When you saw the picture for this article, did you think of a peacock’s feather? These fibers are not harvested from birds, and in fact, the colors come from transparent rubber. As with peacock feathers, they come from the way light reflects off layers of differing materials, this is known as optical interference, and it is the same effect seen on oil slicks. The benefit to using transparent rubber is that the final product is flexible and when drawn, the interference shifts. In short, they change color when stretched.
Most of the sensors we see and feature are electromechanical, which has the drawback that we cannot read them without some form of interface. Something like a microcontroller, gauge, or a slew of 555 timers. Reading a single strain gauge on a torque wrench is not too tricky, but simultaneously reading a dozen gauges spread across a more complex machine such as a quadcopter will probably require graphing software to generate a heat map. With this innovation it could now be done with an on-board camera in real-time. Couple that with machine learning and perhaps you could launch Skynet. Or build a better copter.
The current proof-of-concept weaves the fibers into next-generation bandages to give an intuitive sense of how tightly a dressing should be applied. For the average first-aid responder, the rule is being able to slide a finger between the fabric and skin. That’s an easy indicator, but it only works after the fact whereas saying that the dressing should be orange while wrapping gives constant feedback.
Of all the ways to open up a lock, there are some tried and true methods. Keys, combinations, RFIDs, picks, and explosives have all had their time and place, but now someone else wants to try something new. [Erik] has come up with a lock that opens when it is shown a pattern of colors.
The lock in question uses a set of color coded cards as the “keys”. When the cards are inserted in the lock, a TCS230 color sensor interprets the pattern on the cards and sends the information over to an Arduino Uno. From there, the Arduino can command the physical lock to open if the pattern is a match, although [Erik] is still waiting on the locking mechanism to arrive while he continues to prototype the device.
This is a fairly unique idea with a number of upsides. First, the code can’t be “stolen” from inside a wallet like RFID cards can. (Although if you can take a picture of the card all bets are off.) If you lose your key, you can simply print another one, and the device is able to handle multiple different keys and log the usage of each one. Additionally, no specialized equipment is needed to create the cards, unlike technologies that rely on magnetic strips. Of course, there’s always this classic way of opening doors if you’d rather go old school with your home locks.
Continue reading “Color-Coded Key Opens Doors, Opportunities”
Sorting M&Ms is really only a major concern if you happen to be working on a Van Halen tour, but it’s a fun exercise nonetheless. It’s for this reason we see plenty of sorting projects come our way, varying from the breadboard and cardboard variety, all the way up to final university projects. Today, [Karl] has blessed us with their sculptural-grade offering, and the attention to detail is stunning.
The project has been in gestation in [Karl]’s mind, on and off, for 10 years or so. The big problem centered around reliably separating out one M&M at a time from a hopper of many. From time to time, [Karl] would speak with other builders using similar techniques to his failed experiments, who often reported that the secret to their machine’s reliability was… careful video editing. It was only when a parts sorter flashed across the Hackaday feed that [Karl] found the mechanism that would work to make his project a reality.
Now that the individual candies could readily be separated and fed through a machine, the rest of the project came together quickly. A color sensor was combined with servos and a stepper motor to duct M&Ms into separate flasks.
The real value of this build, however, is in the overall attention paid to the aesthetics of the final product. The device was built to be a kinetic sculpture, able to run reliably with the minimum of attention at the behest of even an untrained user. By carefully optimising the mechanisms inside and building an attractive enclosure, [Karl] has developed something we’d be proud to show off in a living room.
The Vectrex is everybody’s favourite vector-based console from the early 1980s. Vector graphics really didn’t catch on in the videogame market, but the Vectrex has, nonetheless held on to a diehard contingent of fans that continue to tinker with the platform to this day. [Arcade Jason] just so happens to be leading the pack right now.
The Vectrex has always been a monochrome machine, capable of only displaying white lines on its vector monitor. Color was provided by plastic overlays that were stuck to the screen, however this was never considered a particularly mindblowing addition to the console. [Jason] decided he could do better, and dug deep into his collection of vector monitors.
With a 36″ color vector monitor to hand, the Vectrex was laid out on the bench, ready for hacking. The bus heading to one of the DACs was hijacked, and fed through a series of OR and AND logic to generate color signals, since the original Vectrex hardware had no way of doing so. This is then fed to the color monitor, with amazing results.
