binlets/CITATION.cff at master · maurosilber/binlets · GitHub

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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Binlets: Data fusion-aware denoising enables accurate and unbiased quantification of multichannel signals
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Mauro
    family-names: Silberberg
    email: maurosilber@df.uba.ar
    orcid: 'https://orcid.org/0000-0002-2402-1100'
    affiliation: >-
      1. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Buenos Aires, Argentina.
      2. CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA). Buenos Aires, Argentina
  - given-names: Hernán Edgardo
    family-names: Grecco
    email: hgrecco@df.uba.ar
    affiliation: >-
      1. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Buenos Aires, Argentina.
      2. CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA). Buenos Aires, Argentina
    orcid: 'https://orcid.org/0000-0002-1165-4320'
identifiers:
  - type: doi
    value: 10.1016/j.inffus.2023.101999
abstract: >-
  As monitoring multiple signals becomes more cost-effective,
  combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process.
  Here, we present a method based on the Haar wavelet transform
  that trades off resolution against accuracy based on statistical significance.
  By taking advantage of correlations between channels,
  it offers a superior performance compared to denoising each channel separately.
  It outperforms standard wavelet methods when the magnitude of interest in the data-fusion process involves a non-linear transformation or reduction of a multichannel signal.
  We demonstrate its efficacy by benchmarking our method against standard wavelet thresholding for synthetic single and multichannel time series, and a multichannel two-dimensional image.
  The method has a simple interpretation as an adaptive binning of the signal,
  and neither requires training data nor specialized hardware to run fast.
  In addition, a reference Python implementation is available on GitHub and PyPI,
  making it simple to integrate into any analysis pipeline.
license: MIT