_ks3e
Joined 14 years ago
Joined 14 years ago
[1]: https://docs.kenp.io/airpods-max/usb-c-kit/v1dot1_assembly/
I suspect it's fairly challenging to implement since the LIDAR sensor doesn't operate through the lens, so you'd have to continuously align the depth map with the image to account for parallax; plus it's only useful for close-ish distances (since the lasers can't be too powerful) and can cause unwanted focus behavior with windows or reflections.
Is your approach specific to the case where the matrix fits inside cache, but the memory footprint of the basis causes performance issues? Most of the communication-avoiding Krylov works I've seen, e.g [0,1] seem to assume that if the matrix fits, so will its basis, and so end up doing some partitioning row-wise for the 'large matrix' case; I'm curious what your application is.
[0] https://www2.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-..., e.g. page 25. [1] https://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-...
It's a weird process and not particularly user friendly (passkey accounts are tied to a specific passkey and can't have additional ones added, so you need to create a new account if you, say, migrate from one hardware key to another). Hopefully they improve the process before passkey support goes out of beta.
It seems like a system like this would need to respond very quickly to changes in the air mass, and the weight and slow response of an airliner might make this system less feasible unless you could somehow measure airflow a reasonable distance in front of the plane.