Pi-Based Spectrometer Puts The Complexity In The Software

Play around with optics long enough and sooner or later you’re probably going to want a spectrometer. Optical instruments are famously expensive, though, at least for high-quality units. But a useful spectrometer, like this DIY Raspberry Pi-based instrument, doesn’t necessarily have to break the bank.

This one comes to us by way of [Les Wright], whose homebrew laser builds we’ve been admiring for a while now. [Les] managed to keep the costs to a minimum here by keeping the optics super simple. The front end of the instrument is just a handheld diffraction-grating spectroscope, of the kind used in physics classrooms to demonstrate the spectral characteristics of different light sources. Turning it from a spectroscope to a spectrometer required a Raspberry Pi and a camera; mounted to a lens and positioned to see the spectrum created by the diffraction grating, the camera sends data to the Pi, where a Python program does the business of converting the spectrum to data. [Les]’s software is simple by complete, giving a graphical representation of the spectral data it sees. The video below shows the build process and what’s involved in calibrating the spectrometer, plus some of the more interesting spectra one can easily explore.

We appreciate the simplicity and the utility of this design, as well as its adaptability. Rather than using machined aluminum, the spectroscope holder and Pi cam bracket could easily be 3D-printer, and we could also see how the software could be adapted to use a PC and webcam.

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Visual Raspberry Pi With Node-Red And TensorFlow

If you prefer to draw boxes instead of writing code, you may have tried IBM’s Node-RED to create logic with drag-and-drop flows. A recent [TensorFlow] video shows an interview between [Jason Mayes] and [Paul Van Eck] about using TensorFlow.js with Node-RED to create machine learning applications for Raspberry Pi visually. You can see the video, below.

The video doesn’t go into much detail since it is only ten minutes long. But it does show how easy it is to do things like identify images using an existing TensorFlow model. There is a more detailed tutorial available, as well as a corresponding video, which you can see below.

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PiStorm Brings Modern Muscle To The Amiga

The Amiga, well known as the best and greatest computer ever designed, is nonetheless a platform of yesteryear. Its 68K, and later PowerPC, architectures have both been abandoned by the mainstream, and its attractive grey industrial design no longer graces store shelves. That doesn’t mean the platform is dead however, with diehard shredders like [Claude Schwarz] working hard to keep it alive with projects like PiStorm.

PiStorm is a Motorola 68K CPU emulator, running on a Raspberry PI 3A. The Pi uses its GPIOs to interact with a CPLD chip, which acts as the logic glue to allow the modern single board computer to emulate the Amiga’s original processor. However, it’s more than just an easy way to replace or upgrade a CPU. It also offers additional features, like retargetable graphics acceleration, SCSI disk emulation, and the ability to run whatever Kickstart ROM you so desire.

While the initial work has been done on a Pi 3A, [Claude] has also demonstrated some of the basic functionality running on a Pi CM4 too. The benchmarks are more fierce than a Beyoncé Super Bowl half time show, so if you need grunt on your classic Amiga, this could be the way to go. As a bonus, files to build your own are readily available on Github, which should make it a mite more accessible than other Amiga accelerator boards.

We wonder whether this accelerator could be used to hook the Amiga up to Spotify, a la this previous build. Likely, time will tell. Video after the break.

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Heavy Metal Cyberdeck Has An Eye Towards Expansion

Whether we’re talking about Gibson’s Sprawl or our increasingly dystopian reality, one of the defining characteristics of a cyberdeck is that it can be easily customized and upgraded over time. While a few of the builds we’ve covered over the last couple of years have focused more on style than substance, we really appreciate the designs that embrace the concept of modularity to make sure the system can evolve to meet the changing demands of hacking on the go.

To that end, the M3TAL from [BlastoSupreme] is a perfect example of what a cyberdeck should be. Naturally it’s got the cyberpunk aesthetics we’ve come to expect, but more importantly, it’s designed so modifications and repairs are as quick and painless as possible. The trick is the use of a 2020 aluminum extrusion frame, which allows external panels and components to be attached anywhere along the length of the deck using T-Nuts. Similarly, by mounting internal components to “sleds” that ride between the pieces of extrusion, the electronics can easily be removed or swapped out as complete modules.

