Frequency Characteristics of Wavelets

By C. Parameswariah, M. Cox. IEEE Power Engineering Review, 2002

164 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4fe05adba2df0690da22e8c2c1d4ef9d6525dc31

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. M. E. El-Hawary
2. M. Murugappan
3. Nagarajan Ramachandran
4. Martin Buss
5. Y. Sazali

In Control and Systems Engineering

1. M. E. El-Hawary
2. Walid G. Morsi
3. M. Murugappan
4. Nagarajan Ramachandran
5. Martin Buss
6. Y. Sazali
7. Matthias Althoff

In Signal Processing

1. M. E. El-Hawary
2. M. Murugappan
3. Nagarajan Ramachandran
4. Walid G. Morsi
5. Martin Buss
6. Y. Sazali
7. Bao-Liang Lu

Citations in the corpus

• Classification of human emotion from EEG using discrete wavelet transform (2010, methodology): “This process is carried out iteratively producing a set of approximation coefficients and detail coefficients at different levels or scales [23] (Table 1).”
• Feature Extraction and Selection for Emotion Recognition from EEG (2014, background): “Correspondence of frequency bands and wavelet decomposition levels depends on the sampling frequency and is given for our case of fS 1⁄4 512 Hz in the last column of Table 1 [39].”
• Reformulating Three-Phase Power Components Definitions Contained in the IEEE Standard 1459–2000 Using Discrete Wavelet Transform (2007, background): “The main disadvantage of wavelet analysis is the issue of spectral leakage [34]–[36].”