MTRNN) can acquire the capabilities to recognize, generate, and correct sentences by self-organizing in a way that mirrors the hierarchical structure of sentences: characters grouped into words, and words into sentences. The model can control which sentence to generate depending on its initial states (generation phase) and the initial states can be calculated from the target sentence (recognition phase). In an experiment, we trained our model over a set of unannotated sentences from an artificial language, represented as sequences of characters. Once trained, the model could recognize and generate grammatical sentences, even if they were not learned. Moreover, we found that our model could correct a few substitution errors in a sentence, and the correction performance was improved by adding the errors to the training sentences in each training iteration with a certain probability. An analysis of the neural activations in our model revealed that the MTRNN had self-organized, reflecting the hierarchical linguistic structure by taking advantage of the differences in timescale among its neurons: in particular, neurons that change the fastest represented "characters", those that change more slowly, "words", and those that change the slowest, "sentences".

  15. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

    Science.gov (United States)

    Alshehhi, Rasha; Marpu, Prashanth Reddy

    2017-04-01

    Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

  16. Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks.

    Science.gov (United States)

    Selver, M Alper; Güzeliş, Cüneyt

    2009-01-01

    As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT and MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.

  17. Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

    Science.gov (United States)

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

  18. Design Method Based on Routing Tree for Topology Update in Ad Hoc Network

    Institute of Scientific and Technical Information of China (English)

    WEI Zheng-xi; ZHANG Hong; WANG Xiao-ling

    2006-01-01

    A design method based on the tree-model structure for topology update is presented. The routing tree of every node in network is built by defining the data structure and is used to save the topology information of neighbor nodes. The node topology update is accomplished by exchanging their routing trees. For saving the precious wireless bandwidth, the routing tree is sparsely shaped before sending by pruning the redundant routing information. Then, the node topology update is implemented by using algorithms of inserting and deleting routing sub-trees.

  19. Bayesian methods for estimating the reliability in complex hierarchical networks (interim report).

    Energy Technology Data Exchange (ETDEWEB)

    Marzouk, Youssef M.; Zurn, Rena M.; Boggs, Paul T.; Diegert, Kathleen V. (Sandia National Laboratories, Albuquerque, NM); Red-Horse, John Robert (Sandia National Laboratories, Albuquerque, NM); Pebay, Philippe Pierre

    2007-05-01

    Current work on the Integrated Stockpile Evaluation (ISE) project is evidence of Sandia's commitment to maintaining the integrity of the nuclear weapons stockpile. In this report, we undertake a key element in that process: development of an analytical framework for determining the reliability of the stockpile in a realistic environment of time-variance, inherent uncertainty, and sparse available information. This framework is probabilistic in nature and is founded on a novel combination of classical and computational Bayesian analysis, Bayesian networks, and polynomial chaos expansions. We note that, while the focus of the effort is stockpile-related, it is applicable to any reasonably-structured hierarchical system, including systems with feedback.

  20. Virtual and Dynamic Hierarchical Architecture: an overlay network topology for discovering grid services with high performance

    Institute of Scientific and Technical Information of China (English)

    黄理灿; 吴朝晖; 潘云鹤

    2004-01-01

    This paper presents an overlay network topology called Virtual and Dynamic Hierarchical Architecture (VDHA) for discovering Grid services with high performance. Service discovery based on VDHA has scalable, autonomous, efficient, reliable and quick responsive. We propose two service discovery algorithms. Full Search Query and Discovery Protocol (FSQDP) discovers the nodes that match the request message from all N nodes, which has time complexity O(logN), space complexity O(nvg) (nvg being node numbers of each virtual group), and message-cost O(N), and Domain-Specific Query and Discovery Protocol (DSQDP) searches nodes in only specific domains with time complexity O(nvg), space complexity O(nvg), and message-cost O(nvg). In this paper, we also describe VDHA, its formal definition, and Grid Group Management Protocol.

  1. AH-MAC: Adaptive Hierarchical MAC Protocol for Low-Rate Wireless Sensor Network Applications

    Directory of Open Access Journals (Sweden)

    Adnan Ismail Al-Sulaifanie

    2017-01-01

    Full Text Available This paper proposes an adaptive hierarchical MAC protocol (AH-MAC with cross-layer optimization for low-rate and large-scale wireless sensor networks. The main goal of the proposed protocol is to combine the strengths of LEACH and IEEE 802.15.4 while offsetting their weaknesses. The predetermined cluster heads are supported with an energy harvesting circuit, while the normal nodes are battery-operated. To prolong the network’s operational lifetime, the proposed protocol transfers most of the network’s activities to the cluster heads while minimizing the node’s activity. Some of the main features of this protocol include energy efficiency, self-configurability, scalability, and self-healing. The simulation results showed great improvement of the AH-MAC over LEACH protocol in terms of energy consumption and throughput. AH-MAC consumes eight times less energy while improving throughput via acknowledgment support.

  2. Electropolymerized Star-Shaped Benzotrithiophenes Yield π-Conjugated Hierarchical Networks with High Areal Capacitance

    KAUST Repository

    Ringk, Andreas

    2016-03-30

    High-surface-area π-conjugated polymeric networks have the potential to lend outstanding capacitance to supercapacitors because of the pronounced faradaic processes that take place across the dense intimate interface between active material and electrolytes. In this report, we describe how benzo[1,2-b:3,4-b’:5,6-b’’]trithiophene (BTT) and tris-EDOT-benzo[1,2-b:3,4-b’:5,6-b’’]trithiophene (TEBTT) can serve as 2D (trivalent) building blocks in the development of electropolymerized hierarchical π-conjugated frameworks with particularly high areal capacitance. In comparing electropolymerized networks of BTT, TEBTT, and their copolymers with EDOT, we show that P(TEBTT/EDOT)-based frameworks can achieve higher areal capacitance (e.g., as high as 443.8 mF cm-2 at 1 mA cm-2) than those achieved by their respective homopolymers (PTEBTT and PEDOT) in the same experimental conditions of electrodeposition (PTEBTT: 271.1 mF cm-2 (at 1 mA cm-2) and PEDOT: 12.1 mF cm-2 (at 1 mA cm-2)). For example, P(TEBTT/EDOT)-based frameworks synthesized in a 1:1 monomer-to-comonomer ratio show a ca. 35x capacitance improvement over PEDOT. The high areal capacitance measured for P(TEBTT/EDOT) copolymers can be explained by the open, highly porous hierarchical morphologies formed during the electropolymerization step. With >70% capacitance retention over 1,000 cycles (up to 89% achieved), both PTEBTT- and P(TEBTT/EDOT)-based frameworks are resilient to repeated electrochemical cycling and can be considered promising systems for high life cycle capacitive electrode applications.

  3. Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

    Institute of Scientific and Technical Information of China (English)

    Xiao-Lin Li; Jian-Nong Cao

    2008-01-01

    To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network, we present acoordinated scheduling method following the divisible load scheduling paradigm. The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions. We consider a sensor network consisting of several clusters. In a cluster, after related raw data measured by source nodes are collected at the fusion node,in-network data aggregation is further considered. The scheduling strategies consist of two phases: intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Finally, numerical examples are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.

  4. A Dynamic Key Management Scheme Based on Secret Sharing for Hierarchical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Enjian Bai

    2013-01-01

    Full Text Available Since wireless sensor networks (WSN for short are often deployed in hostile environments in many applications, security becomes one of the critical issues in WSN. Moreover, due to the limitation of the sensor nodes, traditional key management schemes are not suitable for it. Thereby,a feasible and efficient key management scheme is an important guarantee for WSN to communicate securely. For the moment, many protocols have been proposed and each has its own advantages. However, these protocols cannot provide sufficient security in many cases, such as node capture attack, which makes WSN more vulnerable than traditional wireless networks. Key protection and revocation issues must be considered with special attention in WSN. To address these two issues, we propose a dynamically clustering key management scheme based on secret sharing for WSN. The scheme combined the hierarchical structure of wireless sensor networks with dynamic key management scheme. The analysis results show that the scheme has strong security and resistance of captured attack, as well as low communicational overhead, and it well meets the requirement of scalability.

  5. Hierarchical network model for the analysis of human spatio-temporal information processing

    Science.gov (United States)

    Schill, Kerstin; Baier, Volker; Roehrbein, Florian; Brauer, Wilfried

    2001-06-01

    The perception of spatio-temporal pattern is a fundamental part of visual cognition. In order to understand more about the principles behind these biological processes, we are analyzing and modeling the presentation of spatio-temporal structures on different levels of abstraction. For the low- level processing of motion information we have argued for the existence of a spatio-temporal memory in early vision. The basic properties of this structure are reflected in a neural network model which is currently developed. Here we discuss major architectural features of this network which is base don Kohonens SOMs. In order to enable the representation, processing and prediction of spatio-temporal pattern on different levels of granularity and abstraction the SOMs are organized in a hierarchical manner. The model has the advantage of a 'self-teaching' learning algorithm and stored temporal information try local feedback in each computational layer. The constraints for the neural modeling and data set for training the neural network are obtained by psychophysical experiments where human subjects' abilities for dealing with spatio-temporal information is investigated.

  6. Hierarchical surface code for network quantum computing with modules of arbitrary size

    Science.gov (United States)

    Li, Ying; Benjamin, Simon C.

    2016-10-01

    The network paradigm for quantum computing involves interconnecting many modules to form a scalable machine. Typically it is assumed that the links between modules are prone to noise while operations within modules have a significantly higher fidelity. To optimize fault tolerance in such architectures we introduce a hierarchical generalization of the surface code: a small "patch" of the code exists within each module and constitutes a single effective qubit of the logic-level surface code. Errors primarily occur in a two-dimensional subspace, i.e., patch perimeters extruded over time, and the resulting noise threshold for intermodule links can exceed ˜10 % even in the absence of purification. Increasing the number of qubits within each module decreases the number of qubits necessary for encoding a logical qubit. But this advantage is relatively modest, and broadly speaking, a "fine-grained" network of small modules containing only about eight qubits is competitive in total qubit count versus a "course" network with modules containing many hundreds of qubits.

  7. APPLICATION OF NEURAL NETWORK WITH MULTI-HIERARCHIC STRUCTURE TO EVALUATE SUSTAINABLE DEVELOPMENT OF THE COAL MINES

    Institute of Scientific and Technical Information of China (English)

    李新春; 陶学禹

    2000-01-01

    The neural network with multi-hierarchic structure is provided in this paper to evaluate sustainable development of the coal mines based on analyzing its effect factors. The whole evaluating system is composed of 5 neural networks.The feasibility of this method has been proved by case study. This study will provide a scientfic and theoretic foundation for evaluating the sustainable development of coal mines.

  8. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

    Directory of Open Access Journals (Sweden)

    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  9. Networks and Spanning Trees: The Juxtaposition of Prüfer and Boruvka

    Science.gov (United States)

    Lodder, Jerry

    2014-01-01

    This paper outlines a method for teaching topics in undergraduate mathematics or computer science via historical curricular modules. The contents of one module, "Networks and Spanning Trees," are discussed from the original work of Arthur Cayley, Heinz Prüfer, and Otakar Boruvka that motivates the enumeration and application of trees in…

  10. The Accuracy of Tree-based Counting in Dynamic Networks

    OpenAIRE

    Krishnamurthy, Supriya; Ardelius, John; Aurell, Erik; Dam, Mads; Stadler, Rolf; Wuhib, Fetahi

    2010-01-01

    Tree-based protocols are ubiquitous in distributed systems. They are flexible, they perform generally well, and, in static conditions, their analysis is mostly simple. Under churn, however, node joins and failures can have complex global effects on the tree overlays, making analysis surprisingly subtle. To our knowledge, few prior analytic results for performance estimation of tree based protocols under churn are currently known. We study a simple Bellman-Ford-like protocol which performs net...

  11. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  12. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

    Directory of Open Access Journals (Sweden)

    Buer Jan

    2004-12-01

    Full Text Available Abstract Background Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear. Results In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators. Conclusion The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E

  13. P-Adic Analog of Navier–Stokes Equations: Dynamics of Fluid’s Flow in Percolation Networks (from Discrete Dynamics with Hierarchic Interactions to Continuous Universal Scaling Model

    Directory of Open Access Journals (Sweden)

    Klaudia Oleschko

    2017-04-01

    Full Text Available Recently p-adic (and, more generally, ultrametric spaces representing tree-like networks of percolation, and as a special case of capillary patterns in porous media, started to be used to model the propagation of fluids (e.g., oil, water, oil-in-water, and water-in-oil emulsion. The aim of this note is to derive p-adic dynamics described by fractional differential operators (Vladimirov operators starting with discrete dynamics based on hierarchically-structured interactions between the fluids’ volumes concentrated at different levels of the percolation tree and coming to the multiscale universal topology of the percolating nets. Similar systems of discrete hierarchic equations were widely applied to modeling of turbulence. However, in the present work this similarity is only formal since, in our model, the trees are real physical patterns with a tree-like topology of capillaries (or fractures in random porous media (not cascade trees, as in the case of turbulence, which we will be discussed elsewhere for the spinner flowmeter commonly used in the petroleum industry. By going to the “continuous limit” (with respect to the p-adic topology we represent the dynamics on the tree-like configuration space as an evolutionary nonlinear p-adic fractional (pseudo- differential equation, the tree-like analog of the Navier–Stokes equation. We hope that our work helps to come closer to a nonlinear equation solution, taking into account the scaling, hierarchies, and formal derivations, imprinted from the similar properties of the real physical world. Once this coupling is resolved, the more problematic question of information scaling in industrial applications will be achieved.

  14. A GENETIC ALGORITHM FOR CONSTRUCTING BROADCAST TREES WITH COST AND DELAY CONSTRAINTS IN COMPUTER NETWORKS

    Directory of Open Access Journals (Sweden)

    Ahmed Y. Hamed

    2015-01-01

    Full Text Available We refer to the problem of constructing broadcast trees with cost and delay constraints in the networks as a delay-constrained minimum spanning tree problem in directed networks. Hence it is necessary determining a spanning tree of minimal cost to connect the source node to all nodes subject to delay constraints on broadcast routing. In this paper, we proposed a genetic algorithm for solving broadcast routing by finding the low-cost broadcast tree with minimum cost and delay constraints. In this research we present a genetic algorithm to find the broadcast routing tree of a given network in terms of its links. The algorithm uses the connection matrix of the given network to find the spanning trees and considers the weights of the links to obtain the minimum spanning tree. Our proposed algorithm is able to find a better solution, fast convergence speed and high reliability. The scalability and the performance of the algorithm with increasing number of network nodes are also encouraging.

  15. The transmission process: A combinatorial stochastic process for the evolution of transmission trees over networks.

    Science.gov (United States)

    Sainudiin, Raazesh; Welch, David

    2016-12-07

    We derive a combinatorial stochastic process for the evolution of the transmission tree over the infected vertices of a host contact network in a susceptible-infected (SI) model of an epidemic. Models of transmission trees are crucial to understanding the evolution of pathogen populations. We provide an explicit description of the transmission process on the product state space of (rooted planar ranked labelled) binary transmission trees and labelled host contact networks with SI-tags as a discrete-state continuous-time Markov chain. We give the exact probability of any transmission tree when the host contact network is a complete, star or path network - three illustrative examples. We then develop a biparametric Beta-splitting model that directly generates transmission trees with exact probabilities as a function of the model parameters, but without explicitly modelling the underlying contact network, and show that for specific values of the parameters we can recover the exact probabilities for our three example networks through the Markov chain construction that explicitly models the underlying contact network. We use the maximum likelihood estimator (MLE) to consistently infer the two parameters driving the transmission process based on observations of the transmission trees and use the exact MLE to characterize equivalence classes over the space of contact networks with a single initial infection. An exploratory simulation study of the MLEs from transmission trees sampled from three other deterministic and four random families of classical contact networks is conducted to shed light on the relation between the MLEs of these families with some implications for statistical inference along with pointers to further extensions of our models. The insights developed here are also applicable to the simplest models of "meme" evolution in online social media networks through transmission events that can be distilled from observable actions such as "likes", "mentions

  16. The Tree Core Problem with Some Constraint in a Tree Network%树状网络上多约束的tree core问题

    Institute of Scientific and Technical Information of China (English)

    杨建芳; 刘建贞

    2012-01-01

    According to background of actual application,because of the processing ability of each equipment is limited generally in the computer and correspondence networks,and the cost between each equipment is controlled.The paper puts forward the tree core problem with degree and radius constraint in a tree network,denoted as problem.The first is to structure-maximal subtree of,then the second is to use the method of dynamic programming to solve problem.It takes time algorithm for this problem.%考虑到在实际应用中,由于计算机和通信网络中一般每个设备的处理能力是有限的,以及控制各设备放置点之间的营运成本,该文在tree core问题的基础上,提出了同时带有度和半径约束的tree core问题,记为(q,l)-DTC问题(Degree constrained Tree Core)。该文先构造出极大子树集,然后在极大子树中利用动态规划的方法,求解(q,l)-DTC问题,可在O(n2)时间内求得该问题的最优解。

  17. An Efficient Admission Control Algorithm for Load Balancing In Hierarchical Mobile IPv6 Networks

    CERN Document Server

    Harini, Prof P

    2009-01-01

    In hierarchical Mobile IPv6 networks, Mobility Anchor Point (MAP) may become a single point of bottleneck as it handles more and more mobile nodes (MNs). A number of schemes have been proposed to achieve load balancing among different MAPs. However, signaling reduction is still imperfect because these schemes also avoid the effect of the number of CNs. Also only the balancing of MN is performed, but not the balancing of the actual traffic load, since CN of each MN may be different. This paper proposes an efficient admission control algorithm along with a replacement mechanism for HMIPv6 networks. The admission control algorithm is based on the number of serving CNs and achieves actual load balancing among MAPs. Moreover, a replacement mechanism is introduced to decrease the new MN blocking probability and the handoff MN dropping probability. By simulation results, we show that, the handoff delay and packet loss are reduced in our scheme, when compared with the standard HMIPv6 based handoff.

  18. A Hybrid System of Hierarchical Planning of Behaviour Selection Networks for Mobile Robot Control

    Directory of Open Access Journals (Sweden)

    Young-Seol Lee

    2014-04-01

    Full Text Available An office delivery robot receives a large amount of sensory data and there is uncertainty in its action outcomes. The robot should not only accomplish its goals using environmental information, but also consider various exceptions simultaneously. In this paper, we propose a hybrid system using hierarchical planning of modular behaviour selection networks to generate autonomous behaviour in the office delivery robot. Behaviour selection networks, one of the well-known behaviour-based methods suitable for goal-oriented tasks, are made up of several smaller behaviour modules. Planning is attached to the construct and adjust sequences of the modules by considering the sub-goals, the priority in each task and the user feedback. This helps the robot to quickly react in dynamic situations as well as achieve global goals efficiently. The proposed system is verified with both the Webot simulator and a Khepera II robot that runs in a real office environment carrying out delivery tasks. Experimental results have shown that a robot can achieve goals and generate module sequences successfully even in unpredictable situations. Additionally, the proposed planning method reduced the elapsed time during tasks by 17.5% since it adjusts the behaviour module sequences more effectively.

