Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks
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.
Drossel, Barbara
2007-01-01
This review explains in a self-contained way the properties of random Boolean networks and their attractors, with a special focus on critical networks. Using small example networks, analytical calculations, phenomenological arguments, and problems to solve, the basic concepts are introduced and important results concerning phase diagrams, numbers of relevant nodes and attractor properties are derived.
Boolean networks as modelling framework
Florian eGreil
2012-08-01
Full Text Available In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and the immediate application in plant biology, references for more in-depths treatment of the mathematical details are also given.
Boolean networks with reliable dynamics
Peixoto, Tiago P
2009-01-01
We investigated the properties of Boolean networks that follow a given reliable trajectory in state space. A reliable trajectory is defined as a sequence of states which is independent of the order in which the nodes are updated. We explored numerically the topology, the update functions, and the state space structure of these networks, which we constructed using a minimum number of links and the simplest update functions. We found that the clustering coefficient is larger than in random networks, and that the probability distribution of three-node motifs is similar to that found in gene regulation networks. Among the update functions, only a subset of all possible functions occur, and they can be classified according to their probability. More homogeneous functions occur more often, leading to a dominance of canalyzing functions. Finally, we studied the entire state space of the networks. We observed that with increasing systems size, fixed points become more dominant, moving the networks close to the frozen...
Forced synchronization of autonomous dynamical Boolean networks
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics
Forced synchronization of autonomous dynamical Boolean networks
Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx [División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A. C., Camino a la Presa San José 2055, Col. Lomas 4 Sección, C.P. 78216, San Luis Potosí, S.L.P. (Mexico); Campos-Cantón, I. [Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, C.P. 78000, San Luis Potosí, S.L.P. (Mexico); Gauthier, Daniel J. [Department of Physics and Center for Nonlinear and Complex Systems, Duke University, Box 90305, Durham, North Carolina 27708 (United States)
2015-08-15
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Boolean network robotics: a proof of concept
Roli, Andrea; Pinciroli, Carlo; Birattari, Mauro
2011-01-01
Dynamical systems theory and complexity science provide powerful tools for analysing artificial agents and robots. Furthermore, they have been recently proposed also as a source of design principles and guidelines. Boolean networks are a prominent example of complex dynamical systems and they have been shown to effectively capture important phenomena in gene regulation. From an engineering perspective, these models are very compelling, because they can exhibit rich and complex behaviours, in spite of the compactness of their description. In this paper, we propose the use of Boolean networks for controlling robots' behaviour. The network is designed by means of an automatic procedure based on stochastic local search techniques. We show that this approach makes it possible to design a network which enables the robot to accomplish a task that requires the capability of navigating the space using a light stimulus, as well as the formation and use of an internal memory.
Boolean Factor Analysis by Attractor Neural Network
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
2007-01-01
Roč. 18, č. 3 (2007), s. 698-707. ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007
Effect of memory in non-Markovian Boolean networks
Ebadi, Haleh; Ausloos, Marcel; Jafari, GholamReza
2016-01-01
One successful model of interacting biological systems is the Boolean network. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function, - one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of a cell cycle network, we discover a power law memory with a more robust dynamics than the Markovian dynamics.
The value of less connected agents in Boolean networks
Epstein, Daniel; Bazzan, Ana L. C.
2013-11-01
In multiagent systems, agents often face binary decisions where one seeks to take either the minority or the majority side. Examples are minority and congestion games in general, i.e., situations that require coordination among the agents in order to depict efficient decisions. In minority games such as the El Farol Bar Problem, previous works have shown that agents may reach appropriate levels of coordination, mostly by looking at the history of past decisions. Not many works consider any kind of structure of the social network, i.e., how agents are connected. Moreover, when structure is indeed considered, it assumes some kind of random network with a given, fixed connectivity degree. The present paper departs from the conventional approach in some ways. First, it considers more realistic network topologies, based on preferential attachments. This is especially useful in social networks. Second, the formalism of random Boolean networks is used to help agents to make decisions given their attachments (for example acquaintances). This is coupled with a reinforcement learning mechanism that allows agents to select strategies that are locally and globally efficient. Third, we use agent-based modeling and simulation, a microscopic approach, which allows us to draw conclusions about individuals and/or classes of individuals. Finally, for the sake of illustration we use two different scenarios, namely the El Farol Bar Problem and a binary route choice scenario. With this approach we target systems that adapt dynamically to changes in the environment, including other adaptive decision-makers. Our results using preferential attachments and random Boolean networks are threefold. First we show that an efficient equilibrium can be achieved, provided agents do experimentation. Second, microscopic analysis show that influential agents tend to consider few inputs in their Boolean functions. Third, we have also conducted measurements related to network clustering and centrality
Neural Network Boolean Factor Analysis and Applications
Húsek, Dušan; Frolov, A.; Polyakov, P.Y.; Snášel, V.
-: WSEAS Press, 2007 - (Katehakis, M.; And ina, D.; Mastorakis, M.), s. 30-35. (Electrical and Computer Engineering Series). ISBN 978-960-6766-21-3. [CIMMACS'07. WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics. Tenerife (ES), 14.12.2007-16.12.2007] R&D Projects: GA MŠk 1M0567; GA AV ČR 1ET100300414; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : Hopfield neural network * boolean factor analysis * unsupervised learning * dimension reduction * data mining Subject RIV: BB - Applied Statistics, Operational Research
Solving the Satisfiability Problem Through Boolean Networks
Roli, Andrea; Milano, Michela
2011-01-01
In this paper we present a new approach to solve the satisfiability problem (SAT), based on boolean networks (BN). We define a mapping between a SAT instance and a BN, and we solve SAT problem by simulating the BN dynamics. We prove that BN fixed points correspond to the SAT solutions. The mapping presented allows to develop a new class of algorithms to solve SAT. Moreover, this new approach suggests new ways to combine symbolic and connectionist computation and provides a general framework f...
Solving the Satisfiability Problem Through Boolean Networks
Roli, Andrea
2011-01-01
In this paper we present a new approach to solve the satisfiability problem (SAT), based on boolean networks (BN). We define a mapping between a SAT instance and a BN, and we solve SAT problem by simulating the BN dynamics. We prove that BN fixed points correspond to the SAT solutions. The mapping presented allows to develop a new class of algorithms to solve SAT. Moreover, this new approach suggests new ways to combine symbolic and connectionist computation and provides a general framework for local search algorithms.
Representations of Boolean Functions by Perceptron Networks
Kůrková, Věra
Prague : Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 68-70 ISBN 978-80-87136-19-5. [ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014] R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : perceptron networks * model complexity * Boolean functions Subject RIV: IN - Informatics, Computer Science
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Ali Muhammad Ali Rushdi; Adnan Ahmad Alsogati
2013-01-01
A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s). Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable a...
Consistent stabilizability of switched Boolean networks.
Li, Haitao; Wang, Yuzhen
2013-10-01
This paper investigates the consistent stabilizability of switched Boolean networks (SBNs) by using the semi-tensor product method, and presents a number of new results. First, an algebraic expression of SBNs is obtained by the semi-tensor product, based on which the consistent stabilizability is then studied for SBNs and some necessary and sufficient conditions are presented for the design of free-form and state-feedback switching signals, respectively. Finally, the consistent stabilizability of SBNs with state constraints is considered and some necessary and sufficient conditions are proposed. The study of illustrative examples shows that the new results obtained in this paper are very effective in designing switching signals for the consistent stabilizability of SBNs. PMID:23787170
Boolean network model predicts knockout mutant phenotypes of fission yeast.
Maria I Davidich
Full Text Available BOOLEAN NETWORKS (OR: networks of switches are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.
Measuring Mutual Information in Random Boolean Networks
Luque, B; Luque, Bartolo; Ferrera, Antonio
1999-01-01
During the last few years an area of active research in the field of complex systems is that of their information storing and processing abilities. Common opinion has it that the most interesting beaviour of these systems is found ``at the edge of chaos'', which would seem to suggest that complex systems may have inherently non-trivial information proccesing abilities in the vicinity of sharp phase transitions. A comprenhensive, quantitative understanding of why this is the case is however still lacking. Indeed, even ``experimental'' (i.e., often numerical) evidence that this is so has been questioned for a number of systems. In this paper we will investigate, both numerically and analitically, the behavior of Random Boolean Networks (RBN's) as they undergo their order-disorder phase transition. We will use a simple mean field approximation to treat the problem, and without lack of generality we will concentrate on a particular value for the connectivity of the system. In spite of the simplicity of our argume...
Enhancing Boolean networks with fuzzy operators and edge tuning
Poret, Arnaud; Monteiro Sousa, Claudio; Boissel, Jean-Pierre
2014-01-01
Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic ...
IMS Algorithm for Learning Representations in Boolean Neural Networks
Biswas, Nripendra N; Murthy, TVMK; Chandrasekhar, M.
1991-01-01
A new algorithm for learning representations in Boolean neural networks, where the inputs and outputs are binary bits, is presented. The algorithm has become feasible because of a newly discovered theorem which states that any non-linearly separable Boolean function can be expressed as a convergent series of linearly separable functions connected by the logical OR (+) and the logical INHIBIT (-) operators. The formation of the series is carried out by many important properties exhibited by th...
The Influence of Canalization on the Robustness of Boolean Networks
Kadelka, Claus; Laubenbacher, Reinhard
2016-01-01
Time- and state-discrete dynamical systems are frequently used to model molecular networks. This paper provides a collection of mathematical and computational tools for the study of robustness in Boolean network models. The focus is on networks governed by $k$-canalizing functions, a recently introduced class of Boolean functions that contains the well-studied class of nested canalizing functions. The activities and sensitivity of a function quantify the impact of input changes on the function output. This paper generalizes the latter concept to $c$-sensitivity and provides formulas for the activities and $c$-sensitivity of general $k$-canalizing functions as well as canalizing functions with more precisely defined structure. A popular measure for the robustness of a network, the Derrida value, can be expressed as a weighted sum of the $c$-sensitivities of the governing canalizing functions, and can also be calculated for a stochastic extension of Boolean networks. These findings provide a computationally eff...
Binary higher order neural networks for realizing Boolean functions.
Zhang, Chao; Yang, Jie; Wu, Wei
2011-05-01
In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Ali Muhammad Ali Rushdi
2013-01-01
Full Text Available A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s. Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable at a latter instance t2 to its value at an earlier instance t1. This equation remains valid when the times t1 and t2 are both augmented by any integral multiple of the underlying time period. In other words, there are infinitely many versions of a reduced scalar equation, any of which is useful for deducing information about the cyclic behavior of the network. However, to obtain correct information about the transient behavior of the network, one must find the true reduced scalar equation for which instances t1 and t2 are minimal. This study investigates the nature, derivation and utilization of reduced scalar equations. It relies on Boolean-algebraic manipulations for the derivation of such equations and suggests that this derivation can be facilitated by seeking certain orthogonality relations among certain successive (albeit not necessarily consecutive instances of the same scalar variable. We demonstrate, contrary to previously published assumptions or assertions, that there is typically no common reduced scalar equation for all the scalar variables. Each variable usually satisfies its own distinct reduced scalar equation. We also demonstrate that the derivation of a reduced scalar equation is achieved not only by proving it but also by disproving an immediately preceding version of it when such a version might exist. We also demonstrate that, despite the useful insight supplied by the reduced scalar equations, they do not provide a total solution like the
Optimal Computation of Symmetric Boolean Functions in Collocated Networks
Kowshik, Hemant
2011-01-01
We consider collocated wireless sensor networks, where each node has a Boolean measurement and the goal is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide exactly optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We also extend the results to the case of integer measurements and certain integer-valued functions. We use lower bounds from communication complexity theory, and provide an achievable scheme using information theoretic tools. Next, we consider the case where nodes measurements are random and drawn from independent Bernoulli distributions. We address the problem of optimal function computation so as to minimize the expected total number of bits that are transmitted. In ...
Boolean network representation of contagion dynamics during a financial crisis
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-01-01
This work presents a network model for representation of the evolution of certain patterns of economic behavior. More specifically, after representing the agents as points in a space in which each dimension associated to a relevant economic variable, their relative "motions" that can be either stationary or discordant, are coded into a boolean network. Patterns with stationary averages indicate the maintenance of status quo, whereas discordant patterns represent aggregation of new agent into the cluster or departure from the former policies. The changing patterns can be embedded into a network representation, particularly using the concept of autocatalytic boolean networks. As a case study, the economic tendencies of the BRIC countries + Argentina were studied. Although Argentina is not included in the cluster formed by BRIC countries, it tends to follow the BRIC members because of strong commercial ties.
Algorithms for Finding Small Attractors in Boolean Networks
Hayashida Morihiro
2007-01-01
Full Text Available A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.
Comparison of Two Neural Networks Approaches to Boolean Matrix Factorization
Polyakov, P.Y.; Frolov, A. A.; Húsek, Dušan
Los Alamitos: IEEE Computer Society, 2009 - (Snášel, V.; Pokorný, J.; Pichappan, P.; El-Qawasmeh, E.), s. 316-321 ISBN 978-1-4244-4614-8. [NDT 2009. International Conference on Networked Digital Technologies /1./. Ostrava (CZ), 29.07.2009-31.07.2009] R&D Projects: GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : data mining * artificial inteligence * neural networks * multivariate statistics * Boolean factor analysis * Hopfield-like neural networks * feed forward neural network Subject RIV: BB - Applied Statistics, Operational Research
Neural Network Based Boolean Factor Analysis of Parliament Voting
Frolov, A. A.; Polyakov, P.Y.; Húsek, Dušan; Řezanková, H.
Heidelberg : Springer, 2006 - (Rizzi, A.; Vichi, M.), s. 861-868 ISBN 3-7908-1708-2. [COMPSTAT 2006. Symposium /17./. Rome (IN), 28.08.2006-01.09.2006] R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Grant ostatní: RFBR(RU) 05-07-90049 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural networks * social networks Subject RIV: BB - Applied Statistics, Operational Research
Self-organized networks of competing boolean agents
Paczuski; Bassler; Corral
2000-04-01
A model of Boolean agents competing in a market is presented where each agent bases his action on information obtained from a small group of other agents. The agents play a competitive game that rewards those in the minority. After a long time interval, the poorest player's strategy is changed randomly, and the process is repeated. Eventually the network evolves to a stationary but intermittent state where random mutation of the worst strategy can change the behavior of the entire network, often causing a switch in the dynamics between attractors of vastly different lengths. PMID:11019043
Chaos synchronization of two stochastically coupled random Boolean networks
Hung, Y.-C. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China) and Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: d9123801@student.nsysu.edu.tw; Ho, M.-C. [Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: t1603@nknucc.nknu.edu.tw; Lih, J.-S. [Department of Physics and Geoscience, National Pingtung University of Education, Pingtung, Taiwan (China); Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China); Jiang, I-M. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China); Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)
2006-07-24
In this Letter, we study the chaos synchronization of two stochastically coupled random Boolean networks (RBNs). Instead of using the 'site-by-site and all-to-all' coupling, the coupling mechanism we consider here is that: the nth cell in a network is linked by an arbitrarily chosen cell in the other network with probability {rho}, and it possesses no links with probability 1-{rho}. The mechanism is useful to investigate the coevolution of biological species via horizontal genetic exchange. We show that the density evolution of networks can be described by two deterministic coupled polynomial maps. The complete synchronization occurs when the coupling parameter exceeds a critical value. Moreover, the reverse bifurcations in inhomogeneous condition are observed and under our discussion.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.
Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations
Heckel, Reinhard; Bossert, Martin
2011-01-01
Consider a large Boolean network with a feed forward structure. Given a probability distribution for the inputs, can one find-possibly small-collections of input nodes that determine the states of most other nodes in the network? To identify these nodes, a notion that quantifies the determinative power of an input over states in the network is needed. We argue that the mutual information (MI) between a subset of the inputs X = {X_1, ..., X_n} of node i and the function f_i(X)$ associated with node i quantifies the determinative power of this subset of inputs over node i. To study the relation of determinative power to sensitivity to perturbations, we relate the MI to measures of perturbations, such as the influence of a variable, in terms of inequalities. The result shows that, maybe surprisingly, an input that has large influence does not necessarily have large determinative power. The main tool for the analysis is Fourier analysis of Boolean functions. Whether a function is sensitive to perturbations or not...
Evolution of a designless nanoparticle network into reconfigurable Boolean logic.
Bose, S K; Lawrence, C P; Liu, Z; Makarenko, K S; van Damme, R M J; Broersma, H J; van der Wiel, W G
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures. PMID:26389658
Evolution and Controllability of Cancer Networks: A Boolean Perspective.
Srihari, Sriganesh; Raman, Venkatesh; Leong, Hon Wai; Ragan, Mark A
2014-01-01
Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of
Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles
Shah Imran
2011-07-01
Full Text Available Abstract Background With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data. Results We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature. Conclusion A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our
A SAT-based algorithm for finding attractors in synchronous Boolean networks.
Dubrova, Elena; Teslenko, Maxim
2011-01-01
This paper addresses the problem of finding attractors in synchronous Boolean networks. The existing Boolean decision diagram-based algorithms have limited capacity due to the excessive memory requirements of decision diagrams. The simulation-based algorithms can be applied to larger networks, however, they are incomplete. We present an algorithm, which uses a SAT-based bounded model checking to find all attractors in a Boolean network. The efficiency of the presented algorithm is evaluated by analyzing seven networks models of real biological processes, as well as 150,000 randomly generated Boolean networks of sizes between 100 and 7,000. The results show that our approach has a potential to handle an order of magnitude larger models than currently possible. PMID:21778527
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
Natalie Berestovsky
Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them
Dynamical modeling of the cholesterol regulatory pathway with Boolean networks
Corcos Laurent
2008-11-01
Full Text Available Abstract Background Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now. Results We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs, as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway. Conclusion We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico
Characterizing short-term stability for Boolean networks over any distribution of transfer functions
Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.
2016-07-01
We present a characterization of short-term stability of Kauffman's N K (random) Boolean networks under arbitrary distributions of transfer functions. Given such a Boolean network where each transfer function is drawn from the same distribution, we present a formula that determines whether short-term chaos (damage spreading) will happen. Our main technical tool which enables the formal proof of this formula is the Fourier analysis of Boolean functions, which describes such functions as multilinear polynomials over the inputs. Numerical simulations on mixtures of threshold functions and nested canalyzing functions demonstrate the formula's correctness.
SAT-based Distributed Reactive Control Protocol Synthesis for Boolean Networks
Sahin, Yunus Emre; Ozay, Necmiye
2016-01-01
This paper considers the synthesis of distributed reactive control protocols for a Boolean network in a distributed manner. We start with a directed acyclic graph representing a network of Boolean subsystems and a global contract, given as an assumption-guarantee pair. Assumption captures the environment behavior, and guarantee is the requirements to be satisfied by the system. Local assumption-guarantee contracts, together with local control protocols ensuring these local contracts, are comp...
Propagation of external regulation and asynchronous dynamics in random Boolean networks
Mahmoudi, Hamed; Pagnani, Andrea; Weigt, Martin; Zecchina, Riccardo
2007-01-01
Boolean Networks and their dynamics are of great interest as abstract modeling schemes in various disciplines, ranging from biology to computer science. Whereas parallel update schemes have been studied extensively in past years, the level of understanding of asynchronous updates schemes is still very poor. In this paper we study the propagation of external information given by regulatory input variables into a random Boolean network. We compute both analytically and numerically the time evol...
Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes
Barnabei, Graziano; Conversano, Ciro; Lensi, Elena
2010-01-01
Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain.
Decisional Processes with Boolean Neural Network: The Emergence of Mental Schemes
Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain. (authors)
Polynomial-Time Algorithm for Controllability Test of a Class of Boolean Biological Networks
Koichi Kobayashi
2010-01-01
Full Text Available In recent years, Boolean-network-model-based approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomial-time algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of large-scale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective.
Relative stability of network states in Boolean network models of gene regulation in development.
Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier; Price, Nathan D; Huang, Sui
2016-01-01
Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. PMID:26965665
Autonomous Modeling, Statistical Complexity and Semi-annealed Treatment of Boolean Networks
Gong, Xinwei
This dissertation presents three studies on Boolean networks. Boolean networks are a class of mathematical systems consisting of interacting elements with binary state variables. Each element is a node with a Boolean logic gate, and the presence of interactions between any two nodes is represented by directed links. Boolean networks that implement the logic structures of real systems are studied as coarse-grained models of the real systems. Large random Boolean networks are studied with mean field approximations and used to provide a baseline of possible behaviors of large real systems. This dissertation presents one study of the former type, concerning the stable oscillation of a yeast cell-cycle oscillator, and two studies of the latter type, respectively concerning the statistical complexity of large random Boolean networks and an extension of traditional mean field techniques that accounts for the presence of short loops. In the cell-cycle oscillator study, a novel autonomous update scheme is introduced to study the stability of oscillations in small networks. A motif that corrects pulse-growing perturbations and a motif that grows pulses are identified. A combination of the two motifs is capable of sustaining stable oscillations. Examining a Boolean model of the yeast cell-cycle oscillator using an autonomous update scheme yields evidence that it is endowed with such a combination. Random Boolean networks are classified as ordered, critical or disordered based on their response to small perturbations. In the second study, random Boolean networks are taken as prototypical cases for the evaluation of two measures of complexity based on a criterion for optimal statistical prediction. One measure, defined for homogeneous systems, does not distinguish between the static spatial inhomogeneity in the ordered phase and the dynamical inhomogeneity in the disordered phase. A modification in which complexities of individual nodes are calculated yields vanishing
Mapping Complex Networks: Exploring Boolean Modeling of Signal Transduction Pathways
Bhardwaj, Gaurav; Wells, Christine P.; Albert, Reka; van Rossum, Damian B.; Patterson, Randen L
2009-01-01
In this study, we explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signaling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP3R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomization of ...
Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
Berlin : Springer, 2010 - (Koronacki, J.; Ras, Z.; Wierzchon, S.; Kacprzyk, J.), s. 501-518 ISBN 978-3-642-05176-0. - (Studies in Computational Intelligence. 262) Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * Hopfield-like neural network * spurious attractors * statistics * bingy data Subject RIV: IN - Informatics, Computer Science
Analysis and control of Boolean networks a semi-tensor product approach
Cheng, Daizhan; Li, Zhiqiang
2010-01-01
This book presents a new approach to the investigation of Boolean control networks, using the semi-tensor product (STP), which can express a logical function as a conventional discrete-time linear system. This makes it possible to analyze basic control problems.
Reverse engineering Boolean networks: from Bernoulli mixture models to rule based systems.
Mehreen Saeed
Full Text Available A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN. In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.
From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits
Bin Shao; Xiang Liu; Dongliang Zhang; Jiayi Wu; Qi Ouyang
2015-01-01
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, ...
Exploring phospholipase C-coupled Ca(2+) signalling networks using Boolean modelling.
Bhardwaj, G; Wells, C P; Albert, R; van Rossum, D B; Patterson, R L
2011-05-01
In this study, the authors explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signalling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP(3)R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomisation of the Boolean operators ablates oscillatory pattern formation. Furthermore, knock-out simulations of the IP(3)R, TRPC3 and multiple other proteins recapitulate experimentally derived results. The potential of this approach can be observed by its ability to predict previously undescribed cellular phenotypes using in vitro experimental data. Indeed, our cellular analysis of the developmental and calcium-regulatory protein, DANGER1a, confirms the counter-intuitive predictions from our Boolean models in two highly relevant cellular models. Based on these results, the authors theorise that with sufficient legacy knowledge and/or computational biology predictions, Boolean networks can provide a robust method for predictive modelling of any biological system. [Includes supplementary material]. PMID:21639591
Detecting small attractors of large Boolean networks by function-reduction-based strategy.
Zheng, Qiben; Shen, Liangzhong; Shang, Xuequn; Liu, Wenbin
2016-04-01
Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2. PMID:26997659
Random Boolean Networks and Attractors of their Intersecting Circuits
Demongeot, Jacques; Elena, Adrien; Noual, Mathilde; Sené, Sylvain
2011-01-01
International audience The multi-scale strategy in studying biological regulatory networks analysis is based on two level of analysis. The first level is structural and consists in examining the architecture of the interaction graph underlying the network and the second level is functional and analyse the regulatory properties of the network. We apply this dual approach to the "immunetworks" involved in the control of the immune system. As a result, we show that the small number of attract...
Dynamical modeling of the cholesterol regulatory pathway with Boolean networks
Corcos Laurent; Kervizic Gwenael
2008-01-01
Abstract Background Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivo...
Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
Wai-Ki Ching
2008-09-01
Full Text Available A Boolean network (BN is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact algorithms.
Claudia Stötzel
Full Text Available In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
HSP70 mediates survival in apoptotic cells – Boolean network prediction and experimental validation
Suhas Vasaikar
2015-08-01
Full Text Available Neuronal stress or injury results in the activation of proteins, which regulate the balance between survival and apoptosis. However, the complex mechanism of cell signalling involving cell death and survival, activated in response to cellular stress is not yet completely understood. To bring more clarity about these mechanisms, a Boolean network was constructed that represented the apoptotic pathway in neuronal cells. FasL and neurotrophic growth factor (NGF were considered as inputs in the absence and presence of heat shock proteins known to shift the balance towards survival by rescuing pro-apoptotic cells. The probabilities of survival, DNA repair and apoptosis as cellular fates, in the presence of either the growth factor or FasL, revealed a survival bias encoded in the network. Boolean predictions tested by measuring the mRNA expression level of caspase-3, caspase-8 and BAX in neuronal Neuro2a (N2a cell line with NGF and FasL as external input, showed positive correlation with the observed experimental results for survival and apoptotic states. It was observed that HSP70 contributed more towards rescuing cells from apoptosis in comparison to HSP27, HSP40 and HSP90. Overexpression of HSP70 in N2a transfected cells showed reversal of cellular fate from FasL-induced apoptosis to survival. Further, the pro-survival role of the proteins BCL2, IAP, cFLIP and NFκB determined by vertex perturbation analysis was experimentally validated through protein inhibition experiments using EM20-25, Embelin and Wedelolactone, which resulted in 1.27-fold, 1.26-fold and 1.46-fold increase in apoptosis of N2a cells. The existence of a one-to-one correspondence between cellular fates and attractor states shows that Boolean networks may be employed with confidence in qualitative analytical studies of biological networks.
The behavior of noise-resilient Boolean networks with diverse topologies
The dynamics of noise-resilient Boolean networks with majority functions and diverse topologies is investigated. A wide class of possible topological configurations is parametrized as a stochastic blockmodel. For this class of networks, the dynamics always undergoes a phase transition from a non-ergodic regime, where a memory of its past states is preserved, to an ergodic regime, where no such memory exists and every microstate is equally probable. Both the average error on the network and the critical value of noise where the transition occurs are investigated analytically, and compared to numerical simulations. The results for 'partially dense' networks, comprising relatively few, but dynamically important nodes, which have a number of inputs that greatly exceeds the average for the entire network, give very general upper bounds on the maximum resilience against noise attainable on globally sparse systems
Critical line in undirected Kauffman Boolean networks - the role of percolation
We show that to describe correctly the position of the critical line in Kauffman random Boolean networks one must take into account percolation phenomena underlying the process of damage spreading. For this reason, since the issue of percolation transition is much simpler in random undirected networks than in the directed ones, we study the Kauffman model in undirected networks. We derive the mean field formula for the critical line in the giant components of these networks, and show that the critical line characterizing the whole network results from the fact that the ordered behavior of small clusters shields the chaotic behavior of the giant component. We also show a possible attitude towards the analytical description of the shielding effect. The theoretical derivations given in this paper very much tally with the numerical simulations done for classical random graphs
Damage spreading in spatial and small-world random boolean networks
Lu, Qiming [Los Alamos National Laboratory; Teuscher, Christof [Los Alamos National Laboratory
2008-01-01
Random Boolean Networks (RBNs) are often used as generic models for certain dynamics of complex systems, ranging from social networks, neural networks, to gene or protein interaction networks. Traditionally, RBNs are interconnected randomly and without considering any spatial arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ({bar K} << 1) and that the critical connectivity of stability K{sub s} changes compared to random networks. At higher {bar K}, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key trade-offs between damage spreading (robustness), the network wiring cost, and the network's communication characteristics.
Variances as order parameter and complexity measure for random Boolean networks
Luque, Bartolo [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain); Ballesteros, Fernando J [Observatori Astronomic, Universitat de Valencia, Ed. Instituts d' Investigacio, Pol. La Coma s/n, E-46980 Paterna, Valencia (Spain); Fernandez, Manuel [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain)
2005-02-04
Several order parameters have been considered to predict and characterize the transition between ordered and disordered phases in random Boolean networks, such as the Hamming distance between replicas or the stable core, which have been successfully used. In this work, we propose a natural and clear new order parameter: the temporal variance. We compute its value analytically and compare it with the results of numerical experiments. Finally, we propose a complexity measure based on the compromise between temporal and spatial variances. This new order parameter and its related complexity measure can be easily applied to other complex systems.
