Kendo, a Japanese martial art, is practiced with a special sword. It’s not a particularly sharp sword, though, since the “blade” is essentially a length of bamboo. For this reason, Kendo practitioners must rely on correct form and technique in order to make sure their practice is as effective as possible, and Cornell students [Iman] and [Weichen] have made a Kendo trainer that helps the swordsmen in their art.
The core of the project is a PIC32 microcontroller hooked up to a set of three piezoelectric sensors and a LSM9DS1 inertial module. The three piezoelectric sensors are attached to a helmet and the inertial module to the sword, and the sensors work together to determine both the location of the strike and whether or not it had enough strength to be considered a “good” strike (the rules of Kendo are beyond the scope of this article). The trainer can then calculate all of the information and provide feedback to the user on a small screen.
While martial-arts related builds seem to be relatively rare, we did find a similar project from back in 2011 called the Virtual Sensei which used a then-popular Kinect in order to track movements. This PIC32-based project, though, seems to be a little more thorough by including the strength of the strike in the information the computer uses, and is probably less expensive to boot!
Continue reading “Microcontroller Studies The Blade”
When the only tool you have is a hammer, all problems look like nails. And if your goal is to emulate the behavior of an FPGA but your only tools are FPGAs, then your nail-and-hammer issue starts getting a little bit interesting. That’s at least what a group of students at Cornell recently found when learning about the Xilinx FPGA used by a researcher in the 1990s by programming its functionality into another FPGA.
Using outdated hardware to recreate a technical paper from decades ago might be possible, but an easier solution was simply to emulate the Xilinx in a more modern FPGA, the Cyclone V FPGA from Terasic. This allows much easier manipulation of I/O as well as reducing the hassle required to reprogram the device. Once all of that was set up, it was much simpler to perform the desired task originally set up in that 90s paper: using evolutionary algorithms to discriminate between different inputs.
While we will leave the investigation into the algorithms and the I/O used in this project as an academic exercise for the reader, this does serve as a good reminder that we don’t always have to have the exact hardware on hand to get the job done. Old computers can be duplicated on less expensive, more modern equipment, and of course video games from days of yore are a snap to play on other hardware now too.
Thanks to [Bruce Land] for the tip!
If you are a science fiction fan, you are probably aware of one of the genre’s oddest dichotomies. A lot of science fiction is concerned about if a robot, alien, or whatever is a person. However — sometimes in the same story — finding life is as easy as asking the science officer with a fancy tricorder. If you go to Mars and meet Marvin, it is pretty clear he’s alive, but faced with a bunch of organic molecules, the task is a bit harder. Now it is going to get harder still because Cornell scientists have created a material that has an artificial metabolism and checks quite a few boxes of what we associate with life. You can read the entire paper if you want more detail.
Three of the things people look for to classify something as alive is that it has a metabolism, self-arranges, and reproduces. There are other characteristics, depending on who you ask, but those three are pretty crucial.
Continue reading “Forget Artificial Intelligence; Think Artificial Life”
When you think of analog computing, it’s possible you don’t typically think of FPGAs. Sure, a few FPGAs will have specialized analog blocks, but usually they are digital devices. [Bruce Land] — a name well-known to Hackaday — has a post about building a digital differential analyzer using an FPGA and it is essentially an analog computer simulated on the digital fabric of an FPGA.
Whereas traditional analog computers use operational amplifiers to do mathematical integration, on the FPGA [Land] uses digital summers The devices simulate a system of differential equations, which can be nonlinear.
Continue reading “FPGA Makes Digital Analog Computer”
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.
Continue reading “This Machine Teaches Sign Language”
If you’re headed over to mainland China as a tourist, it’s possible to get to most of the country by rail. China is huge though, about the same size as the United States and more than twice the size of the European Union. Traveling that much area isn’t particularly easy. There are over 300 train terminals in China, and finding the quickest route somewhere is not obvious at all. This is an engineering challenge waiting to be solve, and luckily some of the students at Cornell Engineering have taken a stab at efficiently navigating China’s rail system using an FPGA.
The FPGA runs an algorithm for finding the shortest route between two points, called Dijkstra’s algorithm. With so many nodes this can get cumbersome for a computer to calculate, but the parallel processing of a dedicated FPGA speeds up the process significantly. The FPGA also includes something called a “hard processor system“, or HPS. This is not a soft-core, but dedicated computing hardware in the form of an ARM Cortex-A9. Testing showed that utilizing both the HPS and the FPGA can speed up the computation by up to ten times over a microcontroller alone.
This project goes into extreme detail on the methodology and the background of the math and coding involved, and is definitely worth a read if you’re interested in FPGAs or traveling salesman-esque problems. FPGAs aren’t the only dedicated hardware you can use to solve these kinds of problems though, if you have a big enough backpack while you’re traveling around China you could also use a different kind of computer.
Continue reading “Using An FPGA To Navigate China’s Railroads”
There’s a great deal of research happening around the topic of autonomous vehicles of all creeds and colours. [Ryan] decided this was an interesting field, and took on an autonomous drone as his final project at Cornell University.
The main idea was to create a drone that could autonomously follow a target which provided GPS data for the drone to follow. [Ryan] planned to implement this by having a smartphone provide GPS coordinates to the drone over WiFi, allowing the drone to track the user.
As this was a university project, he had to take a very carefully considered approach to the build. Given likely constraints on both money and time, he identified that the crux of the project was to develop the autonomous part of the drone, not the drone itself. Thus, off-the-shelf parts were selected to swiftly put together a drone platform that would serve as a test bed for his autonomous brain.
The write up is in-depth and shares all the gritty details of getting the various subsystems of the drone talking together. He also shares issues that were faced with altitude control – without any sensors to determine altitude, it wasn’t possible to keep the drone at a level height. This unfortunately complicated things and meant that he didn’t get to complete the drone’s following algorithm. Such roadblocks are highly common in time-limited university projects, though their educational value cannot be overstated. Overall, while the project may not have met its final goals, it was obviously an excellent learning experience, and one which has taught him plenty about working with drones and the related electronics.
For another take on autonomous flight, check out this high-speed AI racing drone.