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| Why evolution by natural selection didn't make use of RF? |
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| T3sl4co1l:
Static fields are easy: electric fields make your hairs stand on end, or other things; magnetic fields by magnetite crystals usually, I think. Anything faster than nerve response rate though, good luck. Actual "RF", in the MHz to say 10s of THz, far harder to do. Also arguably in the 10s-100s THz, there's thermal radiation, but this is getting into the quantum range, in this case by way of phonons; heat. Bolometry works for anything, of course, I mean, you can certainly feel various wavelengths if they're strong enough (from diathermy to direct microwaves, to the warmth of the sun), and the sensor isn't too thin for the wavelength. Not very sensitive though. Tim |
| RoGeorge:
--- Quote from: tggzzz on August 30, 2023, 08:41:02 am --- --- Quote from: karpouzi9 on August 30, 2023, 02:45:51 am ---... If you subscribe to Fourier's theories, then ... --- End quote --- Doesn't everybody? --- End quote --- That's far from obvious, nor trivial. The continuous sinusoids calculated with Fourier are not really there. The sinusoids are as if it were to be a bunch of added together sinusoids instead of the given shape of a signal, but they are not really there. One can see with an oscilloscope that there is some other wiggly form and no sinusoid in an arbitrary signal. A given shape can be decomposed many ways. One way is to write our shape as a sum of sin and cos functions, thus the Fourier spectrum. But the same signal can be decomposed, for example, in a series of wavelets. Or, maybe with some other kernel function instead of either sin/cos or wavelets. Now, is our arbitrary signal a sum of wavelets? Or a sum of sinusoids? Or some other sum of some other kernel functions? I think the correct answer is neither, our arbitrary signal is just the shape we see on the oscilloscope, not a sum of sinusoids, not a sum of wavelets, or any other sum. It's a happy coincidence that Fourier is particularly useful in engineering, somehow overlaps with the idea of superposition (which only holds for linear systems). Then we are all trained to apply Fourier only where it holds, and we turn a blind eye to what doesn't match, or we learn how to minimize Furrier side effects like the Gibbs artifacts, the need of windowing, the lost of causality. We do that all the time and only consider what we need out of it, up to the point where we start to believe a square wave is made of odd harmonics in a certain ratio, which is not really so. I consider that's just a form of gaslighting, though this time the gaslighting is just an innocent side effect with no malicious intent behind. I consider the sinusoidal components aren't really there, and that can be proved in practice. (An example of causality artifact can be seen in any digital oscilloscope, when sinx/x compensation is on, and the level of a constant 0 or 1 voltage starts to wiggle on the screen before an edge, as if the signal will somehow have premonitions and knows in advance when an edge will come - that's just a Fourier artifact, the logic level can not know the future.) I think it can be proved in practice that the sinusoids from the Fourier spectrum are not really there. For example, if we take a square wave of 1kHz, such a signal is supposed to have plenty of sinusoidal 3kHz in it, according to Fourier. However, one can make a filter that will only respond to a sinusoidal shape of 3kHz, and no other shape than sinusoidal. Such a 3kHz filter won't respond to a square wave of 1kHz, which indicates there is no sinusoidal 3kHz in our 1kHz square. I'll say that's a proof Fourier spectrum is not really there. Now, the funny thing is that what we casually call a 3kHz filter in EE will respond to a 1 kHz square, how so!?! :o But then, what's a filter? And how/why does it work? That's another simple yet difficult question to answer. |
| tggzzz:
--- Quote from: RoGeorge on August 31, 2023, 04:47:32 pm --- --- Quote from: tggzzz on August 30, 2023, 08:41:02 am --- --- Quote from: karpouzi9 on August 30, 2023, 02:45:51 am ---... If you subscribe to Fourier's theories, then ... --- End quote --- Doesn't everybody? --- End quote --- That's far from obvious, nor trivial. The continuous sinusoids calculated with Fourier are not really there. The sinusoids are as if it were to be a bunch of added together sinusoids instead of the given shape of a signal, but they are not really there. One can see with an oscilloscope that there is some other wiggly form and no sinusoid in an arbitrary signal. --- End quote --- That last sentence is wrong, pure and simple. The earlier ones make no sense. For similar reasons I suppose you would claim that the individual colours in white light are not really there. --- Quote ---A given shape can be decomposed many ways. One way is to write our shape as a sum of sin and cos functions, thus the Fourier spectrum. But the same signal can be decomposed, for example, in a series of wavelets. Or, maybe with some other kernel function instead of either sin/cos or wavelets. Now, is our arbitrary signal a sum of wavelets? Or a sum of sinusoids? Or some other sum of some other kernel functions? I think the correct answer is neither, our arbitrary signal is just the shape we see on the oscilloscope, not a sum of sinusoids, not a sum of wavelets, or any other sum. --- End quote --- Observing