Yesterday, my son was thinking of adding a microphone to the design he is working on, and was considering adding a fast Fourier transform (FFT) to detect pitch.

He spent a few hours after his 10 a.m.–5 p.m. theater class reading about the FFT algorithm. He found an implementation of the FFT for an Arduino, which he tried reading along with the FFT explanations he found on the web.  I’m actually surprised the the Arduino was capable of doing an FFT, given the slowness of the processor.  It is true that the example code only does a 64-sample FFT with a sampling rate of 1kHz, using 8-bit samples and 16-bit integer arithmetic, but it is reported to do it at better than 10 FFTs per second.

I also pointed him to the Discrete Cosine Transform (DCT), which has somewhat smaller boundary artifacts, and can be computed about twice as fast, but he hasn’t had time to read that yet.  Somewhat surprisingly the DCT article in Wikipedia much better written than the general one on Fourier Transforms, which uses awkward notation and a rather dry, formal factoid dump.

I wanted to show him that FFT did not make pitch extraction trivial (at least not in real musical contexts), so I wired up a microphone and amplifier to my BitScope Pocket Analyzer and we turned on some Internet radio (our local public radio station, KUSP).  The complex mass of rapidly shifting peaks in the FFT made it clear that tracking a pitch would not be easy.  (I think that there are some useful pitch-extraction algorithms that are based on FFT, but they are not trivial.) I think I convinced him that he is better off trying to extract loudness than pitch, if he wants a useful control parameter for his device.

Incidentally, he spent some time yesterday looking for cheap electret microphones. There are quite a few on the market, but many of them say “hand solder only” on the datasheets—even some of the ones with just solder pads! There are mics that can be reflow soldered, but finding prices for them in the 100s is difficult—they mostly seem to be quotes from the manufacturers only. One promising one is the SiSonic SPQ2410HR5H-PD, which is only  3.76mm by 2.24mm and uses a ball grid array (it costs 92¢ in 100s, substantially more than 57¢ for the cheapest through-hole mic (though that needs to be hand soldered). CORRECTION 2013 July 10—that’s a MEMS silicon mic, not an electret.

We looked at digital mics that do the A-to-D conversion already, but the only one with a useful output format was the ADMP441, which costs $4.52 in 100s (way too expensive). The cheaper (down to about $1.02) digital mics all use PDM (pulse-density modulation), but to get that into a usable form inside the ATMega, he’d have to low-pass filter it and pass it through the A/D converter. Still, that may not be any more expensive than an analog mic, DC-blocking high-pass filter, and amplifier, though using a separate amplifier would let him design for the proper microphone sensitivity.  He’s going to have to figure out whether the board area and parts cost are worth the extra functionality of the microphone.  If the board area is not a problem, he could design the mic in, but then have the devices only partially populated to save parts costs, if necessary.

We also noticed that we could tell the bandwidth of the radio station we were listening to, because there was a very clear drop in the spectrum at 10kHz.  I tried capturing that this afternoon, but the station we listened to had a talk program rather than a music program, and I never captured a moment when they were using the full bandwidth.  I tried a different Internet music source, and got the following plot, which seemed to indicate a 12kHz bandwidth, but that may have been limited by the music recording they were playing, rather than the codec used for transmission over the internet.

Snapshot of FFT showing a bandwidth of about 12kHz. The grid for the spectrum is 10dB per division vertically and 6kHz per division horizontally.

Before we’d played with sound input, we’d looked at sine waves generated by the BitScope, by connecting a wire from the GEN output to the CHA input. I don’t think I was able to explain to him why the windowing function used for removing boundary artifacts in the FFT results in spreading the single-frequency spikes into wider peaks. It was too late at night to go into the theory of transforms and how multiplication in the time domain turns into convolution in the frequency domain, and vice versa. For that matter, he hasn’t even had convolution yet, so some of the fundamentals needed for the explanation were missing.

At one point I thought that the FFT on the Bitscope was a crude rectangular window, but I was informed by the Bitscope people that they use a Kaiser window. I should have been able to tell that they were doing some sort of windowing by seeing the spread of the spikes for sine waves, but I wouldn’t have been able to guess which window. (It may be buried in the documentation somewhere.) Actually, now that I look at the spikes, they seem too wide for a Kaiser window, unless they set the α parameter much too large.  They only need the sidelobes to be about 60dB down, which should be a much narrower main lobe—not the 18-bin width I think I’m seeing. Perhaps there is something else spreading the peaks, not just the Kaiser window.

