We introduce a transformation from time series to complex networks and then study the relative frequency of different subgraphs within that network. The distribution of subgraphs can be used to distinguish between and to characterize different types of continuous dynamics: periodic, chaotic, and periodic with noise. Moreover, although the general types of dynamics generate networks belonging to...

We study the stable phases of an attractor neural network model, with binary units, for hippocampal place cells encoding 1D or 2D spatial maps or environments. Using statistical mechanics tools we show that, below critical values for the noise in the neural response and for the number of environments, the network activity is spatially localized in one environment. We calculate the number of sto...

Most simulation-based noise maps are important for official noise assessment but lack local noise characteristics. The main reasons for this lack of information are that official noise simulations only provide information about expected noise levels, which is limited by the use of large-scale monitoring of noise sources, and are updated infrequently. With the emergence of smart cities and ubiqu...

Although magnetoencephalography (MEG) and electroencephalography (EEG) have been available for decades, their relative merits are still debated. We examined regional differences in signal-to-noise-ratios (SNRs) of cortical sources in MEG and EEG. Data from four subjects were used to simulate focal and extended sources located on the cortical surface reconstructed from high-resolution magnetic r...

Noise-free stochastic resonance is investigated in two chaotic maps with periodically modulated control parameter close to a boundary crisis: the Hénon map and the kicked spin model. Response of the maps to the periodic signal at the fundamental frequency and its higher harmonics is examined. The systems show noise-free stochastic multiresonance, i.e. multiple maxima of the signal-to-noise rati...

We consider the effect of noise on the dynamics generated by volume-preserving maps on a d-dimensional torus. The quantity we use to measure the irreversibility of the dynamics is the dissipation time. We focus on the asymptotic behaviour of this time in the limit of small noise. We derive universal lower and upper bounds for the dissipation time in terms of various properties of the map and it...

Self-organizing maps have been used extensively for condition-based maintenance, where quantization errors of test data referring to the self-organizing maps of healthy training data have been used as features. Researchers have used minimum quantization error as a health indicator, which is sensitive to noise in the training data. Some other researchers have used the average of the quantization...

In this paper, we present a method to enhance noisy depth maps using adaptive steering kernel regression based on distance transform. Dataadaptive kernel regression filters are widely used for image denoising by considering spatial and photometric properties of pixel data. In order to reduce noise in depth maps more efficiently, we adaptively refine the steering kernel regression function accor...

We present a method for joint inversion of teleseismic and ambient noise Rayleigh wave data for phase velocity maps from 18 to 50 s period. We adapt the two-plane wave method for teleseismic data to include ambient noise phase data. We apply themethod to data from Iceland’s ICEMELT and HOTSPOT arrays. Checkerboard tests show that the joint inversion improves phase velocity model recovery over m...