Can someone help me with the matlab code? At the line 12, minimize(norm(s_L1,1));, I'm getting an error which says "Unrecognized function or variable 'cvx_bcompress_mex'. " I've already downloaded and set up the cvx package. I didnt understand what this error means?
Very informative video. Thank you so much. I am quite new to this area and have one question. Correct me if I am asking something silly: with the equation y = C * Phi * s , assume we have an image sensor + binary mask in front of image sensor(0: opaque, 1: transparent). The transparent part on the mask actually represents C. My question is how we design C. I know Phi is DCT related parameters, but how do we design the random binary mask C ?
Sir ......can machine learning is also helpfull in solid mechanics...... If yes then please make a video on it...... I am about to do research in field of solid mechanics so please tell me
I was thinking, could we possibly use some norm other than 1? maybe (this is a complete guess) the naturally occuring signals tend to minimize the sqrt(2) norm. Maybe the l1 norm strips away too much
Thank you professor for presenting nice and useful information and can you please informe me how to compress an image by using compressive sensing Thanks in advance professor.
Here are videos for how to compress an image in Python (th-cam.com/video/uB3v6n8t2dQ/w-d-xo.html) and in Matlab (th-cam.com/video/KGiV_2i713I/w-d-xo.html)
Actually, in compressed sensing we are not going to compress an existing captured image rather we are going to capture a compressed image. Compressive sensing is not a compression technique after capturing the image but it is before capturing.
Thank you for the awesome video! I wonder in the case of the presence of noise (at 7:00 of the video), what happens if we use: subject to 'norm(y-theta*s, 1)', instead of 'norm(y-theta*s, 2)'? A more sparse solution?
Because it's placing a constraint on the distance between y and Theta*s, I think L1 and L2 would work similarly (exactly if we adjust the threshold properly). i.e. |x| < 0.001 and x^2 < 0.001^2 are exactly the same.
Attempting to adapt this to an image, but my code does not go brrr so much as ker-chunk-chunk: stackoverflow.com/questions/66510827/compressed-sensing-why-does-my-convex-solver-fail-when-i-give-it-more-sample Any advice is most appreciated.
FFT, Walsh Hadamard are very tuned to the natural world and will concentrate that type of information down to a few dimensions. Which is unfair sharing. A random projection won't concentrate such information at all and gives fair sharing. At a price. The response to information at one part of an image will be completely different to the same information at different part of the image. A random pattern of sign flips before a FFT or Hadamard give a rough random projection. Repeat for better quality. What would be a better pattern of sign flips that still gave reasonable sharing with more regular response across say an image. I was kind of thinking the parity of an incrementing counter would be a good choice for generating such sign flips. I'll give it a try.
Of course the (Walsh) Hadamard transform is very tied up with parity and Gray codes. For a counter c running between 0 and say 255 the parity function parity(c and s) which outputs +1 or -1 depending on parity even or odd gives the different sequencies of the transform. The basis vectors. For s=0 you get the all 1's vector (sequency 0). For s=128 you get sequency 1 where the first half of the vector is +1's and the second half is -1's. There is one transition between values. The most complicated waveform (but not the highest sequency) is parity(c and 255). Which is very unlikely to occur in natural world data, yet is not random.
That's not going to work. It will just shift around the order of the sequencies. I'll have to think of some other pattern of sign flips that is sub-random and approximately orthogonal to the basis vectors of the Hadamard transform.
SIR, I KNOW YOU HAVE MADE A VIDEO ABOUT KALMAN FILTER MORE THAN AN YEAR AGO SIR BUT THIS IS A SPECIAL REQUEST FROM ME SIR COULD YOU PLEASE MAKE A VIDEO ON HOW TO USE KALMAN FILTER WITH ARDUINO. SIR, PLEASE MAKE A VIDEO ON IT IF YOU CAN 👍👍
Incredibly helpful series! Awesome work!
You're priceless dear Mr Steve
Thanks so much!
Can someone help me with the matlab code? At the line 12, minimize(norm(s_L1,1));, I'm getting an error which says "Unrecognized function or variable 'cvx_bcompress_mex'. " I've already downloaded and set up the cvx package. I didnt understand what this error means?
