A carregar...
Publicação
Using Peano–Hilbert space filling curves for fast bidimensional ensemble EMD realization
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.73 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Empirical mode decomposition (EMD) is a fully unsupervised and data-driven approach to the class of nonlinear and non-stationary signals. A new approach is proposed, namely PHEEMD, to image analysis by using Peano–Hilbert space filling curves to transform 2D data (image) into 1D data, followed by ensemble EMD (EEMD) analysis, i.e., a more robust realization of EMD based on white noise excitation. Tests’ results have shown that PHEEMD exhibits a substantially reduced computational cost compared to other 2D-EMD approaches, preserving, simultaneously, the information lying at the EMD domain; hence, new perspectives for its use in low computational power devices, like portable applications, are feasible.
Descrição
Palavras-chave
Ensemble empirical mode decomposition (EEMD) Fast bidimensional EEMD Peano–Hilbert curves
Contexto Educativo
Citação
Costa et al.: Using Peano–Hilbert space filling curves for fast bidimensional ensemble EMD realization. EURASIP Journal on Advances in Signal Processing 2012 2012:181
Editora
Springer Science and Business Media