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- Hammerstein, Peter, 1971-
(författare)
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Stochastic Resonance and Noise-Assisted Signal Transfer : On Coupling Effects of Stochastic Resonators and Spectral Optimization of Fluctuations in Random Network Switches
- 2004
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent research shows that noise or random fluctuations must not always be destructive in Nature by degrading system performance. On the contrary, in nonlinear systems they can synchronize systems or enhance the quality of signal transmission. The latter possibility is reported in the thesis.The phenomenon of stochastic resonance (SR) is presented and illustrated by an example of a ferromagnetically coupled spin chain, described by the Glauber's stochastic Ising spin model. It is demonstrated that an optimal strength of the next-neighbor interaction is able to improve the SR-effect. A similar mechanism has further been studied on the stochastic nonlinear dynamics of a ferromagnetic stripe domain in an inhomogeneous thin film. SR and its dependence on the domain stiffness, which is due to the exchange interaction, are presented. Experimental parameters for potential verification on Bi-doped epitaxial garnet-ferrite films are proposed. Further-on, a nonlinear model of a junction in neuronal and road structures is studied using various types of noise (stochastic processes) to generate the incoming traffic. It is shown that random fluctuations are able to enhance signal transmission, whereby the zero crossings of colored (1/fk) Gaussian noise is superior to Poissonian noise and, in certain cases, to deterministic, periodic traffic too. Optimal traffic for k ≈ 1 has been found. In case of Gaussian 1/fk noise modulated periodic input, noise-assisted traffic can be observed as well and demonstrate how random fluctuations can enhance the signal traffic efficiency in a network. The effect of an optimal k has finally been applied to a data package network switch, whereby a stochastic data scheduling algorithm is proposed and investigated numerically and analytically.
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| 2. |
- Repsilber, Dirk, 1971-, et al.
(författare)
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Reverse engineering of regulatory networks : simulation studies on a genetic algorithm approach for ranking hypotheses
- 2002
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Ingår i: Biosystems (Amsterdam. Print). - Ireland : Elsevier. - 0303-2647 .- 1872-8324. ; 66:1-2, s. 31-41
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Tidskriftsartikel (refereegranskat)abstract
- Reverse engineering algorithms (REAs) aim at using gene expression data to reconstruct interactions in regulatory genetic networks. This may help to understand the basis of gene regulation, the core task of functional genomics. Collecting data for a number of environmental conditions is necessary to reengineer even the smallest regulatory networks with reasonable confidence. We systematically tested the requirements for the experimental design necessary for ranking alternative hypotheses about the structure of a given regulatory network. A genetic algorithm (GA) was used to explore the parameter space of a multistage discrete genetic network model with fixed connectivity and number of states per node. Our results show that it is not necessary to determine all parameters of the genetic network in order to rank hypotheses. The ranking process is easier the more experimental environmental conditions are used for the data set. During the ranking, the number of fixed parameters increases with the number of environmental conditions, while some errors in the hypothetical network structure may pass undetected, due to a maintained dynamical behaviour.
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