  19. Hierarchical Agglomerative Clustering Schemes for Energy-Efficiency in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Taleb Tariq

    2017-06-01

    Full Text Available Extending the lifetime of wireless sensor networks (WSNs while delivering the expected level of service remains a hot research topic. Clustering has been identified in the literature as one of the primary means to save communication energy. In this paper, we argue that hierarchical agglomerative clustering (HAC provides a suitable foundation for designing highly energy efficient communication protocols for WSNs. To this end, we study a new mechanism for selecting cluster heads (CHs based both on the physical location of the sensors and their residual energy. Furthermore, we study different patterns of communications between the CHs and the base station depending on the possible transmission ranges and the ability of the sensors to act as traffic relays. Simulation results show that our proposed clustering and communication schemes outperform well-knows existing approaches by comfortable margins. In particular, networks lifetime is increased by more than 60% compared to LEACH and HEED, and by more than 30% compared to K-means clustering.

  20. A Tree-Based Data Collecting Network Structure for Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Chi-Tsun Cheng; Chi K. Tse; Francis C. M. Lau

    2008-01-01

    In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly correlated, and hence energy saving using in-network data fusion becomes possible. A traditional data fusion scheme starts with dividing the network into clusters, followed by electing a sensor node as cluster head in each cluster. A cluster head is responsible for collecting data from all its cluster members, performing data fusion on these data and transmitting the fused data to the base station. Assuming that a sensor node is only capable of handling a single node-to-node transmission at a time and each transmission takes T time-slots, a cluster head with n cluster members will take at least Nt time-slots to collect data from all its cluster members. In this paper, a tree-based network structure and its formation algorithms are proposed. Simulation results show that the proposed network structure can greatly reduce the delay in data collection.

  1. A Tree-Based Data Collecting Network Structure for Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Chi-Tsun Cheng; Chi K. Tse; Francis C. M. Lau

    2008-01-01

    In a sensor network with a large numberof densely populated sensor nodes, a single target ofinterest may be detected by multiple sensor nodessimultaneously. Data collected from the sensor nodes areusually highly correlated, and hence energy saving usingin-network data fusion becomes possible. A traditionaldata fusion scheme starts with dividing the network intoclusters, followed by electing a sensor node as clusterhead in each cluster. A cluster head is responsible forcollecting data from all its cluster members, performingdata fusion on these data and transmitting the fused datato the base station. Assuming that a sensor node is onlycapable of handling a single node-to-node transmissionat a time and each transmission takes T time-slots, acluster head with n cluster members will take at least nTtime-slots to collect data from all its cluster members. Inthis paper, a tree-based network structure and itsformation algorithms are proposed. Simulation resultsshow that the proposed network structure can greatlyreduce the delay in data collection.

  2. A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks

    Science.gov (United States)

    González-Parada, Eva; Cano-García, Jose; Aguilera, Francisco; Sandoval, Francisco; Urdiales, Cristina

    2017-01-01

    Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage. PMID:28075364

  3. Self-Construction of Aggregation Tree for Gathering Mobile Data in Wireless Sensor Network

    Science.gov (United States)

    Lee, Sangbin; Kim, Songmin; Kim, Sungjun; Ko, Doohyun; Kim, Bumjin; An, Sunshin

    A network of sensors can be used to obtain state based data from the area in which they are deployed. To reduce costs, the data sent via intermediate sensors to a sink are often aggregated. In this letter, we introduce Self-Construction of Aggregation Tree (SCAT) scheme which uses a novel data aggregation scheme utilizing the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. The static nodes can construct a near- optimal aggregation tree by themselves, using the knowledge of the mobile node, which is a process similar to forming the centralized aggregation tree.

  4. Hierarchical mechanisms of spatially contagious seed dispersal in complex seed-disperser networks.

    Science.gov (United States)

    Fedriani, José M; Wiegand, Thorsten

    2014-02-01

    Intra- and interspecific spatially contagious seed dispersal has far-reaching implications for plant recruitment, distribution, and community assemblage. However, logistical and analytical limitations have curtailed our understanding concerning the mechanisms and resulting spatial patterns of contagious seed dispersal in most systems and, especially, in complex seed-disperser networks. We investigated mechanisms of seed aggregation using techniques of spatial point pattern analysis and extensive data sets on mutispecific endozoochorous seed rain generated by five frugivorous mammals in three Mediterranean shrublands over two seasons. Our novel analytical approach revealed three hierarchical and complementary mechanisms of seed aggregation acting at different levels (fecal samples, seeds, pairs of seed species) and spatial scales. First, the three local guilds of frugivores tended to deliver their feces highly aggregated at small and intermediate spatial scales, and the overall pattern of fecal delivery could be described well by a nested double-cluster Thomas process. Second, once the strong observed fecal aggregation was accounted for, the distribution of mammal feces containing seeds was clustered within the pattern of all feces (i.e., with and without seeds), and the density of fecal samples containing seeds was higher than expected around other feces containing seeds in two out of the three studied seed-disperser networks. Finally, at a finer level, mark correlation analyses revealed that for some plant species pairs, the number of dispersed seeds was positively associated either at small or large spatial scales. Despite the relatively invariant patterning of nested double-clustering, some attributes of endozoochorous seed rain (e.g., intensity, scales of aggregation) were variable among study sites due to changes in the ecological context in which seeds and their dispersers interact. Our investigation disentangles for the first time the hierarchy of synergic

  5. Modeling transcriptional networks regulating secondary growth and wood formation in forest trees.

    Science.gov (United States)

    Liu, Lijun; Filkov, Vladimir; Groover, Andrew

    2014-06-01

    The complex interactions among the genes that underlie a biological process can be modeled and presented as a transcriptional network, in which genes (nodes) and their interactions (edges) are shown in a graphical form similar to a wiring diagram. A large number of genes have been identified that are expressed during the radial woody growth of tree stems (secondary growth), but a comprehensive understanding of how these genes interact to influence woody growth is currently lacking. Modeling transcriptional networks has recently been made tractable by next-generation sequencing-based technologies that can comprehensively catalog gene expression and transcription factor-binding genome-wide, but has not yet been extensively applied to undomesticated tree species or woody growth. Here we discuss basic features of transcriptional networks, approaches for modeling biological networks, and examples of biological network models developed for forest trees to date. We discuss how transcriptional network research is being developed in the model forest tree genus, Populus, and how this research area can be further developed and applied. Transcriptional network models for forest tree secondary growth and wood formation could ultimately provide new predictive models to accelerate hypothesis-driven research and develop new breeding applications.

  6. Hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol

    Institute of Scientific and Technical Information of China (English)

    彭可; 陈际达; 陈岚

    2002-01-01

    Aiming at the weaknesses of LON bus, combining the coexistence of fieldbus and DCS (Distribu-ted Control Systems) in control networks, the authors introduce a hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol. This design adopts LON as the trunk, master-slave RS-422/485 control networks are connected to LON as special subnets by dedicated gateways. It is an implementation method for isomerous control network integration. Data management is ranked according to real-time requirements for different network data. The core components, such as control network nodes, router and gateway, are detailed in the paper. The design utilizes both communication advantage of LonWorks technology and the more powerful control ability of universal MCUs or PLCs, thus it greatly increases system response speed and performance-cost ratio.

  7. Carbon nanotube-based polymer nanocomposites: Fractal network to hierarchical morphology

    Science.gov (United States)

    Chatterjee, Tirtha

    The dispersion of anisotropic nanoparticles such as single-walled carbon nanotubes in polymeric matrices promises the ability to develop advanced materials with controlled and tailored combinations of properties. However, dispersion of such nanotubes in a polymer matrix is an extremely challenging task due to strong attractive interactions between the nanotubes. The successful dispersion of single-walled carbon nanotubes in poly(ethylene oxide) using an anionic surfactant (lithium dodecyl sulfate) as compatibilizer is reported here. The geometrical percolation threshold (pc, in vol %) of nanotubes, as revealed by melt-state rheological measurements, is found to be at ˜ 0.09 vol % loading, which corresponds to an effective tube anisotropy of ˜ 650. The system shows an even earlier development of the electrical percolation at 0.03 vol % SWNT loading as obtained by electrical conductivity measurements. In their quiescent state, the nanotubes show hierarchical fractal network (mass fractal dimension ˜ 2.3 +/- 0.2) made of aggregated flocs. Inside the floc, individual or small bundles of nanotubes overlap each other to form a dense mesh. The interfloc interactions provides the stress bearing capacity for these nano composites and are responsible for the unique modulus scaling of these systems (˜(p-pc)delta, 3.0 ≤ delta ≤ 4.5). The interaction is inversely related to the particle dispersion state, which influences the absolute values of the viscoelastic parameters. As a direct consequence of the self-similar fractal network, the linear flow properties display 'time-temperature-composition' superposition. This superposability can be extended for non-linear deformations when the non-linear properties are scaled by the local strain experienced by the elements of the network. More interestingly, under steady shear, these nanocomposites show network-independent behavior. The absolute stress value is a function of the nanotube loading, but the characteristic time

  8. Trees

    Science.gov (United States)

    Al-Khaja, Nawal

    2007-01-01

    This is a thematic lesson plan for young learners about palm trees and the importance of taking care of them. The two part lesson teaches listening, reading and speaking skills. The lesson includes parts of a tree; the modal auxiliary, can; dialogues and a role play activity.

  9. SO2 Emissions in China – Their Network and Hierarchical Structures

    Science.gov (United States)

    Yan, Shaomin; Wu, Guang

    2017-01-01

    SO2 emissions lead to various harmful effects on environment and human health. The SO2 emission in China has significant contribution to the global SO2 emission, so it is necessary to employ various methods to study SO2 emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO2 emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO2 emissions from power generation sector were highly individualized as small-sized clusters, the SO2 emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO2 emissions from residential sector was not impacted by time, and the SO2 emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO2 emissions from all four sectors and is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measure to combat SO2 emissions. PMID:28387301

  10. A Hierarchical of Security Situation Element Acquisition Mechanism in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Li Fangwei

    2015-08-01

    Full Text Available In wireless sensor network, the processing ability of the sensor nodes is poor. And the security situational element acquisition is also a serious problem. Thus, this paper proposes a hierarchical framework of security situational elements acquisition mechanism. In this framework, support vector machine hyper sphere multi class algorithm is introduced as basic classifier. The method of attribute reduction uses non negative matrix factorization algorithm. The fuzzy classification algorithm used to initialize non negative matrix factorization, in order to avoid the local optimal which is caused by non negative matrix factorization random initialization. In the sink node classification rules and attribute reduction rules are formed by learning. The classification analyses respectively focus on the cluster head and sink node, which can reduce the requirement of the sensor node properties. Attribute reduction before the data transmission, which reduces communication consumption data transmission, improves the performance of classifiers. By simulation analysis, the scheme has preferably accuracy in the situation elements acquisiton, and smaller communication overhead in the process of information transmission.

  11. Interference mitigation for broadcast in hierarchical cell structure networks: Transmission strategy and area spectral efficiency

    KAUST Repository

    Yang, Yuli

    2014-10-01

    In this paper, a hierarchical cell structure (HCS) is considered, where an access point (AP) broadcasts to local nodes (LNs) over orthogonal frequency subbands within a local cell located in a macrocell. Since the local cell shares the spectrum licensed to the macrocell, a given LN is interfered with by the macrocell user (MU)\\'s transmissions over the same subband. To improve the performance of the AP\\'s broadcast service, a novel transmission strategy is proposed to mitigate the interference from the MU to the LN while achieving diversity gain. For the purpose of performance evaluation, the ergodic capacity of the proposed scheme is quantified, and the corresponding closed-form expression is obtained. By comparing with the traditional transmission scheme, which suffers from MU\\'s interference, illustrative numerical results substantiate that the proposed scheme achieves better performance than the traditional scheme as the MU-LN mean channel power gain is larger than half of the AP-LN mean channel power gain. Subsequently, we develop an optimized network design by maximizing the area spectral efficiency (ASE) of the AP\\'s broadcast in the local cell.

  12. A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

    Directory of Open Access Journals (Sweden)

    H.H. Soliman

    2012-11-01

    Full Text Available An explosive growth in the field of wireless sensor networks (WSNs has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

  13. A Secure Hierarchical Identify Authentication Scheme Combining Trust Mechanism in Mobile IPv6 Networks

    Directory of Open Access Journals (Sweden)

    Zhi Zhang

    2009-07-01

    Full Text Available During the last few years, it has become more and more conpeling in mobile applications, mobile IPv6 technology is convenient, but also produces a series of security compromise. Identify authentication is an important part of the network security. In this paper, we proposed a secure identify authentication scheme combining reputation mechanism, which considers inters domain trust relationship between mobile node home domain and the access domain in the pre-handoff procedure and realizes effective mutual authentication between mobile node(MN and the access domain. A dynamic reputation maintenance mechanism for inter domain relationship is also designed. Based SMC signature, a hierarchical signature and verification scheme is designed in one round mutual authentication. Theoretical analysis and numerical results show that proposed scheme is more effective in reducing total authentication and handoff delay and the signaling overhead than relative schemes. Security analysis shows, basing on the security of SMC-IBS, the proposed scheme is sufficient for private key privacy, signature unforgeability. Moreover, our scheme first provide public key revocation and key escrow problem in mobile IPv6 networks’ access authentication.

  14. Learning invariant object recognition from temporal correlation in a hierarchical network.

    Science.gov (United States)

    Lessmann, Markus; Würtz, Rolf P

    2014-06-01

    Invariant object recognition, which means the recognition of object categories independent of conditions like viewing angle, scale and illumination, is a task of great interest that humans can fulfill much better than artificial systems. During the last years several basic principles were derived from neurophysiological observations and careful consideration: (1) Developing invariance to possible transformations of the object by learning temporal sequences of visual features that occur during the respective alterations. (2) Learning in a hierarchical structure, so basic level (visual) knowledge can be reused for different kinds of objects. (3) Using feedback to compare predicted input with the current one for choosing an interpretation in the case of ambiguous signals. In this paper we propose a network which implements all of these concepts in a computationally efficient manner which gives very good results on standard object datasets. By dynamically switching off weakly active neurons and pruning weights computation is sped up and thus handling of large databases with several thousands of images and a number of categories in a similar order becomes possible. The involved parameters allow flexible adaptation to the information content of training data and allow tuning to different databases relatively easily. Precondition for successful learning is that training images are presented in an order assuring that images of the same object under similar viewing conditions follow each other. Through an implementation with sparse data structures the system has moderate memory demands and still yields very good recognition rates.

  15. Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery

    Directory of Open Access Journals (Sweden)

    Hayder Dibs

    2017-06-01

    Full Text Available There has been growing interest in Malaysia to increase the productivity of latex. This made accurate knowledge of rubber tree growth and age distribution a helpful decision making tool for the government, rubber plantation managers, and harvesters. Gathering this information using conventional methods is difficult, time consuming, and limited in spatial coverage. This paper presents hierarchical classification approach to obtain accurate map of rubber tree growth age distribution using SPOT-5 satellite imagery. The objective of the study is to evaluate the performance of pixel-based and object-oriented classifiers for rubber growth classification. At the first level, the general land cover was classified into eight land cover classes (soil, water body, rubber, mature oil palm, young oil palm, forest, urban area, and other vegetation using Mahalanobis distance (MD, k-nearest neighbor (k-NN, and Support Vector Machine (SVM classifiers. Thereafter, the best classification map, k-NN output, was used to select only pixels that belong to the rubber class from the SPOT-5 image. The extracted pixels served as input into the next classification hierarchy where four classifiers, MD, k-NN, SVM, and decision tree (DT, were implemented to map rubber trees into three intra-classes (mature, middle-aged, and young rubbers. The result produced overall accuracy of 97.48%, 96.90%, 96.25%, and 80.80% for k-NN, SVM, MD, and DT respectively. The result indicates that object-oriented classifiers are better than pixel-based methods mapping rubber tree growth.

  16. CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

    Science.gov (United States)

    Franke, R.

    2016-11-01

    In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

  17. The comparison of tree-sibling time consistent phylogenetic networks is graph isomorphism-complete.

    Science.gov (United States)

    Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel

    2014-01-01

    Several polynomial time computable metrics on the class of semibinary tree-sibling time consistent phylogenetic networks are available in the literature; in particular, the problem of deciding if two networks of this kind are isomorphic is in P. In this paper, we show that if we remove the semibinarity condition, then the problem becomes much harder. More precisely, we prove that the isomorphism problem for generic tree-sibling time consistent phylogenetic networks is polynomially equivalent to the graph isomorphism problem. Since the latter is believed not to belong to P, the chances are that it is impossible to define a metric on the class of all tree-sibling time consistent phylogenetic networks that can be computed in polynomial time.

  18. 3D Graphene-Foam-Reduced-Graphene-Oxide Hybrid Nested Hierarchical Networks for High-Performance Li-S Batteries.

    Science.gov (United States)

    Hu, Guangjian; Xu, Chuan; Sun, Zhenhua; Wang, Shaogang; Cheng, Hui-Ming; Li, Feng; Ren, Wencai

    2016-02-24

    A 3D graphene-foam-reduced-graphene-oxide hybrid nested hierarchical network is synthesized to achieve high sulfur loading and content simultaneously, which solves the "double low" issues of Li-S batteries. The obtained Li-S cathodes show a high areal capacity two times larger than that of commercial lithium-ion batteries, and a good cycling performance comparable to those at low sulfur loading.

  19. Node Redeployment Algorithm Based on Stratified Connected Tree for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jun Liu

    2016-12-01

    Full Text Available During the underwater sensor networks (UWSNs operation, node drift with water environment causes network topology changes. Periodic node location examination and adjustment are needed to maintain good network monitoring quality as long as possible. In this paper, a node redeployment algorithm based on stratified connected tree for UWSNs is proposed. At every network adjustment moment, self-examination and adjustment on node locations are performed firstly. If a node is outside the monitored space, it returns to the last location recorded in its memory along straight line. Later, the network topology is stratified into a connected tree that takes the sink node as the root node by broadcasting ready information level by level, which can improve the network connectivity rate. Finally, with synthetically considering network coverage and connectivity rates, and node movement distance, the sink node performs centralized optimization on locations of leaf nodes in the stratified connected tree. Simulation results show that the proposed redeployment algorithm can not only keep the number of nodes in the monitored space as much as possible and maintain good network coverage and connectivity rates during network operation, but also reduce node movement distance during node redeployment and prolong the network lifetime.

