Author: admin
Links - June 7, 2009
- Encoding 2D angles without trigonometry -
- OCT technology development: Where are we now? A commercial perspective -
- Touching the microworld with force-feedback optical tweezers -
- Optical trapping calculations for metal nanoparticles.
Comparison with experimental data for Au and Ag spheres. - - CrunchPad Nears Production, Looks Awesome -
- Freeze, or the Gelatin Gets It -
- Nighttime Sky Over Death Valley -
- Shedding Some Light on Light -
6640m jog
6640 m jog in 45 min 38 s, at a leisurly average pace of 8.7 km/h.
It's funny how in October when it's + 4 C and raining snow/water horizontally you can have this route and the parks all to yourself and hardly meet anyone during the run. Today it's sunny and +20 C and the scene is very different. Every imaginable species of urban park-dweller (and their dog!) is out and about. Jogging in June has much less of a "Hyvä on hiihtäjän hiihdellä" (Finnish skiing poem by Eino Leino) feeling to it compared to October!
Links - May 31, 2009
Links - May 24, 2009
- Quantitative displacement measurement of a nanotube cantilever with nanometer accuracy using epifluorescence microscopy -
- Path integral formulation of your life. -
- Cloud Computing -
- Underdamped modes in a hydrodynamically coupled microparticle system -
- Compton Lecture: Steven Chu -
- Random Laser -
- Physics vs. Chemistry -
Fluorescent DNA
I'm testing an EMCCD camera. This is a video of fluorescently labeled DNA through a 100x epi-fluorescence microscope.
Or you can try a slightly better quality wmv-download (82 Mb)
Once we've had time to practice some more, it should look much cooler, something like these DNA-curtains, or DNA-ejection from bacteriophage lambda. But it's a start.
Also on a youtube near you: molecular motors, TIRFM, optical tweezers setup animation,
Links - May 17, 2009
- Herschel and Planck launched succesfully -
- A microfluidic mixing system for single-molecule measurements -
- Non-Blinking Quantum Dots -
- Trapping probability analysis of a DNA trap using electric and hydrodrag force fields in tapered microchannels -
- Optical tweezers with tips grown at the end of fibers by photopolymerization -
- Math on the Tube -
- Angle of Incidence = Music -
- What the Nighttime Sky Really Looks Like -
- You’ll Shoot Your Eye Out -
- Slo-Mo -
Mowing video moved
Jumpcut is closing, so I needed to move this video to youtube. This relates to my earlier posts here
http://www.anderswallin.net/2007/12/mowing-tactics/
and here
http://www.anderswallin.net/2007/06/an-emergent-spiral/
When I find time to work on this next, there are many ideas for improvements: How to specify only climb/conventional milling (allowing only the right or left side of the cutter to be used). Using a variable step length for the simulation. Simulating dynamics of the macing (controlling the tool with a trajectory generator with acceleration/speed limits etc). How to implement rapid feed between cutting moves? how to choose among many allowed starting points for the cut? Should this use an adaptive resolution model, like a quad-tree? How should G-code be output, a filter which outputs G-code within a specified tolerance of the simulated path would probably be best?
Uniform random points in a circle using polar coordinates
I need this seldom enough to forget how it's done - but then it's annoying to have to think/google for the solution again when I do need it... So I'll document here.
The task is to generate uniformly distributed numbers within a circle of radius R in the (x,y) plane. At first polar coordinates seems like a great idea, and the naive solution is to pick a radius r uniformly distributed in [0, R], and then an angle theta uniformly distributed in [0, 2pi]. BUT, you end up with an exess of points near the origin (0, 0)! This is wrong because if we look at a certain angle interval, say [theta, theta+dtheta], there needs to be more points generated further out (at large r), than close to zero. The radius must not be picked from a uniform distribution, but one that goes as
pdf_r = (2/R^2)*r
That's easy enough to do by calculating the inverse of the cumulative distribution, and we get for r:
r = R*sqrt( rand() )
where rand() is a uniform random number in [0, 1]. Here is a picture:
some matlab code here.
The thinking for generating random points on the surface of a sphere in 3D is very similar. If I get inspired I will do a post on that later, meanwhile you can go read these lecture notes.
Helsinki NIDays 2009
I presented one slide and a 3 minute video as a case-study during the keynote presentation at Helsinki NIDays 2009 this morning.