Last night a pair of young moose showed up in our driveway. Andrea and I went out on the deck and shot some iPhone video of their antics. At one point (see the video below) Deuce went over to the fence and the moose ran over to check him out. It was clear that Deuce was trying to play, but I’m not sure what the moose were thinking.
The iPhone shoots QuickTime movies; the new HTML5 web standard will include a
video tag that indicates to a browser that the file is a video. Firefox 3.5 is the first browser to implement this tag, playing Ogg Theora videos without needing an external video plug-in like Flash or Silverlight or whatever. This is a good thing because it means web developers can stop developing their sites with a bunch of proprietary languages and formats just to show a video.
Unfortunately, getting a QuickTime video from the iPhone into the right format is a bit of a pain, and even after I got it all figured out, the video wouldn’t play once I uploaded it to my hosting provider. But in case it’s useful, here’s the procedure I used. I suspect the
ffmpeg2theora step could probably have been done on my Mac, but it doesn’t appear to be part of
fink so I just installed it on my Linux box and ran it there.
- Drag MOV file onto the New Event icon in the upper left pane of iMovie
- Crop and select time period in the movie in the middle frame
- Create a new project (which appears in the lower left pane)
- Drag the selection from the upper middle frame to the lower middle frame
- Export the movie at Medium quality
- Transfer to Linux
- Run ffmpeg2theora -v 5 -x 350 --aspect 16:9 moose_test.m4v (choosing the proper aspect ratio and size)
In my previous post on my weather station I included a graph of the relationship between the three temperature sensors I’m collecting data from. It turns out that the plot is incorrect because I had messed up the program that converts the raw data from one of my Arduino stations. What became clear after looking at the (fixed!) data is that the enclosure I built isn’t adequate at keeping the sensors cool when in direct sunlight. I could probably improve the design of the enclosure somewhat, but I decided to aspirate the sensors instead.
The photo on the right shows the inside of the enclosure. The sensors are sitting on a platform inside the pipe, and there’s a small muffin fan (the kind you’d use to vent a computer case) on the top. It’s a 12V fan, and at the moment it’s being driven by a 9V AC/DC converter. The plan is to replace the converter with a 12V solar cell that is sufficient to drive the fan. This way it doesn’t consume any electricity, and the fan is only spinning when it’s necessary (when the sun is out). Thus far, I haven’t found a suitable solar panel. The small ones designed to charge a cell phone battery don’t operate at the correct voltage (and probably don’t produce enough current anyway), and the big ones designed to keep a car battery charged are expensive and overpowered for my needs. With winter rapidly approaching, I’ve got plenty of time to figure something out.
The pipe is a piece of 4” sewer pipe that’s been spray painted white and has a series of holes drilled into the bottom. The fan pulls air up through these holes and over the sensor array in the middle. If I had it to do over again, I’d cut the pipe a bit shorter so it’s not so difficult to get into the enclosure. But for keeping the sensors bathed in atmospheric air, it works quite well.
The plot shows the observed temperature for each of the three sensors I’ve got. The blue line is the “west” sensor that’s inside the enclosure I built and is the subject of this post. The red line is the reported temperature from the Rainwise station that sits atop a post attached to the dog yard gate. The green line is the sensor that’s behind the house and under the oil tank. I built the enclosure for the west sensors on July 12th and installed it that evening. You can immediately see the effect of the shielding during the high temperature peak on the 13th. But you can still see the two little peaks that are present in the previous plots. These peaks come from direct sun on the station, split by some trees that shade the station for an hour or two.
On the evening of the 13th I installed the pipe and fan. It was smoky on the 14th and cloudy on the 15th, but you can see the effect of the fan on the following dates. The double peaks are now gone, and the temperature from the west sensors at the high point during the day is now a few degrees cooler than the measurement from the Rainwise station. Also notice that all three sensors are virtually identical on the 15th when it was cloudy and raining.
What this demonstrates to me is that the aspirated west sensor is now the best reference sensor for our site. The Rainwise sensor is a close second, but it’s Gill multi-plate radiation shield isn’t as effective as my aspiration system at reducing the effect of solar heating on the station.
