Talk about lazy. Didnt even zoom in to take the pic.
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The first part of this machine is the memory cell. Its quite simple. Just a decider that says to let through anything greater than 0 and give it a value of 1. This is connected to itself much like a timer or perhaps a memory cell. The advantage is to add to the cell you need a positive number and a negative one to clear it. Each of the lights in a single column attached to the memory cell have an "if unique signal" is greater than 0 condition. So 10 signals per column much like the side scrolling displays.
Now here is where it gets different.
Each of the deciders above the memory cells are following an if signal X (represents the horizontal axis) = its value let the signals through. In another spot the X value is constantly cycling between 0 and 10. This allows for a stream of 10 different signals which are synced with the x axis to control the display. Thereby reducing the number of separate signals from 100 to 10 to control the same 100 lights. On the Y axis (the right side) you have a similar set of deciders which work exactly the same except they use y instead of x. In front of them is a bunch of adders. The symbols in the adders correspond with the symbols in the lamps. and essentially convert any signal sent into them into the value for that lamp.
I hate adding screenshots if they are unneeded. If this is unclear I'll add one.
the x and y axis are controlled by simple counters. The x axis is the standard X<10 kinda counter as well the y. And the y increments every time x=4. It isn't set to 0 because of the layers of combinators between the x and y logic. (its hard to explain but trust me on this). And the first 2 deciders the signal goes through converts positive values to 1 and everything else to positive values. By changing the expected values within these deciders it is possible to change the signal or "channel" the TV is watching from. The 2 logic pieces that "jut out" at the top allow for tuning the display. To keep the timers in sync. Essentially when the values inside the constant maker are changed the difference will be sent to the timers. This makes use of exploiting the delay and addition built into combinators.
The encoders are pretty simple it works the same way but in reverse. So the signals come from the combinators and are only passed through if it is the right signal and position at the same time. The values are programmed in with the same method. A tuning circuit is also included.
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The output adder converts the signals to the desired channel value.
As an added bonus. here is a 96 pixel display that only uses 4 signals and operates at .6hz. I cobbled this together just because I can.
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