research-article

Spectral Representation of Two-Sided Signals from and Applications to Signal Processing

Author: N. G. DokuchaevAuthors Info & Claims

Problems of Information Transmission, Volume 60, Issue 2

Pages 113 - 126

https://doi.org/10.1134/S0032946024020030

Published: 14 October 2024 Publication History

Abstract

We study spectral representation as well as predictability and recoverability problems for nonvanishing discrete-time signals from, i.e., for bounded discrete-time signals, including signals that do not vanish at . We extend the notions of transfer functions, the spectrum gaps, bandlimitness, and filters to these general type signals. We present some frequency conditions for predictability and data recoverability and propose some prediction and data recovery methods.

References

[1]

Dokuchaev N.Predictors for Discrete Time Processes with Energy Decay on Higher FrequenciesIEEE Trans. Signal Process.201260116027-6030https://doi.org/10.1109/TSP.2012.2212436

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[2]

Dokuchaev N.Near-ideal Causal Smoothing Filters for the Real SequencesSignal Process.20161181285-293https://doi.org/10.1016/j.sigpro.2015.07.002

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[3]

Dokuchaev, N.G., Spectral Representation of Nonvanishing Discrete-Time Signals and the Prediction Problem, to appear in Probl. Peredachi Inf., 2024, vol. 60, no. 3.

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Kahane, J.-P., Séries de Fourier absolument convergentes, Berlin: Springer, 1970. https://doi.org/10.1007/978-3-662-59158-1

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Spectral Representation of Two-Sided Signals from and Applications to Signal Processing
1. Hardware
  1. Robustness

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Published In

Problems of Information Transmission Volume 60, Issue 2

Jun 2024

70 pages

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Plenum Press

United States

Publication History

Published: 14 October 2024

Accepted: 10 September 2024

Revision received: 16 August 2024

Received: 18 June 2024

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Abstract

References

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Recommendations

Composite signal representation for fast and storage-efficient processing of encrypted signals

Signal adaptive method for improved space/spatial-frequency representation of nonstationary two-dimensional signals

Distributed signal processing with graph spectral dictionaries

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