For thirty years, the classic synths of the late 70s and early 80s could not be reproduced. Part of the reason for this is market forces — the synth heads of the 80s didn’t want last year’s gear. The other part for the impossibility to build new versions of these synths was the lack of parts. Synths such as the Prophet 5, Fairlight CMI, and Korg Mono/Poly relied on voltage controlled filter ICs — the SSM2044 — that you can’t buy new anymore. If you can source a used one, be prepared to pay $30. New old stock costs about $100.
Now, these chips are being remade. A new hardware revision for this voltage controlled filter has been taped out by the original hardware designer, and these chips are being produced in huge quantities. Instead of $100 for a new old stock chip, this chip will cost about $1.60 in 1000 unit quantities.
The list of synths and music boxes sporting an SSM2044 reads like a Who’s Who of classic electronic music machines. E-Mu Drumulators, Korg polyphonic synths, Crumars, and even a Doepfer module use this chip in the filter section. The new chip — the SSI2144 — supposedly provides the same classic tone but adds a few improvements such as improved pin layouts, an SSOP package, and more consistent operation from device to device.
This news follows the somewhat recent trend of chip fabs digging into classic analog designs of the 70s, realizing the chips are being sold for big bucks on eBay, and releasing it makes sense to spin up a new production line. Last year, the Curtis CEM3340 voltage controlled oscillator was rereleased, giving the Oberheim OB, Roland SH and Jupiter, and the Memory Moog a new lease on life. These chips aren’t only meant to repair broken, vintage equipment; there are a few builders out there who are making new devices with these rereleased classic synths.
We bet when [devttyS0] made his latest video about RF filter design (YouTube, embedded below), he had the old saying in mind: in theory, there’s no difference between theory and practice, but in practice, there is. He starts out pointing how now modern tools will make designing and simulating any kind of filter easy, but the trick is to actually build it in real life and get the same performance. You can see the video below.
One of the culprits, of course, is we tend to design and simulate with perfect components. Wires have zero resistance, capacitance, and inductance. Inductors and capacitance have no parasitic elements in our rosy design world. Even the values of components will vary from their ideal values and may change over time.
Suppose you take a few measurements of a time-varying signal. Let’s say for concreteness that you have a microcontroller that reads some voltage 100 times per second. Collecting a bunch of data points together, you plot them out — this must surely have come from a sine wave at 35 Hz, you say. Just connect up the dots with a sine wave! It’s as plain as the nose on your face.
And then some spoil-sport comes along and draws in a version of your sine wave at -65 Hz, and then another at 135 Hz. And then more at -165 Hz and 235 Hz or -265 Hz and 335 Hz. And then an arbitrary number of potential sine waves that fit the very same data, all spaced apart at positive and negative integer multiples of your 100 Hz sampling frequency. Soon, your very pretty picture is looking a bit more complicated than you’d bargained for, and you have no idea which of these frequencies generated your data. It seems hopeless! You go home in tears.
But then you realize that this phenomenon gives you super powers — the power to resolve frequencies that are significantly higher than your sampling frequency. Just as the 235 Hz wave leaves an apparent 35 Hz waveform in the data when sampled at 100 Hz, a 237 Hz signal will look like 37 Hz. You can tell them apart even though they’re well beyond your ability to sample that fast. You’re pulling in information from beyond the Nyquist limit!
This essential ambiguity in sampling — that all frequencies offset by an integer multiple of the sampling frequency produce the same data — is called “aliasing”. And understanding aliasing is the first step toward really understanding sampling, and that’s the first step into the big wide world of digital signal processing.
Whether aliasing corrupts your pristine data or provides you with super powers hinges on your understanding of the effect, and maybe some judicious pre-sampling filtering, so let’s get some knowledge.
Those of us who have spent a lifetime building electronic projects have probably breathed more solder smoke than we should. This is not an ideal situation as we’ve probably increased our risk of asthma and other medical conditions as a result.