Sine wave and FFT analysis of it (Click on image for larger, clearer picture). Note the harmonic distortion (2nd, 3rd, and 4th harmonic at about –40dB, –45dB, and —53dB).
Good luck figuring out the settings of the Bitscope from the information they show on the display!  TB is the time per division on the x-axis of the plot, while BW=120kHz is the full width of the spectrum (so 12kHz per division), and the sine wave is at about 3kHz.

It is interesting to look a a sine wave that is an exact submultiple of the sampling frequency:

Here is a 2975Hz sine wave, where each period should be 8 samples long. Note the appearance of side bands to either side of the main peak. These artifacts are much smaller if we move to a frequency that does not fit so neatly into the FFT buffer.
Note that the time base is different for this screenshot (20ms/division).

We also looked at the spectra of triangle waves and square waves (since the BitScope waveform generator can do those also). Playing with the duty cycle was fun also. I had not been aware that a duty cycle of 1/n on a square wave or triangle wave suppressed the nth, 2nth, 3nth, … harmonics. I had known that a 50% duty cycle square wave or triangle wave suppressed the even harmonics, but I had never thought about other duty cycles.

Triangle wave with 25% duty cycle, showing suppression of the 4th, 8th, 12th, and 16th harmonics.

Yesterday, my son was thinking of adding a microphone to the design he is working on, and was considering adding a fast Fourier transform (FFT) to detect pitch.

He spent a few hours after his 10 a.m.–5 p.m. theater class reading about the FFT algorithm. He found an implementation of the FFT for an Arduino, which he tried reading along with the FFT explanations he found on the web.  I’m actually surprised the the Arduino was capable of doing an FFT, given the slowness of the processor.  It is true that the example code only does a 64-sample FFT with a sampling rate of 1kHz, using 8-bit samples and 16-bit integer arithmetic, but it is reported to do it at better than 10 FFTs per second.

I also pointed him to the Discrete Cosine Transform (DCT), which has somewhat smaller boundary artifacts, and can be computed about twice as fast, but he hasn’t had time to read that yet.  Somewhat surprisingly the DCT article in Wikipedia much better written than the general one on Fourier Transforms, which uses awkward notation and a rather dry, formal factoid dump.

I wanted to show him that FFT did not make pitch extraction trivial (at least not in real musical contexts), so I wired up a microphone and amplifier to my BitScope Pocket Analyzer and we turned on some Internet radio (our local public radio station, KUSP).  The complex mass of rapidly shifting peaks in the FFT made it clear that tracking a pitch would not be easy.  (I think that there are some useful pitch-extraction algorithms that are based on FFT, but they are not trivial.) I think I convinced him that he is better off trying to extract loudness than pitch, if he wants a useful control parameter for his device.

Incidentally, he spent some time yesterday looking for cheap electret microphones. There are quite a few on the market, but many of them say “hand solder only” on the datasheets—even some of the ones with just solder pads! There are mics that can be reflow soldered, but finding prices for them in the 100s is difficult—they mostly seem to be quotes from the manufacturers only. One promising one is the SiSonic SPQ2410HR5H-PD, which is only  3.76mm by 2.24mm and uses a ball grid array (it costs 92¢ in 100s, substantially more than 57¢ for the cheapest through-hole mic (though that needs to be hand soldered). CORRECTION 2013 July 10—that’s a MEMS silicon mic, not an electret.

We looked at digital mics that do the A-to-D conversion already, but the only one with a useful output format was the ADMP441, which costs $4.52 in 100s (way too expensive). The cheaper (down to about $1.02) digital mics all use PDM (pulse-density modulation), but to get that into a usable form inside the ATMega, he’d have to low-pass filter it and pass it through the A/D converter. Still, that may not be any more expensive than an analog mic, DC-blocking high-pass filter, and amplifier, though using a separate amplifier would let him design for the proper microphone sensitivity.  He’s going to have to figure out whether the board area and parts cost are worth the extra functionality of the microphone.  If the board area is not a problem, he could design the mic in, but then have the devices only partially populated to save parts costs, if necessary.

