We consider distributed state estimation over a resource-limited wireless sensor network. A stochastic sensor activation scheme is introduced to reduce the sensor energy consumption in communications, under which each sensor is activated with a certain probability. When the sensor is activated, it observes the target state and exchanges its estimate of the target state with its neighbors; otherwise, it only receives the estimates from its neighbors. An optimal estimator is designed for each sensor by minimizing its mean-squared estimation error. An upper and a lower bound of the limiting estimation error covariance are obtained. A method of selecting the consensus gain and a lower bound of the activating probability is also provided.
Complex networks with expanding dimensions are studied, where the networks may be directed and weighted, and network nodes are varying in discrete time in the sense that some new nodes may be added and some old nodes may be removed from time to time. A model of such networks in computer data transmission is discussed. Each node on the network has fixed dimensionality, while the dimension of the whole network is defined by the total number of nodes. Based on the spectacular properties of data transmission on computer networks, some new concepts of stable and unstable networks differing from the classical Lyapunov stability are defined. In particular, a special unstable network model, called devil network, is introduced and discussed. It is further found that a variety of structures and connection weights affects the network stability substantially. Several criteria on stability, instability, and devil network are established for a rather general class of networks, where some conditions are actually necessary and sufficient. Mathematically, this paper makes a first attempt to rigorously formulate a fundamental issue of modeling discrete linear time-varying systems with expanding dimensions and study their basic stability property.
No abstract is provided for this article.
No abstract is provided for this article.
This paper studies the chaotification problem of driving a continuous-time system chaotic near its stable limit cycle. The controller is designed to ensure the controlled orbit be bounded and, meanwhile, have positive Lyapunov exponents. A numerical example is given to illustrate the effectiveness of the proposed chaotification algorithm.
Time-delay and unavailability of system states bring extra difficulties to synchronization of chaotic systems. This paper investigates observer-based synchronization of time-delay complex-variable chaotic systems (CVCSs) with complex parameters. Differing from the existing works that considered time delay only in the linear term of some special CVCSs, the two cases of time delay in and out of nonlinear terms in general CVCSs are considered here, respectively. In addition, for the case that system states are not available for control, an observer-based output feedback control approach is developed to estimate the system states and guarantee the time-delay CVCSs to achieve synchronization. Moreover, by combining Lyapunov–Krasovskii function with linear matrix inequality in the complex field, two sufficient criteria are derived to ensure complete synchronization of time-delay CVCSs. Finally, a numerical example is presented to illustrate the theoretical results.
No abstract is provided for this article.
The concept of network resilience has gained increasing attention in the last few decades owing to its great potential in strengthening and maintaining complex systems. From network-based approaches, researchers have explored resilience of real ecological systems comprising diverse types of interactions, such as mutualism, antagonist, and predation, or mixtures of them. In this paper, we propose a dimension-reduction method for analyzing the resilience of hybrid herbivore-plant-pollinator networks. We qualitatively evaluate the contribution of species toward maintaining resilience of networked systems, as well as the distinct roles played by different categories of species. Our findings demonstrate that the strong contributors to network resilience within each category are more vulnerable to extinction. Notably, among the three types of species in consideration, plants exhibit a higher likelihood of extinction, compared to pollinators and herbivores.
In this paper, the problem of making a nonlinear system chaotic by using state-feedback control is studied. The feedback controller uses a simple sine function of the system state, but only one component in each dimension. It is proved, by using the anti-integrable limit method, that the designed control system generates chaos in the sense of Devaney. In fact, the controlled system so designed is a perturbation of the original system, which turns out to be a simple Bernoulli shift.
In this paper, we address routing in complex networks. Routing traffic across a network requires finding best possible paths between sources and destinations. When data traffic changes dynamically, a path that was optimal in the past may not be the best for the next packet. Adapting to traffic changes and finding optimal paths dynamically are challenging tasks. They become more demanding in large and complex networks. In optical burst switching (OBS) networks, two optical bursts contending for the same link need resolution mechanisms other than queueing. Deflection routing protocols are used to override routing tables and "deflect" one of the bursts to a free link. Instead of deflecting bursts at an immediate point of contention, the proposed Random Early Deflection (RED-f) routing protocol triggers deflection ahead of time and, thus, offers additional routing paths and lowers the burst loss rate due to contention. Simulations demonstrate that RED-f enabled nodes in a scale-free complex network reduce burst loss rate by exchanging control information with only few other network nodes.
Exchange arrangements among different countries over the world are foundations of the world economy, which generally stand behind the daily economic evolution. As the first study of the world exchange arrangements web (WEAW), we built a bipartite network with countries as one type of nodes and currencies as the other, and found it to have a prominent scale-free feature with a power-law degree distribution. In a further empirical study of the currency section of the WEAW, we calculated the clustering coefficients, average nearest-neighbors degree, and average shortest distance. As an essential economic network, the WEAW is found to be a correlated disassortative network with a hierarchical structure, possessing a more prominent scale-free feature than the world trade web (WTW).
In this paper, the intrinsic relationship between energy consumption and neural information coding in local neural networks of the cerebral cortex is studied. The energy functions of a variety of membrane potential are obtained under some conditions of mutual coupling at both the supra-threshold and the sub-threshold states in a neural population. These energy functions can accurately reproduce excitatory postsynaptic potentials (EPSP), inhibitory postsynaptic potentials (IPSP), as well as an action potential, found in the experiments of neuro-electrophysiology. Recently, it has been proved that signal transmission and neuronal energetic demands are tightly coupled to information coding in the cerebral cortex in functional magnetic resonance imaging (fMRI) experiments. Therefore, the analytic results obtained in this paper show that the principle of energy coding is quite fundamental and is beneficial to the study of the important scientific problem as how the brain performs coding at the level of local neural networks.
