Obsessively Explaining The Visual Effects In Flight Of The Navigator

[Captain Disillusion] has earned a reputation on YouTube for debunking hoaxes and spreading a healthy sense of skepticism while having some of the highest production value on the platform and pretending to be some kind of inter-dimensional superhero. You’ve likely seen him give a careful explanation of how some viral video was faked alongside a generous dose of sarcastic humor and his own impressive visual effects. VFXcool is a series on his channel that takes deep dives into movies that are historically significant in the effects industry. For this installment, [Captain Disillusion]’s “intern”, [Alan], takes over to breakdown how filmmakers brought a futuristic spaceship to life in 1986’s Flight of the Navigator.

Making a movie requires hacks upon hacks, and that goes double in the era when the technology and techniques we now take for granted were being developed even as they were being put to film. The range of topics covered here is extreme: from full-scale props to models; from robotic motion control rigs to stop motion animation; from early computer graphics to the convoluted optical compositing that was necessary before digital workflows were possible. The tools themselves may be outdated, but understanding the history and the processes allows for a deeper insight into how we accomplish these kinds of effects today. And, really, it’s just so… cool.

[Captain Disillusion]’s previous VFXcool is all about the Back to the Future trilogy, and it’s a little shorter with more information on motion control rigs. We also love seeing how people make DIY effects in their own homes. LEGO actually seems like a pretty popular option for putting together whole scenes in amateur filmmaking.

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Yet Another Rigol DS1054Z Viewer

Tired of squinting at the small numbers on the oscilloscope display, [Alfred] aka [Gaze@] decided to take matters into his own hands and wrote yet another tool to remotely view images from a Rigol DS1054Z. At least that was the initial idea. But, it grew unexpectedly — as [Alfred] says, “the more the project turned out to be fun, the more it got out of hand”. We know the feeling well.

In addition to being able to simply view and export the screen, the program implements waveform measurements (we’re not sure if it is using the measurement ability of the ‘scope, or actually performing measurements in the program). And as you can see in the animated GIF of the program in operation over on the GitHub repository, the numbers are certainly clear and legible. His problem of squinting at the small screen has indeed been solved.

This is coded in Pascal (FPC Lazarus), but we weren’t able to browse the program because [Alfred] hasn’t posted the source code yet. It is written only for Linux, and he has tested it on Ubuntu, Debian, Fedora, and Manjaro. The project relies on Python, PyVisa, and gtk2, and talks to your DS1054Z over USB or LAN. The installation instructions are well documented, but as [Alfred] himself warns, if you encounter trouble arising from subtle dependency version conflicts, you may need to be a nerd and/or a pensioner with unlimited time on your hands to solve them. There is no users guide nor extensive help according to [Alfred]. However, simple hints might be found in hover text or by pressing F1. Disclaimers aside, this looks like an interesting project to try out.

As [Alfred] notes, there are many other tools available to fetch data and images from your Rigol oscilloscope. [Jenny List] wrote a two-part series on using Python to control your test instruments, and here’s an example of a simple Python script that does a screen grab. Do you have a favorite way to remotely operate your oscilloscope? Let us know in the comments below.

Speech Recognition On An Arduino Nano?

Like most of us, [Peter] had a bit of extra time on his hands during quarantine and decided to take a look back at speech recognition technology in the 1970s. Quickly, he started thinking to himself, “Hmm…I wonder if I could do this with an Arduino Nano?” We’ve all probably had similar thoughts, but [Peter] really put his theory to the test.

The hardware itself is pretty straightforward. There is an Arduino Nano to run the speech recognition algorithm and a MAX9814 microphone amplifier to capture the voice commands. However, the beauty of [Peter’s] approach, lies in his software implementation. [Peter] has a bit of an interplay between a custom PC program he wrote and the Arduino Nano. The learning aspect of his algorithm is done on a PC, but the implementation is done in real-time on the Arduino Nano, a typical approach for really any machine learning algorithm deployed on a microcontroller. To capture sample audio commands, or utterances, [Peter] first had to optimize the Nano’s ADC so he could get sufficient sample rates for speech processing. Doing a bit of low-level programming, he achieved a sample rate of 9ksps, which is plenty fast for audio processing.

