Ever since he was a young hacker
[Mark Gibson] has messed with the silver ball.
From Soho down to Denver
he must have fixed them all.
But we ain’t seen anything like this
in any amusement hall.
That darn devious hacker
sure hacks a mean pinball.
He hacks it like an expert,
Becomes part of the machine
Automating all the bumpers
Always wiring clean
His table plays by automation,
And radio control for all
That darn devious hacker
sure hacks a mean pinball.
No matter how fine your fine motor skills may be, it’s really hard to manipulate anything on the stage of a microscope with any kind of accuracy. One jitter or caffeine-induced tremor means the feature of interest on the sample you’re looking at shoots off out of the field of view, and getting back to where you were is a tedious matter of trial and error.
Mechanical help on the microscope stage is nice, and electromechanical help is even better, but a DIY fully motorized microscope stage with complete motion control is the way to go for the serious microscopist on a budget. Granted, not too many people are in [fabiorinaldus]’ position of having a swell microscope like the Olympus IX50, and those that do probably work for an outfit that can afford all the bells and whistles. But this home-brew stage ticks off all the boxes on design and execution. The slide is moved across the stage in two dimensions with small NEMA-8 steppers and microstepping controllers connected to two linear drives that are almost completely 3D-printed. The final resolution on the drives is an insane 0.000027344 mm. An Arduino lives in the custom-built control box and a control pad with joystick, buttons, and an OLED display allow the stage to return to set positions of interest. It’s really quite a build.
We’ve featured a lot of microscope hacks before, most of them concerning the reflective inspection scopes we all seem to covet for SMD work. But that doesn’t mean we haven’t shown love for optical scopes before, and electron microscopes have popped up a time or two as well.
If you’ve ever experienced the heartbreak of finding a seed in your supposedly seedless navel orange, you’ll be glad to hear that with a little work, you can protect yourself with an optical computed tomography scanner to peer inside that slice before popping it into your mouth.
We have to admit to reading this one with a skeptical eye at first. It’s not that we doubt that a DIY CT scanner is possible; after all, we’ve seen examples at least a couple of times before. The prominent DSLR mounted to the scanning chamber betrays the use of visible light rather than X-rays in this scanner — but really, X-ray is just another wavelength of light. If you choose optically translucent test subjects, the principles are all the same. [Jbumstead]’s optical CT scanner is therefore limited to peeking inside things like slices of tomatoes or oranges to look at the internal structure, which it does with impressive resolution.
This scanner also has a decided advantage over X-ray CT scanners in that it can image the outside of an object in the visible spectrum, which makes it a handy 3D-scanner in addition to its use in diagnosing Gummi Bear diseases. In either transmissive or reflective mode, the DSLR is fitted with a telecentric lens and has its shutter synchronized to the stepper-driven specimen stage. Scan images are sent to Matlab for reconstruction of CT scans or to Photoscan for 3D scans.
The results are impressive, although it’s arguably more useful as a scanner. Looking to turn a 3D-scan into a 3D-print? Photogrammetry is where it’s at.
Many of us have put our making/hacking/building skills to use as a favor for our friends and family. [Boris Werner] is no different, he set about creating a music festival stage with Playmobil figures and parts for a couple of friends who were getting married. The miniature performers are 1/24 scale models of the forming family. The bride and groom are on guitar and vocals while junior drums.
Turning children’s toys into a wedding-worthy gift isn’t easy but the level of detail [Boris Werner] used is something we can all learn from. The video after the break does a great job of showing just how many cool synchronized lighting features can be crammed into a tiny stage in the flavor of a real show and often using genuine Playmobil parts. Automation was a mix of MOSFET controlled LEDs for the stage lighting, addressable light rings behind the curtain, a disco ball with a stepper motor and music, all controlled by an Arduino.
Unless you are some kind of Playmobil purist, this is way cooler than anything straight out of the box. This is the first mention of Playmobil on Hackaday but miniatures are hardly a new subject like this similarly scaled space sedan.
If you’ve never been a patient at a sleep laboratory, monitoring a person as they sleep is an involved process of wires, sensors, and discomfort. Seeking a better method, MIT researchers — led by [Dina Katabi] and in collaboration with Massachusetts General Hospital — have developed a device that can non-invasively identify the stages of sleep in a patient.
Approximately the size of a laptop and mounted on a wall near the patient, the device measures the minuscule changes in reflected low-power RF signals. The wireless signals are analyzed by a deep neural-network AI and predicts the various sleep stages — light, deep, and REM sleep — of the patient, negating the task of manually combing through the data. Despite the sensitivity of the device, it is able to filter out irrelevant motions and interference, focusing on the breathing and pulse of the patient.
What’s novel here isn’t so much the hardware as it is the processing methodology. The researchers use both convolutional and recurrent neural networks along with what they call an adversarial training regime:
Our training regime involves 3 players: the feature encoder (CNN-RNN), the sleep stage predictor, and the source discriminator. The encoder plays a cooperative game with the predictor to predict sleep stages, and a minimax game against the source discriminator. Our source discriminator deviates from the standard domain-adversarial discriminator in that it takes as input also the predicted distribution of sleep stages in addition to the encoded features. This dependence facilitates accounting for inherent correlations between stages and individuals, which cannot be removed without degrading the performance of the predictive task.
Anyone out there want to give this one a try at home? We’d love to see a HackRF and GNU Radio used to record RF data. The researchers compare the RF to WiFi so repurposing a 2.4 GHz radio to send out repeating uniformed transmissions is a good place to start. Dump it into TensorFlow and report back.
In specific applications, jet engines are often the most efficient internal combustion engines available. Not just for airplanes, but for anything that needs to run on a wide variety of fuels, operate at a consistent high RPM, or run for an extended amount of time. Of course, most people don’t have an extra $4,000 lying around to buy a small hobby engine, but now there’s a 3D-printed axial compressor available from [noob_sauce].
As an aero propulsion engineer, [noob_sauce] is anything but a novice in the world of jet engines. This design is on its fourth iteration with a working model set to be tested by the end of the month. Additionally, [noob_sauce] created his own software that was necessary for the design of such a small, efficient jet engine which has all been made available on Git. So far the only part that has been completed has been the compressor stage of the engine, but it’s still a very impressive build that we don’t see too often due to the complexity and cost of axial compressor jet engines.
Of course, there are some less-complex jet engines that are available to anyone with access to a hardware store and a welder which don’t require hardly any precision at all. While they’re fun and noisy and relatively easy to build, though, they don’t have near the efficiency of a jet engine like this one. The build is impressive on its own, and also great that [noob_sauce] plans to release all the plans so that anyone can build one of these as well.
If you’ve ever tried to take nice photos of small objects in your home, you might have found that it can be more difficult than it seems. One way to really boost the quality of your photos is to get proper lighting with a good background. The problem is setting up a stage for photos can be expensive and time-consuming. [Spafouxx] shows that you don’t need to sink a lot of money or energy into a setup to get some high quality photos.
His lighting setup is very simple. Two wooden frames are built from scraps of wood. The frames stand upright and have two LED strips mounted horizontally. The LEDs face inwards toward the object of the photos. The light is diffused using ordinary parchment paper that you might use when baking.
The frames are angled to face the backdrop. In this case, the backdrop is made of a piece of A4 printer paper propped up against a plastic drink bottle. The paper is curved in such a way to prevent shadows. For being so simple, the example photo shows how clean the images look in the end.