I had an interesting idea that I intend to pursue more but it's interesting enough in the areas it could potentially link together that I wanted to post the beginning of it here.
The first thought is we should have some simple mathematical operations to describe at least the apparent motion of objects in space from a first person perspective.
The obvious one is of horizontal and vertical rotation, in which case these are easily described by circles in trigonometry and we can begin with trying to describe a simple mechanism to move a circle around in various manners in space relative to an observer.
We have a few operations like determining the elliptical ratio as well rotations and offsets.
Now we can also consider such a circle to be drawn at some frequency or wavelength and effectively it could be a single point sweeping over a cycle. This would make moving these circles around similar to viewing the progressive growth, or shrinkage of a spiral over time.
Now let's compare this with the operation of a radio receiver - we can tune to various stations and this alters the base/reference frequency. If we perform a simple multiplication of this, we get heterodyning http://en.wikipedia.org/wiki/Heterodyne and we're computing basically the sums and differences of our reference frequency with the signal we're analyzing.
This creates redundant mirror images in the signal and the general technique then is to construct a lowpass filter in order to isolate only one version of this signal. (We can ignore the IF amplifier in a typical radio if we have components that are perfect resonators)
The simplest filter is constructed by simply summing two samples in time together. This creates a "comb filter" http://en.wikipedia.org/wiki/Comb_filter, but a comb filter generates periodic repetitions in a spectrum, though if we simply string together a very large number of such filters, similar to a transmission line we can remove this artifact.
What we then have are a series of what I believe are called "half cosine filters". Basically we're just summing together two "adjacent" points in a signal and constructing another signal for which similarly we sum together two adjacent values and we simply repeat this.
If we look at the frequency response of such a filter, we can see this delay in time to be equivalent to summing two phase shifted points of a sine wave (this can be closely associated with memory).
a(t)=sin(f*t)+sin(f*(t-1))
Notice that the peak value of a occurs when f*t and f*(t+1) are symmetrically centered around pi/2, in which case we have an amplitude of sin(pi/2+f/2)+sin(pi/2-f/2) and these two values are equal, so we can rewrite this and normalize the gain to a maximum of 1 at a frequency of 0 and rewrite this in terms of a cosine as cos(f/2).
If we stage n of these filters serially, the response then becomes an exponential decay of cos(f/2)^n.
Now we had previously offset the frequency via. a multiplication with a reference frequency, p, which is sin(p) and this multiplication with the source signal, sin(s), creates the response (via. the product to sum identity http://en.wikipedia.org/wiki/List_of...ric_identities):
sin(p)*sin(s)=(cos(p+s)+cos(p-s))/2
Now we must assume our signal p is significantly larger than 0 (in other words, we're not looking near the "origin" of space as we would then see a mirroring of our signal within the "passband" of the filter) and that our filter response, cos(f/2)^n is sufficiently narrow to reject the cos(p+s) component, such that we have a "small" value of p-s, near 0.
Then the apparent response of this with respect to changes in the frequency, p approximately follows the form cos((p-s)/2)^n.
Let's rewrite this as d=(p-s)/2 to denote the distance or difference between our reference point in a spectral space and let's also write the cosine function in terms of its Taylor series expansion http://en.wikipedia.org/wiki/Taylor_series:
cos(d)^n=(1-d^2/2!+d^4/4!-d^6/6!+...)^n
The most significant term for d~=0 is the d^2/2! term. To keep things simple, we can assume a rescaling of d by sqrt(2) and remove the division by 2.
Now we have a first order approximation of cos(d)^n=(1-d)^n and notice also that on infinitesimal scaling we also have 1-d~=e^-d, and similarly for d~=0, we have, for a constant scaling value, a, cos(a*d)^n~=1-(a*d)^(2n)~=e^-(a*d)^2n (and again, these are under the previous assumptions (in order to construct the first order appearance of a "flat" Euclidean space) of n>>|p|>>|p-s| ... as always, it's important to keep our infinities sorted out
, though I left their specific relationships unspecified, but constructing various relationships between these could, in itself, provide a "signal" to receive and considering that n is constructed of the equivalent of quanta and we have 3 "dimensions" to view as determined by n, f and g, where f(n)~=g(n) and similarly n >> p=f(n) >> |f(n)-g(n)| ).
