The blue board seen above is the guts of a product called the eeColor Color3. It was designed to act as a pass-through between your television and HDMI source device. It boasts the ability to adjust the color saturation to suit any viewing conditions. But [Taylor Killian] could care less about what the thing was made for, he tore it open and used the FPGA inside for his own purposes.
The obvious problem with this compared to a proper dev board is that the pins are not all broken out in a user-friendly way. But he got his hands on it for free after a mail-in-rebate (you might find one online for less than $10 if you’re lucky) and it’s got an Altera Cyclone IV chip with 30k (EP4CE30F23C6N) gates in it so he’s not complaining. The first project he took on with his new toy was to load up an open source Bitcoin mining program. The image above shows it grinding away at 15 megahashes per second while consuming only 2.5 watts. Not bad. Now he just needs to make a modular rack to hold a mining farm.
It’s the end of the semester for [Bruce Land]’s microcontroller design class at Cornell, and the projects coming off the workbench this semester look as awesome as any before. For their final project, [Alexander Wang] and [Bill Jo] designed an audio frequency spectrum analyzer using two microcontrollers in a parallel setup.
This spectrum analyzer takes an audio signal from an iPod, phone, or CD player through a 3.5 mm jack and displays the level for dozens of frequency bands much like an audio visualizer in iTunes or a nice car stereo display. To display these frequency bands, the spectrum analyzer first needs to perform a Fast Fourier Transform on the incoming audio signal. While FFT is extremely fast, the calculations are rather hardware intensive; calculating the frequencies and displaying them on a TV would be a bit much even for the ATMega1284 used in the project.
To graph the audio signal on their small display, [Alexander] and [Bill] broke the build up into two parts – one to do the math on the audio, and another to generate the NTSC video signal for the display.
As seen in the video after the break, the spectrum analyzer works wonderfully, and even though it only functions up to 4kHz, it’s more than enough to see what’s going on in most music.
Continue reading “Building a spectrum analyzer with parallel processing”
To be honest, we’ve heard of dithering but that’s the extent of our knowledge on the topic. After looking through [Windell’s] post about using Dithering in Processing we can now say we’ve got a base of knowledge on the topic.
Dithering is used to produce an image out of two colors that our eyes can put together into something meaningful. The history of the algorithms goes back to monochrome displays. But now the hobby electronics we work with for fun have comparable computing power and perhaps it’s time to rediscover these techniques. [Windell’s] project implements the Atkinson dithering algorithm in real-time on your webcam. He’s doing this in Processing, which should make it pretty easy to port for your own purposes.
So why might you want to use dithering in your own projects? Because if it can be used to make very cool milled artwork there must be other undiscovered uses lurking around your workshop.
[Tomáš], a.k.a. [Frooxius] is playing around with computational theory and processor architectures – a strange hobby in itself, we know – and has created the strangest CPU we’ve ever seen described.
The Weird Processing Unit, or WPU, isn’t designed like the Intel or ARM CPU in your laptop or phone. No, the WPU is a thought experiment in computer design that’s something between being weird for the sake of being weird and throwing stuff at the wall and seeing what sticks.
The WPU only has four instructions, or attoinstructions, to change the state of one of the 64 pins on the computer – set to logical 1, set to logical 0, invert current state, and halt. These instructions are coded with two bits, and the operand (i.e. the wire connected to the computer) is encoded in another six bits.
These 64 wires are divided up into several busses – eight bit address and control busses make up the lowest 16 bits, a 32-bit data bus has a function akin to a register, and a 16-bit ‘Quick aJump bus’ provides the program counter and attocode memory. The highest bit on the WPU is a ‘jump bit’, implemented for unconditional jumps in code.
We’re not even sure the WPU can even be considered a computer. We realize, though, that’s probably not the point; [Tomáš] simply created the WPU to do something out of the ordinary. It’s not meant to be a real, or even useful, CPU; it’s simply a thought experiment to see what is possible by twiddling bits around.
Tip ‘o the hat to [Adam] for sending this one in.
If you ever need to manipulate images really fast, or just want to make some pretty fractals, [Reuben] has just what you need. He developed a neat command line tool to send code to a graphics card and generate images using pixel shaders. Opposed to making these images with a CPU, a GPU processes every pixel in parallel, making image processing much faster.
All the GPU coding is done by writing a bit of code in GLSL. [Reuben]’s command line utility takes that code, sends it to the graphics card, and returns the image calculated by the GPU. It’s very simple for to make pretty Mandebrolt set images and sine wave interference this way, but [Reuben]’s project can do much more than that. By sending an image to the GPU and performing a few operations, [Reuben] can do very fast edge detection and other algorithmic processing on pre-existing images.
So far, [Reuben] has tested his software with a few NVIDIA graphics cards under Windows and Linux, although it should work with any graphics card with pixel shaders.
Although [Reuben] is sending code to his GPU, it’s not quite on the level of the NVIDIA CUDA parallel computing platform; [Reuben] is only working with images. Cleverly written software could get around that, though. Still, even if [Reuben]’s project is only used for image processing, it’s still much faster than any CPU-bound method.
You can grab a copy of [Reuben]’s work over on GitHub.
[Oscar] shows us how to use a Processing sketch for Android to communicate with Bluetooth devices (translated). It turns out this is easier than you might think. Processing and Android are both closely related to Java, and you can just import the Android libraries that deal with Bluetooth within the Processing sketch. That makes it easy to enable the Bluetooth modem when the sketch is launched, and manages connecting with devices as well as sending and receiving data.
For this example [Oscar] is using an Arduino with a Bluetooth module as a test device. His sketch first shows what devices are available, then connects to the one you select from the list. The 11 lines of Arduino code transmit a value via the serial port, and listens back for a command to toggle the LED on pin 13. [Oscar] takes time in his tutorial to show us how each step of the Processing sketch is assembled, instead of only posting the finished code.
[Michael] from Nootropic Design wrote in to share an interesting and fun project he put together using one of the products his company sells. The gadget in question is their “Video Experimenter” shield which was designed for the Arduino. It is typically used to allow the manipulation of composite video streams via overlays and the like, but it can also serve as a video analyzer as well.
When used for video analysis, the board lets you decode closed captioning data, which is exactly what [Michael] did here. He decided it would be fun to scrape the closed captioning information from various shows and commercials to do a little bit of content analysis.
Using a Processing sketch on his Arduino, he reads the closed captioning feed from his cable box, keeping a count of every word mentioned in the broadcast. As the show progresses, his sketch dynamically constructs a cloud that shows the most commonly used words in the video feed.
The results he gets are quite interesting, especially when he watches the nightly news, or some other broadcast with a specific target audience. We think it would be cool to run this application during a political debate or perhaps during a Hollywood awards ceremony to discover which set of speakers is the most vapid.
if you’re interested in learning more about the decoding process, [Michael] has put together a detailed explanation of how the closed captioning data can be pulled from a video stream. For those of you who just want to see the decoder in action, keep reading to see a quick video demonstration.
Continue reading “Analyzing TV’s talking heads with Processing”