Very informative video. Thank you so much. I am quite new to this area and have one question. Correct me if I am asking something silly: with the equation y = C * Phi * s , assume we have an image sensor + binary mask in front of image sensor(0: opaque, 1: transparent). The transparent part on the mask actually represents C. My question is how we design C. I know Phi is DCT related parameters, but how do we design the random binary mask C ?
Sir ......can machine learning is also helpfull in solid mechanics...... If yes then please make a video on it...... I am about to do research in field of solid mechanics so please tell me
I was thinking, could we possibly use some norm other than 1? maybe (this is a complete guess) the naturally occuring signals tend to minimize the sqrt(2) norm. Maybe the l1 norm strips away too much
People do use other norms, p norms. p>=1 will maintain convexity. 0
What about the next step? We have Theta=C*Phi, how do we get Phi to calculate x?
it would be cool to show how hermite transform work, it is popular as sparse domain. beside dft and dct or wavelet
Thank you professor....
Thank you professor for presenting nice and useful information and can you please informe me how to compress an image by using compressive sensing
Thanks in advance professor.
Here are videos for how to compress an image in Python (th-cam.com/video/uB3v6n8t2dQ/w-d-xo.html) and in Matlab (th-cam.com/video/KGiV_2i713I/w-d-xo.html)
Actually, in compressed sensing we are not going to compress an existing captured image rather we are going to capture a compressed image. Compressive sensing is not a compression technique after capturing the image but it is before capturing.
Thank you for the awesome video! I wonder in the case of the presence of noise (at 7:00 of the video), what happens if we use: subject to 'norm(y-theta*s, 1)', instead of 'norm(y-theta*s, 2)'? A more sparse solution?
Because it's placing a constraint on the distance between y and Theta*s, I think L1 and L2 would work similarly (exactly if we adjust the threshold properly). i.e. |x| < 0.001 and x^2 < 0.001^2 are exactly the same.
How do you download cvx ?
Sir can u please make a video on restricted isometry property.
Attempting to adapt this to an image, but my code does not go brrr so much as ker-chunk-chunk:
stackoverflow.com/questions/66510827/compressed-sensing-why-does-my-convex-solver-fail-when-i-give-it-more-sample
Any advice is most appreciated.
FFT, Walsh Hadamard are very tuned to the natural world and will concentrate that type of information down to a few dimensions. Which is unfair sharing. A random projection won't concentrate such information at all and gives fair sharing. At a price. The response to information at one part of an image will be completely different to the same information at different part of the image.
A random pattern of sign flips before a FFT or Hadamard give a rough random projection. Repeat for better quality. What would be a better pattern of sign flips that still gave reasonable sharing with more regular response across say an image. I was kind of thinking the parity of an incrementing counter would be a good choice for generating such sign flips. I'll give it a try.
Of course the (Walsh) Hadamard transform is very tied up with parity and Gray codes. For a counter c running between 0 and say 255 the parity function parity(c and s) which outputs +1 or -1 depending on parity even or odd gives the different sequencies of the transform. The basis vectors. For s=0 you get the all 1's vector (sequency 0). For s=128 you get sequency 1 where the first half of the vector is +1's and the second half is -1's. There is one transition between values.
The most complicated waveform (but not the highest sequency) is parity(c and 255). Which is very unlikely to occur in natural world data, yet is not random.
That's not going to work. It will just shift around the order of the sequencies. I'll have to think of some other pattern of sign flips that is sub-random and approximately orthogonal to the basis vectors of the Hadamard transform.
SIR, I KNOW YOU HAVE MADE A VIDEO ABOUT KALMAN FILTER MORE THAN AN YEAR AGO SIR BUT THIS IS A SPECIAL REQUEST FROM ME SIR COULD YOU PLEASE MAKE A VIDEO ON HOW TO USE KALMAN FILTER WITH ARDUINO. SIR, PLEASE MAKE A VIDEO ON IT IF YOU CAN 👍👍
Thanks for the request. I would check out Brian Douglas's channel, as he has lots of great control+hardware videos.
@@Eigensteve thankyou sir