Cheap Current Probe Gets Good Review

A current probe isn’t a very common fixture on most workbenches because they are pretty expensive. [VoltLog] looks at a fairly inexpensive current probe from Micsig. He seemed impressed with the workmanship and it looks similar to more expensive offerings. There are two models with different bandwidth numbers (800 kHz and 2 MHz). It can measure current on a 10A and 100A scale.

According to [VoltLog] comparable probes from other vendors are more expensive and have lower bandwidth. He also liked that the device powers from USB since most newer scopes will have a USB port available.

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Tesla Turbine Boat Uses Lily Impeller

Typically in the RC community, radio control boats rely on small nitro engines or electric motors to get around. Fitted with traditional propellers, they’re capable of great speed and performance. Of course, there’s more than one way to skin a cat, as [Integza] shows with his latest build.

As far as the boat side of things is concerned, it’s a basic 3D printed single hull design. The innovation comes in the drivetrain, instead. The boat uses compressed air for propulsion, stored in a battery of four soda bottles, pressurized to 6 bar. The compressed air is used to drive a Tesla turbine of [Integza]’s design, which is 3D printed on a resin printer. Rather then driving a propeller, the Tesla turbine instead turns a Lily impeller, which pulls the boat through the water rather than pushing it along. The impeller uses a nature-inspired design, hence the name, and was also 3D printed, making producing its complex geometry a cinch. The guts of a toy radio control car are then used to control the boat.

Understandably, performance is less than stellar. The limited reserves of compressed air can’t propel the boat long, and the combination of the high RPM Tesla turbine and Lily impeller don’t provide a lot of thrust. However, the boat does move under its own power, demonstrating these oddball technologies while doing so.

[Integza] has been working with these technologies for a while; we featured an earlier Tesla turbine build back in 2018. Video after the break.

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Orbital Tracking On The NES

It’s easy to dismiss the original Nintendo Entertainment System as just, well, an entertainment system. But in reality the 6502 based console wasn’t so far removed from early home computers like the Apple II and Commodore 64, and Nintendo even briefly flirted with creating software and accessories geared towards general purpose computing. Though in the end, Mario and friends obviously won out.

Still, we’re willing to bet that nobody at Nintendo ever imagined their plucky little game system would one day be used to track the course of a space station in low Earth orbit. But that’s precisely what [Vi Grey] has done with his latest project, which is part of his overall effort to demonstrate the unexpected capabilities of the iconic NES. While you’ll need a bit of extra hardware to run the program on a real console, there’s no fundamental trickery that would have kept some developer from doing this in 1985 if they’d wanted to.

Raspberry Pi Zero and TAStm32

If you want to see your own 8-bit view of the International Space Station, the easiest way is with an emulator. In that case, [Vi] explains how you can load up his Lua script in Mesen or FCEUX to provide the ROM with the necessary tracking data from the Internet.

To run it on a real NES you’ll not only need some type of flash cart to get the ROM loaded, but also a TAStm32 board that’s used for tool-assisted speedruns. This allows the computer to essentially “type” the orbital data into the NES by emulating rapid controller button presses. That might seem like a tall order, but it’s important to note that neither device requires you to modify the original console; the code itself runs on a 100% stock NES.

If tracking spacecraft isn’t your thing, perhaps you’d be more interested in the some of the work [Vi] has previously done on the NES. We’re particularly fond of his polyglot ROM that is a ZIP file of its own source code.

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Twitter: It’s Not The Algorithm’s Fault. It’s Much Worse.

Maybe you heard about the anger surrounding Twitter’s automatic cropping of images. When users submit pictures that are too tall or too wide for the layout, Twitter automatically crops them to roughly a square. Instead of just picking, say, the largest square that’s closest to the center of the image, they use some “algorithm”, likely a neural network, trained to find people’s faces and make sure they’re cropped in.

The problem is that when a too-tall or too-wide image includes two or more people, and they’ve got different colored skin, the crop picks the lighter face. That’s really offensive, and something’s clearly wrong, but what?

A neural network is really just a mathematical equation, with the input variables being in these cases convolutions over the pixels in the image, and training them essentially consists in picking the values for all the coefficients. You do this by applying inputs, seeing how wrong the outputs are, and updating the coefficients to make the answer a little more right. Do this a bazillion times, with a big enough model and dataset, and you can make a machine recognize different breeds of cat.

What went wrong at Twitter? Right now it’s speculation, but my money says it lies with either the training dataset or the coefficient-update step. The problem of including people of all races in the training dataset is so blatantly obvious that we hope that’s not the problem; although getting a representative dataset is hard, it’s known to be hard, and they should be on top of that.

Which means that the issue might be coefficient fitting, and this is where math and culture collide. Imagine that your algorithm just misclassified a cat as an “airplane” or as a “lion”. You need to modify the coefficients so that they move the answer away from this result a bit, and more toward “cat”. Do you move them equally from “airplane” and “lion” or is “airplane” somehow more wrong? To capture this notion of different wrongnesses, you use a loss function that can numerically encapsulate just exactly what it is you want the network to learn, and then you take bigger or smaller steps in the right direction depending on how bad the result was.

Let that sink in for a second. You need a mathematical equation that summarizes what you want the network to learn. (But not how you want it to learn it. That’s the revolutionary quality of applied neural networks.)

