It sounds pretty good too, but it’s a little hummy – which I think is probably the breadboard.  A very simple circuit, but has a couple options for modding.  There’s a low-pass switch you can throw, and you can swap out the capacitor for different amounts of low-pass.  And you can mess with the kinds of diodes you use, and whatnot.  Fun project, with a Russian name, so I’m going to have fun painting this one.

Most of the trouble with this project was hacking through the various electronics components grab bags I bought to find the right parts.  The numbering schemes on the caps in particular are tricky.  I got a few that I think are caps, but I’m still not sure.  And how do I tell what kind a random diode is?  If I ever find out, I’ll let you know.

This was my workspace on 2-5-2010. Click for a humongous version (1.9mb).

Items of note:

• Ohm’s Law
• Medicine Man balsa wood glider (half finished)
• Make:Electronics book, Maker’s Notebook
• Woolly Mammoth clone guitar pedal, nearly done
• 2.5 gallon fishtank, testing out temperature logging via LM34 and Arduino (see FishApp) for more details.
• There are no less than five computers on/around my desk. Not all are visible.
• Small cheap telescope
• Printing plate of some old ship
• Guitars.
• More guitars.

The mess?  Oh, that just means I’m getting work done.

To refresh your memory, the FishApp keeps track of water changes, and gives you a graph showing weighted-age values for the water in your fish tank.  This requires you to pay attention while you’re doing the water change, and to log in to the website and report how much water you changed when.  Well, I want to have the FishApp sense, measure, and publish water changes for me.  To those ends, I designed a system that can measure the water level in the tank over time, report it to a computer, figure out when and how much water was changed, and report that back to the main fishapp web application.  The measurement is done using a Ping))) ultrasonic rangefinder, and data from that (and other sensors) is fed into a computer.

But just how is the computer supposed to figure out when a water change happened?  It’s input is just a string of numbers, and it’s gotta be smart enough to filter out random noise from tank cleanings, frisky fish, the water filter starting and stopping for one reason or another, or any of a hundred other situations.  What to do?

If you’ve read the last post about the FishApp, you know where to start – smooth out the data.  To recap, I have the sensor set to read the water level every half-second, and report it to the computer.  The raw data is pretty noisy:

but if we make new data points from the median of  every 20 samples, things smooth out pretty quickly:

The system should be able to handle this muuuch easier.

The algorithm works by keeping a queue of recent (smoothed) samples.  By comparing the oldest sample with the newest sample, you can get a “slope” value.   On the graph above, for example, it doesn’t take very many samples until the oldest will be just over 600 but the most recent will be around 800 or so, and you’ll have a large positive slope.  Once the algorithm sees this large positive slope (above a certain trigger value), it knows that a water change is beginning, and notes the water level beforehand.  At some point in the process of a water change, I start filing the tank up again, and we see a large negative slope (around 55-60 on the graph above).  The algorithm notes the capitulation and the minimum water level.  If the absolute value of the slope stays low enough for long enough, the algorithm detects a steady state, and calls it the end of the water change.  The “steps” you see on the graph are because I do my water changes bucket-by-bucket, but because the algorithm is using a slope from a queue maybe 10 samples long, it already does a pretty good job of smoothing these steps out, and not getting too confused.

After running the algorithm against the data above, it worked flawlessly.  Take a look at this graph:

The gray lines are where the important steps in the process were detected.  For example, the first gray line @ x=10 is where the algorithm first noticed we were emptying water out of the tank.  It looks late – and it is, but that’s merely a consequence of using a queue of 10 samples to generate the slope.  The actual “before” water level value it uses is not the one at the gray line, but the minimum one in the queue – which is correct (enough for rock and roll).  Then, at x=54, the algorithm detected a large negative slope and decided that we were filling the tank up again.  It started looking for the start of a steady state, which it found at x=121, and it stayed steady long enough that at x=150, the algorithm wrapped up and decided that we’ve done a water change.  NIFTY!

If you think about what is going on here, it’s really calculus, under the covers.  The slope value is the derivative of the function, and we look for the points that derivative changes sign.  But the function we’re using isn’t perfect, and isn’t continuous, and so we’ve got to build in a little extra wonkitude-resistance.  The algorithm has inputs for the size of the queue, the trigger value for large positive/negative slopes, a tolerance value to ignore noise during steady states, and the number of samples to go during a steady state before deciding we’re really done with the water change – so in theory, this algorithm could be adapted and tuned for a wide range of input sources with little-to-no modification.

Right now, the algorithm is coded in python, but I think it might even be possible to do the crunching on the Arduino.  If I did that and wired the Arduino up to an ethernet shield, I could ALMOST eliminate the computer altogether.  I still need the computer to run the webcam, however, so there’s no point in trying to run this code on the chip or anything like that.  But I think it would be possible in theory, if you don’t want the cam server running.