The M3TAL is currently outfitted with a Raspberry Pi 4 and a pair of 26650 batteries.

Furthering the idea of expandability, [BlastoSupreme] included an authentic 3.5 floppy drive on the M3TAL that allows him to pack an incredible 1.44 MB onto each rugged and portable disk. OK, so maybe the floppy drive isn’t terribly impressive compared to 2021 tech, but it does seem oddly appropriate for a cyberdeck. On the opposite side of the deck there’s a RetroCART slot, which cloaks modern USB devices in clunky faux cartridges. This provides a unified physical format for everything from removable storage to microcontrollers and software defined radio receivers.

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A Lot Of Effort For A Pi Laptop

Building a Raspberry Pi laptop is not that uncommon. In fact, just a few clicks from any of the major electronics suppliers will have the parts needed for such a project speeding on their way to your house in no time at all. But [joekutz] holds the uncontroversial belief that the value in these parts has somewhat diminishing returns, so he struck out to build his own Pi laptop with a €4 DVD player screen and a whole lot of circuit wizardry to make his parts bin laptop work.

The major hurdle that he needed to overcome was how to power both the display and the Pi with the two small battery banks he had on hand. Getting 5V for the Pi was easy enough, but the display requires 8V so he added one lithium ion battery in series (with its own fuse) in order to reach the required voltage. This does make charging slightly difficult but he also has a unique four-pole break-before-make switch on hand which doesn’t exactly simplify things, but it does make the project function without the risk of short-circuiting any of the batteries he used.

The project also makes use of an interesting custom circuit which provides low voltage protection for that one lonely lithium battery as well. All in all it’s a master course in using some quality circuit-building skills and electrical theory to make do with on-hand parts (and some 3D printing) rather than simply buying one’s way out of a problem. And the end result is something that’s great for anything from watching movies to playing some retro games.

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An RP2040 Board Designed For Machine Learning

Machine learning (ML) typically conjures up ideas of fancy code requiring oodles of storage and tons of processing power. However, there are some ML models that, once trained, can readily be run on much more spartan hardware – even a microcontroller! The RP2040, star of the Raspberry Pi Pico, is one such chip up to the task, and [Arducam] have announced a board aiming to employ it to those ends – the Pico4ML.

The board goes heavy on the hardware, equipping the RP2040 with plenty of tools useful for machine learning tasks. There’s a QVGA camera on board, as well as a tiny 0.96″ TFT display. The camera feed can even be streamed live to the screen if so desired. There’s also a microphone to capture audio and an IMU, already baked into the board. This puts object, speech, and gesture recognition well within the purview of the Pico4ML.

Running ML models on a board like the Pico4ML isn’t about robust high performance situations. Instead, it’s intended for applications where low power and portability are key. If you’ve got some ideas on what the Pico4ML could do and do well, sound off in the comments. We’d probably hook it up to a network so we could have it automatically place an order when we yell out for pizza. We’ve covered machine learning on microcontrollers before, too – with a great Remoticon talk on how to get started!

Hamster Goes On Virtual Journey

Hamsters are great pets, especially for those with limited space or other resources. They are fun playful animals that are fairly easy to keep, and are entertaining to boot. [Kim]’s hamster, [Mr. Fluffbutt], certainly fits this mold as well but [Kim] wanted something a little beyond the confines of the habitat and exercise wheel and decided to send him on a virtual journey every time he goes for a run.

The virtual hamster journey is built on an ESP32 microcontroller which monitors the revolutions of the hamster wheel via a hall effect sensor and magnet. It then extrapolates the distance the hamster has run and sends the data to a Raspberry Pi which hosts a MQTT and Node.js server. From there, it maps out an equivalent route according to a predefined GPX route and updates that information live. The hamster follows the route, in effect, every time it runs on the wheel. [Mr Fluffbutt] has made it from the Netherlands to southeastern Germany so far, well on his way to his ancestral home of Syria.

This project is a great way to add a sort of augmented reality to a pet hamster, in a similar way that we’ve seen self-driving fish tanks. Adding a Google Streetview monitor to the hamster habitat would be an interesting addition as well, but for now we’re satisfied seeing the incredible journey that [Mr Fluffbutt] has been on so far.