We also noticed that we could tell the bandwidth of the radio station we were listening to, because there was a very clear drop in the spectrum at 10kHz.  I tried capturing that this afternoon, but the station we listened to had a talk program rather than a music program, and I never captured a moment when they were using the full bandwidth.  I tried a different Internet music source, and got the following plot, which seemed to indicate a 12kHz bandwidth, but that may have been limited by the music recording they were playing, rather than the codec used for transmission over the internet.

Snapshot of FFT showing a bandwidth of about 12kHz. The grid for the spectrum is 10dB per division vertically and 6kHz per division horizontally.

Before we’d played with sound input, we’d looked at sine waves generated by the BitScope, by connecting a wire from the GEN output to the CHA input. I don’t think I was able to explain to him why the windowing function used for removing boundary artifacts in the FFT results in spreading the single-frequency spikes into wider peaks. It was too late at night to go into the theory of transforms and how multiplication in the time domain turns into convolution in the frequency domain, and vice versa. For that matter, he hasn’t even had convolution yet, so some of the fundamentals needed for the explanation were missing.

At one point I thought that the FFT on the Bitscope was a crude rectangular window, but I was informed by the Bitscope people that they use a Kaiser window. I should have been able to tell that they were doing some sort of windowing by seeing the spread of the spikes for sine waves, but I wouldn’t have been able to guess which window. (It may be buried in the documentation somewhere.) Actually, now that I look at the spikes, they seem too wide for a Kaiser window, unless they set the α parameter much too large.  They only need the sidelobes to be about 60dB down, which should be a much narrower main lobe—not the 18-bin width I think I’m seeing. Perhaps there is something else spreading the peaks, not just the Kaiser window.

Sine wave and FFT analysis of it (Click on image for larger, clearer picture). Note the harmonic distortion (2nd, 3rd, and 4th harmonic at about –40dB, –45dB, and —53dB).
Good luck figuring out the settings of the Bitscope from the information they show on the display!  TB is the time per division on the x-axis of the plot, while BW=120kHz is the full width of the spectrum (so 12kHz per division), and the sine wave is at about 3kHz.

It is interesting to look a a sine wave that is an exact submultiple of the sampling frequency:

Here is a 2975Hz sine wave, where each period should be 8 samples long. Note the appearance of side bands to either side of the main peak. These artifacts are much smaller if we move to a frequency that does not fit so neatly into the FFT buffer.
Note that the time base is different for this screenshot (20ms/division).

We also looked at the spectra of triangle waves and square waves (since the BitScope waveform generator can do those also). Playing with the duty cycle was fun also. I had not been aware that a duty cycle of 1/n on a square wave or triangle wave suppressed the nth, 2nth, 3nth, … harmonics. I had known that a 50% duty cycle square wave or triangle wave suppressed the even harmonics, but I had never thought about other duty cycles.

Triangle wave with 25% duty cycle, showing suppression of the 4th, 8th, 12th, and 16th harmonics.

I’ve been thinking of expanding the pressure sensor lab for the Applied Circuits Course to do something more than just measure breath pressure.  Perhaps something that conveys another physics or engineering concept.

One thing I thought of doing was measuring the back pressure and air flow on an aquarium air pump bubbling air into an aquarium. Looking at the tradeoff of flow-rate and back pressure would be a good characterization of an air pump (something I wish was clearly shown in advertisements for aquarium air pumps and air stones).  Measuring flow rate is a bit of a pain—about the best I could think of was to bubble air into an inverted soda bottle (or other known volume) and time it.

This would be a good physics experiment and might even make a decent middle-school product-science science fair project (using a cheap mechanical low-pressure gauge, rather than an electronic pressure sensor), but setting up tanks of water in an electronics lab is a logistic nightmare, and I don’t think I want to go there.

I can generate back pressure with just a simple clamp on the hose, though, and without a flow rate measurement we could do everything completely dry.

Setup for measuring the back pressure for an aquarium air pump (needs USB connection for data logger and power).

Using the Arduino data loggger my son wrote, I recorded the air pressure while adjusting the clamp (to get the y-axis scale correct, I had to use the estimated gain of the amplifier based on resistor sizes used in the amplifier).

The peak pressure, with the clamp sealing the hose shut, seems to be about 14.5 kPa (2.1psi).

I was interested in the fluctuation in the pressure, so I set the clamp to get about half the maximum back pressure, then recorded with the Arduino data logger set to its highest sampling frequency (1ms/sample).

