While you might have never considered the idea, looms—especially the punchcard-driven Jacquard loom, which helped inform both Ada Lovelace and Charles Babbage’s pioneering work—are an important part of computing history. As reported here, Victoria Manganiello and Julian Goldman have created an awe-inspiring ode to this computing heritage in the form of a handwoven tapestry that constantly changes the way it looks, aptly named “Computer 1.0.”

The tapestry, which was recently on display at the Museum of Arts and Design in New York City, stretches nine meters in length and features tubing woven throughout. An Arduino actuates pumps and valves to produce familiar patterns in this tubing with blue-dyed water and air.

These patterns soon become abstract and perhaps more open to interpretation, though with more development it’s noted that images and even smartphone-readable designs could be possible. 

Be sure to see the short demo of this incredible installation in the video below! 

A handwoven textile activated by computer code, Computer 1.0 explores connections between weaving and technology. For the project, Victoria Manganiello invited designer Julian Goldman to collaborate on designing and programming a pump controlled by Arduino microcomputers to move precise sequences of air and liquid through the approximately 2,000 feet of tubing woven through the cloth. The movement of the air and liquid evokes traditional weaving patterns such as bird’s eye, monk’s cloth, and twill. And the operating system—the computer and the pump—is not kept out of sight in the service of the woven screen and the pixelated patterns that run across it, but rather are an integral part of the work; nothing is hidden.

Manganiello’s textile reflects and expands on the ob­scured history of weaving and coding, calling attention to the “under-over, under-over” movement of thread becoming cloth that originally inspired the “zero-one-zero-one” of binary code. The jacquard loom of 1801, which used punch cards to program the movement of thread into increasingly complex woven patterns, is a direct, though frequently forgotten, ancestor of modern computers.

Amazon senior product manager Ben Hamm has a cat named Metric. While this adorable feline friend helped with a rat infestation problem in his apartment, he also likes to take his hunting skills out into nature, bringing… whatever home around one out of 10 nights.

To combat this situation, Hamm used an Amazon DeepLens camera to detect the cat, then examine whether or not it’s carrying something extra, based on a machine learning algorithm trained with over 23,000 images.

If the cat is carrying prey, an Arduino locks the cat out for 15 minutes, while the system texts Hamm pictures. It also gives a donation to the National Audubon Society, described by Hamm in his presentation below, as “blood money.” Currently it only works with Metric, but could be generalized with more cat data if you’re having the same problem.

Even though machine learning AKA ‘deep learning’ / ‘artificial intelligence’ has been around for several decades now, it’s only recently that computing power has become fast enough to do anything useful with the science.

However, to fully understand how a neural network (NN) works, [Dimitris Tassopoulos] has stripped the concept down to pretty much the simplest example possible – a 3 input, 1 output network – and run inference on a number of MCUs, including the humble Arduino Uno. Miraculously, the Uno processed the network in an impressively fast prediction time of 114.4 μsec!

Whilst we did not test the code on an MCU, we just happened to have Jupyter Notebook installed so ran the same code on a Raspberry Pi directly from [Dimitris’s] bitbucket repo.

He explains in the project pages that now that the hype about AI has died down a bit that it’s the right time for engineers to get into the nitty-gritty of the theory and start using some of the ‘tools’ such as Keras, which have now matured into something fairly useful.

In part 2 of the project, we get to see the guts of a more complicated NN with 3-inputs, a hidden layer with 32 nodes and 1-output, which runs on an Uno at a much slower speed of 5600 μsec.

This exploration of ML in the embedded world is NOT ‘high level’ research stuff that tends to be inaccessible and hard to understand. We have covered Machine Learning On Tiny Platforms Like Raspberry Pi And Arduino before, but not with such an easy and thoroughly practical example.

Doom holds a special place as one of the biggest games of the 1990s, as well as being one of the foundational blocks of the FPS genre. Long before 3D accelerators hit the market, iD Software’s hit was being played on computers worldwide, and later spread to all manner of other platforms. [David Ruiz] decided to build a cutdown version for everyone’s favourite, the ATmega328.

Due to the limited resources available, it’s not a direct port of Doom. [David] instead took some sprites and map data from the original game, and built a raycasting engine similar to that of Wolfenstein 3D. Despite the limited memory and CPU cycles, the basic game can run at between 8-11 FPS. There are fancy dithering tricks to help improve the sense of depth, a simplified enemy AI, and even a custom text library for generating the UI.

It’s a great example of what can be done with a seemingly underpowered part. We’ve seen similar work before, with Star Fox replicated on the Arduboy. A hacker’s ingenuity truly knows no bounds.

When riding a motorcycle, it’s important to be seen, and if other vehicles can see your brake lights and turn signals as well, all the better. To help with visibility, YouTuber “MechTools” outfitted his helmet with a brake light and turn indicators that activate along with the motorcycle’s built-in signals.

The video below shows off how it was built, using an Arduino Uno onboard the motorcycle, plus a Nano embedded in the helmet. A pair of nRF24L01 transceivers enable the two Arduinos to communicate wirelessly, and three TIP122 transistors controls the lighting directly for sufficient power output.

While a neat concept, be sure that you don’t compromise your helmet’s structural integrity or legality if you try something similar! Code is available in the video’s description.

