just kidding .. well .. sort of..
About a month ago I noticed my left shin and foot had swollen enormously. There are a few nasty possibilities and a close friend died from one in December, so it was off to the emergency care unit. It turned out to be something easy to deal with, but the exams were interesting. I have this fascination for instrumentation and had a nice conversation with the ultrasound operator. He didn't know what one of the settings on his machine was doing. The name was a giveaway so we talked. In return he gave me a detailed tour of the innards of my legs (good and bad one) and a few other places too. The interface was aimed at experts, but very interesting. He had a rich menu of information to display on the screen - often images and graphs. He could also choose to represent some of then acoustically over speakers or earphones. He said he almost always used the visual displays along with simultaneous audio as combining the channels was much more informative than either alone. It took me back to my first big particle physics experiment.
At a conceptual level particle physics is easy - smash particles together and sort through the debris. The problem is the easy stuff's been done. More exotic interactions are rare and happen at energies high enough that most of the interactions are complicated mixes of well studied results. All of this happens at a furious rate much higher than is encountered anywhere else - there is no way to save all of the data you're measuring. You need to be able to throw away what isn't interesting while keeping the tiny bit that is. When I started we kept one in a few thousand events. Now some of the larger experiments are more like one in a million. This is done with with triggers.
Triggers are logical groupings of signals that give you an early hint, think a hundred nanoseconds or so, that something is candidate for further study. Conceptually it's something like this electron over a certain energy hits this part of your detector, this other part of the detector is completely quiet, and another particle is coming out at nearly a right angle to everything else. It may or may not be real - that's for further study, but it happens it's worth saving. The trigger fires and you save it. We had about a dozen triggers. It was often convenient to look at an event soon it took place to learn about your experiment and possibly even find a hint of something interesting. That's where the title of the post comes in.
A few years before this experiment I had seen Close Encounters of the Third Kind. A meh as a film, but the music caught my attention. The aliens used music to communicate. To signal them, scientists sent played five notes: G, A, F, F (an octave down) and C.
When the spacecraft arrives they used a variation on the theme and the aliens respond. Then something of a jam session takes place.
Designing my triggers it struck me that some had cluster groupings in the detector that could be expressed as a unique three note cord. I knew about synthesizer music (Switched on Bach and Walter/Wendy Carlos) and sort of borrowed a dozen frequency generators from the undergrad teaching lab (they had a lot of them). A bit of tuning and I had a full musical scale. Some of the most important triggers were F major (F-A-C), C major (C-E-G), and D major (D-F#-A) .. there were a couple of minor chords. It worked and proved itself. It was my gateway drug - I've been interested in sound as data and as a computer-human interface ever since. Data sonification can be a nice way to handle human data overload. We seem to be wired for it (some of us more than others), but modern interfaces rarely use it.
Then there's listening...
Imagine a manager stepping onto a railing overlooking the production floor in a 19th century textile mill. He (sadly always a he) would look around, listen and maybe even smell. Machines slightly out of spec would stand out .. a sour note in the harmony. Moving forward most car mechanics use sound as a diagnostic tool and even listen to customer renditions to narrow the problem space. I use it to sort out issues with the washing machine and dryer - usually as a diagnostic tool when something breaks, but I'm beginning to use it as an indicator of future failure.
Consider mechanical devices that may be difficult to fully incorporate into data analysis programs. A feature of mechanical devices is the units of the same model are unique. An xyx -102 refrigerator in your home probably has somewhat different operating characteristics than the same model in your neighbor's home. Monitoring such devices and feeding using the information for optimal efficiency and lifetime can be difficult - especially on older existing hardware. A good example are chillers used in building-scale cooling. Their efficiency improves with increasing load - often dramatically until you reach a point where the unit shakes and is in danger of failing. Each chiller is different. A human operator usually is in charge of several and learns about their individual quirks. A lot of valuable information - often hundreds of thousands of dollars worth - is locked up in a few local experts. An expert from a different building would have a learning curve. Bringing information out of the the human is a major challenge. You have to be clever. You spend a lot of time with chillers.
A neat trick is to study the acoustic signature of chillers as they approach and go through their point of maximum efficiency. They begin to emit a banging sound that changes to a screeching noise. They shake on their mounts. If you can begin to detect this you can back them off a bit and stay near their maximum efficiency while prolonging their life. The easiest way is a microphone or a vibration sensor (which is all a microphone is). It is pretty simple to digitize this and move it into a computer than can offer feedback to the machine control.1 It can also feed into a machine learning program so you can begin to characterize all of your chillers prolonging their lives and predicting when they will fail so maintenance takes the place of failure.
Getting data about the world around us can take a wide variety of sensors, but shortcuts are emerging. Cameras output images, but add a computer and they can do quite a bit more: image recognition, crop analysis, temperature (infrared cameras) ... the list is dramatically expanding. We mostly neglect sound, but microphones are a simple and often inexpensive sensor that with a computer can do much more. Data streams from these powerful computational enhanced sensors can be integrated with other data streams for real machine learning. Opportunistic data hunting.
I have a few of personal interests along these lines. One is the acoustic ecosystem signature of ecosystems. The local woods have coywolves. I've put digital recorders out there just to capture their howls, but it is clear the background sounds might be important. Flora and fauna have their own signatures spanning large frequency and time domains. This is probably near ground floor fun for amateur scientists. I can even imagine it being useful in many sports.
Now think about other senses - there are many more than five.
__________
1 An Arduino and a piezoelectric buzzer/speaker used as a microphone is probably overkill for an application like this.
Comments