As far as seeing and ignoring fixed objects, you can also remove any returns that have a near-zero velocity in radar and focus only on those objects that are moving.
Of course, indoor settings have a lot of non-stationary objects as well that might not be targets of interest to you, like fans, curtains blowing in the breeze, etc. So you can also develop algorithms to remove those signatures too.
Seeing fixed objects can be beneficial as well, for example, if you have a sensor deployed in a room but you don't know a priori what the room looks like. Longitudinal results and long range statistics can take you pretty far in seeing the room extents and layout and furniture, etc. Though a lidar sweep is better if you can get it
Oh Jua, it's cool to see that name. They've been on my radar since I applied to (and was rejected by) them earlier this year. I'll be interested to see the research paper once it is published next week, especially since they claim their model surpasses Aurora and Graphcast
I was about to say "they still torture students this way" but stopped myself when I remembered I took Circuits 1 and 2 back in 2007. So maybe my knowledge is dated too...
It's a weird butterfly effect moment in my career though. I had an awesome professor for circuits 1, and ended up switching majors to EE after that. Then got two more degrees on top of the bachelor's
Very cool project. There are some presentations by the PI on youtube that I recommend searching for. One of the interesting takeaways I had was that they were able to do better with mesoscale phenomena and extreme weather prediction than the other players (like Graphcast and Pangu and FourCastNet), in part due to their technique for training a higher resolution data space (0.1 deg vs 0.25 or 0.5). I also found it interesting that they were able to show a scaling relationship where performance increased by 5% every time they doubled the model size - and their loss was still improving when they had to cut it off due to cost constraints.
Yeah for sure. So say your category is "fruits and vegetables". You might say Avocado, Banana, Celery, etc. The goal is to challenge yourself, and it's more of a stretch than you'd think (but a good exercise). Or at least, it was a stretch for me especially at the beginning
This happened to me following a concussion. I worked with a therapist who suggested a "game" that could help my word recall. Every night before sleeping, pick a category and try to name an example from the category for each letter of the alphabet. Choose a different category every night so you're always challenging yourself.
Of course there's no substitute for working with a PT who specializes in post TBI recovery.
I've gained a lot of my recall back (though not at 100%).
I wouldn't read too much into it. UK is one of my alma maters. Everyone in that area of the US means "University of Kentucky" when they say "UK". It isn't a dig at the United Kingdom nor is it (I assume) an attempt to gain undue credibility by associating with the country. For the people there, UK as the University is simply the first order association for that acronym, rather than what is to them a faraway country that has no bearing on their day-to-day lives.
Of course, indoor settings have a lot of non-stationary objects as well that might not be targets of interest to you, like fans, curtains blowing in the breeze, etc. So you can also develop algorithms to remove those signatures too.
Seeing fixed objects can be beneficial as well, for example, if you have a sensor deployed in a room but you don't know a priori what the room looks like. Longitudinal results and long range statistics can take you pretty far in seeing the room extents and layout and furniture, etc. Though a lidar sweep is better if you can get it