This Raspberry Pi Is A Stereo Camera And So Much More

Over the years we have featured a huge array of projects featuring the Raspberry Pi, but among them there is something that has been missing in all but a few examples. The Raspberry P Compute Module is the essentials of a Pi on a form factor close to that of a SODIMM module, and it is intended as a way to embed a Pi inside a commercial product. It’s refreshing then to see [Eugene]’s StereoPi project, a PCB that accepts a Compute Module and provides interfaces for two Raspberry Pi cameras.

What makes this board a bit special is that as well as the two camera connectors at the required spacing for stereophotography it also brings out all the interfaces you’d expect on a regular Pi, so there is the familiar 40-pin expansion header as well as USB and Ethernet ports. It has a few extras such as a pin-based power connector, and an on-off switch.

Where are they going with this one? So far we’ve seen demonstrations of the rig used to create depth maps with ROS (Robot Operating System). But even more fun is seeing the 3rd-person-view rig shown in the video below. You strap on a backpack that holds the stereo camera above your head, then watch yourself through VR goggles. Essentially you become the video game. We’ve seen this demonstrated before and now it looks like it will be easy to give it a try yourself as StereoPi has announced they’re preparing to crowdfund.

So aside from the stereophotography why is this special? The answer comes in that it is as close as possible to a fresh interpretation of a Raspberry Pi board without being from the Pi Foundation themselves. The Pi processors are not available to third party manufacturers, so aside from the Odroid W (which was made in very limited numbers) we have never seen a significant alternative take on a compatible Raspberry Pi. The idea that this could be achieved through the Compute Module is one that we hope might be taken up by other designers, potentially opening a fresh avenue in the Raspberry Pi story.

The Raspberry Pi Compute Module has passed through two iterations since its launch in 2014, but probably due to the lower cost of a retail Raspberry Pi we haven’t seen it in many projects save for a few game consoles. If the advent of boards like this means we see more of it, that can be no bad thing.

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Machine Learning On Tiny Platforms Like Raspberry Pi And Arduino

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!

Arduino And Pi Share Boardspace

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.

A Star-Trek-Inspired Robot With Raspberry Pi And AI

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.

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Behold The WT-220: A ‘Clever’ VT-220 Terminal

[John Whittington] failed to win a bid for an old VT-220 serial terminal on eBay, so he decided to make his own version and improve it along the way. The result is the Whitterm-220 (or WT-220) which has at its core a Raspberry Pi and is therefore capable of more than just acting as a ‘dumb’ serial terminal.

Rear of the WT-220 with paint-filled laser engraving and all necessary connectors.

The enclosure is made from stacked panels of laser-cut plywood with an acrylic plate on the back for labels and connectors, where [John] worked paint into the label engravings before peeling off the acrylic’s protective film. By applying paint after laser-engraving but before peeling off the film, it acts as a fill and really makes the text pop.

Near the front, one layer of clear acrylic among the plywood layers acts as a light guide and serves as a power indicator, also doing double duty as TX/RX activity lights. When power is on, that layer glows, serving as an attractive indicator that doesn’t interfere with looking at the screen. When data is sent or received, a simple buffer circuit tied to the serial lines lights up LEDs to show TX or RX activity, with the ability to enable or disable this functionality by toggling a GPIO pin. A video overview is embedded below, where you can see the unit in action.

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Vintage Plotter Turned Fruit Spectrometer

Fruit can be a tricky thing: if you buy it ripe you’ll be racing against time to eat the pieces before they turn into a mushy mess, but if you buy the ones which are a bit before their prime it’s not always easy to tell when they’re ready to eat. Do you smell it? Squeeze it? Toss it on the counter to see if it bounces? In the end you forget about them and they go bad anyway. That’s why here at Hackaday we sustain ourselves with only collected rainwater and thermo-stabilized military rations.

But thankfully Cornell students [Christina Chang], [Michelle Feng], and [Russell Silva] have come up with a delightfully high-tech solution to this decidedly low-tech problem. Rather than rely on human senses to determine when a counter full of fruit has ripened, they propose an automated system which uses a motorized spectrometer to scan an arrangement of fruit. The device measures the fruit’s reflectance at 678 nm, which can be used to determine the surface concentration of chlorophyll-a; a prime indicator of ripeness.

