Office Supplies Make Math Sculptures If You Know What You’re Doing

Ever been fiddling around at your desk in the office, wondering if some grander structure might come from an assemblage of paper clips, pens, and binder clips? You’re not alone. Let your mind contemplate these beautiful maths sculptures from [Zachary Abel].

[Zachary] has a knack for both three-dimensional forms and the artistic use of color. His Möbius Clips sculpture ably takes 110 humble pieces of office equipment in multiple colors, and laces them into a continuous strip that has beguiled humanity for generations. The simple paper clip becomes a dodecahedron, a colorful spiralling ball, or a tightly-stitched box. He does great things with playing cards too.

What elevates his work is that there’s a mathematical structure to it. It’s so much more than a pile of stationary, there’s always a geometry, a pattern which your mind latches on to when you see it. He also often shares the mathematical background behind his work, too.

If you’re fumbling about with the contents of your desk drawer while another Zoom meeting drags on, you might want to challenge yourself to draw from [Zachary’s] example. If you pull off something fantastical, do let us know!



Digitally Reading A Micrometer’s Output

If you’re instrumenting your machine tools, or if you’re just curious, you might want to get granular access to the output of a digital micrometer or the like. [Tommy] set his mind to figuring out the communications protocol of the ClockWise Tools dial indicator for this very purpose. And he succeeded!

Work began by finding the clock and signal lines for the gauge. With those identified, and the signals up on an AD2 logic analyzer, it was determined that once every 40 milliseconds, the device sent a data burst of six nibbles separated by 1.58 milliseconds apiece. The device communicates the absolute position of the gauge, and the data can be readily decoded with the aid of an op-amp to help boost up the 1.5-volt logic to a more reasonable level for a modern commodity microcontroller like the Arduino Nano. From there, the information can be trucked over serial to a PC, or you can do just about anything else with it besides.

We’ve seen similar hacks performed on calipers before, too, making automated measurements a breeze. If you’re working on something that needs precise measurements down to the, well… micrometer… this project might be just the thing you’re looking for.

Making Art With Maxwell’s Equations

When you think of art, you might think of portraiture, landscapes, or other kinds of paintings. But mathematics can feel artistic at times, too. We’ve all seen gorgeous Mandelbrot fractals, and less gorgeous Julia fractals, but that’s not all that’s out there. As [Prof. Halim Boutayeb] demonstrates, Maxwell’s equations can show us some real beauty, too.

Find us a cooler graph than this one!

The work involves running simulations of multiple electromagnetic sources moving, bouncing around, interacting, and so on. The art comes in the plotting of the fields, in warm colors or just outright rainbows. The professor does a great job of pairing some of these videos with pumping electronic music, which only adds to the fun.

Of course, the colors are pretty, but there’s a lot of valuable physics going on behind all this. Thankfully, there are all kinds of additional resources linked for those eager to learn about the Finite Difference Time Domain method and how it can be used for valid simulation tasks.

Throw this kind of stuff on a projector at your next rave and you will not be disappointed. Video after the break.

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Hacking An IP Camera To Run Your Own Software

Ah, generic unbranded IP cameras. Safe, secure? Probably not. [Alex] has been hacking around with one of his very own, and he’s recently busted the thing wide open.

Determining that the camera had a software update function built in, [Alex] saw an opening for hijinks. The first issue was that the camera only accepts encrypted update packages, which complicates things somewhat. However, through some smart reverse engineering, the format of the updates and their encryption method became obvious to [Alex]. Oh, and partly because there was a GitHub repository online featuring the source code used by the manufacturer to encrypt their updates. That definitely helped. It also led [Alex] to suspect the manufacturer may not have properly respected the open source license of some of the routines involved.

In the demo of the exploit, [Alex] has the camera reach out to instead of the servers of the original manufacturer. That’s a pretty clear way to show that the camera has been owned.

We first featured [Alex]’s work in this space all the way back in 2019. It’s come a long way since then!