  20. RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations.

    Science.gov (United States)

    Castro, Mauro A A; Wang, Xin; Fletcher, Michael N C; Meyer, Kerstin B; Markowetz, Florian

    2012-04-24

    Visualization and analysis of molecular networks are both central to systems biology. However, there still exists a large technological gap between them, especially when assessing multiple network levels or hierarchies. Here we present RedeR, an R/Bioconductor package combined with a Java core engine for representing modular networks. The functionality of RedeR is demonstrated in two different scenarios: hierarchical and modular organization in gene co-expression networks and nested structures in time-course gene expression subnetworks. Our results demonstrate RedeR as a new framework to deal with the multiple network levels that are inherent to complex biological systems. RedeR is available from http://bioconductor.org/packages/release/bioc/html/RedeR.html.

  1. Combined multi-nozzle deposition and freeze casting process to superimpose two porous networks for hierarchical three-dimensional microenvironment.

    Science.gov (United States)

    Snyder, Jessica E; Hunger, Philipp M; Wang, Chengyang; Hamid, Qudus; Wegst, Ulrike G K; Sun, Wei

    2014-03-01

    An engineered three-dimensional scaffold with hierarchical porosity and multiple niche microenvironments is produced using a combined multi-nozzle deposition-freeze casting technique. In this paper we present a process to fabricate a scaffold with improved interconnectivity and hierarchical porosity. The scaffold is produced using a two-stage manufacturing process which superimposes a printed porous alginate (Alg) network and a directionally frozen ceramic-polymer matrix. The combination of two processes, multi-nozzle deposition and freeze casting, provides engineering control of the microenvironment of the scaffolds over several length scales; including the addition of lateral porosity and the ratio of polymer to ceramic microstructures. The printed polymer scaffold is submerged in a ceramic-polymer slurry and subsequently, both structures are directionally frozen (freeze cast), superimposing and patterning both microenvironments into a single hierarchical architecture. An optional additional sintering step removes the organic material and densifies the ceramic phase to produce a well-defined network of open pores and a homogenous cell wall material composition. The techniques presented in this contribution address processing challenges, such as structure definition, reproducibility and fine adjustments of unique length scales, which one typically encounters when fabricating topological channels between longitudinal and transverse porous networks.

  2. Rooted-tree network for optimal non-local gate implementation

    Science.gov (United States)

    Vyas, Nilesh; Saha, Debashis; Panigrahi, Prasanta K.

    2016-09-01

    A general quantum network for implementing non-local control-unitary gates, between remote parties at minimal entanglement cost, is shown to be a rooted-tree structure. Starting from a five-party scenario, we demonstrate the local implementation of simultaneous class of control-unitary(Hermitian) and multiparty control-unitary gates in an arbitrary n-party network. Previously, established networks are turned out to be special cases of this general construct.

  3. Improved Multispanning Tree Routing Using Efficient and Reliable Routing Algorithm for Irregular Networks

    Directory of Open Access Journals (Sweden)

    L. Keerthana

    2012-06-01

    Full Text Available A Strategy of Multispanning Tree Zone Ordered Label Based routing is improved with Efficient and Reliable (EAR routing for irregular networks is presented and analyzed in this work. Most existing deadlock free routing methods for irregular topologies impose several limitations on node and channel labeling in an irregular network is based on a pre-defined spanning tree.It is not possible to form a deadlock free zone of three or four channel labels for two spanning tree. So this existing Multispanning Tree Zone Ordered Label Based routing is modified with Efficient and Reliable (EAR routing. EAR is based on four parameters length of the path,distance traversed,transmission of link and energy levels to dynamically determine and maintain the best routes.. The simulation results have shown highest packet delivery ratio, minimum latency, and energy consumption.

  4. Simulation of the Tree Algorthm in ATM Networks

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper proposes an algorithm for multicast routing in ATM networks. We define the network cost of a routing as the combination of the cost. If establishing connections, the cost of the overall bandwidth and overall switchings, our routing algorithm works on the original topology of ATM networks with physical switches and physical links, and different switching functionalities of Virtual Path(VP) switches and Virtual Channel(VC) switches in the networks. It generates an optimal multicast routing with the minimal overall network cost. Simulations with MATLAB have been made to compare the quality of the routing generated by our algorithm with those of other two major algorithms.

  5. An empirical assessment of tree branching networks and implications for plant allometric scaling models.

    Science.gov (United States)

    Bentley, Lisa Patrick; Stegen, James C; Savage, Van M; Smith, Duncan D; von Allmen, Erica I; Sperry, John S; Reich, Peter B; Enquist, Brian J

    2013-08-01

    Several theories predict whole-tree function on the basis of allometric scaling relationships assumed to emerge from traits of branching networks. To test this key assumption, and more generally, to explore patterns of external architecture within and across trees, we measure branch traits (radii/lengths) and calculate scaling exponents from five functionally divergent species. Consistent with leading theories, including metabolic scaling theory, branching is area preserving and statistically self-similar within trees. However, differences among scaling exponents calculated at node- and whole-tree levels challenge the assumption of an optimised, symmetrically branching tree. Furthermore, scaling exponents estimated for branch length change across branching orders, and exponents for scaling metabolic rate with plant size (or number of terminal tips) significantly differ from theoretical predictions. These findings, along with variability in the scaling of branch radii being less than for branch lengths, suggest extending current scaling theories to include asymmetrical branching and differential selective pressures in plant architectures.

  6. ORPOM model for optimum distribution of tree ring sampling based on the climate observation network

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Tree ring dating plays an important role in obtaining past climate information.The fundamental study of obtaining tree ring samples in typical climate regions is particularly essential.The optimum distribution of tree ring sampling sites based on climate information from the Climate Observation Network(ORPOM model) is presented in this article.In this setup,the tree rings in a typical region are used for surface representation,by applying excellent correlation with the climate information as the main principle.Taking the Horqin Sandy Land in the cold and arid region of China as an example,the optimum distribution range of the tree ring sampling sites was obtained through the application of the ORPOM model,which is considered a reasonably practical scheme.

  7. The probability of a gene tree topology within a phylogenetic network with applications to hybridization detection.

    Directory of Open Access Journals (Sweden)

    Yun Yu

    Full Text Available Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa.

  8. Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

    Science.gov (United States)

    Mengshoel, Ole J.

    2010-01-01

    One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

  9. Contributions of a global network of tree diversity experiments to sustainable forest plantations

    OpenAIRE

    Verheyen, Kris; Vanhellemont, Margot; Auge, Harald; Baeten, Lander; Baraloto, Christopher; Barsoum, Nadia; Bilodeau-Gauthier, Simon; Bruelheide, Helge; Castagneyrol, Bastien; Godbold, Douglas; Haase, Josephine; Hector, Andy; Jactel, Hervé; Koricheva, Julia; LOREAU, Michel

    2016-01-01

    The area of forest plantations is increasing worldwide helping to meet timber demand and protect natural forests. However, with global change, monospecific plantations are increasingly vulnerable to abiotic and biotic disturbances. As an adaption measure we need to move to plantations that are more diverse in genotypes, species, and structure, with a design underpinned by science. TreeDivNet, a global network of tree diversity experiments, responds to this need by assessing the advantages and...

  10. Minimum-Cost Node-Disjoint Steiner Trees in Series-Parallel Networks

    Directory of Open Access Journals (Sweden)

    Sunil Chopra

    1996-01-01

    , is a Steiner tree spanning Ni for i = 1, ..., r and VNi ∩ VNj = for i ≠ j. The edge-disjoint version of this problem is known to be NP-hard for t. series-parallel graphs (see Rlchey and Parker [5]. In this paper we give a O(n5 algorithm for finding a minimum-cost node-disjoint family of Steiner trees in series-parallel networks. Our algorithm can be extended to k-trees and is polynomial for fixed k.

  11. Parallel Processing for Large-scale Fault Tree in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xinyan Wang

    2013-05-01

    Full Text Available Wireless sensor networks (WSN covers many kinds of technologies, such as technology of sensor, embedded system, wireless communication, etc. WSN is different from the traditional networks in size, communication distance and energy-constrained so as to develop new topology, protocol, quality of service (QoS, and so on. In order to solve the problem of self-organizing in the topology, this paper proposes a novel strategy which is based on communication delay between sensors. Firstly, the gateway selects some boundary nodes to connect. Secondly, the boundary nodes choose inner nodes. The rest may be deduced by analogy. Finally, a net-tree topology with multi-path routing is developed. The analyses of the topology show that net-tree has strong ability in self-organizing and extensible. However, the scale of system is usually very large and complexity so that it is hard to detect the failure nodes when the nodes fail. To solve the greater challenge, the paper proposes to adopt fault tree analysis. Fault tree is a commonly used method to analyze the reliability of a network or system. Based on the fault tree analysis, a parallel computing algorithm is represented to these faults in the net-tree. Firstly, two models for parallel processing are came up and we focus on the parallel processing algorithm based on the cut sets. Then, the speedup ratio is studied. Compare with the serial algorithm, the results of the experiment shows that the efficiency has been greatly improved.

  12. Fast algorithm for the reconciliation of gene trees and LGT networks.

    Science.gov (United States)

    Scornavacca, Celine; Mayol, Joan Carles Pons; Cardona, Gabriel

    2017-04-07

    In phylogenomics, reconciliations aim at explaining the discrepancies between the evolutionary histories of genes and species. Several reconciliation models are available when the evolution of the species of interest is modelled via phylogenetic trees; the most commonly used are the DL model, accounting for duplications and losses in gene evolution and yielding polynomially-solvable problems, and the DTL model, which also accounts for gene transfers and implies NP-hard problems. However, when dealing with non-tree-like evolutionary events such as hybridisations, phylogenetic networks - and not phylogenetic trees - should be used to model species evolution. Reconciliation models involving phylogenetic networks are still at their early days. In this paper, we propose a new reconciliation model in which the evolution of species is modelled by a special kind of phylogenetic networks - the LGT networks. Our model considers duplications, losses and transfers of genes, but restricts transfers to happen through some specific arcs of the network, called secondary arcs. Moreover, we provide a polynomial algorithm to compute the most parsimonious reconciliation between a gene tree and an LGT network under this model. Our method, when combined with quartet decomposition methods to detect putative "highways" of transfers, permits to refine their analyses by allowing to examine the two possible directions of a highway and even consider combinations of highways.

  13. Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways

    Science.gov (United States)

    Zhang, Kui; Busov, Victor; Wei, Hairong

    2017-01-01

    Background Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. Results A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. Conclusions BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental

  14. Contributions of a global network of tree diversity experiments to sustainable forest plantations.

    Science.gov (United States)

    Verheyen, Kris; Vanhellemont, Margot; Auge, Harald; Baeten, Lander; Baraloto, Christopher; Barsoum, Nadia; Bilodeau-Gauthier, Simon; Bruelheide, Helge; Castagneyrol, Bastien; Godbold, Douglas; Haase, Josephine; Hector, Andy; Jactel, Hervé; Koricheva, Julia; Loreau, Michel; Mereu, Simone; Messier, Christian; Muys, Bart; Nolet, Philippe; Paquette, Alain; Parker, John; Perring, Mike; Ponette, Quentin; Potvin, Catherine; Reich, Peter; Smith, Andy; Weih, Martin; Scherer-Lorenzen, Michael

    2016-02-01

    The area of forest plantations is increasing worldwide helping to meet timber demand and protect natural forests. However, with global change, monospecific plantations are increasingly vulnerable to abiotic and biotic disturbances. As an adaption measure we need to move to plantations that are more diverse in genotypes, species, and structure, with a design underpinned by science. TreeDivNet, a global network of tree diversity experiments, responds to this need by assessing the advantages and disadvantages of mixed species plantations. The network currently consists of 18 experiments, distributed over 36 sites and five ecoregions. With plantations 1-15 years old, TreeDivNet can already provide relevant data for forest policy and management. In this paper, we highlight some early results on the carbon sequestration and pest resistance potential of more diverse plantations. Finally, suggestions are made for new, innovative experiments in understudied regions to complement the existing network.

  15. Fluctuation relations between hierarchical kinetically equivalent networks with Arrhenius-type transitions and their roles in systems and structural biology

    Science.gov (United States)

    Deng, De-Ming; Lu, Yi-Ta; Chang, Cheng-Hung

    2017-06-01

    The legality of using simple kinetic schemes to determine the stochastic properties of a complex system depends on whether the fluctuations generated from hierarchical equivalent schemes are consistent with one another. To analyze this consistency, we perform lumping processes on the stochastic differential equations and the generalized fluctuation-dissipation theorem and apply them to networks with the frequently encountered Arrhenius-type transition rates. The explicit Langevin force derived from those networks enables us to calculate the state fluctuations caused by the intrinsic and extrinsic noises on the free energy surface and deduce their relations between kinetically equivalent networks. In addition to its applicability to wide classes of network related systems, such as those in structural and systems biology, the result sheds light on the fluctuation relations for general physical variables in Keizer's canonical theory.

  16. Hierarchical Storage Management at the NASA Center for Computational Sciences: From UniTree to SAM-QFS

    Science.gov (United States)

    Salmon, Ellen; Tarshish, Adina; Palm, Nancy; Patel, Sanjay; Saletta, Marty; Vanderlan, Ed; Rouch, Mike; Burns, Lisa; Duffy, Daniel; Caine, Robert

    2004-01-01

    This paper presents the data management issues associated with a large center like the NCCS and how these issues are addressed. More specifically, the focus of this paper is on the recent transition from a legacy UniTree (Legato) system to a SAM-QFS (Sun) system. Therefore, this paper will describe the motivations, from both a hardware and software perspective, for migrating from one system to another. Coupled with the migration from UniTree into SAM-QFS, the complete mass storage environment was upgraded to provide high availability, redundancy, and enhanced performance. This paper will describe the resulting solution and lessons learned throughout the migration process.

  17. Alternative Path Communication in Wide-Scale Cluster-Tree Wireless Sensor Networks Using Inactive Periods

    Science.gov (United States)

    Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco

    2017-01-01

    The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable technology to deploy wide-scale Wireless Sensor Networks (WSNs). These networks are usually designed to support convergecast traffic, where all communication paths go through the PAN (Personal Area Network) coordinator. Nevertheless, peer-to-peer communication relationships may be also required for different types of WSN applications. That is the typical case of sensor and actuator networks, where local control loops must be closed using a reduced number of communication hops. The use of communication schemes optimised just for the support of convergecast traffic may result in higher network congestion and in a potentially higher number of communication hops. Within this context, this paper proposes an Alternative-Route Definition (ARounD) communication scheme for WSNs. The underlying idea of ARounD is to setup alternative communication paths between specific source and destination nodes, avoiding congested cluster-tree paths. These alternative paths consider shorter inter-cluster paths, using a set of intermediate nodes to relay messages during their inactive periods in the cluster-tree network. Simulation results show that the ARounD communication scheme can significantly decrease the end-to-end communication delay, when compared to the use of standard cluster-tree communication schemes. Moreover, the ARounD communication scheme is able to reduce the network congestion around the PAN coordinator, enabling the reduction of the number of message drops due to queue overflows in the cluster-tree network. PMID:28481245

  18. Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.

    Science.gov (United States)

    Qu, Hong; Yi, Zhang; Yang, Simon X

    2013-06-01

    Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

  19. An Improved Particle Swarm Optimization Based on Deluge Approach for Enhanced Hierarchical Cache Optimization in IPTV Networks

    Directory of Open Access Journals (Sweden)

    M. Somu

    2014-05-01

    Full Text Available In recent years, IP network has been considered as a new delivery network for TV services. A majority of the telecommunication industries have used IP network to offer on-demand services and linear TV services as it can offer a two-way and high-speed communication. In order to effectively and economically utilize the IP network, caching is the technique which is usually preferred. In IPTV system, a managed network is utilized to bring out TV services, the requests of Video on Demand (VOD objects are usually combined in a limited period intensively and user preferences are fluctuated dynamically. Furthermore, the VOD content updates often under the control of IPTV providers. In order to minimize this traffic and overall network cost, a segment of the video content is stored in caches closer to subscribers, for example, Digital Subscriber Line Access Multiplexer (DSLAM, a Central Office (CO and Intermediate Office (IO. The major problem focused in this approach is to determine the optimal cache memory that should be assigned in order to attain maximum cost effectiveness. This approach uses an effective Grate Deluge algorithm based Particle Swarm Optimization (GDPSO approach for attaining the optimal cache memory size which in turn minimizes the overall network cost. The analysis shows that hierarchical distributed caching can save significant network cost through the utilization of the GDPSO algorithm.

  20. A Distance Metric for Tree-Sibling Time Consistent Phylogenetic Networks

    CERN Document Server

    Cardona, Gabriel; Rossello, Francesc; Valiente, Gabriel

    2008-01-01

    The presence of reticulate evolutionary events in phylogenies turn phylogenetic trees into phylogenetic networks. These events imply in particular that there may exist multiple evolutionary paths from a non-extant species to an extant one, and this multiplicity makes the comparison of phylogenetic networks much more difficult than the comparison of phylogenetic trees. In fact, all attempts to define a sound distance measure on the class of all phylogenetic networks have failed so far. Thus, the only practical solutions have been either the use of rough estimates of similarity (based on comparison of the trees embedded in the networks), or narrowing the class of phylogenetic networks to a certain class where such a distance is known and can be efficiently computed. The first approach has the problem that one may identify two networks as equivalent, when they are not; the second one has the drawback that there may not exist algorithms to reconstruct such networks from biological sequences. We present in this pa...

  1. Modeling and Algorithm for Multiple Spanning Tree Provisioning in Resilient and Load Balanced Ethernet Networks

    Directory of Open Access Journals (Sweden)

    Steven S. W. Lee

    2015-01-01

    Full Text Available We propose a multitree based fast failover scheme for Ethernet networks. In our system, only few spanning trees are used to carry working traffic in the normal state. As a failure happens, the nodes adjacent to the failure redirect traffic to the preplanned backup VLAN trees to realize fast failure recovery. In the proposed scheme, a new leaf constraint is enforced on the backup trees. It enables the network being able to provide 100% survivability against any single link and any single node failure. Besides fast failover, we also take load balancing into consideration. We model an Ethernet network as a twolayered graph and propose an Integer Linear Programming (ILP formulation for the problem. We further propose a heuristic algorithm to provide solutions to large networks. The simulation results show that the proposed scheme can achieve high survivability while maintaining load balancing at the same time. In addition, we have implemented the proposed scheme in an FPGA system. The experimental results show that it takes only few μsec to recover a network failure. This is far beyond the 50 msec requirement used in telecommunication networks for network protection.

