Circumvent Facial Recognition With Yarn

Knitwear can protect you from a winter chill, but what if it could keep you safe from the prying eyes of Big Brother as well? [Ottilia Westerlund] decided to put her knitting skills to the test for this anti-surveillance sweater.

[Westerlund] explains that “yarn is a programable material” containing FOR loops and other similar programming concepts transmitted as knitting patterns. In the video (after the break) she also explores the history of knitting in espionage using steganography embedded in socks and other knitwear to pass intelligence in unobtrusive ways. This lead to the restriction of shipping handmade knit goods in WWII by the UK government.

Back in the modern day, [Westerlund] took the Hyperface pattern developed by the Adam Harvey and turned it into a knitting pattern. Designed to circumvent detection by Viola-Jones based facial detection systems, the pattern presents a computer vision system with a number of “faces” to distract it from covered human faces in an image. While the knitted jumper (sweater for us Americans) can confuse certain face detection systems, [Westerlund] crushes our hope of a fuzzy revolution by saying that it is unsuccessful against the increasingly prevalent neural network-based facial detection systems creeping on our day-to-day activities.

The knitting pattern is available if you want to try your hands at it, but [Westerlund] warns it’s a bit of a pain to actually implement. If you want to try knitting and tech mashup, check out this knitting clock or this software to turn 3D models into knitting patterns.

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Using STEP And STL Files In FreeCAD

If you’ve tried FreeCAD, you know that it has a daunting number of workbenches and options. [MangoJelly] has a large number of video tutorials on FreeCAD, and the latest one, below, covers working with STEP and STL with the tool.

If you’ve ever wondered why designers like to work with STEP files and not STL, this video answers that question immediately. A part brought in from a STEP file is closer to the original CAD object. It doesn’t have all the operations that make the part up, but it does have proper faces that you can work with like a normal part. The same part imported from STL, however, is one single mesh.

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Hackaday Links: April 16, 2023

The dystopian future you’ve been expecting is here now, at least if you live in New York City, which unveiled a trio of technology solutions to the city’s crime woes this week. Surprisingly, the least terrifying one is “DigiDog,” which seems to be more or less an off-the-shelf Spot robot from Boston Dynamics. DigiDog’s job is to de-escalate hostage negotiation situations, and unarmed though it may be, we suspect that the mission will fail spectacularly if either the hostage or hostage-taker has seen Black Mirror. Also likely to terrify the public is the totally-not-a-Dalek-looking K5 Autonomous Security Robot, which is apparently already wandering around Times Square using AI and other buzzwords to snitch on people. And finally, there’s StarChase, which is based on an AR-15 lower receiver and shoots GPS trackers that stick to cars so they can be tracked remotely. We’re not sure about that last one either; besides the fact that it looks like a grenade launcher, the GPS tracker isn’t exactly covert. Plus it’s only attached with adhesive, so it seems easy enough to pop it off the target vehicle and throw it in a sewer, or even attach it to another car.

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An aluminium case with a small PCB and two nine-volt batteries inside

A Low-Noise Amplifier To Quantify Resistor Noise

Noise is all around us, and while acoustic noise is easy to spot using our ears, electronic noise is far harder to quantify even with the right instruments. A spectrum analyzer is the most convenient tool for noise measurements, but also adds noise of its own to whatever signal you’re looking at. [Limpkin] has been working on measuring very small noise signals using a spectrum analyzer, and shared his results in a comprehensive blog post.

The target he set himself was to measure the noise produced by a 50 Ohm resistor, which is the impedance most commonly seen on the inputs and outputs of RF systems. The formula for Johnson-Nyquist noise power tells us that the expected noise voltage in a one-hertz bandwidth is just 0.9 nanovolts – tiny by any standard, and an order of magnitude smaller than the noise floor of a typical spectrum analyzer. [Limpkin] therefore designed an amplifier and signal buffer to crank up the noise signal by a factor of 100, using ultra-low noise op amps running off a pair of nine-volt batteries.

There was a problem with this circuit, however: any stray DC voltage present at its input would also be amplified to levels that could damage the analyzer’s sensitive input port. To prevent this, [Limpkin] decided to add a clipper circuit to his amplifier. This consists of a pair of comparators that continuously monitor the amplifier’s output voltage and disconnect it through a silicon switch if it goes beyond 200 millivolts. [Limpkin] packaged his circuit in a beautifully-machined case and ran various tests to ensure the clipper worked reliably even in the presence of fast input transients.

With the clipper in place, it was safe to run the planned noise measurements. The end result? About 0.89 nV, just as predicted by theory. Measuring nanovolt-level signals usually requires extremely accurate equipment and lots of tricks to minimize noise. Sometimes though, noise is just what you need to make a radio transmitter. Thanks for the tip, [alfonso32]!

3D-Printable Foaming Nozzle Shows How They Work

[Jack]’s design for a 3D-printable foaming nozzle works by mixing air with a fluid like liquid soap or hand sanitizer. This mixture gets forced through what looks like layers of fine-mesh sieve and eventually out the end by squeezing the bottle. The nozzle has no moving parts but does have an interesting structure to make this possible.

The fine meshes are formed by multiple layers of bridged filament.

