Search results for "NOISE"
showing 10 items of 1375 documents
Do happy faces really modulate liking for Jackson Pollock art and statistical fractal noise images?
2017
Flexas et al. (2013) demonstrated that happy faces increase preference for abstract art if seen in short succession. We could not replicate their findings. In our first experiment, we tested whether valence, saliency or arousal of facial primes can modulate liking of Jackson Pollock art crops. In the second experiment, the emphasis was on testing another type of abstract visual stimuli which possess similar low-level image features: statistical fractal noise images. Pollock crops were rated significantly higher when primed with happy faces in contrast to neutral faces, but not differently to the no-prime condition. Findings of our study suggest that affective priming with happy faces may be…
Moment equations in a Lotka-Volterra extended system with time correlated noise
2007
A spatially extended Lotka-Volterra system of two competing species in the presence of two correlated noise sources is analyzed: (i) an external multiplicative time correlated noise, which mimics the interaction between the system and the environment; (ii) a dichotomous stochastic process, whose jump rate is a periodic function, which represents the interaction parameter between the species. The moment equations for the species densities are derived in Gaussian approximation, using a mean field approach. Within this formalism we study the effect of the external time correlated noise on the ecosystem dynamics. We find that the time behavior of the $1^{st}$ order moments are independent on th…
Two-species model for spatial distributions of sardine and anchovy: A comparison with real data
2010
We present a study of pattern formation in a set of two coupled equations modeling two competing species. We consider generalized Lotka-Volterra equations in the presence of a multiplicative noise which models the interaction between the species and the environment. The interaction parameter between the species is a random process which obeys a stochastic differential equation with a generalized bistable potential in the presence of a periodic driving term, which accounts for the environment temperature variation.We find noise-induced spatial patterns with strong anti-correlation between the two species. We compare our theoretical results with the experimental data of the spatial distributi…
Digital information receiver based on stochastic resonance
2003
International audience; An electronic receiver based on stochastic resonance is presented to rescue subthreshold modulated digital data. In real experiment, it is shown that a complete data restoration is achieved for both uniform and Gaussian white noise.
Extension of The Stochastic Differential Calculus To Complex Processes
1996
In structural engineering complex processes arise to predict the first excursion failure, fatigue failure, etc. Indeed to solve these problems the envelope function, which is the modulus of a complex process, is usually introduced. In this paper the statistics of the complex response process related to the envelope statistics of linear systems subjected to parametric stationary normal white noise input are evaluated by using extensively the properties of stochastic differential calculus.
Complex Systems: an Interdisciplinary Approach
2011
Two main peculiarities characterize complex systems: the nonlinearity and the noisy environmental interaction. The comprehension of noise role in the dynamics of nonlinear systems plays a key aspect in the efforts devoted to understand and model so-called complex systems.
Population Monte Carlo Schemes with Reduced Path Degeneracy
2017
Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the resampling step. In this paper we focus on novel population Monte Carlo schemes that present enhanced diversity, compared to the standard approach, while keeping the same implementation structure (sample generation, weighting and resampling). The new schemes combine different weighting and resampling strategies to reduce the path degeneracy and achieve a higher performance at the cost of additional low computational complexity cost. Computer si…
Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression
2021
The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real…
A Variational Approach for Denoising Hyperspectral Images Corrupted by Poisson Distributed Noise
2014
Poisson distributed noise, such as photon noise is an important noise source in multi- and hyperspectral images. We propose a variational based denoising approach, that accounts the vectorial structure of a spectral image cube, as well as the poisson distributed noise. For this aim, we extend an approach for monochromatic images, by a regularisation term, that is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a Split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared to a marginal approach on synthetic and real data.
On the effect of analog noise in discrete-time analog computations
1998
We introduce a model for analog computation with discrete time in the presence of analog noise that is flexible enough to cover the most important concrete cases, such as noisy analog neural nets and networks of spiking neurons. This model subsumes the classical model for digital computation in the presence of noise. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the VC-dimension of computational models with analog noise.