@siddgangadhar1234
Prof. Brunton: I've been following your lecture series on the control Bootcamp and data-drive control for the past month or so and I absolutely adore the amount of effort you have put into them! I'm currently studying robotics at CMU and I really hope to meet you some day and shake your hand; it would be an honor!!
@NowanIlfideme
Another application that was in the news recently, as I understand, was the black hole image, where the astronomers had data from specific observatories which are semi-randomly scattered across the surface of the Earth. There they also had to select the image from probable ones that fit the model, so probably a bit more in-depth than shown here...
@rlrfproductions
Really appreciated the practical applications you mentioned near the end
@HelloWorlds__JTS
Note (mainly to my future self): Incoherence in this context (at 2:43) can be thought of as a measurement matrix C that doesn't preferentially select or exclude certain "frequencies" [or whatever conjugate variable is playing the role] over others.
@aliscander92
Great lectures! Steve, could you put all the lectures of Compressed Sensing in one playlist, please!
@jeanbaptisteemmanuelzorg5911
Thanks - I haven’t heard CS described so clearly before
@layaltannous112
Loved the example that was given at the end
@kylebeggs
Just when I thought these videos couldn't get better... now he is throwing jokes in them too 2:50
@Tman7855
Steve you are the MAN!
@sadattahmeed7462
Thank you professor. This was much helpful. How do I calculate the inner product of two matrices of different dimensions (in order to test their coherence)? We usually calculate inner product of vectors, so I am not sure how to do this with matrices. The answers I found online only applies for matrices of the same dimension :(
@fzigunov
This is awesome!! I wonder if our eyes are also are an example of compressed sensing, as we have lower density of cones/rods away from the center of the optic disk?
@EduardoGarcia-tv2fc
So that means that If C has certain properties it could become a filter? (in the example of 8:44 a high frequencies filter?)
@freakphysics
Incredible explanation, Steve. Thank you so much.
@fanalysis6734
I'm a bit confused by x=psi*s. If the data x is sparse in the fourier basis, would psi be the DFT or the IDFT matrix?
@raghibshahriar8447
question: for a x=50*50 pixels original image, guess y=250*1, s=2500*1 ,psi=2500*2500,and that means c=250*2500 but it was supposed to be 50*50 matrix as it says which pixels we are measuring of a 50*50 original image.
@ToufiqMdHossain
Thank you for this video professor.
@dongwengan612
How this video produced? I tried to record a video after a mirror while writing on the mirror and then use the mirror function on iphone to correct it. But it looks like I am using my left hand to write. Is this video record seperately or Mr. Bru write in left hand?
@Veptis
It's interesting to further I watch and I will watch more of the chapter. The random nature makes me question: I'd you build a camera where pixels are more randomly distributed instead of in a nice array: could you actually get the random sample much better than just taking a subsection of the signal? Is the random selection just spatial if you have two dimensional data like an image or could you also take like measurement noise as being the random factor(which is very close to actually random) for inferring the higher accuracy brightness level for pixel. Here you would think for the Luma value as a dimension, where just 0-255 is sparse and you want 0-4095 instead for example.
@pablo_brianese
Compressive sensing is beautiful!
@Seneca_dengo_dengo