something on one type of instrument (e.g. a scope) is irrelevant. Why not choose a spectrum analyser instead? Fourier analysis is a mathematical concept. Ditto wavelet analysis. The validity of one does not invalidate the other. You can also decompose a 2D shape into a set of triangles. Or squares. Or hexagons. --- Quote ---(An example of causality artifact can be seen in any digital oscilloscope, when sinx/x compensation is on, and the level of a constant 0 or 1 voltage starts to wiggle on the screen before an edge, as if the signal will somehow have premonitions and knows in advance when an edge will come - that's just a Fourier artifact, the logic level can not know the future.) --- End quote --- Sigh. You are missing many important concepts there, and I don't have time to attempt to enlighten you. I just refer you to textbooks. The rest of your post isn't even wrong. |
| RoGeorge:
--- Quote from: tggzzz on August 31, 2023, 06:57:17 pm ---I suppose you would claim --- End quote --- We are here to talk about ideas, not to disconsider each other. Well, again, you start with ad hominem innuendos, assuming how you are smart and the others are not. That attitude never made anybody look smart, quite contrary but arrogant. I don't have the time to enlighten you either. |
| TimFox:
--- Quote from: RoGeorge on August 31, 2023, 04:47:32 pm --- --- Quote from: tggzzz on August 30, 2023, 08:41:02 am --- --- Quote from: karpouzi9 on August 30, 2023, 02:45:51 am ---... If you subscribe to Fourier's theories, then ... --- End quote --- Doesn't everybody? --- End quote --- That's far from obvious, nor trivial. The continuous sinusoids calculated with Fourier are not really there. The sinusoids are as if it were to be a bunch of added together sinusoids instead of the given shape of a signal, but they are not really there. One can see with an oscilloscope that there is some other wiggly form and no sinusoid in an arbitrary signal. A given shape can be decomposed many ways. One way is to write our shape as a sum of sin and cos functions, thus the Fourier spectrum. But the same signal can be decomposed, for example, in a series of wavelets. Or, maybe with some other kernel function instead of either sin/cos or wavelets. Now, is our arbitrary signal a sum of wavelets? Or a sum of sinusoids? Or some other sum of some other kernel functions? I think the correct answer is neither, our arbitrary signal is just the shape we see on the oscilloscope, not a sum of sinusoids, not a sum of wavelets, or any other sum. It's a happy coincidence that Fourier is particularly useful in engineering, somehow overlaps with the idea of superposition (which only holds for linear systems). Then we are all trained to apply Fourier only where it holds, and we turn a blind eye to what doesn't match, or we learn how to minimize Furrier side effects like the Gibbs artifacts, the need of windowing, the lost of causality. We do that all the time and only consider what we need out of it, up to the point where we start to believe a square wave is made of odd harmonics in a certain ratio, which is not really so. I consider that's just a form of gaslighting, though this time the gaslighting is just an innocent side effect with no malicious intent behind. I consider the sinusoidal components aren't really there, and that can be proved in practice. (An example of causality artifact can be seen in any digital oscilloscope, when sinx/x compensation is on, and the level of a constant 0 or 1 voltage starts to wiggle on the screen before an edge, as if the signal will somehow have premonitions and knows in advance when an edge will come - that's just a Fourier artifact, the logic level can not know the future.) I think it can be proved in practice that the sinusoids from the Fourier spectrum are not really there. For example, if we take a square wave of 1kHz, such a signal is supposed to have plenty of sinusoidal 3kHz in it, according to Fourier. However, one can make a filter that will only respond to a sinusoidal shape of 3kHz, and no other shape than sinusoidal. Such a 3kHz filter won't respond to a square wave of 1kHz, which indicates there is no sinusoidal 3kHz in our 1kHz square. I'll say that's a proof Fourier spectrum is not really there. Now, the funny thing is that what we casually call a 3kHz filter in EE will respond to a 1 kHz square, how so!?! :o But then, what's a filter? And how/why does it work? That's another simple yet difficult question to answer. --- End quote --- In the real world, to see if there be 3rd harmonic power in a square wave, do the following simple experiment with real equipment. I'm not aware of any realizable (in the technical sense) filter that would discriminate against non-sinusoidal waveforms in the way you suggest. Experiment: start with good square-wave generator, with reasonable rise and fall times, but with stable frequency (from a good clock oscillator); set frequency to 1.00 MHz. Connect directly to modern spectrum analyzer, with synthesized local oscillators and selectable filter bandwidth ("RBW"). Set RBW to minimum value available in the SA, and let the sweep width be set by the default manufacturer's value for that RBW. 1. Set center frequency to 1.00 MHz and observe signal level at 1 MHz. 2. Set center frequency to 3.00 MHz and observe signal level at 3 MHz. Will the ratio between those levels agree with the Fourier series of a 1 MHz square wave? |
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