  2. Linear Network Coding on Multi-Mesh of Trees using All to All Broadcast

    Directory of Open Access Journals (Sweden)

    Nitin Rakesh

    2011-05-01

    Full Text Available We introduce linear network coding on parallel architecture for multi-source finite acyclic network. In this problem, different messages in diverse time periods are broadcast and every non-source node in the network decodes and encodes the message based on further communication.We wish to minimize the communication steps and time complexity involved in transfer of data from node-to-node during parallel communication.We have used Multi-Mesh of Trees (MMT topology for implementing network coding. To envisage our result, we use all-to-all broadcast as communication algorithm.

  3. ROOT: Energy Efficient Routing through Optimized Tree in Sensor Networks

    CERN Document Server

    Chakraborty, Kaushik; Mitra, Swarup Kumar; Naskar, Mrinal Kanti

    2011-01-01

    Due to limitation of battery power, wireless sensor nodes are highly energy constrained. So, to enhance the network lifetime, the protocols which are used in wireless sensor network should be energy efficient. The LEACH and PEGASIS protocols which are elegant solutions to this problem try to minimize the overall energy dissipation by the nodes in the network. While the LEACH protocol randomizes cluster heads to achieve equal energy dissemination, the PEGASIS protocol forms a chain of cluster heads taking rounds in transmitting to the base station. In this paper we propose an energy efficient protocol which combines and thus enhances the performance of LEACH and PEGASIS. Here the base station is located at variable distances from each node due to the random deployment of the sensor nodes. So, each node actually dissipates a different amount of energy during its turn of transmission to the base station. This energy difference between the various nodes keeps on increasing with resulting in poorer network perform...

  4. Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

    Directory of Open Access Journals (Sweden)

    Chen Xiaoyu

    2007-12-01

    Full Text Available Abstract Background In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. Results We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. Conclusion Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

  5. Are communities just bottlenecks? Trees and treelike networks have high modularity

    CERN Document Server

    Bagrow, James P

    2012-01-01

    Much effort has gone into understanding the modular nature of complex networks. Communities, also known as clusters or modules, are densely interconnected groups of nodes that are only sparsely connected to other groups in the network. Discovering high quality communities is a difficult and important problem in a number of areas. The most popular approach is the objective function known as Modularity, used to both discover communities and measure their strength. To understand the modular structure of networks it is then crucial to know how such functions evaluate different topologies, what features they account for and what implicit assumptions they may make. We show that trees and treelike networks can have unexpectedly and often arbitrarily high values of modularity. This is surprising since trees are maximally sparse connected graphs and are not typically considered to possess modular structure, yet the non-local null model used by modularity assigns low probabilities, and thus high significance, to the de...

  6. Distributed Algorithms for Scheduling on Line and Tree Networks

    CERN Document Server

    Chakaravarthy, Venkatesan T; Sabharwal, Yogish

    2012-01-01

    We have a set of processors (or agents) and a set of graph networks defined over some vertex set. Each processor can access a subset of the graph networks. Each processor has a demand specified as a pair of vertices $$, along with a profit; the processor wishes to send data between $u$ and $v$. Towards that goal, the processor needs to select a graph network accessible to it and a path connecting $u$ and $v$ within the selected network. The processor requires exclusive access to the chosen path, in order to route the data. Thus, the processors are competing for routes/channels. A feasible solution selects a subset of demands and schedules each selected demand on a graph network accessible to the processor owning the demand; the solution also specifies the paths to use for this purpose. The requirement is that for any two demands scheduled on the same graph network, their chosen paths must be edge disjoint. The goal is to output a solution having the maximum aggregate profit. Prior work has addressed the above...

  7. On Energy Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks

    CERN Document Server

    Comaniciu, Cristina

    2007-01-01

    In this paper, a hierarchical cross-layer design approach is proposed to increase energy efficiency in ad hoc networks through joint adaptation of nodes' transmitting powers and route selection. The design maintains the advantages of the classic OSI model, while accounting for the cross-coupling between layers, through information sharing. The proposed joint power control and routing algorithm is shown to increase significantly the overall energy efficiency of the network, at the expense of a moderate increase in complexity. Performance enhancement of the joint design using multiuser detection is also investigated, and it is shown that the use of multiuser detection can increase the capacity of the ad hoc network significantly for a given level of energy consumption.

  8. Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.

    Science.gov (United States)

    Mohammadzadeh, Ardashir; Ghaemi, Sehraneh

    2015-09-01

    This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown.

  9. On Energy-Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Poor HVincent

    2007-01-01

    Full Text Available A hierarchical cross-layer design approach is proposed to increase energy efficiency in ad hoc networks through joint adaptation of nodes' transmitting powers and route selection. The design maintains the advantages of the classic OSI model, while accounting for the cross-coupling between layers, through information sharing. The proposed joint power control and routing algorithm is shown to increase significantly the overall energy efficiency of the network, at the expense of a moderate increase in complexity. Performance enhancement of the joint design using multiuser detection is also investigated, and it is shown that the use of multiuser detection can increase the capacity of the ad hoc network significantly for a given level of energy consumption.

  10. Trees

    OpenAIRE

    Henri Epstein

    2016-01-01

    An algebraic formalism, developed with V. Glaser and R. Stora for the study of the generalized retarded functions of quantum field theory, is used to prove a factorization theorem which provides a complete description of the generalized retarded functions associated with any tree graph. Integrating over the variables associated to internal vertices to obtain the perturbative generalized retarded functions for interacting fields arising from such graphs is shown to be possible for a large cate...

  11. Trees

    OpenAIRE

    Epstein, Henri

    2016-01-01

    An algebraic formalism, developped with V. Glaser and R. Stora for the study of the generalized retarded functions of quantum field theory, is used to prove a factorization theorem which provides a complete description of the generalized retarded functions associated with any tree graph. Integrating over the variables associated to internal vertices to obtain the perturbative generalized retarded functions for interacting fields arising from such graphs is shown to be possible for a large cat...

  12. Trees

    CERN Document Server

    Epstein, Henri

    2016-01-01

    An algebraic formalism, developped with V.~Glaser and R.~Stora for the study of the generalized retarded functions of quantum field theory, is used to prove a factorization theorem which provides a complete description of the generalized retarded functions associated with any tree graph. Integrating over the variables associated to internal vertices to obtain the perturbative generalized retarded functions for interacting fields arising from such graphs is shown to be possible for a large category of space-times.

  13. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

    OpenAIRE

    Tai, Kai Sheng; Socher, Richard; Manning, Christopher D

    2015-01-01

    Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of...

  14. Cross-Layer Protocol Combining Tree Routing and TDMA Slotting in Wireless Sensor Networks

    Science.gov (United States)

    Bai, Ronggang; Ji, Yusheng; Lin, Zhiting; Wang, Qinghua; Zhou, Xiaofang; Qu, Yugui; Zhao, Baohua

    Being different from other networks, the load and direction of data traffic for wireless sensor networks are rather predictable. The relationships between nodes are cooperative rather than competitive. These features allow the design approach of a protocol stack to be able to use the cross-layer interactive way instead of a hierarchical structure. The proposed cross-layer protocol CLWSN optimizes the channel allocation in the MAC layer using the information from the routing tables, reduces the conflicting set, and improves the throughput. Simulations revealed that it outperforms SMAC and MINA in terms of delay and energy consumption.

  15. Facile solid-state synthesis of Ni@C nanosheet-assembled hierarchical network for high-performance lithium storage

    Science.gov (United States)

    Gu, Jinghe; Li, Qiyun; Zeng, Pan; Meng, Yulin; Zhang, Xiukui; Wu, Ping; Zhou, Yiming

    2017-08-01

    Micro/nano-architectured transition-metal@C hybrids possess unique structural and compositional features toward lithium storage, and are thus expected to manifest ideal anodic performances in advanced lithium-ion batteries (LIBs). Herein, we propose a facile and scalable solid-state coordination and subsequent pyrolysis route for the formation of a novel type of micro/nano-architectured transition-metal@C hybrid (i.e., Ni@C nanosheet-assembled hierarchical network, Ni@C network). Moreover, this coordination-pyrolysis route has also been applied for the construction of bare carbon network using zinc salts instead of nickel salts as precursors. When applied as potential anodic materials in LIBs, the Ni@C network exhibits Ni-content-dependent electrochemical performances, and the partially-etched Ni@C network manifests markedly enhanced Li-storage performances in terms of specific capacities, cycle life, and rate capability than the pristine Ni@C network and carbon network. The proposed solid-state coordination and pyrolysis strategy would open up new opportunities for constructing micro/nano-architectured transition-metal@C hybrids as advanced anode materials for LIBs.

  16. Core-based Shared Tree Multicast Routing Algorithms for LEO Satellite IP Networks

    Institute of Scientific and Technical Information of China (English)

    Cheng Lianzhen; Zhang Jun; Liu Kai

    2007-01-01

    A new core-based shared tree algorithm, viz core-cluster combination-based shared tree (CCST) algorithm and the weighted version (i.e. w-CCST algorithm) are proposed in order to resolve the channel resources waste problem in typical source-based multicast routing algorithms in low earth orbit (LEO) satellite IP networks. The CCST algorithm includes the dynamic approximate center (DAC)core selection method and the core-cluster combination multicast route construction scheme. Without complicated onboard computation,the DAC method is uniquely developed for highly dynamic networks of periodical and regular movement. The core-cluster combination method takes core node as the initial core-cluster, and expands it stepwise to construct an entire multicast tree at the lowest tree cost by a shortest path scheme between the newly-generated core-cluster and surplus group members, which results in great bandwidth utilization.Moreover, the w-CCST algorithm is able to strike a balance between performance of tree cost and that of end-to-end propagation delay by adjusting the weighted factor to meet strict end-to-end delay requirements of some real-time multicast services at the expense of a slight increase in tree cost. Finally, performance comparison is conducted between the proposed algorithms and typical algorithms in LEO satellite IP networks. Simulation results show that the CCST algorithm significantly decreases the average tree cost against to the others, and also the average end-to-end propagation delay of w-CCST algorithm is lower than that of the CCST algorithm.

  17. Superframe Duration Allocation Schemes to Improve the Throughput of Cluster-Tree Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Erico Leão

    2017-01-01

    Full Text Available The use ofWireless Sensor Network (WSN technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme, or by number of descendant nodes (Nodes-SDA scheme of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks.

  18. Superframe Duration Allocation Schemes to Improve the Throughput of Cluster-Tree Wireless Sensor Networks.

    Science.gov (United States)

    Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco

    2017-01-27

    The use ofWireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme), or by number of descendant nodes (Nodes-SDA scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks.

  19. Superframe Duration Allocation Schemes to Improve the Throughput of Cluster-Tree Wireless Sensor Networks

    Science.gov (United States)

    Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco

    2017-01-01

    The use of Wireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme), or by number of descendant nodes (Nodes-SDA scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks. PMID:28134822

  20. The entire mean weighted first-passage time on infinite families of weighted tree networks

    Science.gov (United States)

    Sun, Yanqiu; Dai, Meifeng; Shao, Shuxiang; Su, Weiyi

    2017-03-01

    We propose the entire mean weighted first-passage time (EMWFPT) for the first time in the literature. The EMWFPT is obtained by the sum of the reciprocals of all nonzero Laplacian eigenvalues on weighted networks. Simplified calculation of EMWFPT is the key quantity in the study of infinite families of weighted tree networks, since the weighted complex systems have become a fundamental mechanism for diverse dynamic processes. We base on the relationships between characteristic polynomials at different generations of their Laplacian matrix and Laplacian eigenvalues to compute EMWFPT. This technique of simplified calculation of EMWFPT is significant both in theory and practice. In this paper, firstly, we introduce infinite families of weighted tree networks with recursive properties. Then, we use the sum of the reciprocals of all nonzero Laplacian eigenvalues to calculate EMWFPT, which is equal to the average of MWFPTs over all pairs of nodes on infinite families of weighted networks. In order to compute EMWFPT, we try to obtain the analytical expressions for the sum of the reciprocals of all nonzero Laplacian eigenvalues. The key step here is to calculate the constant terms and the coefficients of first-order terms of characteristic polynomials. Finally, we obtain analytically the closed-form solutions to EMWFPT on the weighted tree networks and show that the leading term of EMWFPT grows superlinearly with the network size.

  1. Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

    Science.gov (United States)

    Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

    2012-05-30

    The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.

  2. Bistability of mixed states in a neural network storing hierarchical patterns

    Science.gov (United States)

    Toya, Kaname; Fukushima, Kunihiko; Kabashima, Yoshiyuki; Okada, Masato

    2000-04-01

    We discuss the properties of equilibrium states in an autoassociative memory model storing hierarchically correlated patterns (hereafter, hierarchical patterns). We will show that symmetric mixed states (hereafter, mixed states) are bistable on the associative memory model storing the hierarchical patterns in a region of the ferromagnetic phase. This means that the first-order transition occurs in this ferromagnetic phase. We treat these contents with a statistical mechanical method (SCSNA) and by computer simulation. Finally, we discuss a physiological implication of this model. Sugase et al (1999 Nature 400 869) analysed the time-course of the information carried by the firing of face-responsive neurons in the inferior temporal cortex. We also discuss the relation between the theoretical results and the physiological experiments of Sugase et al .

  3. Distributed Algorithm for Constructing Efficient Tree Topology for Message Dissemination in Vehicular Networks

    Directory of Open Access Journals (Sweden)

    S. Kamakshi

    2014-01-01

    Full Text Available Vehicular ad hoc network is an ad hoc network constituted among moving vehicles that have wireless dedicated short range communication (DSRC devices in order to provide ubiquitous connectivity even if the road-side infrastructure is unavailable. Message dissemination in vehicular ad hoc networks is necessary for exchanging information about prevailing traffic information, so that the vehicles can take alternate routes to avoid traffic jam. A major challenge in broadcast protocols is that they result in flooding of messages that reduce the speed of dissemination due to collision. Dissemination of messages using tree topology reduces the number of rebroadcasts. Dynamicity Aware Graph Relabeling System model provides a framework to construct spanning tree in mobile wireless network. In this paper, we propose a new distributed algorithm for constructing an arbitrary spanning tree based on Dynamicity Aware Graph Relabeling System model, which develops a maximum leaf spanning tree in order to reduce the number of rebroadcasts and dissemination time. Our simulation results prove that the number of vehicles rebroadcasting the message is curtailed to 15% and the dissemination time required to achieve 100% reachability is curtailed by 10% under average vehicle density.

  4. Reconstructing missing daily precipitation data using regression trees and artificial neural networks

    Science.gov (United States)

    Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

  5. An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rosana Lachowski

    2015-01-01

    Full Text Available Monitoring and data collection are the two main functions in wireless sensor networks (WSNs. Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.

  6. Packets distribution in a tree-based topology wireless sensor networks

    CSIR Research Space (South Africa)

    Akpakwu, GA

    2016-07-01

    Full Text Available The concept of data distribution within cluster of sensor nodes to the source sink has resulted to intense research in Wireless Sensor Networks (WSNs). In this paper, in order to determine the scheduling length of packet distribution, a tree...

  7. An efficient distributed algorithm for constructing spanning trees in wireless sensor networks.

    Science.gov (United States)

    Lachowski, Rosana; Pellenz, Marcelo E; Penna, Manoel C; Jamhour, Edgard; Souza, Richard D

    2015-01-14

    Monitoring and data collection are the two main functions in wireless sensor networks (WSNs). Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.

  8. Cross Layered Multi-Meshed Tree Scheme for Cognitive Networks

    Science.gov (United States)

    2011-06-01

    routing AODV or DSR can be used. SHARP proactive routing protocol ... AODV . The first is a proactive routing protocol and the second is a reactive routing protocol . We use the proactive routing protocol OLSR to evaluate...connectivity challenges posed by MANETs such as Airborne Networks are the ‘ routing ’ and ‘medium access control’ (MAC) protocols . Such protocols

  9. PLATON: Peer-to-Peer load adjusting tree overlay networks

    NARCIS (Netherlands)

    Lymberopoulos, L.; Pittaras, C.; Grammatikou, M.; Papavassiliou, S.; Maglaris, V.

    2011-01-01

    Peer-to-Peer systems supporting multi attribute and range queries use a number of techniques to partition the multi dimensional data space among participating peers. Load-balancing of data accross peer partitions is necessary in order to avoid the presence of network hotspots which may cause

  10. PLATON: Peer-to-Peer load adjusting tree overlay networks

    NARCIS (Netherlands)

    Lymberopoulos, L.; Pittaras, C.; Grammatikou, M.; Papavassiliou, S.; Maglaris, V.

    2011-01-01

    Peer-to-Peer systems supporting multi attribute and range queries use a number of techniques to partition the multi dimensional data space among participating peers. Load-balancing of data accross peer partitions is necessary in order to avoid the presence of network hotspots which may cause perform

  11. A NOVEL INTRUSION DETECTION MODE BASED ON UNDERSTANDABLE NEURAL NETWORK TREES

    Institute of Scientific and Technical Information of China (English)

    Xu Qinzhen; Yang Luxi; Zhao Qiangfu; He Zhenya

    2006-01-01

    Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network,statistical techniques and expert systems are used to model network packets in the field of intrusion detection.In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is presented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN's capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually "gray boxes" as they can be interpreted easily ifthe number of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset.We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.

  12. H-P2PSIP: Interconnection of P2PSIP domains for Global Multimedia Services based on a Hierarchical DHT Overlay Network

    OpenAIRE

    Martínez-Yelmo, Isaías; Bikfalvi, Alex; Cuevas, Rubén; Guerrero, Carmen; García-Reinoso, Jaime

    2009-01-01

    The IETF P2PSIP WG is currently standardising a protocol for distributed mul- timedia services combining the media session functionality of SIP and the decentralised distribution and localisation of resources in peer-to-peer networks. The current P2PSIP scenarios only consider the infrastructure for the connectivity inside a single domain. This paper proposes an extension of the current work to a hierarchical multi-domain scenario: a two level hierarchical peer-to-peer overlay architecture...

  13. Selective logging in tropical forests decreases the robustness of liana–tree interaction networks to the loss of host tree species

    Science.gov (United States)

    Magrach, Ainhoa; Senior, Rebecca A.; Rogers, Andrew; Nurdin, Deddy; Benedick, Suzan; Laurance, William F.; Santamaria, Luis; Edwards, David P.

    2016-01-01

    Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the ‘health’ and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness, and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growing tree species, which could exacerbate the loss of timber value and carbon storage already associated with logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana–tree interaction networks if heavily infested trees (i.e. the most connected ones) were more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of ‘extinction debt’. PMID:26936241

  14. Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species.