The receptor mosaic hypothesis of the engram: possible relevance of Boolean network modeling.
Zoli, M; Guidolin, D; Fuxe, K; Agnati, L F
1996-09-01
In the past 15 years, several lines of evidence have shown that receptors for chemical signals can interact in domains of the plasma membrane and possibly form molecular circuits encoding logical operators. In this frame, the receptor mosaic hypothesis of the engram was advanced. According to this proposal, aggregates of different receptor species (mosaics) may form in neuronal membranes (typically synapses) and constitute a memory trace (engram) of its activity. In the present paper, we present an attempt to model the functioning of aggregates of interacting receptors in membrane domains by means of random Boolean networks. PMID:8968825
Húsek, Dušan; Frolov, A. A.; Polyakov, P.Y.; Řezanková, H.; Snášel, V.
Lisabon : Instituto Nacional de Estatística, 2008 - (Gomes, M.; Pinto Martins, J.; Silva, J.), s. 3739-3742 ISBN 978-972-673-992-0. [ISI 2007. Session of the International Statistical Institute /56./. Lisboa (PT), 22.08.2007-29.08.2007] R&D Projects: GA AV ČR 1ET100300414 Grant ostatní: RFBR(RU) 05-07-90049 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * document classification * automatic concepts search * unsupervised learning * neural network Subject RIV: BB - Applied Statistics, Operational Research
Neural Network Based Boolean Factor Analysis: Efficient Tool for Automated Topics Search.
Húsek, Dušan; Frolov, A. A.; Polyakov, P.Y.; Řezanková, H.
Amman: Applied Science Private University, 2006 - (Issa, G.; El-Qawasmeh, E.; Raho, G.), s. 321-327 ISBN 9957-8592-0-X. [CSIT 2006. International Multiconference on Computer Science and Information Technology /4./. Amman (JO), 05.04.2006-07.04.2006] R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural networks * associative memory * clustering * web searching * semantic web * information retrieval * document indexing * document classification * document processing * data mining * machine learning Subject RIV: BB - Applied Statistics, Operational Research
Damage spreading in spatial and small-world random Boolean networks
Lu, Qiming; Teuscher, Christof
2014-02-01
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities (K¯≪1) and that the critical connectivity of stability Ks changes compared to random networks. At higher K¯, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size N increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
高阶布尔网络的结构%Structure of higher order Boolean networks*
李志强; 赵寅; 程代展
2011-01-01
The higher order Boolean (control) network is introduced and its topological structure is studied.Using semi-tensor product of matrices,its dynamics is converted into two algebraic forms,which are standard discrete-time dynamic systems.The one-to-one correspondence of the network dynamics and its first algebraic form is proved,and certain topological structures,including fixed points,cycles,and transient time,of higher order Boolean (control) networks are revealed.The relationship between the original system and its second algebraic form is also studied.%介绍高阶布尔（控制）网络,并研究了其拓扑结构.以矩阵的半张量积作为工具,把高阶布尔网络的动态过程转化为2种标准离散事件动态系统的代数形式.证明了高阶布尔网络和第1代数形式的一一对应关系,并由此得到其拓扑结构（不动点、极限圈以及暂态期等）.还研究了高阶布尔网络系统与它第2代数形式的关系.
Diffusion Adaptation over Networks
Sayed, Ali H
2012-01-01
Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to changing data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative networks. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation; 2. Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of Ste...
Damage Spreading in Spatial and Small-world Random Boolean Networks
Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.
2014-02-18
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach
Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering. (paper)
Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach
Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru
2014-12-01
Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.
Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis
Frolov, A.; Húsek, Dušan; Polyakov, P.Y.
Vol. 1. Berlin: Springer, 2012 - (Wang, J.; Yen, G.; Polycarpou, M.), s. 1-10. (Lecture Notes in Computer Science. 7367). ISBN 978-3-642-31345-5. ISSN 0302-9743. [ISNN 2012. International Symposium on Neural Networks /9./. Shenyang (CN), 11.07.2012-14.07.2012] R&D Projects: GA ČR GAP202/10/0262 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : Associative Neural Network * Likelihood Maximization * Boolean Factor Analysis * Binary Matrix factorization * Noise XOR Mixing * Plato Problem * Information Gain * Bars problem * Data Mining * Dimension Reduction * Hebbian Learning * Anti-Hebbian Learning Subject RIV: IN - Informatics, Computer Science
Chaudhuri, Arijit
2014-01-01
Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.
Yih-Lon Lin
2013-01-01
Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.
Adaptive network countermeasures.
McClelland-Bane, Randy; Van Randwyk, Jamie A.; Carathimas, Anthony G.; Thomas, Eric D.
2003-10-01
This report describes the results of a two-year LDRD funded by the Differentiating Technologies investment area. The project investigated the use of countermeasures in protecting computer networks as well as how current countermeasures could be changed in order to adapt with both evolving networks and evolving attackers. The work involved collaboration between Sandia employees and students in the Sandia - California Center for Cyber Defenders (CCD) program. We include an explanation of the need for adaptive countermeasures, a description of the architecture we designed to provide adaptive countermeasures, and evaluations of the system.
Zhu, Zheng; Andresen, Juan Carlos; Moore, M A; Katzgraber, Helmut G
2014-02-01
We study the equilibrium and nonequilibrium properties of Boolean decision problems with competing interactions on scale-free networks in an external bias (magnetic field). Previous studies at zero field have shown a remarkable equilibrium stability of Boolean variables (Ising spins) with competing interactions (spin glasses) on scale-free networks. When the exponent that describes the power-law decay of the connectivity of the network is strictly larger than 3, the system undergoes a spin-glass transition. However, when the exponent is equal to or less than 3, the glass phase is stable for all temperatures. First, we perform finite-temperature Monte Carlo simulations in a field to test the robustness of the spin-glass phase and show that the system has a spin-glass phase in a field, i.e., exhibits a de Almeida-Thouless line. Furthermore, we study avalanche distributions when the system is driven by a field at zero temperature to test if the system displays self-organized criticality. Numerical results suggest that avalanches (damage) can spread across the whole system with nonzero probability when the decay exponent of the interaction degree is less than or equal to 2, i.e., that Boolean decision problems on scale-free networks with competing interactions can be fragile when not in thermal equilibrium. PMID:25353433
Detecting a Singleton Attractor in a Boolean Network Utilizing SAT Algorithms
Tamura, Takeyuki; Akutsu, Tatsuya
The Boolean network (BN) is a mathematical model of genetic networks. It is known that detecting a singleton attractor, which is also called a fixed point, is NP-hard even for AND/OR BNs (i.e., BNs consisting of AND/OR nodes), where singleton attractors correspond to steady states. Though a naive algorithm can detect a singleton attractor for an AND/OR BN in O(n 2n) time, no O((2-ε)n) (ε > 0) time algorithm was known even for an AND/OR BN with non-restricted indegree, where n is the number of nodes in a BN. In this paper, we present an O(1.787n) time algorithm for detecting a singleton attractor of a given AND/OR BN, along with related results. We also show that detection of a singleton attractor in a BN with maximum indegree two is NP-hard and can be polynomially reduced to a satisfiability problem.
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
2010-01-01
Roč. 73, č. 7-9 (2010), s. 1394-1404. ISSN 0925-2312 R&D Projects: GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * Hopfield neural Network * unsupervised learning * dimension reduction * data mining Subject RIV: IN - Informatics, Computer Science Impact factor: 1.429, year: 2010
Computational complexity of Boolean functions
Boolean functions are among the fundamental objects of discrete mathematics, especially in those of its subdisciplines which fall under mathematical logic and mathematical cybernetics. The language of Boolean functions is convenient for describing the operation of many discrete systems such as contact networks, Boolean circuits, branching programs, and some others. An important parameter of discrete systems of this kind is their complexity. This characteristic has been actively investigated starting from Shannon's works. There is a large body of scientific literature presenting many fundamental results. The purpose of this survey is to give an account of the main results over the last sixty years related to the complexity of computation (realization) of Boolean functions by contact networks, Boolean circuits, and Boolean circuits without branching. Bibliography: 165 titles.
Parameter Learning of Boolean Bayesian Networks%布尔型贝叶斯网络参数学习
吴永广; 周兴旺
2015-01-01
布尔型贝叶斯网络是一类由布尔型变量组成的网络，它能够以线性多变量函数描述，使计算和处理上灵活高效。通过运用连接树算法对络进行分块化处理的方法，可以提高算法的效率，然后以传统的最大似然估计方法对布尔型网络的参数进行学习。服从同一分布律的贝叶斯网络参数学习算法发展比较成熟，这类以狄利克雷或者高斯分布为基础的算法在应用领域中难以发挥其应有的价值。相比之下，基于布尔型贝叶斯网络下的参数学习更贴近于应用，在人工智能和数据挖掘等领域有很好的发展前景。%Boolean Bayesian network is a class of Bayesian networks which are made up of Boolean varia-bles. The method to describe the network with a multi-linear function is flexible and efficient to compute and process variables. By introducing Junction Tree algorithm,the network can be divided into blocks which can make it easy to calculate. Then the traditional maximum likelihood estimation method was used for learning Boolean networks. Parameter learning algorithm following the same distribution is more ma-ture,but this kind of algorithm based on Dirichlet or Gaussian distribution is difficult to play its proper val-ue in practice. In contrast,parameter learning based on Boolean networks gets close to applications. It has good prospects for development in areas such as artificial intelligence and data mining.
Adaptive Dynamic Bayesian Networks
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Húsek, Dušan; Moravec, P.; Snášel, V.; Frolov, A.; Řezanková, H.; Polyakov, P.Y.
Berlin : Springer, 2007 - (Ghosh, A.; De, R.), s. 235-243 ISBN 978-3-540-77045-9. - (Lecture Notes in Computer Science. 4815). [PReMI 2007. International Conference /2./. Kolkata (IN), 18.12.2007-22.12.2007] R&D Projects: GA MŠk(CZ) 1M0567; GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural network * dimension reduction * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research
Boolean implication networks derived from large scale, whole genome microarray datasets
Sahoo, Debashis; Dill, David L.; Gentles, Andrew J.; Tibshirani, Robert; Plevritis, Sylvia K.
2008-01-01
We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three sp...
Generalized Adaptive Artificial Neural Networks
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
Boolean Differential Operators
Catumba, Jorge; Diaz, Rafael
2012-01-01
We consider four combinatorial interpretations for the algebra of Boolean differential operators. We show that each interpretation yields an explicit matrix representation for Boolean differential operators.
Memory-Based Boolean Game and Self-Organized Phenomena on Networks
HUANG Zi-Gang; WU Zhi-Xi; GUAN Jian-Yue; WANG Ying-Hai
2006-01-01
@@ We study a memory-based Boolean game (MBBG) taking place on a regular ring, wherein each agent acts according to its local optimal states of the last M time steps recorded in memory, and the agents in the minority are rewarded. One free parameter p between 0 and 1 is introduced to denote the strength of the agent willing to make a decision according to its memory. It is found that giving proper willing strength p, the MBBG system can spontaneously evolve to a state of performance better than the random game; while for larger p, the herd behaviour emerges to reduce the system profit. By analysing the dependence of dynamics of the system on the memory capacity M, we find that a higher memory capacity favours the emergence of the better performance state, and effectively restrains the herd behaviour, thus increases the system profit. Considering the high cost of long-time memory, the enhancement of memory capacity for restraining the herd behaviour is also discussed,and M = 5 is suggested to be a good choice.
Computation emerges from adaptive synchronization of networking neurons.
Zanin, Massimiliano; Del Pozo, Francisco; Boccaletti, Stefano
2011-01-01
The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain. PMID:22073167
Computation emerges from adaptive synchronization of networking neurons.
Massimiliano Zanin
Full Text Available The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.
Combinatorics of Boolean automata circuits dynamics
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2012-01-01
International audience In line with fields of theoretical computer science and biology that study Boolean automata networks to model regulation networks, we present some results concerning the dynamics of networks whose underlying structures are oriented cycles, that is, Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain them. Our work fo...
Investigating Boolean Matrix Factorization
Snášel, V.; Platoš, J.; Krömer, P.; Húsek, Dušan; Neruda, Roman; Frolov, A. A.
- : ACM, 2008 - (Ding, C.; Li, T.; Zhu, S.), s. 18-25 ISBN 978-1-60558-307-5. [DMMT'08. Workshop in Conjunction with SIGKDD 2008 /14./. Las Vegas (US), 24.08.2008-24.08.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * nonnegative matrix factorization * neural networks * information retrieval * data mining * binary data Subject RIV: BB - Applied Statistics, Operational Research http://users.cs.fiu.edu/~taoli/kdd08-workshop/DMMT08-Proceedings.pdf
Decoupled Adapt-then-Combine diffusion networks with adaptive combiners
Fernandez-Bes, Jesus; Arenas-García, Jerónimo; Silva, Magno T. M.; Azpicueta-Ruiz, Luis A.
2015-01-01
In this paper we analyze a novel diffusion strategy for adaptive networks called Decoupled Adapt-then-Combine, which keeps a fully local estimate of the solution for the adaptation step. Our strategy, which is specially convenient for heterogeneous networks, is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Such comparison shows the need of implementing adaptive combiners for both schemes to obtain a good performance in cas...
Hu, Mingxiao; Shen, Liangzhong; Zan, Xiangzhen; Shang, Xuequn; Liu, Wenbin
2016-01-01
Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behavior of systems. In this paper, we investigate the less-studied one-bit perturbation, which falls under the category of structural intervention. Previous works focused on finding the optimal one-bit perturbation to maximally alter the steady-state distribution (SSD) of undesirable states through matrix perturbation theory. However, the application of the SSD is limited to Boolean networks with about ten genes. In 2007, Xiao et al. proposed to search the optimal one-bit perturbation by altering the sizes of the basin of attractions (BOAs). However, their algorithm requires close observation of the state-transition diagram. In this paper, we propose an algorithm that efficiently determines the BOA size after a perturbation. Our idea is that, if we construct the basin of states for all states, then the size of the BOA of perturbed networks can be obtained just by updating the paths of the states whose transitions have been affected. Results from both synthetic and real biological networks show that the proposed algorithm performs better than the exhaustive SSD-based algorithm and can be applied to networks with about 25 genes. PMID:27196530
Evolution of regulatory networks towards adaptability and stability in a changing environment.
Lee, Deok-Sun
2014-11-01
Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments. PMID:25493848
Evolution of regulatory networks towards adaptability and stability in a changing environment
Lee, Deok-Sun
2014-11-01
Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.
Montanaro, Ashley; Osborne, Tobias J.
2008-01-01
In this paper we introduce the study of quantum boolean functions, which are unitary operators f whose square is the identity: f^2 = I. We describe several generalisations of well-known results in the theory of boolean functions, including quantum property testing; a quantum version of the Goldreich-Levin algorithm for finding the large Fourier coefficients of boolean functions; and two quantum versions of a theorem of Friedgut, Kalai and Naor on the Fourier spectra of boolean functions. In o...
Boolean reasoning the logic of boolean equations
Brown, Frank Markham
2012-01-01
A systematic treatment of Boolean reasoning, this concise, newly revised edition combines the works of early logicians with recent investigations, including previously unpublished research results. Brown begins with an overview of elementary mathematical concepts and outlines the theory of Boolean algebras. Two concluding chapters deal with applications. 1990 edition.
Andersen, Henrik Reif; Hulgaard, Henrik
2002-01-01
This paper presents a new data structure called boolean expression diagrams (BEDs) for representing and manipulating Boolean functions. BEDs are a generalization of binary decision diagrams (BDDs) which can represent any Boolean circuit in linear space. Two algorithms are described for transforming...
布尔表达式的化简与并行排序网络验证%Boolean expression simplification and parallel sort network validation
王德才; 徐建国; 吴哲辉; 罗永亮; 王传民
2009-01-01
To design an effective tool that can be used to verify the correctness of a parallel sorting network, a Boolean expression sim-plification algorithm based on the [0,1] theory and Boolean function of the characteristics and the nature is put forward, based on this algorithm a validation tool is designed. The characteristics and the nature of [0,1] theory and Boolean function are discussed and the natures that are helpful to simplify of the operation are pointed out. The tool can be used for the design of parallel sorting networks based on the parameters of the network graphics, and it can automatically generate the Boolean expressions and simplify it. The tool's output will be helpful to analyze the network, and it can also be used to design and optimize the sort network. Finally, the validity of the tool is demonstrated by the application.%为设计出能够验证并行排序网络正确性的有效工具,根据[0,1]原理和布尔函数的特点和性质,提出一种布尔表达式的化简算法,并根据此算法设计出验证工具.对[0,1]原理和布尔函数的特点和性质进行了讨论,指出有利于化简操作的性质.设计出的工具能够根据并行排序网络的参数显示网络图形、自动生成布尔表达式并实现化简验证,工具的输出有利于对排序网络的分析,也可以用于辅助排序网络的设计和优化.实验结果表明了该工具的有效性.
Controllability and observability of Boolean control networks%布尔控制网络的能控性与能观性
李志强; 宋金利
2013-01-01
Using the semi-tensor product,we convert the Boolean control network to its algebraic form.From the structure matrix of Boolean control network,the controllability and observability of the Boolean control network are discussed.A novel necessary and sufficient condition for controllability,which improves the recent results,is given.The new controllability condition eliminates the redundant computation of controllability matrix.The highest power of matrix is reduced from 2m+n to 2 n.Also,a sufficient condition for observability is obtained,which can be computed easily.A numerical example is presented to show the applicability of our controllability and observability condition.%利用矩阵的半张量积,布尔控制网络被转化为离散时间系统.本文从离散时间系统的结构矩阵出发,讨论了逻辑控制系统的能控能观性条件,得到了一个新的能控性条件.新的条件简化了原有能控性矩阵的计算复杂性,矩阵的最高阶数由原来的2m+n降到了2n.另外,还得到了检验布尔控制网络能观性的条件.与原有条件相比,新的条件更容易计算检验.最后,给出一个实例,检验给出的能控能观性判断条件的正确性.
Solomon, Alan D
2012-01-01
REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Boolean Algebra includes set theory, sentential calculus, fundamental ideas of Boolean algebras, lattices, rings and Boolean algebras, the structure of a Boolean algebra, and Boolean
Fluctuating epidemics on adaptive networks
Shaw, Leah B
2008-01-01
A model for epidemics on an adaptive network is considered. Nodes follow an SIRS (susceptible-infective-recovered-susceptible) pattern. Connections are rewired to break links from non-infected nodes to infected nodes and are reformed to connect to other non-infected nodes, as the nodes that are not infected try to avoid the infection. Monte Carlo simulation and numerical solution of a mean field model are employed. The introduction of rewiring affects both the network structure and the epidemic dynamics. Degree distributions are altered, and the average distance from a node to the nearest infective increases. The rewiring leads to regions of bistability where either an endemic or a disease-free steady state can exist. Fluctuations around the endemic state and the lifetime of the endemic state are considered. The fluctuations are found to exhibit power law behavior.
Adaptive Dynamics of Regulatory Networks: Size Matters
Martinetz Thomas
2009-01-01
Full Text Available To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.
Caglar, Mehmet Umut; Pal, Ranadip
2011-03-01
Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.
Cho Kwang-Hyun; Choi Sun; Kwon Yung-Keun
2007-01-01
Abstract Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a fee...
Monotone Boolean functions are an important object in discrete mathematics and mathematical cybernetics. Topics related to these functions have been actively studied for several decades. Many results have been obtained, and many papers published. However, until now there has been no sufficiently complete monograph or survey of results of investigations concerning monotone Boolean functions. The object of this survey is to present the main results on monotone Boolean functions obtained during the last 50 years
Tucker, Jerry H.; Tapia, Moiez A.; Bennett, A. Wayne
1988-01-01
The concept of Boolean integration is developed, and different Boolean integral operators are introduced. Given the changes in a desired function in terms of the changes in its arguments, the ways of 'integrating' (i.e. realizing) such a function, if it exists, are presented. The necessary and sufficient conditions for integrating, in different senses, the expression specifying the changes are obtained. Boolean calculus has applications in the design of logic circuits and in fault analysis.
Andersen, Henrik Reif; Hulgaard, Henrik
This paper presents a new data structure called Boolean Expression Diagrams (BEDs) for representing and manipulating Boolean functions. BEDs are a generalization of Binary Decision Diagrams (BDDs) which can represent any Boolean circuit in linear space and still maintain many of the desirable...... properties of BDDs. Two algorithms are described for transforming a BED into a reduced ordered BDD. One closely mimics the BDD apply-operator while the other can exploit the structural information of the Boolean expression. The efficacy of the BED representation is demonstrated by verifying that the...
Adaptive Networks: the Governance for Sustainable Development
S.G. Nooteboom (Sibout)
2006-01-01
textabstractIn this book, I reconstruct how policy makers, working together in what I term adaptive networks, have enabled a breakthrough in thinking about sustainable mobility in certain policy circles. I define the conduct of leading actors in these adaptive networks as sustainable change manag
Dynamical Adaptation in Terrorist Cells/Networks
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure.......Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...
Adaptive cluster synchronization in complex dynamical networks
Cluster synchronization is investigated in different complex dynamical networks. In this Letter, a novel adaptive strategy is proposed to make a complex dynamical network achieve cluster synchronization, where the adaptive strategy of one edge is adjusted only according to its local information. A sufficient condition about the global stability arbitrarily grouped of cluster synchronization is derived. Several numerical simulations show the effectiveness of the adaptive strategy.
Recruitment dynamics in adaptive social networks
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2011-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the...
Analysis and Control of Boolean Networks:A Semi-tensor Product Approach%布尔网络的分析与控制-矩阵半张量积方法
程代展; 齐洪胜; 赵寅
2011-01-01
布尔网络是描述基因调控网络的一个有力工具.由于系统生物学的发展,布尔网络的分析与控制成为生物学与系统控制学科的交叉热点.本文综述作者用其原创的矩阵半张量积方法在布尔网络的分析与控制中得到的一系列结果.内容包括:布尔网络的拓扑结构,布尔控制网络的能控、能观性与实现,布尔网络的稳定性和布尔控制网络的镇定,布尔控制网络的干扰解耦,布尔(控制)网络的辨识,以及布尔网络的最优控制等.%Boolean network is a powerful tool for describing gene regulatory network. With the development of the systems biology, the analysis and control of Boolean networks become a hot topic for multidisciplinary research. This paper surveys some recent results obtained in the analysis and control of Boolean networks using semi-tensor product of matrices. The contents of this paper include the topological structure of Boolean networks, the controllability and observability, realization, stability and stabilization, disturbance decoupling, identification, and optimal control of Boolean (control) networks.
Boolean nested canalizing functions: a comprehensive analysis
Li, Yuan; Murrugarra, David; Aguilar, Boris; Laubenbacher, Reinhard
2012-01-01
Boolean network models of molecular regulatory networks have been used successfully in computational systems biology. The Boolean functions that appear in published models tend to have special properties, in particular the property of being nested canalizing, a property inspired by the concept of canalization in evolutionary biology. It has been shown that networks comprised of nested canalizing functions have dynamic properties that make them suitable for modeling molecular regulatory networks, namely a small number of (large) attractors, as well as relatively short limit cycles. This paper contains a detailed analysis of this class of functions, based on a novel normal form as polynomial functions over the Boolean field. The concept of layer is introduced that stratifies variables into different classes depending on their level of dominance. Using this layer concept a closed form formula is derived for the number of nested canalizing functions with a given number of variables. Additional metrics analyzed in...
Energy-efficient adaptive wireless network design
Havinga, Paul J. M.; Smit, Gerard J.M.; Bos, Martinus
2000-01-01
Energy efficiency is an important issue for mobile computers since they must rely on their batteries. We present an energy-efficient highly adaptive architecture of a network interface and novel data link layer protocol for wireless networks that provides quality of service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and an acceptable quality of service. The paper provides a review of ideas and...
Adaptive Control Based On Neural Network
Wei, Sun; Lujin, Zhang; Jinhai, Zou; Siyi, Miao
2009-01-01
In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed cont...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential.However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and ...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile; Beale, R
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and...
On the number of attractors of Boolean automata circuits
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2009-01-01
In line with fields of theoretical computer science and biology that study Boolean automata networks often seen as models of regulation networks, we present some results concerning the dynamics of networks whose underlying interaction graphs are circuits, that is Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain them. Our work focuses on th...
Recruitment dynamics in adaptive social networks.
Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B
2013-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). PMID:25395989
Recruitment dynamics in adaptive social networks
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)
New Measure of Boolean Factor Analysis Quality
Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.
Vol. 1. Heidelberg: Springer, 2011 - (Dobnikar, A.; Lotrič, U.; Šter, B.), s. 100-109. (Lecture Notes in Computer Science. 6593). ISBN 978-3-642-20281-0. ISSN 0302-9743. [ICANNGA'2011. International Conference /10./. Ljubljana (SI), 14.04.2011-16.04.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * information gain * expectation-maximization * associative memory * neural network application * Boolean matrix factorization * bars problem * Hopfield neural network Subject RIV: IN - Informatics, Computer Science
Boolean filters of distributive lattices
M. Sambasiva Rao
2013-07-01
Full Text Available In this paper we introduce the notion of Boolean filters in a pseudo-complemented distributive lattice and characterize the class of all Boolean filters. Further a set of equivalent conditions are derived for a proper filter to become a prime Boolean filter. Also a set of equivalent conditions is derived for a pseudo-complemented distributive lattice to become a Boolean algebra. Finally, a Boolean filter is characterized in terms of congruences.
Network measures for characterising team adaptation processes
Barth, S.K.; Schraagen, J.M.C.; Schmettow, M.
2015-01-01
The aim of this study was to advance the conceptualisation of team adaptation by applying social network analysis (SNA) measures in a field study of a paediatric cardiac surgical team adapting to changes in task complexity and ongoing dynamic complexity. Forty surgical procedures were observed by tr
Neural network with dynamically adaptable neurons
Tawel, Raoul (Inventor)
1994-01-01
This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
Stratification and enumeration of Boolean functions by canalizing depth
He, Qijun; Macauley, Matthew
2016-01-01
Boolean network models have gained popularity in computational systems biology over the last dozen years. Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions. The canalizing depth of a function describes how many canalizing variables can be recursively "picked off", until a non-canalizing function remains. In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial. This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth. As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
Stratification and enumeration of Boolean functions by canalizing depth
He, Qijun
2015-01-01
Boolean network models have gained popularity in computational systems biology over the last dozen years. Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions. The canalizing depth of a function describes how many canalizing variables can be recursively picked off, until a non-canalizing function remains. In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial. This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth. As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
In-Network Adaptation of Video Streams Using Network Processors
Mohammad Shorfuzzaman
2009-01-01
problem can be addressed, near the network edge, by applying dynamic, in-network adaptation (e.g., transcoding of video streams to meet available connection bandwidth, machine characteristics, and client preferences. In this paper, we extrapolate from earlier work of Shorfuzzaman et al. 2006 in which we implemented and assessed an MPEG-1 transcoding system on the Intel IXP1200 network processor to consider the feasibility of in-network transcoding for other video formats and network processor architectures. The use of “on-the-fly” video adaptation near the edge of the network offers the promise of simpler support for a wide range of end devices with different display, and so forth, characteristics that can be used in different types of environments.
Lahoz-Beltra, R; Hameroff, S R; Dayhoff, J E
1993-01-01
Adaptive behaviors and dynamic activities within living cells are organized by the cytoskeleton: intracellular networks of interconnected protein polymers which include microtubules (MTs), actin, intermediate filaments, microtubule associated proteins (MAPs) and other protein structures. Cooperative interactions among cytoskeletal protein subunit conformational states have been used to model signal transmission and information processing. In the present work we present a theoretical model for molecular computing in which Boolean logic is implemented in parallel networks of individual MTs interconnected by MAPs. Conformational signals propagate on MTs as in data buses and in the model MAPs are considered as Boolean operators, either as bit-lines (like MTs) where a signal can be transported unchanged between MTs ('BUS-MAP'), or as bit-lines where a Boolean operation is performed in one of the two MAP-MT attachments ('LOGIC-MAP'). Three logic MAPs have been defined ('NOT-MAP, 'AND-MAP', 'XOR-MAP') and used to demonstrate addition, subtraction and other arithmetic operations. Although our choice of Boolean logic is arbitrary, the simulations demonstrate symbolic manipulation in a connectionist system and suggest that MT-MAP networks can perform computation in living cells and are candidates for future molecular computing devices. PMID:8318677
Boolean gates on actin filaments
Siccardi, Stefano; Tuszynski, Jack A.; Adamatzky, Andrew
2016-01-01
Actin is a globular protein which forms long polar filaments in the eukaryotic cytoskeleton. Actin networks play a key role in cell mechanics and cell motility. They have also been implicated in information transmission and processing, memory and learning in neuronal cells. The actin filaments have been shown to support propagation of voltage pulses. Here we apply a coupled nonlinear transmission line model of actin filaments to study interactions between voltage pulses. To represent digital information we assign a logical TRUTH value to the presence of a voltage pulse in a given location of the actin filament, and FALSE to the pulse's absence, so that information flows along the filament with pulse transmission. When two pulses, representing Boolean values of input variables, interact, then they can facilitate or inhibit further propagation of each other. We explore this phenomenon to construct Boolean logical gates and a one-bit half-adder with interacting voltage pulses. We discuss implications of these findings on cellular process and technological applications.
