Day: November 21, 2017

Hackers Vs. Mold: Building A Humidistat Fan

November 21, 2017 by 29 Comments

Having a mold problem in your home is terrible, especially if you have an allergy to it. It can be toxic, aggravate asthma, and damage your possessions. But let’s be honest, before you even get to those listed issues, having mold where you live feels disgusting.

You can clean it with the regular use of unpleasant chemicals like bleach, although only with limited effectiveness. So I was not particularly happy to discover mold growing on the kitchen wall, and decided to do science at it. Happily, I managed to fix my mold problems with a little bit of hacker ingenuity.

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Mad Eye For The WiFi

November 21, 2017 by 12 Comments

In the Harry Potter universe, Professor Moody was, perhaps unfairly, given the nickname Mad Eye for the prosthetic eye he wore. His eye remains a challenge for technically-minded cosplayers aiming to recreate the look and feel of this unique piece of headgear. [cyborgworkshop] had already mastered the basic eye, but wanted to take things further.

The original build relied on a sub-micro servo to move the eyeball. This was done at random as an attempt to simulate the eye’s behaviour in the books and films. However, wanting more, [cyborgworkshop] decided to make the eye more reactive to its surrounding environment. Using the Adafruit Huzzah, a breakout board for the ESP8266, code was whipped up to detect the number of WiFi access points in the area. The more access points, the more frequent and erratic the movement of the eye. Occasional slower periods of movement are coded in before the eye resumes its wild darting once more, depending on just how saturated the local WiFi environment is.

It’s a great twist on the project, and [cyborgworkshop] has provided more details on the initial build, too. If you think you’re having déja vu, check out this build using recycled parts.

Prototyping, Making A Board For, And Coding An ARM Neural Net Robot

November 21, 2017 by 1 Comment

[Sean Hodgins]’s calls his three-part video series an Arduino Neural Network Robot but we’d rather call it an enjoyable series on prototyping, designing a board with surface mount parts, assembling it, and oh yeah, putting a neural network on it, all the while offering plenty of useful tips.

In part one, prototype and design, he starts us out with a prototype using a breadboard. The final robot isn’t on an Arduino, but instead is on a custom-made board built around an ARM Cortex-M0+ processor. However, for the prototype, he uses a SparkFun SAM21 Arduino-sized board, a Pololu DRV8835 dual motor driver board, four photoresistors, two motors, a battery, and sundry other parts.

Once he’s proven the prototype works, he creates the schematic for his custom board. Rather than start from scratch, he goes to SparkFun’s and Pololu’s websites for the schematics of their boards and incorporates those into his design. From there he talks about how and why he starts out in a CAD program, then moves on to KiCad where he talks about his approach to layout.

Part two is about soldering and assembly, from how he sorts the components while still in their shipping packages, to tips on doing the reflow in a toaster oven, and fixing bridges and parts that aren’t on all their pads, including the microprocessor.

In Part three he writes the code. The robot’s objective is simple, run away from the light. He first tests the photoresistors without the motors and then writes a procedural program to make the robot afraid of the light, this time with the motors. Finally, he writes the neural network code, but not before first giving a decent explanation of how the neural network works. He admits that you don’t really need a neural network to make the robot run away from the light. But from his comparisons of the robot running using the procedural approach and then the neural network approach, we think the neural network one responds better to what would be the in-between cases for the procedural approach. Admittedly, it could be that a better procedural version could be written, but having the neural network saved him the trouble and he’s shown us a lot that can be reused from the effort.

In case you want to replicate this, [Sean]’s provided a GitHub page with BOM, code and so on. Check out all three parts below, or watch just the parts that interest you.

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