Much of the world appears to revolve around sports, and sports tracking is a pretty big business. So how do people keep up with their favorite team? Well, [Jackson] and [Mourad] decided to devise a custom IoT solution.
Their system is a bit convoluted, so bear with us. First, they tell Alexa whether or not the team won or lost that week. Alexa then sends that information to IFTTT where two different Particle Argon boards are constantly polling the results to decide how to respond next. One Particle responds by lighting up an LED, green for a win and red for a loss. Another Particle board displays the results on an LCD screen. But this is where things get tricky. One of the more confusing aspects of their design is one of the Particle boards then signals back to IFTTT, telling it to tally the number of wins and losses. This seems a bit roundabout since the system started with IFTTT in the first place. Regardless, they seemed to be happy with the result and I’m sure they learned something in the process.
Okay, so this doesn’t really use a taser — that’s just click bait and we apologize. An actual taser would be a terrible way to train yourself to be a better typist, because depending on where you choose to deliver the shock, you could damage your typing nerves pretty quickly with a few milliamps at 50,000 volts.
The main brain of this pain trainer is a Particle Argon board which has I/O pins that can be controlled from the web. When the website detects a typo, it sends a signal to the Argon, which turns on a relay that activates the shock mechanism. What’s most impressive is that [nobody6502] doesn’t have a full-blown computer and programmed everything on an iPad. Check out the build video after the break.
We’ve all seen recreations of the famous double-slit experiment, which showed that light can behave both as a wave and as a particle. Or rather, it’s likely that what we’ve seen is the results of the double-slit experiment, that barcode-looking pattern of light and dark stripes, accompanied by some handwaving about classical versus quantum mechanics. But if you’ve got 20 minutes to invest, this video of the whole double-slit experiment cuts through the handwaving and opens your eyes to the quantum world.
For anyone unfamiliar with the double-slit experiment, [Huygens Optics] actually doesn’t spend that much time explaining the background. Our explainer does a great job on the topic, but suffice it to say that when coherent light passes through two closely spaced, extremely fine openings, a characteristic pattern of alternating light and dark bands can be observed. On the one hand, this demonstrates the wave nature of light, just as waves on the ocean or sound waves interfere constructively and destructively. On the other hand, the varying intensity across the interference pattern suggests a particle nature to light.
To resolve this conundrum, [Huygens] jumps right into the experiment, which he claims can be done with simple, easily sourced equipment. This is belied a little by the fact that he used photolithography to create his slits, but it should still be possible to reproduce with slits made in more traditional ways. The most fascinating bit of this for us was the demonstration of single-photon self-interference using nothing but neutral density filters and a CCD camera. The explanation that follows of how it can be that a single photon can pass through both slits at the same time is one of the most approachable expositions on quantum mechanics we’ve ever heard.
Small waterways give life in the form of drinking and irrigation water, but can also be very destructive when flooding occurs. In the US, monitoring of these waterways is done by mainly by the USGS, with accurate but expensive monitoring stations. This means that there is a limit to how many monitoring stations can be deployed. In an effort to come up with a more cost-efficient monitoring solution, [Rohan Menon] and [Ian Vernooy] created Aquametric, a simple water level, temperature and conductivity measuring station.
The device is built around a Particle Electron that features a STM32 microcontroller and a 3G modem. An automotive ultrasonic sensors measures water level, a thermistor measures temperature and a pair of parallel aluminum plates are used to measure conductivity. All the data from the prototype is output to a live dashboard. The biggest challenges for the system came with field deployment.
The great outdoors can be rather merciless with our ideas and electronic devices. [Rohan] and [Ian] did some tests with LoRa, but quickly found that the terrain severely limited the effective range. Power was another challenge, first testing with a solar panel and lithium battery. This proved unreliable especially at temperatures near freezing, so they decided to use 18 AA batteries instead and optimized power usage.
The mounting system is still an ongoing challenge. A metal pole driven into the riverbed at a wider part ended up bent (probably from ice sheets) and covered in debris to the point that it affected water level readings. They then moved to a narrower and shallower section in the hopes of avoiding debris, but the rocky bottom prevented them from effectively driving in a pole. So the mounted the pole on a steel plate which was then packet with rock to keep it in place. This too failed when it tipped over from rising water levels, submerging the entire sensor unit. Surprisingly it survived with only a little moisture getting inside.
