If you want to add humidity and temperature sensors to your home automation sensor, you can — like [Maker’s Fun Duck] did — buy some generic ones for about a buck. For a dollar, you get a little square LCD with sensors and a button. You even get the battery. Can you reprogram the firmware to bend it to your will? As [Duck] shows in the video, you can.

The device advertises some custom BLE services, but [Duck] didn’t want to use the vendor’s phone app, so he cracked the case open. Inside was a microcontroller with Bluetooth, an LCD driver, a sensor IC, and very little else.

The processor is an ARM Cortex M0, a PHY6222 with very low power consumption. The LCD is a very cheap panel with no drivers onboard. All the drive electronics are on the PCB. The sensor is a CHT8305C which uses I2C.

Luckily, the PHY6222 has a publically available SDK with English documentation. The PCB has two sets of UART pads and it is possible to flash the chip via one of the UARTs.

Eventually, [Duck] put a custom firmware on the box, but we were intrigued by the idea that for a buck you could get a little low-power ARM module with an LCD and a sensor. It seems like you could do more with this, although we are sure the LCD is not very general purpose, surely this little box could act as a panel meter, a countdown timer, or lots of other things with some custom firmware.

These are, of course, knock offs of the slightly more expensive Xiaomi sensors, and those are flashable, too. We aren’t sure how accurate either sensor is, but humidity measurement is a complex topic.

Most of us using desktop computers, and plenty of us on laptops, have some sort of fan or pump installed in our computer to remove heat and keep our machines running at the most optimum temperature. That’s generally a good thing for performance, but comes with a noise pollution cost. It’s possible to build fanless computers, though, which are passively cooled by using larger heat sinks with greater thermal mass, or by building more efficient computers, or both. But sometimes even fanless designs can benefit from some forced air, so [Sasa] built this system for cooling fanless systems with fans.

The main advantage of a system like this is that the fans on an otherwise fanless system remain off when not absolutely necessary, keeping ambient noise levels to a minimum. [Sasa] does have a few computers with fans, and this system helps there as well. Each fan module is WiFi-enabled, allowing for control of each fan on the system to be set up and controlled from a web page. It also can control 5V and 12V fans automatically with no user input, and can run from any USB power source, so it’s not necessary to find a USB-PD-compatible source just to run a small fan.

Like his previous project, this version is built to easily integrate with scripting and other third-party software, making it fairly straightforward to configure in a home automation setup or with any other system that is monitoring a temperature. It doesn’t have to be limited to a computer, either; [Sasa] runs one inside a server cabinet that monitors the ambient temperature in the cabinet, but it could be put to use anywhere else a fan is needed. Perhaps even a hydroponic setup.

Over at the University of Wisconsin’s Undergraduate Projects Lab (UPL) there’s been a way to check whether this room is open for general use by CS undergraduates and others practically for most of the decades that it has existed. Most recently [Andrew Moses] gave improving on the then latest, machine vision-based iteration a shot. Starting off with a historical retrospective, the 1990s version saw a $15 camera combined with a Mac IIcx running a video grabber, an FTP server and an HP workstation that’d try to fetch the latest FTP image.

As the accuracy of this system means the difference between standing all forlorn in front of a closed UPL door and happily waddling into the room to work on some projects, it’s obvious that any new system had to be as robust as possible. The machine vision based version that got installed previously seemed fancy: it used a Logitech C920 webcam, a YOLOv7 MV model to count humanoids and a tie into Discord to report the results. The problem here was that this would sometimes count items like chairs as people, and there was the slight issue that people in the room didn’t equate an open door, as the room may be used for a meeting.

Thus the solution was changed to keeping track of whether the door was open, using a sensor on the two doors into the room. Sadly, the captive-portal-and-login-based WiFi made the straightforward approach with a reed sensor, a magnet and an ESP32 too much of a liability. Instead the sensor would have to communicate with a device in the room that’d be easier to be updated, ergo a Zigbee-using door sensor, Raspberry Pi with Zigbee dongle and Home Assistant (HA) was used.

One last wrinkle was the need to use a Cloudflare-based tunnel add-on to expose the HA API from the outside, but now at long last the UPL door status can be checked with absolute certainty that it is correct. Probably.

Featured image: The machine vision-based room occupancy system at UoW’s UPL. (Credit: UPL, University of Wisconsin)

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