    Science.gov (United States)

    Magrach, Ainhoa; Senior, Rebecca A; Rogers, Andrew; Nurdin, Deddy; Benedick, Suzan; Laurance, William F; Santamaria, Luis; Edwards, David P

    2016-03-16

    Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the 'health' and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness, and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growing tree species, which could exacerbate the loss of timber value and carbon storage already associated with logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana-tree interaction networks if heavily infested trees (i.e. the most connected ones) were more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of 'extinction debt'. © 2016 The Author(s).

  15. Tree Tensor Network State with Variable Tensor Order: An Efficient Multireference Method for Strongly Correlated Systems.

    Science.gov (United States)

    Murg, V; Verstraete, F; Schneider, R; Nagy, P R; Legeza, Ö

    2015-03-10

    We study the tree-tensor-network-state (TTNS) method with variable tensor orders for quantum chemistry. TTNS is a variational method to efficiently approximate complete active space (CAS) configuration interaction (CI) wave functions in a tensor product form. TTNS can be considered as a higher order generalization of the matrix product state (MPS) method. The MPS wave function is formulated as products of matrices in a multiparticle basis spanning a truncated Hilbert space of the original CAS-CI problem. These matrices belong to active orbitals organized in a one-dimensional array, while tensors in TTNS are defined upon a tree-like arrangement of the same orbitals. The tree-structure is advantageous since the distance between two arbitrary orbitals in the tree scales only logarithmically with the number of orbitals N, whereas the scaling is linear in the MPS array. It is found to be beneficial from the computational costs point of view to keep strongly correlated orbitals in close vicinity in both arrangements; therefore, the TTNS ansatz is better suited for multireference problems with numerous highly correlated orbitals. To exploit the advantages of TTNS a novel algorithm is designed to optimize the tree tensor network topology based on quantum information theory and entanglement. The superior performance of the TTNS method is illustrated on the ionic-neutral avoided crossing of LiF. It is also shown that the avoided crossing of LiF can be localized using only ground state properties, namely one-orbital entanglement.

  16. The Inverse 1-Median Problem on Tree Networks with Variable Real Edge Lengths

    Directory of Open Access Journals (Sweden)

    Longshu Wu

    2013-01-01

    Full Text Available Location problems exist in the real world and they mainly deal with finding optimal locations for facilities in a network, such as net servers, hospitals, and shopping centers. The inverse location problem is also often met in practice and has been intensively investigated in the literature. As a typical inverse location problem, the inverse 1-median problem on tree networks with variable real edge lengths is discussed in this paper, which is to modify the edge lengths at minimum total cost such that a given vertex becomes a 1-median of the tree network with respect to the new edge lengths. First, this problem is shown to be solvable in linear time with variable nonnegative edge lengths. For the case when negative edge lengths are allowable, the NP-hardness is proved under Hamming distance, and strongly polynomial time algorithms are presented under l1 and l∞ norms, respectively.

  17. Categorizing Ideas about Trees: A Tree of Trees

    Science.gov (United States)

    Fisler, Marie; Lecointre, Guillaume

    2013-01-01

    The aim of this study is to explore whether matrices and MP trees used to produce systematic categories of organisms could be useful to produce categories of ideas in history of science. We study the history of the use of trees in systematics to represent the diversity of life from 1766 to 1991. We apply to those ideas a method inspired from coding homologous parts of organisms. We discretize conceptual parts of ideas, writings and drawings about trees contained in 41 main writings; we detect shared parts among authors and code them into a 91-characters matrix and use a tree representation to show who shares what with whom. In other words, we propose a hierarchical representation of the shared ideas about trees among authors: this produces a “tree of trees.” Then, we categorize schools of tree-representations. Classical schools like “cladists” and “pheneticists” are recovered but others are not: “gradists” are separated into two blocks, one of them being called here “grade theoreticians.” We propose new interesting categories like the “buffonian school,” the “metaphoricians,” and those using “strictly genealogical classifications.” We consider that networks are not useful to represent shared ideas at the present step of the study. A cladogram is made for showing who is sharing what with whom, but also heterobathmy and homoplasy of characters. The present cladogram is not modelling processes of transmission of ideas about trees, and here it is mostly used to test for proximity of ideas of the same age and for categorization. PMID:23950877

  18. A Multiple-Dimensional Tree Routing Protocol for Multisink Wireless Sensor Networks Based on Ant Colony Optimization

    OpenAIRE

    Hui Zhou; Dongliang Qing; Xiaomei Zhang; Honglin Yuan; Chen Xu

    2012-01-01

    Routing protocol is an important topic in the wireless sensor networks. For MultiSink wireless sensor networks, the routing protocol designs and implementations are more difficult due to the structure complexity. The paper deals with the problem of a multiple-dimensional tree routing protocol for multisink wireless sensor networks based on ant colony optimization. The proposed protocol is as follows: (1) listening mechanism is used to establish and maintain multidimensional tree routing topol...

  19. An Enhanced Tree-Shaped Adachi-Like Chaotic Neural Network Requiring Linear-Time Computations

    Science.gov (United States)

    Qin, Ke; Oommen, B. John

    The Adachi Neural Network (AdNN) [1-5], is a fascinating Neural Network (NN) which has been shown to possess chaotic properties, and to also demonstrate Associative Memory (AM) and Pattern Recognition (PR) characteristics. Variants of the AdNN [6,7] have also been used to obtain other PR phenomena, and even blurring. A significant problem associated with the AdNN and its variants, is that all of them require a quadratic number of computations. This is essentially because all their NNs are completely connected graphs. In this paper we consider how the computations can be significantly reduced by merely using a linear number of computations. To do this, we extract from the original complete graph, one of its spanning trees. We then compute the weights for this spanning tree in such a manner that the modified tree-based NN has approximately the same input-output characteristics, and thus the new weights are themselves calculated using a gradient-based algorithm. By a detailed experimental analysis, we show that the new linear-time AdNN-like network possesses chaotic and PR properties for different settings. As far as we know, such a tree-based AdNN has not been reported, and the results given here are novel.

  20. Hierarchical spatial segregation of two Mediterranean vole species: the role of patch-network structure and matrix composition.

    Science.gov (United States)

    Pita, Ricardo; Lambin, Xavier; Mira, António; Beja, Pedro

    2016-09-01

    According to ecological theory, the coexistence of competitors in patchy environments may be facilitated by hierarchical spatial segregation along axes of environmental variation, but empirical evidence is limited. Cabrera and water voles show a metapopulation-like structure in Mediterranean farmland, where they are known to segregate along space, habitat, and time axes within habitat patches. Here, we assess whether segregation also occurs among and within landscapes, and how this is influenced by patch-network and matrix composition. We surveyed 75 landscapes, each covering 78 ha, where we mapped all habitat patches potentially suitable for Cabrera and water voles, and the area effectively occupied by each species (extent of occupancy). The relatively large water vole tended to be the sole occupant of landscapes with high habitat amount but relatively low patch density (i.e., with a few large patches), and with a predominantly agricultural matrix, whereas landscapes with high patch density (i.e., many small patches) and low agricultural cover, tended to be occupied exclusively by the small Cabrera vole. The two species tended to co-occur in landscapes with intermediate patch-network and matrix characteristics, though their extents of occurrence were negatively correlated after controlling for environmental effects. In combination with our previous studies on the Cabrera-water vole system, these findings illustrated empirically the occurrence of hierarchical spatial segregation, ranging from within-patches to among-landscapes. Overall, our study suggests that recognizing the hierarchical nature of spatial segregation patterns and their major environmental drivers should enhance our understanding of species coexistence in patchy environments.

  1. An efficient algorithm for finding optimal gain-ratio multiple-split tests on hierarchical attributes in decision tree learning

    Energy Technology Data Exchange (ETDEWEB)

    Almuallim, H. [King Fahd Univ. of Petroleum & Minerals, Dhahran (Saudi Arabia); Akiba, Yasuhiro; Kaneda, Shigeo [NTT Communication Science Labs., Kanagawa (Japan)

    1996-12-31

    Given a set of training examples S and a tree-structured attribute x, the goal in this work is to find a multiple-split test defined on x that maximizes Quinlan`s gain-ratio measure. The number of possible such multiple-split tests grows exponentially in the size of the hierarchy associated with the attribute. It is, therefore, impractical to enumerate and evaluate all these tests in order to choose the best one. We introduce an efficient algorithm for solving this problem that guarantees maximizing the gain-ratio over all possible tests. For a training set of m examples and an attribute hierarchy of height d, our algorithm runs in time proportional to dm, which makes it efficient enough for practical use.

  2. Comparing M5 Model Trees and Neural Networks for River Level Forecasting

    Science.gov (United States)

    Khan, S.; See, L.

    2005-12-01

    Artificial neural networks (ANNs) have been the subject of much research activity in hydrological modelling over the last decade yet this represents only one data-driven modelling approach from among a very rich set. M5 model trees are an example of a technique that has had little application in the hydrological domain yet the results are promising (Solomatine and Xue, 2004). They are a machine learning approach that combines regression trees and classification. The input space is partitioned into subsets based on entropy measures, and regression equations are then fit to these subsets. The advantages over ANNs are (a) their ability to provide knowledge in the form of a decision tree and (b) much faster training times. This has important implications for operational use as they are not black box models. In this study ANNs, M5 model trees and time series analysis have been used to develop models to predict river levels at a gauging station in the River Ouse catchment in Northern England. Two lead times have been used: t+6 and t+24 hours. The input data consisted of historical levels at the gauging stations, upstream level data and rainfall from five rain gauges across the catchment, determined by correlation with the output. The results of the study showed that the ANNs outperformed both the M5 model trees and time series approaches when considering global goodness-of-fit measures such as root mean squared error and coefficient of efficiency. However, the difference in performance between the ANNs and M5 model trees was not large, e.g. 1 percent difference in coefficient of efficiency for t+6 hours. When considering the longer lead time of t+24 hours, the performance of the ANNs and M5 model trees almost converged. The M5 model tree, however, also provides the rules of operation. The first partition for both the t+6 and t+24 hour models was determined by the value of the river level at one of the upstream stations. The individual regression equations associated with

  3. Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

    Directory of Open Access Journals (Sweden)

    Hong Cheng

    2017-01-01

    Full Text Available (1 Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN; (2 Methods: Two back propagation neural network (BPNN models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image, pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3 Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2 between the estimated and the actual weighted yield, mean forecast error (MFE, mean absolute percentage error (MAPE, and root mean square error (RMSE were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit and improved near harvest (red, large fruit to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv.

  4. Landslide Susceptibility Mapping of Tegucigalpa, Honduras Using Artificial Neural Network, Bayesian Network and Decision Trees

    Science.gov (United States)

    Garcia Urquia, E. L.; Braun, A.; Yamagishi, H.

    2016-12-01

    Tegucigalpa, the capital city of Honduras, experiences rainfall-induced landslides on a yearly basis. The high precipitation regime and the rugged topography the city has been built in couple with the lack of a proper urban expansion plan to contribute to the occurrence of landslides during the rainy season. Thousands of inhabitants live at risk of losing their belongings due to the construction of precarious shelters in landslide-prone areas on mountainous terrains and next to the riverbanks. Therefore, the city is in the need for landslide susceptibility and hazard maps to aid in the regulation of future development. Major challenges in the context of highly dynamic urbanizing areas are the overlap of natural and anthropogenic slope destabilizing factors, as well as the availability and accuracy of data. Data-driven multivariate techniques have proven to be powerful in discovering interrelations between factors, identifying important factors in large datasets, capturing non-linear problems and coping with noisy and incomplete data. This analysis focuses on the creation of a landslide susceptibility map using different methods from the field of data mining, Artificial Neural Networks (ANN), Bayesian Networks (BN) and Decision Trees (DT). The input dataset of the study contains geomorphological and hydrological factors derived from a digital elevation model with a 10 m resolution, lithological factors derived from a geological map, and anthropogenic factors, such as information on the development stage of the neighborhoods in Tegucigalpa and road density. Moreover, a landslide inventory map that was developed in 2014 through aerial photo interpretation was used as target variable in the analysis. The analysis covers an area of roughly 100 km2, while 8.95 km2 are occupied by landslides. In a first step, the dataset was explored by assessing and improving the data quality, identifying unimportant variables and finding interrelations. Then, based on a training

  5. Embedding global and collective in a torus network with message class map based tree path selection

    Science.gov (United States)

    Chen, Dong; Coteus, Paul W.; Eisley, Noel A.; Gara, Alan; Heidelberger, Philip; Senger, Robert M; Salapura, Valentina; Steinmacher-Burow, Burkhard; Sugawara, Yutaka; Takken, Todd E.

    2016-06-21

    Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computer program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.

  6. Data compression studies for NOAA Hyperspectral Environmental Suite (HES) using 3D integer wavelet transforms with 3D set partitioning in hierarchical trees

    Science.gov (United States)

    Huang, Bormin; Huang, Hung-Lung; Chen, Hao; Ahuja, Alok; Baggett, Kevin; Schmit, Timothy J.; Heymann, Roger W.

    2004-02-01

    The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Hyperspectral sounder data is a particular class of data requiring high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Hence compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are processed with the 3D set partitioning in hierarchical trees (SPIHT) scheme followed by context-based arithmetic coding. SPIHT provides better coding efficiency than Shapiro's original embedded zerotree wavelet (EZW) algorithm. We extend the 3D SPIHT scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.

  7. A blind hierarchical coherent search for gravitational-wave signals from coalescing compact binaries in a network of interferometric detectors

    Energy Technology Data Exchange (ETDEWEB)

    Bose, Sukanta; Dayanga, Thilina; Ghosh, Shaon; Talukder, Dipongkar, E-mail: sukanta@wsu.edu, E-mail: wdayanga@wsu.edu, E-mail: shaonghosh@mail.wsu.edu, E-mail: talukder_d@wsu.edu [Department of Physics and Astronomy, Washington State University, 1245 Webster, Pullman, WA 99164-2814 (United States)

    2011-07-07

    We describe a hierarchical data analysis pipeline for coherently searching for gravitational-wave signals from non-spinning compact binary coalescences (CBCs) in the data of multiple earth-based detectors. This search assumes no prior information on the sky position of the source or the time of occurrence of its transient signals and, hence, is termed 'blind'. The pipeline computes the coherent network search statistic that is optimal in stationary, Gaussian noise. More importantly, it allows for the computation of a suite of alternative multi-detector coherent search statistics and signal-based discriminators that can improve the performance of CBC searches in real data, which can be both non-stationary and non-Gaussian. Also, unlike the coincident multi-detector search statistics that have been employed so far, the coherent statistics are different in the sense that they check for the consistency of the signal amplitudes and phases in the different detectors with their different orientations and with the signal arrival times in them. Since the computation of coherent statistics entails searching in the sky, it is more expensive than that of the coincident statistics that do not require it. To reduce computational costs, the first stage of the hierarchical pipeline constructs coincidences of triggers from the multiple interferometers, by requiring their proximity in time and component masses. The second stage follows up on these coincident triggers by computing the coherent statistics. Here, we compare the performances of this hierarchical pipeline with and without the second (or coherent) stage in Gaussian noise. Although introducing hierarchy can be expected to cause some degradation in the detection efficiency compared to that of a single-stage coherent pipeline, nevertheless it improves the computational speed of the search considerably. The two main results of this work are as follows: (1) the performance of the hierarchical coherent pipeline on

  8. Hierarchical weeping willow nano-tree growth and effect of branching on dye-sensitized solar cell efficiency.

    Science.gov (United States)

    Herman, Indria; Yeo, Junyeob; Hong, Sukjoon; Lee, Daeho; Nam, Koo Hyun; Choi, Jun-ho; Hong, Won-hwa; Lee, Dongjin; Grigoropoulos, Costas P; Ko, Seung Hwan

    2012-05-17

    In this paper we have demonstrated the simple, low cost, low temperature, hydrothermal growth of weeping willow ZnO nano-trees with very long branches to realize high efficiency dye-sensitized solar cells (DSSCs). We also discuss the effects of branching on solar cell efficiency. By introducing branched growth on the backbone ZnO nanowires (NWs), the short circuit current density and the overall light conversion efficiency of the branched ZnO NW DSSCs increased to almost four times that for vertically grown ZnO NWs. The efficiency increase is attributed to the increase in surface area for higher dye loading and light harvesting and also to reduced charge recombination through direct conduction along the crystalline ZnO branches. As the length of the branches increased, the branches became flaccid and the increase in solar cell efficiency slowed down because the effective surface area increase was hindered by branch bundling during the drying process and subsequent decrease in the dye loading.

  9. The algebra of the general Markov model on phylogenetic trees and networks.

    Science.gov (United States)

    Sumner, J G; Holland, B R; Jarvis, P D

    2012-04-01

    It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.

  10. Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach.

    Science.gov (United States)

    Pham, Lisa M; Carvalho, Luis; Schaus, Scott; Kolaczyk, Eric D

    Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.

  11. Beyond the locally tree-like approximation for percolation on real networks

    CERN Document Server

    Radicchi, Filippo

    2016-01-01

    Theoretical attempts proposed so far to describe ordinary percolation processes on real-world networks rely on the locally tree-like ansatz. Such an approximation, however, holds only to a limited extent, as real graphs are often characterized by high frequencies of short loops. We present here a theoretical framework able to overcome such a limitation for the case of site percolation. Our method is based on a message passing algorithm that discounts redundant paths along triangles in the graph. We systematically test the approach on 98 real-world graphs and on synthetic networks. We find excellent accuracy in the prediction of the whole percolation diagram, with significant improvement with respect to the prediction obtained under the locally tree-like approximation. Residual discrepancies between theory and simulations do not depend on clustering and can be attributed to the presence of loops longer than three edges. We present also a method to account for clustering in bond percolation, but the improvement...

  12. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Tso, Geoffrey K.F.; Yau, Kelvin K.W. [City University of Hong Kong, Kowloon, Hong Kong (China). Department of Management Sciences

    2007-09-15

    This study presents three modeling techniques for the prediction of electricity energy consumption. In addition to the traditional regression analysis, decision tree and neural networks are considered. Model selection is based on the square root of average squared error. In an empirical application to an electricity energy consumption study, the decision tree and neural network models appear to be viable alternatives to the stepwise regression model in understanding energy consumption patterns and predicting energy consumption levels. With the emergence of the data mining approach for predictive modeling, different types of models can be built in a unified platform: to implement various modeling techniques, assess the performance of different models and select the most appropriate model for future prediction. (author)

  13. ON MULTICAST TREE CONSTRUCTION IN IPV4-IPV6 HYBRID NETWORK

    Institute of Scientific and Technical Information of China (English)

    Zhang Chao; Zhang Yuan; Huang Yongfeng; Li Xing

    2010-01-01

    With the IPv4 addresses exhausting and IPv6 emerging,the Peer-to-Peer (P2P) overlay is becoming increasingly heterogeneous and complex: pure IPv4,dual stack and pure IPv6 hosts coexist,and the connectivity limitation between IPv4 and IPv6 hosts requires the overlay protocols to be fit for this hybrid situation. This paper sets out to answer the question of how to construct multicast tree on top of IPv4-IPv6 hybrid network. Our solution is a New Greedy Algorithm (NGA) which eliminates the problem of joining failure in the hybrid network and keeps the efficiency of greedy algorithm in tree construction. Simulation results show that our algorithm has excellent performance,which is very close to the optimal in many cases.

