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I am not sure what you mean by compressed sensing - if you're only interested information such as "child-sized object moving rapidly across field of view", then

I am not sure what you mean by compressed sensing - if you're only interested information such as "child-sized object moving rapidly across field of view", then you can get away with smaller sensors, but here, CS is also talking about faces being recognized in a crowd. That's going to require a relatively bulky camera system, just because you need to collect a large amount of photons (which requires an aperture/lens of certain size), and record them on a sensor which can decide which direction they came from (also needs a certain size). This is the reason why flies and bees have eyes that are (small) webcam-sized, and they probably can't pick out facial features, even if they cared to or had the brain to process them. For the sequencing, I am really a lot less sure what's ultimately possible, but I wanted to call attention to possible overhyping. If you read the manufacturer's advertisement for the USB genome scanner, it starts out by saying that it accepts a drop of saliva or blood and starts producing a genome. Then it sort of segues into saying that you'd want to separate out the chromosome which you want sequenced (which involves centrifuging the cells to separate the chromosomes by size), and cut that chromosome into manageable pieces (wet chemistry, more centrifuging), and then it will read most of that chromosome in its 6-hour lifetime, but of course you'll only have piecewise sequences to put together. Uh, that was not what you said in the beginning, and I have a feeling there are more gotchas - this is, after all, still just the advertisement.

maxerickson 14y ago
So how about finding the chromosomes using some sort of molecular sieve? Or thousands and thousands of them, if they don't work very well. I'm asking that from a completely naive point of view, but it seems to me that there is a good chance of completely new methods being created, not just the scaling down of existing methods.
Dn_Ab 14y ago
A good explanation of compressed sensing: http://terrytao.wordpress.com/2007/04/13/compressed-sensing-and-single-pixel-cameras/ http://terrytao.wordpress.com/2007/04/13/compressed-sensing-.... I don't see it working for facial recognition as is but who knows what will happen when you combine this, clever linear coding, local networks and ubiquitous computing. Faces are highly compressible wrt recognition (we do it quickly at poor conditions). I have little doubt that certain timings, exploiting patterns in movements and clever arrangements of the devices will allow tiny, energy efficient cameras to recognize even faces. For sequencing, I agree. I don't see having a bunch of tiny sequencers auto sequencing everything to be particularly feasible or leading to anything like what he mentions. But then again, we are both thinking linearly when the tech is improving 'exponentially'. It is possible that sequencers will be chemical computers or DNA based or who knows? Current preprocessing limitations and seperability difficulties could turn out to be one of those "I can't believe they used to think that was hard!" moments. Regardless, the scenario itself, that of being able to monitor vastly more data at a biological and ecosystem level is almost certain. And that is what is important - not nitpicking the details of implementation. What happens to society when you have sensors and substantial computing power everywhere? This is one of the scenarios in Vinge's singularity...
anamax 14y ago
> I am not sure what you mean by compressed sensing Google "compressed sensing" Add "site:stanford.edu" for some focus. > faces being recognized in a crowd. That's going to require a relatively bulky camera system, just because you need to collect a large amount of photons (which requires an aperture/lens of certain size Turns out that you don't. See Candes talk on http://www.stanford.edu/class/ee380/ay1011.html http://www.stanford.edu/class/ee380/ay1011.html and Baron's talk on http://www.stanford.edu/class/ee380/ay0607.html http://www.stanford.edu/class/ee380/ay0607.html
- labcomputer 14y ago
  I'm not sure what this comment is supposed to add. Maybe to say that you don't need to resolve an image very well to detect a face? In any event, the resolution of your imaging system is fundamentally limited by it's aperture. That is a limitation dictated by physics. You can play fun games with synthetic aperture provided that you can recover the phase of the incoming radiation, but recovering the phase of 700nm radiation is really hard to do. There seems to be a misconception that a single-pixel detection system is somehow immune to diffraction, but this is simply not the case.

maxerickson 14y ago

So how about finding the chromosomes using some sort of molecular sieve? Or thousands and thousands of them, if they don't work very well. I'm asking that from a completely naive point of view, but it seems to me that there is a good chance of completely new methods being created, not just the scaling down of existing methods.

Dn_Ab 14y ago

A good explanation of compressed sensing: http://terrytao.wordpress.com/2007/04/13/compressed-sensing-and-single-pixel-cameras/ http://terrytao.wordpress.com/2007/04/13/compressed-sensing-.... I don't see it working for facial recognition as is but who knows what will happen when you combine this, clever linear coding, local networks and ubiquitous computing. Faces are highly compressible wrt recognition (we do it quickly at poor conditions). I have little doubt that certain timings, exploiting patterns in movements and clever arrangements of the devices will allow tiny, energy efficient cameras to recognize even faces. For sequencing, I agree. I don't see having a bunch of tiny sequencers auto sequencing everything to be particularly feasible or leading to anything like what he mentions. But then again, we are both thinking linearly when the tech is improving 'exponentially'. It is possible that sequencers will be chemical computers or DNA based or who knows? Current preprocessing limitations and seperability difficulties could turn out to be one of those "I can't believe they used to think that was hard!" moments. Regardless, the scenario itself, that of being able to monitor vastly more data at a biological and ecosystem level is almost certain. And that is what is important - not nitpicking the details of implementation. What happens to society when you have sensors and substantial computing power everywhere? This is one of the scenarios in Vinge's singularity...

anamax 14y ago

> I am not sure what you mean by compressed sensing Google "compressed sensing" Add "site:stanford.edu" for some focus. > faces being recognized in a crowd. That's going to require a relatively bulky camera system, just because you need to collect a large amount of photons (which requires an aperture/lens of certain size Turns out that you don't. See Candes talk on http://www.stanford.edu/class/ee380/ay1011.html http://www.stanford.edu/class/ee380/ay1011.html and Baron's talk on http://www.stanford.edu/class/ee380/ay0607.html http://www.stanford.edu/class/ee380/ay0607.html

labcomputer 14y ago
I'm not sure what this comment is supposed to add. Maybe to say that you don't need to resolve an image very well to detect a face? In any event, the resolution of your imaging system is fundamentally limited by it's aperture. That is a limitation dictated by physics. You can play fun games with synthetic aperture provided that you can recover the phase of the incoming radiation, but recovering the phase of 700nm radiation is really hard to do. There seems to be a misconception that a single-pixel detection system is somehow immune to diffraction, but this is simply not the case.