Boolean Delay Equations: A Simple Way of Looking at Interactions and Extreme Events
Ghil, Michael
2013-04-01
Boolean Delay Equations (BDEs) are semi-discrete dynamical models with Boolean-valued variables that evolve in continuous time. Systems of BDEs can be classified into conservative or dissipative, in a manner that parallels the classification of ordinary or partial differential equations. Solutions to certain conservative BDEs exhibit growth of complexity in time; such BDEs can be seen therefore as metaphors for biological evolution or human history. Dissipative BDEs are structurally stable and exhibit multiple equilibria and limit cycles, as well as more complex, fractal solution sets, such as Devil's staircases and ``fractal sunbursts.'' BDE systems have been used as highly idealized models of climate change on several time scales, as well as in earthquake modeling and prediction, and in genetics. BDEs with an infinite number of variables on a regular spatial grid have been called "partial BDEs" and we discuss connections with other types of discrete dynamical systems, including cellular automata and Boolean networks. We present recent BDE work on damage propagation in networks, with an emphasis on production-chain models. This formalism turns out to be well adapted to investigating propagation of an initial damage due to a climatic or other natural disaster. It thus serves to study economic impacts of extreme events, as well as extreme disruption of a network of interactions.
Adaptation by Plasticity of Genetic Regulatory Networks
Brenner, Naama
2007-03-01
Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.
Bayesian Network Models for Adaptive Testing
Plajner, Martin; Vomlel, Jiří
Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf
Adaptive scheduling in cellular access, wireless mesh and IP networks
Nieminen, Johanna
2011-01-01
Networking scenarios in the future will be complex and will include fixed networks and hybrid Fourth Generation (4G) networks, consisting of both infrastructure-based and infrastructureless, wireless parts. In such scenarios, adaptive provisioning and management of network resources becomes of critical importance. Adaptive mechanisms are desirable since they enable a self-configurable network that is able to adjust itself to varying traffic and channel conditions. The operation of adaptive me...
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem
2005-01-01
Full Text Available Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized artificial neural network model. The system allows automatic generation of individualised courses according to the learners goal and previous knowledge and can dynamically adapt the course according to the learners success in acquiring knowledge. Several experiments showed the effectiveness of the proposed method.
Inadmissible Class of Boolean Functions under Stuck-at Faults
Das, Debesh K.; Chowdhury, Debabani; Bhattacharya, Bhargab B; Sasao, Tsutomu
2013-01-01
Many underlying structural and functional factors that determine the fault behavior of a combinational network, are not yet fully understood. In this paper, we show that there exists a large class of Boolean functions, called root functions, which can never appear as faulty response in irredundant two-level circuits even when any arbitrary multiple stuck-at faults are injected. Conversely, we show that any other Boolean function can appear as a faulty response from an irredundant realization ...
Fault Tolerant Boolean Satisfiability
Roy, A
2011-01-01
A delta-model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yielding a new satisfying assignment. These satisfying assignments represent robust solutions to optimization problems (e.g., scheduling) where it is possible to recover from unforeseen events (e.g., a resource becoming unavailable). The concept of delta-models was introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that finding delta-models for general Boolean formulas is NP-complete. In this paper, we extend that result by studying the complexity of finding delta-models for classes of Boolean formulas which are known to have polynomial time satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT, dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have polynomial time tests for delta-models, testing w...
Geometric Operators on Boolean Functions
Frisvad, Jeppe Revall; Falster, Peter
2007-01-01
In truth-functional propositional logic, any propositional formula represents a Boolean function (according to some valuation of the formula). We describe operators based on Decartes' concept of constructing coordinate systems, for translation of a propositional formula to the image of a Boolean function. With this image of a Boolean function corresponding to a propositional formula, we prove that the orthogonal projection operator leads to a theorem describing all rules of inference in propo...
Algorithms for Weighted Boolean Optimization
Manquinho, Vasco; Marques-Silva, Joao; Planes Cid, Jordi
2009-01-01
The Pseudo-Boolean Optimization (PBO) and Maximum Satisfiability (MaxSAT) problems are natural optimization extensions of Boolean Satisfiability (SAT). In the recent past, different algorithms have been proposed for PBO and for MaxSAT, despite the existence of straightforward mappings from PBO to MaxSAT and viceversa. This papers proposes Weighted Boolean Optimization (WBO), a new uni- fied framework that aggregates and extends PBO and MaxSAT. In addition, the paper proposes...
On Capability-Related Adaptation in Networked Service Systems
Finn Arve Aagesen; Patcharee Thongtra
2012-01-01
Adaptability is a property related to engineering as well as to the execution of networked service systems. This publication considers issues of adaptability both within a general and a scoped view. The generalview considers issues of adaptation at two levels: 1) System of entities, functions and adaptability types, and 2) Architectures supporting adaptability. Adaptability types defined are capability-related, functionality-related and context-related adaptation. The scoped view of the publi...
Cooperative Media Streaming Using Adaptive Network Compression
Møller, Janus Heide; Sørensen, Jesper Hemming; Krigslund, Rasmus;
2008-01-01
an adaptive hybrid between LC and MDC. In order to facilitate the use of MDC-CC, a new overlay network approach is proposed, using tree of meshes. A control system for managing description distribution and compression in a small mesh is implemented in the discrete event simulator NS-2. The two...... media distribution using traditional approaches. In particular, the asymmetric relationship between the uplink and the downlink bandwidth makes the cooperative distribution difﬁcult. A promising concept, termed MDC with Conditional Compression (MDC-CC), has been proposed [11], which essentially acts as...
Adaptive-network models of swarm dynamics
Huepe, Cristian [614 N Paulina Street, Chicago, IL 60622-6062 (United States); Zschaler, Gerd; Do, Anne-Ly; Gross, Thilo, E-mail: cristian@northwestern.edu [Max-Planck-Institut fuer Physik komplexer Systeme, Noethnitzer Strasse 38, 01187 Dresden (Germany)
2011-07-15
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Adaptive-network models of swarm dynamics
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Quantitative Adaptive RED in Differentiated Service Networks
LONG KePing(隆克平); WANG Qian(王茜); CHENG ShiDuan(程时端); CHEN JunLiang(陈俊亮)
2003-01-01
This paper derives a quantitative model between RED (Random Early Detection)maxp and committed traffic rate for token-based marking schemes in DiffServ IP networks. Then,a DiffServ Quantitative RED (DQRED) is presented, which can adapt its dropping probabilityto marking probability of the edge router to reflect not only the sharing bandwidth but also therequirement of performance of these services. Hence, DQRED can cooperate with marking schemesto guarantee fairness between different DiffServ AF class services. A new marking probabilitymetering algorithm is also proposed to cooperate with DQRED. Simulation results verify thatDQRED mechanism can not only control congestion of DiffServ network very well, but also satisfydifferent quality requirements of AF class service. The performance of DQRED is better than thatof WRED.
Adaptive bipartite consensus on coopetition networks
Hu, Jiangping; Zhu, Hong
2015-07-01
In this paper, a bipartite consensus tracking problem is considered for a group of autonomous agents on a coopetition network, on which the agents interact cooperatively and competitively simultaneously. The coopetition network involves positive and negative edges and is conveniently modeled by a signed graph. Additionally, the dynamics of all the agents are subjected to unknown disturbances, which are represented by linearly parameterized models. An adaptive estimation scheme is designed for each agent by virtue of the relative position measurements and the relative velocity measurements from its neighbors. Then a consensus tracking law is proposed for a new distributed system, which uses the relative measurements as the new state variables. The convergence of the consensus tracking error and the parameter estimation are analyzed even when the coopetition network is time-varying and no more global information about the bounds of the unknown disturbances is available to all the agents. Finally, some simulation results are provided to demonstrate the formation of the bipartite consensus on the coopetition network.
Symmetry in Boolean Satisfiability
Fadi A. Aloul
2010-06-01
Full Text Available This paper reviews recent approaches on how to accelerate Boolean Satisfiability (SAT search by exploiting symmetries in the problem space. SAT search algorithms traverse an exponentially large search space looking for an assignment that satisfies a set of constraints. The presence of symmetries in the search space induces equivalence classes on the set of truth assignments. The goal is to use symmetries to avoid traversing all assignments by constraining the search to visit a few representative assignments in each equivalence class. This can lead to a significant reduction in search runtime without affecting the completeness of the search.
Boolean Orthogonalizing Combination Methods
Yavuz Can
2015-05-01
Full Text Available In this paper a new logical operation method called “ presented. It is used to calculate the difference, but also the complement of a function as well as the EXOR and EXNOR of two minterms respectively two ternary respectively two ternary-vector logical operation method called “orthogonal OR advantages of both methods are their results, which are already available form that has an essential advantage for continuing calculations. Since it applies, an orthogonal disjunctive normal form is equal to orthogonal antivalence normal form, subsequent Boolean differential calculus will be simplified.
Fault Tolerant Boolean Satisfiability
Roy, A
2011-01-01
A delta-model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yielding a new satisfying assignment. These satisfying assignments represent robust solutions to optimization problems (e.g., scheduling) where it is possible to recover from unforeseen events (e.g., a resource becoming unavailable). The concept of delta-models was introduced by Ginsberg, Parkes and Roy (AAAI 1998...
Approximate Reasoning with Fuzzy Booleans
Broek, van den P.M.; Noppen, J.A.R.
2004-01-01
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp ante
Geometric Operators on Boolean Functions
Frisvad, Jeppe Revall; Falster, Peter
of a few geometric operators working on the images of Boolean functions. The operators we describe, arise from the niche area of array-based logic and have previously been tightly bound to an array-based representation of Boolean functions. We redefine the operators in an abstract form to make them...
Cryptographic Boolean functions and applications
Cusick, Thomas W
2009-01-01
Boolean functions are the building blocks of symmetric cryptographic systems. Symmetrical cryptographic algorithms are fundamental tools in the design of all types of digital security systems (i.e. communications, financial and e-commerce).Cryptographic Boolean Functions and Applications is a concise reference that shows how Boolean functions are used in cryptography. Currently, practitioners who need to apply Boolean functions in the design of cryptographic algorithms and protocols need to patch together needed information from a variety of resources (books, journal articles and other sources). This book compiles the key essential information in one easy to use, step-by-step reference. Beginning with the basics of the necessary theory the book goes on to examine more technical topics, some of which are at the frontier of current research.-Serves as a complete resource for the successful design or implementation of cryptographic algorithms or protocols using Boolean functions -Provides engineers and scient...
Cardinal invariants on Boolean algebras
Monk, J Donald
2014-01-01
This book is concerned with cardinal number valued functions defined for any Boolean algebra. Examples of such functions are independence, which assigns to each Boolean algebra the supremum of the cardinalities of its free subalgebras, and cellularity, which gives the supremum of cardinalities of sets of pairwise disjoint elements. Twenty-one such functions are studied in detail, and many more in passing. The questions considered are the behaviour of these functions under algebraic operations such as products, free products, ultraproducts, and their relationships to one another. Assuming familiarity with only the basics of Boolean algebras and set theory, through simple infinite combinatorics and forcing, the book reviews current knowledge about these functions, giving complete proofs for most facts. A special feature of the book is the attention given to open problems, of which 185 are formulated. Based on Cardinal Functions on Boolean Algebras (1990) and Cardinal Invariants on Boolean Algebras (1996) by the...
Probabilistic Adaptive Anonymous Authentication in Vehicular Networks
Yong Xi; Ke-Wei Sha; Wei-Song Shi; Loren Schwiebert; Tao Zhang
2008-01-01
Vehicular networks have attracted extensive attention in recent years for their promises in improving safety and enabling other value-added services. Most previous work focuses on designing the media access and physical layer protocols.Privacy issues in vehicular systems have not been well addressed. We argue that privacy is a user-specific concept, and a good privacy protection mechanism should allow users to select the levels of privacy they wish to have. To address this requirement, we propose an adaptive anonymous authentication mechanism that can trade off the anonymity level with computational and communication overheads (resource usage). This mechanism, to our knowledge, is the first effort on adaptive anonymous authentication. The resources used by our protocol are few. A high traffic volume of 2000 vehicles per hour consumes about 60kbps bandwidth, which is less than one percent of the bandwidth of DSRC (Dedicated Short Range Communications). By using adaptive anonymity, the protocol response time can further be improved 2～4 times with lessthan 20% bandwidth overheads.
Integrated Adaptive Analysis and Visualization of Satellite Network Data Project
National Aeronautics and Space Administration — We propose to develop a system that enables integrated and adaptive analysis and visualization of satellite network management data. Integrated analysis and...
Opportunistic Adaptive Relaying in Cognitive Radio Networks
Jaafar, Wael; Haccoun, David
2012-01-01
Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called "Adaptive relaying scheme 1"), where one relay node can assist the primary or the secondary transmission with the objective of improving the outage probability of the secondary transmission with respect to a primary outage probability threshold. Upper bound expressions of the secondary outage probability using the proposed scheme are derived over Rayleigh fading channels. Numerical and simulation results show that the secondary outage probability using the proposed scheme is lower than that of other relaying schemes. Then, we extend the proposed scheme to the case where the relay node has the ability to decode both the primary and secondary signals and also can assist simultaneously both transmissions. Simulations show the performance improvement that can be obtained due to this extension in terms of...
Link-based formalism for time evolution of adaptive networks
Zhou, Jie; Chen, Guanrong
2013-01-01
Network topology and nodal dynamics are two fundamental stones of adaptive networks. Detailed and accurate knowledge of these two ingredients is crucial for understanding the evolution and mechanism of adaptive networks. In this paper, by adopting the framework of the adaptive SIS model proposed by Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the information of degree correlation of the network, we propose a link-based formalism for describing the system dynamics with high accuracy and subtle details. Several specific degree correlation measures are introduced to reveal the coevolution of network topology and system dynamics.
Adaptive training of feedforward neural networks by Kalman filtering
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
On the Number of Attractors of Positive and Negative Boolean Automata Circuits.
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2010-01-01
International audience In line with fields of theoretical computer science and biology that study Boolean automata networks often seen as models of regulation networks, we present some results concerning the dynamics of networks whose underlying interaction graphs are circuits, that is, Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain t...
On Kolmogorov's superpositions and Boolean functions
Beiu, V.
1998-12-31
The paper overviews results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on an explicit numerical (i.e., constructive) algorithm for Kolmogorov's superpositions they will show that for obtaining minimum size neutral networks for implementing any Boolean function, the activation function of the neurons is the identity function. Because classical AND-OR implementations, as well as threshold gate implementations require exponential size (in the worst case), it will follow that size-optimal solutions for implementing arbitrary Boolean functions require analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Speed Adaptation in Urban Road Network Management
Raiyn Jamal
2016-06-01
Full Text Available Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS and global navigation satellite system (GNSS. Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.
Opinion dynamics on an adaptive random network
Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.
2009-04-01
We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.
Brain network adaptability across task states.
Elizabeth N Davison
2015-01-01
Full Text Available Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific and across (task-general brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
Techniques for solving Boolean equation systems
Keinänen, Misa
2006-01-01
Boolean equation systems are ordered sequences of Boolean equations decorated with least and greatest fixpoint operators. Boolean equation systems provide a useful framework for formal verification because various specification and verification problems, for instance, μ-calculus model checking can be represented as the problem of solving Boolean equation systems. The general problem of solving a Boolean equation system is a computationally hard task, and no polynomial time solution technique ...
A more robust Boolean model describing inhibitor binding
Zhaoqian Steven XIE; Chao TANG
2008-01-01
From the first application of the Boolean model to the cell cycle regulation network of budding yeast, new regulative pathways have been discovered, par-ticularly in the G1/S transition circuit. This discovery called for finer modeling to study the essential biology, and the resulting outcomes are first introduced in the ar-ticle. A traditional Boolean network model set up for the new G1/S transition circuit shows that it cannot correctly simulate real biology unless the model parameters are fine tuned. The deficiency is caused by an overly coarse-grained description of the inhibitor binding process, which shall be overcome by a two-vector model proposed whose robustness is surveyed using random perturba-tions. Simulations show that the proposed two-vector model is much more robust in describing inhibitor binding processes within the Boolean framework.
Adaptive-impulsive synchronization of uncertain complex dynamical networks
This Letter studies adaptive-impulsive synchronization of uncertain complex dynamical networks. Based on the stability analysis of impulsive system, several network synchronization criteria for local and global adaptive-impulsive synchronization are established. Numerical example is also given to illustrate the results
A Note on the Inversion Complexity of Boolean Functions in Boolean Formulas
Morizumi, Hiroki
2008-01-01
In this note, we consider the minimum number of NOT operators in a Boolean formula representing a Boolean function. In circuit complexity theory, the minimum number of NOT gates in a Boolean circuit computing a Boolean function $f$ is called the inversion complexity of $f$. In 1958, Markov determined the inversion complexity of every Boolean function and particularly proved that $\\lceil \\log_2(n+1) \\rceil$ NOT gates are sufficient to compute any Boolean function on $n$ variables. As far as we...
Synchronization of general complex networks via adaptive control schemes
Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik
2014-03-01
In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
Yuntao Shi; Bo Liu; Xiao Han
2013-01-01
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results.
Electrooptical adaptive switching network for the hypercube computer
Chow, E.; Peterson, J.
1988-01-01
An all-optical network design for the hyperswitch network using regular free-space interconnects between electronic processor nodes is presented. The adaptive routing model used is described, and an adaptive routing control example is presented. The design demonstrates that existing electrooptical techniques are sufficient for implementing efficient parallel architectures without the need for more complex means of implementing arbitrary interconnection schemes. The electrooptical hyperswitch network significantly improves the communication performance of the hypercube computer.
LTE Adaptation for Mobile Broadband Satellite Networks
Bastia Francesco
2009-01-01
Full Text Available One of the key factors for the successful deployment of mobile satellite systems in 4G networks is the maximization of the technology commonalities with the terrestrial systems. An effective way of achieving this objective consists in considering the terrestrial radio interface as the baseline for the satellite radio interface. Since the 3GPP Long Term Evolution (LTE standard will be one of the main players in the 4G scenario, along with other emerging technologies, such as mobile WiMAX; this paper analyzes the possible applicability of the 3GPP LTE interface to satellite transmission, presenting several enabling techniques for this adaptation. In particular, we propose the introduction of an inter-TTI interleaving technique that exploits the existing H-ARQ facilities provided by the LTE physical layer, the use of PAPR reduction techniques to increase the resilience of the OFDM waveform to non linear distortion, and the design of the sequences for Random Access, taking into account the requirements deriving from the large round trip times. The outcomes of this analysis show that, with the required proposed enablers, it is possible to reuse the existing terrestrial air interface to transmit over the satellite link.
Temporal percolation of a susceptible adaptive network
Valdez, L D; Braunstein, L A
2013-01-01
In the last decades, due to the appearance of many diseases such as SARS and the H1N1 flu strain, many authors studied the impact of the disease spreading in the evolution of the infected individuals using the susceptible-infected-recovered model. However, few authors focused on the temporal behavior of the susceptible individuals. Recently it was found that in an epidemic spreading, the dynamic of the size of the biggest susceptible cluster can be explained by a temporal node void percolation [Valdez et al PLoS ONE 7, e44188 (2012)]. It was shown that the size of the biggest susceptible cluster is the order parameter of this temporal percolation where the control parameter can be related to the number of links between susceptible individuals at a given time. As a consequence, there is a critical time at which the biggest susceptible cluster is destroyed. In this paper, we study the susceptible-infected-recovered model in an adaptive network where an intermittent social distancing strategy is applied. In this...
Fuzzy Optimized Metric for Adaptive Network Routing
Ahmad Khader Haboush
2012-04-01
Full Text Available Network routing algorithms used today calculate least cost (shortest paths between nodes. The cost of a path is the sum of the cost of all links on that path. The use of a single metric for adaptive routing is insufficient to reflect the actual state of the link. In general, there is a limitation on the accuracy of the link state information obtained by the routing protocol. Hence it becomes useful if two or more metrics can be associated to produce a single metric that can describe the state of the link more accurately. In this paper, a fuzzy inference rule base is implemented to generate the fuzzy cost of each candidate path to be used in routing the incoming calls. This fuzzy cost is based on the crisp values of the different metrics; a fuzzy membership function is defined. The parameters of these membership functions reflect dynamically the requirement of the incoming traffic service as well as the current state of the links in the path. And this paper investigates how three metrics, the mean link bandwidth, queue utilization and the mean link delay, can be related using a simple fuzzy logic algorithm to produce a optimized cost of the link for a certain interval that is more „precise‟ than either of the single metric, to solve routing problem .
Adaptive Mobile Positioning in WCDMA Networks
Dong B.
2005-01-01
Full Text Available We propose a new technique for mobile tracking in wideband code-division multiple-access (WCDMA systems employing multiple receive antennas. To achieve a high estimation accuracy, the algorithm utilizes the time difference of arrival (TDOA measurements in the forward link pilot channel, the angle of arrival (AOA measurements in the reverse-link pilot channel, as well as the received signal strength. The mobility dynamic is modelled by a first-order autoregressive (AR vector process with an additional discrete state variable as the motion offset, which evolves according to a discrete-time Markov chain. It is assumed that the parameters in this model are unknown and must be jointly estimated by the tracking algorithm. By viewing a nonlinear dynamic system such as a jump-Markov model, we develop an efficient auxiliary particle filtering algorithm to track both the discrete and continuous state variables of this system as well as the associated system parameters. Simulation results are provided to demonstrate the excellent performance of the proposed adaptive mobile positioning algorithm in WCDMA networks.
Dynamic multimedia stream adaptation and rate control for heterogeneous networks
SZWABE Andrzej; SCHORR Andreas; HAUCK Franz J.; KASSLER Andreas J.
2006-01-01
Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.
Adapting Bayes Network Structures to Non-stationary Domains
Nielsen, Søren Holbech; Nielsen, Thomas Dyhre
2008-01-01
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradu......When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...
Information Theoretic Adaptive Tracking of Epidemics in Complex Networks
Harrington, Patrick L
2013-01-01
Adaptively monitoring the states of nodes in a large complex network is of interest in domains such as national security, public health, and energy grid management. Here, we present an information theoretic adaptive tracking and sampling framework that recursively selects measurements using the feedback from performing inference on a dynamic Bayesian Network. We also present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of hidden node states resulting from actively monitoring the network. Since traditional epidemic thresholds are derived using observation independent Markov chains, the threshold of the posterior should more accurately model the true phase transition of a network. The adaptive tracking framework and epidemic threshold should provide insight into modeling the dynamic response of the updated posterior to active intervention and control policies while monitoring modern complex networks.
Adaptive cluster synchronization of directed complex networks with time delays.
Heng Liu
Full Text Available This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.
Robust adaptive neural network control with supervisory controller
张天平; 梅建东
2004-01-01
The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed.The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.Simulation results demonstrate the effectiveness of the approach.
Generalized join-hemimorphisms on Boolean algebras
Sergio Celani
2003-01-01
We introduce the notions of generalized join-hemimorphism and generalized Boolean relation as an extension of the notions of join-hemimorphism and Boolean relation, respectively. We prove a duality between these two notions. We will also define a generalization of the notion of Boolean algebra with operators by considering a finite family of Boolean algebras endowed with a generalized join-hemimorphism. Finally, we define suitable notions of subalgebra, congruences, Boole...
On the Performance of Adaptive Modulation in Cognitive Radio Networks
Foukalas, F.; Karetsos, G. T.
2013-01-01
We study the performance of cognitive radio networks (CRNs) when incorporating adaptive modulation at the physical layer. Three types of CRNs are considered, namely opportunistic spectrum access (OSA), spectrum sharing (SS) and sensing-based SS. We obtain closed-form expressions for the average spectral efficiency achieved at the secondary network and the optimal power allocation for both continuous and discrete rate types of adaptive modulation assuming perfect channel state information. The...
Boolean Operations on Conic Polygons
Yong-Xi Gong; Yu Liu; Lun Wu; Yu-Bo Xie
2009-01-01
An algorithm for Boolean operations on conic polygons is proposed. Conic polygons are polygons consisting of conic segments or bounded conics with directions. Preliminaries of Boolean operations on general polygons are presented. In our algorithm, the intersection points and the topological relationships between two conic polygons are computed. Boundaries are obtained by tracking path and selecting uncrossed boundaries following rule tables to build resulting conic polygons.We define a set of rules for the intersection, union, and subtraction operations on conic polygons. The algorithm considers degeneration cases such as homology, complement, interior, and exterior. The algorithm is also evaluated and implemented.
Adaptive nonlinear control using input normalized neural networks
An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small
Traffic flow on realistic road networks with adaptive traffic lights
de Gier, Jan; Rojas, Omar
2010-01-01
We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adative vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, the total travel time is smallest when using the joint upstream-downstream control strategy.
Stochastic analysis of epidemics on adaptive time varying networks
Kotnis, Bhushan; Kuri, Joy
2013-06-01
Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
Linking Individual and Collective Behavior in Adaptive Social Networks
Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.
2016-03-01
Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.
Boolean modeling in systems biology: an overview of methodology and applications
Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology. (paper)
Implementation of an Adaptive Learning System Using a Bayesian Network
Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki
2015-01-01
An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…
Adaptation to synchronization in phase-oscillator networks
Arizmendi, Fernando; Zanette, Damian H.
2008-01-01
We introduce an adaptation algorithm by which an ensemble of coupled oscillators with attractive and repulsive interactions is induced to adopt a prescribed synchronized state. While the performance of adaptation is controlled by measuring a macroscopic quantity, which characterizes the achieved degree of synchronization, adaptive changes are introduced at the microscopic level of the interaction network, by modifying the configuration of repulsive interactions. This scheme emulates the disti...
Collaborative Trust Networks in Engineering Design Adaptation
Atkinson, Simon Reay; Maier, Anja; Caldwell, Nicholas;
2011-01-01
); applying the Change Prediction Method (CPM) tool. It posits the idea of the ‘Networks-in-Being’ with varying individual and collective characteristics. [Social] networks are considered to facilitate information exchange between actors. At the same time, networks failing to provide trusted-information can...... hinder effective communication and collaboration. Different combinations of trust may therefore improve or impair the likelihood of information flow, transfer and subsequent action (cause and effect). This paper investigates how analysing different types of network-structures-in-being can support......Within organisations, decision makers have to rely on collaboration with other actors from different disciplines working within highly dynamic and distributed associated networks of varying size and scales. This paper develops control and influence networks within Design Structure Matrices (DSM...
Adaptive optimization and control using neural networks
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers constructed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua's circuit network example is finally given to show the effectiveness of the proposed synchronization criteria. (general)
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features,including adaptation,robustness and distributed,decentralized nature,which are well suited for routing in modern communication networks.This paper describes an adaptive swarm-based routing algorithm that increases convergence speed,reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Model for cascading failures with adaptive defense in complex networks
This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology, where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them. The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures. Particularly, there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding, a costless strategy of defense for preventing cascade breakdown is proposed. It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes. (general)
Adaptive Neurons For Artificial Neural Networks
Tawel, Raoul
1990-01-01
Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.
Network-topology-adaptive quantum conference protocols
Zhang Sheng; Wang Jian; Tang Chao-Jing; Zhang Quan
2011-01-01
As an important application of the quantum network communication,quantum multiparty conference has made multiparty secret communication possible.Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology.However,the topology of the quantum network significantly affects the communication efficiency,e.g.,parallel transmission in a channel with limited bandwidth.We have proposed two distinctive protocols,which work in two basic network topologies with efficiency higher than the existing ones.We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping.Another protocol,based on quantum multicasting with quantum data compression,which can improve the efficiency of the network,works in the star-like network.The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption.In general,the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem; Toufik Sari; Mokhtar Sellami
2005-01-01
Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized art...
Cardinal invariants on Boolean algebras
Monk, J Donald
2009-01-01
Deals with cardinal number valued functions defined for any Boolean algebra. This title considers the behavior of these functions under algebraic operations such as products, free products, ultraproducts, and their relationships to one another. It covers topics such as ultraproducts and Fedorchukis theorem
Boolean-Valued Belief Functions
Kramosil, Ivan
2002-01-01
Roč. 31, č. 2 (2002), s. 153-181. ISSN 0308-1079 R&D Projects: GA AV ČR IAA1030803 Institutional research plan: AV0Z1030915 Keywords : Dempster-Schafer theory * Boolean algebra Subject RIV: BA - General Mathematics Impact factor: 0.241, year: 2002
Evolutionary Design of Boolean Functions
WANG Zhang-yi; ZHANG Huan-guo; QIN Zhong-ping; MENG Qing-shu
2005-01-01
We use evolutionary computing to synthesize Boolean functions randomly. By using specific crossover and mutation operator in evolving process and modifying search space and fitness function, we get some high non-linearity functions which have other good cryptography characteristics such as autocorrelation etc. Comparing to other heuristic search techniques, evolutionary computing approach is more effective because of global search strategy and implicit parallelism.