  14. Discovering a junction tree behind a Markov network by a greedy algorithm

    CERN Document Server

    Szantai, Tamas

    2011-01-01

    In an earlier paper we introduced a special kind of k-width junction tree, called k-th order t-cherry junction tree in order to approximate a joint probability distribution. The approximation is the best if the Kullback-Leibler divergence between the true joint probability distribution and the approximating one is minimal. Finding the best approximating k-width junction tree is NP-complete if k>2. In our earlier paper we also proved that the best approximating k-width junction tree can be embedded into a k-th order t-cherry junction tree. We introduce a greedy algorithm resulting very good approximations in reasonable computing time. In this paper we prove that if the Markov network underlying fullfills some requirements then our greedy algorithm is able to find the true probability distribution. Our algorithm uses just the k-th order marginal probability distributions as input. We compare the results of the greedy algorithm proposed in this paper with the greedy algorithm proposed by Malvestuto in 1991.

  15. Enhanced Clustering Techniques for Hyper Network Planning using Minimum Spanning Trees and Ant-Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Lamiaa F. Ibrahim

    2011-01-01

    Full Text Available Problem statement: The process of network planning is divided into two sub steps. The first step is determining the location of the Multi Service Access Node (MSAN. The second step is the construction of subscriber network lines from MSAN to subscribers to satisfy optimization criteria and design constraints. Due to the complexity of this process artificial intelligence and clustering techniques have been successfully deployed to solve many problems. The problems of the locations of MSAN, the cabling layout and the computation of optimum cable network layouts have been addressed in this study. The proposed algorithm, Clustering density-Based Spatial of Applications with Noise original, minimal Spanning tree and modified Ant-Colony-Based algorithm (CBSCAN-SPANT, used two clustering algorithms which are density-based and agglomerative clustering algorithm using distances which are shortest paths distance and satisfying the network constraints. This algorithm used wire and wireless technology to serve the subscribers demand and place the switches in a real optimal place. Approach: The density-based Spatial Clustering of Applications with Noise original (DBSCAN algorithm has been modified and a new algorithm (NetPlan algorithm has been proposed by the author in a recent work to solve the first step in the problem of network planning. In the present study, the NetPlan algorithm is modified by introduce the modified Ant-Colony-Based algorithm to find the optimal path between any node and the corresponding MSAN node in the first step of network planning process to determine nodes belonging to each cluster. The second step, in the process of network planning, is also introduced in the present study. For each cluster, the optimal cabling layout from each MSAN to the subscriber premises is determining by introduce the Prime algorithm which construct minimal spanning tree. Results: Experimental results and analysis indicate that the

  16. Community detection in networks based on minimum spanning tree and modularity

    Science.gov (United States)

    Saoud, Bilal; Moussaoui, Abdelouahab

    2016-10-01

    In this paper we propose a novel splitting and merging method for community detection in which a minimum spanning tree (MST) of dissimilarity between nodes in graph is employed. In the splitting process, edges with high dissimilarity in the MST are removed to construct small disconnected subgroups of nodes from the same community. In the merging process, subgroup pairs are iteratively merged to identify the final community structure maximizing the modularity. The proposed method requires no parameter. We provide a general framework for implementing such a method. Experimental results obtained by applying the method on computer-generated networks and different real-world networks show the effectiveness of the proposed method.

  17. Immunizations on small worlds of tree-based wireless sensor networks

    DEFF Research Database (Denmark)

    Li, Qiao; Zhang, Bai-Hai; Cui, Ling-Guo

    2012-01-01

    , are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses. With the former strategy, the infection extends exponentially, although the immunization effectively reduces the contagion speed. With the latter strategy, recurrent contagion oscillations occur in the small world......The sensor virus is a serious threat, as an attacker can simply send a single packet to compromise the entire sensor network. Epidemics become drastic with link additions among sensors when the small world phenomena occur. Two immunization strategies, uniform immunization and temporary immunization...

  18. A new approach for effectively determining fracture network connections in fractured rocks using R tree indexing

    Institute of Scientific and Technical Information of China (English)

    LIU Hua-mei; WANG Ming-yu; SONG Xian-feng

    2011-01-01

    Determinations of fracture network connections would help the investigators remove those “meaningless” no-flow-passing fractures,providing an updated and more effective fracture network that could considerably improve the computation efficiency in the pertinent numerical simulations of fluid flow and solute transport.The effective algorithms with higher computational efficiency are needed to accomplish this task in large-scale fractured rock masses.A new approach using R tree indexing was proposed for determining fracture connection in 3D stochastically distributed fracture network.By comparing with the traditional exhaustion algorithm,it was observed that from the simulation results,this approach was much more effective; and the more the fractures were investigated,the more obvious the advantages of the approach were.Furthermore,it was indicated that the runtime used for creating the R tree indexing has a major part in the total of the runtime used for calculating Minimum Bounding Rectangles(MBRs),creating the R tree indexing,precisely finding out fracture intersections,and identifying flow paths,which are four important steps to determine fracture connections.This proposed approach for the determination of fracture connections in three-dimensional fractured rocks are expected to provide efficient preprocessing and critical database for practically accomplishing numerical computation of fluid flow and solute transport in large-scale fractured rock masses.

  19. A general strategy to determine the congruence between a hierarchical and a non-hierarchical classification

    Directory of Open Access Journals (Sweden)

    Marín Ignacio

    2007-11-01

    Full Text Available Abstract Background Classification procedures are widely used in phylogenetic inference, the analysis of expression profiles, the study of biological networks, etc. Many algorithms have been proposed to establish the similarity between two different classifications of the same elements. However, methods to determine significant coincidences between hierarchical and non-hierarchical partitions are still poorly developed, in spite of the fact that the search for such coincidences is implicit in many analyses of massive data. Results We describe a novel strategy to compare a hierarchical and a dichotomic non-hierarchical classification of elements, in order to find clusters in a hierarchical tree in which elements of a given "flat" partition are overrepresented. The key improvement of our strategy respect to previous methods is using permutation analyses of ranked clusters to determine whether regions of the dendrograms present a significant enrichment. We show that this method is more sensitive than previously developed strategies and how it can be applied to several real cases, including microarray and interactome data. Particularly, we use it to compare a hierarchical representation of the yeast mitochondrial interactome and a catalogue of known mitochondrial protein complexes, demonstrating a high level of congruence between those two classifications. We also discuss extensions of this method to other cases which are conceptually related. Conclusion Our method is highly sensitive and outperforms previously described strategies. A PERL script that implements it is available at http://www.uv.es/~genomica/treetracker.

  20. Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

    Science.gov (United States)

    Wu, Xin-Ye; Zheng, Zhi-Gang

    2013-08-01

    A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

  1. Energy Efficient Backoff Hierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Jun Wang; Yong-Tao Cao; Jun-Yuan Xie; Shi-Fu Chen

    2011-01-01

    Compared with flat routing protocols, clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we integrate the multi-hop technique with a backoff-based clustering algorithm to organize sensors. By using an adaptive backoff strategy, the algorithm not only realizes load balance among sensor node, but also achieves fairly uniform cluster head distribution across the network. Simulation results also demonstrate our algorithm is more energy-efficient than classical ones. Our algorithm is also easily extended to generate a hierarchy of cluster heads to obtain better network management and energy-efficiency.

  2. Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

    Science.gov (United States)

    Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

    2016-08-01

    Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

  3. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

    Directory of Open Access Journals (Sweden)

    Parvin Jeffrey

    2010-12-01

    Full Text Available Abstract Background Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. Results In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2 were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM. A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes. We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes and targeted TFs (25% of common TFs. The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. Conclusions Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to

  4. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

    Science.gov (United States)

    2010-01-01

    Background Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. Results In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes. We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. Conclusions Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the

  5. Cross-linked carbon network with hierarchical porous structure for high performance solid-state electrochemical capacitor

    Science.gov (United States)

    Cheng, Yongliang; Huang, Liang; Xiao, Xu; Yao, Bin; Hu, Zhimi; Li, Tianqi; Liu, Kang; Zhou, Jun

    2016-09-01

    The development of portable electronics strongly requires flexible, lightweight, and inexpensive energy-storage devices with high power density, long cycling stability, and high reliability. In this work, we prepare a flexible solid-state electrochemical capacitor using cross-linked hierarchical porous carbon network as electrode material via electrospinning and carbonization process. This device can reversibly deliver a maximum energy density of 10.18 W h/kg with excellent cycling stability which achieves 95% capacitance retention after 20000 charge/discharge cycles. Moreover, it also demonstrates outstanding mechanical flexibility and excellent capacitance retention even when the device is repeatedly bended 10000 cycles under 90°. All of these results suggest its promising perspective in flexible energy storage device.

  6. Virtual and Dynamic Hierarchical Architecture:an overlay network topology for discovering grid services with high performance

    Institute of Scientific and Technical Information of China (English)

    黄理灿; 吴朝晖; 潘云鹤

    2004-01-01

    This paper presents an overlay network topology called Virtual and Dynamic Hierarchical Architecture (VDHA) for discovering Grid services with high performance. Service discovery based on VDHA has scalable, autonomous, efficient, reliable and quick responsive. We propose two service discovery algorithms. Full Search Query and Discovery Protocol (FSQDP) discovers the nodes that match the request message from all N nodes, which has time complexity O(logN), space complexity O(nvg) (nvg being node numbers of each virtual group), and message-cost O(N), and Domain-Specific Query and Discovery Protocol (DSQDP) searches nodes in only specific domains with time complexity O(nvg), space complexity O(nvg), and message-cost O(nvg). In this paper, we also describe VDHA, its formal definition, and Grid Group Management Protocol.

  7. Criticality governed by the stable renormalization fixed point of the Ising model in the hierarchical small-world network.

    Science.gov (United States)

    Nogawa, Tomoaki; Hasegawa, Takehisa; Nemoto, Koji

    2012-09-01

    We study the Ising model in a hierarchical small-world network by renormalization group analysis and find a phase transition between an ordered phase and a critical phase, which is driven by the coupling strength of the shortcut edges. Unlike ordinary phase transitions, which are related to unstable renormalization fixed points (FPs), the singularity in the ordered phase of the present model is governed by the FP that coincides with the stable FP of the ordered phase. The weak stability of the FP yields peculiar criticalities, including logarithmic behavior. On the other hand, the critical phase is related to a nontrivial FP, which depends on the coupling strength and is continuously connected to the ordered FP at the transition point. We show that this continuity indicates the existence of a finite correlation-length-like quantity inside the critical phase, which diverges upon approaching the transition point.

  8. Hierarchical Group Based Mutual Authentication and Key Agreement for Machine Type Communication in LTE and Future 5G Networks

    Directory of Open Access Journals (Sweden)

    Probidita Roychoudhury

    2017-01-01

    Full Text Available In view of the exponential growth in the volume of wireless data communication among heterogeneous devices ranging from smart phones to tiny sensors across a wide range of applications, 3GPP LTE-A has standardized Machine Type Communication (MTC which allows communication between entities without any human intervention. The future 5G cellular networks also envisage massive deployment of MTC Devices (MTCDs which will increase the total number of connected devices hundredfold. This poses a huge challenge to the traditional cellular system processes, especially the traditional Mutual Authentication and Key Agreement (AKA mechanism currently used in LTE systems, as the signaling load caused by the increasingly large number of devices may have an adverse effect on the regular Human to Human (H2H traffic. A solution in the literature has been the use of group based architecture which, while addressing the authentication traffic, has their share of issues. This paper introduces Hierarchical Group based Mutual Authentication and Key Agreement (HGMAKA protocol to address those issues and also enables the small cell heterogeneous architecture in line with 5G networks to support MTC services. The aggregate Message Authentication Code based approach has been shown to be lightweight and significantly efficient in terms of resource usage compared to the existing protocols, while being robust to authentication message failures, and scalable to heterogeneous network architectures.

  9. RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks

    NARCIS (Netherlands)

    Labraoui, Nabila; Gueroui, Mourad; Aliouat, Makhlouf; Petit, Jonathan

    2011-01-01

    In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge

  10. RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks

    NARCIS (Netherlands)

    Labraoui, Nabila; Gueroui, Mourad; Aliouat, Makhlouf; Petit, Jonathan

    2011-01-01

    In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge arb

  11. Self-organized Critical Model Based on Complex Brain Networks with Hierarchical Organization

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ying-Yue; ZHANG Gui-Qing; YANG Qiu-Ying; CHEN Tian-Lun

    2008-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modelling each cortical area with a subnetwork of interacting excitable neurons.We find that the avalanche of our model on different levels exhibits power-law.Furthermore the power-law exponent of the distribution and the average avalanche Size are affected by the topology of the network.

  12. On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks

    CERN Document Server

    Amancio, Diego R; Costa, Luciano da F; 10.1209/0295-5075/99/48002

    2013-01-01

    Many features of complex systems can now be unveiled by applying statistical physics methods to treat them as social networks. The power of the analysis may be limited, however, by the presence of ambiguity in names, e.g., caused by homonymy in collaborative networks. In this paper we show that the ability to distinguish between homonymous authors is enhanced when longer-distance connections are considered, rather than looking at only the immediate neighbors of a node in the collaborative network. Optimized results were obtained upon using the 3rd hierarchy in connections. Furthermore, reasonable distinction among authors could also be achieved upon using pattern recognition strategies for the data generated from the topology of the collaborative network. These results were obtained with a network from papers in the arXiv repository, into which homonymy was deliberately introduced to test the methods with a controlled, reliable dataset. In all cases, several methods of supervised and unsupervised machine lear...

  13. A small-world network derived from the deterministic uniform recursive tree by line graph operation

    Science.gov (United States)

    Hou, Pengfeng; Zhao, Haixing; Mao, Yaping; Wang, Zhao

    2016-03-01

    The deterministic uniform recursive tree ({DURT}) is one of the deterministic versions of the uniform recursive tree ({URT}). Zhang et al (2008 Eur. Phys. J. B 63 507-13) studied the properties of DURT, including its topological characteristics and spectral properties. Although DURT shows a logarithmic scaling with the size of the network, DURT is not a small-world network since its clustering coefficient is zero. Lu et al (2012 Physica A 391 87-92) proposed a deterministic small-world network by adding some edges with a simple rule in each DURT iteration. In this paper, we intoduce a method for constructing a new deterministic small-world network by the line graph operation in each DURT iteration. The line graph operation brings about cliques at each node of the previous given graph, and the resulting line graph possesses larger clustering coefficients. On the other hand, this operation can decrease the diameter at almost one, then giving the analytic solutions to several topological characteristics of the model proposed. Supported by The Ministry of Science and Technology 973 project (No. 2010C B334708); National Science Foundation of China (Nos. 61164005, 11161037, 11101232, 11461054, 11551001); The Ministry of education scholars and innovation team support plan of Yangtze River (No. IRT1068); Qinghai Province Nature Science Foundation Project (Nos. 2012-Z-943, 2014-ZJ-907).

  14. A data management proposal to connect in a hierarchical way nodes of the Spanish Long Term Ecological Research (LTER) network

    Science.gov (United States)

    Fuentes, Daniel; Pérez-Luque, Antonio J.; Bonet García, Francisco J.; Moreno-LLorca, Ricardo A.; Sánchez-Cano, Francisco M.; Suárez-Muñoz, María

    2017-04-01

    The Long Term Ecological Research (LTER) network aims to provide the scientific community, policy makers, and society with the knowledge and predictive understanding necessary to conserve, protect, and manage the ecosystems. LTER is organized into networks ranging from the global to national scale. In the top of network, the International Long Term Ecological Research (ILTER) Network coordinates among ecological researchers and LTER research networks at local, regional and global scales. In Spain, the Spanish Long Term Ecological Research (LTER-Spain) network was built to foster the collaboration and coordination between longest-lived ecological researchers and networks on a local scale. Currently composed by nine nodes, this network facilitates the data exchange, documentation and preservation encouraging the development of cross-disciplinary works. However, most nodes have no specific information systems, tools or qualified personnel to manage their data for continued conservation and there are no harmonized methodologies for long-term monitoring protocols. Hence, the main challenge is to place the nodes in its correct position in the network, providing the best tools that allow them to manage their data autonomously and make it easier for them to access information and knowledge in the network. This work proposes a connected structure composed by four LTER nodes located in southern Spain. The structure is built considering hierarchical approach: nodes that create information which is documented using metadata standards (such as Ecological Metadata Language, EML); and others nodes that gather metadata and information. We also take into account the capacity of each node to manage their own data and the premise that the data and metadata must be maintained where it is generated. The current state of the nodes is a follows: two of them have their own information management system (Sierra Nevada-Granada and Doñana Long-Term Socio-ecological Research Platform) and

  15. Brain networks for confidence weighting and hierarchical inference during probabilistic learning.

    Science.gov (United States)

    Meyniel, Florent; Dehaene, Stanislas

    2017-05-09

    Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.

  16. Two-user opportunistic scheduling using hierarchical modulations in wireless networks with heterogenous average link gains

    KAUST Repository

    Hossain, Md Jahangir

    2010-03-01

    Our contribution, in this paper, is two-fold. First, we analyze the performance of a hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) scheme, which was proposed by the authors, in a fading environment where different users have different average link gains. Specifically, we present a new expression for the spectral efficiency (SE) of the users and using this expression, we compare the degrees of fairness (DOF) of the TBS scheme with that of classical single user opportunistic scheduling schemes, namely, absolute carrier-to-noise ratio (CNR) based single-best user scheduling (SBS) and normalized CNR based proportional fair scheduling (PFS) schemes. The second contribution is that we propose a new hybrid two-user opportunistic scheduling (HTS) scheme based on our earlier proposed TBS scheme. This HTS scheme selects the first user based on the largest absolute CNR value among all the users while the second user is selected based on the ratios of the absolute CNRs to the corresponding average CNRs of the remaining users. The total transmission rate i.e., the constellation size is selected according to the absolute CNR of the first best user. The total transmission rate is then allocated among these selected users by joint consideration of their absolute CNRs and allocated number of information bit(s) are transmitted to them using hierarchical modulations. Numerical results are presented for a fading environment where different users experience independent but non-identical (i.n.d.) channel fading. These selected numerical results show that the proposed HTS scheme can considerably increase the system\\'s fairness without any degradation of the link spectral efficiency (LSE) i.e., the multiuser diversity gain compared to the classical SBS scheme. These results also show that the proposed HTS scheme has a lower fairness in comparison to the PFS scheme which suffers from a considerable degradation in LSE. © 2010 IEEE.

