Higher-Order Wavelet Filters
Create a wavelet filter at arbitrary scale.
wavelet.filter(wf.name, filter.seq = "L", n = 512)
wf.name |
Character string of wavelet filter. |
filter.seq |
Character string of filter sequence. |
n |
Length of zero-padded filter. Frequency resolution will be
|
Uses cascade subroutine to compute higher-order wavelet
coefficient vector from a given filtering sequence.
Vector of wavelet coefficients.
B. Whitcher
Bruce, A. and H.-Y. Gao (1996). Applied Wavelet Analysis with S-PLUS, Springer: New York.
Doroslovacki, M. L. (1998) On the least asymmetric wavelets, IEEE Transactions on Signal Processing, 46, No. 4, 1125-1130.
Daubechies, I. (1992) Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM: Philadelphia.
Morris and Peravali (1999) Minimum-bandwidth discrete-time wavelets, Signal Processing, 76, No. 2, 181-193.
Nielsen, M. (2001) On the Construction and Frequency Localization of Finite Orthogonal Quadrature Filters, Journal of Approximation Theory, 108, No. 1, 36-52.
## Figure 4.14 in Gencay, Selcuk and Whitcher (2001)
par(mfrow=c(3,1), mar=c(5-2,4,4-1,2))
f.seq <- "HLLLLL"
plot(c(rep(0,33), wavelet.filter("mb4", f.seq), rep(0,33)), type="l",
xlab="", ylab="", main="D(4) in black, MB(4) in red")
lines(c(rep(0,33), wavelet.filter("d4", f.seq), rep(0,33)), col=2)
plot(c(rep(0,35), -wavelet.filter("mb8", f.seq), rep(0,35)), type="l",
xlab="", ylab="", main="D(8) in black, -MB(8) in red")
lines(c(rep(0,35), wavelet.filter("d8", f.seq), rep(0,35)), col=2)
plot(c(rep(0,39), wavelet.filter("mb16", f.seq), rep(0,39)), type="l",
xlab="", ylab="", main="D(16) in black, MB(16) in red")
lines(c(rep(0,39), wavelet.filter("d16", f.seq), rep(0,39)), col=2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.