Wavelets for the Analysis, Estimation, and Synthesis of Scaling Data
By Patrice Abry, Patrick Flandrin, Murad S. Taqqu, D. Veitch. 2002
440 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a00bf665dbcbd786e456185885fdaa09d69f41dc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Vladas Pipiras
- Joan Bruna
- Jean-Chrysostome Bolot
- Paulo Gonçalves
- Gilles Faÿ
- Murad S. Taqqu
- Eric Moulines
- Franccois Roueff
In Computer Vision and Pattern Recognition
- Franccois Roueff
- Vladas Pipiras
- Patrick Flandrin
- Gabriel Rilling
- Paulo Gonçalves
- Gilles Faÿ
- Eric Moulines
- Joan Bruna
Citations in the corpus
- Estimators of Long-Memory: Fourier versus Wavelets (2008, methodology): “d regression method was first developed by Abry and Veitch (1998) under the simplifying assumption that the wavelet coefficients are uncorrelated; see also Veitch and Abry (1999) and the review articles Abry et al. (2000) and Abry et al. (2003). In addition, see Veitch et al. (2003) for the automatic s”
- Empirical mode decomposition as a filter bank (2004, background): “1, the collection of all such filters tend to organize in a filter bank structure which is reminiscent of what is classically observed in wavelet decompositions in similar situations [1].”
- Analytic evaluation of RED performance (2000, background): “Furthermore, TCP traffic itself is bursty 1 [16], [1], which means that a loss event at a router tends to involve many packets at a time, leading to reduced throughput and synchronization between TCP connections sharing the ressources of that router.”
- Wavelet-Type Expansion of the Rosenblatt Process (2004, methodology): “Practical implementation of synthesis of fBm was proposed by Abry and Sellan [3] (see also Abry, Flandrin, Taqqu, and Veitch [ 1 ] for a summary) and is further explored by Pipiras [24, 25].”
- Wavelet construction of Generalized Multifractional processes. (2007, background): “This is why FBM has now become a standard model: it is used in many areas such as hydrology, economics, finance, physics and telecommunications [1, 17, 26, 36].”
- Scattering Representations for Recognition (2013, background): “Some authors [Jaf00; AFTV00] use this property to distinguish ‘monofractals’ from ‘multifractals’, which contain singularities of different Hölder exponents.”