It has become more common over the years to see fume extraction systems and filters as part of the professional soldering environment, and this trend has also started to appear in the world of the home solderer. As always, where commercial products go the hardware hacker will never be far behind. We’ve seen people producing their own soldering fume filters using computer fans.
A particularly neat example comes via [Engineer of None], who has posted an Instructable and the YouTube video shown below the break for a filter mounted on a desk lamp. A toaster is used to heat a piece of acrylic. The softened plastic is then shaped to fit the contours of the lamp. The lamp’s articulated arm is perfect for placing light and fume extraction exactly where it is needed. It’s not the most complex of hacks, but we’d have one like it on our bench without a second thought. We would probably add an activated carbon filter to ours though.
One of the first frustrating situations a beginning microcontroller programmer will come across is the issue of debouncing switches. Microcontrollers are faster than switches, and the switch has yet to be built that can change state in zero time like they can on paper. This hurdle is easily overcome, but soon we are all faced with another issue: filtering noise from an analog signal. Luckily [Paul Martinsen] has put together a primer of three different ways to use an Arduino to filter signals.
The first (and fastest, simplest, etc.) way to filter an analog signal is to sample a bunch of times and then average all of the samples together. This will eliminate most outliers and chatter without losing much of the information. From there, the tutorial moves on to programming a running average to help increase the sample time (but consume much more memory). Finally, [Paul] takes a look at exponential filters, which are recursive, use less memory, and can be tweaked to respond to changes in different ways.
[Paul] discusses all of the perks and downsides of each method and provides examples for each as well. It’s worth checking out, whether you’re a seasoned veteran who might glean some nuance or you’re a beginner who hasn’t even encountered this problem yet. And if you’re still working on debouncing a digital input, we have you covered there, too.
As anyone who is a veteran of many RF projects will tell you, long component leads can be your undoing. Extra stray capacitances, inductances, and couplings can change the properties of your design to the point at which it becomes unfit for purpose, and something of a black art has evolved in the skill of reducing these effects.
RF Biscuit is [Georg Ottinger]’s attempt to simplify some of the challenges facing the RF hacker. It’s a small PCB with a set of footprints that can be used to make a wide range of surface-mount filters, attenuators, dummy loads, and other RF networks with a minimum of stray effects. Provision has been made for a screening can, and the board uses edge-launched SMA connectors. So far he’s demonstrated it with a bandpass filter and a dummy load, but he suggests it should also be suitable for amplifiers using RF gain blocks.
It’s a tough challenge, to produce a universal board for multiple projects with very demanding layout requirements such as those you’d find in the RF field. We’re anxious to see whether the results back up the promise, and whether the idea catches on.
This appears to be the first RF network prototyping board we’ve featured here at Hackaday. We’ve featured crystal filters before, and dummy loads though, but nothing that brings them all together. What would you build on your RF Biscuit?
Measuring the body’s electrical signals is a neat trick… if you can get your equipment dialed in enough to establish dependable measurements. The technique is called Surface ElectroMyography (SEMG) though you’ll hear many call this ECG. They’re essentially the same technology; the Electro CardioGraph instruments monitor the activity of the heart while SEMG Instruments monitor electrical signals used to control other muscles. Both types of hardware amount to an instrumentation type amplifier and some form of I/O or display.
This topic has been in my back pocket for many months now. Back in May we Hackaday’ites descended on New York City for the Disrupt NY Hackathon event. We arrived a day or so early so that we might better peruse the Korean BBQ joints and check out the other electronics that NY has to offer. On Saturday we gathered around, each shouting out the size of his or her t-shirt preference as we covered up our black Hackaday logo tees with maroon maroon ones (sporting the Hackaday logo of course) for a 24-hour craze of hardware hacking.
There were two individuals at our tables who were both hacking away on hardware to measure the electrical field produced by the body’s muscles in some form or another. The electrical signals measured from the skin are small, and need careful consideration to measure the signal despite the noise. This is a fun experiment that lets you work with both Instrumentation Amplifiers and OpAmps to achieve a usable signal from the movement of your body.