Also, many consider space time to be describable by 4 dimensions, but this is not inherently true relative to an observer as an observer, such as the Earth can specific all events in space in terms of a time (similarly altitude) and two angles of rotation. Notice that time and distance are equated in physics, hence it is redundant to both contain time and a distance value as a description of an event. The only reason this is done is to "objectify" the view in physics and attempt to simultaineously describe many observers views, but this later fails when we then consider ourselves to both be able to localize them at a distance as well as synchronize the time (and then we have created two unknowns, where there is only one).
So we have a description that constructs a potentially finite and closed 1-D "universe" with positions specified in terms of spectral characteristics and a "local" bandwidth gain approximating a gaussian form (close associations with atomic properties and Pascal's triangle here)
Let's consider that we have a highly chaotic spectrum and that we're trying to isolate some signal within this. In this case we can assume a very low signal to noise ratio http://en.wikipedia.org/wiki/Signal-to-noise_ratio.
If we have a constant amplitude signal at some frequency, s, and we "move"/tune our reference, p, near s, then the amplitude of this signal changes relative to (I'm skipping constant scaling factors and generally working with just the first order approximations, though these details are not insignificant over large distances and can create distortions to the perception of space when n is finite) cos(|p-s|)^n~=e^-|p-s|^2n.
Now if we look at the theoretical lower limit for the capacity of detecting information, we have a Shannon limit http://en.wikipedia.org/wiki/Shannon...artley_theorem of:
C=B*log(1+sig/noise), where B is the bandwidth of the signal (though we should similarly include a constant scaling factor if we want to determine a level of confidence), s and n are the amplitudes of the signal and "noise" respectively and C is the equivalent quantity of information.
With a fixed bandwidth (and level of confidence), we then have C proportional to:
C~=log(1+sig/noise) and sig~=(e^-d^2n)^2=e^-d^4n, so
C~=log(1+(e^-d^4n)/noise)
With noise>>sig, once again ignoring scaling, we have (e^-d^4n)/noise near 0 and log(1+sig/noise)~=sig/noise
In this case, with a large noise (we can assume a chaotic vacuum energy) the minimum theoretical signal is proportional to the energy of it, though large signal energies can appear distorted in size and "shrink" due to a large "mass"/energy of it is not true that noise>>sig, because we then approach the logarithmic "distortion" to the linear approximation.
There are still lots of additional things to look at, but overall it's a very interesting model how motion in space could be perceived as sweeping through a spectrum of "channels" of information and the associated distortions that would appear to exist "moving" past these along with properties associated with distortions arising from a finite "universe"/spectrum as well as distortions arising from a "saturation" of energy in a space creating an appearance of a non-linearity in its information content as it begins to approach densities of a potential chaotic vacuum energy.
And all this from something, in many ways, more simple than an old crystal radio set.
I've also previously found some rather interesting structures embedded with chaotic noise sources, such as this from analyzing various delayed versions of the evolution of a logistic function.
Look at the 6th image in this:
http://www.toequest.com/forum/member...art-logic.html
This is also similar to my comments regarding folding a "time line" of a single deterministic algorithm to generate different dimensions of data.
Anyway, there do appear to be many correlations with physics in this, though if this model is correct, then there are also many areas of distortions that can be looked at, including what occurs when p~=0 and we look at the "origin" of space(That could be a fun spot to check) as well as how the divergence between the parabolic form ("flat" or Euclidean view) and the cosine form ("closed"/warped space) can affect the appearance of physical properties on different scales.
There should also be simple mechanisms that can be added to this to generate rotations in perspective (and this could potentially arise if we use the imaginary components of rotation as well).
P.S.: No, I don't think a truly "objective" universe is closed, but in terms of a finite set of physical properties making the measurements, then yes, we should expect that to only describe a finite component of it.


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