  17. QoS Supported IPTV Service Architecture over Hybrid-Tree-Based Explicit Routed Multicast Network

    Directory of Open Access Journals (Sweden)

    Chih-Chao Wen

    2012-01-01

    Full Text Available With the rapid advance in multimedia streaming and multicast transport technology, current IP multicast protocols, especially PIM-SM, become the major channel delivery mechanism for IPTV system over Internet. The goals for IPTV service are to provide two-way interactive services for viewers to select popular program channel with high quality for watching during fast channel surfing period. However, existing IP multicast protocol cannot meet above QoS requirements for IPTV applications between media server and subscribers. Therefore, we propose a cooperative scheme of hybrid-tree based on explicit routed multicast, called as HT-ERM to combine the advantages of shared tree and source tree for QoS-supported IPTV service. To increase network utilization, the constrained shortest path first (CSPF routing algorithm is designed for construction of hybrid tree to deliver the high-quality video stream over watching channel and standard quality over surfing channel. Furthermore, the Resource Reservation Protocol- Traffic Engineering (RSVP-TE is used as signaling mechanism to set up QoS path for multicast channel admission control. Our simulation results demonstrated that the proposed HT-ERM scheme outperforms other multicast QoS-based delivery scheme in terms of channel switching delay, resource utilization, and blocking ratio for IPTV service.

  18. Is Light-Tree Structure Optimal for Multicast Routing in Sparse Light Splitting WDM Networks?

    CERN Document Server

    Zhou, Fen; Cousin, Bernard; 10.1109/ICCCN.2009.5235386

    2010-01-01

    To minimize the number of wavelengths required by a multicast session in sparse light splitting wavelength division multiplexing (WDM) networks, a light-hierarchy structure, which occupies the same wavelength on all links, is proposed to span as many destinations as possible. Different from a light-tree, a light-hierarchy accepts cycles, which are used to traverse crosswise a 4-degree (or above) multicast incapable (MI) node twice (or above) and switch two light signals on the same wavelengths to two destinations in the same multicast session. In this paper, firstly, a graph renewal and distance priority light-tree algorithm (GRDP-LT) is introduced to improve the quality of light-trees built for a multicast request. Then, it is extended to compute light-hierarchies. Obtained numerical results demonstrate the GRDP-LT light-trees can achieve a much lower links stress, better wavelength channel cost, and smaller average end-to-end delay as well as diameter than the currently most efficient algorithm. Furthermore...

  19. A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Matheswaran Saravanan

    2014-01-01

    Full Text Available Wireless sensor network (WSN consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST. The proposed algorithm computes the distance-based Minimum Spanning Tree (MST of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm.

  20. Inferring regulatory networks from expression data using tree-based methods.

    Directory of Open Access Journals (Sweden)

    Vân Anh Huynh-Thu

    Full Text Available One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene is predicted from the expression patterns of all the other genes (input genes, using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

  1. Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

    Science.gov (United States)

    Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

    2017-02-01

    Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

  2. Hierarchical block structures and high-resolution model selection in large networks

    CERN Document Server

    Peixoto, Tiago P

    2013-01-01

    Discovering the large-scale topological features in empirical networks is a crucial tool in understanding how complex systems function. However most existing methods used to obtain the modular structure of networks suffer from serious problems, such as the resolution limit on the size of communities, where smaller but well-defined clusters are not detectable when the network becomes large. This phenomenon occurs for the very popular approach of modularity optimization, but also for more principled ones based on statistical inference and model selection. Here we construct a nested generative model which, through a complete description of the entire network hierarchy at multiple scales, is capable of avoiding this limitation, and enables the detection of modular structure at levels far beyond those possible by current approaches. Even with this increased resolution, the method is based on the principle of parsimony, and is capable of separating signal from noise, and thus will not lead to the identification of ...

  3. A Tree Based Self-routing Scheme for Mobility Support in Wireless Sensor Networks

    Science.gov (United States)

    Kim, Young-Duk; Yang, Yeon-Mo; Kang, Won-Seok; Kim, Jin-Wook; An, Jinung

    Recently, WSNs (Wireless Sensor Networks) with mobile robot is a growing technology that offer efficient communication services for anytime and anywhere applications. However, the tiny sensor node has very limited network resources due to its low battery power, low data rate, node mobility, and channel interference constraint between neighbors. Thus, in this paper, we proposed a tree based self-routing protocol for autonomous mobile robots based on beacon mode and implemented in real test-bed environments. The proposed scheme offers beacon based real-time scheduling for reliable association process between parent and child nodes. In addition, it supports smooth handover procedure by reducing flooding overhead of control packets. Throughout the performance evaluation by using a real test-bed system and simulation, we illustrate that our proposed scheme demonstrates promising performance for wireless sensor networks with mobile robots.

  4. Multipath Routing for Self-Organizing Hierarchical Mobile Ad-Hoc Networks – A Review

    OpenAIRE

    Udayachandran Ramasamy; Sankaranarayanan, K.

    2010-01-01

    Security has become a primary concern for providing protected communication between mobile nodes in a hostile environment. The characteristics of Ad-hoc networks (dynamic topology, infrastructure less, variable capacity links, etc) are origin of many issues. Limited bandwidth, energy constraints, high cost security are the encountered problems. This type of networks pose particular challenges in terms of Quality of Service (QoS) and performance. In this paper, the issues of multipath routing ...

  5. Inter-Cluster Routing Authentication for Ad Hoc Networks by a Hierarchical Key Scheme

    Institute of Scientific and Technical Information of China (English)

    Yueh-Min Huang; Hua-Yi Lin; Tzone-I Wang

    2006-01-01

    Dissimilar to traditional networks, the features of mobile wireless devices that can actively form a network without any infrastructure mean that mobile ad hoc networks frequently display partition due to node mobility or link failures. These indicate that an ad hoc network is difficult to provide on-line access to a trusted authority server. Therefore,applying traditional Public Key Infrastructure (PKI) security framework to mobile ad hoc networks will cause insecurities.This study proposes a scalable and elastic key management scheme integrated into Cluster Based Secure Routing Protocol (CBSRP) to enhance security and non-repudiation of routing authentication, and introduces an ID-Based internal routing authentication scheme to enhance the routing performance in an internal cluster. Additionally, a method of performing routing authentication between internal and external clusters, as well as inter-cluster routing authentication, is developed.The proposed cluster-based key management scheme distributes trust to an aggregation of cluster heads using a threshold scheme faculty, provides Certificate Authority (CA) with a fault tolerance mechanism to prevent a single point of compromise or failure, and saves CA large repositories from maintaining member certificates, making ad hoc networks robust to malicious behaviors and suitable for numerous mobile devices.

  6. Decentralized cooperative unmanned aerial vehicles conflict resolution by neural network-based tree search method

    Directory of Open Access Journals (Sweden)

    Jian Yang

    2016-09-01

    Full Text Available In this article, a tree search algorithm is proposed to find the near optimal conflict avoidance solutions for unmanned aerial vehicles. In the dynamic environment, the unmodeled elements, such as wind, would make UAVs deviate from nominal traces. It brings about difficulties for conflict detection and resolution. The back propagation neural networks are utilized to approximate the unmodeled dynamics of the environment. To satisfy the online planning requirement, the search length of the tree search algorithm would be limited. Therefore, the algorithm may not be able to reach the goal states in search process. The midterm reward function for assessing each node is devised, with consideration given to two factors, namely, the safe separation requirement and the mission of each unmanned aerial vehicle. The simulation examples and the comparisons with previous approaches are provided to illustrate the smooth and convincing behaviours of the proposed algorithm.

  7. Decentralized Lifetime Maximizing Tree with Clustering for Data Delivery in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Deepali Virmani

    2011-09-01

    Full Text Available A wireless sensor network has a wide application domain which is expanding everyday and they have been deployed pertaining to their application area. An application independent approach is yet to come to terms with the ongoing exploitation of the WSNs. In this paper we propose a decentralized lifetime maximizing tree for application independent data aggregation scheme using the clustering for data delivery in WSNs. The proposed tree will minimize the energy consumption which has been a resisting factor in the smooth working of WSNs as well as minimize the distance between the communicating nodes under the control of a sub-sink which further communicate and transfer data to the sink node.

  8. Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

    Institute of Scientific and Technical Information of China (English)

    Yanlin He; Yuan Xu; Zhiqiang Geng; Qunxiong Zhu

    2015-01-01

    To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network (AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts:groups of subnets based on well trained Auto-associative Neural Networks (AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method, the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification (EDAC) is adopted. Soft sensor using AHNN based on EDAC (EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid (PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.

  9. Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing

    Directory of Open Access Journals (Sweden)

    Saima Zafar

    2016-10-01

    Full Text Available A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN. Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called ‘active network subset’ that satisfies the existing network traffic conditions while switching off the residual unused network components. A ‘passive network subset’ is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%–70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision.

  10. Efficient hierarchical analysis of the stability of a network through dimensional reduction of its influence topology

    CERN Document Server

    Kinkhabwala, Ali

    2013-01-01

    The connection between network topology and stability remains unclear. General approaches that clarify this relationship and allow for more efficient stability analysis would be desirable. In this manuscript, I examine the mathematical notion of influence topology, which is fundamentally based on the network reaction stoichiometries and the first derivatives of the reactions with respect to each species at the steady state solution(s). The influence topology is naturally represented as a signed directed bipartite graph with arrows or blunt arrows connecting a species node to a reaction node (positive/negative derivative) or a reaction node to a species node (positive/negative stoichiometry). The set of all such graphs is denumerable. A significant reduction in dimensionality is possible through stoichiometric scaling, cycle compaction, and temporal scaling. All cycles in a network can be read directly from the graph of its influence topology, enabling efficient and intuitive computation of the principal minor...

  11. Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness.

    Science.gov (United States)

    Zhang, Jing; Chu, Haitao; Hong, Hwanhee; Virnig, Beth A; Carlin, Bradley P

    2015-07-28

    Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random. However, in randomized controlled trials, nonignorable missingness or missingness not at random may occur due to deliberate choices of treatments at the design stage. In addition, those undertaking a network meta-analysis may selectively choose treatments to include in the analysis, which may also lead to missingness not at random. In this paper, we extend our previous work to incorporate missingness not at random using selection models. The proposed method is then applied to two network meta-analyses and evaluated through extensive simulation studies. We also provide comprehensive comparisons of a commonly used contrast-based method and the arm-based method via simulations in a technical appendix under missing completely at random and missing at random.

  12. AtPID: the overall hierarchical functional protein interaction network interface and analytic platform for Arabidopsis.

    Science.gov (United States)

    Li, Peng; Zang, Weidong; Li, Yuhua; Xu, Feng; Wang, Jigang; Shi, Tieliu

    2011-01-01

    Protein interactions are involved in important cellular functions and biological processes that are the fundamentals of all life activities. With improvements in experimental techniques and progress in research, the overall protein interaction network frameworks of several model organisms have been created through data collection and integration. However, most of the networks processed only show simple relationships without boundary, weight or direction, which do not truly reflect the biological reality. In vivo, different types of protein interactions, such as the assembly of protein complexes or phosphorylation, often have their specific functions and qualifications. Ignorance of these features will bring much bias to the network analysis and application. Therefore, we annotate the Arabidopsis proteins in the AtPID database with further information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways. The latest updated AtPID database is available at http://www.megabionet.org/atpid/.

  13. New approach for phylogenetic tree recovery based on genome-scale metabolic networks.

    Science.gov (United States)

    Gamermann, Daniel; Montagud, Arnaud; Conejero, J Alberto; Urchueguía, Javier F; de Córdoba, Pedro Fernández

    2014-07-01

    A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

  14. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

    Science.gov (United States)

    Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

  15. Hierarchal Variable Switching Sets of Interacting Multiple Model for Tracking Maneuvering Targets in Sensor Network

    Directory of Open Access Journals (Sweden)

    Seham Moawoud Ay Ebrahim

    2013-01-01

    Full Text Available Tracking maneuvering targets introduce two major directions to improve the Multiple Model (MM approach: Develop a better MM algorithm and design a better model set. The Interacting Multiple Model (IMM estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is the ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the use of too many models is performance-wise as bad as that of too few models. In this paper we show that the use of too many models is performance-wise as bad as that of too few models. To overcome this we divide the models into a small number of sets, tuning these sets during operation at the right operating set. We proposed Hierarchal Switching sets of IMM (HSIMM. The state space of the nonlinear variable is divided into sets each set has its own IMM. The connection between them is the switching algorithm which manages the activation and termination of sets. Also the re-initialization process overcomes the error accumulation due to the targets changes from one model to another. This switching can introduce a number of different models while no restriction on their number. The activation of sets depends on the threshold value of set likely hood. As the likely hood of the set is higher than threshold it is active otherwise it is minimized. The result is the weighted sum of the output of active sets. The computational time is minimum than introduced by IMM and VIMM. HSIMM introduces less error as the noise increase and there is no need for re adjustment to the Covariance as the noise increase so it is more robust against noise and introduces minimum computational time.

  16. Optimizing data access for wind farm control over hierarchical communication networks

    DEFF Research Database (Denmark)

    Madsen, Jacob Theilgaard; Findrik, Mislav; Madsen, Tatiana Kozlova

    2016-01-01

    and communication networks on the controller performance. We start by investigating the effects of a communication network that introduces delays in the information access for the central controller. The control performance as measured by accumulated fatigue is shown to be significantly impacted by communication....... This information quality metric is called mismatch probability, mmPr, and is used to express quantitatively the information accuracy in a given scenario. Lastly measurements of different communication technologies have been performed in order to carry out the analysis in a practically relevant scenario......In this paper we investigate a centralized wind farm controller which runs periodically. The controller attempts to reduce the damage a wind turbine sustains during operation by estimating fatigue based on the wind turbine state. The investigation focuses on the impact of information access...

  17. A hierarchical network approach for modeling Rift Valley fever epidemics with applications in North America.

    Directory of Open Access Journals (Sweden)

    Ling Xue

    Full Text Available Rift Valley fever is a vector-borne zoonotic disease which causes high morbidity and mortality in livestock. In the event Rift Valley fever virus is introduced to the United States or other non-endemic areas, understanding the potential patterns of spread and the areas at risk based on disease vectors and hosts will be vital for developing mitigation strategies. Presented here is a general network-based mathematical model of Rift Valley fever. Given a lack of empirical data on disease vector species and their vector competence, this discrete time epidemic model uses stochastic parameters following several PERT distributions to model the dynamic interactions between hosts and likely North American mosquito vectors in dispersed geographic areas. Spatial effects and climate factors are also addressed in the model. The model is applied to a large directed asymmetric network of 3,621 nodes based on actual farms to examine a hypothetical introduction to some counties of Texas, an important ranching area in the United States of America. The nodes of the networks represent livestock farms, livestock markets, and feedlots, and the links represent cattle movements and mosquito diffusion between different nodes. Cattle and mosquito (Aedes and Culex populations are treated with different contact networks to assess virus propagation. Rift Valley fever virus spread is assessed under various initial infection conditions (infected mosquito eggs, adults or cattle. A surprising trend is fewer initial infectious organisms result in a longer delay before a larger and more prolonged outbreak. The delay is likely caused by a lack of herd immunity while the infection expands geographically before becoming an epidemic involving many dispersed farms and animals almost simultaneously. Cattle movement between farms is a large driver of virus expansion, thus quarantines can be efficient mitigation strategy to prevent further geographic spread.

  18. A hierarchical network approach for modeling Rift Valley fever epidemics with applications in North America.

    Science.gov (United States)

    Xue, Ling; Cohnstaedt, Lee W; Scott, H Morgan; Scoglio, Caterina

    2013-01-01

    Rift Valley fever is a vector-borne zoonotic disease which causes high morbidity and mortality in livestock. In the event Rift Valley fever virus is introduced to the United States or other non-endemic areas, understanding the potential patterns of spread and the areas at risk based on disease vectors and hosts will be vital for developing mitigation strategies. Presented here is a general network-based mathematical model of Rift Valley fever. Given a lack of empirical data on disease vector species and their vector competence, this discrete time epidemic model uses stochastic parameters following several PERT distributions to model the dynamic interactions between hosts and likely North American mosquito vectors in dispersed geographic areas. Spatial effects and climate factors are also addressed in the model. The model is applied to a large directed asymmetric network of 3,621 nodes based on actual farms to examine a hypothetical introduction to some counties of Texas, an important ranching area in the United States of America. The nodes of the networks represent livestock farms, livestock markets, and feedlots, and the links represent cattle movements and mosquito diffusion between different nodes. Cattle and mosquito (Aedes and Culex) populations are treated with different contact networks to assess virus propagation. Rift Valley fever virus spread is assessed under various initial infection conditions (infected mosquito eggs, adults or cattle). A surprising trend is fewer initial infectious organisms result in a longer delay before a larger and more prolonged outbreak. The delay is likely caused by a lack of herd immunity while the infection expands geographically before becoming an epidemic involving many dispersed farms and animals almost simultaneously. Cattle movement between farms is a large driver of virus expansion, thus quarantines can be efficient mitigation strategy to prevent further geographic spread.

  19. Non-Linear Behaviour Of Gelatin Networks Reveals A Hierarchical Structure

    KAUST Repository

    Yang, Zhi

    2015-12-14

    We investigate the strain hardening behaviour of various gelatin networks - namely physically-crosslinked gelatin gel, chemically-crosslinked gelatin gels, and a hybrid gels made of a combination of the former two - under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillation shear protocols. Further, the internal structures of physically-crosslinked gelatin gel and chemically-crosslinked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically-crosslinked network, whereas in the physically-crosslinked gels a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as correlation length (ξ), cross-sectional polymer chain radius (Rc), and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physically-crosslinked and chemically crosslinked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized non-linear elastic theory we used to fit our stress-strain curves. The chemical crosslinking that generates coils and aggregates hinders the free stretching of the triple helices bundles in the physically-crosslinked gels.

