8,188 research outputs found
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
Efficient Rewirings for Enhancing Synchronizability of Dynamical Networks
In this paper, we present an algorithm for optimizing synchronizability of
complex dynamical networks. Based on some network properties, rewirings, i.e.
eliminating an edge and creating a new edge elsewhere, are performed
iteratively avoiding always self-loops and multiple edges between the same
nodes. We show that the method is able to enhance the synchronizability of
networks of any size and topological properties in a small number of steps that
scales with the network size.Although we take the eigenratio of the Laplacian
as the target function for optimization, we will show that it is also possible
to choose other appropriate target functions exhibiting almost the same
performance. The optimized networks are Ramanujan graphs, and thus, this
rewiring algorithm could be used to produce Ramanujan graphs of any size and
average degree
Nonuniform random geometric graphs with location-dependent radii
We propose a distribution-free approach to the study of random geometric
graphs. The distribution of vertices follows a Poisson point process with
intensity function , where , and is a
probability density function on . A vertex located at
connects via directed edges to other vertices that are within a cut-off
distance . We prove strong law results for (i) the critical cut-off
function so that almost surely, the graph does not contain any node with
out-degree zero for sufficiently large and (ii) the maximum and minimum
vertex degrees. We also provide a characterization of the cut-off function for
which the number of nodes with out-degree zero converges in distribution to a
Poisson random variable. We illustrate this result for a class of densities
with compact support that have at most polynomial rates of decay to zero.
Finally, we state a sufficient condition for an enhanced version of the above
graph to be almost surely connected eventually.Comment: Published in at http://dx.doi.org/10.1214/11-AAP823 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Appearance-based localization for mobile robots using digital zoom and visual compass
This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally
Asimov's Coming Back
Ever since the word ‘ROBOT’ first appeared in a science\ud
fiction in 1921, scientists and engineers have been trying\ud
different ways to create it. Present technologies in\ud
mechanical and electrical engineering makes it possible\ud
to have robots in such places as industrial manufacturing\ud
and assembling lines. Although they are\ud
essentially robotic arms or similarly driven by electrical\ud
power and signal control, they could be treated the\ud
primitive pioneers in application. Researches in the\ud
laboratories go much further. Interdisciplines are\ud
directing the evolution of more advanced robots. Among these are artificial\ud
intelligence, computational neuroscience, mathematics and robotics. These disciplines\ud
come closer as more complex problems emerge.\ud
From a robot’s point of view, three basic abilities are needed. They are thinking\ud
and memory, sensory perceptions, control and behaving. These are capabilities we\ud
human beings have to adapt ourselves to the environment. Although\ud
researches on robots, especially on intelligent thinking, progress slowly, a revolution\ud
for biological inspired robotics is spreading out in the laboratories all over the world
Semantic labeling of places using information extracted from laser and vision sensor data
Indoor environments can typically be divided into places with different functionalities like corridors, kitchens,
offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly,
we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor
environments
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