Upgrading PC Cooling With Software

As computing power increases with each new iteration of processors, actual power consumption tends to increase as well. All that waste heat has to go somewhere, and while plenty of us are content to add fans and heat sinks for a passable air-cooled system there are others who prefer a liquid cooling solution of some sort. [Cal] uses a liquid cooler on his system, but when he upgraded his AMD chip to one with double the number of cores he noticed the cooling fans on the radiator were ramping quickly and often. To solve this problem he turned to Python instead of building a new cooling system.

The reason for the rapid and frequent fan cycling was that the only trigger for the cooling fans available on his particular motherboard is CPU temperature. For an air cooled system this might be fine, but a water cooled system with much more thermal mass should be better able to absorb these quick changes in CPU temperature without constantly adjusting fan speed. Using a python script set up to run as a systemd service, the control loop monitors not only the CPU temperature but also the case temperature and the temperature of the coolant, and then preferentially tries to dump heat from the CPU into the thermal mass of the water cooler before much ramping of cooling fans happens.

An additional improvement here is that the fans can run at a much lower speed, reducing dust in the computer case and also reducing noise compared to before the optimizations. The computer now reportedly runs almost silently unless it has been under load for several minutes. The script is specific to this setup but easily could be modified for other computers using liquid cooling, and using Grafana to monitor the changes can easily be done as [Cal] also demonstrates when calibrating and testing the system. On the other hand, if you prefer a more flashy cooling system as a living room centerpiece, we have you covered there as well.

An illustration of jellyfish swimming in the ocean by Rebecca Konte. The jellyfish are wearing cones on their "heads" to streamline their swimming that contain some sort of electronics inside.

The Six Million Dollar Jellyfish

What if you could rebuild a jellyfish: better, stronger, faster than it was before? Caltech now has the technology to build bionic jellyfish.

Studying the ocean given its influence on the rest of the climate is an important scientific task, but the wild pressure differences as you descend into the eternal darkness make it a non-trivial engineering problem. While we’ve sent people to the the deepest parts of the ocean, submersibles are much too expensive and risky to use for widespread data acquisition.

The researchers found in previous work that making a cyborg jellyfish was more effective than biomimetic jellyfish robots, and have now given the “biohybrid robotic jellyfish” a 3D-printed, neutrally buoyant, swimming cap. In combination with the previously-developed “pacemaker,” these cyborg jellyfish can explore the ocean (in a straight line) at 4.5x the speed of a conventional moon jelly while carrying a scientific payload. Future work hopes to make them steerable like the well-known robo-cockroaches.

If you’re interested in some other attempts to explore Earth’s oceans, how about drift buoys, an Open CTD, or an Open ROV? Just don’t forget to keep the noise down!

Continue reading “The Six Million Dollar Jellyfish”

FedEx Robot Solves Complex Packing Problems

Despite the fact that it constantly seems like we’re in the midst of a robotics- and artificial intelligence-driven revolution, there are a number of tasks that continue to elude even the best machine learning algorithms and robots. The clothing industry is an excellent example, where the flimsy materials can easily trip up robotic manipulators. But one task like this that seems like it might soon be solve is packing cargo into trucks, as FedEx is trying to do with one of their new robots.

Part of the reason this task is so difficult is that packing problems, similar to “traveling salesman” problems, are surprisingly complex. The packages are not presented to the robot in any particular order, and need to be efficiently placed according to weight and size. This robot, called DexR, uses artificial intelligence paired with an array of sensors to get an idea of each package’s dimensions, which allows it to then plan stacking and ordering configurations and ensure secure fits between all of the other packages. The robot must also be capable of quickly adapting if any packages shift during stacking and re-order or re-stack them.

As robotics platforms and artificial intelligence continue to improve, it’s likely we’ll see a flurry of complex problems like these solved by machine instead of by human. Tackling real-world tasks are often more complex than they seem, as anyone with a printer an a PC LOAD LETTER error can attest to, even handling single sheets of paper can be a difficult task for a robot. Interfacing with these types of robots can be a walk in the park, though, provided you read the documentation first.

$1 Graphene Sensor Identifies Safe Water

If you live in a place where you can buy Arduinos and Raspberry Pis locally, you probably don’t spend much time worrying about your water supply. But in some parts of the world, it is nothing to take for granted, bad water accounts for as many as 500,000 deaths worldwide every year. Scientists have reported a graphene sensor they say costs a buck and can detect dangerous bacteria and heavy metals in drinking water.

The sensor uses a GFET — a graphene-based field effect transistor to detect lead, mercury, and E. coli bacteria. Interestingly, the FETs transfer characteristic changes based on what is is exposed to. We were, frankly, a bit surprised that this is repeatable enough to give you useful data. But apparently, it is especially when you use a neural network to interpret the results.

