15,573 research outputs found
Classification of scale-free networks
While the emergence of a power law degree distribution in complex networks is
intriguing, the degree exponent is not universal. Here we show that the
betweenness centrality displays a power-law distribution with an exponent \eta
which is robust and use it to classify the scale-free networks. We have
observed two universality classes with \eta \approx 2.2(1) and 2.0,
respectively. Real world networks for the former are the protein interaction
networks, the metabolic networks for eukaryotes and bacteria, and the
co-authorship network, and those for the latter one are the Internet, the
world-wide web, and the metabolic networks for archaea. Distinct features of
the mass-distance relation, generic topology of geodesics and resilience under
attack of the two classes are identified. Various model networks also belong to
either of the two classes while their degree exponents are tunable.Comment: 6 Pages, 6 Figures, 1 tabl
An LMI Approach to Discrete-Time Observer Design with Stochastic Resilience
Much of the recent work on robust control or observer design has focused on preservation of stability of the controlled system or the convergence of the observer in the presence of parameter perturbations in the plant or the measurement model. The present work addresses the important problem of stochastic resilience or non-fragility of a discrete-time Luenberger observer which is the maintenance of convergence and/or performance when the observer is erroneously implemented possibly due to computational errors i.e. round off errors in digital implementation or sensor errors, etc. A common linear matrix inequality framework is presented to address the stochastic resilient design problem for various performance criteria in the implementation based on the knowledge of an upper bound on the variance of the random error in the observer gain. Present results are compared to earlier designs for stochastic robustness. Illustrative examples are given to complement the theoretical results
Resilient Observer Design for Discrete-Time Nonlinear Systems with General Criteria
A class of discrete-time nonlinear system and measurement equations having incrementally conic nonlinearities and finite energy disturbances is considered. A linear matrix inequality based resilient observer design approach is presented to guarantee the satisfaction of a variety of performance criteria ranging from simple estimation error boundedness to dissipativity in the presence of bounded perturbations on the gain. Some simulation examples are included to illustrate the proposed design methodology
Stochastically Resilient Observer Design for a Class of Continuous-Time Nonlinear Systems
This work addresses the design of stochastically resilient or non-fragile continuous-time Luenberger observers for systems with incrementally conic nonlinearities. Such designs maintain the convergence and/or performance when the observer gain is erroneously implemented due possibly to computational errors i.e. round off errors in computing the observer gain or changes in the observer parameters during operation. The error in the observer gain is modeled as a random process and a common linear matrix inequality formulation is presented to address the stochastically resilient observer design problem for a variety of performance criteria. Numerical examples are given to illustrate the theoretical results
Near-field spectroscopy of bimodal size distribution of InAs/AlGaAs quantum dots
We report on high-resolution photoluminescence (PL) spectroscopy of spatial
structure of InAs/AlGaAs quantum dots (QDs) by using a near-field scanning
optical microscope (NSOM). The double-peaked distribution of PL spectra is
clearly observed, which is associated with the bimodal size distribution of
single QDs. In particular, the size difference of single QDs, represented by
the doublet spectral distribution, can be directly observed by the NSOM images
of PL.Comment: 3pages, 3figue
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