Now imagine, as happened to Google, your algorithm fits “gorilla” to the image of a black person. That’s wrong, but it’s categorically differently wrong from simply fitting “airplane” to the same person. How do you write the loss function that incorporates some penalty for racially offensive results? Ideally, you would want them to never happen, so you could imagine trying to identify all possible insults and assigning those outcomes an infinitely large loss. Which is essentially what Google did — their “workaround” was to stop classifying “gorilla” entirely because the loss incurred by misclassifying a person as a gorilla was so large.

This is a fundamental problem with neural networks — they’re only as good as the data and the loss function. These days, the data has become less of a problem, but getting the loss right is a multi-level game, as these neural network trainwrecks demonstrate. And it’s not as easy as writing an equation that isn’t “racist”, whatever that would mean. The loss function is being asked to encapsulate human sensitivities, navigate around them and quantify them, and eventually weigh the slight risk of making a particularly offensive misclassification against not recognizing certain animals at all.

I’m not sure this problem is solvable, even with tremendously large datasets. (There are mathematical proofs that with infinitely large datasets the model will classify everything correctly, so you needn’t worry. But how close are we to infinity? Are asymptotic proofs relevant?)

Anyway, this problem is bigger than algorithms, or even their writers, being “racist”. It may be a fundamental problem of machine learning, and we’re definitely going to see further permutations of the Twitter fiasco in the future as machine classification is being increasingly asked to respect human dignity.

Linear Clock Ratchets Up The Action

On the face of it, making a clock that displays the time by moving a pointer along a linear scale shouldn’t be too hard. After all, steppers and linear drives should do the job in a jiffy. Throw an Arduino in and Bob’s your uncle, right?

Wrong. At least that’s not the way [Leo Fernekes] decided to build this unique ratcheting linear clock, a brilliant decision that made the project anything but run-of-the-mill. The idea has been kicking around in [Leo]’s head for years, and there it stayed until inspiration came in the unlikely form of [This Old Tony], one of our favorite YouTube machinists. [Old Tony] did a video on the simple genius of latching mechanisms, like the ones in retractable pens, and that served as an “A-ha!” moment for [Leo]. For a ratchet, he used a strip of bandsaw blade oriented so the teeth point upward. A complex bit of spring steel, bent to engage with the blade’s teeth, forms a pawl to keep the pointer moving upward until it reaches the top.

[Leo] decided early on that this would be an impulse clock, like the type used in schools and factories. He used a servo to jog a strip of tape upward once each minute; the tape is engaged by jaws that drag the pointer along with it, moving the pawl up the ratchet by one tooth and lifting the pointer one minute closer to the top. The pointer releases at the top and falls back to start the cycle over; to arrest its freefall, [Leo] had the genius idea of attaching magnets and using eddy currents induced in the aluminum frame for the job. Finished off with a 3D-printed Art Deco scale, the clock is a unique timepiece that’s anything but boring.

We really appreciate [Leo]’s unique and creative take on projects, and his range. Check out his everlasting continuity tester and his phage-like sentry gun for some neat build details.

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Fermenting Yogurt With The Help Of Hardware

Fermentation is a natural process that has been exploited by humanity for millennia. Behind such favorites as cheese and beer, it takes just the right conditions to get the desired results. To aid in this process, and to explore the crafts of their ancestors, [Victoria] and [Petar] created an electronic fermentation quilt.

Bulgarian yogurt was the tasty end result from this work.

Anyone familiar with breadmaking will be familiar with throwing a cloth over dough when left to rest. This is all about temperature management, providing optimum conditions for the yeast to work their magic. This fermentation quilt takes things to the next level, integrating soft heater pads and temperature sensing hardware into the fabric itself. Rather than acting as a simple insulator, the quilt can actively supply heat where needed, switching off when reaching the set temperature. In this example, the quilt is set to maintain a temperature of 45 degrees for the optimum production of Bulgarian yogurt.

The fermentation quilt serves as an excellent example of what can be achieved when combining textiles with smart electronics. Tools like Adafruit’s Lilypad and conductive thread all come together to make this a functional and useful device, and shows that electronic textiles aren’t just limited to blinky wearables.

Fermentation is a popular topic among hackers, with [Trent Fehl]’s Supercon talk at the 2019 Supercon covering similar ground from a sourdough perspective. It goes to show that hardware skills can pay off in the kitchen, too!

Internet Connected E-Paper Message Board

Are you still writing notes on paper and sticking them to the fridge like it’s the ’80s? Well, if you are, and you read this site, you’d probably like to upgrade to something a bit more 21st century. And, thanks to robot maker [James Bruton], you can leave your old, last century, message taking behind as he has a tutorial up showing you how to build an internet connected e-paper message display board. And, if you have a Raspberry Pi, an e-paper display and adapters just lying around doing nothing, then this project will cost you less than the buck that paper and a magnet will cost you.

Sarcasm aside, this is a pretty nice project. As mentioned, the base of this is a Raspberry Pi – [James] uses a Pi 4, but you could get away with an older, lower powered model as well. This powers the cheap(-ish) e-paper display he found online, which comes with the necessary adapters for the Pi, as well as a python library to write to the display. [James] uses a Google Sheet as the cloud storage for the message board, and there is some python code to access the cells in the Sheet and print them on the display if anything has changed. A cron job runs the script every 5 minutes to catch changes in the messages.

As with most of the projects that [James] does, he gives a good overview in the video and goes over the process of finding the hardware and writing and updating the script. He’s put the script and details as well as the CAD file for the frame he created for the project up on GitHub. [James] has been featured several times on the site before, check out some of his projects.

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