Function Generator With Zero CPU Cycles

October 22, 2014 by Brian Benchoff 34 Comments

No one is sitting around their workbench trying to come up with the next great oscilloscope or multimeter, but function generators still remain one of the pieces of test equipment anyone – even someone with an Arduino starter pack – can build at home. Most of these function generators aren’t very good; you’re lucky if you can get a sine wave above the audio spectrum. [Bruce Land] had the idea to play around with DMA channels on a PIC32 and ended up with a function generator that uses zero CPU cycles. It’s perfect for a homebrew function generator build, or even a very cool audio synthesizer.

The main obstacles to generating a good sine wave at high frequencies are a high sample rate and an accurate DAC. For homebrew function generators, it’s usually the sample rate that’s terrible; it’s hard pushing bits out a port that fast. By using the DMA channel on a PIC32, [Bruce] can shove arbitrary waveforms out of the chip without using any CPU cycles. By writing a sine wave, or any other wave for that matter, to memory, the PIC32 will just spit them out and leave the CPU to do more important work.

[Bruce] was able to generate a great-looking sine wave up to 200 kHz, and the highest amplitude of the harmonics was about 40db below the fundamental up to 100 kHz. That’s a spectacular sine wave, and the perfect basis for a DIY function generator build.

Star Wars: Yoda Stories with a modified tile set

Reverse Engineering Star Wars: Yoda Stories

October 8, 2014 by Eric Evenchick 22 Comments

Star Wars: Yoda Stories was released by LucasArts in 1997 to minimal critical acclaim. As IGN said, “like Phantom Menace proved, just because it’s Star Wars doesn’t mean it’s good.” This didn’t stop [Zach] from playing it, and years later, taking an interest in reverse engineering the game.

[Zach]’s reverse engineering of Star Wars: Yoda Stories (google cache) takes a look at the game’s data file. This binary file is parsed by the game at run time to extract sound effects, sprites, and map tiles. Perhaps the best known game data file type was Doom’s WAD file, which had purpose built editing programs from third parties.

After a quick look at the data file in HxD, [Zach] began writing scripts in C# to extract different sections of the data file. Once the sections were found, more code was used to apply a color palette and generate bitmaps.

In the end, [Zach] managed to get a couple thousand tiles of the game’s data. He found some interesting ones, such as the sports car that he replaced the X-Wing with in his mod. The engine for an earlier Lucasarts game, Indiana Jones and His Desktop Adventures, should be very similar, and once we find the Mac install disk and a copy of ResEdit, we’ll post something on Hackaday.io.

Winning Game-App Contests With Computer Vision

October 6, 2014 by Mike Szczys 10 Comments

[Gadget Addict] found out about a contest being held by a shoe seller. Their mobile app has a game very much like Bejeweled. The high scorer each month gets £500. His choices were to be better at the game than everyone else, or to be smarter. He chose the latter by writing a computer vision program to play the game.

There are two distinct parts of a hack like this one. The first is just figuring out a way to programmatically detect the game board and correctly identify each icon on it. This is an iPad game. [Gadget Addict] is mirroring the screen on his laptop, which gives him easy access to the game board and also allows for simulated swipes for automatic play. Above you can see two examples where black pixels may be counted in order to identify the icon. A set of secondary checks differentiates similar entries after the first filtering. The other part of the hack involves writing the algorithms to solve for the best move.

If you liked this one, check out a super-fast Bejeweled solver from several years back. We should also mention that this was just a proof of concept and [GA] never actually entered the contest.

Running Golang On The Intel Edison

September 25, 2014 by Eric Evenchick 20 Comments

While most embedded development is still done in C and/or assembly, some people are working with more modern languages. The team over at Gobot has successfully managed to get Go running on the Intel Edison.

The Go programming language, which has been around for about five years, compiles to machine code like C. It has a number of modern features including concurrency, garbage collection, and packages.

We’ve looked at the Edison on Hackaday before, and even took a detailed look at the hardware. It features a Quark SoC, Bluetooth, and WiFi, which makes it well suited for connected devices.

Getting Go to work on the Edison hardware wasn’t particularly difficult, since it supports the Pentium instruction set and MMX. However, a library was needed to interface with the Edison’s peripherals. The Gobot team whipped up gobot-intel-iot, which makes it easy to work with GPIO, I2C, and PWM.

