Abstract: In a multi-cultural learning context, where ties are
weak and dynamic, combining qualitative with quantitative research
methods may be more effective. Such a combination may also allow
us to answer different types of question, such as about people’s
perception of the network. In this study the use of observation,
interviews and photos were explored as ways of enhancing data from
social network questionnaires. Integrating all of these methods was
found to enhance the quality of data collected and its accuracy, also
providing a richer story of the network dynamics and the factors that
shaped these changes over time.
Abstract: Statistical analysis of electrophysiological recordings
obtained under, e.g. tactile, stimulation frequently suggests participation
in the network dynamics of experimentally unobserved “hidden"
neurons. Such interneurons making synapses to experimentally
recorded neurons may strongly alter their dynamical responses to
the stimuli. We propose a mathematical method that formalizes this
possibility and provides an algorithm for inferring on the presence
and dynamics of hidden neurons based on fitting of the experimental
data to spike trains generated by the network model. The model
makes use of Integrate and Fire neurons “chemically" coupled
through exponentially decaying synaptic currents. We test the method
on simulated data and also provide an example of its application to
the experimental recording from the Dorsal Column Nuclei neurons
of the rat under tactile stimulation of a hind limb.
Abstract: P2P Networks are highly dynamic structures since
their nodes – peer users keep joining and leaving continuously. In the
paper, we study the effects of network change rates on query routing
efficiency. First we describe some background and an abstract system
model. The chosen routing technique makes use of cached metadata
from previous answer messages and also employs a mechanism for
broken path detection and metadata maintenance. Several metrics are
used to show that the protocol behaves quite well even with high rate
of node departures, but above a certain threshold it literally breaks
down and exhibits considerable efficiency degradation.
Abstract: A network of coupled stochastic oscillators is
proposed for modeling of a cluster of entangled qubits that is
exploited as a computation resource in one-way quantum
computation schemes. A qubit model has been designed as a
stochastic oscillator formed by a pair of coupled limit cycle
oscillators with chaotically modulated limit cycle radii and
frequencies. The qubit simulates the behavior of electric field of
polarized light beam and adequately imitates the states of two-level
quantum system. A cluster of entangled qubits can be associated
with a beam of polarized light, light polarization degree being
directly related to cluster entanglement degree. Oscillatory network,
imitating qubit cluster, is designed, and system of equations for
network dynamics has been written. The constructions of one-qubit
gates are suggested. Changing of cluster entanglement degree caused
by measurements can be exactly calculated.
Abstract: As wireless sensor networks are energy constraint networks
so energy efficiency of sensor nodes is the main design issue.
Clustering of nodes is an energy efficient approach. It prolongs the
lifetime of wireless sensor networks by avoiding long distance communication.
Clustering algorithms operate in rounds. Performance of
clustering algorithm depends upon the round time. A large round
time consumes more energy of cluster heads while a small round
time causes frequent re-clustering. So existing clustering algorithms
apply a trade off to round time and calculate it from the initial
parameters of networks. But it is not appropriate to use initial
parameters based round time value throughout the network lifetime
because wireless sensor networks are dynamic in nature (nodes can be
added to the network or some nodes go out of energy). In this paper
a variable round time approach is proposed that calculates round
time depending upon the number of active nodes remaining in the
field. The proposed approach makes the clustering algorithm adaptive
to network dynamics. For simulation the approach is implemented
with LEACH in NS-2 and the results show that there is 6% increase
in network lifetime, 7% increase in 50% node death time and 5%
improvement over the data units gathered at the base station.
Abstract: Video sensor networks operate on stringent requirements
of latency. Packets have a deadline within which they have
to be delivered. Violation of the deadline causes a packet to be
treated as lost and the loss of packets ultimately affects the quality
of the application. Network latency is typically a function of many
interacting components. In this paper, we propose ways of reducing
the forwarding latency of a packet at intermediate nodes. The
forwarding latency is caused by a combination of processing delay
and queueing delay. The former is incurred in order to determine the
next hop in dynamic routing. We show that unless link failures in a
very specific and unlikely pattern, a vast majority of these lookups
are redundant. To counter this we propose source routing as the
routing strategy. However, source routing suffers from issues related
to scalability and being impervious to network dynamics. We propose
solutions to counter these and show that source routing is definitely
a viable option in practical sized video networks. We also propose a
fast and fair packet scheduling algorithm that reduces queueing delay
at the nodes. We support our claims through extensive simulation on
realistic topologies with practical traffic loads and failure patterns.