New Sampling Techniques Make Up For Lost Data

An unnamed reader writes: "Professors at Vanderbilt and the University of Connneticut have published a non-uniform sampling theory that could yield better quality digital signals than the standard Uniform sampling techniques pioneered by Shannon at Bell Labs. The Vanderbilt press release and link to the published paper can be found here."

Comments

Some useful niche applications

[michaelmalak]

Think about computer displays. Would you ever want to have to deal with non-square pixels? Sometimes, yes, like in the CGA days where the goal was to display 80 columns while keeping memory and bandwidth costs down. In general, it's a PITA. Now multiply that pain by not only having non-square pixels but where the pixels also come in various sizes.

What's the practicality of this? Well, spiral MRIs, for example, where for mechanical reasons you don't want to have to stop-and-start the very heavy "scanner", wasting time and jarring sensitive equipment. As I said, niche applications.

As for compressing audio, there are already plenty of other psychoacoustic compression schemes -- whether non-uniform sampling is better or worse will likely depend on the application.

Re:Brain scans?

[Hal-9001]

Any medical imaging technique can only be so accurate, due to either machine or physical limitations. This defines a maximal meaningful sampling rate or resolution for that imaging modality. For example, positron emission tomography (PET) has a physical resolution limit of 10mm because positrons can propagate up to 10mm from where they are generated before they decay into gamma radiation that can be detected by the machine. With this technique, a doctor can get an image with better than 10mm resolution, something that the machine by itself could never do.

BTW, sampling doesn't mean that you're guessing. The sampled data points are the actual measured values of the signal at specified points in time or space. You have to sample because there is no way that you could collect all values for the signal for all points in time or space, and there is usually a sampling rate at which point you're collecting more data than you need to accurately represent the signal.