The fluctuation in back pressure seems to have both a 60Hz and a 420Hz component with back pressure at about half maximum.

Because the Arduino data logger has trouble dealing with audio frequency signals, I decided to take another look at the signals using the Bitscope pocket analyzer.

The waveform for the pressure fluctuations from the AQT3001 air pump, with the back pressure about 7.5kPa (half the maximum).

One advantage of using the Bitscope is that it has FFT analysis:

Spectrum for the back pressure fluctuation. One can see many of the multiples of 60Hz, with the particularly strong peak at 420Hz.

I was also interested in testing a Whisper40 air pump (a more powerful, but quieter, pump). When I clamped the hose shut for that air pump, the hi-gain output of the amplifier for the pressure sensor saturated, so I had to use the low gain output to determine the maximum pressure (24.8kPA, or about 3.6psi). The cheap Grafco clamp that I used is a bit hard to get complete shutoff with (I needed to adjust the position of the tubing and use pliers to turn the knob).  It is easy to get complete shutoff if the tube is folded over, but then modulation of less than complete shutoff is difficult.

The fluctuation in pressure shows a different waveform from the AQT3001:

The Whisper40 air pump, with the clamp set to get a bit less than half the maximum back pressure, produces a 60Hz sawtooth pressure waveform, without the strong 420Hz component seen from the AQT3001. The peak-to-peak fluctuation in pressure seems to be largest around this back pressure. The 3kPa fluctuation is larger than for the AQT3001, but the pump seems quieter.

The main noise from the pump is not from the fluctuation in the pressure in the air hose, but radiation from the case of the pump. That noise seems to be least when the back pressure is about 1.1kPa (not at zero, surprisingly). The fluctuation is then all positive pressure, ranging from 0 to 2.2kPa and is nearly sinusoidal, with some 2nd and 3rd harmonic.

As the back pressure increases for the Whisper40, the 2nd, 3rd, and 4th harmonics get larger, but the 60Hz fundamental gets smaller. The 4th harmonic is maximized (with the 1st through 4th harmonics almost equal) at about 22.8kPa, above which all harmonics get smaller, until the air hose is completely pinched off and there is no pressure variation.

When driving the large airstone in our aquarium, the Whisper40 has a back pressure of about 7.50kPa (1.1psi) with a peak-to-peak fluctuation of about 2.6kPa.

I’m not sure whether this air-pump back-pressure experiment is worth adding to the pressure sensor lab.  If I decide to do it, we would need to get a dozen cheap air pumps.  The Tetra 77853 Whisper 40 air pump is $11.83 each from Amazon, but the smaller one for 10-gallon aquariums is only $6.88.  With 12 Ts and 12 clamps, this would cost about $108, which is not a significant cost for the lab.

I’ve been thinking of expanding the pressure sensor lab for the Applied Circuits Course to do something more than just measure breath pressure.  Perhaps something that conveys another physics or engineering concept.

One thing I thought of doing was measuring the back pressure and air flow on an aquarium air pump bubbling air into an aquarium. Looking at the tradeoff of flow-rate and back pressure would be a good characterization of an air pump (something I wish was clearly shown in advertisements for aquarium air pumps and air stones).  Measuring flow rate is a bit of a pain—about the best I could think of was to bubble air into an inverted soda bottle (or other known volume) and time it.

This would be a good physics experiment and might even make a decent middle-school product-science science fair project (using a cheap mechanical low-pressure gauge, rather than an electronic pressure sensor), but setting up tanks of water in an electronics lab is a logistic nightmare, and I don’t think I want to go there.

I can generate back pressure with just a simple clamp on the hose, though, and without a flow rate measurement we could do everything completely dry.

Setup for measuring the back pressure for an aquarium air pump (needs USB connection for data logger and power).

Using the Arduino data loggger my son wrote, I recorded the air pressure while adjusting the clamp (to get the y-axis scale correct, I had to use the estimated gain of the amplifier based on resistor sizes used in the amplifier).

The peak pressure, with the clamp sealing the hose shut, seems to be about 14.5 kPa (2.1psi).

I was interested in the fluctuation in the pressure, so I set the clamp to get about half the maximum back pressure, then recorded with the Arduino data logger set to its highest sampling frequency (1ms/sample).

The fluctuation in back pressure seems to have both a 60Hz and a 420Hz component with back pressure at about half maximum.

