Hackaday Links: January 19, 2020

We’ve seen some interesting pitches in personal ads before, but this one takes the cake. Japanese billionaire Yusaku Maezawa is looking for a date to go along with him on his paid trip to the Moon, with the hope of finding a life partner. Maezawa is slated to be SpaceX’s first commercial lunar flyby customer, and will make the trip no earlier than 2023. That should give him plenty of time to go through the 20,000 applications he received from single women 20 and older with bright personalities and positive attitudes. And he should have plenty of time to make an awesome mixtape for the ride.

Imagine snooping through your kid’s garbage can only to find a used syringe lying in there. Most of us would likely be able to tell that the syringe once contained thermal compound or solder paste and be suitably proud of the little chip off the block, but apparently Cooler Master has fielded enough calls from panicked normie parents that they decided to change the design of their applicators. Given the design of the new applicator we doubt that’s really the reason, but it’s a good marketing story, and we can totally see how someone could mistake the old applicator for something illicit.

It looks as though SpaceX could be getting itself into legal trouble with its Starlink launches. Or more correctly, the FCC might, having apparently violated the National Environmental Policy Act, a Nixon-era law that requires government agencies to consider the environmental impact of any projects they approve. The Federal Communications Commission has been using a loophole in the law to claim a “categorical exemption” from these reviews when approving communications projects, particularly space-based projects. It’s not clear whether space is legally considered part of the environment, so the lawyers are hashing that out. If the FCC gets sued and loses, it’s not clear what happens to the existing Starlink satellites or future launches. Stay tuned for details.

Don’t forget that the Open Hardware Summit is coming soon. The 2020 meeting is the 10th anniversary of the confab, to be held on March 13 in New York. Hackaday is, of course, a proud sponsor of the conference, and our own Sophi Kravtiz will be the keynote speaker! Get your tickets soon.

Tired of off-loading data manipulation and analysis tasks to R in your Python programs? Then you’re probably already aware of Pandas, the Python library that converts data into dataframe objects for easier manipulation. Pandas has (have?) been in pre-release for years, but there’s now a legit 1.0.0 release candidate available. Now might be the time for you Python data mungers to get onboard the Pandas Express.

And finally, the Consumer Electronics Show is a yearly gift to anyone in the tech media, providing as it does so many examples of outrageous uses for the latest technology. To wit, we have LuluPet, the world’s first feces-analyzing cat litter box. LuluPet uses a built-in camera along with IR sensors and an “AI chip” to monitor your cat’s dookie and provide an alert if anything looks awry. On the one hand, inspecting cat poop is a job we’d love to outsource, but on the other hand, most cats we know are quick to cover the evidence of their excretions with kitty litter, leaving a clay-encrusted blob rather than the turds with defined borders that would seem to be needed for image recognition to do its job. We’ll reserve judgment on this one until we see a review.

Training Bats In The Random Forest With The Confusion Matrix

When exploring the realm of Machine Learning, it’s always nice to have some real and interesting data to work with. That’s where the bats come in – they’re fascinating animals that emit very particular ultrasonic calls that can be recorded and analysed with computer software to get a fairly good idea of what species they are. When analysed with an FFT spectogram, we can see the individual call shapes very clearly.

Creating an open source classifier for bats is also potentially useful for the world outside of Machine Learning as it could not only enable us to more easily monitor bats themselves, but also the knock on effects of modern farming methods on the natural environment. Bats feed on moths and other night flying insects which themselves have been decimated in numbers. Even in the depths of the countryside here in the UK these insects are a fraction of the population that they used to be 30 years ago, but nobody seems to have monitored this decline.

So getting back to our spectograms, it would be perfectly reasonable to throw these images at a convolutional neural network (CNN) and use an image feature-recognition strategy. But I wanted to explore the depths of the mysterious Random Forest. Continue reading “Training Bats In The Random Forest With The Confusion Matrix”

Improving The Accuracy Of Gas Sensors

If you need a sensor to detect gasses of some sort, you’ll probably be looking at the MQ series of gas sensors. These small metal cylinders contain a heater and some electrochemical sensor. Wire the heater up to a voltage, and connect one end of the resistor to an ADC, and you have a sensor for alcohol vapors, hydrogen sulfide, carbon monoxide, or ozone, depending on which model of sensor you’ve picked up.

These are simple analog devices, and as you would expect they’re sensitive to both temperature and humidity. [Davide Gironi] wanted a more accurate gas sensor, so he’s diving into a bit of overengineering and correlating the output of these sensors against temperature and humidity.

There’s a difference between accuracy and precision, and if you want to calibrate gas sensors, you’ll need to calibrate them against something. Instead of digging out a gas sensor of known precision, [Davide] took the easy way out: he graphed the curves on the datasheets for these sensors. It’s brilliant in its simplicity.

These numbers were thrown into R, and with a bit of work, [Davide] had a look up table of various concentrations of gasses plotted against certain resistances. In testing these sensors, he found a higher correlation between humidity and temperature and gas concentrations, which one would expect.

The files for these sensors are available on [Davide]’s website, and he included a neat little video showing everyone what went into these calculations. You can check that out below.

Continue reading “Improving The Accuracy Of Gas Sensors”

Raspberry Pi And R


[Stephen] picked up a Raspberry Pi to do a little hardware hacking and add a blinking LED to the many feathers in his software development hat. He picked up an analog to digital converter and a temperature sensor that would serve him well in a few projects he wanted to put together, including a weather station and a small Pi-controlled home brewing setup. He ended up not liking Python, and didn’t like the C-ness of wiringPi. He’s a scientist, so he’s most comfortable with R and Matlab. Of course, playing around with a R and a Raspberry Pi means replicating his sensor-reading code in R.

[Stephen] put together a neat little package that will allow him to read his sensors over an SPI bus with his Raspberry Pi. Yes, this functionality can easily be duplicated with Python, but if you’re looking to generate beautiful graphs, or just do a whole lot of statistics on something, R is the tool you need.

It’s a cool project, even if it is only measuring the temperature. Using R for the nerd cred isn’t bad, either.

BeagleBoard Cluster

What do you do after you make a BeagleBoard graphing calculator? [Matt] over at Liquidware Antipasto made a BeagleBoard Elastic R Cluster that fits in a briefcase. Ten BeagleBoards, are connected to each other though USB to ethernet adapters and a pair of ethernet switches connected to a wireless router. The cost for this cluster comes in around $2000 and while consuming less than 40 watts of power, out-paces a $4500 laptop. How might you use this cluster? What improvements would you make? Continue reading “BeagleBoard Cluster”