If that sounds a bit above your pay grade, don’t worry. The students were able to build a functional prototype using a 1980’s era plotter, a Raspberry Pi, and a low-cost AS7263 NIR spectral sensor from SparkFun which just so happens to have a peak responsivity of 680 nm. The scanning is performed by a PIC32MX250F128B development board with an attached TFT LCD display so the results can be easily viewed. The Raspberry Pi is used in conjunction with a Adafruit PCA9685 I2C PWM driver to control the plotter’s stepper motors. The scanning and motor control could be done with the PIC32 alone, but to save time the students decided to use the Raspberry Pi to command the PCA9685 as that was what the documentation and software was readily available for.

To perform a scan, the stepper motors home the AS7263 sensor module, and then passes it under the fruit which is laying on a clear acrylic sheet. Moving the length of the acrylic sheet, the sensor is able to scan not only multiple pieces of fruit but the entirety of each piece; allowing it to determine for example if a section of a banana has already turned. The relative ripeness of the fruit is displayed to the user on the LCD display as a heatmap: the brighter the color the more ripe it is.

At the end of their paper, [Christina], [Michelle], and [Russell] note that while the scanner worked well there’s still room for improvement. A more scientific approach to calculating how ripe each fruit is would make the device more accurate and take out the guess work on the part of the end user, and issues with darker colored fruit could potentially be resolved with additional calibration.

While a spectrometer might sound like the kind of equipment that only exists in multi-million dollar research laboratories, we occasionally see projects like this which make the technology much more accessible. This year we saw a compact spectrometer in the Hackaday Prize, and going a bit farther back in time we even featured a roundup of some of the most impressive spectrometer builds on Hackaday.io.

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A Pi Cluster To Hang In Your Stocking With Care

It’s that time of year again, with the holidays fast approaching friends and family will be hounding you about what trinkets and shiny baubles they can pretend to surprise you with. Unfortunately there’s no person harder to shop for than the maker or hacker: if we want it, we’ve probably already built the thing. Or at least gotten it out of somebody else’s trash.

But if they absolutely, positively, simply have to buy you something that’s commercially made, then you could do worse than pointing them to this very slick Raspberry Pi cluster backplane from [miniNodes]. With the ability to support up to five of the often overlooked Pi Compute Modules, this little device will let you bring a punchy little ARM cluster online without having to build something from scratch.

The Compute Module is perfectly suited for clustering applications like this due to its much smaller size compared to the full-size Raspberry Pi, but we don’t see it get used that often because it needs to be jacked into an appropriate SODIMM connector. This makes it effectively useless for prototyping and quickly thrown together hacks (I.E. everything most people use the Pi for), and really only suitable for finished products and industrial applications. It’s really the line in the sand between playing around with the Pi and putting it to real work.

[miniNodes] calls their handy little device the Carrier Board, and beyond the obvious five SODIMM slots for the Pis to live in, there’s also an integrated gigabit switch with an uplink port to get them all connected to the network. The board powers all of the nodes through a single barrel connector on the side opposite the Ethernet jack, leaving behind the masses of spider’s web of USB cables we usually see with Pi clusters.

The board doesn’t come cheap at $259 USD, plus the five Pi Compute Modules which will set you back another $150. But for the ticket price you’ll have a 20 core ARM cluster with 5 GB of RAM and 20 GB of flash storage in a 200 x 100 millimeter (8 x 4 inch) footprint, with an energy consumption of under 20 watts when running at wide open throttle. This could be an excellent choice for mobile applications, or if you just want to experiment with parallel processing on a desktop-sized device.

Amazon is ready for the coming ARM server revolution, are you? Between products like this and the many DIY ARM clusters we’ve seen over the years, it looks like we’re going to be dragging the plucky architecture kicking and screaming into the world of high performance computing.

[Thanks to Baldpower for the tip.]