Winners Of Hackaday’s Data Loggin’ Contest: Bluetooth Gardening, Counting Cups, And Predicting Rainfall

The votes for Hackaday’s Data Loggin’ Contest have been received, saved to SD, pushed out to MQTT, and graphed. Now it’s time to announce the three projects that made the most sense out of life’s random data and earned themselves a $100 gift certificate for Tindie, the Internet’s foremost purveyor of fine hand-crafted artisanal electronics.

First up, and winner of the Data Wizard category, is this whole-garden soil moisture monitor by [Joseph Eoff]. You might not realize it from the picture at the top of the page, but lurking underneath the mulch of that lovely garden is more than 20 Bluetooth soil sensors arranged in a grid pattern. All of the data is sucked up by a series of solar powered ESP32 access points, and ultimately ends up on a Raspberry Pi by way of MQTT. Here, custom Python software generates a heatmap that indicates possible trouble spots in the garden. With its easy to understand visualization of what’s happening under the surface, this project perfectly captured the spirit of the category.

Next up is the Nespresso Shield from [Steadman]. This clever gadget literally listens for the telltale sounds of the eponymous coffee maker doing its business to not only estimate your daily consumption, but warn you when the machine is running low on water. The clever non-invasive method of pulling data from a household appliance made this a strong entry for the Creative Genius category.

Last but certainly not least is this comprehensive IoT weather station that uses machine learning to predict rainfall. With crops and livestock at risk from sudden intense storms, [kutluhan_aktar] envisions this device as an early warning for farmers. The documentation on this project, from setting up the GPRS-enabled ESP8266 weather station to creating the web interface and importing all the data into TensorFlow, is absolutely phenomenal. This project serves as a invaluable framework for similar DIY weather detection and prediction systems, which made it the perfect choice for our World Changer category.

There may have only been three winners this time around, but the legendary skill and creativity of the Hackaday community was on full display for this contest. A browse through the rest of the submissions is highly recommended, and we’re sure the creators would love to hear your feedback and suggestions in the comments.

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Practical Sensors: The Many Ways We Measure Heat Electronically

Measuring temperature turns out to be a fundamental function for a huge number of devices. You furnace’s programmable thermostat and digital clocks are obvious examples. If you just needed to know if a certain temperature is exceeded, you could use a bimetalic coil and a microswitch (or a mercury switch as was the method with old thermostats). But these days we want precision over a range of readings, so there are thermocouples that generate a small voltage, RTDs that change resistance with temperature, thermistors that also change resistance with temperature, infrared sensors, and vibrating wire sensors. The bandgap voltage of a semiconductor junction varies with temperature and that’s predictable and measurable, too. There are probably other methods too, some of which are probably pretty creative.

Bimetalic coil by [Hustvede], CC-BY-SA 3.0.
You can often think of creative ways to do any measurement. There’s an old joke about the smart-alec student in physics class. The question was how do you find the height of a building using a barometer. One answer was to drop the barometer from the top of the building and time how long it takes to hit the ground. Another answer — doubtlessly an engineering student — wanted to find the building engineer and offer to give them the barometer in exchange for the height of the building. By the same token, you could find the temperature by monitoring a standard thermometer with a camera or even a level sensor which is a topic for another post.

The point is, there are plenty of ways to measure anything, but in every case, you are converting what you want to know (temperature) into something you know how to measure like voltage, current, or physical position. Let’s take a look at how some of the most interesting temperature sensors accomplish this.

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Signal Conditioning Hack Chat This Wednesday

Join us on Wednesday, February 17 at noon Pacific for the Signal Conditioning Hack Chat with Jonathan Foote!

The real world is a messy place, because very little in it stays in a static state for very long. Things are always moving, vibrating, heating up or cooling down, speeding up or slowing down, or even changing in ways that defy easy description. But these changes describe the world, and understanding and controlling these changes requires sensors that can translate them into usable signals — “usable” being the key term.

Making a signal work for you usually requires some kind of signal processing — perhaps an amplifier to boost a weak signal from a strain gauge, or a driver for a thermocouple. Whatever the case, pulling a useful signal that represents a real-world process from the background noise of all the other signals going on around it can be challenging, as can engineering systems that can do the job in sometimes harsh environments. Drivers, filters, amplifiers, and transmitters must all work together to get the clearest picture of what’s going on in a system, lest bad data lead to bad decisions.

To help us understand the world of signal conditioning, Jonathan Foote will drop by the Hack Chat. You may remember Jonathan as the “recovering scientist” who did a great Remoticon talk on virtual modular synthesizers. It turns out that synths are just a sideline for Dr. Foote, who has a Ph.D. in Electrical Engineering and a ton of academic experience. He’s a bit of a Rennaissance man when it comes to areas of interest — machine learning, audio analysis, robotics, and of course, signal processing. He’ll share some insights on how to pull signals from the real world and put them to work.

join-hack-chatOur Hack Chats are live community events in the Hackaday.io Hack Chat group messaging. This week we’ll be sitting down on Wednesday, February 17 at 12:00 PM Pacific time. If time zones have you tied up, we have a handy time zone converter.

Click that speech bubble to the right, and you’ll be taken directly to the Hack Chat group on Hackaday.io. You don’t have to wait until Wednesday; join whenever you want and you can see what the community is talking about.

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Raspberry Pi Helps Racer Master The Track

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.

The Raspberry Pi based data acquisition system.

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.

In the past we’ve seen onboard data collection systems make real-world races look more like their virtual counterparts, but it seems like the solution [Douglas] has come up with is more practical in the heat of the moment.

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Into The Belly Of The Beast With Placemon

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.

We have another outstanding example of sensing household events without directly interfacing with an appliance, and bringing a sensor suite to your car might be up your alley.

Obstacle Avoidance For Drones, Learned From Mosquitoes

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.

Although this sensing method might not replace ultrasonic or time-of-flight sensors for drones, it does show that there is still a lot we can learn from nature, and that simpler is usually better. We’ve already seen simple insect-inspired navigation for drone swarms, as well as an optical navigation device for humans that works without satellites and only requires a view of the sky. Thanks for the tip [Qes]! Continue reading “Obstacle Avoidance For Drones, Learned From Mosquitoes”

Free Cloud Data Logging Courtesy Of Google

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.