A sandbox game generally refers to an open world computer simulation, but Tim Callinan’s fall semester project takes things in a different direction—an actual sandbox controlled by an Arduino and gShield.

Callinan’s Zen table is capable of producing complicated designs in the sand, using a single steel marble that appears to magically move across the surface by itself.

Underneath the sand assembly is a stepper-controlled gantry that acts like a laser cutter or other CNC tool, dragging the marble around with a powerful magnet. The results are stars, rotating squares, and other kaleidoscopic patterns, beautifully edge-lit with a series of RGB LEDs.

Several other classmates were involved in making this build a reality, including Mark Morello, who wired and programmed the device.

If you’ve ever wanted to plot shapes using an oscilloscope, YouTuber Electronoobs reveals the tricks in his latest video. In it, he draws a Christmas tree, along with a few other shapes, and while that holiday is now past, there’s always the 2019. Of course, you don’t have to wait, as these concepts can be applied to anything you like throughout the year!

In the video, Electronoobs uses an Arduino Nano to produce PWM signals on two channels, filtering each of them with a capacitor and resistor. As he explains, shapes must be fairly simple, and end in the same place they started. Even with these restrictions, once the oscilloscope it turned to x/y plot mode and the signal is tuned in, the results are quite good.

In this tutorial we will use two pins from the Arduino to create fast PWM signals. With a small filter, we change the amplitude of that signal according to the width of the PWM pulse. By that, we can draw shapes on the oscilloscope when in XY mode.

Code for the project can be found in Electronoobs’ write-up here.

Machine learning is starting to come online in all kinds of arenas lately, and the trend is likely to continue for the forseeable future. What was once only available for operators of supercomputers has found use among anyone with a reasonably powerful desktop computer. The downsizing isn’t stopping there, though, as Microsoft is pushing development of machine learning for embedded systems now.

The Embedded Learning Library (ELL) is a set of tools for allowing Arduinos, Raspberry Pis, and the like to take advantage of machine learning algorithms despite their small size and reduced capability. Microsoft intended this library to be useful for anyone, and has examples available for things like computer vision, audio keyword recognition, and a small handful of other implementations. The library should be expandable to any application where machine learning would be beneficial for a small embedded system, though, so it’s not limited to these example applications.

There is one small speed bump to running a machine learning algorithm on your Raspberry Pi, though. The high processor load tends to cause small SoCs to overheat. But adding a heatsink and fan is something we’ve certainly seen before. Don’t let your lack of a supercomputer keep you from exploring machine learning if you see a benefit to it, and if you need more power than just one Raspberry Pi you can always build a cluster to get your task done just a little bit faster, too.

Thanks to [Baldpower] for the tip!

A Raspberry Pi Zero (W) and Arduino are very different animals, the prior has processing power and connectivity while the latter has some analog to digital converters (ADCs) and nearly real-time reactions. You can connect them to one another with a USB cable and for many projects that will happily wed the two. Beyond that, we can interface this odd couple entirely through serial, SPI, I2C, and logic-level signaling. How? Through a device by [cburgess] that is being called an Arduino shield that supports a Pi0 (W). Maybe it is a cape which interfaces with Arduino. The distinction may be moot since each board has a familiar footprint and both of them are found here.

Depending on how they are set up and programmed, one can take control over the other, or they could happily do their own thing and just exchange a little information. This board is like a marriage counselor between a Raspberry Pi and an Arduino. It provides the level-shifting so they don’t blow each other up and libraries so they can speak nicely to one another. If you want to dig a bit deeper into this one, design files and code examples are on available.

Perhaps we’ll report on this board at the heart of a pinball machine retrofit, a vintage vending machine restoration, or maybe a working prop replica from the retro bar in Back to the Future II.

Digitizing an object usually means firing up a CAD program and keeping the calipers handy, or using a 3D scanner to create a point cloud representing an object’s surfaces. [Dzl] took an entirely different approach with his DIY manual 3D digitizer, a laser-cut and 3D printed assembly that uses rotary encoders to create a turntable with an articulated “probe arm” attached.

Each joint of the arm is also an encoder, and by reading the encoder values and applying a bit of trigonometry, the relative position of the arm’s tip can be known at all times. Manually moving the tip of the arm from point to point on an object therefore creates measurements of that object. [Dzl] successfully created a prototype to test the idea, and the project files are available on GitHub.