To analyze the utterances, he first divided each sample utterance into 50 ms segments. Think of dividing a single spoken word into its different syllables. Like analyzing the “se-” in “seven” separate from the “-ven.” 50 ms might be too long or too short to capture each syllable cleanly, but hopefully, that gives you a good mental picture of what [Peter’s] program is doing. He then calculated the energy of 5 different frequency bands, for every segment of every utterance. Normally that’s done using a Fourier transform, but the Nano doesn’t have enough processing power to compute the Fourier transform in real-time, so Peter tried a different approach. Instead, he implemented 5 sets of digital bandpass filters, allowing him to more easily compute the energy of the signal in each frequency band.

The energy of each frequency band for every segment is then sent to a PC where a custom-written program creates “templates” based on the sample utterances he generates. The crux of his algorithm is comparing how closely the energy of each frequency band for each utterance (and for each segment) is to the template. The PC program produces a .h file that can be compiled directly on the Nano. He uses the example of being able to recognize the numbers 0-9, but you could change those commands to “start” or “stop,” for example, if you would like to.

[Peter] admits that you can’t implement the type of speech recognition on an Arduino Nano that we’ve come to expect from those covert listening devices, but he mentions small, hands-free devices like a head-mounted multimeter could benefit from a single word or single phrase voice command. And maybe it could put your mind at ease knowing everything you say isn’t immediately getting beamed into the cloud and given to our AI overlords. Or maybe we’re all starting to get used to this. Whatever your position is on the current state of AI, hopefully, you’ve gained some inspiration for your next project.

All-Wheel Drive Bicycle Using Hand Drill Parts

A skilled mountain biker can cross some extreme terrain, but [The Q] thought there might be room for improvement, so he converted a fat bike to all-wheel drive.

The major challenge here is transferring pedal power to the front wheels, especially around the headset. [The Q] solved this by effectively building a differential from the parts of a very old hand drill. Since the front wheel needs to rotate at the same speed as the rear, one long chain loops from the rear wheel to the headset, tensioned by a pair of derailleurs. This front sprocket turns a series of spur gears and bevel gear arranged around the headset, which transfers the power down to the front wheel via another chain.

It would be interesting to feel what the bike rides like in soft sand, mud, and over rocks. We can see it has some advantages in those conditions but were unsure if it would be enough to offset the penalty in weight and complexity. The additional chains and gears certainly look like they’re asking to catch foliage, clothing, and maybe even skin. However, we suspect [The Q] was more likely doing it for the challenge of the build, which we can certainly appreciate. With the rise of e-bikes, adding a hub motor to the front wheel seems like a simpler option.

We’ve seen several interesting bicycle hacks over the years, including a strandbeest rear end, 3D printed tires and an automatic shifter. Continue reading “All-Wheel Drive Bicycle Using Hand Drill Parts”

ESP8266 Adds WiFi To A 433 MHz Weather Station

There’s no shortage of cheap weather stations on the market that pull in data from several wireless sensors running in the 433 to 900 MHz range and present you with a slick little desktop display, but that’s usually where the flow of information stops. Looking to bridge the gap and bring all that local climate data onto the Internet, [Jonathan Diamond] decided to reverse engineer how his weather station worked.

The first phase of this project involved an RTL-SDR receiver, GNURadio, and a sprinkling of Python. [Jonathan] was able to lock onto the signal and piece together the data packets that reported variables such as temperature, wind speed, and rainfall. Each one of these was a small puzzle in itself, and in the end, there’s still a few bits which he hasn’t quite figured out. But he at least had enough to move onto the next step.

Tapping into the radio module.

