EXCELLENT!
Thank you a fine metaphor.
My experience with BIN data acquisition comes mostly from Energy Dispersive X
Ray analysis.
Each photon of X-rays produces electron hole pairs in direct proportion to
their photon energy. Assuming only one event happens during any given sample
period, one accumulates bins at each energy of photons the were emitted from
the sample. The photon energy associated with a particular bin tells you what
element is present and the number of photons in that bin bin tells you how much
of that element is present.
Over time, a really high signal to noise is accomplished. Of course, this is
only true for analytical signals that can be held constant for a time long
enough for the spectrum to accumulate.
Gary
> On Sep 12, 2016, at 9:55 AM, Katz Ajamas <ajamas.rn@gmail.com> wrote:
>
> Dr Gary-
>
> It might be helpful to consider extremes.
>
> One bit sampling.
>
> The only information collected is whether the current sample is greater
> than or less than the preceding sample. This is one bit A/D, the smallest
> digital dynamic range one can get. If samples are close enough together(in
> time relative to the bandwidth) we can recreate a complex signal from this
> lowest possible digital dynamic range. Consider pointillism(dot frequency)
> in black and white as an two dimensional version of this.
>
> Bin sampling.
>
> You mentioned familiarity with bin sampling. The level of a sample is
> measured and recorded. Doing this at the Nyquist frequency allows for
> recreation of a the signal. Consider halftone(dot size at a fixed
> frequency) in black and white as a two dimensional example.
>
> A combination of the two approaches allows for optimization. One can
> compare radios in dynamic range by looking at the sample rate combined with
> the number of bits in each sample. Consider B&W photographic film as a two
> dimensional example.
>
> 73, -Bob ah7i/w4
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