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Recovering Data From Noise
An anonymous reader tips an account up at Wired of a hot new field of mathematics and applied algorithm research called "compressed sensing" that takes advantage of the mathematical concept of sparsity to recreate images or other datasets from noisy, incomplete inputs. "[The inventor of CS, Emmanuel] Candès can envision a long list of applications based on what he and his colleagues have accomplished. He sees, for example, a future in which the technique is used in more than MRI machines. Digital cameras, he explains, gather huge amounts of information and then compress the images. But compression, at least if CS is available, is a gigantic waste. If your camera is going to record a vast amount of data only to throw away 90 percent of it when you compress, why not just save battery power and memory and record 90 percent less data in the first place? ... The ability to gather meaningful data from tiny samples of information is also enticing to the military."
The military probably wants the ability to send/receive without revealing the data or the location of its source to the enemy. For example, its nuclear subs need to surface in order to communicate, and they don't want the enemy to be able to use triangulation to pinpoint the location of the subs. So, they make the data they're transmitting appear as noise. That way if the enemy happens to be listening on that frequency, they don't detect anything.
Because it's hard to know what is needed and what isn't to produce a photograph that still looks good to a human, and pushing that computing power down to the camera sensors where power is more limited than a computer is unlikely to save either time or power.
If you read the article, the rest of that quote makes a lot more sense. Here it is in context:
If your camera is going to record a vast amount of data only to throw away 90 percent of it when you compress, why not just save battery power and memory and record 90 percent less data in the first place? For digital snapshots of your kids, battery waste may not matter much; you just plug in and recharge. “But when the battery is orbiting Jupiter,” Candès says, “it’s a different story.” Ditto if you want your camera to snap a photo with a trillion pixels instead of a few million.
So, while this strategy might not be implemented in my Canon Powershot anytime soon, it sounds like a really great idea for exploration or just limited resources in general. I was thinking more along the lines of making really crappy resolution low power cameras that are very cheap but distributing them with this software that takes the images on your computer and processes them to make them highly defined images.
My work here is dung.
In fact, it's expected to be used to increase the aperture of cameras. The advantage of this, is that using random patterns you could be able to determine the kernel of the convolving pattern in the picture, therefore, you would be able to re-focus the image after it was taken. In regular photography that kernel is normally Gaussian and very hard to de-blur. But using certain patterns when taking the picture (probably implemented as micro-mirrors), you could, easily do this in post processing.
MOD PARENT UP for this: "This algorithm doesn't create absent data nor does it infer it, it just makes the uncertainties it has "nicer" than the usual smoothing."
Fraud alert: The title, "Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples" should have been "A better smoothing algorithm".
In fact, it's expected to be used to increase the aperture of cameras. The advantage of this, is that using random patterns you could be able to determine the kernel of the convolving pattern in the picture, therefore, you would be able to re-focus the image after it was taken. In regular photography that kernel is normally Gaussian and very hard to de-blur. But using certain patterns when taking the picture (probably implemented as micro-mirrors), you could, easily do this in post processing.
You people think in such limited terms. The military uses rapid frequency shifting and spread spectrum communications to avoid jamming. Such technology could be used to more rapidly identify the keys and encoding of such transmissions, as well as decreasing the amount of energy required to create an effective jamming signal by several orders of magnitude across the spectrum used if any pattern could be identified. Currently, massive antenna arrays are required to provide the resolution necessary to conduct such an attack. This makes the jamming equipment more mobile, and more effective at the same time. A successful attack on that vector could effectively kill most low-power communications capabilities of a mobile force, or at least increase the error rate (hello Shannon's Law) to the point where the signal becomes unusable. The Air Force is particularily dependent on realtime communications that rely on low-power signal sources.
If nothing else, getting a signal lock would at least tell you what's in the air. Stealth be damned -- you get a signal lock on the comms, which are on most of the time these days, and you don't need radar. Just shoot in the general direction of Signal X and *bang*. Anything that reduces the noise floor generates a greater exposure area for these classes of sigint attacks. Cryptologists need not apply.
#fuckbeta #iamslashdot #dicemustdie
Amusingly enough, the idea of compressed sensing (I will rephrase for clarity) that a minimal sampling is needed for working with high dimensional data that can be described in a much smaller subspace at any given time has been used to describe neural processes in the visual cortex (V1). [See Redwood Center for Theoretical Neuroscience, https://redwood.berkeley.edu/%5D. The lingo used is a bit different than the CS community, but the math is essentially the same. The point being that compressed sensing could lead to answers a lot more natural for human perception than simply canceling out high frequencies.
Also the point is that CS leads to [near] perfect reconstruction for signals of a certain nature rather than the fuzzyness that comes from some other algorithms that do not take the inherent sparsity of the signal into account.
@humanity: *facepalm*