I don't see anything in that thread about non-uniform sampling. I
looked at the PDF files linked to in the thread and I don't see
anything amazing. I would suggest that the author is comparing his
own product to standard filters without us knowing anything about how
they were set up. All filters have parameters that are adjusted to
suit the needs. As to the specific examples, how do you know if the
data was selected to show their filter at its best while other data
might show it as inferior to the others?

Just like when a CPU vendor benchmarks his chips against a competitor,
they don't give the competitor a chance to set up their machine, the
vendor doing the benchmark sets up both machines and obviously tweeks
his own as best he can.

Rick


On Feb 3, 7:18*pm, Jessica <pt...@live.com> wrote:
> I appreciate all the advice. *After doing more research, I have
> determined that I am looking for a filter with "non-uniform
> sampling". *I also found this thread interesting:
>
> http://www.dsprelated.com/showmessage/4806/2.php
>
> On Feb 2, 9:30*am, "Michael Plante" <michael.pla...@gmail.com> wrote:
>
> > Jerry wrote:
> > >Jessica wrote:
> > >> 1. Average sample rate is in the ms range, but can vary to seconds /
> > >> minutes when there is no activity to be measured.
> > >> 2. Most of the sample intervals are in the ms range, but accuracy down
> > >> to the microsecond is possible. *However, at that level, mostly noise
> > >> is recorded. *I want to downsample to one measurement per second or
> > >> even minute.
> > >> 3. I am not sure how to quantify this, the signal measured does
> > >> contain a lot of noise, but ideally, I would like to use a Hull Moving
> > >> Average to smooth the data.
>
> > >> I appreciate your help.
>
> > >> On Feb 1, 5:29 pm, Richard Owlett <rowl...@pcnetinc.com> wrote:
> > >>> Jessica wrote:
> > >>>> I am looking for an open-source smoother to down-sample data. I know
> > >>>> there are many such as Hull Moving average, but I have data logged
> > at
> > >>>> the ms scale at non-fixed time intervals and I want to sample it on
> > >>>> the second range at fixed time intervals. Does anyone know of a good
> > >>>> smoother to do this? The key is that the input data is not logged at
> > a
> > >>>> constant frequency.
> > >>> Can't answer your specific question. *BUT* I can suggest
> > >>> information to give so you can get a meaningful answer:
>
> > >>> 1. What is the average sample rate?
> > >>> 2. What is the minimum and maximum time between samples?
> > >>> * * 2a. Possibly, what is the distribution of sample intervals?
> > >>> 3. What is your desired fixed "sample rate"?
> > >>> 4. What accuracy constraints of fit of reconstructed signal to
> > >>> * * the actual? [Would linear interpolation between points be
> > >>> * * adequate or would a 310947 degree polynomial be needed?
> > >>> * * [Alright already, the degree was tongue in cheek, but intent
> > >>> * * *was serious.]
>
> > >>> The art of getting "right answer" is asking "right question" ;>
> > >>> [I *KNOW* from personal embarrassing experience.]
>
> > >Another question: How does the sampling rate compare to the bandwidth of
> > >your data?
>
> > If this is the same application, and if I'm understanding correctly, there
> > is no continuous form of the data:
>
> >http://groups.google.com/group/comp....hread/da25715e...
>
> > Knowing the bandwidth would probably boil down to deciding which
> > frequencies are "noise", but that doesn't necessarily mean a low-pass
> > filter is a good idea.
>
> > Jessica: *what is stopping you from using the HMA that you want to use?
> > Is it a question of whether it's the right tool? *I'm not familiar with
> > this estimator (other than reading an article on it just now that givesa
> > formula, but not much insight), but I would think it'd be important to come
> > up with a model (this is an art, and if you get it wrong, the results will
> > probably not tell you; Rune has a lot of good stuff to say about this )
> > of both the trends you want to track, as well as the "noise" you don't.
> > You might look for: *random walks, periodic components (once a day, once an
> > hour, etc), random ramps, etc..
>
>