To illustrate the effect of changing the Gaussian convolution kernel size, I generated a series of 64x64x64 3D noise texture arrays using the code from my 3D MATLAB noise (continued) post:
After the break, see how increasing the size of the convolution kernel affects the quality of the 3D noise.
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So, I have figured out a way to generate some nice, 3D noise that wraps in all 3 dimensions offline, which allows it to be read into a 3D texture in OpenGL. Here is an example video of slices of the 3D texture:
Continue after the break to see how this 3D noise was generated.
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I am creating random 3D shapes for my post-doctoral research and need to generate 2D and 3D Perlin noise textures in MATLAB (for later use in OpenGL). If you’re not familiar with Perlin noise, it is procedurally generated random noise that appears organic due to it high and low-frequency noise content.
We have a Arduino Fio temperature logger, so now maybe we can increase the accuracy by adding an external temperature sensor. I have a couple of DS18B20 Programmable Resolution 1-Wire Digital Thermometers, so I thought, heck, let’s try one out!
These temperature sensors are much more accurate out-of-the-box, so I don’t need to deal with calibration (which I did need to worry about with the internal thermometer). In addition, using separate, discrete components allows for the possibility of putting temperature sensors directly on/in whatever you may want to measure (rather than merely measuring the ambient temperature) and the potential for multiple temperature sensors with a single Arduino Fio (which are available at Amazon.com).
Let’s extend the low power Ardunio Fio + Xbee setup that I previously blogged about. I wanted to see if I could create a simple wireless temperature sensor that could allow for long(er) term logging. Interestingly, the ATmega328P on the Arduino Fio has both a “secret” internal thermometer and internal voltmeter, meaning that I could (potentially) create a wireless sensor with no external additional external components (other than the Fio, XBee, and battery).
So, taking advantage of the available hardware and the code available, I went about creating a wireless temperature logger using an Arduino Fio (available from Amazon.com) and two XBees (one for the Fio and one for the coordinator).
As usually, I have been very sporadic in posting new/updated projects due to my prioritization of my doctoral work (i.e., not much time for fun little electronics projects!). However, I’ve been playing around with the Arduino Fio (available from Amazon.com) in my free time for a little while now, so I wanted to post some notes on a very low power usage setup that I was able to put together.
As my free hobby time has dwindled, so has the time I’ve been able to devote to debugging programs written for my little Microchip PICs. So, given my limited time, I decided to dive right into Arduinos — which utilize a higher-level programming language, making things a little quicker and easier for me to tinker — and try to get some wireless communication working. After looking through the possibilities, I settled on the Arduino Fio. The Arduino Fio is a great little Arduino-compatible board that includes a socket for an XBee 802.15.4 wireless module along with a LiPo plug and charger circuit.
What is everyone excited for??? More math! Ok, maybe not, but I’ll come back to the electronics projects later (including investigating the Parallax RFID reader, Arduino FIO, and heck, maybe even some more LED fun). In the meantime, let’s get back to math.
What do the volume calculations look like, and how would one go about calculating the center of mass/geometric centroid of a spherical plot? More after the break…
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My current research at Rutgers focuses on identifying the cognitive mechanisms that are employed when perceiving the shape and structure of 3D objects. One of the little problems that I had to overcome was finding out how to calculate the center of mass/gravity of a 3D surface of revolution analytically. Specifically, I needed to calculate the COM for conical frustums with and without an attached part (which was also analytically generated).
Unfortunately, it wasn’t as easy to derive as I assumed, but the steps toward that end are pretty straightforward. After the break you can see the general equation I ultimately derived for calculating the COM in 3D-space.
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