cool idea. care to share more? my thought would be continuously measuring antenna impedance over time, and looking for a peak in this signal's spectrum near the rotation frequency. could also try with a photodiode (+ optional light source, or use ambient light) (this would be similar to an optical computer mouse), which i guess could be more directional, and thus give a stronger signal
Combat robots are more like RC cars than autonomous robots, the driver stays in control of the bot over a radio link. I use ESP-NOW, and get about 6000 packets per second from the transmitter to the ESP32 in the box, and each comes with radio link stats, signal strength and Channel State Information. Using autocorrelation on the signal strength gives you the rotation period, and using that you can establish a Phase Locked Loop to remove the drift of integrating rotation speed to get absolute rotation angle.
wow, i had heard of transistor thermal runaway, but i didn't know you could reach silicon melting point temperatures. though i guess in hindsight it makes sense that, considering that during the manufacturing process semiconductors are already exposed to very high temperatures, if you are instantly permanently altering a device with heat (as you do when you destroy it through thermal runaway), you must be reaching comparably high temperatures.
what type of people look at heat transfer at the device level in an IC (and at such short time scales)? where can i learn more about that? this looks like it could have an impact in analog power circuits, because even if you don't destroy the transistor you could alter its characteristics, but i haven't seen a lot of attention being paid to that. personally i guess i figured individual transistors never went significantly beyond the temperature ranges we already consider for the circuit as a whole.
the biggest question for me is how robust are these designs.
in the journal articles they did show measurements of real devices which agreed fine with predictions, but i didn't find them addressing it explicitly in the text. also, some systems they presented contained subblocks that were conventionally designed that could be carrying some of the weight.
or maybe i'm just sour that they're coming for my job? or maybe that's what they want us to think?
i think what wins in practice is simple ideas that can work in spite of all manufacturing and environment variations, and model limitations -- think stuff like feedback and symmetry. and what they show here is the opposite of that.
i've done blind optimization of circuit parameters some times only to end up realizing some pretty simple such ideas that i'd missed (like "you need symmetry here" or "you just need more bandwidth here") and made complete sense when you thought about them. so i wonder if we can't tweak a few pixels in their structures and reveal something simpler.
> but i didn't find them addressing it explicitly in the text
Yes, this is exactly what bothers me about this article and about a few similar articles published in the past, that they do not contain any evidence that their claims about the usefulness of AI in design are true.
In TFA it says that the role of AI is replacing the electromagnetic simulator in the optimization process, by guessing the behavior of the structure, which is many orders of magnitude faster than a simulation.
This sounds plausible, but in order to believe this I would want to see the differences between AI guesses and real measurements, in the case of structures with geometries that are very different from those used in the training of the AI.
Also I would want to see exactly with which simulators they have compared the speed of the AI model.
There are various simulation approaches for electromagnetic fields and electronic circuits, that can trade-off accuracy for speed, so I am not convinced that AI inference takes necessarily much less time than some faster low-accuracy methods of simulation, which would still be more accurate and more reliable than AI guesses.
Since you beat me to it, I'll add something that relates relates you were saying on "realizing some pretty simple... ideas".
I think a big plus of computer aided design like this is "innovization"[1]. Somewhat awkward term. But, a system like this leading one to deeper understanding of a particular process is the general idea. It's a fun feeling in practice.
> How generalizable are these methods? Can they consistently deliver truly high performance? Can we get to a place where AI produces designs that maximize every conceivable trade-off, holistically optimizing every parameter to its most ideal physical state? .... AI can hallucinate a design that creates bad circuits that don’t work. This means verification methods need to remain under human oversight.
And they are essentially correct. We need better validation and verification methods, both software and hardware to keep in check the mistakes of automated random processes.
> the biggest question for me is how robust are these designs.
Maybe it doesn't matter?
I mean, of course it matters. But most of this sort of design space is effectively NP-complete, where the creation starts with a blank schematic page and has an impossibly large search space, but where the checking of the design is much simpler.
> also, obligatory mention: "genetic antennas"
Exactly. How does this work? When confronted with the question, of course, everybody gets all excited about the constrained randomness of the GA, but if you think about it, what really makes it work is that there is a comparatively cheap test for fitness for purpose.
