Martin Hasler

Optimal and suboptimal chaos receivers

The paper describes the state of the art in the design of receivers for chaos-based digital communication over noisy channels. An information theoretic analysis of the potential of chaos in digital communication schemes is given, underlining that there is no fundamental principle that speaks against the use of chaos in digital communications. The design of the optimal receiver based on statistically optimal detection is shown. The channel model considered throughout the paper is that of additive white Gaussian noise. While the optimal receiver provides the base for assessing the potential of chaos-based schemes in this application, suboptimal versions thereof as presented in the paper allow efficient implementations.

Reservoir optimization in recurrent neural networks using kronecker kernels

In this paper, using the mathematical properties of self-kronecker-production of small size random matrices, a simple but effective method is presented to optimize the reservoir of an echo state network given a certain task. The experimental results investigating the NARMA system show that few steps of the proposed optimization process can lead to a near optimum solution.

A Weighting Scheme for Enhancing Community Detection in Networks

Many algorithms have recently been proposed for finding communities in networks. By definition, a community is a subset of vertices with a high number of connections among the vertices, but only few connections with other vertices. The worst drawback of most of the proposed algorithms is their computational complexity which is usually an exponentially increasing function of the number of the vertices. Newman-Fast is a well-known community detection algorithm which is suitable for large networks due to its low computational cost. Although the performance of this algorithm is good for well structured networks, it does not perform well for more fuzzy-clustered networks. In this paper, we propose a weighting scheme which considerably enhances the performance of the Newman-Fast algorithm with a little effort. We also show that the modified algorithm effectively enhances the community discovery process in both computer-generated and real-world networks.

Classes of stochastically switched (blinking) systems

It is investigated to what extent the trajectories of a stochastically switched (blinking) system follow the corresponding trajectories of the averaged system. Four cases have to be distinguished, depending on whether or not the averaged system has a unique attractor and whether or not the attractor(s) is (are) invariant under the dynamics of the blinking system. The corresponding asymptotic behavior of the trajectories of the blinking system is described and illustrative examples are given