05 Jun '09 17:31>
Does anybody here know about them? I'm particularly interested in them in the context of filtering.
Originally posted by PalynkaFrom what I can find, Kalman is about system modeling. I may have had another term for it at uni.
I'm interested in their use as filters. How would you say that they compare in terms of properties to, say, a Kalman filter?
Originally posted by gezzaThanks, that's exactly one of the things I was looking for.
From what I can find, Kalman is about system modeling. I may have had another term for it at uni.
I looked at them more in terms of Wavelet Transform. Similar to Fourier Transform, but you are not using sine waves, but rather wavelets. The FT is great for time invariant signals, but does not show discontinuities (e.g. signal starting). WT copes better w ...[text shortened]... ou want to design a low pass filter, then the sine representation of the FT is more appropriate.
Originally posted by PalynkaWavelets are more akin to pulses, a one time affair. Frequency domain is about recurring waves, a sine wave, for instance. A single cycle of a sine wave could be analyzed by wavelets better than frequency domains. What frequency area are you interested in filtering? I have an interest in hi Q low frequency filters myself, related to brain wave activity, frequencies on the order of 1 to 20 Hz, how to implement one in a simple op amp circuit. I need to have 4 or 5 bands, so 5 separate filters each reacting to a specific band, say 1 to 5 Hz, 6 to 10, 11 to 15, 16 to 20, etc.
Thanks, that's exactly one of the things I was looking for.
Do you know how they would differ in terms of frequency domain?
Originally posted by sonhouseI'm interested in filtering recurring waves which can have pulses, so to speak. The idea is to take into account possible pulses (e.g deep short-lived recessions) in macroeconomic time-series.
Wavelets are more akin to pulses, a one time affair. Frequency domain is about recurring waves, a sine wave, for instance. A single cycle of a sine wave could be analyzed by wavelets better than frequency domains. What frequency area are you interested in filtering? I have an interest in hi Q low frequency filters myself, related to brain wave activity, fre ...[text shortened]... arate filters each reacting to a specific band, say 1 to 5 Hz, 6 to 10, 11 to 15, 16 to 20, etc.
Originally posted by PalynkaAccording to this Wiki article, the father wavelet is made up so as to eliminate the need for integration, and it talks about mother wavelets and a lot more, maybe you can get some use out of it:
I'm interested in filtering recurring waves which can have pulses, so to speak. The idea is to take into account possible pulses (e.g deep short-lived recessions) in macroeconomic time-series.
These series have a long-term (think trend) and a short-term component (think business cycle). Filtering is often used to try to extract what are long-run very low ...[text shortened]... and frequency domain (isn't that the point of the separation into father and mother wavelets?)
Originally posted by PalynkaNot sure if this is going to help you, but I'll give it a go. This is how I used these things.
I'm interested in filtering recurring waves which can have pulses, so to speak. The idea is to take into account possible pulses (e.g deep short-lived recessions) in macroeconomic time-series.
These series have a long-term (think trend) and a short-term component (think business cycle). Filtering is often used to try to extract what are long-run very low ...[text shortened]... and frequency domain (isn't that the point of the separation into father and mother wavelets?)