It’s All In The Libs – Building A Plugin System Using Dynamic Loading

July 12, 2018 by Sven Gregori 17 Comments

Shared libraries are our best friends to extend the functionality of C programs without reinventing the wheel. They offer a collection of exported functions, variables, and other symbols that we can use inside our own program as if the content of the shared library was a direct part of our code. The usual way to use such libraries is to simply link against them at compile time, and let the linker resolve all external symbols and make sure everything is in place when creating our executable file. Whenever we then run our executable, the loader, a part of the operating system, will try to resolve again all the symbols, and load every required library into memory, along with our executable itself.

But what if we didn’t want to add libraries at compile time, but instead load them ourselves as needed during runtime? Instead of a predefined dependency on a library, we could make its presence optional and adjust our program’s functionality accordingly. Well, we can do just that with the concept of dynamic loading. In this article, we will look into dynamic loading, how to use it, and what to do with it — including building our own plugin system. But first, we will have a closer look at shared libraries and create one ourselves.

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Keep It Close: A Private Git Server Crash Course

June 27, 2018 by Tom Nardi 65 Comments

At this point, everyone has already heard that Microsoft is buying GitHub. Acquisitions of this scale take time, but most expect everything to be official by 2019. The general opinion online seems to be one of unease, and rightfully so. Even if we ignore Microsoft’s history of shady practices, there’s always an element of unease when somebody new takes over something you love. Sometimes it ends up being beneficial, the beginning of a new and better era. But sometimes…

Let’s not dwell on what might become of GitHub. While GitHub is the most popular web-based interface for Git, it’s not the only one. For example GitLab, a fully open source competitor to GitHub, is reporting record numbers of new repositories being created after word of the Microsoft buyout was confirmed. But even GitLab, while certainly worth checking out in these uncertain times, might be more than you strictly need.

Let’s be realistic. Most of the software projects hackers work on don’t need even half the features that GitHub/GitLab offer. Whether you’ve simply got a private project you want to maintain revisions of, or you’re working with a small group collaboratively in a hackerspace setting, you don’t need anything that isn’t already provided by the core Git software.

Let’s take a look at how quickly and easily you can setup a private Git server for you and your colleagues without having to worry about Microsoft (or anyone else) having their fingers around your code.

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Arduino And Pidgin C++

June 23, 2018 by Al Williams 87 Comments

What do you program the Arduino in? C? Actually, the Arduino’s byzantine build processes uses C++. All the features you get from the normal libraries are actually C++ classes. The problem is many people write C and ignore the C++ features other than using object already made for them. Just like traders often used pidgin English as a simplified language to talk to non-English speakers, many Arduino coders use pidgin C++ to effectively code C in a C++ environment. [Bert Hubert] has a two-part post that isn’t about the Arduino in particular, but is about moving from C to a more modern C++.

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Calm Down: It’s Only Assembly Language

June 19, 2018 by Al Williams 43 Comments

Based on [Ben Jojo’s] title — x86 Assembly Doesn’t have to be Scary — we assume that normal programmers fear assembly. Most hackers don’t mind it, but we also don’t often have an excuse to program assembly for desktop computers.

In fact, the post is really well suited for the typical hacker because it focuses the on real mode of an x86 processor after it boots. What makes this tutorial a little more interesting than the usual lecture is that it has interactive areas, where a VM runs your code in the browser after assembling with NASM.

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Using TensorFlow To Recognize Your Own Objects

May 23, 2018 by Steven Dufresne 9 Comments

When the time comes to add an object recognizer to your hack, all you need do is choose from many of the available ones and retrain it for your particular objects of interest. To help with that, [Edje Electronics] has put together a step-by-step guide to using TensorFlow to retrain Google’s Inception object recognizer. He does it for Windows 10 since there’s already plenty of documentation out there for Linux OSes.

You’re not limited to just Inception though. Inception is one of a few which are very accurate but it can take a few seconds to process each image and so is more suited to a fast laptop or desktop machine. MobileNet is an example of one which is less accurate but recognizes faster and so is better for a Raspberry Pi or mobile phone.

You’ll need a few hundred images of your objects. These can either be scraped from an online source like Google’s images or you get take your own photos. If you use the latter approach, make sure to shoot from various angles, rotations, and with different lighting conditions. Fill your background with various other things and even have some things partially obscuring your objects. This may sound like a long, tedious task, but it can be done efficiently. [Edje Electronics] is working on recognizing playing cards so he first sprinkled them around his living room, added some clutter, and walked around, taking pictures using his phone. Once uploaded, some easy-to-use software helped him to label them all in around an hour. Note that he trained on 24 different objects, which are the number of different cards you get in a pinochle deck.

You’ll need to install a lot of software and do some configuration, but he walks you through that too. Ideally, you’d use a computer with a GPU but that’s optional, the difference being between three or twenty-four hours of training. Be sure to both watch his video below and follow the steps on his Github page. The Github page is kept most up-to-date but his video does a more thorough job of walking you through using the software, such as how to use the image labeling program.

Why is he training an object recognizer on playing cards? This is just one more step in making a blackjack playing robot. Previously he’d done an impressive job using OpenCV, even though the algorithm handled non-overlapping cards only. Google’s Inception, however, recognizes partially obscured cards. This is a very interesting project, one which we’ll be keeping an eye on. If you have any ideas for him, leave them in the comments below.

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Machine Learning Crash Course From Google

May 22, 2018 by Al Williams 9 Comments

We’ve been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even classifying radio signals. If you’ve yet to climb the learning curve, you might be interested in a new free class from Google using TensorFlow.

Of course, we’ve covered tutorials for TensorFlow before, but this is structured as a 15 hour class with 25 lessons and 40 exercises. Of course, it is also from the horse’s mouth, so to speak. Google says the class will answer questions like:

How does machine learning differ from traditional programming?
What is loss, and how do I measure it?
How does gradient descent work?
How do I determine whether my model is effective?
How do I represent my data so that a program can learn from it?
How do I build a deep neural network?

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Hands-On: Flying Drones With Scratch

May 22, 2018 by Al Williams 8 Comments

I’ll admit it. I have a lot of drones. Sitting at my desk I can count no fewer than ten in various states of flight readiness. There are probably another half dozen in the garage. Some of them cost almost nothing. Some cost the better part of a thousand bucks. But I recently bought a drone for $100 that is both technically interesting and has great potential for motivating kids to learn about programming. The Tello is a small drone from a company you’ve never heard of (Ryze Tech), but it has DJI flight technology onboard and you can program it via an API. What’s more exciting for someone learning to program than using it to fly a quadcopter?

For $100, the Tello drone is a great little flyer. I’d go as far as saying it is the best $100 drone I’ve ever seen. Normally I don’t suggest getting a drone with no GPS since the price on those has come down. But the Tello optical sensor does a great job of keeping the craft stable as long as there is enough light for it to see. In addition, the optical sensor works indoors unlike GPS.

But if that was all there was to it, it probably wouldn’t warrant a Hackaday post. What piqued my interest was that you can program the thing using a PC. In particular, they use Scratch — the language built at MIT for young students. However, the API is usable from other languages with some work.

Information about the programming environment is rather sparse, so I dug in to find out how it all worked.

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