After using my Arduino Fio to make an Arduino LCD oscilloscope, I wanted to try adding a second channel. Using the single channel Arduino FIO LCD Oscilloscope code as a template, I modified the project and added a second channel.
Here’s a short demonstration video of the new two channel project:
Click through the break to check out the code.
Read More
Ok, so this post is not shape or electronics related, but still a fun little side project. Since I started working at Justus-Liebig-Universität in Gießen, Germany as a postdoc, my wife and I have cooked as much homemade food as possible. Germany is full of fantastic, inexpensive, and fresh ingredients, so we want to take advantage of whatever is available seasonally.
This week, blueberries were on sale, 0,49€ for 200g, and puff pastry dough is really inexpensive as well, 0,69€ for a sheet. So, here’s my first attempt at a “foodie” video demonstrating how I made puff pastries with fresh blueberry compote, with CC-licensed music by Jill Zimmerman.
It has been 7 years (!) since I posted my PIC18F2550 KS0108 Graphical LCD Oscilloscope code and schematics. I have long since taken the circuit apart, sold my PIC microcontrollers, and moved on in my life (as one can surmise from my most recent posts detailing my graduate and postdoctoral work). However, I still get inquiries about the Microchip PIC oscilloscope, so I decided to recreate it using a simpler setup using my Arduino Fio.
Here’s a short teaser video just to show that, yes, it works (going through a couple different sine wave frequencies, some random noise, etc. just to illustrate it working):
Click through the break to get more information on the setup.
Read More
In research I will be presenting in a few days at VSS (the Vision Sciences Society annual meeting), I will be demonstrating how we may use orientation flow fields of texture and shading when making perceptual judgments of 3D shape structure (see Fleming, Holtmann-Rice, & Bülthoff, 2011 for additional information). Since I find visualizations fun, I decided to use some spare CPU cycles overnight to visualize the orientation fields of a rotating blobby object.
The object on the left in the above video is a textured and shaded object with a small amount of specular reflection (lit using the Debvec Funston Beach at Sunset light probe). On the right, I’m illustrating the dominant orientations in the image, across the surface of the object.
Click through for some more visualizations.
Read More
I recently published an article in the Journal of Vision with my PhD advisor, Manish Singh, and my current Postdoctoral advisor, Roland W. Fleming:
Cholewiak, S. A., Fleming, R. W., & Singh, M. (2013). Visual perception of the physical stability of asymmetric three-dimensional objects. Journal of Vision, 13(4), 1–13. doi: 10.1167/13.4.12
Here’s the abstract:
Visual estimation of object stability is an ecologically important judgment that allows observers to predict the physical behavior of objects. A natural method that has been used in previous work to measure perceived object stability is the estimation of perceived “critical angle”—the angle at which an object appears equally likely to fall over versus return to its upright stable position. For an asymmetric object, however, the critical angle is not a single value, but varies with the direction in which the object is tilted. The current study addressed two questions: (a) Can observers reliably track the change in critical angle as a function of tilt direction? (b) How do they visually estimate the overall stability of an object, given the different critical angles in various directions? To address these questions, we employed two experimental tasks using simple asymmetric 3D objects (skewed conical frustums): settings of critical angle in different directions relative to the intrinsic skew of the 3D object (Experiment 1), and stability matching across 3D objects with different shapes (Experiments 2 and 3). Our results showed that (a) observers can perceptually track the varying critical angle in different directions quite well; and (b) their estimates of overall object stability are strongly biased toward the minimum critical angle (i.e., the critical angle in the least stable direction). Moreover, the fact that observers can reliably match perceived object stability across 3D objects with different shapes suggests that perceived stability is likely to be represented along a single dimension.
Want to cite us? Click through for the BibTeX source.
Read More