After the break, the team demos PWM on the Edison using Go.
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Rigging Your 3D Models In The Real-World

September 2, 2014 by Marsh 21 Comments

Computer animation is a task both delicate and tedious, requiring the manipulation of a computer model into a series of poses over time saved as keyframes, further refined by adjusting how the computer interpolates between each frame. You need a rig (a kind of digital skeleton) to accurately control that model, and researcher [Alec Jacobson] and his team have developed a hands-on alternative to pushing pixels around.

3D Rig with Control Curves — Control curves (the blue circles) allow for easier character manipulation.

The skeletal systems of computer animated characters consists of kinematic chains—joints that sprout from a root node out to the smallest extremity. Manipulating those joints usually requires the addition of easy-to-select control curves, which simplify the way joints rotate down the chain. Control curves do some behind-the-curtain math that allows the animator to move a character by grabbing a natural end-node, such as a hand or a foot. Lifting a character’s foot to place it on chair requires manipulating one control curve: grab foot control, move foot. Without these curves, an animator’s work is usually tripled: she has to first rotate the joint where the leg meets the hip, sticking the leg straight out, then rotate the knee back down, then rotate the ankle. A nightmare.

[Alec] and his team’s unique alternative is a system of interchangeable, 3D-printed mechanical pieces used to drive an on-screen character. The effect is that of digital puppetry, but with an eye toward precision. Their device consists of a central controller, joints, splitters, extensions, and endcaps. Joints connected to the controller appear in the 3D environment in real-time as they are assembled, and differences between the real-world rig and the model’s proportions can be adjusted in the software or through plastic extension pieces.

The plastic joints spin in all 3 directions (X,Y,Z), and record measurements via embedded Hall sensors and permanent magnets. Check out the accompanying article here (PDF) for specifics on the articulation device, then hang around after the break for a demonstration video.

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Open Source Marker Recognition For Augmented Reality

August 24, 2014 by Matt Terndrup 17 Comments

marker

[Bharath] recently uploaded the source code for an OpenCV based pattern recognition platform that can be used for Augmented Reality, or even robots. It was built with C++ and utilized the OpenCV library to translate marker notations within a single frame.

The program started out by focusing in on one object at a time. This method was chosen to eliminate the creation of additional arrays that contained information of all of the blobs inside the image; which could cause some problems.

Although this implementation did not track marker information through multiple frames, it did provide a nice foundation for integrating pattern recognition into computer systems. The tutorial was straightforward and easy to ready. The entire program and source code can be found on Github which comes with a ZERO license so that anyone can use it. A video of the program comes up after the break:

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Hyperlapse Makes Your HeadCam Videos Awesome

August 12, 2014 by Adam Fabio 34 Comments

hyperlapse First person video – between Google Glass, GoPro, and other sports cameras, it seems like everyone has a camera on their head these days. If you’re a surfer or skydiver, that might make for some awesome footage. For the rest of us though, it means hours of boring video. The obvious way to fix this is time-lapse. Typically time-lapse throws frames away. Taking 1 of every 10 frames results in a 10x speed increase. Unfortunately, speeding up a head mounted camera often leads to a video so bouncy it can’t be watched without an air sickness bag handy. [Johannes Kopf], [Michael Cohen], and [Richard Szeliski] at Microsoft Research have come up with a novel solution to this problem with Hyperlapse.

Hyperlapse photography is not a new term. Typically, hyperlapse films require careful planning, camera rigs, and labor-intensive post-production to achieve a usable video. [Johannes] and team have thrown computer vision and graphics algorithms at the problem. The results are nothing short of amazing.

The full details are available in the team’s report (35MB PDF warning). To obtain usable data, the fisheye lenses often used on these cameras must be calibrated. The team accomplished that with the OCamCalib toolbox. Imported video is broken down frame by frame. Using structure from motion algorithms, hyperlapse creates a 3D models of the various scenes in the video. With the scenes in this virtual world, the camera can be moved and aimed at will. The team’s algorithms then pick a smooth path that follows the original cameras trajectory. Once the camera’s position is known, it’s simply a matter of rendering the final video.

The results aren’t perfect. The mountain climbing scenes show some artifacts caused by the camera frame rate and exposure changing due to the varied lighting conditions. People appear and disappear in the bicycling portion of the video.

~~One thing the team doesn’t mention is how long the process takes~~. We’re sure this kind of rendering must require some serious time and processing power. Still, the output video is stunning.

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