What’s more, there is the possibility the device could find other contaminants like pesticides. While the materials in the sensor might have cost a dollar, it sounds like you’d need a big equipment budget to reproduce these. There are silicon wafers, spin coating, oxygen plasma, and lithography. Not something you’ll whip up in the garage this weekend.

Still, it is interesting to see a FET used this way and a cheap way to monitor water quality would be welcome. Using machine learning with water sensors isn’t a new idea. Of course, the sensor is one part of the equation. Monitoring is the other.

Weather Station With Distributed Sensors

Building a weather station is a fairly common project that plenty of us have taken on, and for good reason. They can be built around virtually any microcontroller or full-scale computer, can have as many or few sensors as needed, and range from simple, straightforward projects to more complex systems capable of doing things like sending data off to weather services like Weather Underground. This weather station features a few innovations we don’t often see, though, with a modular and wireless design that makes it versatile and easy to scale up or down as needed.

Each of the modules in this build use the ESP32 platform, which simplifies design and also takes care of the wireless capability needed. The base station gets a few extra sensors including those for carbon dioxide, volatile organic compounds, and nitrogen oxides. It also includes a screen which can be used to display a wide variety of data gathered locally but also includes forecast information fetched from the free OpenWeatherMap API. For the sensor modules, BME280 sensors are used for temperature, pressure, and humidity and each module includes its own solar panel and battery with the ESP32 chips set to operate using as little energy as possible.

One of the things that helps easily integrate all of the sensor modules is the use of ESP-NOW, which we have seen a few times before. It essentially eliminates the need for a router and allows ESP modules to connect directly with one another. The build also goes into detail about most of the aspects of this project including the programming of the GUI that the ESP32 base station displays on its screen, so for anyone looking to start their own weather station project this should be an excellent guide. Make sure to check out this one as well if you want to send all of your weather data to Weather Underground.

An image of two dogs and a bison wearing harnesses with the energy harvesting system. Text next to the animals says Dog 1 (Exp. 1), Dog 2 (Exp. 2), Dog 2 (Exp. 3), and Wisent (Exp. 4)

Kinefox Tracks Wildlife For A Lifetime

Radio trackers have become an important part of studying the movements of wildlife, but keeping one running for the life of an animal has been challenging. Researchers have now developed a way to let wildlife recharge trackers via their movements.

With trackers limited to less than 5% of an animal’s total mass to prevent limitations to the their movement, it can be especially difficult to fit trackers with an appropriately-sized battery pack to last a lifetime. Some trackers have been fitted with solar cells, but besides issues with robustness, many animals are nocturnal or live in dimly-lit spaces making this solution less than ideal. Previous experiments with kinetically-charged trackers were quite bulky.

The Kinefox wildlife tracking system uses an 18 g, Kinetron MSG32 kinetic energy harvesting mechanism to power the GPS and accelerometer. Similar to the mechanical systems found in automatic winding watches, this energy harvester uses a pendulum glued to a ferromagnetic ring which generates power as it moves around a copper coil. Power is stored in a Li-ion capacitor rated for 20,000 charge/discharge cycles to ensure better longevity than would be afforded by a Li-ion battery. Data is transmitted via Sigfox to a cloud-based database for easy access.

If you want to build one to track your own pets, the files and BOM are available on GitHub. We’ve featured other animal trackers before for cats and dogs which are probably also applicable to bison.

Custom Glove Guides Wearers’ Dreams

For as much advancement as humanity has made in modern medicine even in the last century alone, there’s still plenty we don’t understand about the human body. That’s particularly true of the brain, where something as common as dreams are the subject of active debate about their fundamental nature, if they serve any purpose, and where they originate. One research team is hoping to probe a little further into this mystery, and has designed a special glove to help reach a little deeper into the subconscious brain.

The glove, called Dormio, has a number of sensors and feedback mechanisms which researchers hope will help explore the connection between dreaming and creativity. Volunteers were allowed to take a nap while wearing the glove, which can detect the moment they began entering a specific stage of sleep. At that point, the device would provide an audio cue to seed an idea into the dreams, in this case specifically prompting the sleeper to think about trees. Upon awakening, all reported dreaming about trees specifically, and also demonstrated increased creativity in tests compared to control groups.

While this might not have the most obvious of implications, opening the brain up to being receptive of more creative ideas can have practical effects beyond the production of art or music. For example, the researchers are also investigating whether the glove can help individuals with post-traumatic stress disorder manage nightmares. From a technical perspective this glove isn’t much different from some other devices we’ve seen before, and replicating one to perform similar functions might be possible for most of us willing to experiment on ourselves.