Star Trek is often credited with helping spur the development of technologies we have today — the go-to example being cell phones. When a Star Trek April Fool’s product inspires a maker to build the real thing? Well, that seems par for the course. [MS3FGX] decided to make it so. The 3D printed Star Trek-themed phone dock acts as a Bluetooth speaker and white noise generator. The result is shown off in the video below and equals the special effects you expect to find on the silver screen.
Taking a few liberties from the product it’s based on — which was much larger and had embedded screens — makes [MS4FGX]’s version a little more practical. Two industrial toggle switches control a tech cube nightlight and the internal Bluetooth speaker. An NFC tag behind the phone dock launches the pre-installed LCARS UI app and turns on the phone’s Bluetooth. Despite being a challenge for [MS3FGX] to design, the end product seems to work exactly as intended.
Continue reading “Star Trek Phone Dock Might as Well Be From Picard’s Night Stand”
[Jordi] made this awesome looking mini amplifier which has a rather unusual feature. He’s calling it the Bizarre Mini Amplifier because it also has a white noise generator built right into it! Bizarre right?
Now, most people would just find a suitable amplifier and put it into a nice box, but not [Jordi]! He’s designed the amplifier circuit from the ground up! It features four distinct stages like most typical amplifiers:
- Impedance Adapt Stage: Two OPAMPS for both the left and right channels — The high input impedance allows for different audio sources to be connected without affecting the output.
- Mixer stage: Combines the left, right and noise signals into one, using a third OPAMP. A potentiometer is the output resistor which allows for the volume control.
- Filter Stage: A simple filter stage that uses a R-C low-pass filter, another potentiometer controls the tone.
- Power Stage: A final power amplifier to boost the output.
After building the circuit, there was a bit of troubleshooting to get it to work properly, so if you’re interested [Jordi] has done a great write-up of this on his blog.
Finally, he decided to add a white noise generator after he discovered it helps him sleep. This is the one part of the project that he didn’t actually go into detail for! But, considering it’s just white noise, we could probably figure out what he did. Stick around after the break to see the device in action!
Continue reading “Bizarre Mini Amplifier + White Noise Generator?”
Even though rand() may be a good enough random number generator for making a video game, the patterns of random bits it spits out may not be sufficient for applications requiring truly random data. [Giorgio] built his own random number generator, and after many statistical tests it ended up being random enough for a few very complex calculations.
Previously, we saw [Giorgio] generate random numbers with a Chua circuit, but for all the complexity of building an electronic strange attractor there’s actually a much simpler source of random data: a white noise generator.
[Giorgio]’s random number generator for this project is just a pair of resistors, with an op-amp buffer, amplifier, and current switch to turn analog data into a digital output of random 1s and 0s. [Giorgio] sampled this data by plugging the digital out into one of the GPIO pins of a Raspberry Pi and recording the data with s small script.
To verify his sequence of bits was actually random, [Giorgio] performed a few tests on the data, some more reliable in determining randomness than others.
Because every project needs a few awesome visualizations, [Giorgio] plotted each sequence of bits as either a black or white pixel in a bitmap. The resulting image certainly looks like television static, so there are no obvious problems with the data.
[Giorgio] also performed an interesting Monte Carlo simulation with his megabytes of random data: By plotting points on a plane (with a range from 0,0 to 1,1), [Giorgio] can approximate the value of π by testing if a point is inside a circle with a radius of 1. The best approximation of pi using 10,000 points of random data came out to be 3.1436
Of course [Giorgio] put his random data through a few proper statistical tests such as rngtest and dieharder, passing all the tests of randomness with flying colors. An interesting build that shows a small glimpse of how hard generating really random numbers actually is.