  20. Admission Control for Multiservices Traffic in Hierarchical Mobile IPv6 Networks by Using Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Jung-Shyr Wu

    2012-01-01

    Full Text Available CAC (Call Admission Control plays a significant role in providing QoS (Quality of Service in mobile wireless networks. In addition to much research that focuses on modified Mobile IP to get better efficient handover performance, CAC should be introduced to Mobile IP-based network to guarantee the QoS for users. In this paper, we propose a CAC scheme which incorporates multiple traffic types and adjusts the admission threshold dynamically using fuzzy control logic to achieve better usage of resources. The method can provide QoS in Mobile IPv6 networks with few modifications on MAP (Mobility Anchor Point functionality and slight change in BU (Binding Update message formats. According to the simulation results, the proposed scheme presents good performance of voice and video traffic at the expenses of poor performance on data traffic. It is evident that these CAC schemes can reduce the probability of the handoff dropping and the cell overload and limit the probability of the new call blocking.

  1. Energy Efficient Zone Division Multihop Hierarchical Clustering Algorithm for Load Balancing in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Ashim Kumar Ghosh

    2011-12-01

    Full Text Available Wireless sensor nodes are use most embedded computing application. Multihop cluster hierarchy has been presented for large wireless sensor networks (WSNs that can provide scalable routing, data aggregation, and querying. The energy consumption rate for sensors in a WSN varies greatly based on the protocols the sensors use for communications. In this paper we present a cluster based routing algorithm. One of our main goals is to design the energy efficient routing protocol. Here we try to solve the usual problems of WSNs. We know the efficiency of WSNs depend upon the distance between node to base station and the amount of data to be transferred and the performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. This algorithm makes the best use of node with low number of cluster head know as super node. Here we divided the full region in four equal zones and the centre area of the region is used to select for super node. Each zone is considered separately and the zone may be or not divided further that’s depending upon the density of nodes in that zone and capability of the super node. This algorithm forms multilayer communication. The no of layer depends on the network current load and statistics. Our algorithm is easily extended to generate a hierarchy of cluster heads to obtain better network management and energy efficiency.

  2. A high-resolution monitoring network investigating stem growth of tropical forest trees

    Science.gov (United States)

    Hofhansl, F.; De Araujo, A. C.; DeLucia, E. H.

    2015-12-01

    The proportion of carbon (C) allocated to tree stems is an important determinant of the C sink-strength of global forest ecosystems. Understanding the mechanisms controlling stem growth is essential for parameterization of global vegetation models and to accurately predict C sequestration of forest ecosystems. However, we still lack a thorough understanding of intra-annual variations in stem growth of tropical forest ecosystems, which could be especially prone to projected climatic changes. We here present high-resolution data (≤ 6 µm; ≥ 1 min) from a novel monitoring network of wireless devices for automated measurement of expansion and contraction in tree diameter using a membrane potentiometer, as well as point dendrometers on phloem and xylem to analyze diurnal changes in stem growth. Our results indicate that diurnal changes in stem diameter were associated with sap flow and related to seasonal variations in daytime temperature and water availability, such that daily maximum stem growth was positively related to temperature during the wet season but showed the opposite trend during the onset of the dry season. We show that high-resolution monitoring of stem growth of tropical trees is crucial to determine the response to intra-annual climate variation and therefore will be key to accurately predict future responses of tropical aboveground C storage, and should be of special interest for tropical ecosystem research and earth system science.

  3. Fast method of constructing image correlations to build a free network based on image multivocabulary trees

    Science.gov (United States)

    Zhan, Zongqian; Wang, Xin; Wei, Minglu

    2015-05-01

    In image-based three-dimensional (3-D) reconstruction, one topic of growing importance is how to quickly obtain a 3-D model from a large number of images. The retrieval of the correct and relevant images for the model poses a considerable technological challenge. The "image vocabulary tree" has been proposed as a method to search for similar images. However, a significant drawback of this approach is identified in its low time efficiency and barely satisfactory classification result. The method proposed is inspired by, and improves upon, some recent methods. Specifically, vocabulary quality is considered and multivocabulary trees are designed to improve the classification result. A marked improvement was, indeed, observed in our evaluation of the proposed method. To improve time efficiency, graphics processing unit (GPU) computer unified device architecture parallel computation is applied in the multivocabulary trees. The results of the experiments showed that the GPU was three to four times more efficient than the enumeration matching and CPU methods when the number of images is large. This paper presents a reliable reference method for the rapid construction of a free network to be used for the computing of 3-D information.

  4. Spring temperature variability over Turkey since 1800 CE reconstructed from a broad network of tree-ring data

    Science.gov (United States)

    Köse, Nesibe; Tuncay Güner, H.; Harley, Grant L.; Guiot, Joel

    2017-01-01

    The meteorological observational period in Turkey, which starts ca. 1930 CE, is too short for understanding long-term climatic variability. Tree rings have been used intensively as proxy records to understand summer precipitation history of the region, primarily because they have a dominant precipitation signal. Yet, the historical context of temperature variability is unclear. Here, we used higher-order principle components of a network of 23 tree-ring chronologies to provide a high-resolution spring (March-April) temperature reconstruction over Turkey during the period 1800-2002. The reconstruction model accounted for 67 % (Adj. R2 = 0.64, p tree-ring network (first principle component) showed highly significant correlations with gridded summer drought index reconstruction over Turkey and Mediterranean countries. Our results showed that, beside the dominant precipitation signal, a temperature signal can be extracted from tree-ring series and they can be useful proxies in reconstructing past temperature variability.

  5. Effective Network Intrusion Detection using Classifiers Decision Trees and Decision rules

    Directory of Open Access Journals (Sweden)

    G.MeeraGandhi

    2010-11-01

    Full Text Available In the era of information society, computer networks and their related applications are the emerging technologies. Network Intrusion Detection aims at distinguishing the behavior of the network. As the network attacks have increased in huge numbers over the past few years, Intrusion Detection System (IDS is increasingly becoming a critical component to secure the network. Owing to large volumes of security audit data in a network in addition to intricate and vibrant properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this work, the field of machine learning attempts to characterize how such changes can occur by designing, implementing, running, and analyzing algorithms that can be run on computers. The discipline draws on ideas, with the goal of understanding the computational character of learning. Learning always occurs in the context of some performance task, and that a learning method should always be coupled with a performance element that uses the knowledge acquired during learning. In this research, machine learning is being investigated as a technique for making the selection, using as training data and their outcome. In this paper, we evaluate the performance of a set of classifier algorithms of rules (JRIP, Decision Tabel, PART, and OneR and trees (J48, RandomForest, REPTree, NBTree. Based on the evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The empirical simulation result shows the comparison between the noticeable performance improvements. The classification models were trained using the data collected from Knowledge Discovery Databases (KDD for Intrusion Detection. The trained models were then used for predicting the risk of the attacks in a web server environment or by any network administrator or any Security Experts. The

  6. Enhanced Deployment Strategy for Role-based Hierarchical Application Agents in Wireless Sensor Networks with Established Clusterheads

    Science.gov (United States)

    Gendreau, Audrey

    Efficient self-organizing virtual clusterheads that supervise data collection based on their wireless connectivity, risk, and overhead costs, are an important element of Wireless Sensor Networks (WSNs). This function is especially critical during deployment when system resources are allocated to a subsequent application. In the presented research, a model used to deploy intrusion detection capability on a Local Area Network (LAN), in the literature, was extended to develop a role-based hierarchical agent deployment algorithm for a WSN. The resulting model took into consideration the monitoring capability, risk, deployment distribution cost, and monitoring cost associated with each node. Changing the original LAN methodology approach to model a cluster-based sensor network depended on the ability to duplicate a specific parameter that represented the monitoring capability. Furthermore, other parameters derived from a LAN can elevate costs and risk of deployment, as well as jeopardize the success of an application on a WSN. A key component of the approach presented in this research was to reduce the costs when established clusterheads in the network were found to be capable of hosting additional detection agents. In addition, another cost savings component of the study addressed the reduction of vulnerabilities associated with deployment of agents to high volume nodes. The effectiveness of the presented method was validated by comparing it against a type of a power-based scheme that used each node's remaining energy as the deployment value. While available energy is directly related to the model used in the presented method, the study deliberately sought out nodes that were identified with having superior monitoring capability, cost less to create and sustain, and are at low-risk of an attack. This work investigated improving the efficiency of an intrusion detection system (IDS) by using the proposed model to deploy monitoring agents after a temperature sensing

  7. Hierarchically designed agarose and poly(ethylene glycol) interpenetrating network hydrogels for cartilage tissue engineering.

    Science.gov (United States)

    DeKosky, Brandon J; Dormer, Nathan H; Ingavle, Ganesh C; Roatch, Christopher H; Lomakin, Joseph; Detamore, Michael S; Gehrke, Stevin H

    2010-12-01

    A new method for encapsulating cells in interpenetrating network (IPN) hydrogels of superior mechanical integrity was developed. In this study, two biocompatible materials-agarose and poly(ethylene glycol) (PEG) diacrylate-were combined to create a new IPN hydrogel with greatly enhanced mechanical performance. Unconfined compression of hydrogel samples revealed that the IPN displayed a fourfold increase in shear modulus relative to a pure PEG-diacrylate network (39.9 vs. 9.9 kPa) and a 4.9-fold increase relative to a pure agarose network (8.2 kPa). PEG and IPN compressive failure strains were found to be 71% ± 17% and 74% ± 17%, respectively, while pure agarose gels failed around 15% strain. Similar mechanical property improvements were seen when IPNs-encapsulated chondrocytes, and LIVE/DEAD cell viability assays demonstrated that cells survived the IPN encapsulation process. The majority of IPN-encapsulated chondrocytes remained viable 1 week postencapsulation, and chondrocytes exhibited glycosaminoglycan synthesis comparable to that of agarose-encapsulated chondrocytes at 3 weeks postencapsulation. The introduction of a new method for encapsulating cells in a hydrogel with enhanced mechanical performance is a promising step toward cartilage defect repair. This method can be applied to fabricate a broad variety of cell-based IPNs by varying monomers and polymers in type and concentration and by adding functional groups such as degradable sequences or cell adhesion groups. Further, this technology may be applicable in other cell-based applications where mechanical integrity of cell-containing hydrogels is of great importance.

  8. Metapopulation modelling of riparian tree species persistence in river networks under climate change.

    Science.gov (United States)

    Van Looy, Kris; Piffady, Jérémy

    2017-11-01

    Floodplain landscapes are highly fragmented by river regulation resulting in habitat degradation and flood regime perturbation, posing risks to population persistence. Climate change is expected to pose supplementary risks in this context of fragmented landscapes, and especially for river systems adaptation management programs are developed. The association of habitat quality and quantity with the landscape dynamics and resilience to human-induced disturbances is still poorly understood in the context of species survival and colonization processes, but essential to prioritize conservation and restoration actions. We present a modelling approach that elucidates network connectivity and landscape dynamics in spatial and temporal context to identify vital corridors and conservation priorities in the Loire river and its tributaries. Alteration of flooding and flow regimes is believed to be critical to population dynamics in river ecosystems. Still, little is known of critical levels of alteration both spatially and temporally. We applied metapopulation modelling approaches for a dispersal-limited tree species, white elm; and a recruitment-limited tree species, black poplar. In different model steps the connectivity and natural dynamics of the river landscape are confronted with physical alterations (dams/dykes) to species survival and then future scenarios for climatic changes and potential adaptation measures are entered in the model and translated in population persistence over the river basin. For the two tree species we highlighted crucial network zones in relation to habitat quality and connectivity. Where the human impact model already shows currently restricted metapopulation development, climate change is projected to aggravate this persistence perspective substantially. For both species a significant drawback to the basin population is observed, with 1/3 for elm and ¼ for poplar after 25 years already. But proposed adaptation measures prove effective to even

  9. A Hierarchical Network of Provably Optimal Learning Control Systems: Extensions of the Associative Control Process (ACP) Network

    Science.gov (United States)

    1993-01-01

    either behavior or learning, The modified ACP network does not have a negative reinforcement center yet is able to reproduce the simulation results of...Assuming N = 0 at all times, there is only one reinforcement center that is active, the positive reinforcement ccnter (PC). The negative ... reinforcement center (NC) is never active, and the weights associated with NC never change. The state is sensed through the sensors, and the actions are

  10. Cellulose nanofiber-templated three-dimension TiO2 hierarchical nanowire network for photoelectrochemical photoanode

    Science.gov (United States)

    Li, Zhaodong; Yao, Chunhua; Wang, Fei; Cai, Zhiyong; Wang, Xudong

    2014-12-01

    Three dimensional (3D) nanostructures with extremely large porosity possess a great promise for the development of high-performance energy harvesting and storage devices. In this paper, we developed a high-density 3D TiO2 fiber-nanorod (NR) heterostructure for efficient photoelectrochemical (PEC) water splitting. The hierarchical structure was synthesized on a ZnO-coated cellulose nanofiber (CNF) template using atomic layer deposition (ALD)-based thin film and NR growth procedures. The tubular structure evolution was in good agreement with the recently discovered vapor-phase Kirkendall effect in high-temperature ALD processes. The NR morphology was formed via the surface-reaction-limited pulsed chemical vapor deposition (SPCVD) mechanism. Under Xenon lamp illumination without and with an AM 1.5G filter or a UV cut off filter, the PEC efficiencies of a 3D TiO2 fiber-NR heterostructure were found to be 22-249% higher than those of the TiO2-ZnO bilayer tubular nanofibers and TiO2 nanotube networks that were synthesized as reference samples. Such a 3D TiO2 fiber-NR heterostructure offers a new route for a cellulose-based nanomanufacturing technique, which can be used for large-area, low-cost, and green fabrication of nanomaterials as well as their utilizations for efficient solar energy harvesting and conversion.

  11. Simple and difficult mathematics in children: a minimum spanning tree EEG network analysis.

    Science.gov (United States)

    Vourkas, Michael; Karakonstantaki, Eleni; Simos, Panagiotis G; Tsirka, Vasso; Antonakakis, Marios; Vamvoukas, Michael; Stam, Cornelis; Dimitriadis, Stavros; Micheloyannis, Sifis

    2014-07-25

    Sensor-level network characteristics associated with arithmetic tasks varying in complexity were estimated using tools from modern network theory. EEG signals from children with math difficulties (MD) and typically achieving controls (NI) were analyzed using minimum spanning tree (MST) indices derived from Phase Lag Index values - a graph method that corrects for comparison bias. Results demonstrated progressive modulation of certain MST parameters with increased task difficulty. These findings were consistent with more distributed network activation in the theta band, and greater network integration (i.e., tighter communication between involved regions) in the alpha band as task demands increased. There was also evidence of stronger intraregional signal inter-dependencies in the higher frequency bands during the complex math task. Although these findings did not differ between groups, several MST parameters were positively correlated with individual performance on psychometric math tasks involving similar operations, especially in the NI group. The findings support the potential utility of MST analyses to evaluate function-related electrocortical reactivity over a wide range of EEG frequencies in children.

  12. Prognostic transcriptional association networks: a new supervised approach based on regression trees

    Science.gov (United States)

    Nepomuceno-Chamorro, Isabel; Azuaje, Francisco; Devaux, Yvan; Nazarov, Petr V.; Muller, Arnaud; Aguilar-Ruiz, Jesús S.; Wagner, Daniel R.

    2011-01-01

    Motivation: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains. Results: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine. Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/. Contact: inepomuceno@us.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21098433

  13. A CAD System for Identification and Classification of Breast Cancer Tumors in DCE-MR Images Based on Hierarchical Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Rastiboroujeni

    2015-06-01

    Full Text Available In this paper, we propose a computer aided diagnosis (CAD system based on hierarchical convolutional neural networks (HCNNs to discriminate between malignant and benign tumors in breast DCE-MRIs. A HCNN is a hierarchical neural network that operates on two-dimensional images. A HCNN integrates feature extraction and classification processes into one single and fully adaptive structure. It can extract two-dimensional key features automatically, and it is relatively tolerant to geometric and local distortions in input images. We evaluate CNN implementation learning and testing processes based on gradient descent (GD and resilient back-propagation (RPROP approaches. We show that, proposed HCNN with RPROP learning approach provide an effective and robust neural structure to design a CAD base system for breast MRI, and has potential as a mechanism for the evaluation of different types of abnormalities in medical images.

  14. Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

    Science.gov (United States)

    Li, Qingquan; Zeng, Zhe; Zhang, Tong; Li, Jonathan; Wu, Zhongheng

    2011-02-01

    Optimal paths computed by conventional path-planning algorithms are usually not "optimal" since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.

  15. Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

    Directory of Open Access Journals (Sweden)

    Liang Ding

    2007-11-01

    Full Text Available Wireless multimedia sensor networks (WMSN have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

  16. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2014-01-01

    Full Text Available This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users’ demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators’ service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

  17. An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan; Qin, Jin

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

  18. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

  19. Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human-viral Infection Patterns.

    Science.gov (United States)

    Smoly, Ilan; Carmel, Amir; Shemer-Avni, Yonat; Yeger-Lotem, Esti; Ziv-Ukelson, Michal

    2016-03-01

    Network querying is a powerful approach to mine molecular interaction networks. Most state-of-the-art network querying tools either confine the search to a prespecified topology in the form of some template subnetwork, or do not specify any topological constraints at all. Another approach is grammar-based queries, which are more flexible and expressive as they allow for expressing the topology of the sought pattern according to some grammar-based logic. Previous grammar-based network querying tools were confined to the identification of paths. In this article, we extend the patterns identified by grammar-based query approaches from paths to trees. For this, we adopt a higher order query descriptor in the form of a regular tree grammar (RTG). We introduce a novel problem and propose an algorithm to search a given graph for the k highest scoring subgraphs matching a tree accepted by an RTG. Our algorithm is based on the combination of dynamic programming with color coding, and includes an extension of previous k-best parsing optimization approaches to avoid isomorphic trees in the output. We implement the new algorithm and exemplify its application to mining viral infection patterns within molecular interaction networks. Our code is available online.

  20. Village agroforestry systems and tree-use practices: A case study in Sri Lanka. Multipurpose tree species network research series

    Energy Technology Data Exchange (ETDEWEB)

    Wickramasinghe, A.

    1992-01-01

    Village agroforestry systems in Sri Lanka have evolved through farmers' efforts to meet their survival needs. The paper examines farmers' land-use systems and their perceptions of the role of trees in the villages of Bambarabedda and Madugalla in central Sri Lanka. The benefits of village agroforestry are diverse food, fuelwood, fodder, timber, and mulch, but food products are of outstanding importance. The ability of Artocarpus heterophyllus (the jackfruit tree) and Cocos nucifera (coconut) to ensure food security during the dry season and provide traditional foods throughout the year, as well as to grow in limited space, make them popular crops in the two study villages. The study recommends that further research precede the formulation of agricultural interventions and that efforts to promote improved tree varieties recognize farmers' practices and expressed needs.