An Mbed In Your Browser

December 17, 2017 by 7 Comments

If you have dabbled in the world of ARM microcontrollers, you might be familiar with the Mbed platform, a software abstraction layer for a range of ARM-based small dev boards. If you don’t have an Mbed board but fancy giving it a go, you might imagine that you’d be out of luck, but [Jan Jongboom] could have an answer to your problem in the form of an Mbed simulation in your browser.

We’re not high-end ARM microcontroller developers here at Hackaday so beyond observing that it brings the Mbed abstraction layer binaries to the browser through the magic of Emscripten it’s best to point the curious at its GitHub repository. But we can see its attraction as a means to take a look at Mbed, and given that [Jan] describes himself as “a developer and evangelist currently working on the Internet of Things for ARM“, it’s safe to say this one comes as they say, from the horse’s mouth.

The Mbed board that is probably most famous is the education-focused micro:bit, but there are plenty of others on the market. Back in 2015 we published a getting started guide, if you are new to the Mbed.

Via Hacker News.

Cryptanalyse Your Air Con

December 17, 2017 by 19 Comments

Infrared remote controls are simple and ubiquitous. Emulating them with the aid of a microcontroller is a common project that hackers use to control equipment as diverse as televisions, cable boxes, and home stereos. Some air conditioners can be a little more complicated, however, but [Ken]’s here to help.

The root of the problem is that the air conditioner remote was using a non-obvious checksum to verify if commands received were valid. To determine the function generating the checksum, [Ken] decided to bust out the tools of differential cryptanalysis. This involves carefully varying the input to a cryptographic function and comparing it to the differences in the output.

With 35 signals collected from the remote, a program was written to find input data that varied by just one bit. The checksum outputs were then compared to eventually put together the checksum function.

[Ken] notes that the function may not be 100% accurate, as they’re only using a limited sample of data in which not all the bytes change significantly. However, it shows that a methodical approach is valuable when approaching such projects.

Thirsty for more checksum-busting action? Check out this hacked weather station.

Powering Your Mining Rig The Right Way

December 17, 2017 by 36 Comments

It happens to the best of us. We power up our project and immediately run into issues. Be it spotty communication or microcontroller reset or any number of bugs that have us mystified and picking though our code… only to find that it’s a power supply issue. Anyone who has tried doing Raspberry Pi stuff and depended on the USB power from their PC has certainly been bit by this.

It’s the same with larger, more power hungry projects as well. [Nerd Ralph] has been running a mining rig for a few years now, and has learned just how important proper power supply management can be. His strategy involves using interlocks to ensure everything powers up at the same time to avoid feedback problems, running a separate ground wire between all GPU cards and the PSU and running the supplies at 220 for the NA folks.

Be sure to check out [Nerd Ralph’s] blog for more details and tips to power your own mining rig.

Final Project For Better Sleep

December 17, 2017 by 14 Comments

It’s that time of year again, and students around the world are scrambling (or have already scrambled) to finish their final projects for the semester. And, while studying for finals prevents many from sleeping an adequate amount, [Julia] and [Nick] are seeking to maximize “what little sleep the [Electrical and Computer Engineering] major allows” them by using their final project to measure sleep quality.

To produce a metric for sleep quality, [Julia] and [Nick] set out to measure various sleep-related activities, specifically heart rate, motion and breath frequency. During the night, an Arduino Nano mounted to a glove collects data from the various sensors mounted to the user, all the while beaming the data to a stationary PIC for analysis and storage. When the user awakes, they can view their sleep report on a TFT display at the PIC base station. Ideally, users would use this data to test different habits in order to get the best nights sleep possible.

Interestingly, the group chose to implement their own heart rate sensor. With an IR transmitter, IR phototransistor and an OP amp, the group illuminates user’s fingers and measure reflection to detect heartbeats. This works because the amount of IR reflected from the user’s finger changes with blood pressure and blood oxygen level, which also happen to change when the heart is beating. There were some bumps along the road when it came to the heartbeat sensor (the need to use a finger instead of the wrist forced them to use a glove instead of a wristband), but we think it’s super cool and totally worth it. In addition to heart rate, motion is measured by an accelerometer and breath is measured by a flex sensor wrapped around the user’s chest.

With all of their data beamed back by a pair of nRF24L01s, the PIC computes the sleep “chaos” which is exactly what it sounds like: it describes just how chaotic the user slept by looking for acyclic and sudden movement. Using this metric, combined with information from breathing and heart rate, the PIC computes a percentage for good sleep where 100% is a great night and 0% means you might have been just as well off pulling an all-nighter. And, to top it all off, the PIC saves your data to an SD card for easy after-the-fact review.

