Multiresolution Feature Extraction

Multiresolution Feature Extraction is a time series feature extraction method that decomposes a signal into multiple temporal or frequency resolutions using hierarchical representations—such as wavelet transforms or filter banks—to capture both coarse-grained trends and fine-grained local patterns distinctive from single-resolution approaches.

120

Publications

5K

Citations

367

Authors

187

Institutions

Publications per year

2017–2026

31

Authors

367

  1. MS
    M.M.A. Salama

    University of Waterloo, Canada

    4 publications · 283 citations

    4

    h-index

  2. AC
    A.Y. Chikhani

    Royal Military College of Canada, Canada

    4 publications · 283 citations

    4

    h-index

  3. AG
    A.M. Gaouda

    University of Waterloo, Canada

    3 publications · 244 citations

    3

    h-index

  4. MU
    Michaël Unser

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 359 citations

    2

    h-index

  5. MR
    Mohammed Rziza

    Mohammed V University, Morocco

    2 publications · 28 citations

    2

    h-index

1–5 of 367

Venues

  1. 6

    h-index

  2. 6

    h-index

  3. Pattern Recognition

    3 publications · 242 citations

    3

    h-index

  4. IEEE Transactions on Image Processing

    3 publications · 235 citations

    3

    h-index

  5. IEEE Power Engineering Review

    2 publications · 143 citations

    2

    h-index

1–5