Looking to give himself a competitive edge, racer [Douglas Hedges] wanted to come up with a system that could give him real-time feedback on how his current performance compared to his previous fastest lap time. Armed with a Raspberry Pi and some Python libraries, he set out to add a simple telemetry system to his car. But as is often the case with these kind of projects, things just started snowballing from there.
At the most basic level, his system uses GPS position and speed information to light up a strip of RGB LEDs on the dashboard: green means he’s going faster than the previous best lap, and red means he isn’t. Any interface more complex than that would just be a distraction while he focuses on the track. But that doesn’t mean the Raspberry Pi can’t collect data for future review after the race is over.
With the basic functionality in place, [Douglas] turned his attention to collecting engine performance data. It turned out the car already had some pre-existing equipment for collecting metrics such as the air-fuel ratio and RPM, which he was able to connect to the Raspberry Pi thanks to its use of a well documented protocol. On top of that he added a Labjack U3 data acquisition system which let him pull in additional information like throttle position and coolant temperature. Grafana is used to visualize all of this data after the race, though it sounds like he’s also considering adding a cellular data connection vehicle data can be streamed out in real-time.
No, no, at first we thought it was a Pokemon too, but Placemon monitors your place, your home, your domicile. Instead of a purpose-built device, like a CO detector or a burglar alarm, this is a generalized monitor that streams data to a central processor where machine learning algorithms notify you if something is awry. In a way, it is like a guard dog who texts you if your place is unusually cold, on fire, unlawfully occupied, or underwater.
[anfractuosity] is trying to make a hacker-friendly version based on inspiration from a scientific paper about general-purpose sensing, which will have less expensive components but will lose accuracy. For example, the article suggests thermopile arrays, like low-resolution heat-vision, but Placemon will have a thermometer, which seems like a prudent starting place.
The PCB is ready to start collecting sound, temperature, humidity, barometric pressure, illumination, and passive IR then report that telemetry via an onboard ESP32 using Wifi. A box utilizing Tensorflow receives the data from any number of locations and is training to recognize a few everyday household events’ sensor signatures. Training starts with events that are easy to repeat, like kitchen sounds and appliance operations. From there, [anfractuosity] hopes that he will be versed enough to teach it new sounds, so if a pet gets added to the mix, it doesn’t assume there is an avalanche every time Fluffy needs to go to the bathroom.
Our understanding of the sensory capabilities of animals has a lot of blanks, and often new discoveries serve as inspiration for new technology. Researchers from the University of Leeds and the Royal Veterinary College have found that mosquitos can navigate in complete darkness by detecting the subtle changes in the air flow created when they fly close to obstacles. They then used this knowledge to build a simple but effective sensor for use on drones.
Extremely sensitive receptors at the base of the antennae on mosquitoes’ heads, called the Johnston’s organ, allow them to sense these tiny changes in airflow. Using fluid dynamics simulations based on high speed photography, the researchers found that the largest changes in airflow occur over the mosquito’s head, which means the receptors are in exactly the right place. From their data, scientists predict that mosquitos could possibly detect surfaces at a distance of more than 20 wing lengths. Considering how far 20 arm lengths is for us, that’s pretty impressive. If you can get past the paywall, you can read the full article from the Science journal.
Using their newfound knowledge, the researchers equipped a small drone with probe tubes connected to differential pressure sensors. Using these sensors the drone was able to effectively detect when it got close to the wall or floor, and avoid a collision. The sensors also require very little computational power because it’s only a basic threshold value. Check out the video after the break.
Pushing all of your data into “The Cloud” sounds great, until you remember that what you’re really talking about is somebody else’s computer. That means all your hard-crunched data could potentially become inaccessible should the company running the service go under or change the rules on you; a situation we’ve unfortunately already seen play out.
Which makes this project from [Zoltan Doczi] and [Róbert Szalóki] so appealing. Not only does it show how easy it can be to shuffle your data through the tubes and off to that big data center in the sky, but they send it to one of the few companies that seem incapable of losing market share: Google. But fear not, this isn’t some experimental sensor API that the Big G will decide it’s shutting down next Tuesday in favor of a nearly identical service with a different name. All your precious bits and bytes will be stored in one of Google’s flagship products: Sheets.
It turns out that Sheets has a “Deploy as Web App” function that will spit out a custom URL that clients can use to access the spreadsheet data. This project shows how that feature can be exploited with the help of a little Python code to push data directly into Google’s servers from the Raspberry Pi or other suitably diminutive computer.