Because the Arduino data logger has trouble dealing with audio frequency signals, I decided to take another look at the signals using the Bitscope pocket analyzer.

The waveform for the pressure fluctuations from the AQT3001 air pump, with the back pressure about 7.5kPa (half the maximum).

One advantage of using the Bitscope is that it has FFT analysis:

Spectrum for the back pressure fluctuation. One can see many of the multiples of 60Hz, with the particularly strong peak at 420Hz.

I was also interested in testing a Whisper40 air pump (a more powerful, but quieter, pump). When I clamped the hose shut for that air pump, the hi-gain output of the amplifier for the pressure sensor saturated, so I had to use the low gain output to determine the maximum pressure (24.8kPA, or about 3.6psi). The cheap Grafco clamp that I used is a bit hard to get complete shutoff with (I needed to adjust the position of the tubing and use pliers to turn the knob).  It is easy to get complete shutoff if the tube is folded over, but then modulation of less than complete shutoff is difficult.

The fluctuation in pressure shows a different waveform from the AQT3001:

The Whisper40 air pump, with the clamp set to get a bit less than half the maximum back pressure, produces a 60Hz sawtooth pressure waveform, without the strong 420Hz component seen from the AQT3001. The peak-to-peak fluctuation in pressure seems to be largest around this back pressure. The 3kPa fluctuation is larger than for the AQT3001, but the pump seems quieter.

The main noise from the pump is not from the fluctuation in the pressure in the air hose, but radiation from the case of the pump. That noise seems to be least when the back pressure is about 1.1kPa (not at zero, surprisingly). The fluctuation is then all positive pressure, ranging from 0 to 2.2kPa and is nearly sinusoidal, with some 2nd and 3rd harmonic.

As the back pressure increases for the Whisper40, the 2nd, 3rd, and 4th harmonics get larger, but the 60Hz fundamental gets smaller. The 4th harmonic is maximized (with the 1st through 4th harmonics almost equal) at about 22.8kPa, above which all harmonics get smaller, until the air hose is completely pinched off and there is no pressure variation.

When driving the large airstone in our aquarium, the Whisper40 has a back pressure of about 7.50kPa (1.1psi) with a peak-to-peak fluctuation of about 2.6kPa.

I’m not sure whether this air-pump back-pressure experiment is worth adding to the pressure sensor lab.  If I decide to do it, we would need to get a dozen cheap air pumps.  The Tetra 77853 Whisper 40 air pump is $11.83 each from Amazon, but the smaller one for 10-gallon aquariums is only $6.88.  With 12 Ts and 12 clamps, this would cost about $108, which is not a significant cost for the lab.

Learn how to use the TI ADS1110 16-bit ADC with Arduino in chapter fifty-three of my Arduino Tutorials. The first chapter is here, the complete series is detailed here.

Updated 02/07/2013

Introduction

Moving on from the last chapter where we explained an 8-bit ADC, in this instalment we have the Texas Instruments ADS1110 – an incredibly tiny but useful 16-bit analogue-to-digital converter IC.  It can operate between 2.7 and 5.5 V so it’s also fine for Arduino Due and other lower-voltage development boards. This is a quick guide to get you going with the ADS1110 ready for further applications. Before continuing any further, please download the data sheet (pdf) as it will be useful and referred to during this tutorial. The ADS1110 gives you the option of a more accurate ADC than offered by the Arduino’s 10-bit ADCs – and it’s relatively easy to use. The only block for some is the package type – it’s only available in SOT23-6:

So unless you’re making a customised PCB, some sort of breakout will be required. One useful example is the Schmartboard we reviewed earlier:

The ADS1110 uses the I2C bus for communication, so if this is new to you – please review the I2C tutorials before continuing. And as there’s only six pins you can’t set the bus address – instead, you can select from six variants of the ADS1110 – each with their own address (see page two of the data sheet). As you can see the in the photo above, ours is marked “EDO” which matches to the bus address 1001000 or 0x48h. And with the example circuits we’ve used 10kΩ pull-up resistors on the I2C bus. You can use the ADS1110 as either a single-ended or differential ADC –  But first we need to examine the configuration register which is used to control various attributes, and the data register.

