186,906 research outputs found
Mathematical models for estimating radio channels utilization when transmitting real-time flows in mobile ad hoc network
The wireless self-organized network functioning efficiency is considered from its radio channels utilization point of view. In order to increase the radio channels utilization it is offered to carry out flow smoothing of the arriving requests for audio-and video flow transfer due to their buffering. Definition of the radio channel utilization indicator is given. Mathematical models for radio channels utilization assessment by real-time flows transfer in the wireless self-organized network are presented. Estimated experiments results according to the average radio channel utilization productivity with and without buffering of the arriving inquiries are shown. It is proved that due to requests for real-time flows transfer buffering in the wireless self-organized network it is possible to increase the radio channels utilization indicator significantly.Keywords: mobile ad hoc network, mathematical model, real-time flows, indicator of the radio channel utilizatio
Fluid flows shaping organism morphology
A dynamic self-organized morphology is the hallmark of network-shaped
organisms like slime moulds and fungi. Organisms continuously re-organize their
flexible, undifferentiated body plans to forage for food. Among these organisms
the slime mould Physarum polycephalum has emerged as a model to investigate how
organism can self-organize their extensive networks and act as a coordinated
whole. Cytoplasmic fluid flows flowing through the tubular networks have been
identified as key driver of morphological dynamics. Inquiring how fluid flows
can shape living matter from small to large scales opens up many new avenues
for research.Comment: 5 pages, 2 figures, perspectiv
Self-Control of Traffic Lights and Vehicle Flows in Urban Road Networks
Based on fluid-dynamic and many-particle (car-following) simulations of
traffic flows in (urban) networks, we study the problem of coordinating
incompatible traffic flows at intersections. Inspired by the observation of
self-organized oscillations of pedestrian flows at bottlenecks [D. Helbing and
P. Moln\'ar, Phys. Eev. E 51 (1995) 4282--4286], we propose a self-organization
approach to traffic light control. The problem can be treated as multi-agent
problem with interactions between vehicles and traffic lights. Specifically,
our approach assumes a priority-based control of traffic lights by the vehicle
flows themselves, taking into account short-sighted anticipation of vehicle
flows and platoons. The considered local interactions lead to emergent
coordination patterns such as ``green waves'' and achieve an efficient,
decentralized traffic light control. While the proposed self-control adapts
flexibly to local flow conditions and often leads to non-cyclical switching
patterns with changing service sequences of different traffic flows, an almost
periodic service may evolve under certain conditions and suggests the existence
of a spontaneous synchronization of traffic lights despite the varying delays
due to variable vehicle queues and travel times. The self-organized traffic
light control is based on an optimization and a stabilization rule, each of
which performs poorly at high utilizations of the road network, while their
proper combination reaches a superior performance. The result is a considerable
reduction not only in the average travel times, but also of their variation.
Similar control approaches could be applied to the coordination of logistic and
production processes
Intrinsic adaptation in autonomous recurrent neural networks
A massively recurrent neural network responds on one side to input stimuli
and is autonomously active, on the other side, in the absence of sensory
inputs. Stimuli and information processing depends crucially on the qualia of
the autonomous-state dynamics of the ongoing neural activity. This default
neural activity may be dynamically structured in time and space, showing
regular, synchronized, bursting or chaotic activity patterns.
We study the influence of non-synaptic plasticity on the default dynamical
state of recurrent neural networks. The non-synaptic adaption considered acts
on intrinsic neural parameters, such as the threshold and the gain, and is
driven by the optimization of the information entropy. We observe, in the
presence of the intrinsic adaptation processes, three distinct and globally
attracting dynamical regimes, a regular synchronized, an overall chaotic and an
intermittent bursting regime. The intermittent bursting regime is characterized
by intervals of regular flows, which are quite insensitive to external stimuli,
interseeded by chaotic bursts which respond sensitively to input signals. We
discuss these finding in the context of self-organized information processing
and critical brain dynamics.Comment: 24 pages, 8 figure
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