We’ve seen countless different robot kits promoted for STEM education, every one of which can perform the robotic “Hello World” task of line following. Many were in attendance at Maker Faire Bay Area 2019 toiling in their endless loops. Walking past one such display by Microduino, Inc. our attention was caught by a demonstration of their mCookie modules in action: installing a peripheral module took less than a second with a “click” of magnets finding each other.

Many Arduino projects draw from an ecosystem of Arduino shields. Following that established path, Microduino had offered tiny Arduino-compatible boards and peripherals which connected with pins and headers just like their full-sized counterparts. Unfortunately their tiny size also meant their risk of pin misalignment and corresponding damage would be higher as well. mCookie addresses this challenge by using pogo pins for electrical contacts, and magnets to ensure proper alignment. Now even children with not-quite-there-yet dexterity can assemble these modules, opening up a market to a younger audience.

Spring loaded electric connections are a popular choice for programming jigs, and we’ve seen them combined with magnets for ideas like modular keyboards, and there are also LittleBits for building simple circuits. When packaged with bright colorful LEGO-compatible plastic mounts, we have the foundation of an interesting option for introductory electronics and programming. Microduino’s focus at Maker Faire was promoting their Itty Bitty Buggy, which at $60 USD is a significantly more affordable entry point to intelligent LEGO creations than LEGO’s own $300 USD Mindstorm EV3. It’ll be interesting to see if these nifty mCookie modules will help Microduino differentiate themselves from other LEGO compatible electronic kits following a similar playbook.

Rub two pieces of metal against each other hard enough, and it won’t be long before they heat up sufficiently to cause problems. That’s especially true when one is a workpiece and one is a tool edge, and the problems that arise from failing to manage the heat produced by friction can cost you dearly.

The traditional way of dealing with this is by pumping heavy streams of liquid coolant at the workpiece, but while that works, it creates problems of its own. That’s where minimum quantity lubrication comes in. MQL uses a fine mist of lubricant atomized in a stream of compressed air, which saves on lube and keeps swarf cleaner for easier recycling. The gear needed for MQL can be pricey though, so [brockard] decided to add homebrew MQL to his CNC router, with great results.

The video below shows the whole process, from raw metal to finished system – skip ahead to about 12 minutes if you just want to see final testing, but be warned that you’ll be missing some high-quality machining. The finished pump is a double-piston design, with each side driven by a cam rotated by a servo. An Arduino controls the speed of the motor based on the current settings; the pump is turned on and off through G-code control of a relay.

The lubricant stream is barely visible in the video, as opposed to the sloshing mess of traditional flood coolants, and seems much more suitable for a hobbyist-grade CNC setup. Need to build a CNC router before you build this? You can do much worse than this one.

Thanks for the tip, [Jasper Jans].

We’ve heard of beer pong, but we’re not sure we’ve heard of wine pong. And certainly never wine pong automated with a ping pong ball throwing robot like this one.

There’s not a huge amount of detail available in the video below, and no build log per se. But [Electron Dust] has a few shots in the video that explain what’s going on, as well as a brief description in a reddit thread about the device. The idea is to spin a ball up to a steady speed and release it the same way every time. The rig itself is made of wood and spun by plain brushed DC motors – [Electron Dust] explains that he chose them over PWM servos to simplify things and eliminate uncertainty in the release point. The ball is retained by a pair of arms, each controlled by a pair of hobby servos. An Arduino spins along with everything else and counts 50 revolutions before triggering the servos to retract and release the ball. A glass positioned at the landing spot captures the ball perfectly once everything is dialed in.

Here’s hoping that build details end up on his blog soon, as they did for this audio-feedback juggling machine. And while we certainly like this project, it might be cool if it could aim the ball into the glass. Or it could always reposition the target on the fly.

If you like solving puzzles out in the real world, you’ve probably been to an escape room before, or are at least familiar with its concept of getting (voluntarily) locked inside a place and searching for clues that will eventually lead to a key or door lock combination that gets you out again. And while there are plenty of analog options available to implement this, the chances are you will come across more and more electronics-infused puzzles nowadays, especially if it fits the escape room’s theme itself. [Alastair Aitchison] likes to create such puzzles and recently discovered how he can utilize a USB powered plasma globe as a momentary switch in one of his installations.

The concept is pretty straightforward, [Alastair] noticed the plasma globe will draw significantly more current when it’s being touched compared to its idle state, which he measures using an INA219 current shunt connected to an Arduino. As a demo setup in his video, he uses two globes that will trigger a linear actuator when touched at the same time, making it an ideal multiplayer installation. Whether the amount of fingers, their position on the globe, or movement make enough of a reliable difference in the current consumption to implement a more-dimensional switch is unfortunately not clear, but definitely something worth experimenting with.

In case you’re planning to build your own escape room and are going for the Mad Scientist Laboratory theme, you’ll obviously need at least one of those plasma globes sparking in a corner anyway, so this will definitely come in handy — maybe even accompanied by something slightly larger? And for all other themes, you can always resort to an RFID-based solution instead.

If you’ve been waiting for a new way to generate geometric art, then be sure to check out the Cycloid-O-Matic from InventorArtist Darcy Whyte.

This three-axis cycloid drawing machine is something of an update on the classic spirograph toy, but instead of (only) using an arrangement of gears, it incorporates stepper motors to create smooth curving patterns.

Control is accomplished via an Arduino Uno and GRBL shield, while a single motor rotates the paper in a circle on top of a lazy Susan. A pen is held above in a linkage system, actuated by two steppers that spin to move the linkages and draw in the X/Y plane.