Now at this point, he could have pulled the data right out of the air with his RTL-SDR. But looking to push his skills to the next level, [Jonathan] decided to open up the base station and isolate its receiver. Since he already decoded the packets on the RF side, he knew exactly what he was looking for with his oscilloscope and logic analyzer. Once he was tapped into the feed coming from the radio, the final step was writing some code for the ESP8266 that could listen on the line, interpret the data packets, and push the resulting variables out over the network.

In this case, [Jonathan] decided to funnel all the data into Weather Underground by way of the Personal Weather Station API. This not only let him view the data through their web interface and smartphone application, but brought their hyperlocal forecasting technology into the mix at no extra charge. If you’re not interested in sharing your info with the public, it would be a trivial matter to change the firmware so the data is published to a local MQTT broker, or whatever else floats your proverbial boat.

If you’re really lucky, your own weather station may already have an ESP8266 onboard and is dumping all its collected data to the serial port. But if not, projects like this one that break down how to reverse engineer a wireless signal can be a great source of inspiration and guidance should you decide to try and crack the code.

Flat-Pack Pasta: Like Ikea Furniture Without The Weird Wrench

When it comes to food packaging, there’s no bigger scam than potato chip bags, right? People complain about the air (nitrogen, actually) inside, but it’s there for a reason — nitrogen pushes out oxygen, so the chips live in a state of factory-fresh dormancy until you rip open the bag and release the gas. If you want flat-pack chips, there’s always those uniformly-shaped potato slurry wafers that come in a can. But even those usually manage to have a few broken ones.

On the other hand, no one complains about the extra space in their box of fusilli — that would be silly. But seriously, successfully shipping fragile foods requires either flat packing or a lot of extra space, especially if that food comes in a myriad of fun 3D shapes like pasta does. Everybody knows that 3D pasta is superior to flat pasta because it holds sauces so much better. The pasta must be kept intact!

The great thing about pasta as a food is that it’s simple to make, and it’s more nutritious than potato chips. Because of these factors, pasta is often served in extreme situations to large groups of people, like soldiers and the involuntarily displaced. But storing large quantities of shapely pasta takes up quite a bit of space. And because of all that necessary air, much of the packaging goes to waste.

So what if you could keep your plethora of pasta in, say, a filing cabinet? A research team led by the Morphing Matter Lab at Carnegie Mellon University have created a way to make flat-pack pasta that springs to life after a few minutes in boiling water.

Continue reading “Flat-Pack Pasta: Like Ikea Furniture Without The Weird Wrench”

Voidstar’s Vitals, Visualized For Video

Great news for fans of [Voidstar Labs] — [Zack] is going to be streaming future builds live on YouTube instead of trying to keep up with a grueling and limiting schedule of releasing a build video every week. The only problem is that the wall behind him is totally blank and boring, which matters quite a bit for pretty much any streamer that doesn’t broadcast from a hot tub. Well, not anymore! Now the wall has twenty square feet of rainbow hexagons, because blinkenlights.

But these aren’t just any blinkenlights. They’re informative. They dance to the beat of [Zack]’s bio-metrics, or in other words, they are visualizing how sweaty and anxious [Zack] may be at a given moment, and turning that information into art.

At the heart of this build is a brand-new bio-metric board called the EmotiBit which boasts sixteen sensors in a small package, including a pulse oximeter. The EmotiBit sends vitals to [Zack]’s PC, which is running an oscilloscope app to interpret the signals. Then they are sent over Open Sound Control to an ESP32, which runs the light show.

Like [Zack] says in the video after the break, this isn’t a terribly difficult project, but the construction takes time. [Zack] used aluminum extrusion meant for under-cabinet lighting and ran forty strips of fourteen DotStar LEDs each. The nodes are printed in carbon-fiber PLA and hold the lights away from the wall so it looks cooler. Worried about the current draw? It’s okay, because the brightness and number of lit LEDs at any one time is limited. Add in the fact that none of the LEDs are ever turned off — they fade by one percent each loop — and you have some really cool animations. Check them out after the break.

Want some localized blinkenlights to wear about town? Wear your heart on your sleeve and show them how hard you’re crushing the elliptical at the gym.

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