TCP-Adaptive in High Speed Long Distance Networks
Quan Liu
2014-02-01
Full Text Available With the development of high performance computing and increasing of network bandwidth, more and more applications require fast data transfer over high-speed long-distance networks. Research shows that the standard TCP Reno cannot fulfill the requirement of fast transfer of massive data due to its conservative congestion control mechanism. Some works have been proposed to improve the TCP throughput performance using more aggressive window increasing tactics and obtain substantial achievements. However, they cannot be strictly proved to be comprehensively suitable for high-speed complex network environments. In this paper, we propose TCP-Adaptive, an adaptive congestion control algorithm adjusting the increasing congestion window dynamically. The algorithm improves logarithmic detection procedure for available bandwidth in the flow path by distinguishing the first detection in congestion avoidance and retransmission timeout. On the other hand, an adaptive control algorithm is proposed to achieve better performance in high-speed long-distance networks. The algorithm uses round trip time (RTT variations to predict the congestion trends to update the increments of congestion window. Simulations verify the property of TCP-Adaptive and show satisfying performance in throughput, RTT fairness aspects over high-speed long-distance networks. Especially in sporadic loss environment, TCP-Adaptive shows a significant adaptability with the variations of link quality
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Gross, Thilo
2015-01-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks
E M Saad; Awadalla, M. H.; R. R. Darwish
2009-01-01
Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive ...
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Rocha, Luis E. C.; Gross, Thilo
2016-04-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Time scales in evolutionary game on adaptive networks
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Time scales in evolutionary game on adaptive networks
Cong, Rui, E-mail: congrui0000@126.com [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wu, Te; Qiu, Yuan-Ying [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wang, Long [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Center for Systems and Control, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing (China)
2014-02-01
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can describe other adaptive network dynamics involving collective...
Time-adaptive and history-adaptive multicriterion routing in stochastic, time-dependent networks
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan;
2009-01-01
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' model and the more flexible "history-adaptive'' one. We point out several properties of the sets of efficient solutions found under the two models. We also devise a method...
Implementing Boolean Matrix Factorization
Neruda, Roman; Snášel, V.; Platoš, J.; Krömer, P.; Húsek, Dušan; Frolov, A. A.
Vol. Part I. Berlin : Springer, 2008 - (Kůrková, V.; Neruda, R.; Koutník, J.), s. 543-552 ISBN 978-3-540-87535-2. - (Lecture Notes in Computer Science. 5163). [ICANN 2008. International Conference on Artificial Neural Network s /18./. Prague (CZ), 03.09.2008-06.09.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : factor analysis * genetic algorithm * neural network s Subject RIV: IN - Informatics, Computer Science
Adaptive intelligent power systems: Active distribution networks
Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Boccaletti, Stefano; Caldarelli, Guido
2016-06-01
Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but also beneficial for the functioning of a variety of systems. We here consider an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity, and show that it self-organizes from fragmented and incoherent states to connected and synchronized ones. The synchronization and percolation are associated to abrupt transitions, and they are concurrently (and significantly) enhanced as compared to the non-adaptive case. Finally we provide evidence that only partial adaptation is sufficient to determine these enhancements. Our study, therefore, indicates that inclusion of simple adaptive mechanisms can efficiently describe some emergent features of networked systems’ collective behaviors, and suggests also self-organized ways to control synchronization and percolation in natural and social systems.
Controling contagious processes on temporal networks via adaptive rewiring
Belik, Vitaly; Hövel, Philipp
2015-01-01
We consider recurrent contagious processes on a time-varying network. As a control procedure to mitigate the epidemic, we propose an adaptive rewiring mechanism for temporary isolation of infected nodes upon their detection. As a case study, we investigate the network of pig trade in Germany. Based on extensive numerical simulations for a wide range of parameters, we demonstrate that the adaptation mechanism leads to a significant extension of the parameter range, for which most of the index nodes (origins of the epidemic) lead to vanishing epidemics. We find that diseases with detection times around a week and infectious periods up to 3 months can be effectively controlled. Furthermore the performance of adaptation is very heterogeneous with respect to the index node. We identify index nodes that are most responsive to the adaptation strategy and quantify the success of the proposed adaptation scheme in dependence on the infectious period and detection times.
Network Reliability Algorithm Based on Pathset Matrix and Boolean Operation%基于路集矩阵与布尔运算的网络可靠度算法
高会生; 展敬宇; 王博颖; 李潇睿
2012-01-01
This paper analyzes the network reliability algorithm based on pathset matrix, and there exists a serious combination explosion problem in this algorithm. Aiming at this problem, it proposes a network reliability algorithm based on pathset matrix and boolean operation. The concept of bit vector is introduced. In addition, the pre-process of special pathsets and count of all-one bit vectors are also implied. Experimental results show that it not only increases the memory utilization, reduce the redundancy but also relieve the combination explosion problem in some degree.%分析基于路集矩阵与布尔运算的网络可靠度算法,指出其存在组合爆炸问题.为此,提出一种改进算法,引入位矢量以减少内存需求,对特殊路集进行预处理并统计全1位矢量.实验结果表明,改进算法可提高内存利用率、减少冗余运算,能在一定程度上缓解组合爆炸问题.
Adaptive Capacity Management in Bluetooth Networks
Son, L.T.
With the Internet and mobile wireless development, accelerated by high-speed and low cost VLSI device evolution, short range wireless communications have become more and more popular, especially Bluetooth. Bluetooth is a new short range radio technology that promises to be very convenient, low...... power, and low cost mobile ad hoc solution for the global interconnection of all mobile devices. To implement Bluetooth network as a true mobile ad hoc wireless network operating in short radio range, highly dynamic network environment, low power, and scarce resources, many new research challenges occur......, such as limited wireless bandwidth operation, routing, scheduling, network control, etc. Currently Bluetooth specification particularly does not describe in details about how to implement Quality of Service and Resource Management in Bluetooth protocol stacks. These issues become significant, when the number...
Adaptive computational resource allocation for sensor networks
WANG Dian-hong; FEI E; YAN Yu-jie
2008-01-01
To efficiently utilize the limited computational resource in real-time sensor networks, this paper focu-ses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economies. It designs a mieroeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simula-tion in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational re-sources according to the priority of tasks, achieves the superior allocation performance and equilibrium perform-ance compared to traditional allocation policies, and ultimately prolongs the system lifetime.
Bifurcation Analysis of Equilibria in Competitive Logistic Networks with Adaptation
Raimondi, A.; Tebaldi, C.
2008-04-01
A general n-node network is considered for which, in absence of interactions, each node is governed by a logistic equation. Interactions among the nodes take place in the form of competition, which also includes adaptive abilities through a (short term) memory effect. As a consequence the dynamics of the network is governed by a system of n2 nonlinear ordinary differential equations. As a first step, equilibria and their stability are investigated analytically for the general network in dependence of the relevant parameters, namely the strength of competition, the adaptation rate and the network size. The existence of classes of invariant subspaces, related to symmetries, allows the introduction of a reduced model, four dimensional, where n appears as a parameter, which give full account of existence and stability for the equilibria in the network.
Scalable Lunar Surface Networks and Adaptive Orbit Access
Wang, Xudong
2015-01-01
Teranovi Technologies, Inc., has developed innovative network architecture, protocols, and algorithms for both lunar surface and orbit access networks. A key component of the overall architecture is a medium access control (MAC) protocol that includes a novel mechanism of overlaying time division multiple access (TDMA) and carrier sense multiple access with collision avoidance (CSMA/CA), ensuring scalable throughput and quality of service. The new MAC protocol is compatible with legacy Institute of Electrical and Electronics Engineers (IEEE) 802.11 networks. Advanced features include efficiency power management, adaptive channel width adjustment, and error control capability. A hybrid routing protocol combines the advantages of ad hoc on-demand distance vector (AODV) routing and disruption/delay-tolerant network (DTN) routing. Performance is significantly better than AODV or DTN and will be particularly effective for wireless networks with intermittent links, such as lunar and planetary surface networks and orbit access networks.
Explosive Synchronization and Emergence of Assortativity on Adaptive Networks
JIANG Hui-Jun; WU Hao; HOU Zhong-Huai
2011-01-01
@@ We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks,following an Achlioptas process based on dynamic clustering information.During each adaptive step of the network topology,a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules(PRs) applied to the dynamic clusters.Particularly,two types of PRs are considered,namely,the min-PR and max-PR.We demonstrate that the synchronization transition becomes explosive in both cases.Interestingly,we find that the min-PR rule can lead to disassortativity of the network topology,while the max-PR rule leads to assortativity.%We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks, following an Achlioptas process based on dynamic clustering information. During each adaptive step of the network topology, a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules (PRs) applied to the dynamic clusters. Particularly, two types of PRs are considered, namely, the min-PR and max-PR. We demonstrate that the synchronization transition becomes explosive in both cases. Interestingly, we find that the min-PR rule can lead to disassortativity of the network topology, while the max-PR rule leads to assortativity.
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu;
2012-01-01
known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control......Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and...... model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the...
Analysis of adaptive algorithms for an integrated communication network
Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim
1985-01-01
Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.
A candidate multimodal functional genetic network for thermal adaptation
Katharina C. Wollenberg Valero
2014-09-01
Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren; Leonard, Naomi Ehrich
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcatio...
Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks
Fafoutis, Xenofon; Dragoni, Nicola
2012-01-01
ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....
Scalable Harmonization of Complex Networks With Local Adaptive Controllers
Kárný, Miroslav; Herzallah, R.
-, - (2016). ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Feedback * Feedforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.699, year: 2014 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Cost-Optimal Execution of Trees of Boolean Operators with Shared Streams
Casanova, Henri; Lim, Lipyeow; Robert, Yves; Vivien, Frédéric; Zaidouni, Dounia
2013-01-01
The processing of queries expressed as trees of boolean operators applied to predicates on sensor data streams has several applications in mobile computing. Sensor data must be retrieved from the sensors to a query processing device, such as a smartphone, over one or more network interfaces. Retrieving a data item incurs a cost, e.g., an energy expense that depletes the smartphone's battery. Since the query tree contains boolean operators, part of the tree can be shortcircuited depending on t...
Radio propagation and adaptive antennas for wireless communication networks
Blaunstein, Nathan
2014-01-01
Explores novel wireless networks beyond 3G, and advanced 4G technologies, such as MIMO, via propagation phenomena and the fundamentals of adapted antenna usage.Explains how adaptive antennas can improve GoS and QoS for any wireless channel, with specific examples and applications in land, aircraft and satellite communications.Introduces new stochastic approach based on several multi-parametric models describing various terrestrial scenarios, which have been experimentally verified in different environmental conditionsNew chapters on fundamentals of wireless networks, cellular and non-cellular,
Boolean Models of Biological Processes Explain Cascade-Like Behavior.
Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen
2016-01-01
Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes. PMID:26821940
QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Mohammad Abdur Razzaque
2014-12-01
Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.
Adaptive Data Transmission in Multimedia Networks
Manimegalai Parry
2005-01-01
Full Text Available This study suggests a method where the packet size of each source is adjusted according to the network bandwidth. A controller is used to trace the data transmission rate at the router. An algorithm is developed and coded in Tool Command Language. Simulation is performed on NS-2 using 4 different test cases and the results show that the proposed algorithm avoids router queue overflow.
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Study on Adaptive Control with Neural Network Compensation
单剑锋; 黄忠华; 崔占忠
2004-01-01
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify pareters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.
An Adaptive Complex Network Model for Brain Functional Networks
Gomez Portillo, Ignacio J.; Gleiser, Pablo M.
2009-01-01
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show diffe...
Maximizing Quality and Performance of Network Through Adaptive Traffic Engineering
Sameera Pallavi; Ch.Sandeep; P.Pramod Kumar
2013-01-01
Network management systems are to handle traffic dynamics in order to ensure congestion free network with highest throughput. IP environments are able to provide simple facilities for forwarding and routing packets. However, in presence of dynamic traffic conditions efficient management of resources is yet to be achieved. Recently Ning Wang et al. proposed a traffic engineering system which can ynamically adapt to traffic conditions with the help of virtual routing topologies. It has two majo...
Adaptive projective synchronization with different scaling factors in networks
Guo Liu-Xiao; Xu Zhen-Yuan; Hu Man-Feng
2008-01-01
We study projective synchronization with different scaling factors (PSDF) in N coupled chaotic systems networks.By using the adaptive linear control,some sufficient criteria for the PSDF in symmetrical and asymmetrical coupled networks are separately given based on the Lyapunov function method and the left eigenvalue theory.Numerical simulations for a generalized chaotic unified system are illustrated to verify the theoretical results.
Optimization of an adaptive neural network to predict breathing
Murphy, Martin J; Pokhrel, Damodar
2008-01-01
Purpose: To determine the optimal configuration and performance of an adaptive feed forward neural network filter to predict breathing in respiratory motion compensation systems for external beam radiation therapy. Method and Materials: A two-layer feed forward neural network was trained to predict future breathing amplitudes for 27 recorded breathing histories. The prediction intervals ranged from 100 to 500 ms. The optimal sampling frequency, number of input samples, training rate, and numb...
Quantum algorithms for testing Boolean functions
Erika Andersson
2010-06-01
Full Text Available We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the Bernstein-Vazirani quantum algorithm can deterministically identify which one of these Boolean functions we are given using just one single function query. The same quantum algorithm can also be used to learn which input variables other types of Boolean functions depend on, with a success probability that depends on the form of the Boolean function that is tested, but does not depend on the total number of input variables. We also outline a procedure to futher amplify the success probability, based on another quantum algorithm, the Grover search.
Social Networking Adapted for Distributed Scientific Collaboration
Karimabadi, Homa
2012-01-01
Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and
Boolean Reasoning with Graphs of Partitions
Goossens, Daniel
2010-01-01
version longue du papier court "A Dynamic Boolean Knowledge Base" accepté à ICTAI 2010. This paper presents an implemented architecture for easy learning, reorganizing and navigation into a Boolean knowledge base. As the base grows with new definitions and constraints, it is normalized by the closure of a completion operator. This normalization allows arbitrary formats for Boolean expressions. It ensures basic reasoning abilities and spontaneously organizes intermingled taxonomies of conce...
Model Checking of Boolean Process Models
Schneider, Christoph; Wehler, Joachim
2011-01-01
In the field of Business Process Management formal models for the control flow of business processes have been designed since more than 15 years. Which methods are best suited to verify the bulk of these models? The first step is to select a formal language which fixes the semantics of the models. We adopt the language of Boolean systems as reference language for Boolean process models. Boolean systems form a simple subclass of coloured Petri nets. Their characteristics are low tokens to mode...
Estimation of Boolean Factor Analysis Performance by Informational Gain
Frolov, A.; Húsek, Dušan; Polyakov, P.Y.
Berlin : Springer, 2010 - (Snášel, V.; Szczepaniak, P.; Abraham, A.; Kacprzyk, J.), s. 83-94 ISBN 978-3-642-10686-6. - (Advances in Intelligent and Soft Computing. 67). [AWIC 2009. Atlantic Web Intelligence Conference /6./. Prague (CZ), 09.09.2009-11.09.2009] Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * informational gain * Hopfield-like network Subject RIV: IN - Informatics, Computer Science
Progress in Applications of Boolean Functions
Sasao, Tsutomu
2010-01-01
This book brings together five topics on the application of Boolean functions. They are 1. Equivalence classes of Boolean functions: The number of n-variable functions is large, even for values as small as n = 6, and there has been much research on classifying functions. There are many classifications, each with their own distinct merit. 2. Boolean functions for cryptography: The process of encrypting/decrypting plain text messages often depends on Boolean functions with specific properties. For example, highly nonlinear functions are valued because they are less susceptible to linear attacks.
Adaptive synchronization in an array of asymmetric coupled neural networks
Gao Ming; Cui Bao-Tong
2009-01-01
This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
An Adaptive Neural Network Model for Nonlinear Programming Problems
Xiang-sun Zhang; Xin-jian Zhuo; Zhu-jun Jing
2002-01-01
In this paper a canonical neural network with adaptively changing synaptic weights and activation function parameters is presented to solve general nonlinear programming problems. The basic part of the model is a sub-network used to find a solution of quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an external computer or a special analog or digital processor that adjusts the weights and parameters, one then solves general nonlinear programming problems. Convergence proof and numerical results are given.
High dynamic adaptive mobility network model and performance analysis
LIU Hui; ZHANG Jun
2008-01-01
Since mobile networks are not currently deployed on a large scale, research in this area is mostly by simulation. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Based on random direction mobility model, a high dynamic adaptive mo-bility network model is proposed in the paper. The algorithms and modeling are mainly studied and explained in detail. The technique keystone is that normal dis-tribution is combined with uniform distribution and inertial feedback control is combined with kinematics, through the adaptive control on nodes speed and pre-diction tracking on nodes routes, an adaptive model is designed, which can be used in simulations to produce realistic and dynamic network scenarios. It is the adaptability that nodes mobile parameters can be adjusted randomly in three-dimensional space. As a whole, colony mobility can show some rules. Such ran-dom movement processes as varied speed and dwells are simulated realistically. Such problems as sharp turns and urgent stops are smoothed well. The model can be adapted to not only common dynamic scenarios, but also high dynamic sce-narios. Finally, the mobility model performance is analyzed and validated based on random dynamic scenarios simulations.
In-network adaptation of SHVC video in software-defined networks
Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos
2016-04-01
Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and
Genetic adaptation of the antibacterial human innate immunity network
Lazarus Ross
2011-07-01
Full Text Available Abstract Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.
Adaptive Control of Flexible Redundant Manipulators Using Neural Networks
SONG Yimin; LI Jianxin; WANG Shiyu; LIU Jianping
2006-01-01
An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted.The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors.A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator.The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced.The neuro-identifier and the neurocontroller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC.By adjusting the neuro-identifier and the neuro-controller alternatively,the manipulator was controlled on line for achieving the desired dynamic performance.Finally,a planar 3R redundant manipulator with one smart link was utilized as an illustrative example.The simulation results proved the validity of the control strategy.
Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks
Di Mauro, Alessio; Dragoni, Nicola
2015-01-01
Energy Harvesting - Wireless Sensor Networks (EH-WSNs) constitute systems of networked sensing nodes that are capable of extracting energy from the environment and that use the harvested energy to operate in a sustainable state. Sustainability, seen as design goal, has a significant impact...... on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key...... reinforcement scheme specifically designed for EH-WSNs. The proposed scheme allows each node to take into consideration and adapt to the amount of energy available in the system. In particular, we present two approaches, one static and one fully dynamic, and we discuss some experimental results....
Network Experiences Lead to the Adaption of a Firm’s Network Competence
Bianka Kühne
2011-12-01
Full Text Available Networks become increasingly important as external sources of innovation for firms. Through networks firms get incontact with different actors with whom they can exchange information and collaborate. A firm’s ability to be asuccessful network actor depends on its network competence. This term can be defined as having the necessaryknowledge, skills and qualifications for networking as well as using them effectively. In this paper we investigate thelink between a firm’s network competence and the benefits resulting from it in a two‐way direction. First, thenetwork competence of the firm facilitates the adoption of information from other network actors which may leadto innovation success. Second the perceived network benefits shall in their turn influence the network competenceof the firm. Consequently, firms will adapt their network strategy corresponding their experiences. The objective ofthis paper is to investigate the dynamics of networking and its influence on the firm’s network competence. For thisexploratory research 3 Belgian networks are examined. In‐depth interviews are used in combination with semistructuredinterview guides to conduct the research. Our results indicate that some firms perceive benefits fromtheir network efforts, for others it is more a burden. Furthermore, in some of our cases we found that positiveexperiences with clear benefits motivate the firm to enhance its network competence. This is illustrated by the factthat collaborations are more frequently initiated, trust is more easily build, firms are more open to communicateinformation and the confidentiality threshold is overcome.
Boolean Search: Current State and Perspectives.
Frants, Valery I.; Shapiro, Jacob; Taksa, Isak; Voiskunskii, Vladimir G.
1999-01-01
Discusses the use of Boolean logic in information-retrieval systems and analyzes existing criticisms of operational systems. Considers users' ability to use and understand Boolean operators, ranking, the quality of query formulations, and negative effects of criticism; and concludes that criticism is directed at the methodology employed in…
Version Spaces and Generalized Monotone Boolean Functions
J.C. Bioch (Cor); T. Ibaraki
2002-01-01
textabstractWe consider generalized monotone functions f: X --> {0,1} defined for an arbitrary binary relation <= on X by the property x <= y implies f(x) <= f(y). These include the standard monotone (or positive) Boolean functions, regular Boolean functions and other interesting functions as speci
Spontaneous formation of dynamical groups in an adaptive networked system
Li Menghui; Guan Shuguang [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Lai, C-H, E-mail: tsllm@nus.edu.s [Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Singapore), National University of Singapore, Kent Ridge, Singapore 119260 (Singapore)
2010-10-15
In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tran, Tich Phuoc; Tran, Dat; Nguyen, Cuong Duc
2009-01-01
This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tich Phuoc Tran
2009-10-01
Full Text Available This article applies Machine Learning techniques to solve Intrusion Detection problems withincomputer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN, which integrates an adaptive boosting technique and a semi-parametric neural network to obtain good trade-off between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD-99 intrusion benchmark indicate that our model outperforms other state-of-the-art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.
Reliable adaptive multicast protocol in wireless Ad hoc networks
Sun Baolin; Li Layuan
2006-01-01
In wireless ad hoc network environments, every link is wireless and every node is mobile. Those features make data lost easily as well as multicasting inefficient and unreliable. Moreover, Efficient and reliable multicast in wireless ad hoc network is a difficult issue. It is a major challenge to transmission delays and packet losses due to link changes of a multicast tree at the provision of high delivery ratio for each packet transmission in wireless ad hoc network environment.In this paper, we propose and evaluate Reliable Adaptive Multicast Protocol (RAMP) based on a relay node concept. Relay nodes are placed along the multicast tree. Data recovery is done between relay nodes. RAMP supports a reliable multicasting suitable for mobile ad hoc network by reducing the number of packet retransmissions. We compare RAMP with SRM (Scalable Reliable Multicast). Simulation results show that the RAMP has high delivery ratio and low end-to-end delay for packet transmission.
Self-Adaptive Networked Entities for Building Pervasive Computing Aschitectures
Daněk, Martin; Philippe, J.-M.; Bartosinski, Roman; Honzík, Petr; Gamrat, Ch.
Heidelberg: Springer, 2008 - (Hornby, G.; Sekanina, L.; Haddow, P.), s. 94-105 ISBN 978-3-540-85856-0. ISSN 0302-9743. [International Conference on Evolvable System s: From Biology to Harware, 8th International Conference, ICES 2008. Praha (CZ), 22.09.2008-24.09.2008] R&D Projects: GA MŠk(CZ) 1M0567 EU Projects: European Commission(XE) 027611 - AETHER Institutional research plan: CEZ:AV0Z10750506 Keywords : Self-adaptation * FPGA * Simulink Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2008/ZS/danek-self- adaptive networked entities for building pervasive computing aschitectures.pdf
Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism
HE Hong; QIAN Feng
2007-01-01
Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIFA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way.
Adaptive self-organization in a realistic neural network model
Meisel, Christian; Gross, Thilo
2009-12-01
Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing operates close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how neural networks could remain close to a critical point while undergoing a continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf, introducing a generic mechanism of robust self-organized criticality in adaptive networks. Here, we address the question whether this mechanism is relevant for real neural networks. We show in a realistic model that spike-time-dependent synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength, relating them to the critical state of the network. These results suggest that the interplay between dynamics and topology may be essential for neural information processing.
Adaptive Data Rates for Flexible Transceivers in Optical Networks
Brian Thomas Teipen
2012-05-01
Full Text Available Efforts towards commercializing higher-speed optical transmission have demonstrated the need for advanced modulation formats, several of which require similar transceiver hardware architecture. Adaptive transceivers can be built to have a number of possible operational configurations selected by software. Such software-defined transceiver configurations can create specific modulation formats to support sets of data rates, corresponding tolerances to system impairments, and sets of electronic digital signal processing schemes chosen to best function in a given network environment. In this paper, we discuss possibilities and advantages of reconfigurable, bit-rate flexible transceivers, and their potential applications in future optical networks.
Real-Time Adaptive Color Segmentation by Neural Networks
Duong, Tuan A.
2004-01-01
Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural
Rescue of endemic states in interconnected networks with adaptive coupling.
Vazquez, F; Serrano, M Ángeles; Miguel, M San
2016-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771
Dynamic data-driven sensor network adaptation for border control
Bein, Doina; Madan, Bharat B.; Phoha, Shashi; Rajtmajer, Sarah; Rish, Anna
2013-06-01
Given a specific scenario for the border control problem, we propose a dynamic data-driven adaptation of the associated sensor network via embedded software agents which make sensor network control, adaptation and collaboration decisions based on the contextual information value of competing data provided by different multi-modal sensors. We further propose the use of influence diagrams to guide data-driven decision making in selecting the appropriate action or course of actions which maximize a given utility function by designing a sensor embedded software agent that uses an influence diagram to make decisions about whether to engage or not engage higher level sensors for accurately detecting human presence in the region. The overarching goal of the sensor system is to increase the probability of target detection and classification and reduce the rate of false alarms. The proposed decision support software agent is validated experimentally on a laboratory testbed for multiple border control scenarios.
Adaptive Medium Access Control Protocol for Wireless Body Area Networks
Javaid, N.; Ahmad, A.; A. Rahim; Z.A. Khan; M. Ishfaq; Qasim, U.
2014-01-01
Wireless Body Area Networks (WBANs) are widely used for applications such as modern health-care systems, where wireless sensors (nodes) monitor the parameter(s) of interest. Nodes are provided with limited battery power and battery power is dependent on radio activity. MAC protocols play a key role in controlling the radio activity. Therefore, we present Adaptive Medium Access Control (A-MAC) protocol for WBANs supported by linear programming models for the minimization of energy consumption ...
Supervised Learning in Adaptive DNA Strand Displacement Networks.
Lakin, Matthew R; Stefanovic, Darko
2016-08-19
The development of engineered biochemical circuits that exhibit adaptive behavior is a key goal of synthetic biology and molecular computing. Such circuits could be used for long-term monitoring and control of biochemical systems, for instance, to prevent disease or to enable the development of artificial life. In this article, we present a framework for developing adaptive molecular circuits using buffered DNA strand displacement networks, which extend existing DNA strand displacement circuit architectures to enable straightforward storage and modification of behavioral parameters. As a proof of concept, we use this framework to design and simulate a DNA circuit for supervised learning of a class of linear functions by stochastic gradient descent. This work highlights the potential of buffered DNA strand displacement as a powerful circuit architecture for implementing adaptive molecular systems. PMID:27111037
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
Expectation-Maximization Approach to Boolean Factor Analysis
Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.
Piscataway: IEEE, 2011, s. 559-566. ISBN 978-1-4244-9636-5. [IJCNN 2011. International Joint Conference on Neural Networks. San Jose (US), 31.07.2011-05.08.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * bars problem * dendritic inhibition * expectation-maximization * neural network application * statistics Subject RIV: IN - Informatics, Computer Science
Adaptive network dynamics and evolution of leadership in collective migration
Pais, Darren; Leonard, Naomi E.
2014-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can be extended to study the dynamics of collective tracking or collective learning more generally. Thus, this work may inform the design of robotic networks where agents use decentralized strategies that balance direct environmental measurements with agent interactions.
Adaptive local routing strategy on a scale-free network
Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load (packet queue length) and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated (whose queue lengths are longer than their critical queue lengths), more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies. (general)
Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?
Vaswani, Sharan; Lakshmanan, Laks V. S.
2016-01-01
Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users, to give free samples or discounts to, up front. A disadvantage of this setting is that the marketer is forced to select all the seeds based solely on a diffusion model. If some of the selected seeds do not perform well, there is no opportunity to course-correct. A more practical setting is the adaptive setting in which the ma...
Adaptive comanagement of a marine protected area network in Fiji.
Weeks, Rebecca; Jupiter, Stacy D
2013-12-01
Adaptive management of natural resources is an iterative process of decision making whereby management strategies are progressively changed or adjusted in response to new information. Despite an increasing focus on the need for adaptive conservation strategies, there remain few applied examples. We describe the 9-year process of adaptive comanagement of a marine protected area network in Kubulau District, Fiji. In 2011, a review of protected area boundaries and management rules was motivated by the need to enhance management effectiveness and the desire to improve resilience to climate change. Through a series of consultations, with the Wildlife Conservation Society providing scientific input to community decision making, the network of marine protected areas was reconfigured so as to maximize resilience and compliance. Factors identified as contributing to this outcome include well-defined resource-access rights; community respect for a flexible system of customary governance; long-term commitment and presence of comanagement partners; supportive policy environment for comanagement; synthesis of traditional management approaches with systematic monitoring; and district-wide coordination, which provided a broader spatial context for adaptive-management decision making. Co-Manejo Adaptativo de una Red de Áreas Marinas Protegidas en Fiyi. PMID:24112643
Mining TCGA data using Boolean implications.
