It’s no surprise that we here at Hackaday are big fans of Fritzing KiCad. But to a beginner (or a seasoned veteran!) the learning curve can be cliff-like in its severity. In 2016 we published a piece linking to project by friend-of-the-Hackaday [Chris Gammell] called Contextual Electronics, his project to produce formalized KiCad training. Since then the premier “Getting to Blinky” video series has become an easy recommendation for anyone looking to get started with Libre EDA. After a bit of a hiatus [Chris] is back with bite sized videos exploring every corner of the KiCad-o-verse.
The original Getting to Blinky series is a set of 10 videos up to 30 minutes long that walks through everything from setting up the the KiCad interface through soldering together some perfect purple PCBs. They’re exhaustive in coverage and a great learning resource, but it’s mentally and logistically difficult to sit down and watch hours of content. Lately [Chris] has taken a new tack by producing shorter 5 to 10 minute snapshots of individual KiCad features and capabilities. We’ve enjoyed the ensuing wave of learning in our Youtube recommendations ever since!
Not long ago, machines grew their skills when programmers put their noses to the grindstone and mercilessly attacked those 104 keys. Machine learning is turning some of that around by replacing the typing with humans demonstrating the actions they want the robot to perform. Suddenly, a factory line-worker can be a robot trainer. This is not new, but a robot needs thousands of examples before it is ready to make an attempt. A new paper from researchers at the University of California, Berkeley, are adding the ability to infer so robots can perform after witnessing a task just one time.
A robotic arm with no learning capability can only be told to go to (X,Y,Z), pick up a thing, and drop it off at (X2, Y2, Z2). Many readers have probably done precisely this in school or with a homemade arm. A learning robot generates those coordinates by observing repeated trials and then copies the trainer and saves the keystrokes. This new method can infer that when the trainer picks up a piece of fruit, and drops it in the red bowl, that the robot should make sure the fruit ends up in the red bowl, not just the location where the red bowl was before.
The ability to infer is built from many smaller lessons, like moving to a location, grasping, and releasing and those are trained with regular machine learning, but the inference is the glue that holds it all together. If this sounds like how we teach children or train workers, then you are probably thinking in the right direction.
Sign language can like any language be difficult to learn if you’re not immersed in it, or at least learning from someone who is fluent. It’s not easy to know when you’re making minor mistakes or missing nuances. It’s a medium with its own unique issues when learning, so if you want to learn and don’t have access to someone who knows the language you might want to reach for the next best thing: a machine that can teach you.
This project comes from three of [Bruce Land]’s senior electrical and computer engineering students, [Alicia], [Raul], and [Kerry], as part of their final design class at Cornell University. Someone who wishes to learn the sign language alphabet slips on a glove outfitted with position sensors for each finger. A computer inside the device shows each letter’s proper sign on a screen, and then checks the sensors from the glove to ensure that the hand is in the proper position. Two letters include making a gesture as well, and the device is able to track this by use of a gyroscope and compass to ensure that the letter has been properly signed. It appears to only cover the alphabet and not a wider vocabulary, but as a proof of concept it is very effective.
The students show that it is entirely possible to learn the alphabet reliably using the machine as a teaching tool. This type of technology could be useful for other applications as well, such as gesture recognition for a human interface device. If you want to see more of these interesting and well-referenced senior design builds we’ve featured quite a few, from polygraph machines to a sonar system for a bicycle.
Most humans take a year to learn their first steps, and they are notoriously clumsy. [Hartvik Line] taught a robotic cat to walk [YouTube link] in less time, but this cat had a couple advantages over a pre-toddler. The first advantage was that it had four legs, while the second came from a machine learning technique called genetic algorithms that surpassed human fine-tuning in two hours. That’s a pretty good benchmark.
The robot itself is an impressive piece inspired by robots at EPFL, a research institute in Switzerland. All that Swiss engineering is not easy for one person to program, much less a student, but that is exactly what happened. “Nixie,” as she is called, is a part of a master thesis for [Hartvik] at the University of Stavanger in Norway. Machine learning efficiency outstripped human meddling very quickly, and it can even relearn to walk if the chassis is damaged.
As William Gibson once noted, the future is already here, it just isn’t equally distributed. That’s especially true for those of us with disabilities. [Abishek Singh] wanted to do something about that, so he created a way for the hearing-impaired to use Amazon’s Alexa voice service. He did this using a TensorFlow deep learning network to convert American Sign Language (ASL) to speech and a speech-to-text converter to interpret the response. This all runs on a laptop, so it should work with any voice interface with a bit of tweaking. In particular, [Abishek] seems to have created a custom bit of ASL to trigger Alexa. Perhaps the next step would be to use a robotic arm to create the output directly in ASL and cut out the Echo device completely? [Abishek] has not released the code for this project yet, but he has released the code for other projects, such as Peeqo, the robot that responds with GIFs.
Connecting computers to human brains is currently limited to the scope of science fiction and a few cutting-edge laboratories. Tapping into some nerves farther from our central wetware is possible and [Peter Buczkowski] shows us his stylish machine for implanting a pattern into our brains without actively having to memorize anything.
His Medium Machine leverages a TENS unit to activate forearm muscles in a pattern programmed into an Arduino. Users place their forearm across two aluminum electrodes mounted on a tasteful wooden platform and extend a single finger over a button. Electrical impulses trigger the muscles which press the button. That’s all. After repeating the pattern a few times, the users should be able to recite it back on command even if they aren’t aware of what it means. If this sounds like some [Johnny Mnemonic] memory cache, you are absolutely correct. This project draws inspiration from the [William Gibson] novel which became a [Keanu Reeves] movie.
At what age did you begin learning about electronics? What was the state of the art available to you at the time and what kinds of things were you building? For each reader these answers can be wildly different. Our technology advances so quickly that each successive generation has a profoundly different learning experience. This makes it really hard to figure out what basic knowledge today will be most useful tomorrow.
Do you know the forward voltage drop of a diode? Of course you do. Somewhere just below 0.7 volts, give or take a few millivolts, of course given that it is a silicon diode. If you send current through a 1N4148, you can be pretty certain that the cathode voltage will be that figure below the anode, every time. You probably also have a working knowledge that a germanium diode or a Schottky diode will have a lower forward voltage, and you’ll know in turn that a bipolar transistor will begin to turn on when the voltage between its base and emitter achieves that value. If you know Ohm’s Law, you can now set up a biasing network and without too many problems construct a transistor amplifier.