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Seiko Had A Smartwatch In 1984

You might think of the smartwatch era as beginning with Apple, relatively recently. Or, you might think back to those fancy Timex models with the datalink thing going on in the 1990s. Seiko can beat them all, though, with its UC-2000 smartwatch that debuted all the way back in 1984.

The UC2200 was the bigger docking station of the two.

The UC-2000 very much looks cutting edge for its era, and absolutely ancient today. It featured a 4-bit CPU, 2 kilobytes of RAM, and 6 kilobytes of ROM. Display was via a simple 10×4 character LCD in a rectangular form factor, with four buttons along the bottom. Branded as a “personal information processor,” it was intended for use with the UC-2100 dock. This added a full physical QWERTY keyboard that interacted with the UC-2000 when the two were combined together. Alternatively, you could go for the UC-2200, which not only had a keyboard but also a thermal printer to boot. Oh, and ROM packs for Microsoft Basic, games, or an English-to-Japanese translator.

What could you do on this thing? Well, it had basic watch functions, so it told the time, acted as a stop watch, and an alarm, of course. But you could also use it to store two memos of up to 1000 characters each, schedule appointments, and do basic calculations.

The one thing this smartwatch was missing? Connectivity. It couldn’t get on the Internet, nor could it snatch data from the ether via radio or any other method. By today’s measures, it wouldn’t qualify as much of a smartwatch at all. Moreso a personal organizer that fit on the wrist. Still, for its day, this thing really was a whole computer that fit on your wrist.

Would you believe we’ve seen the UC-2000 before? In fact, we’ve even seen it hacked to play Tetris! Video of that wonderful feat after the break.
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Playing Rock, Paper Scissors With A Time Of Flight Sensor

You can do all kinds of wonderful things with cameras and image recognition. However, sometimes spatial data is useful, too. As [madmcu] demonstrates, you can use depth data from a time-of-flight sensor for gesture recognition, as seen in this rock-paper-scissors demo.

If you’re unfamiliar with time-of-flight sensors, they’re easy enough to understand. They measure distance by determining the time it takes photons to travel from one place to another. For example, by shooting out light from the sensor and measuring how long it takes to bounce back, the sensor can determine how far away an object is. Take an array of time-of-flight measurements, and you can get simple spatial data for further analysis.

The build uses an Arduino Uno R4 Minima, paired with a demo board for the VL53L5CX time-of-flight sensor. The software is developed using NanoEdge AI Studio. In a basic sense, the system uses a machine learning model to classify data captured by the time-of-flight sensor into gestures matching rock, paper, or scissors—or nothing, if no hand is present. If you don’t find [madmcu]’s tutorial enough, you can take a look at the original version from STMicroelectronics, too.

It takes some training, and it only works in the right lighting conditions, but this is a functional system that can determine real hand sign and play the game. We’ve seen similar techniques help more advanced robots cheat at this game before, too! What a time to be alive.

CUDA, But Make It AMD

Compute Unified Device Architecture, or CUDA, is a software platform for doing big parallel calculation tasks on NVIDIA GPUs. It’s been a big part of the push to use GPUs for general purpose computing, and in some ways, competitor AMD has thusly been left out in the cold. However, with more demand for GPU computation than ever, there’s been a breakthrough. SCALE from [Spectral Compute] will let you compile CUDA applications for AMD GPUs.

SCALE allows CUDA programs to run as-is on AMD GPUs, without modification. The SCALE compiler is also intended as a drop-in swap for nvcc, right down to the command line options. For maximum ease of use, it acts like you’ve installed the NVIDIA Cuda Toolkit, so you can build with cmake just like you would for a normal NVIDIA setup. Currently, Navi 21 and Navi 31 (RDNA 2.0 and RDNA 3.0) targets are supported, while a number of other GPUs are undergoing testing and development.

The basic aim is to allow developers to use AMD hardware without having to maintain an entirely separate codebase. It’s still a work in progress, but it’s a promising tool that could help break NVIDIA’s stranglehold on parts of the GPGPU market.