Subarna Sinha
Full Text Available Boolean implications (if-then rules provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression from the glioblastoma (GBM and ovarian serous cystadenoma (OV data sets from The Cancer Genome Atlas (TCGA. We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/.
Adaptive Congestion Control Protocol (ACCP for Wireless Sensor Networks
James DzisiGadze
2013-10-01
Full Text Available In Wireless Sensor Networks (WSN when an event is detected there is an increase in data traffic that mightlead to packets being transmitted through the network close to the packet handling capacity of the WSN.The WSN experiences a decrease in network performance due to packet loss, long delays, and reduction inthroughput. In this paper we developed an adaptive congestion control algorithm that monitors networkutilization and adjust traffic levels and/or increases network resources to improve throughput and conserveenergy. The traffic congestion control protocol DelStatic is developed by introducing backpressuremechanism into NOAH. We analyzed various routing protocols and established that DSR has a higherresource congestion control capability. The proposed protocol, ACCP uses a sink switching algorithm totrigger DelStatic or DSR feedback to a congested node based on its Node Rank. From the simulationresults, ACCP protocol does not only improve throughput but also conserves energy which is critical tosensor application survivability on the field. Our Adaptive Congestion control achieved reliability, highthroughput and energy efficiency.
LAMAN: Load Adaptable MAC for Ad Hoc Networks
Realp Marc
2005-01-01
Full Text Available In mobile ad hoc radio networks, mechanisms on how to access the radio channel are extremely important in order to improve network efficiency. In this paper, the load adaptable medium access control for ad hoc networks (LAMAN protocol is described. LAMAN is a novel decentralized multipacket MAC protocol designed following a cross-layer approach. Basically, this protocol is a hybrid CDMA-TDMA-based protocol that aims at throughput maximization in multipacket communication environments by efficiently combining contention and conflict-free protocol components. Such combination of components is used to adapt the nodes' access priority to changes on the traffic load while, at the same time, accounting for the multipacket reception (MPR capability of the receivers. A theoretical analysis of the system is developed presenting closed expressions of network throughput and packet delay. By simulations the validity of our analysis is shown and the performances of a LAMAN-based system and an Aloha-CDMA-based one are compared.
Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt
2013-09-01
Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. PMID:22726907
Adaptive topology evolution in information-sharing social networks
Chen, Duanbing; Lu, Linyuan; Medo, Matus; Zhang, Yi-Cheng; Zhou, Tao
2011-01-01
The advent of Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. In order to get the picture it is important to address issues like who people do follow and how they search for better information sources. In this work we conduct an empirical analysis on different on-line social networking sites, and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. Hence these abstract rules help to create networks which are both effective in information diffusion and people friendly.
Adaptive Decision-Making Scheme for Cognitive Radio Networks
Alqerm, Ismail
2014-05-01
Radio resource management becomes an important aspect of the current wireless networks because of spectrum scarcity and applications heterogeneity. Cognitive radio is a potential candidate for resource management because of its capability to satisfy the growing wireless demand and improve network efficiency. Decision-making is the main function of the radio resources management process as it determines the radio parameters that control the use of these resources. In this paper, we propose an adaptive decision-making scheme (ADMS) for radio resources management of different types of network applications including: power consuming, emergency, multimedia, and spectrum sharing. ADMS exploits genetic algorithm (GA) as an optimization tool for decision-making. It consists of the several objective functions for the decision-making process such as minimizing power consumption, packet error rate (PER), delay, and interference. On the other hand, maximizing throughput and spectral efficiency. Simulation results and test bed evaluation demonstrate ADMS functionality and efficiency.
Network and adaptive system of systems modeling and analysis.
Lawton, Craig R.; Campbell, James E. Dr. (.; .); Anderson, Dennis James; Eddy, John P.
2007-05-01
This report documents the results of an LDRD program entitled ''Network and Adaptive System of Systems Modeling and Analysis'' that was conducted during FY 2005 and FY 2006. The purpose of this study was to determine and implement ways to incorporate network communications modeling into existing System of Systems (SoS) modeling capabilities. Current SoS modeling, particularly for the Future Combat Systems (FCS) program, is conducted under the assumption that communication between the various systems is always possible and occurs instantaneously. A more realistic representation of these communications allows for better, more accurate simulation results. The current approach to meeting this objective has been to use existing capabilities to model network hardware reliability and adding capabilities to use that information to model the impact on the sustainment supply chain and operational availability.
Generalizing Boolean Satisfiability II: Theory
Dixon, H E; Luks, E M; Parkes, A J; 10.1613/jair.1555
2011-01-01
This is the second of three planned papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal is to define a representation in which this structure is apparent and can easily be exploited to improve computational performance. This paper presents the theoretical basis for the ideas underlying ZAP, arguing that existing ideas in this area exploit a single, recurring structure in that multiple database axioms can be obtained by operating on a single axiom using a subgroup of the group of permutations on the literals in the problem. We argue that the group structure precisely captures the general structure at which earlier approaches hinted, and give numerous examples of its use. We go on to extend the Davis-Putnam-Logemann-...
Local Correction of Boolean Functions
Alon, Noga
2011-01-01
A Boolean function f over n variables is said to be q-locally correctable if, given a black-box access to a function g which is "close" to an isomorphism f_sigma of f, we can compute f_sigma(x) for any x in Z_2^n with good probability using q queries to g. We observe that any k-junta, that is, any function which depends only on k of its input variables, is O(2^k)-locally correctable. Moreover, we show that there are examples where this is essentially best possible, and locally correcting some k-juntas requires a number of queries which is exponential in k. These examples, however, are far from being typical, and indeed we prove that for almost every k-junta, O(k log k) queries suffice.
Densities of mixed volumes for Boolean models
Weil, Wolfgang
2001-01-01
In generalization of the well-known formulae for quermass densities of stationary and isotropic Boolean models, we prove corresponding results for densities of mixed volumes in the stationary situation and show how they can be used to determine the intensity of non-isotropic Boolean models Z in d-dimensional space for d = 2, 3, 4. We then consider non-stationary Boolean models and extend results of Fallert on quermass densities to densities of mixed volumes. In particular, we present explicit...
Translating Pseudo-Boolean Constraints into CNF
Aavani, Amir
2011-01-01
A Pseudo-Boolean constraint is a linear constraint over Boolean variables. This kind of constraints has been widely used in expressing NP-complete problems. This paper introduces a new algorithm for translating Pseudo-Boolean constraints into CNF clauses. The CNF produced by the proposed encoding has small size, and we also characterize the constraints for which one can expect the SAT solvers to perform well on the produced CNF. We show that there are many constraints for which the proposed encoding has a good performance.
Boolean Models of Biosurfactants Production in Pseudomonas fluorescens
Richard, Adrien; Rossignol, Gaelle; Comet, Jean-Paul; Bernot, Gilles; Guespin-Michel, Jannine; Merieau, Annabelle
2012-01-01
Cyclolipopeptides (CLPs) are biosurfactants produced by numerous Pseudomonas fluorescens strains. CLP production is known to be regulated at least by the GacA/GacS two-component pathway, but the full regulatory network is yet largely unknown. In the clinical strain MFN1032, CLP production is abolished by a mutation in the phospholipase C gene () and not restored by complementation. Their production is also subject to phenotypic variation. We used a modelling approach with Boolean networks, which takes into account all these observations concerning CLP production without any assumption on the topology of the considered network. Intensive computation yielded numerous models that satisfy these properties. All models minimizing the number of components point to a bistability in CLP production, which requires the presence of a yet unknown key self-inducible regulator. Furthermore, all suggest that a set of yet unexplained phenotypic variants might also be due to this epigenetic switch. The simplest of these Boolean networks was used to propose a biological regulatory network for CLP production. This modelling approach has allowed a possible regulation to be unravelled and an unusual behaviour of CLP production in P. fluorescens to be explained. PMID:22303435
An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks
Prasad, R Murali
2010-01-01
Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet been investigated. Moreover, they mainly consider non real- time connections in IEEE 802.16e networks. In this paper, we propose to design an adaptive power efficient packet scheduling algorithm that provides a minimum fair allocation of the channel bandwidth for each packet flow and additionally minimizes the power consumption. In the adaptive scheduling algorithm, packets are transmitted as per allotted slots from different priority of traffic classes adaptively, depending on the channel condition. Suppose if the buffer s...
Adaptive neural network error control for generalized perturbation theory
This paper addresses the issue of adaptive error control within generalized perturbation theory (GPT). The strategy herein assessed considers an artificial neural network (ANN) error estimator. The underlying tool facilitating this research is the FORMOSA-P code, a pressurized water reactor (PWR) nuclear fuel management optimization package, which combines simulated annealing and nodal GPT. A number of applications exist where traditional GPT may be limited by the magnitude of perturbations, which it can accurately handle. In fact, other alternative such as supervariational techniques (i.e., n'th-order GPT) and/or multireference strategies (i.e., rodded adjoints) are being sought for boiling water reactor and rodded applications. A perhaps not-so-obvious alternative could be to employ a neural network for adaptive error control within GPT. This study presents the results of two ANN models. The first model constitutes an intensively well-trained ANN used to contrast its global core parameter (i.e., keff) prediction capability versus that of a GPT model. The second model is a similar ANN intended for adaptive GPT error correction. In other words, the latter ANN is trained on-the-fly within the scope of a standard FORMOSA-P calculation
Rescue of endemic states in interconnected networks with adaptive coupling
Vazquez, F; Miguel, M San
2015-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads only if the two layers are interconnected, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network, the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that finite-size effects are amplified by the rewiring, as the...
Multisource Adaptive Data Distribution and Routing in Wireless Sensor Networks
Mukherjee, Subhabrata; Naskar, Mrinal K; Mukherjee, Amitava
2012-01-01
The wireless sensor network is a collection of energy-constrained nodes. Their objective is to sense, collect and process information for some ad-hoc purpose. Typically the nodes are deployed in geographically inaccessible regions. Thus the most challenging task is to design a network with minimal power consumption. As the nodes have to collect and process data very fast, minimizing data delivery time is another objective. In addition to this, when multiple sources transmit data simultaneously, the network load gradually increases and it may lead to congestion. In this paper we have proposed an adaptive framework in which multiple sources transmit data simultaneously with minimal end-to-end data delivery time and minimal energy consumption besides ensuring that congestion remains at an optimum low so that minimal number of data packets are dropped. This paper presents an adaptive framework to achieve the above-mentioned objectives. This framework has been used over Mac 802.11 and extensive simulations have be...
Effects of adaptive dynamical linking in networked games
Yang, Zhihu; Li, Zhi; Wu, Te; Wang, Long
2013-10-01
The role of dynamical topologies in the evolution of cooperation has received considerable attention, as some studies have demonstrated that dynamical networks are much better than static networks in terms of boosting cooperation. Here we study a dynamical model of evolution of cooperation on stochastic dynamical networks in which there are no permanent partners to each agent. Whenever a new link is created, its duration is randomly assigned without any bias or preference. We allow the agent to adaptively adjust the duration of each link during the evolution in accordance with the feedback from game interactions. By Monte Carlo simulations, we find that cooperation can be remarkably promoted by this adaptive dynamical linking mechanism both for the game of pairwise interactions, such as the Prisoner's Dilemma game (PDG), and for the game of group interactions, illustrated by the public goods game (PGG). And the faster the adjusting rate, the more successful the evolution of cooperation. We also show that in this context weak selection favors cooperation much more than strong selection does. What is particularly meaningful is that the prosperity of cooperation in this study indicates that the rationality and selfishness of a single agent in adjusting social ties can lead to the progress of altruism of the whole population.
An adaptive blind watermarking scheme utilizing neural network for synchronization
WU Jian-zhen; XIE Jian-ying; YANG Yu-pu
2007-01-01
An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image's brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.
Boolean Logic with Fault Tolerant Coding
Alagoz, B. Baykant
2009-01-01
Error detectable and error correctable coding in Hamming space was researched to discover possible fault tolerant coding constellations, which can implement Boolean logic with fault tolerant property. Basic logic operators of the Boolean algebra were developed to apply fault tolerant coding in the logic circuits. It was shown that application of three-bit fault tolerant codes have provided the digital system skill of auto-recovery without need for designing additional-fault tolerance mechanisms.
Sensitivity versus block sensitivity of Boolean functions
Virza, Madars
2010-01-01
Determining relationship between sensitivity and block sensitivity of Boolean functions is of interest for computational complexity theory. We construct a sequence of Boolean functions with bs(f) = 1/2 (s(f))^2+ 1/2 s(f). The best known separation previously was bs(f) = 1/2 (s(f))^2 due to Rubinstein (1995). We also report results of computer search for functions with at most 12 variables.
Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks
Bartolini, Novella; la Porta, Thomas; Petrioli, Chiara; Silvestri, Simone
2010-01-01
In this paper we address the problem of prolonging the lifetime of wireless sensor networks (WSNs) deployed to monitor an area of interest. In this scenario, a helpful approach is to reduce coverage redundancy and therefore the energy expenditure due to coverage. We introduce the first algorithm which reduces coverage redundancy by means of Sensor Activation and sensing Radius Adaptation (SARA)in a general applicative scenario with two classes of devices: sensors that can adapt their sensing range (adjustable sensors) and sensors that cannot (fixed sensors). In particular, SARA activates only a subset of all the available sensors and reduces the sensing range of the adjustable sensors that have been activated. In doing so, SARA also takes possible heterogeneous coverage capabilities of sensors belonging to the same class into account. It specifically addresses device heterogeneity by modeling the coverage problem in the Laguerre geometry through Voronoi-Laguerre diagrams. SARA executes quickly and is guarante...
An Adaptive Amplifier System for Wireless Sensor Network Applications
Carlos Marqués; Eduardo Romero; Mónica Lovay; Gabriela Peretti
2012-01-01
This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA) for rec...
Minimum-Risk Path Finding by an Adaptive Amoebal Network
Nakagaki, Toshiyuki; Iima, Makoto; Ueda, Tetsuo; Nishiura, Yasumasa; Saigusa, Tetsu; Tero, Atsushi; Kobayashi, Ryo; Showalter, Kenneth
2007-08-01
When two food sources are presented to the slime mold Physarum in the dark, a thick tube for absorbing nutrients is formed that connects the food sources through the shortest route. When the light-avoiding organism is partially illuminated, however, the tube connecting the food sources follows a different route. Defining risk as the experimentally measurable rate of light-avoiding movement, the minimum-risk path is exhibited by the organism, determined by integrating along the path. A model for an adaptive-tube network is presented that is in good agreement with the experimental observations.
Adaptive Framework for Data Distribution in Wireless Sensor Networks
Mukherjee, Subhabrata; Mukherjee, Amitava
2012-01-01
In recent years, the wireless sensor network (WSN) is playing a key role in sensing, collecting and disseminating information in various applications. An important feature associated with WSN is to develop an efficient data distribution and routing scheme to ensure better quality of service (QoS) that reduces the power consumption and the end-to-end data delivery time. In this work, we propose an adaptive framework to transmit data packets from a source to the sink in WSN across multiples paths with strategically distributed data packets so as to minimize the power consumption as well as the end-to-end data delivery time.
LOAD AWARE ADAPTIVE BACKBONE SYNTHESIS IN WIRELESS MESH NETWORKS
Yuan Yuan; Zheng Baoyu
2009-01-01
Wireless Mesh Networks (WMNs) are envisioned to support the wired backbone with a wireless Backbone Networks (BNet) for providing internet connectivity to large-scale areas.With a wide range of internet-oriented applications with different Quality of Service (QoS) requirement,the large-scale WMNs should have good scalability and large bandwidth.In this paper,a Load Aware Adaptive Backbone Synthesis (LAABS) algorithm is proposed to automatically balance the traffic flow in the WMNs.The BNet will dynamically split into smaller size or merge into bigger one according to statistic load information of Backbone Nodes (BNs).Simulation results show LAABS generates moderate BNet size and converges quickly,thus providing scalable and stable BNet to facilitate traffic flow.
Strategic tradeoffs in competitor dynamics on adaptive networks
Hébert-Dufresne, Laurent; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric
2016-01-01
Non-linear competitor dynamics have been studied on several non-trivial but static network structures. We consider a general model on adaptive networks and interpret the resulting structure as a signature of competitor strategies. We combine the voter model with a directed stochastic block model to encode how a strategy targets competitors (i.e., an aggressive strategy) or its own type (i.e., a defensive strategy). We solve the dynamics in particular cases with tradeoffs between aggressiveness and defensiveness. These tradeoffs yield interesting behaviors such as long transient dynamics, sensitive dependence to initial conditions, and non-transitive dynamics. Not only are such results reminiscent of classic voting paradoxes but they also translate to a dynamical view of political campaign strategies. Finally, while in a two competitor system there exists an optimal strategy that balances aggressiveness and defensiveness, three competitor systems have no such solution. The introduction of extreme strategies ca...
Feedback Stabilization over Wireless Network Using Adaptive Coded Modulation
Li Yang; Xin-Ping Guan; Cheng-Nian Long; Xiao-Yuan Luo
2008-01-01
In this paper, we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS)to improve the energy efficiency and increase the data rate over a fading channel. To capture the characteristics of varying rate,interference, and routing in wireless transmission channels, the concepts of equivalent delay (ED) and networked condition index (NCI)are introduced. Also, the analytic lower and upper bounds of EDs are obtained. Furthermore, we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission. Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI).Numerical results show the validity and advantage of our proposed control strategies.
Adaptive model predictive process control using neural networks
Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.
1997-01-01
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Multiple cardiac arrhythmia recognition using adaptive wavelet network.
Lin, Chia-Hung; Chen, Pei-Jarn; Chen, Yung-Fu; Lee, You-Yun; Chen, Tainsong
2005-01-01
This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN). Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method. PMID:17281539
ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK
BASSEL SOLAIMANE
2011-01-01
Full Text Available Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’s blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.
Adaptive Fault Tolerant Routing In Interconnection Networks: A Review
B.V.Suresh Kumar
2011-05-01
Full Text Available A multi-processor / computer systems are connected by varieties of interconnection networks. To enable any nonfaulty component (Node / Link to communicate with any other non-faulty component in an injured interconnection network, the information on component failure is to be made available to non-faulty components, so as to route messages around the faulty components. In this paper we have reviewed to adaptive routing schemes proposed by Dally and Aloki , Glass and Ni ,and also the implementation details of reliable router. Moreover , it is proved that these schemes of routing messages via shortest paths with high probability and the expected length of routing path is very close to that of shortest path.
The emergence of complexity and restricted pleiotropy in adapting networks
Le Nagard Hervé
2011-11-01
Full Text Available Abstract Background The emergence of organismal complexity has been a difficult subject for researchers because it is not readily amenable to investigation by experimental approaches. Complexity has a myriad of untested definitions and our understanding of its evolution comes primarily from static snapshots gleaned from organisms ranked on an intuitive scale. Fisher's geometric model of adaptation, which defines complexity as the number of phenotypes an organism exposes to natural selection, provides a theoretical framework to study complexity. Yet investigations of this model reveal phenotypic complexity as costly and therefore unlikely to emerge. Results We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher's geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment. Conclusions Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to
Providing Adapted Contextual Information in an Overlay Vehicular Network
Andrés Muñoz
2010-01-01
Full Text Available Current vehicular networks are developed upon commercial solutions based on cellular networks (CNs or vehicular ad-hoc networks (VANETs, both present in numerous research proposals. Current approximations are not enough to cover the communication necessities of several applications at the same time, and they are not suitable for future vehicular pervasive services. The vehicular network presented in this paper fills the existent gap between solutions lacking in flexibility, mainly supported by an infrastructure deployment, and those highly local and distributed, such as sole-VANET approximations. In this manner, an overlay communication platform which can work over the CN basis has been designed and developed. This architecture is complemented by an additional support of an information system located at the infrastructure side. Moreover, since most of the information received from current notification services is not relevant for the driver, an additional subsystem has been devised to provide adapted information to users. This has been carried out by means of an ontology model which represents users' preferences and contextual information. Finally, using a whole prototype of the telematic platform, the performance of this interring process has been evaluated to point out its impact on the system operation.
Trajanovski, S.; Guo, D.; Van Mieghem, P.F.A.
2015-01-01
The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways: (i
Adaptive control of call acceptance in WCDMA network
Milan Manojle Šunjevarić
2013-10-01
Full Text Available In this paper, an overview of the algorithms for access control in mobile wireless networks is presented. A review of adaptive control methods of accepting a call in WCDMA networks is discussed, based on the overview of the algorithms used for this purpose, and their comparison. Appropriate comments and conculsions in comparison with the basic characteristics of these algorithms are given. The OVSF codes are explained as well as how the allocation method influences the capacity and probability of blocking.. Introduction We are witnessing a steady increase in the number of demands placed upon modern wireless networks. New applications and an increasing number of users as well as user activities growth in recent years reinforce the need for an efficient use of the spectrum and its proper distribution among different applications and classes of services. Besides humans, the last few years saw different computers, machines, applications, and, in the future, many other devices, RFID applications, and finally networked objects, as a new kind of wireless networks "users". Because of the exceptional rise in the number of users, the demands placed upon modern wireless networks are becoming larger, and spectrum management plays an important role. For these reasons, choosing an appropriate call admission control algorithm is of great importance. Multiple access and resource management in wireless networks Radio resource management of mobile networks is a set of algorithms to manage the use of radio resources with the aim is to maximize the total capacity of wireless systems with equal distribution of resources to users. Management of radio resources in cellular networks is usually located in the base station controller, the base station and the mobile terminal, and is based on decisions made on appropriate measurement and feedback. It is often defined as the maximum volume of traffic load that the system can provide for some of the requirements for the
Two Expectation-Maximization Algorithms for Boolean Factor Analysis
Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.
2014-01-01
Roč. 130, 23 April (2014), s. 83-97. ISSN 0925-2312 R&D Projects: GA ČR GAP202/10/0262 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean Factor analysis * Binary Matrix factorization * Neural networks * Binary data model * Dimension reduction * Bars problem Subject RIV: IN - Informatics, Computer Science Impact factor: 2.083, year: 2014
A National Climate Change Adaptation Network for Protecting Water Security
Weaver, A.; Sauchyn, D.; Byrne, J. M.
2009-12-01
Water security and resource-dependent community-survival are being increasingly challenged as a consequence of climate change, and it is urgent that we plan now for the security of our water supplies which support our lives and livelihoods. However, the range of impacts of climate change on water availability, and the consequent environmental and human adaptations that are required, is so complex and serious that it will take the combined work of natural, health and social scientists working with industries and communities to solve them. Networks are needed that will identify crucial water issues under climate change at a range of scales in order to provide regionally-sensitive, solutions-oriented research and adaptation. We suggest national and supra-national water availability and community sustainability issues must be addressed by multidisciplinary research and adaptation networks. The work must be driven by a bottom-up research paradigm — science in the service of community and governance. We suggest that interdisciplinary teams of researchers, in partnership with community decision makers and local industries, are the best means to develop solutions as communities attempt to address future water demands, protect their homes from infrastructure damage, and meet their food, drinking water, and other essential resource requirements. The intention is to cover: the impact of climate change on Canadian natural resources, both marine and terrestrial; issues of long-term sustainability and resilience in human communities and the environments in which they are embedded; the making and moving of knowledge, be that between members of Indigenous and non-Indigenous communities, researchers of different disciplines, communities, industry, policymakers and the academy and the crucial involvement of the various orders of government in the response to water problems, under conditions of heightened uncertainty. Such an adaptation network must include a national
A DYNAMIC APPROACH FOR RATE ADAPTATION IN MOBILE ADHOC NETWORKS
Suganya Subramaniam
2013-01-01
Full Text Available A Mobile Ad hoc Network (MANET is a collection of mobile nodes with no fixed infrastructure. The absence of central authorization facility in dynamic and distributed environment affects the optimal utilization of resources like, throughput, power and bandwidth. Rate adaptation is the key technique to optimize the resource throughput. Some recently proposed rate adaptations use Request to Send/Clear to Send (RTS/CTS to suppress the collision effect by differentiating collisions from channel errors. This study presents a methodology to detect the misbehavior of nodes in MANET and proposed the new dynamic algorithm for rate adaptation which in turn can improve the throughput. The proposed approach is implemented in the distributed stipulating architecture with core and access routers. This method does not require additional probing overhead incurred by RTS/CTS exchanges and may be practically deployed without change in firmware. The collision and channel error occurrence will be detected by core router and intimated to the access router to choose alternate route and retain the current rate for transmission. The extensive simulation results demonstrate the effectiveness of proposed method by comparing with existing approaches.
Computation as an emergent feature of adaptive synchronization
Zanin, M.; Papo, D.; Sendiña-Nadal, I.; Boccaletti, S.
2011-12-01
We report on the spontaneous emergence of computation from adaptive synchronization of networked dynamical systems. The fundamentals are nonlinear elements, interacting in a directed graph via a coupling that adapts itself to the synchronization level between two input signals. These units can emulate different Boolean logics, and perform any computational task in a Turing sense, each specific operation being associated with a given network's motif. The resilience of the computation against noise is proven, and the general applicability is demonstrated with regard to periodic and chaotic oscillators, and excitable systems mimicking neural dynamics.
Institutional networks and adaptive water governance in the Klamath River Basin, USA.
Polycentric networks of formal organizations and informal stakeholder groups, as opposed to centralized institutional hierarchies, can be critically important for strengthening the capacity of governance systems to adapt to unexpected social and biophysical change. Adaptive gover...
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Juntao Fei; Zhe Wang
2013-01-01
An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achie...
Xueling Jiang
2014-01-01
Full Text Available The problem of adaptive asymptotical synchronization is discussed for the stochastic complex dynamical networks with time-delay and Markovian switching. By applying the stochastic analysis approach and the M-matrix method for stochastic complex networks, several sufficient conditions to ensure adaptive asymptotical synchronization for stochastic complex networks are derived. Through the adaptive feedback control techniques, some suitable parameters update laws are obtained. Simulation result is provided to substantiate the effectiveness and characteristics of the proposed approach.
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan; Ehrgott, Matthias
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' route choice and the more flexible "history-adaptive'' route choice. We point out some interesting properties of the sets of efficient solutions ("strategies'') found un...... under the two models. We also suggest possible directions for improving computational techniques....
Kamalakshi.N
2009-11-01
Full Text Available Today's wireless networks are highly heterogeneous, with mobile devices consisting of multiple wireless network interfaces (WNICs. Since battery lifetime is limited, power management of the interfaces has become essential with flexible and open architecture, capable of supporting various types of networks, terminals and applications. However how to integrate the protocols to meet the heterogeneous network environments becomes a significant challenge in the fourth generation wireless network. Adaptive protocols are proposed to solve heterogeneity problem in future wireless networks. This paper discusses two protocols R²CP, and RCP and feasibility of RCP protocols applied to the manage power efficiently and adaptive Congestion control on heterogeneous wireless network.
Flexible and dynamic network coding for adaptive data transmission in DTNs
Radenkovic, Milena; Zakhary, Sameh
2012-01-01
Existing network coding approaches for Delay-Tolerant Networks (DTNs) do not detect and adapt to congestion in the network. In this paper we describe CafNC (Congestion aware forwarding with Network Coding) that combines adaptive network coding and adaptive forwarding in DTNs. In CafNC each node learns the status of its neighbours, and their egonetworks in order to detect coding opportunities, and codes as long as the recipients can decode. Our flexible design allows CafNC to efficiently supp...
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
Zhu, Darui, E-mail: zdarui@163.com [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an 710049 (China); School of Electrical Engineering, Xi' an Jiaotong University, Xi' an 710049 (China); Liu, Chongxin; Yan, Bingnan [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an 710049 (China); School of Electrical Engineering, Xi' an Jiaotong University, Xi' an 710049 (China)
2014-01-24
We introduce a chaos model for a permanent-magnet synchronous motor and construct a coupled chaotic motor in a complex dynamic network using the Newman–Watts small-world network algorithm. We apply adaptive pinning control theory for complex networks to obtain suitable adaptive feedback gain and the number of nodes to be pinned. Nodes of low degree are pinned to realize global asymptotic synchronization in the complex network. The proposed adaptive pinning controller is added to the complex motor network for simulation and verification.
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
We introduce a chaos model for a permanent-magnet synchronous motor and construct a coupled chaotic motor in a complex dynamic network using the Newman–Watts small-world network algorithm. We apply adaptive pinning control theory for complex networks to obtain suitable adaptive feedback gain and the number of nodes to be pinned. Nodes of low degree are pinned to realize global asymptotic synchronization in the complex network. The proposed adaptive pinning controller is added to the complex motor network for simulation and verification.