Configuration register

Turn to page eleven of the data sheet. The configuration register is one byte in size, and as the ADS1110 resets on a power-cycle – you need to reset the register if your needs are different to the defaults. The data sheet spells it out quite neatly… bits 0 and 1 determine the gain setting for the PGA (programmable gain amplifier). If you’re just measuring voltages or experimenting, leave these as zero for a gain of 1V/V. Next, the data rate for the ADS1110 is controlled with bits 2 and 3. If you have continuous sampling turned on, this determines the number of samples per second taken by the ADC.

After some experimenting with an Arduino Uno we found the values returned from the ADC were a bit off when using the fastest rate, so leave it as 15 SPS unless required otherwise. Bit 4 sets either continuous sampling (0) or one-off sampling (1). Ignore bits 5 and 6, however they’re always set as 0. Finally bit 7 – if you’re in one-off sampling mode, setting it to 1 requests a sample – and reading it will tell you if the returned data is new (0) or old (1). You can check that the value measured is a new value – if the first bit of the configuration byte that comes after the data is 0, it’s new. If it returns 1 the ADC conversion hasn’t finished.

Data register

As the ADS1110 is a 16-bit ADC, it returns the data over two bytes – and then follows with the value of the configuration register. So if you request three bytes the whole lot comes back. The data is in “two’s complement” form, which is a method of using signed numbers with binary. Converting those two bytes is done by some simple maths. When sampling at 15 SPS, the value returned by the ADS1110 (not the voltage)  falls between -32768 and 32767. The higher byte of the value is multiplied by 256, then added to the lower byte – which is then multiplied by 2.048 and finally divided by 32768. Don’t panic, as we do this in the example sketch below.

Single-ended ADC mode

In this mode you can read a voltage that falls between zero and 2.048 V (which also happens to be the inbuilt reference voltage for the ADS1110). The example circuit is simple (from the data sheet):

Don’t forget the 10kΩ pull-up resistors on the I2C bus. The following sketch uses the ADS1110 in the default mode, and simply returns the voltage measured:

// Example 53.1 - ADS1110 single-sided voltmeter (0~2.048VDC)

#include "Wire.h"
#define ads1110 0x48
float voltage, data;
byte highbyte, lowbyte, configRegister;
void setup()
{
 Serial.begin(9600);
 Wire.begin();
}
void loop()
{
 Wire.requestFrom(ads1110, 3);
 while(Wire.available()) // ensure all the data comes in
 {
 highbyte = Wire.read(); // high byte * B11111111
 lowbyte = Wire.read(); // low byte
 configRegister = Wire.read();
 }

 data = highbyte * 256;
 data = data + lowbyte;
 Serial.print("Data >> ");
 Serial.println(data, DEC);
 Serial.print("Voltage >> ");
 voltage = data * 2.048 ;
 voltage = voltage / 32768.0;
 Serial.print(voltage, DEC);
 Serial.println(" V");
 delay(1000);
}

Once uploaded, connect the signal to measure and open the serial monitor – you’ll be presented with something similar to:

If you need to alter the gain of the internal programmable gain amplifier of the ADC – you’ll need to write a new byte into the configuration register using:

Wire.beginTransmission(ads1110);
Wire.write(configuration byte); 
Wire.endTransmission();

before requesting the ADC data. This would be 0x8D, 0x8E or 0x8F for gain values of 2, 4 and 8 respectively – and use 0x8C to reset the ADS1110 back to default.

Differential ADC mode

In this mode you can read the difference between two voltages that each fall between zero and 5 V. The example circuit is simple (from the data sheet):

We must note here (and in the data sheet) that the ADS1110 can’t accept negative voltages on either of the inputs. You can use the previous sketch for the same results – and the resulting voltage will be the value of Vin- subtracted from Vin+. For example, if you had 2 V on Vin+ and 1 V on Vin- the resulting voltage would be 1 V (with the gain set to 1).

Conclusion

Once again I hope you found this of interest, and possibly useful. And if you enjoy my tutorials, or want to introduce someone else to the interesting world of Arduino – check out my new book “Arduino Workshop” from No Starch Press.

In the meanwhile have fun and keep checking into tronixstuff.com. Why not follow things on twitter, Google+, subscribe  for email updates or RSS using the links on the right-hand column? And join our friendly Google Group – dedicated to the projects and related items on this website. Sign up – it’s free, helpful to each other –  and we can all learn something.

The post Tutorial – Arduino and the TI ADS1110 16-bit ADC appeared first on tronixstuff.

