It’s the latest in instrumentation for the well-appointed shop — an acoustically coupled fast Fourier transform tachometer. Sounds expensive, but it’s really just using a smartphone spectrum analyzer app to indirectly measure tool speeds. And it looks like it could be incredibly handy.
Normally, non-contact tachometers are optically coupled, using photoreceptors to measure light flashing off of a shaft or a tool. But that requires a clear view of the machine, often putting hands far too close to the danger zone. [Matthias Wandel]’s method doesn’t require line of sight because it relies on a cheap spectrum analyzer app to listen to a machine’s sound. The software displays peaks at various frequencies, and with a little analysis and some simple math, the shaft speed of the machine can be determined. [Matthias] explains how to exclude harmonics, where to find power line hum, isolating commutator artifacts, and how to do all the calculations. You’ll need to know a little about your tooling to get the right RPM, and obviously you’ll be limited by the audio frequency response of your phone or tablet. But we think this is a great tip.
[Matthias] is no stranger to shop innovations and putting technology to work in simple but elegant ways. We wonder if spectrum analysis could be used to find harmonics and help with his vibration damping solution for a contractor table saw.
Continue reading “The Tachometer Inside Your Smartphone”
[JBeale] squeezed every last drop of performance from a $5 Doppler radar module, and the secrets of that success are half hardware, half firmware, and all hack.
On the hardware side, the first prototype radar horn was made out of cardboard with aluminum foil taped around it. With the concept proven, [JBeale] made a second horn out of thin copper-clad sheets, but reports that the performance is just about the same. The other hardware hack was simply to tack a wire on the radar module’s analog output and add a simple op-amp gain stage, which extended the sensing range well beyond the ten feet or so that these things are usually used for.
With all that signal coming in, [JBeale] separates out the noise by taking an FFT of the Doppler frequency-shift signal. Figuring that people walk around 2.2 miles per hour, [JBeale] focuses on the corresponding 70 Hz frequency bin and finds that the radar will detect people out to 80 feet. Wow!
This trick of taking an el-cheapo radar unit and amplifying the signal to do something useful isn’t new to Hackaday. [Mathieu] did it with the very same HB-100 unit way back in 2013, and then again with a more modern CDM324 model. But [JBeale]’s hacked horn and clever backend processing push out the limits of what you can expect to do with these cheap units. Kudos.
If you are a wine, beer, or cider maker, you’ll know the ritual of checking for fermentation. As the yeast does its work of turning sugar into alcohol, carbon dioxide bubbles froth on the surface of your developing brew, and if your fermentation container has an airlock, large bubbles pass through the water within it on a regular basis. Your ears become attuned to the regular “Plop… plop… plop” sound they make, and from their interval you can tell what stage you have reached.
[Chris] automated this listening for fermentation bubbles, placing a microphone next to his airlock and detecting amplitude spikes through two techniques: one using an FFT algorithm and the other a bandpass filter. Both techniques yielded similar graphs for fermentation activity over time.
He has a few ideas for improvement, but notes that his system is vulnerable to external noises. There is also an admission that using light to detect bubbles might be a more practical solution as we have shown you more than once with other projects, but as with so many projects on these pages, it is the joy of the tech as much as the practicality that matters.
All other things being equal, signals with wider bandwidth can carry more information. Sometimes that information is data, but sometimes it is frequency. AM radio stations (traditionally) used about 30 kHz of bandwidth, while FM stations consume nearly 200 kHz. Analog video signals used to take up even more space. However, your brain is a great signal processor. To understand speech, you don’t need very high fidelity reproduction.
Radio operators have made use of that fact for years. Traditional shortwave broadcasts eat up about 10kHz of bandwidth, but by stripping off the carrier and one sideband, you can squeeze the voice into about 3 kHz and it still is intelligible. Typical voice codecs (that is, something that converts speech to digital data and back) use anywhere from about 6 kbps to 64 kbps.
[David Rowe] wants to change that. He’s working on a codec for ham radio use that can compress voice to 700 bits per second. He is trying to keep the sound quality similar to his existing 1,300 bit per second codec and you can hear sound samples from both in his post. You’ll notice the voices sound almost like old-fashioned speech synthesis, but it is intelligible.
Continue reading “Voice at 700 Bits Per Second”
Everyone’s a critic, but it’s hard to argue with success. And that’s exactly what [agp.cooper] has with his ATtiny85-based spectrum analyzer devices.
The “normal” way to build a spectrum analyzer is to collect a bunch of samples and run a Fast Fourier Transform (FFT) on them all in one shot. As the name implies, the FFT is fast, and the result is the frequency components of the sampled data. [agp.cooper]’s “wrong” way to do it takes the Goertzel algorithm, which is used for detecting the intensity of a particular frequency, and scanning across the frequency range of interest. It’s a lot slower than a single FFT but, importantly for the ATtiny85 that he implements this on, it’s less demanding of the RAM.
Continue reading “Making a Spectrum Analyzer the Wrong Way on an ATtiny85”
Swear on broadcast television and they’re going to bleep out the audio to protect the sensibilities of the general public. Swear bleeps are fairly standardised at 1kHz, or so [mechatronicsguy] tells us. You learn something new every day.
OK, it’s not as though there’s an ISO document somewhere detailing the exact tone to use when someone says a naughty word on camera, it is far more likely that a 1kHz tone is the most likely frequency to be at hand in a studio. It’s so ubiquitous that even audio engineers with nowhere near perfect pitch can identify it, and one to which an acquaintance of ours swears years of exposure have given his ears a selective notch filter.
Armed with this information, [mechatronicsguy] created a fun project. As a fan of the [electroBOOM] Youtube channel he made a set of LED eyebrows for a picture of his bleep-prone hero, and using a Teensy with its audio and FFT libraries he made them light up whenever a 1kHz tone is detected. It’s not the most amazing of hacks, but if you find yourself in need of a smile on a chilly November morning then maybe it’ll have the same effect on you as it did with us. He’s posted a quick video of the ‘brows in action which we’ve embedded below the break.
Continue reading “Swear Bleep Detecting Eyebrows”
In part one, I compared the different Analog to Digital Converters (ADC) and the roles and properties of Delta Sigma ADC’s. I covered a lot of the theory behind these devices, so in this installment, I set out to find a design or two that would help me demonstrate the important points like oversampling, noise shaping and the relationship between the signal-to-noise ratio and resolution.
Check out part one to see the block diagrams of what what got us to here. The schematics shown below are of a couple of implementations that I played with depicting a single-order and a dual-order Delta Sigma modulators.
Basically I used a clock enabled, high speed comparator, with two polarities in case I got it the logic backwards in my current state of burn out to grey matter ratio. The video includes the actual schematic used.
Since I wasn’t designing for production I accepted the need for three voltages since my bench supply was capable of providing them and this widget is destined for the drawer with the other widgets made for just a few minutes of video time anyway. Continue reading “Tearing into Delta Sigma ADCs Part 2”