Adaptive hybrid simulations for multiscale stochastic reaction networks
The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest
Duality theories for Boolean algebras with operators
Givant, Steven
2014-01-01
In this new text, Steven Givant—the author of several acclaimed books, including works co-authored with Paul Halmos and Alfred Tarski—develops three theories of duality for Boolean algebras with operators. Givant addresses the two most recognized dualities (one algebraic and the other topological) and introduces a third duality, best understood as a hybrid of the first two. This text will be of interest to graduate students and researchers in the fields of mathematics, computer science, logic, and philosophy who are interested in exploring special or general classes of Boolean algebras with operators. Readers should be familiar with the basic arithmetic and theory of Boolean algebras, as well as the fundamentals of point-set topology.
Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning
Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers
An Adaptive Channel Model for VBLAST in Vehicular Networks
Ghassan M. T. Abdalla
2009-01-01
Full Text Available The wireless transmission environment in vehicular ad hoc systems varies from line of sight with few surroundings to rich Rayleigh fading. An efficient communication system must adapt itself to these diverse conditions. Multiple antenna systems are known to provide superior performance compared to single antenna systems in terms of capacity and reliability. The correlation between the antennas has a great effect on the performance of MIMO systems. In this paper we introduce a novel adaptive channel model for MIMO-VBLAST systems in vehicular ad hoc networks. Using the proposed model, the correlation between the antennas was investigated. Although the line of sight is ideal for single antenna systems, it severely degrades the performance of VBLAST systems since it increases the correlation between the antennas. A channel update algorithm using single tap Kalman filters for VBLAST in flat fading channels has also been derived and evaluated. At 12 dB Es/N0, the new algorithm showed 50% reduction in the mean square error (MSE between the actual channel and the corresponding updated estimate compared to the MSE without update. The computational requirement of the proposed algorithm for a p×q VBLAST is 6p×q real multiplications and 4p×q real additions.
Scalable Lunar Surface Networks and Adaptive Orbit Access Project
National Aeronautics and Space Administration — Innovative network architecture, protocols, and algorithms are proposed for both lunar surface networks and orbit access networks. Firstly, an overlaying...
Chertkov, Michael [Los Alamos National Laboratory
2012-07-24
The goal of the DTRA project is to develop a mathematical framework that will provide the fundamental understanding of network survivability, algorithms for detecting/inferring pre-cursors of abnormal network behaviors, and methods for network adaptability and self-healing from cascading failures.
AQM Algorithm with Adaptive Reference Queue Threshold for Communication Networks
Liming Chen
2012-09-01
Full Text Available Nowadays, congestion in communication networks has been more intractable than ever before due to the explosive growth of network scale and multimedia traffic. Active queue management (AQM algorithms had been proposed to alleviate congestion to improve quality of service (QoS, but existing algorithms often suffer from some flaws in one aspect or another. In this paper, a novel AQM algorithm with adaptive reference queue threshold (ARTAQM is proposed of which the main innovative contributions are recounted as follows. First, traffic is predicted to calculate the packet loss ratio (PLR and the traffic rate based on traffic prediction algorithm. Second, by means of periodical measurements, a weighted PLR is obtained to dynamically adjust packet dropping probability in ARTAQM algorithm. Third, ARTAQM algorithm runs in both coarse and fine granularities. In coarse granularity, the mismatch of the predicted traffic rate and link capacity can adjusts the reference queue length in every period, while in fine granularity, reference queue remains fixed and the instantaneous queue is adjusted packet by packet in one period. Simulation results indicate that ARTAQM algorithm not only maintains stable queue and fast response speed, but has lower PLR and higher link utilization as well.
Discrete rate and variable power adaptation for underlay cognitive networks
Abdallah, Mohamed M.
2010-01-01
We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power adaptation under the constraints of maximum average transmit power and maximum average interference power allowed at the primary receiver due to the existence of an interference link between the secondary transmitter and the primary receiver. We first find the optimal discrete rates assuming a predetermined partitioning of the signal-to-noise ratio (SNR) of both the secondary and interference links. We then present an iterative algorithm for finding a suboptimal partitioning of the SNR of the interference link assuming a fixed partitioning of the SNR of secondary link selected for the case where no interference link exists. Our numerical results show that the average spectral efficiency attained by using the iterative algorithm is close to that achieved by the computationally extensive exhaustive search method for the case of Rayleigh fading channels. In addition, our simulations show that selecting the optimal partitioning of the SNR of the secondary link assuming no interference link exists still achieves the maximum average spectral efficiency for the case where the average interference constraint is considered. © 2010 IEEE.
Algorithms for Boolean Function Query Properties
Aaronson, Scott
2001-01-01
We present new algorithms to compute fundamental properties of a Boolean function given in truth-table form. Specifically, we give an O(N^2.322 log N) algorithm for block sensitivity, an O(N^1.585 log N) algorithm for `tree decomposition,' and an O(N) algorithm for `quasisymmetry.' These algorithms are based on new insights into the structure of Boolean functions that may be of independent interest. We also give a subexponential-time algorithm for the space-bounded quantum query complexity of...
Fast Gr\\"obner Basis Computation for Boolean Polynomials
Hinkelmann, Franziska
2010-01-01
We introduce the Macaulay2 package BooleanGB, which computes a Gr\\"obner basis for Boolean polynomials using a binary representation rather than symbolic. We compare the runtime of several Boolean models from systems in biology and give an application to Sudoku.
A finite alternation result for reversible boolean circuits
Selinger, Peter
2016-01-01
We say that a reversible boolean function on n bits has alternation depth d if it can be written as the sequential composition of d reversible boolean functions, each of which acts only on the top n-1 bits or on the bottom n-1 bits. We show that every reversible boolean function of n >= 4 bits has alternation depth 9.
Suppressing Halo-chaos for Intense Ion Beamby Neural Network Adaptation Control Strategy
FANGJin-qing; LUOXiao-shu; WENGJia-qiang; ZHULun-wu
2003-01-01
Neural network has some advantages of adaptation, learn-self, self-organization and suitable for high-dimension for various applications in many fields, especially among them the feed-forward back-propagating neural network self-adaptation method is suitable for control of nonlinear systems.
Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks.
Ranganathan, Mohan Krishna; Kilmartin, Liam
2005-09-01
Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays. Index Terms-Fuzzy delay trend analysis, intertalkspurt, intratalkspurt, multilayer perceptrons (MLPs), network delay estimation, playout buffering, playout delay adaptation, time delay neural networks (TDNNs), voice over Internet protocol (VoIP). PMID:16252825
Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain
Frolov, A.; Húsek, Dušan; Polyakov, P.Y.
2016-01-01
Roč. 27, č. 3 (2016), s. 538-550. ISSN 2162-237X R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:67985807 Keywords : associative memory * bars problem (BP) * Boolean factor analysis (BFA) * data mining * dimension reduction * Hebbian learning rule * information gain * likelihood maximization (LM) * neural network application * recurrent neural network * statistics Subject RIV: IN - Informatics, Computer Science Impact factor: 4.291, year: 2014
Some Aspects of Boolean Valued Analysis
Kusraev, A. G.; Kutateladze, S. S.
2015-01-01
This is a survey of some recent applications of Boolean valued analysis to operator theory and harmonic analysis. Under consideration are pseudoembedding operators, the noncommutative Wickstead problem, the Radon-Nikodym Theorem for JB-algebras, and the Bochner Theorem for lattice-valued positive definite mappings on locally compact groups.
Demonstrating Boolean Logic Using Simple Electrical Circuits
McElhaney, Kevin W.
2004-01-01
While exploring the subject of geometric proofs, boolean logic operators AND and OR can be used to allow students to visualize their true-or-false patterns. An activity in the form of constructing electrical circuits is illustrated to explain the concept.
Boolean Queries Optimization by Genetic Algorithms
Húsek, Dušan; Owais, S.S.J.; Krömer, P.; Snášel, Václav
2005-01-01
Roč. 15, - (2005), s. 395-409. ISSN 1210-0552 R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary algorithms * genetic algorithms * genetic programming * information retrieval * Boolean query Subject RIV: BB - Applied Statistics, Operational Research
Evolutionary Algorithms for Boolean Queries Optimization
Húsek, Dušan; Snášel, Václav; Neruda, Roman; Owais, S.S.J.; Krömer, P.
2006-01-01
Roč. 3, č. 1 (2006), s. 15-20. ISSN 1790-0832 R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary algorithms * genetic algorithms * information retrieval * Boolean query Subject RIV: BA - General Mathematics
A complexity theory based on Boolean algebra
Skyum, Sven; Valiant, Leslie
1985-01-01
A projection of a Boolean function is a function obtained by substituting for each of its variables a variable, the negation of a variable, or a constant. Reducibilities among computational problems under this relation of projection are considered. It is shown that much of what is of everyday rel...
A Boolean Map Theory of Visual Attention
Huang, Liqiang; Pashler, Harold
2007-01-01
A theory is presented that attempts to answer two questions. What visual contents can an observer consciously access at one moment? Answer: only one feature value (e.g., green) per dimension, but those feature values can be associated (as a group) with multiple spatially precise locations (comprising a single labeled Boolean map). How can an…
Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Network
Priya Mohite
2015-01-01
The data fusion process has led to an evolution for emerging Wireless Sensor Networks (WSNs) and examines the impact of various factors on energy consumption. Significantly there has always been a constant effort to enhance network efficiency without decreasing the quality of information. Based on Adaptive Fusion Steiner Tree (AFST), this paper proposes a heuristic algorithm called Modified Adaptive Fusion Steiner Tree (M-AFST) for energy efficient routing which not only does adaptively adjus...
On adaptive control of mobile slotted aloha networks
Lim J.-T.
1995-01-01
Full Text Available An adaptive control scheme for mobile slotted ALOHA is presented and the effect of capture on the adaptive control scheme is investigated. It is shown that with the proper choice of adaptation parameters the adaptive control scheme can be made independent of the effect of capture.
A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks
Loureiro, Antonio A. F.; Carlos M. S. Figueiredo; Eduardo F. Nakamura
2009-01-01
Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts ...
Ali Amiri
2016-01-01
Efficient management of bandwidth in wireless networks is a critical factor for a successful communication system. Special features of wireless networks such user mobility and growth of wireless applications and their high bandwidth intensity create a major challenge to utilize bandwidth resources optimally. In this research, we propose a model for an adaptable network bandwidth management method that combines bandwidth reservation and bandwidth adaptation to reduce call blocking ...
Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays
Weiyuan Ma
2014-12-01
Full Text Available In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain complex networks with time delays. Besides, the system parameters in the uncertain network can be identified in the process of synchronization. To demonstrate the validity of the proposed method, several illustrative examples are presented.
Adaptive approach to global synchronization of directed networks with fast switching topologies
Global synchronization of directed networks with switching topologies is investigated. It is found that if there exists at least one directed spanning tree in the network with the fixed time-average topology and the time-average topology is achieved sufficiently fast, the network will reach global synchronization for appreciate coupling strength. Furthermore, this appreciate coupling strength may be obtained by local adaptive approach. A sufficient condition about the global synchronization is given. Numerical simulations verify the effectiveness of the adaptive strategy.
An Adaptive Amplifier System for Wireless Sensor Network Applications
Mónica Lovay
2012-01-01
Full Text Available This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA for reconfiguring the node resources that runs on a host computer. We adopt a PSoC device from Cypress for the node implementation. The performance evaluation of the scheme presented is made by adopting four different types of fault models in the amplifier gains. The fault simulation results show that GA finds the target gain with low error, maintains the bandwidth above the minimum tolerable bandwidth, and presents a runtime lower than exhaustive search method.
ADAPTIVELY IMPROVING LONG DISTANCE NETWORK TRANSFERS WITH LOGISTICS
LaBissoniere, D.; Roche, K.
2007-01-01
Long distance data movement is an essential activity of modern computing. However, the congestion control mechanisms in the Internet’s Transmission Control Protocol (TCP) severely limit the bandwidth achieved by long distance data transfers. The throughput of such transfers can be improved by applying the logistical technique of breaking a single long distance transfer into multiple shorter transfers. This technique can result in signifi cantly improved throughput while still respecting the shared nature of the Internet by not attempting to circumvent the TCP congestion controls. This technique has been incorporated into an algorithm which attempts to dynamically schedule transfers for optimal throughput. The algorithm couples graph techniques with real-time latency and bandwidth measurements to discover the best path and adaptively respond to network dynamics. The algorithm shows improvements in speed and fl exibility over standard data transfer methods such as FTP. Specifi c transfers tests performed between Oak Ridge National Laboratory and a destination in Sunnyvale, CA show throughput increases by a factor of two.
Efficient community-based control strategies in adaptive networks
Most studies on adaptive networks concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structure in the transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing the modularity, we investigate the evolution of community structure during the transient process, and find that a strong community structure is induced by the rewiring mechanism in the early stage of epidemic dynamics, which, remarkably, delays the outbreak of disease. We then study the effects of control strategies started at different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not ‘the earlier, the better’ for the implementation of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of a strong community structure. For the immunization strategy, immunizing the susceptible nodes on susceptible–infected links and immunizing susceptible nodes randomly have similar control effects. However, for the quarantine strategy, quarantining the infected nodes on susceptible–infected links can yield a far better result than quarantining infected nodes randomly. More significantly, the community-based quarantine strategy performs better than the community-based immunization strategy. This study may shed new light on the forecast and the prevention of epidemics among humans. (paper)
Distributed reinforcement learning for adaptive and robust network intrusion response
Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel
2015-07-01
Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.
Adaptive autonomous Communications Routing Optimizer for Network Efficiency Management Project
National Aeronautics and Space Administration — Maximizing network efficiency for NASA's Space Networking resources is a large, complex, distributed problem, requiring substantial collaboration. We propose the...
"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis
Arellano Gustavo
2011-12-01
Full Text Available Abstract Background In Thomas' formalism for modeling gene regulatory networks (GRNs, branching time, where a state can have more than one possible future, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a asynchrony, (b incompletely specified behavior, and (c interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulators, because infinitely many paths may appear, limiting ordinary simulators to statistical conclusions. Model checkers for branching time, by contrast, are able to prove properties in the presence of infinitely many paths. Results We have developed Antelope ("Analysis of Networks through TEmporal-LOgic sPEcifications", http://turing.iimas.unam.mx:8080/AntelopeWEB/, a model checker for analyzing and constructing Boolean GRNs. Currently, software systems for Boolean GRNs use branching time almost exclusively for asynchrony. Antelope, by contrast, also uses branching time for incompletely specified behavior and environment interaction. We show the usefulness of modeling these two phenomena in the development of a Boolean GRN of the Arabidopsis thaliana root stem cell niche. There are two obstacles to a direct approach when applying model checking to Boolean GRN analysis. First, ordinary model checkers normally only verify whether or not a given set of model states has a given property. In comparison, a model checker for Boolean GRNs is preferable if it reports the set of states having a desired property. Second, for efficiency, the expressiveness of many model checkers is limited, resulting in the inability to express some interesting properties of Boolean GRNs. Antelope tries to overcome these two drawbacks: Apart from reporting the set of all states having a given property, our model checker can express, at the expense of efficiency, some properties that ordinary
V. M. Varatharaju; Badrilal Mathur; Udhayakumar
2011-01-01
Problem statement: The tuning methodology for the parameters of adaptive speed controller causes a transient deviation of the response from the set reference following variation in load torque in a permanent-magnet brushless DC (BLDC) motor drive system. Approach: This study develops a mathematical model of the BLDC drive system, firstly. Secondly, discusses a design of the closed loop drive system employing the Adaptive-Network-based Fuzzy Interference System (ANFIS). The nonlinear simulatio...
Design of artificial genetic regulatory networks with multiple delayed adaptive responses
Kaluza, Pablo
2016-01-01
Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.
Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min
2015-12-01
In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.
Construction of a new adaptive wavelet network and its learning algorithm
无
2001-01-01
A new adaptive learning algorithm for constructing and training wavelet networks is proposed based on the time-frequency localization properties of wavelet frames and the adaptive projection algorithm. The exponential convergence of the adaptive projection algorithm in finite-dimensional Hilbert spaces is constructively proved, with exponential decay ratios given with high accuracy. The learning algorithm can sufficiently utilize the time-frequency information contained in the training data, iteratively determines the number of the hidden layer nodes and the weights of wavelet networks, and solves the problem of structure optimization of wavelet networks. The algorithm is simple and efficient, as illustrated by examples of signal representation and denoising.
Breast image feature learning with adaptive deconvolutional networks
Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.
2012-03-01
Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).
Adaptive robotic control driven by a versatile spiking cerebellar network.
Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio
2014-01-01
The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions. PMID:25390365
Towards a Framework for Self-Adaptive Reliable Network Services in Highly-Uncertain Environments
Grønbæk, Lars Jesper; Schwefel, Hans-Peter; Ceccarelli, Andrea;
2010-01-01
In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation actions. Inherent challenges exists to identify good and timely decision strategies...... to improve resilience of end-node services. In this paper we present a framework, called ODDR (Observation, Diagnosis, Decision, Remediation), for improving resilience of network based services through integration of self-adaptive monitoring services, network diagnosis, decision actions, and finally...
A study of task-based strategies for adaptively constructive neural networks
The authors investigated the strategies for optimizing neural networks under the unified frame based on task, focused for constructive neural networks on two typical and practical schemes, which are adaptively constructive neural networks by growing hidden or layers of hidden nodes and by growing sub net. With the Layer Multinet Model proposed by the research group, the authors investigated task-based algorithms for constructive neural networks, their perspective, strength and weakness
This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.
Boolean representations of simplicial complexes and matroids
Rhodes, John
2015-01-01
This self-contained monograph explores a new theory centered around boolean representations of simplicial complexes leading to a new class of complexes featuring matroids as central to the theory. The book illustrates these new tools to study the classical theory of matroids as well as their important geometric connections. Moreover, many geometric and topological features of the theory of matroids find their counterparts in this extended context. Graduate students and researchers working in the areas of combinatorics, geometry, topology, algebra and lattice theory will find this monograph appealing due to the wide range of new problems raised by the theory. Combinatorialists will find this extension of the theory of matroids useful as it opens new lines of research within and beyond matroids. The geometric features and geometric/topological applications will appeal to geometers. Topologists who desire to perform algebraic topology computations will appreciate the algorithmic potential of boolean represent...
Fast Vertical Mining Using Boolean Algebra
Hosny M. Ibrahim
2015-01-01
Full Text Available The vertical association rules mining algorithm is an efficient mining method, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantage of scanning database many times like Apriori algorithm. In vertical mining, frequent itemsets can be represented as a set of bit vectors in memory, which enables for fast computation. The sizes of bit vectors for itemsets are the main space expense of the algorithm that restricts its expansibility. Therefore, in this paper, a proposed algorithm that compresses the bit vectors of frequent itemsets will be presented. The new bit vector schema presented here depends on Boolean algebra rules to compute the intersection of two compressed bit vectors without making any costly decompression operation. The experimental results show that the proposed algorithm, Vertical Boolean Mining (VBM algorithm is better than both Apriori algorithm and the classical vertical association rule mining algorithm in the mining time and the memory usage.
Boolean Factor Analysis by Expectation-Maximization Method
Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.
Heidelberg : Springer, 2013 - (Kudělka, M.; Pokorný, J.; Snášel, V.; Abraham, A.), s. 243-254 ISBN 978-3-642-31602-9. ISSN 2194-5357. - (Advances in Intelligent Systems and Computing. 179). [IHCI 2011. International Conference on Intelligent Human Computer Interaction /3./. Prague (CZ), 29.08.2011-31.08.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural networks * hidden pattern search * Boolean factor analysis * generative model * information redundancy * exceptation-maximization Subject RIV: IN - Informatics, Computer Science
Efficient Analog Circuits for Boolean Satisfiability
Yin, Xunzhao; Sedighi, Behnam; Varga, Melinda; Ercsey-Ravasz, Maria; Toroczkai, Zoltan; Hu, Xiaobo Sharon
2016-01-01
Efficient solutions to NP-complete problems would significantly benefit both science and industry. However, such problems are intractable on digital computers based on the von Neumann architecture, thus creating the need for alternative solutions to tackle such problems. Recently, a deterministic, continuous-time dynamical system (CTDS) was proposed (Nature Physics, 7(12), 966 (2011)) to solve a representative NP-complete problem, Boolean Satisfiability (SAT). This solver shows polynomial ana...
Using Genetic Algorithms for Boolean Queries Optimization
Húsek, Dušan; Snášel, Václav; Owais, S.S.J.; Krömer, P.
Calgary: ACTA Press, 2005 - (Hamza, M.), s. 178-184 ISBN 0-88986-510-8. [IASTED International Conference on Internet and Multimedia Systems and Applications /9./. Honolulu (US), 15.08.2005-17.08.2005] R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : genetic algorithms * information retrieval * Boolean query * genetic programming Subject RIV: BA - General Mathematics
On the Implementation of Boolean Matrix Factorization
Snášel, V.; Krömer, P.; Platoš, J.; Húsek, Dušan
Los Alamitos: IEEE, 2008, s. 554-558. ISBN 978-0-7695-3299-8. [ETID '08. International Workshop on Evolutionary Techniques /2./, in collocation with DEXA 2008 International Conference /19./. Turin (IT), 01.09.2008-05.09.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : data mining * genetic algorithms * Boolean factorization * binary data * machine learning * feature extraction Subject RIV: IN - Informatics, Computer Science
Adaptive routing in wireless communication networks using swarm intelligence
Arabshahi, P.; Gray, A.; Kassabalidis, I.; Das, A.; Narayanan, S.; Sharkawi, M. El; Marks, R. J.
2001-01-01
In this paper we focus on the network routing problem, and survey swarm intelligent approaches for its efficient solution, after a brief overview of power-aware routing schemes, which are important in the network examples outlined above.
Dynamic adaptable overlay networks for personalised service delivery
Mathieu, B.; Stiemerling, M.; Soveri, M.C.; Galis, A.; Jean, K.; Ocampo, R.; Lai, Z.; Kampmann, M.; Tariq, M.A.; Balos, K.; Ahmed, O.K.; Busropan, B.J.; Prins, M.J.
2007-01-01
Overlay Networks have been designed as a promising solution to deliver new services via the use of intermediate nodes, acting as proxies or relays. This concept enables to hide the heterogeneity and variability of the underlying networks. In the Ambient Networks (ANs) project, the objectives are to
Exploring Educational and Cultural Adaptation through Social Networking Sites
Ryan, Sherry D.; Magro, Michael J.; Sharp, Jason H.
2011-01-01
Social networking sites have seen tremendous growth and are widely used around the world. Nevertheless, the use of social networking sites in educational contexts is an under explored area. This paper uses a qualitative methodology, autoethnography, to investigate how social networking sites, specifically Facebook[TM], can help first semester…
Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune; Andrea Francini; Lisa Zhang
2011-07-12
This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet. The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks
Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin;
2015-01-01
mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...
Ma, Chuang; Zhang, Hai-Feng
2016-01-01
So far, many network-structure-based link prediction methods have been proposed. However, these traditional methods were proposed by highlighting one or two structural features of networks, and then use the methods to implement link prediction in different networks. In many cases, the performance is not ideal since each network has its unique underlying structural features. In this article, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same networks, their inner structural features are utterly different. Inspired by these facts, an \\emph{adaptive} link prediction method is proposed to incorporate multiple structural features from the perspective of combination optimization. In the model, the weight of each structural feature is \\emph{adaptively } determined by logistic regression but not be artificially given in advance. According to our experimental results, we find that the logistic regression based link ...
Mapping DSP algorithms to a reconfigurable architecture Adaptive Wireless Networking (AWGN)
Rauwerda, Gerard
2003-01-01
This report will discuss the Adaptive Wireless Networking project. The vision of the Adaptive Wireless Networking project will be given. The strategy of the project will be the implementation of multiple communication systems in dynamically reconfigurable heterogeneous hardware. An overview of a wireless LAN communication system, namely HiperLAN/2, and a Bluetooth communication system will be given. Possible implementations of these systems in a dynamically reconfigurable architecture are dis...
Adaptivity Support for MPSoCs Based on Process Migration in Polyhedral Process Networks
Emanuele Cannella; Onur Derin; Paolo Meloni; Giuseppe Tuveri; Todor Stefanov
2012-01-01
System adaptivity is becoming an important feature of modern embedded multiprocessor systems. To achieve the goal of system adaptivity when executing Polyhedral Process Networks (PPNs) on a generic tiled Network-on-Chip (NoC) MPSoC platform, we propose an approach to enable the run-time migration of processes among the available platform resources. In our approach, process migration is allowed by a middleware layer which comprises two main components. The first component concerns the inter-ti...
An Adaptive WLAN Interference Mitigation Scheme for ZigBee Sensor Networks
Jo Woon Chong; Chae Ho Cho; Ho Young Hwang; Dan Keun Sung
2015-01-01
We propose an adaptive interference avoidance scheme that enhances the performance of ZigBee networks by adapting ZigBees' transmissions to measured wireless local area network (WLAN) interference. Our proposed algorithm is based on a stochastic analysis of ZigBee operation that is interfered with by WLAN transmission, given ZigBee and WLAN channels are overlaid in the industrial, scientific, and medical (ISM) band. We assume that WLAN devices have higher transmission power than ZigBee device...
Boolean Logic Optimization in Majority-Inverter Graphs
Amarù, Luca; Gaillardon, Pierre-Emmanuel; De Micheli, Giovanni
2015-01-01
We present a Boolean logic optimization framework based on Majority-Inverter Graph (MIG). An MIG is a directed acyclic graph consisting of three-input majority nodes and regular/complemented edges. Current MIG optimization is supported by a consistent algebraic framework. However, when algebraic methods cannot improve a result quality, stronger Boolean methods are needed to attain further optimization. For this purpose, we propose MIG Boolean methods exploiting the error masking property of m...
Using Boolean Constraint Propagation for Sub-clause Deduction
Darras, Sylvain; Dequen, Gilles; Devendeville, Laure; Mazure, Bertrand; Ostrowski, Richard; Sais, Lahkdar
2005-01-01
Boolean Constraint Propagation (BCP) is recognized as one of the most use- ful technique for efficient satisfiability checking. In this paper a new extension of the scope of boolean constraint propagation is proposed. It makes an original use of BCP to achieve further reduction of boolean formulas. Considering the impli- cation graph generated by the constraint propagation process as a resolution tree, sub-clauses from the original formula can be deduced. Then, we show how such extension can ...
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani;
2014-01-01
on the TCP protocol for reliability in data delivery. TCP is known to drop its throughput performance by several fold in the presence of even 1% or 2% packet losses, which are common in wireless systems. This will force DASH to settle at a much lower video resolution, thus reducing the user's quality...... of experience. We show that the use of FRANC, an adaptive network coding protocol that provides both low delay and high throughput to upper layers, as a reliability mechanism for TCP can significantly increase video quality. As part of our analysis, we benchmark the performance of various TCP versions......, including CUBIC, Reno, Veno, Vegas, and Westwood+, under different packet loss rates in wireless systems using a real testbed with Raspberry Pi devices. Our goal was to choose the most promising TCP version in terms of delay performance, in this case TCP Reno, and make a fair comparison between TCP running...
Lubov Muhamedovna Mkrtchyan
2015-01-01
Full Text Available The development of a network communicative space causes the transformation of labor and actualizes the problem of employment as the main indicator of social security. In this regard, the article considers one of the mechanisms ensuring social security of the person as the cultural development of society and the ability to create and maintain a system of values and professional orientations of the person - effective professional adaptation, requirements to which are largely determined by the ability to manage information network technologies, professional and informational culture, the ability to self-education. In its turn, social security in modern conditions acquires the features of socio-network security as a stable state of human security and protection from the negative effects of network communications, which can modify the behavior of a person, his values, and professional orientations and mislead him in the information space. Therefore, this article analyzes the relationship of professional adaptation and professional culture which in the context of network communication is determined by specific activities of the network professional communities, propagating certain values and behaviors. The level of information culture requires from individuals not only systematic knowledge and skills in information technology, but also the ability to think critically, to provide appropriate selection of information, to oppose network risks and threats, to use the communication network as an adaptive resource in the process of self-education.
Nonlinear functional approximation with networks using adaptive neurons
Tawel, Raoul
1992-01-01
A novel mathematical framework for the rapid learning of nonlinear mappings and topological transformations is presented. It is based on allowing the neuron's parameters to adapt as a function of learning. This fully recurrent adaptive neuron model (ANM) has been successfully applied to complex nonlinear function approximation problems such as the highly degenerate inverse kinematics problem in robotics.
Combinational Logic-Level Verification using Boolean Expression Diagrams
Hulgaard, Henrik; Williams, Poul Frederick; Andersen, Henrik Reif
1997-01-01
Boolean Expression Diagrams (BEDs) is a new data structure for representing and manipulating Boolean functions. BEDs are a generalization of Binary Decision Diagrams (BDDs) that are capable of representing any Boolean circuit in linear space and still maintain many of the desirable properties of...... BDDs. This paper demonstrates that BEDs are well suited for solving the combinational logic-level verification problem which is, given two combinational circuits, to determine whether they implement the same Boolean functions. Based on all combinational circuits in the ISCAS 85 and LGSynth 91...