It looks like Jack Eisenmann has done it again. A couple of years after the hobbyist hacker built his first 8-bit computer, he's cobbled together yet another one, but this time with a whopping 16 cores. Appropriately dubbed the DUO Mega, the multicore wonder is made with 16 ATMega328p microcontrollers, each connected to an 8-bit data bus and designed to interpret a custom bytecode that runs the software. Compiled inside a nondescript plastic bin, the machine is also comprised of 16MHz crystal oscillators attached to each of the aforementioned cores, three Arduino UNO boards, 32kb of SRAM, 512kb of flash memory, eight breadboards, an Ethernet shield, a VGA out port and a multitude of components that combine to look like that mess of wires seen above. Because of all that processing power, this relatively primitive machine multitasks beautifully and can perform complicated calculations at an impressive clip. To get a demo of what this marvelous feat of DIY computing can do, have a peek at Eisenmann's video, complete with an 8-bit soundtrack, after the break.

Filed under: Desktops

Comments

Via: Hackaday

Source: DUO Mega

Electric vehicle charging stations aren't cheap: one of the most affordable Level 2 (240V) units sells for $450 and only supplies 16A. Electric Motor Works (EMW) -- which is best known for its electric conversion kits -- wants to change this with JuiceBox, a 15kW Level 2 EV charger that costs just $99 in kit form (plus $10 shipping). The device, which is launching on Kickstarter today, supplies up to 62A and operates on both 120V and 240V. It's built around an Arduino microcontroller and EMW is making both the hardware and software open source.

But wait, there's more! The company is also crowdfunding a Premium Edition of JuiceBox ($199 in kit form) which adds time-of-day charging, a color LCD, ground-fault plus output protection (for outdoor use) and a unique enclosure (hopefully as funky as the one in the picture above). While the DIY kits only require basic assembly and soldering skills, you'll be able to buy fully assembled versions for $100 more. At $329 (shipped), a ready-to-use JuiceBox Premium Edition undercuts other similar charging stations by several hundred dollars. The catch? You'll have to supply your own cables (or buy them separately from EMW), including one with a standard J1772 EV connector. Hit the source link below to check out the campaign, and take a look at the PR after the break.

Filed under: Transportation

Comments

Source: EMW (Kickstarter)

Conventional wisdom would suggest that making a for-real Katamari Damacy ball would be tricky, but that didn't stop Chris McInnis, Ron LeBlanc and Tom Gwozdz from taking up the challenge. As part of the Nuit Blanche festival in London, Canada (which also included some building-projected gaming), they were able to fashion their very own Katamari ball from a yoga ball, some stickers, wood, an Arduino microcontroller, several optical mice and a dissected DualShock 2 controller. See how it steers after the break.

Filed under: Gaming, Peripherals, Science, Alt

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Via: UbercoolStuffldnont (YouTube)

Writing code for an Arduino-friendly board is relatively easy; creating the board is the hard part, unless you live and breathe electrical engineering. If HackEDA has its way, however, the design process could be almost as easy as window shopping. Its new Kickstarter-backed project lets tinkerers choose from a list of parts and get a made-to-order board without knowing a lick about PCB assembly -- algorithms sort out the finer details. While the initial effort includes just 36 combos based around an Atmega328 processor, contributors who want tangible hardware can pay anything from $30 for a bare board through to $10,000 for the first stages of mass production. The truly committed will have to wait until December for the finished goods, but those willing to try HackEDA can use its existing web tool for free.

Filed under: Misc

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Source: Kickstarter, HackEDA

Bike sharing systems like New York's Citi Bike may be taking off, but it's doubtful that many participants can find every station without checking a map. Thankfully, Adafruit has unveiled a smart helmet project that could help at least a few of those riders get to their destinations while keeping their eyes on the road. The DIY effort feeds locations to an Arduino-based Flora board and its positioning add-ons, which in turn use a string of NeoPixel LEDs on the helmet as turn indicators. Commuters just have to watch for blinking lights to know where to go next. While the system isn't easy to set up when cyclists have to manually enter coordinates, it is flexible: the open-source code lets it adapt to most any bike sharing system or headpiece. As long as you can get over looking like a Christmas tree on wheels while you navigate, you can build a smart helmet of your own using the instructions at the source link.

Filed under: GPS, Transportation, Alt

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Source: Adafruit