General Chemotactic Model of Oscillators

Tanaka, Dan

doi:10.1103/physrevlett.99.134103

Cited by 133 publications

(147 citation statements)

References 25 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…Other methods for detection of triplet pairing using superconducting junctions were proposed in Refs. [9,10].The predicted spin accumulation originates from the Andreev bound states at the interface between the singlet and triplet superconductors. Because of the mismatch between the spin symmetries of pairing, the spin-up anddown Andreev bound states have different energies.…”

mentioning

confidence: 98%

Spontaneous Spin Accumulation in Singlet-Triplet Josephson Junctions

Sengupta

Yakovenko

2008

Phys. Rev. Lett.

View full text Add to dashboard Cite

We study the Andreev bound states in a Josephson junction between a singlet and a triplet superconductors. Because of the mismatch in the spin symmetries of pairing, the energies of the spin-up and -down quasiparticles are generally different. This results in imbalance of spin populations and net spin accumulation at the junction in equilibrium. This effect can be detected using probes of local magnetic field, such as the scanning SQUID, Hall, and Kerr probes. It may help to identify potential triplet pairing in (TMTSF)2X, Sr2RuO4, and oxypnictides.

show abstract

mentioning

confidence: 98%

Spontaneous Spin Accumulation in Singlet-Triplet Josephson Junctions

Sengupta

Yakovenko

2008

Phys. Rev. Lett.

View full text Add to dashboard Cite

show abstract

“…Examples include the coordinated motions of robots [8], vehicles [9], and groups of animals [10], in which cooperative dynamics emerge. Especially, there are many examples where synchronization plays a crucial role: chemotaxis [11], mobile ad hoc networks [12], and wireless sensor networks [13]. Despite the importance of this topic, prior research on synchronization in time-dependent networks of populations of agents has concentrated so far on two special cases: (i) where the network topology changes fast [14][15][16][17] and (ii) where the population is dense and arranged in a ring [18].…”

mentioning

confidence: 99%

Synchronization in networks of mobile oscillators

2011

View full text Add to dashboard Cite

We present a model of synchronization in networks of autonomous agents where the topology changes due to agents motion. We introduce two timescales, one for the topological change and another one for local synchronization. If the former scale is much shorter, an approximation that averages out the effect of motion is available. Here we show, however, that the time required for synchronization achievement is larger than the prediction of the approximation in the opposite case, especially close to the continuum percolation transition point. The simulation results are confirmed by means of spectral analysis of the time-dependent Laplacian matrix. Our results show that the tradeoff between these two timescales, which have opposite effects on synchronization, should be taken into account for the design of mobile device networks. After an initial period of characterizing complex networks in terms of local and global statistical properties (e.g., Ref.[1]), attention turned to the dynamics of their interacting units [2]. A widely studied example of such behavior is synchronization of coupled oscillators arranged into complex networks. The interplay between topology and dynamics is crucial for synchronization achievement (see Ref.[3] and references therein). In most studies of such systems the network has a fixed structure, but there are also many interesting scenarios where the topology changes over time in various fields, such as power transmission system [4], consensus problem [5], mobile communication [6], and functional brain networks [7].Within the general framework of time-dependent or evolving networks, we can identify the particular case of networks whose nodes represent physical agents that move around but interact with each other only when they are close enough. Examples include the coordinated motions of robots [8], vehicles [9], and groups of animals [10], in which cooperative dynamics emerge. Especially, there are many examples where synchronization plays a crucial role: chemotaxis [11], mobile ad hoc networks [12], and wireless sensor networks [13]. Despite the importance of this topic, prior research on synchronization in time-dependent networks of populations of agents has concentrated so far on two special cases: (i) where the network topology changes fast [14][15][16][17] and (ii) where the population is dense and arranged in a ring [18]. In the former case, the fast-switching approximation (FSA), which averages out the effect of agent motion, is commonly used. However, for better understanding of synchronization of mobile agents and design of an efficient network, it is very important to clarify when and how FSA fails.In order to study this point, this Rapid Communication proposes a general framework in which agents perform random walks in a two-dimensional (2D) plane. We consider that each agent possesses a mobile wireless device whose state is characterized by a phase variable, and the phases approach one another through the interaction between agents within a certain spatial range. This model is we...

show abstract

“…19,20,35,36 We plot the synchronization time T s , i.e., the time at which DuðT s Þ < 10 À3 is achieved for the first time (Fig. 2(b)), but eventually it can exit this regime.…”

Section: Numerical Resultsmentioning

confidence: 99%

Synchronization of mobile chaotic oscillator networks

Fujiwara

Kurths

Dı́az-Guilera

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to the transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.

show abstract

General Chemotactic Model of Oscillators

Cited by 133 publications

References 25 publications

Spontaneous Spin Accumulation in Singlet-Triplet Josephson Junctions

Spontaneous Spin Accumulation in Singlet-Triplet Josephson Junctions

Synchronization in networks of mobile oscillators

Synchronization of mobile chaotic oscillator networks

Contact Info

Product

Resources

About