Q-Dice – Quantum Powered Dice Roller

Use background radiation to make your dice rolls! Features multiple dice types and guaranteed to make your DnD session more interesting!

Usually computers use numerical methods to generate random numbers. These are called pseudo-random number generators and, as the name implies, are not truly randomly generator.

True random number generators can use physical phenomena, virtually impossible to predict, to generate random numbers. Some of these use methods based on optical properties, radiation, raindrops, or even lava-lamps!

This project uses the detection of ionizing radiation coming from radioactive sources, such as cosmic rays as the source for randomness for dice rolls. The detection of radiation is made using a Geiger-Muller tube which generates an interrupt on the microcontroller that calculates the dice roll.

Read more…

An Explanation of a Classic Semiconductor Riddle

Back in 1996, Bob Pease posed an experiment in an April Fools column. “Take an ordinary NPN transistor, ground the base, pull the emitter up to 12 V with a 1 KΩ resistor and measure the collector voltage referenced to ground.” Do the experiment, and you might be surprised to find a small negative voltage present on the collector. [Filip Piorski] has always loved the riddle, and has explained how it works in a Youtube video.

The key to the trick is the breakdown voltage of the transistor; normally somewhere around 7-8 volts for a typical small NPN transistor. At this point, where the base-emitter junction enters the breakdown regime, it begins to emit light. This light actually travels through the silicon lattice, where it reaches the base-collector junction, which acts like a photodiode under the right conditions. This generates the negative voltage seen at the collector under these conditions.

Read more…

This sensory extension puppet lets you detect magnetic fields like a bird

Birds have an amazing sense of direction that aids in migrating across vast distances, and scientists think this is due to their ability to detect magnetic fields — just like a compass. Chris Hill on Instructables wanted a way to experience this for himself by using a sensor and some sort of feedback mechanism to feel a magnetic field’s directionality and strength

Read more…

This is Grand Theft Auto as made by AI

Called GAN Theft Auto, this is GTA V as you have never seen it before.

Artificial intelligence figures prominently in a wide swath of games, mostly in the way NPCs react to situations. But AI is capable of much more than having a character duck behind a corner during a shootout and, going in a completely different direction, a couple of AI researchers tapped into Nvidia’s GameGAN neural network to create GAN Theft Auto—an entirely AI-generated version of Grand Theft Auto V. The result is pretty remarkable.

The playable demo consists of driving down a short stretch of highway in GTA V. From a modern graphics standpoint, it is not what you would expect—the scene is highly pixelated, and even after upsampling the output, there is still a haze, as if playing GTA V in a dream state.

Read more…

Stream TV from your RaspberryPi with Tvheadend

In this tutorial, we will be showing you how to stream TV from your Raspberry Pi using Tvheadend and a USB Digital TV Tuner.
Tvheadend is a popular open-source TV streaming and recording server that can run on a Raspberry Pi.

The Tvheadhend software has a wide range of support for various input sources allowing it to function with most TV tuners.
Using this software, you can use your Raspberry Pi to record and stream live tv to various sources.
You can even watch the TV streams provided by the Tvheadend software using the Kodi media center or the VLC media player.

Read more…

Tesla flexes its overkill infotainment system by running “Cyberpunk 2077” at 60 fps

When Tesla formally announced the Model S Plaid in its Q4 and FY 2020 Update Letter, the company was quick to point out that company’s new flagship sedan would feature a 10-teraflop infotainment system. Musk even went on to say later that the new Model S and Model X’s infotainment system would be powerful enough to run PS5-level games. 

Read more…

The Piano Metronome is key to keeping the beat

In the world of music, being able to keep time accurately is vital when playing a piece, as even small deviations in timing can cause the notes played to sound “off.” Ordinarily a device called a metronome is used to provide consistent ticks that the musician can use, but most are not that visually interesting. This is what inspired ChristineNZ over on Instructables to create her own metronome that uses an Arduino Uno to both show the beat and produce a small noise. 

Read more…

RaspberryPi Zero Takes the Wheel in Miniature Fighting Robot

Looking to capitalize on his familiarity with the Raspberry Pi, [Sebastian Zen Tatum] decided to put the diminutive Pi Zero at the heart of his “antweight” fighting robot, $hmoney. While it sounds like there were a few bumps in the road early on, the tuxedoed bot took home awards from the recent Houston Mayhem 2021 competition, proving the year of Linux on the battle bot is truly upon us.

Compared to using traditional hobby-grade RC hardware, [Sebastian] says using the Pi represented a considerable cost savings. With Python and evdev, he was able to take input from a commercial Bluetooth game controller and translate it into commands for the GPIO-connected motor controllers. For younger competitors especially, this more familiar interface can be seen as an advantage over the classic RC transmitter.

Read more…

Hand Gesture Recognition Raspberry TensorFlow

Hand gesture recognition based on Raspberry Camera and TensorFlow. All the steps are described from the dataset creation to the final deploy.

The idea behind this project is to create a device able to drive an actuator based on the gesture of the hand’s fingers.

The project is specialized on recognizing streaming images of the hand taken by the raspberry-pi camera.

The data set of the images used to train the model was created ad hoc with images taken from the Raspberry Camera only (not other devices) with a neutral background.

The model is based on the transfer learning of the Inception v3 model, customized to handle the project requirements. The last layer was removed from the Inception v3 model and a few layers were added to be customized with the new dataset and to provide the output for just four cases.

The model was trained with the images collected and pre-classified earlier on a desktop (32 Gb ram + GPU). Once the model was trained and tested, it was exported to the Raspberry Pi.

Read more…