Software Design Patterns for Real Hardware

Here on Hackaday, we’re generally designers of hacks that live in the real world. Nevertheless, I’m convinced that some of the most interesting feats are at the mercy of what’s possible in software, not hardware. Without the right software, no 3D printer could print and no quadcopter could fly. The source code that drives these machines may take months of refinement to flesh out their structure. In a nutshell, these software packages are complicated, and they don’t happen overnight.

So how do they happen; better yet: how could we make that happen? How do we write software that’s flexible enough to coexist peacefully on all sorts of hardware variations?

What separates the big open-source software projects like ROS, LinuxCNC, and Multiwii from the occastional hackathon code are the underlying principles that govern how they’re written. In object-oriented programming, these principles are called design patterns. In the next couple posts, I’m going to crack the lid on a few of these. What’s more, I’ll package them into real-world examples so that we can see these patterns interact with real hardware. The next time you pop open the source code for a big open source hardware project, I hope you can tease out some of these patterns in the code. Better yet, I hope you carry these patterns into your next robot and machine project. Let’s get started.

For readability, all of the examples run in Python3. The snippets below are truncated for brevity, but the real examples in the repository will work if you’ve got a similar hardware setup.

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Shmoocon 2016: GPUs and FPGAs to Better Detect Malware

One of the big problems in detecting malware is that there are so many different forms of the same malicious code. This problem of polymorphism is what led Rick Wesson to develop icewater, a clustering technique that identifies malware.

Presented at Shmoocon 2016, the icewater project is a new way to process and filter the vast number of samples one finds on the Internet. Processing 300,000 new samples a day to determine if they have polymorphic malware in them is a daunting task. The approach used here is to create a fingerprint from each binary sample by using a space-filling curve. Polymorphism will change a lot of the bits in each sample, but as with human fingerprints, patterns are still present in this binary fingerprints that indicate the sample is a variation on a previously known object.
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