Yao, Wei; Fang, Jiakun; Zhao, Ping;
2013-01-01
In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have the...
Improved adaptive-threshold burst assembly in optical burst switching networks
Jiuru Yang; Gang Wang; Shilou Jia
2007-01-01
An improved adaptive-threshold burst assembly algorithm is proposed to alleviate the limitation of conventional assembly schemes on data loss and delay. The algorithm will adjust the values of assembly factors according to variant traffic regions. And the simulation results show that, by using the adaptive-factor adaptive assembly scheme, the performance of networks is extensively enhanced in terms of burst loss probability and average queuing delay.
FANG Jin-Qing; LUO Xiao-Shu; HUANG Guo-Xian
2006-01-01
Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field adaptive control based on the neuralnetwork with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.
Network Latency Adaptive Tempo in the Public Sound Objects System
Barbosa, Álvaro; Cardoso, Jorge; Geiger, Günter
2005-01-01
In recent years Computer Network-Music has increasingly captured the attention of the Computer Music Community. With the advent of Internet communication, geographical displacement amongst the participants of a computer mediated music performance achieved world wide extension. However, when established over long distance networks, this form of musical communication has a fundamental problem: network latency (or net-delay) is an impediment for real-time collaboration. From a recent study, carr...
Transparent Adaptable Network Access and Service Content Differentiation
Senneset, Thomas
2006-01-01
Today s most advanced mobile devices support communication through a variety of network technologies; GSM (including GPRS and EDGE), UMTS, WLAN, Bluetooth, and IR. This master thesis characterizes different network technologies and protocols available to mobile devices, phones in particular. In addition, service provisioning capabilities over different types of networks are identified. Internet access till now has been provided over GSM or UMTS, often via a WAP Gateway. With a WAP Gateway, ...
Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen
2015-04-01
In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should
Adaptive Reference Control for Pressure Management in Water Networks
Kallesøe, Carsten; Jensen, Tom Nørgaard; Wisniewski, Rafal
2015-01-01
Water scarcity is an increasing problem worldwide and at the same time a huge amount of water is lost through leakages in the distribution network. It is well known that improved pressure control can lower the leakage problems. In this work water networks with a single pressure actuator and several...... consumers are considered. Under mild assumptions on the consumption pattern and hydraulic resistances of pipes we use properties of the network graph and Kirchhoffs node and mesh laws to show that simple relations exist between the actuator pressure and critical point pressures inside the network...
Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks
Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments. (paper)
Towards adaptive security for convergent wireless sensor networks in beyond 3G environments
Mitseva, Anelia; Aivaloglou, Efthimia; Marchitti, Maria-Antonietta;
2010-01-01
The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context-aware adapt...
Adaptive pinning synchronization in fractional-order uncertain complex dynamical networks with delay
Liang, Song; Wu, Ranchao; Chen, Liping
2016-02-01
Based on the stability theory of fractional-order systems, synchronization of general fractional-order uncertain complex networks with delay is investigated in this paper. By the inequality of the fractional derivative and the comparison principle of the linear fractional equation with delay, synchronization of complex networks with delay is realized under adaptive control. Some sufficient criteria ensuring local asymptotical synchronization under adaptive control and global asymptotical synchronization under adaptive pinning control are derived, respectively. Finally, numerical simulations are presented to demonstrate the validity and feasibility of the proposed synchronization criteria.
Zu Yun-Xiao; Zhou Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed,and a fitness function is provided.Simulations are conducted using the adaptive niche immune genetic algorithm,the simulated annealing algorithm,the quantum genetic algorithm and the simple genetic algorithm,respectively.The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation,and has quick convergence speed and strong global searching capability,which effectively reduces the system power consumption and bit error rate.
Tat-Bao-Thien Nguyen
2014-01-01
Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
Li, Ning; Cao, Jinde
2015-01-01
In this paper, we investigate synchronization for memristor-based neural networks with time-varying delay via an adaptive and feedback controller. Under the framework of Filippov's solution and differential inclusion theory, and by using the adaptive control technique and structuring a novel Lyapunov functional, an adaptive updated law was designed, and two synchronization criteria were derived for memristor-based neural networks with time-varying delay. By removing some of the basic literature assumptions, the derived synchronization criteria were found to be more general than those in existing literature. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results. PMID:25299765
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. (geophysics, astronomy, and astrophysics)
Adaptive Relay Activation in the Network Coding Protocols
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank
2015-01-01
State-of-the-art Network coding based routing protocols exploit the link quality information to compute the transmission rate in the intermediate nodes. However, the link quality discovery protocols are usually inaccurate, and introduce overhead in wireless mesh networks. In this paper, we present...
Adaptive Epidemic Dynamics in Networks: Thresholds and Control
Xu, Shouhuai; Lu, Wenlian; Xu, Li; Zhan, Zhenxin
2013-01-01
Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily ...
Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling
A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication. (general)
Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network
Manjula Devi, R.; R. C. Suganthe; S. KUPPUSWAMI
2013-01-01
Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do ...
Adaptive Dvisible Load Scheduling Strategies for Workstation Clusters with Unknown Network Resources
Ghose, Debasish; Kim, Hyoung Joong; Kim, Tae Hoon
2005-01-01
Conventional divisible load scheduling algorithms attempt to achieve optimal partitioning of massive loads to be distributed among processors in a distributed computing system in the presence of communication delays in the network. However, these algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present an adaptive strategy that estimates network parameter values...
An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks
Jian Dong; Xiao Ren; Decheng Zuo; Hongwei Liu
2014-01-01
The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P) networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS) can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on ret...
Toward an Adaptive Learning System Framework: Using Bayesian Network to Manage Learner Model
Viet Anh Nguyen
2012-12-01
Full Text Available This paper represents a new approach to manage learner modeling in an adaptive learning system framework. It considers developing the basic components of an adaptive learning system such as the learner model, the course content model and the adaptation engine. We use the overlay model and Bayesian network to evaluate learners’ knowledge. In addition, we also propose a new content modeling method as well as adaptation engine to generate adaptive course based on learner’s knowledge. Based on this approach, we developed an adaptive learning system named is ACGS-II, that teaches students how to design an Entity Relationship model in a database system course. Empirical testing results for students who used the application indicate that our proposed model is very helpful as guidelines to develop adaptive learning system to meet learners’ demands.
Structural self-assembly and avalanchelike dynamics in locally adaptive networks
Gräwer, Johannes; Modes, Carl D.; Magnasco, Marcelo O.; Katifori, Eleni
2015-07-01
Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. In addition, we find that the long term equilibration dynamics exhibit behavior reminiscent of glassy systems characterized by long periods of slow changes punctuated by bursts of reorganization events.
Lu LU; Fagui LIU; Weixiang SHI
2008-01-01
In this paper, a novel control law is presented, which uses neural-network techniques to approximate the affine class nonlinear system having unknown or uncertain dynamics and noise disturbances. It adopts an adaptive control law to adjust the network parameters online and adds another control component according to H-infinity control theory to attenuate the disturbance. This control law is applied to the position tracking control of pneumatic servo systems. Simulation and experimental results show that the tracking precision and convergence speed is obviously superior to the results by using the basic BP-network controller and self-tuning adaptive controller.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
Cluster synchronization in the adaptive complex dynamical networks via a novel approach
This Letter investigates cluster synchronization in the adaptive complex dynamical networks with nonidentical nodes by a local control method and a novel adaptive strategy for the coupling strengths of the networks. In this approach, the coupling strength of each node adjusts adaptively only based on the state information of its neighborhood. By means of the proposed scheme, the sufficient conditions for achieving cluster synchronization are derived analytically by utilizing Lyapunov stability theory. It is demonstrated that the synchronization performance is sensitively affected by the control gain, the inner-coupling matrix and the network topological structure. The numerical simulations are performed to verify the effectiveness of the theoretical results. - Highlights: → We present a more realistic adaptive complex network model with diverse nodes. → The local controllers are designed based the community structure of the network. → Each node's coupling strength adapts self only by the state of its neighborhood. → The synchronization effect is affected by the network structure and control gain. → The Cluster synchronization method is robust against noise perturbation.
An OCP Compliant Network Adapter for GALS-based SoC Design Using the MANGO Network-on-Chip
Bjerregaard, Tobias; Mahadevan, Shankar; Olsen, Rasmus Grøndahl;
2005-01-01
The demand for IP reuse and system level scalability in System-on-Chip (SoC) designs is growing. Network-onchip (NoC) constitutes a viable solution space to emerging SoC design challenges. In this paper we describe an OCP compliant network adapter (NA) architecture for the MANGO NoC. The NA...... decouples communication and computation, providing memory-mapped OCP transactions based on primitive message-passing services of the network. Also, it facilitates GALS-type systems, by adapting to the clockless network. This helps leverage a modular SoC design flow. We evaluate performance and cost of 0.......13 um CMOS standard cell instantiations of the architecture....
Adaptive Control of Networked Systems in the Presence of Bounded Disturbances
A. H. Tahoun
2007-01-01
Full Text Available The insertion of data network in the feedback adaptive control loops makes the analysis and design of networked control systems more complex than traditional control systems. This paper addresses the adaptive stabilization problem of linear time-invariant networked control systems when the measurements of the plant states are corrupted by bounded disturbances. The case of state feedback is treated in which only an upper bound on the norm of matrix A is needed. The problem is to find an upper bound on the transmission period h that guarantees the stability of the overall adaptive networked control system under an ideal transmission process, i.e. no transmission delay or packet dropout. Rigorous mathematical proofs are established, that relies heavily on Lyapunov's stability criterion and dead-zone Technique. Simulation results are given to illustrate the efficacy of our design approach.
Yang Fang
2014-01-01
Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.
Learning Boolean functions with concentrated spectra
Mixon, Dustin G.; Peterson, Jesse
2015-08-01
This paper discusses the theory and application of learning Boolean functions that are concentrated in the Fourier domain. We first estimate the VC dimension of this function class in order to establish a small sample complexity of learning in this case. Next, we propose a computationally efficient method of empirical risk minimization, and we apply this method to the MNIST database of handwritten digits. These results demonstrate the effectiveness of our model for modern classification tasks. We conclude with a list of open problems for future investigation.
Towards boolean operations with thermal photons
Ben-Abdallah, Philippe
2016-01-01
The Boolean algebra is the natural theoretical framework for a classical information treatment. The basic logical operations are usually performed using logic gates. In this Letter we demonstrate that NOT, OR and AND gates can be realized exploiting the near-field radiative interaction in N-body systems with phase change materials. With the recent development of a photon thermal transistor and thermal memory, this result paves the way for a full information treatment and smart solutions for active thermal management at nanoscale with photons.
Multiple-model-and-neural-network-based nonlinear multivariable adaptive control
Yue FU; Tianyou CHAI
2007-01-01
A multivariable adaptive controller feasible for implementation on distributed computer systems (DCS) is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.
Adaptive Resource Allocation For MAI Minimization In Wireless Adhoc Network
Mohammed Abdul Waheed
2011-05-01
Full Text Available Coding-based solutions for MANETs have emerged as a basic solution to current high rate data accessing in adhoc network. This has become essential related to the absence of centralized control such as a monitoring station. A code assignment protocol is needed to assign distinct codes to different terminals. This problem is less effective in small networks, but becomes dominative in large networks where the numbers of code sequence are lesser than the number of terminals to code, demanding reuse of the codes. The issue of code allocation in communication is focused in this paper with the evaluation of MAI in wireless network. Unlike previously proposed protocols in this paper a focus for the multiple access interference (MAI, thereby addressing the limiting near-far problem that decreases the throughput performance in MANETs is made. The code assignment scheme is developed for the proper usage of users code under MANETs communication to minimize the MAI impact.
Adaptive artificial neural network for autonomous robot control
Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.
1992-01-01
The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.
Networked Adaptive Interactive Hybrid Systems (NAIHS) for multiplatform engagement capability
Kester, L.J.H.M.
2008-01-01
Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public
Method for designing networking adaptive interactive hybrid systems
Kester, L. J.H.M.
2010-01-01
Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public
Constructions of vector output Boolean functions with high generalized nonlinearity
KE Pin-hui; ZHANG Sheng-yuan
2008-01-01
Carlet et al. recently introduced generalized nonlinearity to measure the ability to resist the improved correlation attack of a vector output Boolean function. This article presents a construction of vector output Boolean functions with high generalized nonlinearity using the sample space. The relation between the resilient order and generalized nonlinearity is also discussed.
E-Referencer: Transforming Boolean OPACs to Web Search Engines.
Khoo, Christopher S. G.; Poo, Danny C. C.; Toh, Teck-Kang; Hong, Glenn
E-Referencer is an expert intermediary system for searching library online public access catalogs (OPACs) on the World Wide Web. It is implemented as a proxy server that mediates the interaction between the user and Boolean OPACs. It transforms a Boolean OPAC into a retrieval system with many of the search capabilities of Web search engines.…
IMPLEMENTATION OF ADAPTIVE ZONE ROUTING PROTOCOL FOR WIRELESS NETWORKS
T. RAVI NAYAK; SAKE. POTHALAIAH; Dr. K ASHOK BABU
2010-01-01
Mobile Ad hoc wireless Networks (MANETs) that do not need any fixed infrastructure. They are characterized by dynamic topology due to node mobility, limited channel bandwidth, and limited battery power of nodes. The key challenge in the design of ad hoc networks is the development of dynamic routing protocols that can efficiently findroutes between two communicating nodes. Thus, many ad hoc routing protocols have been proposed in recent years. All these routing protocols attempt to provide a ...
Adaptive network traffic management for multi user virtual environments
Oliver, Iain Angus
2011-01-01
Multi User Virtual Environments (MUVE) are a new class of Internet application with a significant user base. This thesis adds to our understanding of how MUVE network traffic fits into the mix of Internet traffic, and how this relates to the application's needs. MUVEs differ from established Internet traffic types in their requirements from the network. They differ from traditional data traffic in that they have soft real-time constraints, from game traffic in that their bandwidth requi...
Smart Grid Adaptive Volt-VAR Optimization in Distribution Networks
Manbachi, Moein
2015-01-01
The electrical distribution networks across the world are witnessing a steady infusion of smart grid technologies into every aspect of their infrastructure and operations. Technologies such as Energy Management Systems (EMS), Distribution Management Systems (DMS) and Advanced Metering Infrastructure (AMI) have partially addressed the needs of the distribution networks for automation, control, monitoring and optimization. Many utilities intend to explore the capabilities of advanced AMI system...
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tich Phuoc Tran; Longbing Cao; Dat Tran; Cuong Duc Nguyen
2009-01-01
This article applies Machine Learning techniques to solve Intrusion Detection problems withincomputer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspac...
A Security Adaptation Reference Monitor for Wireless Sensor Network
El-Maliki, Tewfiq; Seigneur, Jean-Marc
2012-01-01
Security in Wireless Sensor Network has become a hot research topic due to their wide deployment and the increasing new runtime attacks they are facing. We observe that traditional security protocols address conventional security problems and cannot deal with dynamic attacks such as sinkhole dynamic behavior. Moreover, they use resources, and limit the efficient use of sensor resources and inevitably the overall network efficiency is not guaranteed. Therefore, the requirements of new security...
Epidemic Dynamics On Information-Driven Adaptive Networks
Zhan, Xiu-Xiu; Sun, Gui-Quan; Zhang, Zi-Ke
2015-01-01
can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice give a visual representation about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, oscillatory, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects h...
Boolean delay equations: A simple way of looking at complex systems
Ghil, Michael; Zaliapin, Ilya; Coluzzi, Barbara
2008-12-01
Boolean Delay Equations (BDEs) are semi-discrete dynamical models with Boolean-valued variables that evolve in continuous time. Systems of BDEs can be classified into conservative or dissipative, in a manner that parallels the classification of ordinary or partial differential equations. Solutions to certain conservative BDEs exhibit growth of complexity in time; such BDEs can be seen therefore as metaphors for biological evolution or human history. Dissipative BDEs are structurally stable and exhibit multiple equilibria and limit cycles, as well as more complex, fractal solution sets, such as Devil’s staircases and “fractal sunbursts.” All known solutions of dissipative BDEs have stationary variance. BDE systems of this type, both free and forced, have been used as highly idealized models of climate change on interannual, interdecadal and paleoclimatic time scales. BDEs are also being used as flexible, highly efficient models of colliding cascades of loading and failure in earthquake modeling and prediction, as well as in genetics. In this paper we review the theory of systems of BDEs and illustrate their applications to climatic and solid-earth problems. The former have used small systems of BDEs, while the latter have used large hierarchical networks of BDEs. We moreover introduce BDEs with an infinite number of variables distributed in space (“partial BDEs”) and discuss connections with other types of discrete dynamical systems, including cellular automata and Boolean networks. This research-and-review paper concludes with a set of open questions.
Boolean model of Yeast Apoptosis as a tool to study yeast and human apoptotic regulations
MarijaCvijovic
2012-12-01
Full Text Available Programmed cell death (PCD is an essential cellular mechanism that is evolutionary conserved, mediated through various pathways and acts by integrating different stimuli. Many diseases such as neurodegenerative diseases and cancers are found to be caused by, or associated with, regulations in the cell death pathways. Yeast Saccharomyces cerevisiae, is a unicellular eukaryotic organism that shares with human cells components and pathways of the PCD and is therefore used as a model organism. Boolean modelling is becoming promising approach to capture qualitative behaviour and describe essential properties of such complex networks. Here we present large literature-based and to our knowledge first Boolean model that combines pathways leading to apoptosis (a type of PCD in yeast. Analysis of the yeast model confirmed experimental findings of anti-apoptotic role of Bir1p and pro-apoptotic role of Stm1p and revealed activation of the stress protein kinase Hog proposing the maximal level of activation upon heat stress. In addition we extended the yeast model and created an in silico humanized yeast in which human pro- and anti-apoptotic regulators Bcl-2 family and Valosin-contain protein (VCP are included in the model. We showed that accumulation of Bax in in silico humanized yeast shows apoptotic markers and that VCP is essential target of Akt Signaling. The presented Boolean model provides comprehensive description of yeast apoptosis network behaviour. Extended model of humanized yeast gives new insights of how complex human disease like neurodegenration can initially be tested.
An Optimized Technique of Increasing the Performance of Network Adapter on EML Layer
Prashanth L
2012-08-01
Full Text Available Simple Network Adapter initially which acts as an interface between the Transaction server and Network Elements communicates over the channel through tcppdu. Presently the disadvantage being involved in tcppdu is to maintain the channel contention, reservation of channel bandwidth. The disadvantage being involved is certain features, version of network elements communicates by receiving the xml over the socket. So, it’s not possible to change the entire framework, but by updating the framework an XML Over Socket(XOS formation should be supported. The XOS implementation is being performed using Java language through mainly in JVM. Such that by this deployment machines would become easier and form a good communication gap between them. This simple network adapter being developed should support operations of the North bounded server and gives an established authorized, secured, reliable portal. The interface being developed should provide a good performance in meeting the network demands and operated conversions of respective objects
Adaptive Security Architecture based on EC-MQV Algorithm in Personal Network (PN)
Mihovska, Albena D.; Prasad, Neeli R.
2007-01-01
Abstract — Personal Networks (PNs) have been focused on in order to support the user’s business and private activities without jeopardizing privacy and security of the users and their data. In such a network, it is necessary to produce a proper key agreement method according to the feature...... of the network. One of the features of the network is that the personal devices have deferent capabilities such as computational ability, memory size, transmission power, processing speed and implementation cost. Therefore an adaptive security mechanism should be contrived for such a network of various device...... combinations based on user’s location and device’s capability. The paper proposes new adaptive security architecture with three levels of asymmetric key agreement scheme by using context-aware security manager (CASM) based on elliptic curve cryptosystem (EC-MQV)....
Analysis of utility-theoretic heuristics for intelligent adaptive network routing
Mikler, A.R.; Honavar, V.; Wong, J.S.K. [Iowa State Univ., Ames, IA (United States)
1996-12-31
Utility theory offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion optimization problem in a dynamic and uncertain environment. In this paper, we incrementally develop a set of heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. We present an analysis of properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics and identify the conditions under which they are guaranteed to route messages along an optimal path. The paper concludes with a discussion of the relevance of the theoretical results presented in the paper to the design of intelligent autonomous adaptive communication networks and an outline of some directions of future research.
CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK
薛建中; 郑崇勋; 闫相国
2004-01-01
Objective This paper presents classifications of mental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCI) schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0%. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classifier for on-line BCI scheme.
ADAPTIVE FLIGHT CONTROL SYSTEM OF ARMED HELICOPTER USING WAVELET NEURAL NETWORK METHOD
ZHURong-gang; JIANGChangsheng; FENGBin
2004-01-01
A discussion is devoted to the design of an adaptive flight control system of the armed helicopter using wavelet neural network method. Firstly, the control loop of the attitude angle is designed with a dynamic inversion scheme in a quick loop and a slow loop. respectively. Then, in order to compensate the error caused by dynamic inversion, the adaptive flight control system of the armed helicopter using wavelet neural network method is put forward, so the BP wavelet neural network and the Lyapunov stable wavelet neural network are used to design the helicopter flight control system. Finally, the typical maneuver flight is simulated to demonstrate its validity and effectiveness. Result proves that the wavelet neural network has an engineering practical value and the effect of WNN is good.
Using social network analysis to evaluate health-related adaptation decision-making in Cambodia.
Bowen, Kathryn J; Alexander, Damon; Miller, Fiona; Dany, Va
2014-02-01
Climate change adaptation in the health sector requires decisions across sectors, levels of government, and organisations. The networks that link these different institutions, and the relationships among people within these networks, are therefore critical influences on the nature of adaptive responses to climate change in the health sector. This study uses social network research to identify key organisational players engaged in developing health-related adaptation activities in Cambodia. It finds that strong partnerships are reported as developing across sectors and different types of organisations in relation to the health risks from climate change. Government ministries are influential organisations, whereas donors, development banks and non-government organisations do not appear to be as influential in the development of adaptation policy in the health sector. Finally, the study highlights the importance of informal partnerships (or 'shadow networks') in the context of climate change adaptation policy and activities. The health governance 'map' in relation to health and climate change adaptation that is developed in this paper is a novel way of identifying organisations that are perceived as key agents in the decision-making process, and it holds substantial benefits for both understanding and intervening in a broad range of climate change-related policy problems where collaboration is paramount for successful outcomes. PMID:24487452
From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks
Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming
2016-03-01
The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.
An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks
Jian Dong
2014-09-01
Full Text Available The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on retransmission mechanism has been employed widely in many P2P applications; however, Chen’s classic adaptive model cannot describe this kind of detection strategy. In order to provide an efficient service of failure detection in P2P systems, this paper establishes a novel QoS evaluation model for the baseline detection strategy. The relationship between the detection period and the QoS is discussed and on this basis, an adaptive failure detector (B-AFD is proposed, which can meet the quantitative QoS metrics under changing network environment. Meanwhile, it is observed from the experimental analysis that B-AFD achieves better detection accuracy and time with lower detection overhead compared to the traditional baseline strategy and the adaptive detectors based on Chen’s model. Moreover, B-AFD has better adaptability to P2P network.
Modelling Self-Adaptive Networked Entities in Matlab/Simulink
Bartosinski, Roman; Daněk, Martin; Honzík, Petr; Kadlec, Jiří
Praha: Humusoft, 2007, s. 1-8. ISBN 978-80-7080-658-6. [Technical Computing Prague 2007. Praha (CZ), 14.11.2007-14.11.2007] R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : SANE * self- adaptive system * FPGA * simulation Subject RIV: JC - Computer Hardware ; Software
Cherenkov ring recognition using a non-adaptable network
We introduce a very simple and efficient technique to recognize one or several circles in a given pattern. The algorithm can (and in practical application should) be implemented as a massively parallel architecture, in which connections between units are not adaptable. We show examples of recognition of several (eventually overlapping) circles and a practical case of particle identification. (orig.)
Kirstie Cadger
2016-07-01
Full Text Available Social ties play an important role in agricultural knowledge exchange, particularly in developing countries with high exposure to agriculture development interventions. Institutions often facilitate agricultural training projects, with a focus on agroecological practices, such as agroforestry and agrobiodiversity. The structural characteristics of social networks amongst land managers influences decision-making to adopt such adaptive agroecoloigcal practice; however, the extent of knowledge transfer beyond direct project participants is often unknown. Using a social network approach, we chart the structure of agrarian knowledge networks (n = 131 in six communities, which have been differentially exposed to agriculture development interventions in Ghana. Farmer network size, density and composition were distinctly variable; development project-affiliated farmers were embedded in larger networks, had non-affiliated farmers within their networks, were engaged in more diverse agricultural production and reported adopting and adapting agroecological practice more frequently. Such bridging ties that link across distinctive groups in a network can expose network members to new and innovative agroecological practices, such as increasing agrobiodiversity, thus, contributing to livelihood strategies that mitigate environmental and market risk. Furthermore, we show that these knowledge networks were crop-specific where network size varied given the type of crop produced. Such factors, which may influence the rate and extent of agroecological knowledge diffusion, are critical for the effectiveness of land management practices as well as the persistence of agriculture development interventions.
Terse Integer Linear Programs for Boolean Optimization
Christoph Buchheim
2009-05-01
Full Text Available We present a new polyhedral approach to nonlinear Boolean optimization. Compared to other methods, it produces much smaller integer programming models, making it more efficient from a practical point of view. We mainly obtain this by two different ideas: first, we do not require the objective function to be in any normal form. The transformation into a normal form usually leads to the introduction of many additional variables or constraints. Second, we reduce the problem to the degree-two case in a very efficient way, by slightly extending the dimension of the original variable space. The resulting model turns out to be closely related to the maximum cut problem; we show that the corresponding polytope is a face of a suitable cut polytope in most cases. In particular, our separation problem reduces to the one for the maximum cut problem. In practice, the approach appears to be very competitive for unconstrained Boolean optimization problems. First experimental results, which have been obtained for some particularly hard instances of the Max-SAT Evaluation 2007, show that our very general implementation can outperform even special-purpose Max-SAT solvers. The software is accessible online under “we.logoptimize.it”.
Utilizing Network QoS for Dependability of Adaptive Smart Grid Control
Madsen, Jacob Theilgaard; Kristensen, Thomas le Fevre; Olsen, Rasmus Løvenstein;
2014-01-01
A smart grid is a complex system consisting of a wide range of electric grid components, entities controlling power distribution, generation and consumption, and a communication network supporting data exchange. This paper focuses on the influence of imperfect network conditions on smart grid con......- trollers, and how this can be counteracted by utilizing Quality of Service (QoS) information from the communication network. Such an interface between grid controller and network QoS is particularly relevant for smart grid scenarios that use third party communication network infrastructure, where...... modification of networking and lower layer protocols are impossible. This paper defines a middleware solution for adaptation of smart grid control, which uses network QoS information and interacts with the smart grid controller to increase dependability. In order to verify the methodology, an example scenario...
Delay-induced diversity of firing behavior and ordered chaotic firing in adaptive neuronal networks
In this paper, we study the effect of time delay on the firing behavior and temporal coherence and synchronization in Newman–Watts thermosensitive neuron networks with adaptive coupling. At beginning, the firing exhibit disordered spiking in absence of time delay. As time delay is increased, the neurons exhibit diversity of firing behaviors including bursting with multiple spikes in a burst, spiking, bursting with four, three and two spikes, firing death, and bursting with increasing amplitude. The spiking is the most ordered, exhibiting coherence resonance (CR)-like behavior, and the firing synchronization becomes enhanced with the increase of time delay. As growth rate of coupling strength or network randomness increases, CR-like behavior shifts to smaller time delay and the synchronization of firing increases. These results show that time delay can induce diversity of firing behaviors in adaptive neuronal networks, and can order the chaotic firing by enhancing and optimizing the temporal coherence and enhancing the synchronization of firing. However, the phenomenon of firing death shows that time delay may inhibit the firing of adaptive neuronal networks. These findings provide new insight into the role of time delay in the firing activity of adaptive neuronal networks, and can help to better understand the complex firing phenomena in neural networks.
Adaptive and Decentralized Operator Placement for In-Network Query Processing
Bonfils, Boris; Bonnet, Philippe
2003-01-01
In-network query processing is critical for reducing network traffic when accessing and manipulating sensor data. It requires placing a tree of query operators such as filters and aggregations but also correlations onto sensor nodes in order to minimize the amount of data transmitted in the network....... In this paper, we show that this problem is a variant of the task assignment problem for which polynomial algorithms have been developed. These algorithms are however centralized and cannot be used in a sensor network. We describe an adaptive and decentralized algorithm that progressively refines the...
