Interplanetary probes were a constant in the tech news bulletins of the 1960s and 1970s. The Space Race was at its height, and alongside their manned flights the two superpowers sent unmanned missions throughout the Solar System. By the 1980s and early 1990s the Space Race had cooled down, the bean counters moved in, and aside from the spectacular images of the planets periodically arriving from the Voyager series of craft there were scant pickings for the deep space enthusiast.
The launch in late 1996 of the Mars Pathfinder mission with its Sojourner rover then was exciting news indeed. Before Spirit, the exceptionally long-lived Opportunity, and the relatively huge Curiosity rover (get a sense of scale from our recent tour of JPL), the little Sojourner operated on the surface of the planet for 85 days, and proved the technology for the rovers that followed.
In these days of constant online information we’d see every nuance of the operation as it happened, but those of us watching with interest in 1997 missed one of the mission’s dramas. Pathfinder’s lander suffered what is being written up today as the first bug on Mars. When the lander collected Martian weather data, its computer would crash.
Like many other spacecraft, the lander’s computer system ran the real-time OS VxWorks. Of the threads running on the craft, the weather thread was a low priority, while the more important task of servicing its information bus was a high priority one. The weather task would hog the resources, causing the operating system equivalent of an unholy row in our Martian outpost. A priority inversion bug, and one that had been spotted before launch but assigned a low priority.
You can’t walk up to a computer on another planet and swap out a few disks, so the Pathfinder team had to investigate the problem on their Earthbound replica of the lander. The fix involved executing some C code on an interpreter prompt on the spacecraft itself, something that would give most engineers an extremely anxious moment.
The write-up is an interesting read, it’s a translation from a Russian original that is linked within it. If the work of the JPL scientists and engineers interests you, this talk from the recent Hackaday superconference might be of interest.
[via Hacker News]
If you really want to hack software, you are going to face a time when you have to take apart someone’s machine code. If you aren’t very organized, it might even be your own — source code does get lost. If you want to impress everyone, you’ll just read through the hex code (well, the really tough old birds will read it in binary). That was hard to do even when CPUs only had a handful of instructions.
A more practical approach is to use a tool called a disassembler. This is nothing more than a program that converts numeric machine code into symbolic instructions. The devil, of course, is in the details. Real programs are messy. The disassembler can’t always figure out the difference between code and data, for example. The transition points between data and code can also be tricky.
When Not to Use
If you are coding your own program in assembly, a disassembler isn’t usually necessary. The disassembly can’t recover things like variable names, some function names, and — of course — comments. If you use a high-level language and you want to check your compiler output, you can easily have the compiler provide assembly language output (see below).
The real value of a disassembler is when you don’t have the source code. But it isn’t easy, especially for anything nontrivial. Be prepared to do a lot of detective work in most cases.
Continue reading “Disassembly Required”
Deep Learning — the use of neural networks with modern techniques to tackle problems ranging from computer vision to speech recognition and synthesis — is certainly a current buzzword. However, at the core is a set of powerful methods for organizing self-learning systems. Multi-layer neural networks aren’t new, but there is a resurgence of interest primarily due to the availability of massively parallel computation platforms disguised as video cards.
The problem is getting started in something like this. There are plenty of scholarly papers that can be hard to wade through. Or you can grab some code from GitHub and try to puzzle it out.
A better idea would be to take a free class entitled: Practical Deep Learning for Coders, Part 1. The course is free unless you count your investment in time. They warn you to expect to commit about ten hours a week for seven weeks to complete the course. You can see the first installment in the video, below. Continue reading “Practical Deep Learning”
Space. The final frontier. Unfortunately, the vast majority of us are planet-locked until further notice. If you are dedicated hobbyist astronomer, you probably already have the rough positions of the planets memorized. But what if you want to know them exactly from the comfort of your room and educate yourself at the same time? [Shubham Paul] has gone the extra parsec to build a Real-Time Planet Tracker that calculates their locations using Kepler’s Laws with exacting precision.
An Arduino Mega provides the brains, while 3.5-turn-pan and 180-degree-tilt servos are the brawn. A potentiometer and switch allow for for planet and mode selection, while a GPS module and an optional MPU9250 gyroscope/magnetometer let it know where you are. Finally a laser pointer shows the planet’s location in a closed room. And then there’s code: a lot of code.
The hardware side of things — as [Shubham Paul] clarifies — looks a little unfinished because the focus of the project is the software with the intent to instruct. They have included all the code they wrote for the RTPT, providing a breakdown in each section for those who are looking to build their own.
Continue reading “Real-Time Planet Tracker With Laser-Point Accuracy”
[Gerardo Iglesias Galván] decided he wanted to try his hand at bug-bounty hunting — where companies offer to pay hackers for finding vulnerabilities. Usually, this involves getting a device or accessing a device on the network, attacking it as a black box, and finding a way in. [Gerrado] realized that some vendors now supply virtual images of their appliances for testing, so instead of attacking a device on the network, he put the software in a virtual machine and attempted to gain access to the device. Understanding the steps he took can help you shore up your defenses against criminals, who might be after more than just a manufacturer’s debugging bounty.
Continue reading “Hacking a Device That Lives Inside the Matrix”
When you have a large software development team working on a project, monitoring the build server is an important part of the process. When a message comes in from your build servers, you need to take time away from what you’re doing to make sure the build’s not broken and, if it’s broken because of something you did, you have to stop what you’re doing, start fixing it and let people know that you’re on it.
[ridingintraffic]’s team uses Jenkins to automatically build their project and if there’s a problem, it sends a message to a Slack channel. This means the team needs to be monitoring the Slack channel, which can lead to some delays. [ridingintraffic] wanted immediate knowledge of a build problem, so with some software, IoT hardware, and a rotating hazard warning light, the team now gets a visible message that there’s a build problem.
An Adafruit Huzzah ESP8266 board is used as the controller, connected to some RF controlled power outlets via a 434MHz radio module. To prototype the system, [ridingintraffic] used an Arduino hooked up to one of the RF modules to sniff out the codes for turning the power outlets on and off from their remotes. With the codes in hand, work on the Huzzah board began.
An MQTT broker is used to let the Huzzah know when there’s been a build failure. If there is, the Huzzah turns the light beacon on via the power outlets. A bot running on the Slack channel listens for a message from one of the developers saying that problem is being worked on, and when it gets it, it sends the MQTT broker a message to turn the beacon off.
There’s also some separation between the internal network, the Huzzahs, and the Slack server on the internet, and [ridingintraffic] goes over the methods used to communicate between the layers in a more detailed blog post. Now, the developers in [ridingintraffic]’s office don’t need to be glued to the Slack channel, they will not miss the beacon when it signals to start panicking!
Last time, I talked about a simple kind of neural net called a perceptron that you can cause to learn simple functions. For the purposes of experimenting, I coded a simple example using Excel. That’s handy for changing things on the fly, but not so handy for putting the code in a microcontroller. This time, I’ll show you how the code looks in C++ and also tell you more about what you can do when faced with a more complex problem.
Continue reading “Perceptrons in C++”