We remember the earlier version of this project and it’s great to see how it’s been updated with improvements like the addition of a turntable with an encoder. DIY 3D digitizing takes all kinds of approaches, and one example was this unit that used four Raspberry Pi Zeros and four cameras to generate high quality 3D scans.

When [314Reactor] got a robot car kit, he knew he wanted to add some extra things to it. At about the same time he was watching a Star Trek episode that featured exocomps — robots that worked in dangerous areas. He decided to use those fictional devices to inspire his modifications to the car kit. Granted, the fictional robots were intelligent and had a replicator. So you know he won’t make an actual working replica. But then again, the ones on the TV show didn’t have all that either.

A Raspberry Pi runs Tensorflow using the standard camera.  This lets it identify objects of interest (assuming it gets them right) and sends the image back to the operator along with some identifying information. The kit already had an Arduino onboard and the new robot talks to it via a serial port. You can see a video about the project, below.

The design is complicated a bit by the fact that the original kit uses a Bluetooth adapter to send and receive serial commands from a mobile device. However, the controller software with the kit, though, allows for extra buttons, so the Arduino can receive command and send them to the Pi.

The code for the robot — known as Scorpion — is available on GitHub. The extra commands relate to the camera and also some servos that move pincers to mimic the TV robot. Images return to the operator via the Telegram cloud service.

We have to admit, the Scorpion isn’t quite the same as an exocomp. But we can see the influence on the design. It wasn’t smart enough to identify itself in the mirror, so we don’t think it achieved sentience.

We’ve seen smart robots using Tensorflow before. If you prefer, you can always try OpenCV.

If you’d like an easy way to accomplish repetitive biological experiments, the OpenLH presents a great option for automating these tasks. 

The heart of the system is the Arduino Mega-controlled uArm Swift Pro robot, which is equipped with a custom end effector and syringe pump. This enables it to dispense liquids with an average error of just .15 microliters.

A Python/Blockly interface allows the OpenLH to be set up for creative exploration, and because of the arm’s versatility, it could later be modified for 3D printing, laser cutting, or any number of other robotic duties. 

Liquid handling robots are robots that can move liquids with high accuracy allowing to conduct high throughput experiments such as large scale screenings, bioprinting and execution of different protocols in molecular microbiology without a human hand, most liquid handling platforms are limited to standard protocols.

The OpenLH is based on an open source robotic arm (uArm Swift Pro) and allows creative exploration. With the decrease in cost of accurate robotic arms we wanted to create a liquid handling robot that will be easy to assemble, made by available components, will be as accurate as gold standard and will cost less than $1,000. In addition the OpenLH is extendable, meaning more features can be added such as a camera for image analysis and real time decision making or setting the arm on a linear actuator for a wider range. In order to control the arm we made a simple Blockly interface and a picture to print interface block for bioprinting images.

We wanted to build a tool that would be used by students, bioartists, biohackers and community biology labs around the world.

The OpenLH can be seen in the video below, bioprinting with pigment-expressing E. coli bacteria.

The Spresense development board is Sony’s debut into the Maker market for microcontrollers, and it’s an impressive one.

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While you may not consider in detail how your coffee is roasted, those that produce it must pay special attention to make sure that the end product is consistent. Equipment to help analyze roasted coffee is normally quite expensive, but using a near-infrared sensor, Arduino Uno and Bluetooth module, Spencer Corry was able to make his own analysis setup.

As shown in the video below, after calibration, beans are inserted into the analysis chamber using a tryer scoop. Light is shined onto the roasted beans, and the intensity of the reflected near-infrared radiation is analyzed in six different wavelengths. Intensity data is then transmitted via Bluetooth to a smart device, which can be used to make sure things are roasted perfectly.

There has recently been a growth of small roasting companies offering custom in-house roasts. These companies are looking for less expensive alternatives to hiring and training a roast master or using the expensive Agtron Process Analyzer. The Degree of Roast Infrared Analyzer for Coffee Roasters, as described in this document, is meant to be an inexpensive means of measuring the degree of roast of coffee beans. The Degree of Roast Infrared Analyzer uses a tryer, a tool found on coffee roasters used to sample the coffee during roasting, to hold a sample of coffee. The tryer is inserted into the analyzer where the AS7263 NIR Spectral sensor is used to measure 6 different infrared bands (610, 680, 730, 760, 810, and 860nm). The reflectance measurements are transmitted via Bluetooth and can then be correlated to the degree of roast. The analyzer must first be calibrated by pressing a button on the inside of the box in which the PVC is used as a white balance as it has a relatively flat reflectance in the spectral range detected by the sensor.