The commented code that powers the project can be found here along with a parts list in their project write-up.

This device assumes that sleeping is the issue, but if waking up if your problem, we’ve already got you covered, aggressive alarm clock style. For those already on top of their sleep, you might want some help with lucid dreaming.

Video of the project explained by [Julia] and [Nick] after the break.

Continue reading “Final Project For Better Sleep” →

Google's AIY Vision Kit exploded view

Google’s AIY Vision Kit Augments Pi With Vision Processor

December 17, 2017 by 29 Comments

Google has announced their soon to be available Vision Kit, their next easy to assemble Artificial Intelligence Yourself (AIY) product. You’ll have to provide your own Raspberry Pi Zero W but that’s okay since what makes this special is Google’s VisionBonnet board that they do provide, basically a low power neural network accelerator board running TensorFlow.

AIY VisionBonnet with Myriad 2 (MA2450) chip
AIY VisionBonnet with Myriad 2 (MA2450) chip

The VisionBonnet is built around the Intel® Movidius™ Myriad 2 (aka MA2450) vision processing unit (VPU) chip. See the video below for an overview of this chip, but what it allows is the rapid processing of compute-intensive neural networks. We don’t think you’d use it for training the neural nets, just for doing the inference, or in human terms, for making use of the trained neural nets. It may be worth getting the kit for this board alone to use in your own hacks. An alternative is to get Modivius’s Neural Compute Stick, which has the same chip on a USB stick for around $80, not quite double the Vision Kit’s $45 price tag.

The Vision Kit isn’t out yet so we can’t be certain of the details, but based on the hardware it looks like you’ll point the camera at something, press a button and it will speak. We’ve seen this before with this talking object recognizer on a Pi 3 (full disclosure, it was made by yours truly) but without the hardware acceleration, a single object recognition took around 10 seconds. In the vision kit we expect the recognition will be in real-time. So the Vision Kit may be much more dynamic than that. And in case it wasn’t clear, a key feature is that nothing is done on the cloud here, all processing is local.

The kit comes with three different applications: an object recognition one that can recognize up to 1000 different classes of objects, another that recognizes faces and their expressions, and a third that detects people, cats, and dogs. While you can get up to a lot of mischief with just that, you can run your own neural networks too. If you need a refresher on TensorFlow then check out our introduction. And be sure to check out the Myriad 2 VPU video below the break.

Continue reading “Google’s AIY Vision Kit Augments Pi With Vision Processor” →

Neural Network Learns SDR Ham Radio

December 16, 2017 by 19 Comments

Identifying ham radio signals used to be easy. Beeps were Morse code, voice was AM unless it sounded like Donald Duck in which case it was sideband. But there are dozens of modes in common use now including TV, digital data, digital voice, FM, and more coming on line every day. [Randaller] used CUDA to build a neural network that could interface with an RTL-SDR dongle and can classify the signals it hears. Since it is a neural network, it isn’t so much programmed to do it as it is trained. The proof of concept has training to distinguish FM, SECAM, and tetra. However, you can train it to recognize other modulation schemes if you want to invest the time into it.

Continue reading “Neural Network Learns SDR Ham Radio” →

If 3D Printer, Then Custom Aluminum Extrusion Brackets

December 16, 2017 by 63 Comments

Aluminum extrusions are a boon for mechanical assemblies, but they require a stock of brackets and other hardware to be kept on hand. [mightynozzle] has decided to make things a little easier for prototyping and low-stress assemblies by creating a collection of 3D printable brackets for aluminum extrusions. 3D printing your own bracket hardware means faster prototyping, and if the assemblies don’t need the extra strength and rigidity of metal brackets you can just stick with the 3D printed versions.

The files are on Thingiverse, and include STL files of common brackets as well as an OpenSCAD script for customizing. Not familiar with OpenSCAD? No problem, we have a quick primer with examples.

This project showcases two things well. The first is that while brackets are not particularly expensive or hard to obtain, it can still be worth 3D printing them to reduce the overall amount of hardware one needs to keep on hand to make prototyping faster. The other is that 3D printing can shine when it comes to the creation of things like brackets: a few dimes’ worth of plastic can be turned into precise yet geometrically simple objects that would be a pain to make by other means. It certainly beats sitting on one’s hands waiting for parts to be delivered.