At most, the avalanche is detected within five minutes of its occurrence. The detection workflow processes five minute chunks of data at a time and only pulls out the significantly powerful events using spectral flux based thresholding. This event selection acts as a filter, removing the small, insignificant non-events before classification.

To speed things up, the event selection could be performed in an online fashion (as the data is being received)- at which point the avalanche could be detected within the first five seconds!!

I hope that answers your question!!

Compressive sampling (or sensing) has been growing ever since its formulation in the mid 2000’s. Instead of trying to explain how compressive sampling (or sensing) works in this small area (and without mathematical symbols), I will instead direct you to three publications of increasing complexity. The first is from Wired magazine…

1) “Fill in the Blanks: Using Math to Turn Low-Res Data Sets Into Hi-Res Samples”
http://www.wired.com/magazine/2010/02/ff_algori...

2) “Compressed Sensing: Making Every Pixel Count”
http://www.ams.org/samplings/math-history/hap7-...

3) “An Introduction to Compressive Sampling”
http://dsp.rice.edu/sites/dsp.rice.edu/files/cs...

I’m sorry for my lack luster answer, but it’s simply too hard for me to convey how compressive sampling works without mathematical symbols, a white board, or even just hand gestures..

Compressive sampling has been used in all sorts of domains, including MRI data acquisition and reconstruction, geophysical inversions, and (so-called) analog to information conversion. A team at Rice University even created a single pixel camera!!!

Rice University maintains a terrific list of resources and publications related to compressive sampling. Here’s a link: http://dsp.rice.edu/cs

There are two sets of previous research that my work improves upon. The first comes from France and the second from Iceland. In the French version, researchers were able to reach >90% accuracy in classifying avalanches, but the classification scheme was NOT TRAINED AUTOMATICALLY! Instead, the French researchers had human experts create fuzzy logic rules to discern, for example, an avalanche from thunder. My research improves upon this work by making the training completely automatic; the workflow does not require any human judgement other than identifying several avalanches in the initial training set. In the Iceland version, researchers automatically trained machine learning models to identify avalanches, but only reached 65% accuracy. My work, on the other hand, reaches >90% accuracy with fully automated training.

We use a simple star topology with long-range radios (802.15.4 XBee Pro with one mile LOS range). Additionally, we sleep the radio and employ a double buffering scheme to reduce the radio’s uptime.

In terms of power consumption, the sensor nodes must be designed to last all winter without battery replacement. This is because the sensor nodes have and will be deployed in potentially dangerous (avalanche-prone) areas that are difficult to reach. Thus, in our Swiss installment we used a 10W solar panel and 30 Ah battery. (There is the added challenge of poor battery performance at cold temperatures to boot).

Because of the added cost per sensor node (i.e., solar panel + charge controller + larger battery), decreasing the sensor node’s power consumption (using compression, for example) is of paramount importance. Additionally, this is why compressive sampling is so attractive- we can place hard guarantees on the amount of compression (and thus power consumption) that will occur.

Thank you!! I had a great time putting this video together..

To answer your question, there are two sets of previous research that my work improves upon. The first comes from France and the second from Iceland. In the French version, researchers were able to reach >90% accuracy in classifying avalanches, but the classification scheme was NOT TRAINED AUTOMATICALLY! Instead, the French researchers had human experts create fuzzy logic rules to discern, for example, an avalanche from thunder. My research improves upon this work by making the training completely automatic; the workflow does not require any human judgement other than identifying several avalanches in the initial training set. In the Iceland version, researchers automatically trained machine learning models to identify avalanches, but only reached 65% accuracy. My work, on the other hand, reaches >90% accuracy with fully automated training.

Geography affects the effective distance and line of sight direction of the wireless transmissions. The geology alters the seismic waveforms and velocities as well; e.g., the seismic signal from the avalanche will travel faster in granite than in sedimentary rock..

Few avalanches go undetected.. Even if the avalanche is “remote”, i.e., far away from a major highway or recreational area, then it’s still useful information. In avalanche forecasting, knowing when and where avalanches are occurring is really useful information. Often times an avalanche will run on an “indicator path” before sliding on something with higher risk..

That’s an interesting proposition.. i.e., trying to identify riders in the seismic data. Another more practical way would be to use infrared beam sensors at the top and bottom of a known slide path, and track how many people visit a particular ski run. (We’ve built two different prototypes of a system called SkinTrack that was designed to count the number of people and beacons going through backcountry access gates..). Perhaps we could combine our technologies..?

Thanks for the question!

Here’s an article about “SkinTrack”: the backcountry access gate monitoring system that we have been working on: http://arc.lib.montana.edu/snow-science/objects...