What’s tiny and on track to be worth $22 billion dollars by 2018? MEMS (Micro Electrical Mechanical Systems). That’s a catch-all phrase for microscopic devices that have moving parts. Usually, the component sizes range from 0.1 mm to 0.001 mm, which is tiny, indeed. There are some researchers working with even smaller components, sometimes referenced as NEMS (Nano Electrical Mechanical Systems).
MEMS have a wide range of applications including ink jet printers, accelerometers, gyroscopes, microphones, pressure sensors, displays, and more. Many of the sensors in a typical cell phone would not be possible without MEMS. There are many ways that MEMS devices are built, but just to get a flavor, consider the cantilever (see right), one of the most common MEMS constructions.
Sometimes I need to be able to take photographs of very small things, and the so-called macro mode on my point-and-shoot camera just won’t cut it. And it never hurts to have an inspection scope on hand for tiny soldering jobs, either, though I prefer a simple jeweler’s loupe in one eye for most tasks. So I sent just over $40 off to my close friend Alibaba, and a few weeks later was the proud owner of a halfway usable inspection scope that records stills or video to an SD card.
Unfortunately, it’s only halfway useable because of chintzy interface design and a wobbly mount. So I spent an afternoon, took the microscope apart, and got it under microcontroller control, complete with WiFi and a scripting language. Much better! Now I can make microscope time-lapses, but much more importantly I can take blur-free photos without touching the wiggly rig. It was a fun hack, so I thought I’d share. Read on!
What if every time you learned something new, you forgot a little of what you knew before? That sort of overwriting doesn’t happen in the human brain, but it does in artificial neural networks. It’s appropriately called catastrophic forgetting. So why are neural networks so successful despite this? How does this affect the future of things like self-driving cars? Just what limit does this put on what neural networks will be able to do, and what’s being done about it?
Numerical weights in an artificial neural network
Neurons in the brain
The way a neural network stores knowledge is by setting the values of weights (the lines in between the neurons in the diagram). That’s what those lines literally are, just numbers assigned to pairs of neurons. They’re analogous to the axons in our brain, the long tendrils that reach out from one neuron to the dendrites of another neuron, where they meet at microscopic gaps called synapses. The value of the weight between two artificial neurons is roughly like the number of axons between biological neurons in the brain.
To understand the problem, and the solutions below, you need to know a little more detail.
Have you, dear reader, ever needed to plot the position of a swimming pool noodle in 3D and in real time? Of course you have, and today, you’re in luck! I’ve compiled together a solution that’s sure to give you the jumpstart on solving this “problem-you-never-knew-you-had.”
Ok, there’s a bit of a story behind this one. Back in my good-ol’ undergrad days, I got the chance to play with tethered underwater robots. I remember fumbling about thinking: “Hmm, with this robot tether, wouldn’t it be sweet to string up a set of IMUs down the length of the tether to estimate the robot’s location in 3-space?” A few years later, I cooked together this IMU Noodle project to play with some real hardware in the spirit of solving that problem. With a little quaternion math, a nifty IMU, and some custom PCBAs, this idea has gone from some idle brain-ramble into a real device. It’s an incredibly interesting example of using available hardware and a little ingenuity to build a system that is unique and dependable.
As for why? I first saw an IMU noodle pop up on these pages back in 2012 and I was baffled. I just had to build one! Now complete, I figured that there’s enough math and fun-loving electronics nuggets to merit a full article for this month’s after-hour adventures. Dear reader, let me tell you a wonderful story where math meets electronics and works up the courage to ask it out for brunch.
If you have ever read advanced textbooks or papers about electronics, you may have been surprised to see the use of complex numbers used in the analysis of AC circuits. A complex number has two parts: a real part and an imaginary part. I’ve often thought that a lot of books and classes just kind of gloss over what this really means. What part of electricity is imaginary? Why do we do this?
The short answer is phase angle: the time delay between a voltage and a current in a circuit. How can an angle be a time? That’s part of what I’ll need to explain.
First, consider a resistor. If you apply a voltage to it, a certain current will flow that you can determine by Ohm’s law. If you know the instantaneous voltage across the resistor, you can derive the current and you can find the power–how much work that electricity will do. That’s fine for DC current through resistors. But components like capacitors and inductors with an AC current don’t obey Ohm’s law. Take a capacitor. Current only flows when the capacitor is charging or discharging, so the current through it relates to the rate of change of the voltage, not the instantaneous voltage level.
That means that if you plot the sine wave voltage against the current, the peak of the voltage will be where the current is minimal, and the peak current will be where the voltage is at zero. You can see that in this image, where the yellow wave is voltage (V) and the green wave is current (I). See how the green peak is where the yellow curve crosses zero? And the yellow peak is where the green curve crosses zero?
These linked sine and cosine waves might remind you of something — the X and Y coordinates of a point being swept around a circle at a constant rate, and that’s our connection to complex numbers. By the end of the post, you’ll see it isn’t all that complicated and the “imaginary” quantity isn’t imaginary at all.
Hackaday is all about the neat hacks and the repurposing of old components into new projects, but many people then try to take those projects and turn them into businesses. We’ve seen lots of people offer their stuff as kits and sell them on Tindie, with the rare few going on to develop a consumer electronic product at scale.
The Hackaday Prize 2017 Best Product highlights this journey. “Scale” itself is a vague term, but essentially it means to be able to produce enough to meet market demand. We hope that market demand is roughly 7 billion units, purchasing yearly, but the reality is that it is somewhere between 1 and a few hundred thousand, with very big differences in manufacturing at each order of magnitude. So how do you start with a proof of concept and design your product from the very beginning to be optimized to scale to meet whatever demand you can handle?
Guitarists are a special breed, and many of them have a close connection with the instruments they play. It might be a specific brand of guitar, or a certain setup required to achieve the sound they’re looking for. No one has a closer bond with an instrument than Brian May to his Red Special. The guitar he toured with and played through his career with Queen and beyond had very humble beginnings. It was built from scratch by Brian and his father Harold May.
It was the early 1960’s and a young teenaged Brian May wanted an electric guitar. The problem was that the relatively new instruments were still quite expensive — into the hundreds of dollars. Well beyond the means of the modest family’s budget. All was not lost though. Brian’s father Harold was an electrical engineer and a hacker of sorts. He built the family’s radio, TV, and even furniture around the house. Harold proposed the two build a new electric guitar from scratch as a father-son project. This was the beginning of a two-year odyssey that resulted in the creation of one of the world’s most famous musical instruments.
Brian was already an accomplished guitarist, learning first on his dad’s George Formby Banjo-ukulele, and graduating to an Egmond acoustic guitar. Brian’s first forays into electric guitars came from experimenting with that Egmond. If you look close, you can even see the influence it had on the final design of the Red Special.