3D Lightning - In Google Earth

View the 3D reconstruction in Google Earth; download the .kmz file here.

3D Lightning

Reddit is a great website, where the ability to share and discuss things on the web gives some great little discoveries. Things that would otherwise seem impossibly unlikely, like two people in completely different places getting a photo of the same lightning bolt, suddenly pop up all the time.

 Pic by chordnine

 Pic by Bobo1010

Having two pictures of the exact same lightning bolt lets you do something pretty amazing; reconstruct its path in 3D. In this case because the precise location and elevation of the photographers isn't known this is slightly more art than science, but it is still fun!

These are the two bolts, scaled to approximately the same size:

It is immediately clear that they are taken from about the same direction but different heights: the second bolt looks squashed vertically. This means the pair of images are roughly a stereo pair, but with a vertical shift instead of a horizontal. This is just like the pair of images you would see with your eyes if you had two eyes positioned vertically instead of horizontally on your head.

To analyse this the first step is to trace the lightning bolt, making sure that every point in one image matches up to the corresponding point in the other image, then record the coordinates of all the points. This gives a nice table of numbers where you can calculate the difference in x and y position in the two images.

Now we need to do some maths... except I don't like doing complicated maths and it turns out there is a big simplification you can make! If both pictures are taken from a long way away from the lightning bolt (i.e. the object has quite a small angular size in the image) then the shift in position between the images is proportional to the distance from the camera. Bigger shifts mean that bit of the bolt is closer to the camera. This approximation is pretty accurate for the majority of cameras, so I used it here.

The other problem is the proportionality factor. If one part of the lightning bolt shifts twice as much between the two images as another part that means it is twice as close. But twice as close as what? Without knowing exactly where the cameras were positioned that means only the relative distance, not absolute distance, can be calculated. Oh well, close enough!

So what does the lightning bolt look like in 3D? I plugged the coordinates into Blender and this is the result:

Pretty amazing really!

Software used:

ImageJ: Image analysis.

Blender: 3D modelling and rendering.

Pebble

My Pebble arrived! It may be a one of the smartest watches around, but it is also shiny and curvaceous. Explore the curves and reflections in my macro photos in this flickr set....

 Software used:
ImageJ: Photograph animation
UFRaw: Raw extraction

Cilia

Each term I make a research comic for the Oxford University Biochemical Society magazine called Phenotype. This term's topic; cilia! These organelles can be found on a huge number of eukaryotic cells, ranging from nearly every cell in your body, to free living single cell microorganisms and protozoan parasites. The most famous function of cilia is swimming or moving a cell's surroundings, like the sperm flagellum (flagellum and cilium are different names for the same structure) or the cells in your lungs which help keep them clear of mucous.

Cilia are famous for their movement, but cilia are one of our the most multifunctional cell structures and have extremely important sensory and development functions. Can you guess which four if the five classic senses (touch, taste, smell, hearing, sight) need flagella to work?

This term's research comic feature in OUBS Phenotype is all about the diverse functions of cilia/flagella. Check out the comic here, or download the whole issue for free here.

Software used:
Autodesk Sketchbook Pro: Drawing the cells.
Inkscape: Page layout.

Cloud memory

We all know the feeling; you know you know the answer to something but you can't quite bring it to mind... But you remember exactly how to find it online! It's a well known problem that Google is messing with our heads, and its about time we had a name for this new kind of memory. How about:

cloud memory

"I know this great bike shop, but I can't remember what its called! I need to check my cloud memory"

"It's totally in my cloud memory, can I borrow your phone to find it?"

"Exams are so pointless these days, who needs to learn facts when we have such good cloud memory?"

Thanks to Sam Dean for the idea, and you read it here first!

Shooting Jupiter's Moons

It was a nice clear night, so I cracked the camera out to see how clear a picture I could get of the moon.

 Not bad, considering my 5 year old camera really isn't a telescope!

It really wasn't much of a challenge, though in some ways you wouldn't expect it to be. The moon is quite close to Earth in the grand scheme of things; 'only' 249,986 miles on this particular night. It is also normally the biggest and brightest object in the night sky, running at about magnitude -10 on that night.

So I set myself a more difficult challenge... How about aiming for a picture of some moons around a different planet? Jupiter is the obvious choice because it has four huge moons (the Galilean moons Ganymede, Callisto, Io and Europa) and sits relatively close to the sun. Jupiter is also nice and bright and easy to spot in the night sky, although is still about 200 times fainter than our moon.

The problem with taking a picture of Jupiter's moons lies in their sheer distance from Earth. Jupiter was 531,833,620 miles (over half a billion miles) from Earth on that night. This causes two problems: Firstly because the moons are over 2000 times further away from Earth than our moon they appear much much smaller in the sky. Secondly Jupiter's moons are also about 5 times further from the sun than ours, which means they are illuminated much more weakly by the sun than our moon... Together this means that Jupiters moons appear about 2-5 million times fainter than our moon in the night sky. A proper challenge!

So what kind of picture can you get of Jupiter and its moons? It took some tweaking to get a good picture (I had to capture 20 images, align them and average them together to remove the background noise) but here it is:

Huh, that looks plausible... A big blob (Jupiter) and four smaller blobs (its moons?). It was easy to check where Jupiter's moons are expected to be:

The positions of the Galilean moons: 16/04/2013 20:58

A perfect match! Even down to the brightnesses of individual moons with Ganymede appearing brightest and Callisto faintest. This really is quite incredible; with a standard, modern, off-the-shelf camera and lens you can get a clear picture of the Galilean moons. In comparison in 1610, when Galileo discovered these moons, he was at the cutting edge of optical technology. 403 years for technology to go from a cutting edge revolutionary idea, to a cheap consumer commodity.

At the moment astronomers are just about able to image some planets around other stars. Now imagine in 403 years time, the year 2416; will people be able to buy some consumer camera, pop out into the garden one evening and take a picture of planets around another star?

Software used:
ImageJ: Image processing
Stellarium: Simulated images

The geeky details:
Canon EOS 450D
Sigma 18-200mm f/3.5-6.3 DC OS HSM
The lens was used at 200mm, maximum aperture (f/6.3), with focus set manually to infinity. 20 images of Jupiter were captured at ISO 800 with a 2.5 exposure time. Short exposures and high ISO have to be used because through a telephoto lens the stars drift rapidly through Earth's rotation. The moons were visible in the raw images, but to get a clear and less noisy image they had to be aligned and averaged in ImageJ.

This one goes out to the immunologists...

If you are totally confused start reading here, and here. Oh, and it's not called a FACS plot by the way.
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