For the 2020 Hackaday Prize, Field Ready and Conservation X Labs have issued challenges that need require some careful consideration and testing to build things that can survive the real world. So go forth and hack!
The long-held dream of wireless network hackers everywhere is to dispense with centralised network infrastructure, and instead rely on a distributed network in which the clients perform the role of distribution and routing of traffic. These so-called mesh networks promise scalability and simplicity on paper, but are in practice never as easy to implement as the theory might suggest. Much venture capital has been burned over the years by startups chasing that particular dream, yet most of our wireless connectivity still follows a hub topology.
An exciting development in our sphere concerning mesh networking came in early 2018, when Particle, the purveyors of wireless-equipped dev boards, launched their third generation of products. These offered mesh networking alongside their other features, but this week they have announced that they’ll no longer be developing that particular side of their offering. The Wi-Fi-equipped Argon and Cellular-equipped Boron will remain on sale, but they will henceforth discontinue the mesh-only Xenon. Existing owners of the now orphaned board will be compensated with store credit.
Their rationale for discontinuing mesh networking is interesting, and reflects on the sentiment in our first paragraph. Mesh networking is hard, and in particular their attempt to make it work with zero configuration was simply not successful. But then they talk about the realisation that maybe mesh networking was not the right solution for the IoT applications the boards were being used in, and perhaps another technology such as LoRa would be more appropriate.
So the mesh experiment from Particle is over, but the company and its connected dev boards are very much still with us. We salute them for being bold enough to try it, and we wonder when we’ll next find a piece of similar mesh networking hardware.
With the release of TensorFlow Lite at Google I/O 2019, the accessible machine learning library is no longer limited to applications with access to GPUs. You can now run machine learning algorithms on microcontrollers much more easily, improving on-board inference and computation.
[Brandon Satrom] published a demo on how to run TFLite on Particle devices (tested on Photon, Argon, Boron, and Xenon) making it possible to make predictions on live data with pre-trained models. While some of the easier computation that occurs on MCUs requires manipulating data with existing equations (mapping analog inputs to a percentage range, for instance), many applications require understanding large, complex sets of sensor data gathered in real time. It’s often more difficult to get accurate results from a simple equation.
The current method is to train ML models on specialty hardware, deploy the models on cloud infrastructure, and backhaul sensor data to the cloud for inference. By running the inference and decision-making on-board, MCUs can simply take action without backhauling any data.
He starts off by constructing a simple TGLite model for MCU execution, using mean squared error for loss and stochastic gradient descent for the optimization. After training the model on sample data, you can save the model and convert it to a C array for the MCU. On the MCU, you can load the model, TFLite libraries, and operations resolver, as well as instantiate an interpreter and tensors. From there you invoke the model on the MCU and see your results!
Over the years we’ve seen several attempts at adding Internet connectivity to the lowly wired doorbell. Generally, these projects aim to piggyback on the existing wiring, bells, and buttons rather than replace them entirely. Which invariably means at some point the AC wiring is going to need to interface with a DC microcontroller. This is often where things get interesting, as it seems everyone has a different idea on how best to bridge these two systems.
That’s the point where [Ben Brooks] found himself not so long ago. While researching the best way to tap into the 20 VAC pumping through his doorbells, he found a forum post where somebody was experimenting with optocouplers. As is unfortunately so often the case, the forum thread never really had a conclusion, and it wasn’t clear if the original poster ever figured it out.
[Ben] liked the idea though, so he thought he would give it a shot. But before investing in real optocouplers, he created his own DIY versions to use as a proof of concept. He put a standard LED and photoresistor together with a bit of black tape, and connected the LED to the doorbell line with a resistor. Running the LED on 60 Hz AC meant it was flickering rapidly, but for the purposes of detecting if there was voltage on the line, it worked perfectly.
Wanting something slightly more professional for the final product, [Ben] eventually evolved his proof of concept to include a pair of 4N35s, a custom PCB, and a 3D printed enclosure. Powered by a Particle Xenon, the device uses IFTTT to fire off smartphone notifications and blink the lights in the house whenever somebody pushes the bell.