7,029 research outputs found
Mutual synchronization and clustering in randomly coupled chaotic dynamical networks
We introduce and study systems of randomly coupled maps (RCM) where the
relevant parameter is the degree of connectivity in the system. Global
(almost-) synchronized states are found (equivalent to the synchronization
observed in globally coupled maps) until a certain critical threshold for the
connectivity is reached. We further show that not only the average
connectivity, but also the architecture of the couplings is responsible for the
cluster structure observed. We analyse the different phases of the system and
use various correlation measures in order to detect ordered non-synchronized
states. Finally, it is shown that the system displays a dynamical hierarchical
clustering which allows the definition of emerging graphs.Comment: 13 pages, to appear in Phys. Rev.
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
We investigate the relationship of resting-state fMRI functional connectivity
estimated over long periods of time with time-varying functional connectivity
estimated over shorter time intervals. We show that using Pearson's correlation
to estimate functional connectivity implies that the range of fluctuations of
functional connections over short time scales is subject to statistical
constraints imposed by their connectivity strength over longer scales. We
present a method for estimating time-varying functional connectivity that is
designed to mitigate this issue and allows us to identify episodes where
functional connections are unexpectedly strong or weak. We apply this method to
data recorded from participants, and show that the number of
unexpectedly strong/weak connections fluctuates over time, and that these
variations coincide with intermittent periods of high and low modularity in
time-varying functional connectivity. We also find that during periods of
relative quiescence regions associated with default mode network tend to join
communities with attentional, control, and primary sensory systems. In
contrast, during periods where many connections are unexpectedly strong/weak,
default mode regions dissociate and form distinct modules. Finally, we go on to
show that, while all functional connections can at times manifest stronger
(more positively correlated) or weaker (more negatively correlated) than
expected, a small number of connections, mostly within the visual and
somatomotor networks, do so a disproportional number of times. Our statistical
approach allows the detection of functional connections that fluctuate more or
less than expected based on their long-time averages and may be of use in
future studies characterizing the spatio-temporal patterns of time-varying
functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
A New Approach to Coding in Content Based MANETs
In content-based mobile ad hoc networks (CB-MANETs), random linear network
coding (NC) can be used to reliably disseminate large files under intermittent
connectivity. Conventional NC involves random unrestricted coding at
intermediate nodes. This however is vulnerable to pollution attacks. To avoid
attacks, a brute force approach is to restrict the mixing at the source.
However, source restricted NC generally reduces the robustness of the code in
the face of errors, losses and mobility induced intermittence. CB-MANETs
introduce a new option. Caching is common in CB MANETs and a fully reassembled
cached file can be viewed as a new source. Thus, NC packets can be mixed at all
sources (including the originator and the intermediate caches) yet still
providing protection from pollution. The hypothesis we wish to test in this
paper is whether in CB-MANETs with sufficient caches of a file, the performance
(in terms of robustness) of the restricted coding equals that of unrestricted
coding.
In this paper, we examine and compare unrestricted coding to full cache
coding, source only coding, and no coding. As expected, we find that full cache
coding remains competitive with unrestricted coding while maintaining full
protection against pollution attacks
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