If you are a watcher of the world of drones, or multirotors, you may have a fixed idea of what one of these aircraft looks like in your mind. There will be a central pod containing batteries and avionics, with a set of arms radiating from it, each of which will have a motor and a propeller on its end. You are almost certainly picturing a four-rotor design, such as the extremely popular DJI Phantom series of craft.
Of course, four-rotor designs are just one of many possible configurations of a multirotor. You will commonly see octocopters, but sometimes we’ve brought you craft that really put the “multi” in “multirotor”. If the computer can physically control a given even number of motors, within reason, it can be flown.
There is one type of multirotor you don’t see very often though, the trirotor. Three propellers on a drone is a rare sight, and it’s something we find surprising because it’s a configuration that can have some surprising benefits. To think about why, it’s worth taking a look at some of the characteristics of a three-rotor machine’s flight.
It is hard to get very far into electronics without knowing Ohm’s law. Named after [Georg Ohm] it describes current and voltage relationships in linear circuits. However, there are two laws that are even more basic that don’t get nearly the respect that Ohm’s law gets. Those are Kirchhoff’s laws.
In simple terms, Kirchhoff’s laws are really an expression of conservation of energy. Kirchhoff’s current law (KCL) says that the current going into a single point (a node) has to have exactly the same amount of current going out of it. If you are more mathematical, you can say that the sum of the current going in and the current going out will always be zero, since the current going out will have a negative sign compared to the current going in.
You know the current in a series circuit is always the same, right? For example, in a circuit with a battery, an LED, and a resistor, the LED and the resistor will have the same current in them. That’s KCL. The current going into the resistor better be the same as the current going out of it and into the LED.
This is mostly interesting when there are more than two wires going into one point. If a battery drives 3 magically-identical light bulbs, for instance, then each bulb will get one-third of the total current. The node where the battery’s wire joins with the leads to the 3 bulbs is the node. All the current coming in, has to equal all the current going out. Even if the bulbs are not identical, the totals will still be equal. So if you know any three values, you can compute the fourth.
The current from the battery has to equal the current going into the battery. The two resistors at the extreme left and right have the same current through them (1.56 mA). Within rounding error of the simulator, each branch of the split has its share of the total (note the bottom leg has 3K total resistance and, thus, carries less current).
If you happened to look up during a drive down a suburban street in the US anytime during the 60s or 70s, you’ll no doubt have noticed a forest of TV antennas. When over-the-air TV was the only option, people went to great lengths to haul in signals, with antennas of sometimes massive proportions flying over rooftops.
Outdoor antennas all but disappeared over the last third of the 20th century as cable providers became dominant, cast to the curb as unsightly relics of a sad and bygone era of limited choices and poor reception. But now cheapskates cable-cutters like yours truly are starting to regrow that once-thick forest, this time lofting antennas to receive digital programming over the air. Many of the new antennas make outrageous claims about performance or tout that they’re designed specifically for HDTV. It’s all marketing nonsense, of course, because then as now, almost every TV antenna is just some form of the classic Yagi design. The physics of this antenna are fascinating, as is the story of how the antenna was invented.
If you were an engineering student around the end of the 1980s or the start of the 1990s, your destiny most likely lay in writing 8051 firmware for process controllers or becoming a small cog in a graduate training scheme at a large manufacturer. It was set out for you as a limited set of horizons by the university careers office, ready for you to discover as only a partial truth after graduation.
But the chances are that if you were a British engineering student around that time you didn’t fancy any of that stuff. Instead you harboured a secret dream to be [Tim Hunkin]’s apprentice. Of course, if you aren’t a Brit, and maybe you are from a different generation, you’ll have responded quizzically to that name. [Tim Hunkin]? Who?
[Tim Hunkin] is a British engineer, animator, artist and cartoonist who has produced a long series of very recognisable mechanical devices for public display, including clocks, arcade machines, public spectacles, exhibits and collecting boxes for museums, and much more. He came to my attention as an impressionable young engineer with his late 1980s to early 1990s British TV series The Secret Life Of Machines, in which he took everyday household and office machines and appliances and explained and deconstructed them in an accessible manner for the public.
Artificial Intelligence is playing an ever increasing role in the lives of civilized nations, though most citizens probably don’t realize it. It’s now commonplace to speak with a computer when calling a business. Facebook is becoming scary accurate at recognizing faces in uploaded photos. Physical interaction with smart phones is becoming a thing of the past… with Apple’s Siri and Google Speech, it’s slowly but surely becoming easier to simply talk to your phone and tell it what to do than typing or touching an icon. Try this if you haven’t before — if you have an Android phone, say “OK Google”, followed by “Lumos”. It’s magic!
Advertisements for products we’re interested in pop up on our social media accounts as if something is reading our minds. Truth is, something is reading our minds… though it’s hard to pin down exactly what that something is. An advertisement might pop up for something that we want, even though we never realized we wanted it until we see it. This is not coincidental, but stems from an AI algorithm.
At the heart of many of these AI applications lies a process known as Deep Learning. There has been a lot of talk about Deep Learning lately, not only here on Hackaday, but all over the interwebs. And like most things related to AI, it can be a bit complicated and difficult to understand without a strong background in computer science.
If you’re familiar with my quantum theory articles, you’ll know that I like to take complicated subjects, strip away the complication the best I can and explain it in a way that anyone can understand. It is the goal of this article to apply a similar approach to this idea of Deep Learning. If neural networks make you cross-eyed and machine learning gives you nightmares, read on. You’ll see that “Deep Learning” sounds like a daunting subject, but is really just a $20 term used to describe something whose underpinnings are relatively simple.
As Internet security has evolved it has gotten easier to lock your systems down. Many products come out of the box pre-configured to include decent security practices, and most of the popular online services have wised up about encryption and password storage. That’s not to say that things are perfect, but as the computer systems get tougher to crack, the bad guys will focus more on the unpatchable system in the mix — the human element.
History Repeats Itself
Ever since the days of the ancient Greeks, and probably before that, social engineering has been one option to get around your enemy’s defences. We all know the old tale of Ulysses using a giant wooden horse to trick the Trojans into allowing a small army into the city of Troy. They left the horse outside the city walls after a failed five-year siege, and the Trojans brought it in. Once inside the city walls a small army climbed out in the dead of night and captured the city.
How different is it to leave a USB flash drive loaded with malware around a large company’s car park, waiting for human curiosity to take over and an employee to plug the device into a computer hooked up to the corporate network? Both the wooden horse and the USB drive trick have one thing in common, humans are not perfect and make decisions which can be irrational. Continue reading “Social Engineering is on The Rise: Protect Yourself Now”→
JeVois is a small, open-source, smart machine vision camera that was funded on Kickstarter in early 2017. I backed it because cameras that embed machine vision elements are steadily growing more capable, and JeVois boasts an impressive range of features. It runs embedded Linux and can process video at high frame rates using OpenCV algorithms. It can run standalone, or as a USB camera streaming raw or pre-processed video to a host computer for further action. In either case it can communicate to (and be controlled by) other devices via serial port.
But none of that is what really struck me about the camera when I received my unit. What really stood out was the demo mode. The team behind JeVois nailed an effective demo mode for a complex device. That didn’t happen by accident, and the results are worth sharing.