As I've mentioned before, I keep a list of potential topics that I choose from now and then, and this week I thought I'd look back at an article that caught my interest 2 years ago. Scientific American had a story about a beetle that looked for recently-burned forests using a process called stochastic resonance. The beetles use this process to sense heat from great distances, when normally those heat signals would fall below the background levels. Paradoxically, they do this by adding more noise to the signal. I was curious if I could model this type of effect, to get a better feel for how it works.
In its simplest form, we have 3 parameters for this system: The signal strength, the amount of noise added to that, and the threshold for detection. The principle is that even if the signal is smaller than the noise, we still have signal + noise > noise. That means if we can pick our threshold so that noise < threshold < noise + signal, we'll be able to pick up the signal.
Following an example used in the Wikipedia article above, I decided to use a black & white image as the target signal. I settled on one of the more iconic photos of a certain physicist. Below, you'll find the 3 controls I described. Try turning down the overall signal, then adjust the noise and threshold to pick out different features.
Noise |
Threshold |
Signal |
No comments:
Post a Comment