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Filtering by nonlinear systems
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008Synchronization of nonlinear systems forced by external signals is formalized as the response of a nonlinear filter. Sufficient conditions for a nonlinear system to behave as a filter are given. Some examples of generalized chaos synchronization are shown to actually be special cases of nonlinear filtering.
Campos Cantón, E. +2 more
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Journal of Nonlinear Science, 1997
It is shown how it is possible to combine Fourier projections with local nonlinear prediction to provide a methodology which can, in principle, recognize independent dynamical signals. That methodology is applied to a variety of chaotic signals with superimposed the sine waves. Moreover, it is shown how sine wave frequency can be recognized dynamically
Kember, G., Fowler, A. C., Evans, H. B.
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It is shown how it is possible to combine Fourier projections with local nonlinear prediction to provide a methodology which can, in principle, recognize independent dynamical signals. That methodology is applied to a variety of chaotic signals with superimposed the sine waves. Moreover, it is shown how sine wave frequency can be recognized dynamically
Kember, G., Fowler, A. C., Evans, H. B.
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Optics Letters, 1996
We demonstrate a nonlinear Christiansen filter in both the steady state and the transient regimes and in both the self- and the cross-modulation modes. Experiments in which a thermal nonlinearity was used agree well with predictions based on a modified scattering theory model.
S D, Vartak, N M, Lawandy
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We demonstrate a nonlinear Christiansen filter in both the steady state and the transient regimes and in both the self- and the cross-modulation modes. Experiments in which a thermal nonlinearity was used agree well with predictions based on a modified scattering theory model.
S D, Vartak, N M, Lawandy
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Characterising auditory filter nonlinearity
Hearing Research, 1994An important aspect of auditory nonlinearity is that psychoacoustically measured auditory filters broaden as the level at which they are measured increases. However, it is not yet clear whether the change in filter shape is controlled primarily by the level of the probe or that of the masker.
Rosen, Stuart, Baker, Richard
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Neural Systems as Nonlinear Filters
Neural Computation, 2000Experimental data show that biological synapses behave quite differently from the symbolic synapses in all common artificial neural network models. Biological synapses are dynamic; their “weight” changes on a short timescale by several hundred percent in dependence of the past input to the synapse.
Wolfgang Maass 0001, Eduardo D. Sontag
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IEEE Transactions on Signal Processing, 1991
A nonlinear filter is introduced whose output is nearest in distance to the output of a FIR (finite impulse response) linear phase filter; hence it is called the NFIR filter. After choosing the impulse response of the FIR filter properly, the NFIR filter can clean impulsive noise and preserve edges of signals.
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A nonlinear filter is introduced whose output is nearest in distance to the output of a FIR (finite impulse response) linear phase filter; hence it is called the NFIR filter. After choosing the impulse response of the FIR filter properly, the NFIR filter can clean impulsive noise and preserve edges of signals.
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Nonlinear multiscale filtering
IEEE Signal Processing Magazine, 2002In this article, we give an overview of scale-spaces and their application to noise suppression and segmentation of 1-D signals and 2-D images. Several prototypical problems serve as our motivation. We review several scale-spaces (linear Gaussian, Perona-Malik, and SIDE-stabilized inverse diffusion equation) and discuss their advantages and ...
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Introducing Legendre nonlinear filters
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014This paper introduces a novel sub-class of linear-in-the-parameters nonlinear filters, the Legendre nonlinear filters. Their basis functions are polynomials, specifically, products of Legendre polynomial expansions of the input signal samples. Legendre nonlinear filters share many of the properties of the recently introduced classes of Fourier ...
Alberto Carini +3 more
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A new class of nonlinear filters-neural filters
IEEE Transactions on Signal Processing, 1993A class of nonlinear filters based on threshold decomposition and neural networks is defined. It is shown that these neural filters include all filters defined either by continuous functions, such as linear finite impulse response (FIR) filters, or by Boolean functions, such as generalized stack filters.
Lin Yin, Jaakko Astola, Yrjö Neuvo
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Gaussian filter for nonlinear filtering problems
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002We develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the proposed filter.
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