Creating a foam with liquid soap requires roughly one part soap to nine parts air. The idea is that the resulting foam makes more efficient use of the liquid soap compared to dispensing an un-lathered goop directly onto one’s hands.

The really neat part is that the fine mesh structure inside the nozzle is created by having the printer stretch multiple layers of filament across the open span on the inside of the model. This is a technique similar to that used for creating bristles on 3D-printed brushes.

While this sort of thing may require a bit of expert tweaking to get the best results, it really showcases the way the fundamentals of how filament printers work. Once one knows the process, it can be exploited to get results that would be impossible elsewhere. Here are a few more examples of that: printing only a wall’s infill to allow airflow, manipulating “vase mode” to create volumes with structural ribs, and embedding a fine fabric mesh (like tulle) as either a fan filter or wearable and flexible armor. Everything’s got edge cases, and clever people can do some pretty neat things with them (when access isn’t restricted, that is.)

Timeframe: The Little Desk Calendar That Could

Usually, the problem comes before the solution, but for [Stavros], the opposite happened. A 4.7″ E-Ink screen with integrated battery management and ESP32 caught his eye, and he bought it and started thinking about what he wanted to do with it. The Timeframe is a sleek desk calendar based around the integrated e-ink screen.

[Stavros] found the device’s MicroPython support was a little lackluster, and often failed to draw. He found a project that used an older but modified library for driving the e-ink display which worked quite well. However, the older library didn’t support portrait orientation or other niceties. Rather than try and create something complex in C, he moved the complexity to a server environment he knew more about. With the help of CoPilot, he got some code that would wake up the ESP32 every half hour, download an image from a server, and then display it. A Python script uses a headless browser to visit Google Calendar, resize the window, take a screenshot, and then upload it.

The hardest part of the exercise was getting authentication with Google working reliably. A white sleek 3D printed case wraps the whole affair in an aesthetically pleasing shell. So far, this has been a great story of someone building something for themselves and using their strengths. Where’s the hack?

The hack comes when [Stavros] tried squeezing his calendar into a case that was too tight and cracked the screen. Suddenly a large portion of the screen wouldn’t draw. He turned what was broken into something new by mapping out the area that didn’t draw and converting the Python to draw weather information with Pillow rather than screenshot a webpage: clever reuse and a way to make good out of a bad accident.

The code is up on GitLab, and the 3D files for the case are available on Printables. You can also find the project on, as it was an entry into our recently concluded Low-Power Contest. Unfortunately, while the Timeframe is pretty power efficient, it doesn’t last as long as this calendar with a 50-year battery life.

Detecting Anti-Neutrinos From Distant Fission Reactors Using Pure Water At SNO+

Although neutrinos are exceedingly common, their near-massless configuration means that their presence is rather ephemeral. Despite billions of them radiating every second towards Earth from sources like our Sun, most of them zip through our bodies and this very planet without ever interacting with either. This property is also what makes studying these particles that are so fundamental to our understanding so complicated. Fortunately recently published results by researchers behind the SNO+ neutrino detector project shows that we may see a significant bump in our neutrino detection sensitivity.

The Sudbury Neutrino Detector (Courtesy of SNO)
The Sudbury Neutrino Detector (Courtesy of SNO)

In their paper (preprint) in APS Physical Review Letters, the researchers describe how during the initial run of the new SNO+ neutrino detector they were able to detect anti-neutrinos originating from nuclear fission reactors over 240 kilometers away, including Canadian CANDU and US LWR types. This demonstrated the low detection threshold of the  SNO+ detector even in its still incomplete state between 2017 and 2019. Filled with just heavy water and during the second run with the addition of nitrogen to keep out radioactive radon gas from the surrounding rock of the deep mine shaft, SNO+ as a Cherenkov detector accomplished a threshold of 1.4 MeV at its core, more than sufficient to detect the 2.2 MeV gamma radiation from the inverse beta decays (IBD) that the detector is set up for.

The SNO+ detector is the evolution of the original Sudbury Neutrino Observatory (SNO), located 2.1 km below the surface in the Creighton Mine. SNO ran from 1999 to 2006, and was part of the effort to solve the solar neutrino problem, which ultimately revealed the shifting nature of neutrinos via neutrino oscillation. Once fully filled with 780 tons of linear alkylbenzene as a scintillator, SNO+ will investigate a number of topics, including neutrinoless double beta decay (Majorana fermion), specifically the confounding question regarding whether neutrinos are its own antiparticle or not

The focus of SNO+ on nearby nuclear fission reactors is due to the constant beta decay that occurs in their nuclear fuel, which not only produces a lot of electron anti-neutrinos. This production happens in a very predictable manner due to the careful composition of nuclear fuel. As the researchers noted in their paper, SNO+ is accurate enough to detect when a specific reactor is due for refueling, on account of its change in anti-neutrino emissions. This is a property that does not however affect Canadian CANDU PHWRs, as these are constantly refueled, making their neutrino production highly constant.

Each experiment by SNO+ produces immense amounts of data (hundreds of terabytes per year) that takes a while to process, but if these early results are anything to judge by, then SNO+ may progress neutrino research as much as SNO and kin have previously.