Sam Pearsall
2005-12-01
Full Text Available Adaptive ecosystem management (AEM requires building and managing an interorganizational network of stakeholders to conserve ecosystem integrity while sustaining ecosystem services. This paper demonstrates the usefulness of applying the concepts of interorganizational networks and learning organizations to AEM. A case study of the lower Roanoke River in North Carolina illustrates how an AEM network can evolve to guide stakeholders in creating a shared framework for generative learning, consensus building through collaboration, and decision making. Environmental professionals can use this framework to guide institutional arrangements and to coordinate the systematic development of cohesive interorganizational AEM networks.
Study on the Robot Robust Adaptive Control Based on Neural Networks
温淑焕; 王洪瑞; 吴丽艳
2003-01-01
Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Disruption prediction with adaptive neural networks for ASDEX Upgrade
In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the 'novelty' of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated.
Adaptive feature annotation for large video sensor networks
Cai, Yang; Bunn, Andrew; Liang, Peter; Yang, Bing
2013-10-01
We present an adaptive feature extraction and annotation algorithm for articulating traffic events from surveillance cameras. We use approximate median filter for moving object detection, motion energy image and convex hull for lane detection, and adaptive proportion models for vehicle classification. It is found that our approach outperforms three-dimensional modeling and scale-independent feature transformation algorithms in terms of robustness. The multiresolution-based video codec algorithm enables a quality-of-service-aware video streaming according to the data traffic. Furthermore, our empirical data shows that it is feasible to use the metadata to facilitate the real-time communication between an infrastructure and a vehicle for safer and more efficient traffic control.
Disruption prediction with adaptive neural networks for ASDEX Upgrade
Cannas, B.; Fanni, A. [Electrical and Electronic Engineering Dept., University of Cagliari, Piazza D' Armi, 09123 Cagliari (Italy); Pautasso, G. [Max-Planck-Institut fuer Plasmaphysik, EURATOM Association, Garching (Germany); Sias, G., E-mail: giuliana.sias@diee.unica.it [Electrical and Electronic Engineering Dept., University of Cagliari, Piazza D' Armi, 09123 Cagliari (Italy)
2011-10-15
In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the 'novelty' of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated.
Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks
Ali, Borhanuddin Mohd; Sali, Aduwati
2016-01-01
The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications. PMID:27257964
Effects of Implementing Adaptable Channelization in Wi-Fi Networks
Abid Hussain
2016-01-01
Full Text Available The unprecedented increase of wireless devices is now facing a serious threat of spectrum scarcity. The situation becomes even worse due to inefficient frequency distribution protocols, deployed in trivial Wi-Fi networks. The primary source of this inefficiency is static channelization used in wireless networks. In this work, we investigate the use of dynamic and flexible channelization, for optimal spectrum utilization in Wi-Fi networks. We propose optimal spectrum sharing algorithm (OSSA and analyze its effect on exhaustive list of essential network performance measuring parameters. The elementary concept of the proposed algorithm lies in the fact that frequency spectrum should be assigned to any access point (AP based on its current requirement. The OSSA algorithm assigns channels with high granularity, thus maximizing spectrum utilization by more than 20% as compared to static width channel allocation. This optimum spectrum utilization, in turn, increases throughput by almost 30% in many deployment scenarios. The achieved results depict considerable decrease in interference, while simultaneously increasing range. Similarly signal strength values at relatively longer distances improve significantly at narrower channel widths while simultaneously decreasing bit error rates. We found that almost 25% reduction in interference is possible in certain scenarios through proposed algorithm.
Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks.
Al-Medhwahi, Mohammed; Hashim, Fazirulhisyam; Ali, Borhanuddin Mohd; Sali, Aduwati
2016-01-01
The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications. PMID:27257964
Social Networks-Based Adaptive Pairing Strategy for Cooperative Learning
Chuang, Po-Jen; Chiang, Ming-Chao; Yang, Chu-Sing; Tsai, Chun-Wei
2012-01-01
In this paper, we propose a grouping strategy to enhance the learning and testing results of students, called Pairing Strategy (PS). The proposed method stems from the need of interactivity and the desire of cooperation in cooperative learning. Based on the social networks of students, PS provides members of the groups to learn from or mimic…
Zeng, Yuanyuan; Sreenan, Cormac J; Sitanayah, Lanny; Xiong, Naixue; Park, Jong Hyuk; Zheng, Guilin
2011-01-01
Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work. PMID:22163774
Guilin Zheng
2011-03-01
Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.
Yuanyuan Zeng
2010-06-01
Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.
Robust adaptive synchronization of general dynamical networks with multiple delays and uncertainties
LU YIMING; HE PING; MA SHU-HUA; LI GUO-ZHI; MOBAYBEN SALEH
2016-06-01
In this article, a general complex dynamical network which contains multiple delays and uncertainties is introduced, which contains time-varying coupling delays, time-varying node delay, and uncertainties of both the inner- and outer-coupling matrices. A robust adaptive synchronization scheme for these general complex networks with multiple delays and uncertainties is established and raised by employing the robust adaptive control principle and the Lyapunov stability theory. We choose some suitable adaptive synchronization controllers to ensure the robust synchronization of this dynamical network. The numerical simulations of the time-delay Lorenz chaotic system as local dynamical node are provided to observe and verify the viability and productivity of the theoretical research in this paper. Compared to the achievement of previous research, theresearch in this paper seems quite comprehensive and universal.
Enabling Adaptive Rate and Relay Selection for 802.11 Mobile Ad Hoc Networks
Mehta, Neil; Wang, Wenye
2011-01-01
Mobile ad hoc networks (MANETs) are self-configuring wireless networks that lack permanent infrastructure and are formed among mobile nodes on demand. Rapid node mobility results in dramatic channel variation, or fading, that degrades MANET performance. Employing channel state information (CSI) at the transmitter can improve the throughput of routing and medium access control (MAC) protocols for mobile ad hoc networks. Several routing algorithms in the literature explicitly incorporate the fading signal strength into the routing metric, thus selecting the routes with strong channel conditions. While these studies show that adaptation to the time-variant channel gain is beneficial in MANETs, they do not address the effect of the outdated fading CSI at the transmitter. For realistic mobile node speeds, the channel gain is rapidly varying, and becomes quickly outdated due the feedback delay. We analyze the link throughput of joint rate adaptation and adaptive relay selection in the presence of imperfect CSI. Mor...
Adaptive learning with guaranteed stability for discrete-time recurrent neural networks
无
2007-01-01
To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15X15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.
Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks
Hu, Yunan; Jin, Yuqiang; Li, Jing
2003-09-01
Based on fully tuned RBF neural networks and backstepping control techniques, a novel nonlinear adaptive control scheme is proposed for missile control systems with a general set of uncertainties. The effect of the uncertainties is synthesized one term in the design procedure. Then RBF neural networks are used to eliminate its effect. The nonlinear adaptive controller is designed using backstepping control techniques. The control problem is resolved while the control coefficient matrix is unknown. The adaptive tuning rules for updating all of the parameters of the fully tuned RBF neural networks are firstly derived by the Lyapunov stability theorem. Finally, nonlinear 6-DOF numerical simulation results for a BTT missile model are presented to demonstrate the effectiveness of the proposed method.
Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network
Yundi Chu
2015-01-01
Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.
Decentralized direct adaptive neural network control for a class of interconnected systems
Zhang Tianping; Mei Jiandong
2006-01-01
The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconnections is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized direct adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local information. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
Adaptive Home System Using Wireless Sensor Network And Multi Agent System
Jayarani Kamble
2014-03-01
Full Text Available Smart Home is an emerging technology growing continuously which includes number of new technologies which helps to improve human’s quality of living. This paper proposes an adaptive home system for optimum utilization of power, through Artificial Intelligence and Wireless Sensor network. Artificial Intelligence is a technology for developing adaptive system that can perceive the enviornmrnt, learn from the environment and can make decision using Rule based system.Zigbee is a wireless sensor network used to efficiently deliver solution for an energy management and efficiency for adaptive home. An algorithm used in adaptive home system is based on software agent approach that reduce the energy consumption at home by considering the user’s occupancy, temperature and user’s preferences as input to the system.
A novel adaptive modulation and coding strategy based on partial feedback for enhanced MBMS network
SHENG Yu; PENG Mu-gen; WANG Wen-bo
2008-01-01
The difference in link condition of broadcast/multicast users and the limitation of uplink resource, make itdifficult to utilize adaptive modulation and coding (AMC) in theenhanced multimedia broadcast and multicast service (E-MBMS)network. To obtain the improvement of system throughput, thisstudy proposes an adaptive modulation and coding schemebased on partial feedback, by which only partial users whosechannel qualities are lower than the system threshold need tomake a response to the modulation coding scheme (MCS)adaptation procedure. By this investigation, an adaptive schemecan be introduced in the E-MBMS network. Both the theoreticalanalysis and simulation results demonstrate the efficiency of theproposed strategy, in which the performance is close to the idealone and has a significant throughput improvement whencompared with that of the fixed MCS transmission scheme.
Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems
Samira Chabaa
2009-01-01
Full Text Available Problem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been reported by several researchers. The goal of the present contribution was to complement these previous researches by predicting network traffic data. Approach: The Adaptive Neuro-Fuzzy Inference System (ANFIS was realized by an appropriate combination of fuzzy systems and neural networks. It was applied in different applications which have been increased in recent years and have multidisciplinary in several domains with a high accuracy. For this reason, we used a set of input and output data of packet transmission over Internet Protocol (IP networks as input and output of ANFIS to develop a model for predicting data. Results: ANFIS was compared with some existing model based on Volterra system with Laguerre functions. The obtained results demonstrate that the sequences of generated values have the same statistical characteristics as those really observed. Furthermore, the relative error using ANFIS model was better than this obtained by Volterra system model. Conclusion: The developed model fits well real data and can be used for predicting purpose with a high accuracy.
RESEARCH ON ADAPTIVE COMPRESSION CODING FOR NETWORK CODING IN WIRELESS SENSOR NETWORK
Liu Ying; Yang Zhen; Mei Zhonghui; Kong Yuanyuan
2012-01-01
Based on the sequence entropy of Shannon information theory,we work on the network coding technology in Wireless Sensor Network (WSN).In this paper,we take into account the similarity of the transmission sequences at the network coding node in the multi-sources and multi-receivers network in order to compress the data redundancy.Theoretical analysis and computer simulation results show that this proposed scheme not only further improves the efficiency of network transmission and enhances the throughput of the network,but also reduces the energy consumption of sensor nodes and extends the network life cycle.
A QoS-Driven Self-Adaptive Architecture For Wireless Sensor Networks
Jemal, Ahmed; Ben Halima, Riadh
2013-01-01
6 pages International audience Recently, Wireless Sensor Networks (WSN) have become increasingly used to perform distributed sensing and convey useful information. These kinds of environments are complex, heterogeneous and often affected by unpredictable behavior and poor management. This fostered considerable research on designs and techniques that enhance these systems with an adaptation behavior. In this paper, we focus on the self-adaptation branch of the research and give an overvi...
A Flow-Level Performance Model for Mobile Networks Carrying Adaptive Streaming Traffic
Bonald, Thomas; Elayoubi, Salah-Eddine; Lin, Yu-Ting
2015-01-01
International audience This paper proposes a performance model for mobile networks carrying adaptive streaming traffic. The proposed model takes into account the flow dynamics in addition to the main parameters influencing the performance of adaptive streaming, such as the playout buffer and the video bit rates. We show how to compute several performance metrics like the average video bit rate, the deficit rate, defined as the probability of having an instantaneous throughput lower than th...
Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks
Bin Xu; Jin Qi; Chunxia Zhou; Xiaoxuan Hu; Bianjia Xu; Yanfei Sun
2015-01-01
The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. ...
Profile-based adaptive anomaly detection for network security.
Zhang, Pengchu C. (Sandia National Laboratories, Albuquerque, NM); Durgin, Nancy Ann
2005-11-01
As information systems become increasingly complex and pervasive, they become inextricably intertwined with the critical infrastructure of national, public, and private organizations. The problem of recognizing and evaluating threats against these complex, heterogeneous networks of cyber and physical components is a difficult one, yet a solution is vital to ensuring security. In this paper we investigate profile-based anomaly detection techniques that can be used to address this problem. We focus primarily on the area of network anomaly detection, but the approach could be extended to other problem domains. We investigate using several data analysis techniques to create profiles of network hosts and perform anomaly detection using those profiles. The ''profiles'' reduce multi-dimensional vectors representing ''normal behavior'' into fewer dimensions, thus allowing pattern and cluster discovery. New events are compared against the profiles, producing a quantitative measure of how ''anomalous'' the event is. Most network intrusion detection systems (IDSs) detect malicious behavior by searching for known patterns in the network traffic. This approach suffers from several weaknesses, including a lack of generalizability, an inability to detect stealthy or novel attacks, and lack of flexibility regarding alarm thresholds. Our research focuses on enhancing current IDS capabilities by addressing some of these shortcomings. We identify and evaluate promising techniques for data mining and machine-learning. The algorithms are ''trained'' by providing them with a series of data-points from ''normal'' network traffic. A successful algorithm can be trained automatically and efficiently, will have a low error rate (low false alarm and miss rates), and will be able to identify anomalies in ''pseudo real-time'' (i.e., while the intrusion is still in progress
Jinhai Liu
2012-01-01
Full Text Available A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN maintains the simple architecture of FMNN for fast learning and testing while rewriting the membership function, the expansion and contraction rules for hyperbox generation to solve the confusion problems in the hyperbox overlap region. Meanwhile, principle component analysis is adopted to finish dataset dimensionality reduction for increasing learning efficiency. After training, the confidence coefficient of each hyperbox is calculated based on the distribution of samples. During classifying procedure, utilizing adaptive genetic algorithm to complete parameter optimization for AFMN can also fasten the entire procedure than traversal method. For conditions where training samples are insufficient, data core weight updating is indispensible to enhance the robustness of classifier and the modified membership function can adjust itself according to the input varieties. The paper demonstrates the performance of AFMN through substantial examples in terms of classification accuracy and operating speed by comparing it with FMNN, GFMN, and FMCN.
Design of artificial genetic regulatory networks with multiple delayed adaptive responses*
Kaluza, Pablo; Inoue, Masayo
2016-06-01
Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways. Supplementary material in the form of one nets file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2016-70172-9
Diana Göhringer; Lukas Meder; Stephan Werner; Oliver Oey; Jürgen Becker; Michael Hübner
2012-01-01
This paper presents the hardware architecture and the software abstraction layer of an adaptive multiclient Network-on-Chip (NoC) memory core. The memory core supports the flexibility of a heterogeneous FPGA-based runtime adaptive multiprocessor system called RAMPSoC. The processing elements, also called clients, can access the memory core via the Network-on-Chip (NoC). The memory core supports a dynamic mapping of an address space for the different clients as well as different data transfer ...
Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teachers' College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Sun Wen [School of Mathematics and Information, Yangtze University, Hubei Jingzhou 434023 (China)
2010-04-05
This Letter investigates the synchronization of a general complex dynamical network with non-derivative and derivative coupling. Based on LaSalle's invariance principle, adaptive synchronization criteria are obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-derivative and derivative coupling can asymptotically synchronize to a given trajectory, and several useful criteria for synchronization are given. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, simulations results show the method is effective.
Skin Color Segmentation in YCBCR Color Space with Adaptive Fuzzy Neural Network (Anfis
Mohammad Saber Iraji
2012-05-01
Full Text Available In this paper, an efficient and accurate method for human color skin recognition in color images with different light intensity will proposed .first we transform inputted color image from RGB color space to YCBCR color space and then accurate and appropriate decision on that if it is in human color skin or not will be adopted according to YCBCR color space using fuzzy, adaptive fuzzy neural network(anfis methods for each pixel of that image. In our proposed system adaptive fuzzy neural network(anfis has less error and system worked more accurate and appropriative than prior methods.
Ordered Boolean List (OBL): reducing the footprint for evaluating Boolean expressions.
Rossignac, Jaroslaw Jarek
2011-09-01
An Expanded Boolean Expression (EBE) does not contain any XOR or EQUAL operators. The occurrence of each variable is a different literal. We provide a linear time algorithm that converts an EBE of n literals into a logically equivalent Ordered Boolean List (OBL) and show how to use the OBL to evaluate the EBE in n steps and O(log log n) space, if the values of the literals are each read once in the order prescribed by the OBL. (An evaluation workspace of 5 bits suffices for all EBEs of up to six billion literals.) The primary application is the SIMD architecture, where the same EBE is evaluated in parallel for different input vectors when rendering solid models on the GPU directly from their Constructive Solid Geometry (CSG) representation. We compare OBL to the Reduced Ordered Binary Decision Diagram (ROBDD) and suggest possible applications of OBL to logic verification and to circuit design. PMID:21737862
A simple mechanical system for studying adaptive oscillatory neural networks
Jouffroy, Guillaume; Jouffroy, Jerome
Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... robots, lamprey model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller....../guidance system. After a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....
Strong Attractors in Stochastic Adaptive Networks: Emergence and Characterization
Santos, Augusto Almeida; Krishnan, Ramayya; Moura, José M F
2016-01-01
We propose a family of models to study the evolution of ties in a network of interacting agents by reinforcement and penalization of their connections according to certain local laws of interaction. The family of stochastic dynamical systems, on the edges of a graph, exhibits \\emph{good} convergence properties, in particular, we prove a strong-stability result: a subset of binary matrices or graphs -- characterized by certain compatibility properties -- is a global almost sure attractor of the family of stochastic dynamical systems. To illustrate finer properties of the corresponding strong attractor, we present some simulation results that capture, e.g., the conspicuous phenomenon of emergence and downfall of leaders in social networks.
Using Social Network Analysis to Evaluate Health-Related Adaptation Decision-Making in Cambodia
Kathryn J. Bowen
2014-01-01
Full Text Available Climate change adaptation in the health sector requires decisions across sectors, levels of government, and organisations. The networks that link these different institutions, and the relationships among people within these networks, are therefore critical influences on the nature of adaptive responses to climate change in the health sector. This study uses social network research to identify key organisational players engaged in developing health-related adaptation activities in Cambodia. It finds that strong partnerships are reported as developing across sectors and different types of organisations in relation to the health risks from climate change. Government ministries are influential organisations, whereas donors, development banks and non-government organisations do not appear to be as influential in the development of adaptation policy in the health sector. Finally, the study highlights the importance of informal partnerships (or ‘shadow networks’ in the context of climate change adaptation policy and activities. The health governance ‘map’ in relation to health and climate change adaptation that is developed in this paper is a novel way of identifying organisations that are perceived as key agents in the decision-making process, and it holds substantial benefits for both understanding and intervening in a broad range of climate change-related policy problems where collaboration is paramount for successful outcomes.
Adaptive Neural Network Controller for Thermogenerator Angular Velocity Stabilization System
Horvat, Krunoslav; Šoić, Ines; Kuljača, Ognjen
2013-01-01
The paper presents an analytical and simulation approach for the selection of activation functions for the class of neural network controllers for ship’s thermogenerator angular velocity stabilization system. Such systems can be found in many ships. A Lyapunov-like stability analysis is performed in order to obtain a weight update law. A number of simulations were performed to find the best activation function using integral error criteria and statistical T-tests.
A scalable, adaptive, and extensible data center network architecture
Al-Fares, Mohammad Abdulaziz
2012-01-01
Today's largest data centers contain tens of thousands of servers, and they will encompass hundreds of thousands in the very near future. These machines are designed to serve a rich mix of applications and clients with significant aggregate bandwidth requirements; distributed computing frameworks like MapReduce/Hadoop significantly stress the network interconnect, which when compounded with progressively oversubscribed topologies and inefficient multipath forwarding, can cause a major bottlen...
ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK
BASSEL SOLAIMANE; ADNENE CHERIF; SAMEH OUESLATI,
2011-01-01
Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS) model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’...
Adaptive routing for intermittently connected mobile ad hoe networks
M. Musolesi; Hailes, S; Mascolo, C.
2005-01-01
The vast majority of mobile ad hoc networking research makes a very large assumption: that communication can only take place between nodes that are simultaneously accessible within in the same connected cloud (i.e., that communication is synchronous). In reality, this assumption is likely to be a poor one, particularly for sparsely or irregularly populated environments.In this paper we present the Context-Aware Routing (CAR) algorithm. CAR is a novel approach to the provision of asynchronous ...
On the Adaptability of Neural Network Image Super-Resolution
Chua, Kah Keong; Tay, Yong Haur
2012-01-01
In this paper, we described and developed a framework for Multilayer Perceptron (MLP) to work on low level image processing, where MLP will be used to perform image super-resolution. Meanwhile, MLP are trained with different types of images from various categories, hence analyse the behaviour and performance of the neural network. The tests are carried out using qualitative test, in which Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The r...
Molecular networks of human muscle adaptation to exercise and age.
Phillips, Bethan E.; Williams, John P; Thomas Gustafsson; Claude Bouchard; Tuomo Rankinen; Steen Knudsen; Kenneth Smith; Timmons, James A.; Atherton, Philip J.
2013-01-01
Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%),...
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
Hosseini-Asl, Ehsan; Keynto, Robert; El-Baz, Ayman
2016-01-01
Early diagnosis, playing an important role in preventing progress and treating the Alzheimer\\{'}s disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capt...
Adapting Mobile Beacon-Assisted Localization in Wireless Sensor Networks
Wei Dong; Kougen Zheng; Guodong Teng
2009-01-01
The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free,...
Distributed MAC and Rate Adaptation for Ultrasonically Networked Implantable Sensors
Santagati, G. Enrico; Melodia, Tommaso; Galluccio, Laura; Palazzo, Sergio
2013-01-01
The use of miniaturized biomedical devices implanted in the human body and wirelessly internetworked is promising a significant leap forward in medical treatment of many pervasive diseases. Recognizing the well-understood limitations of traditional radio-frequency wireless communications in interconnecting devices within the human body, in this paper we propose for the first time to develop network protocols for implantable devices based on ultrasonic transmissions. We start off by assessing ...
Molecular networks of human muscle adaptation to exercise and age
Phillips, Bethan E.; Williams, John P; Gustafsson, Thomas; Bouchard, Claude; Rankinen, Tuomo; Knudsen, Steen; Smith, Kenneth; Timmons, James A; Atherton, Philip J
2013-01-01
Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%),...
Molecular Networks of Human Muscle Adaptation to Exercise and Age
Phillips, Bethan E.; Williams, John P; Gustafsson, Thomas; Bouchard, Claude; Rankinen, Tuomo; Knudsen, Steen; Smith, Kenneth; Timmons, James A; Atherton, Philip J
2013-01-01
Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%),...
Selective adaptation in networks of heterogeneous populations: model, simulation, and experiment.
Avner Wallach
2008-02-01
Full Text Available Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems, this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies, it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various subpopulations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions that were corroborated in both computer simulations and in cultures of cortical neurons developing in vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases.
A hybrid adaptive routing algorithm for event-driven wireless sensor networks.
Figueiredo, Carlos M S; Nakamura, Eduardo F; Loureiro, Antonio A F
2009-01-01
Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207
Schleussner, Carl-Friedrich; Engemann, Denis A; Levermann, Anders
2015-01-01
Human behaviour is largely shaped by local social interactions and depends on the structure of connections between individuals in social networks. These two dimensions of behaviour selection are commonly studied in isolation by different disciplines and are often treated as independent processes. To the contrary, empirical findings on spread of behaviour in social networks suggest that local interactions between individuals and network evolution are interdependent. Empirical evidence, however, remains inconclusive as social network studies often suffer from limited sample sizes or are prohibitive on ethical grounds. Here we introduce a co-evolutionary adaptive network model of social behaviour selection that provides insights into generative mechanisms by resolving both these aspects through computer simulations. We considered four complementary models and evaluated them with regard to emulating empirical behaviour dynamics in social networks. For this purpose we modelled the prevalence of smoking and and the...
SA-MAC:Self-Stabilizing Adaptive MAC Protocol for Wireless Sensor Networks
波澄; 韩君泽; 李向阳; 王昱; 肖波
2014-01-01
A common method of prolonging the lifetime of wireless sensor networks is to use low power duty cycling protocol. Existing protocols consist of two categories: sender-initiated and receiver-initiated. In this paper, we present SA-MAC, a self-stabilizing adaptive MAC protocol for wireless sensor networks. SA-MAC dynamically adjusts the transmission time-slot, waking up time-slot, and packet detection pattern according to current network working condition, such as packet length and wake-up patterns of neighboring nodes. In the long run, every sensor node will find its own transmission phase so that the network will enter a stable stage when the network load and qualities are static. We conduct extensive experiments to evaluate the energy consumption, packet reception rate of SA-MAC in real sensor networking systems. Our results indicate that SA-MAC outperforms other existing protocols.
Valdovinos, Fernanda S; Brosi, Berry J; Briggs, Heather M; Moisset de Espanés, Pablo; Ramos-Jiliberto, Rodrigo; Martinez, Neo D
2016-10-01
Much research debates whether properties of ecological networks such as nestedness and connectance stabilise biological communities while ignoring key behavioural aspects of organisms within these networks. Here, we computationally assess how adaptive foraging (AF) behaviour interacts with network architecture to determine the stability of plant-pollinator networks. We find that AF reverses negative effects of nestedness and positive effects of connectance on the stability of the networks by partitioning the niches among species within guilds. This behaviour enables generalist pollinators to preferentially forage on the most specialised of their plant partners which increases the pollination services to specialist plants and cedes the resources of generalist plants to specialist pollinators. We corroborate these behavioural preferences with intensive field observations of bee foraging. Our results show that incorporating key organismal behaviours with well-known biological mechanisms such as consumer-resource interactions into the analysis of ecological networks may greatly improve our understanding of complex ecosystems. PMID:27600659
A Turnover based Adaptive HELLO Protocol for Mobile Ad Hoc and Sensor Networks
Ingelrest, François; Mitton, Nathalie; Simplot-Ryl, David
2007-01-01
International audience We present a turnover based adaptive HELLO protocol (TAP), which enables nodes in mobile networks to dynamically adjust their HELLO messages frequency depending on the current speed of nodes. To the best of our knowledge, all existing solutions are based on specific assumptions (\\eg{} slotted networks) and/or require specific hardware (\\eg{} GPS) for speed evaluation. One of the key aspects of our solution is that no additional hardware is required since it does not ...
An adaptive distributed admission approach in Bluetooth network with QoS provisions
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
2002-01-01
In this paper, a method of adaptive distributed admission with end-to-end Quality of Service (QoS) provisions for real time and non real time tra°cs in Blue-tooth networks is highlighted, its mathematic background is analyzed and a simulation with bursty tra°c sources, Interrupted Bernoulli Process...... (IBP), is carried out. The simulation results show that the performance of Bluetooth network is improved when applying the distributed admission method...
Modular neural networks and distributed adaptive search for traveling salesman algorithms
Nygard, Kendall E.; Kadaba, Nagesh
1990-08-01
A modular system of neural networks and a genetic algorithm are employed at a meta level to control solvers for the traveling salesman problem. The neural networks extract features of the input problem and recommend an instantiation of the solver to apply. The genetic algorithm conducts an adaptive search that further refines the parameters that control the work of the solvers. The result is a system that consistently produces very high quality solutions to traveling salesman problems. 1.
Based on the comparison theorem for the stability of impulsive control system, adaptive-impulsive synchronization in drive-response networks of continuous systems with time-delay and non-time-delay is investigated. And the continuous control input, the simple updated laws and a linear impulsive controller are proposed. Moreover, two numerical examples are presented to verify the effectiveness and correctness of the theorem, using the energy resource system and Lue's system as the nodes of the networks.
An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network
Ming Li; Wenqiang Du; Fuzhong Nian
2014-01-01
The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same tim...
Long Cheng; Yan Wang; Shuai Li
2015-01-01
With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective funct...
Adaptive reconfiguration of fractal small-world human brain functional networks
Bassett, Danielle S; Meyer-Lindenberg, Andreas; Achard, Sophie; Duke, Thomas; Bullmore, Edward
2006-01-01
Brain function depends on adaptive self-organization of large-scale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequency-specific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a ...
Sun, Mei; Zeng, Changyan; Tao, Yangwei; Tian, Lixin
2009-08-01
Based on the comparison theorem for the stability of impulsive control system, adaptive-impulsive synchronization in drive-response networks of continuous systems with time-delay and non-time-delay is investigated. And the continuous control input, the simple updated laws and a linear impulsive controller are proposed. Moreover, two numerical examples are presented to verify the effectiveness and correctness of the theorem, using the energy resource system and Lü's system as the nodes of the networks.
Bezzo, Nicola; Davalos, Patricio J. Cruz; Sorrentino, Francesco; Fierro, Rafael
2013-01-01
In this paper we propose an application of adaptive synchronization of chaos to detect changes in the topology of a mobile robotic network. We assume that the network may evolve in time due to the relative motion of the mobile robots and due to unknown environmental conditions, such as the presence of obstacles in the environment. We consider that each robotic agent is equipped with a chaotic oscillator whose state is propagated to the other robots through wireless communication, with the goa...