• Corpus ID: 243985877

Processing of large sets of stochastic signals: filtering based on piecewise interpolation technique

@article{Torokhti2021ProcessingOL,
  title={Processing of large sets of stochastic signals: filtering based on piecewise interpolation technique},
  author={Anatoli Torokhti},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.06041}
}
  • A. Torokhti
  • Published 11 November 2021
  • Computer Science
  • ArXiv
Suppose K Y and K X are large sets of observed and reference signals, respectively, each containing N signals. Is it possible to construct a filter F : K Y → K X that requires a priori information only on few signals, p ≪ N , from K X but performs better than the known filters based on a priori information on every reference signal from K X ? It is shown that the positive answer is achievable under quite unrestrictive assumptions. The device behind the proposed method is based on a special… 

Figures from this paper

References

SHOWING 1-10 OF 30 REFERENCES

Filtering and compression for infinite sets of stochastic signals

Generic Weighted Filtering of Stochastic Signals

Variations of the degrees of freedom improve the performance of the proposed filters, in particular, the error associated with the filter concatenation decreases as the filter number k in the Concatenation increases.

Polynomial-Based Interpolation Filters—Part I: Filter Synthesis

This paper introduces a generalized design method for polynomial-based interpolation filters. These filters can be implemented by using a modified Farrow structure, where the fixed finite impulse

Optimal reduced-rank estimation and filtering

An alternating power (AP) method for computing the optimal reduced-rank estimators and filters is derived and analyzed, which is a generalization of the conventional power method for subspace computation, shown to be globally and exponentially convergent under weak conditions.

Piecewise linear system modeling based on a continuous threshold decomposition

The optimum design of the class of PWL filters introduced in this paper can be postulated as a least squares problem whose variables separate into a linear and a nonlinear part.

A Technique for Image Denoising Based on Adaptive Piecewise Linear Filters and Automatic Parameter Tuning

  • F. Russo
  • Computer Science
    2005 IEEE Instrumentationand Measurement Technology Conference Proceedings
  • 2005
A novel technique for image denoising that adopts multi-pass processing based on a two-parameter piecewise linear filter that aims at removing the noise without blurring the image structure is presented.

A Multistage Representation of the Wiener Filter Based on Orthogonal Projections

It is demonstrated that the cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes and is capable of outperforming the more complex eigendecomposition-based methods.

Adaptive nonlinear digital filter with canonical piecewise-linear structure

A novel adaptive nonlinear filter with the least-mean-square (LMS) error criterion is presented. It is based on the so-called canonical piecewise-linear structure. As an alternative to approaches