[Jason]’s setup is capable of generating 8 colors on the screen, and it’s almost by some weird coincidence that this really does make the classic Vectrex games pop in a way they never have before. It’s also a testament to a simpler time that it’s possible to hack this console’s video signals on a breadboard; modern hardware runs much too fast to get away with such hijinx.
It’s an epic hack that through experimentation and some serendipity, has turned out some exciting results. [Jason] is now in the process of taking this to the next level, experimenting with adding color intensity control and other features to the mix.
It’s not [Jason]’s first time around these parts, either – we saw his big-screen Vectrex just a month ago!
[Thanks to Morris for the tip!]
Continue reading “Vectrex, Finally In Color”
Citizen scientist extraordinaire [Thought Emporium] put out a new video about colorful quantum dots which can be seen below the break. Quantum dots are a few nanometers wide and you can tell which size they are by which color they fluoresce. Their optical and electrical properties vary proportionally with size so red will behave differently than purple but we doubt they will taste like “cherry” and “grape.” Let’s not find out. This makes sense when you realize that a diamond will turn into black powder if you pulverize it. Carbon is funny like that.
[Thought Emporium] uses the video for two purposes. The first is to demonstrate the process he uses to make different size quantum dot in his home lab. The second purpose is to implore the scientific community, in general, to take better care when publishing scientific papers. A flimsy third reason is to show that the show must go on. Partway through, all the batteries for his light were dead so he hastily soldered a connection for his benchtop power supply.
We’ve mentioned [Thought Emporium] a few times before. Another of his carbon-based experiments involved graphene creation. How about magnetic DNA extraction? [Thought Emporium] did that too. If you can’t get enough magnets, how about implanting one?
Continue reading “Carbon Quantum Dots in Your Favorite Color”
When you need to quantify the color of an object, you’ve got quite a few options. You can throw a Raspberry Pi camera and OpenCV at the problem and approach it through software, or you can buy an off-the-shelf RGB sensor and wire it up to an Arduino. Or you can go back to basics and build this reflective RGB sensor from an LED and a photocell.
The principle behind [TechMartian]’s approach is simplicity itself: shine different colored lights on an object and measure how much light it reflects. If you know the red, green, and blue components of the light that correspond to maximum reflectance, then you know the color of the object. Their sensor uses a four-lead RGB LED, but we suppose a Neopixel could be used as well. The photosensor is a simple cadmium sulfide cell, which measures the intensity of light bouncing back from an object as an Arduino drives the LED through all possible colors with PWM signals. The sensor needs to be white balanced before use but seems to give sensible results in the video below. One imagines that a microcontroller-free design would be possible too, with 555s sweeping the PWN signals and op-amps taking care of detection.
And what’s the natural endpoint for a good RGB sensor? A candy sorter, or course, of which we have many examples, from the sleek and polished to the slightly more hackish.
Continue reading “Color Sensor from an RGB LED and a Photocell”
Color palettes are key to any sort of visual or graphic design. A designer has to identify a handful of key colours to make a design work, making calls on what’s eye catching or what sets the mood appropriately. One of the problems is that it relies heavily on subjective judgement, rather than any known mathematical formula. There are rules one can apply, but rules can also be artistically broken, so it’s never a simple task. To this end, [Jack Qiao] created colormind.io, a tool that uses neural nets to generate color palettes.
It’s a fun tool – there’s a selection of palettes generated from popular media and sunset photos, as well as the option to generate custom palettes yourself. Colours can be locked so you can iterate around those you like, finding others that match well. The results are impressive – the tool is able to generate palettes that seem to blend rather well. We were unable to force it to generate anything truly garish despite a few attempts!
The blog explains the software behind the curtain. After first experimenting with a type of neural net known as an LSTM, [Jack] found the results too bland. The network was afraid to be wrong, so would choose values very much “in the middle”, leading to muted palettes of browns and greys. After switching to a less accuracy-focused network known as a GAN, the results were better – [Jack] says the network now generates what it believes to be “plausible” palettes. The code has been uploaded to GitHub if you’d like to play around with it yourself.
Check out this primer on neural nets if you’d like to learn more. We’d like to know – how do you pick a palette when starting a project? Let us know in the comments.