The host computer gets this stream of numbers, and has to have some way of determining when I’ve done a water change, and how much water I’ve changed. I got the feeling that random variation (noise) in the data from the sensor could throw off whatever method I use to compute all of this, so I needed to figure out exactly what the data looks like coming in to the computer, preferably saving it so I can test my algorithms against it without having to do a water change after each revision – that would be a heck of a lot worse than just waiting for the code to recompile.

So I fired up Arduino and Python did a water change, saving the raw data from the Ping))) sensor to a file. Without further ado, this is what a water change looks like to a computer:

Pretty cool, huh?  When I do a water change, I siphon water out of the tank into a 3 gallon bucket, and empty it a bucket at a time, until I’ve taken out as much as I like, and then I re-fill the think 3 gallons at a time.  You end up with the very visible “steps” on the graph.  While the data looks mostly consistent, you can see some wonkiness in some of the steps – which almost looks like thick lines.  The sensor isn’t quite reading the distance regularly in this case.  This looks to me like the kind of data which could really throw of my detection algorithm.  So I had the bright idea of taking a median of 5 samples for each data point and using that series for detection.  Here’s what a median-of-five graph looks like:

Median of Five Graph

You should notice two things: 1) there are fewer datapoints by an order of five, because of the median, and 2) the curve is smoother.  I could prove this by computing the standard deviation on some of those trouble spots from above, but I don’t think it’s necessary: it’s plain to see when graphed.  The data could still be better though – look at the jaggies around 100.  We’ve got plenty of data, so we should be able to create a very smooth line and still have enough resolution to see each step, etc.

I’ll spare you all the gory details, but suffice it to say that the larger the median used, the better.  A median of 10 was better, but still not good.  A median of 15 was nearly perfect, but there was still just a little weirdness.  A median of 20 was perfect:

Median of 20 graph

That’s more like it.  We’ve still got enough data there to see the water change in detail, but smoothed out all the ugliness that could throw off the computer.  Cool stuff.

More details on the detection algorithm’s implementation to follow.  Charts generated with my new favorite tool, Python.

This is my version of a Woolly Mammoth guitar pedal I’m going to call “Mastodon Dave.” This is the test wiring.  Yet to be done is to get Kristin to paint the case, and then I’ll do the final wiring.

I’m working on a balsa glider with plans I got from (the incomparable) Make Magazine.  Here I am working on the critical step of joining the two sides of the fuselage together.

You can see that the two sides are more or less straight.  After doing some more finicky work, sanding, gluing, tweaking, and fussing, it looks even a little better now.  In the background of the above photo, you can see my new telescope – the latest accoutrement for my long series of intellectual obsessions.

The fuselage nearing completion.  Shaping the nose cone was a little hard.

Here’s a little trick I picked up from this guy’s photoset – bevel the edges of the equipment bay hatch so it will slide in and out rather than tack-gluing it or using tabs like the plans said.  I am really happy with the job I did on it, too, and when it is closed, you can barely tell there are two seams there.

I’m also working on the physical hardware for the sensors for my Fishapp. It’s coming along well, but slowly, as the mounting and hardware is the hardest thing about those kinds of projects for me.

Happy Making!

Water age & nitrate levels over time

This chart shows the average age (in days) of the water in my tank compared with the levels of Nitrate I measure using an aquarium test kit.  I compute the water age using information I record about water changes I do to my tank (a more detailed explanation can be found in the original FishApp post), and Nitrate is a mildly-bad chemical the can build up in your tank over time.  It’s the end-product of the Nitrogen Cycle in most fish tanks, and can only be removed by water changes or chemical absorption (which some plants and special filter media can do).

At least that’s the theory.  What this chart is showing me is that the theory seems to actually work out in practice, and that my tests are precise enough to actually be useful – always a good thing.  Even though I only have five data points for the nitrate series (because I don’t always test as much as I should), it is easy to see the nitrate curve following the water age curve.  They actually track pretty well, I think. You can see an ugly spike in the water age when I wasn’t paying enough attention to the tank, and the resulting high nitrate levels, which backed down after a series of regular water changes.  When the water age started creeping back up again, so did Nitrate levels, and then both went down again after we moved from Tennessee to North Carolina (and changed out about 2/3 of the tank water for fresh in the process).  Very cool.

Since nitrate is the last state that fish poo reaches in the nitrogen cycle, it would not surprise me if there were a delay between water age and its effects on nitrate levels.  I don’t really have the data to support that right now, but I will try to be more diligent in my testing, and perhaps we can figure that out soon.

If there are any aquarium owners out there who are interested in the FishApp, you can sign up free and track your own fish tank in a similar fashion.  Here’s a link to sign up.

Check out what happens when one of the Cory’s gets too close. They are so oblivious to their surroundings sometimes… We had heard horror stories about Rams getting aggressive when mating and corys losing their eyes… Luckily none of that here.
I also got some stills.