Here they’re using a temperature and humidity sensor, but the only limitation is your imagination. As an added bonus, the chart and graph functions in Sheets can be used to make high-quality visualizations of your recorded data at no extra charge.
You might be wondering what would happen if a bunch of hackers all over the world started pushing data into Sheets every few seconds. Honestly, we don’t know. The last time we showed how you could interact with one of their services in unexpected ways, Google announced they were retiring it on the very same day. It was probably just a coincidence, but to be on the safe side, we’d recommend keeping the update frequency fairly low.
Back in 2012, before the service was even known as Google Sheets, we covered how you could do something very similar by manually assembling HTTP packets containing your data. We’d say this validates the concept for long-term data storage, but clearly the methodology has changed considerably in the intervening years. Somebody else’s computer, indeed.
[TJ] is a surfer, and drives his car to get to the beach. But when he gets there he’s faced with a dilemma that most surfers have: either put his key in your baggies (shorts) or wetsuit and hope it doesn’t get lost during a wipeout, or stash it on the rear wheel of his car. Hiding the keyfob by the car isn’t an option because it can open the car doors just by being in proximity to the car. He didn’t want to risk losing it to the ocean either, so he built a waveguide of sorts for his key out of aluminum foil that lets him lock the key in the car without locking himself out.
Over a series of trials, [TJ] found out that his car, a 2017 Chevy Cruze, has a series of sensors in it which can determine the location of the keyfob based on triangulation. If it thinks the keyfob is outside of the car, it allows the door to be locked or unlocked with a button on the door handle. If the keyfob is inside the car, though, it prevents the car from locking via the door handles so you don’t accidentally lock yourself out. He found out that he could “focus” the signals of the specific sensors that make the car think the keyfob is outside by building an open Faraday cage.
The only problem now is that while the doors can be locked, they could also can be unlocked. To solve that problem he rigged up an ESP32 to a servo to open and close the opening in the Faraday cage. This still means there’s a hidden device used to activate the ESP32, but odds are that it’s a cheaper device to replace than a modern car key and improves security “through obscurity“. If you have any ideas for improving [TJ]’s build, though, leave them in the comments below. Surfers across the world from [TJ] to the author would be appreciative.
For his final project in UCLA’s Physics 4AL program, [Timothy Kanarsky] used a NodeMCU to smarten up a carefully dissected NERF football. With the addition to dual MPU6050 digital accelerometers and some math, the ball can calculate things like the distance traveled and angular velocity. With a 9 V alkaline battery and a voltage regulator board along for the ride it seems like a lot of weight to toss around; but of course nobody on the Hackaday payroll has thrown a ball in quite some time, so we’re probably not the best judge of such things.
Even if you’re not particularly interested in refining your throw, there’s a lot of fascinating science going on in this project; complete with fancy-looking equations to make you remember just how poorly you did back in math class.
As [Timothy] explains in the write-up, the math used to find velocity and distance traveled with just two accelerometers is not unlike the sort of dead-reckoning used in intercontinental ballistic missiles (ICBMs). Since we’ve already seen model rockets with their own silos, seems all the pieces are falling into place.
The NodeMCU polls the accelerometers every 5 milliseconds, and displays the data on web page complete with scrolling graphs of acceleration and angular velocity. When the button on the rear of the ball is pressed, the data is instead saved to basic Comma Separated Values (CSV) file that’s served up to clients with a minimal FTP server. We might not know much about sportsball, but we definitely like the idea of a file server we can throw at people.
Home automation is a popular project to undertake but its complexity can quickly become daunting, especially if you go further than controlling a few lights (or if you’re a renter). To test the waters you may want to start with something like this home safety monitor, which is an IoT device based on an Arduino. It allows remote monitoring of a home for things such as temperature, toxic gasses, light, and other variables, which is valuable even if you don’t need or want to control anything.
The device is built around an Arduino Nano 33 IOT which has WiFi and Bluetooth capabilities as well as some integrated security features. This build features a number of sensors including pressure/humidity, a gas/smoke detector, and a light sensor. To report all of the information it gathers around the home, an interface with Ubidots is configured to allow easy (and secure) access to the data gathered by the device.
The PCB and code for the project are all provided on the project page, and there are a number of other options available if Ubidots isn’t your preferred method of interfacing with the Internet of Things. You might even give Mozilla’s WebThings a shot if you’re so inclined.