Before I went to California I decided I was finally ready to become a cell phone user with Apple’s iPhone. The contract (two years) is ridiculously long, AT&T doesn’t have the best record when it comes to service and upgrades (we don’t have 3G here in Fairbanks yet…), and I’m uncomfortable with how tight Apple is with their software and hardware. But a cell phone with a fast processor, lots of storage, built-in GPS, compass, motion sensors and a camera that can shoot stills and video? Plus a wide range of apps and an SDK to allow me to develop my own? I couldn’t resist. After verifying we could get service at our house (typically two, weak bars), we signed up and I picked up my phone at the Apple store in San Francisco on my way to Berkeley. Andrea got hers a few days later here in Fairbanks.
The iPhone is everything I thought it would be. The hardware and most of the software is incredible, and it’s already changing (improving, thus far) the way I live my life. The closed nature of it still bothers me, but I’m willing to give it a chance before I consider jailbreaking it.
What does this have to do with The Woodwright’s Shop and St. Roy? Well, one of the apps I have on my iPhone is called What’s On, and as I was perusing the TV schedule to see what baseball game was on FOX (what a surprise: Red Sox vs. Yankees) I noticed that The Woodwright’s Shop had a little HD icon next to it. Sure enough, when I flipped it on, there was St. Roy boring star-shaped holes into walnut in full high-definition video!
After a week and a half in Berkeley and a quick trip to Anchorage for work, I finally feel like things are getting back to normal. Last weekend we bought a bunch of stuff at the Farmer’s Market, including a small basket of raspberries. I had planned to make some raspberry bars, but when I looked at them today, they seemed a bit soft for something like that. On Sunday we picked a half gallon of blueberries from the power line, so making preserves from the combination seemed like a good idea. Here’s the recipe I used:
- 1 small basket fresh raspberries (approximately 2 cups)
- Same weight of fresh blueberries (~2 cups)
- 1 apple, peeled, cored, and chopped small
- Juice from ½ lemon
- 1 T water
- 3 cups sugar
- Bring fruit and liquids to a boil.
- Boil 5 minutes to soften apples.
- Add sugar and mix.
- Purée in food processor.
- Return to boil.
- Boil 10–15 minutes until the pectin is ready (it gels on the back of a spoon as it cools)
- Pour or ladle into hot canning jars (2 pint jars for this quantity), boil in hot water bath for 10 minutes.
I’m not sure if the apple is necessary, but the recipe I was looking at included pectin and I didn’t have any. The Interwebs informed me that apples contain a lot of pectin, so I threw that in the pot as well. The food processor step was included because I wasn’t convinced the apples would get mushed up enough to disappear in the mix.
Yesterday I looked at how wind might be affecting my bicycling to and from work. Today I’ll examine the idea that Miller Hill is confounding the effect of wind on average speed by excluding this portion of the trip from the analysis. To do this, I include a bounding box comparison in the SQL statement that extracts the wind factors for track points. The additional WHERE condition looks like this:
ST_Within(point_utm, ST_SetSRID(ST_MakeBox2D(ST_Point(454861,7193973), ST_Point(458232,7199159)), 32606))
The same ST_Within test is used in the calculation of average speed for each of the trips from work to home. After compiling the wind factors and average speeds, we compare the two using R. Here are the updated results:
lm(formula = mph ~ wind, data = data) Residuals: Min 1Q Median 3Q Max -1.87808 -0.55299 0.04038 0.62790 1.19076 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16.8544 0.2176 77.442 <2e-16 *** wind 0.3896 0.2002 1.946 0.0683 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9445 on 17 degrees of freedom Multiple R-squared: 0.1822, Adjusted R-squared: 0.1341 F-statistic: 3.788 on 1 and 17 DF, p-value: 0.06834
This time around the model and both coefficients are statistically significant (finally!), and “wind factor” is positively correlated with my average speed over the part of the route that doesn’t include Miller Hill and Railroad drive. It’s not a major contributor, but it does explain approximately 18% of the variation in average speed.