Slowly, very slowly, the time when we don’t subject embedded beginners to AVRs and PICs is coming. At a glacial pace, FPGA development platforms are becoming ever more capable and less expensive. [Eric Brombaugh] has been playing around with both ARMs and FPGAs for a while now and decided to combine these two loves into a single board that’s capable of a lot.
This board is fittingly called an STM32F303 + ice5 development board, and does exactly what it says on the tin. There’s an STM32F303 on board providing a 32-bit CPU running at 72 MHz, 48 kB of SRAM, a quarter meg of Flash, and enough peripherals to keep anyone happy. The FPGA side of this board is a Lattice iCE5 with about 3k Look Up Tables (LUTs), and one time programmable non-volatile config memory.
The connections between the ARM and FPGA include a dedicated SPI port, and enough GPIOs to implement full-duplex I2S and a USART. Like all good projects, [Eric] has shared all the files, schematics, and BOMs required to make this board your very own reality, and has provided a few links to the development toolchains. While the FPGA is from Lattice’s ice40 family, it’s not supported by the Open Source Project Icestorm toolchain. Still, it’s a very capable board for ARM and FPGA development.
It’s not surprising that Analog to Digital Converters (ADC’s) now employ several techniques to accomplish higher speeds and resolutions than their simpler counterparts. Enter the Delta-Sigma (Δ∑) ADC which combines a couple of techniques including oversampling, noise shaping and digital filtering. That’s not to say that you need several chips to accomplish this, these days single chip Delta-Sigma ADCs and very small and available for a few dollars. Sometimes they are called Sigma-Delta (∑Δ) just to confuse things, a measure I applaud as there aren’t enough sources of confusion in the engineering world already.
I’m making this a two-parter. I will be talking about some theory and show the builds that demonstrate Delta-Sigma properties and when you might want to use them.
To decode the device’s packets he reached for his RTL-SDR receiver and took a look at it in software. GQRX confirmed the presence of the carrier and allowed him to record a raw I/Q file, which he could then supply to Inspectrum to analyse the packet structure. He found it to be a simple on-off keying scheme, with bits expressed through differing pulse widths. He was then able to create a Gnu Radio project to read and decode them in real time.
Emulating the transmitter was then a fairly straightforward process of generating a 350MHz clock using the on-board PLL and gating it with his generated data stream to provide modulation. The result was able to control his fan with a short wire antenna, indeed he was worried that it might also be doing so for other similar fans in his apartment complex. You can take a look at his source code on GitHub if you would like to try something similar.
It’s worth pointing out that a transmitter like this will radiate a significant amount of harmonics at multiples of its base frequency, and thus without a filter on its output is likely to cause interference. It will also be breaking all the rules set out by whoever the spectrum regulator is where you live, despite its low power. However it’s an interesting project to read, with its reverse engineering and slightly novel use of an FPGA.
A few years ago, [Artem] learned about ways to focus sound in an issue of Popular Mechanics. If sound can be focused, he reasoned, it could be focused onto a plane of microphones. Get enough microphones, and you have a ‘sound camera’, with each microphone a single pixel.
Movies and TV shows about comic books are now the height of culture, so a device using an array of microphones to produce an image isn’t an interesting demonstration of FFT, signal processing, and high-speed electronic design. It’s a Daredevil camera, and it’s one of the greatest builds we’ve ever seen.
[Artem]’s build log isn’t a step-by-step process on how to make a sound camera. Instead, he went through the entire process of building this array of microphones, and like all amazing builds the first step never works. The first prototype was based on a flatbed scanner camera, simply a flatbed scanner in a lightproof box with a pinhole. The idea was, by scanning a microphone back and forth, using the pinhole as a ‘lens’, [Artem] could detect where a sound was coming from. He pulled out his scanner, a signal generator, and ran the experiment. It didn’t work. The box was not soundproof, the inner chamber should have been anechoic, and even if it worked, this camera would only be able to produce an image or two a minute.
The idea sat in the shelf of [Artem]’s mind for a while, and along the way he learned about FFT and how the gigantic Duga over the horizon radar actually worked. Math was the answer, and by using FFT to transform a microphones signals from up-and-down to buckets of frequency and intensity, he could build this camera.
That was the theory, anyway. Practicality has a way of getting in the way, and to build this gigantic sound camera he would need dozens of microphones, dozens of amplifiers, and a controller with enough analog pins, DACs, and processing power to make sense of all of this.
This complexity collapsed when [Artem] realized there was an off-the-shelf part that was a perfect microphone camera pixel. MEMS microphones, like the kind found in smartphones, take analog sound and turn it into a digital signal. Feed this into a fast enough microcontroller, and you can perform FFT on the signal and repeat the same process on the next pixel. This was the answer, and the only thing left to do was to build a board with an array of microphones.
[Artem]’s camera microphone is constructed out of several modules, each of them consisting of an 8×8 array of MEMS microphones, controlled via FPGA. These individual modules can be chained together, and the ‘big build’ is a 32×32 array. After a few problems with manufacturing, the board actually worked. He was recording 64 channels of audio from a single panel. Turning on the FFT visualization and pointing it at a speaker revealed that yes, he had indeed made a sound camera.
The result is a terribly crude movie with blobs of color, but that’s the reality of a camera that only has 32×32 resolution. Right now the sound camera works, the images are crude, and [Artem] has a few ideas of where to go next. A cheap PC is fast enough to record and process all the data, but now it’s an issue of bandwidth; 30 sounds per second is a total of 64 Mbps of data. That’s doable, but it would need another FPGA implementation.
Is this sonic vision? Yes, technically the board works. No, in that the project is stalled, and it’s expensive by any electronic hobbyist standards. Still, it’s one of the best to grace our front page.
It always surprises us that magnetic levitation seems to have two main purposes: trains and toys. It is reasonably inexpensive to get floating Bluetooth speakers, globes, or just floating platforms for display. The idea is reasonably simple, especially if you only care about levitation in two dimensions. You let an electromagnet pull the levitating object (which is, of course, ferrous). A sensor detects when the object is at a certain height and shuts off the magnet. The object falls, which turns the magnet back on, repeating the process. If you do it right, the object will reach equilibrium and hover near the sensor.
Some students at Cornell University decided to implement the control loop to produce levitation using an Altera FPGA. An inductive sensor determined the position of an iron ball. The device uses a standard proportional integral derivative (PID) control loop. The control loop and PWM generation occur in the FPGA hardware. You can see a video of their result, below.
The semester is wrapping up at Cornell, and that means it’s time for the final projects from [Bruce Land]’s lab. Every year we see some very cool projects, and this year is no exception. For their project, [Andre] and [Scott] implemented the audio processing unit (APU) of the Nintendo Entertainment System (NES). This is the classic chiptune sound that regaled a generation with 8-bit sounds that aren’t really eight bits, with the help of a 6502 CPU that isn’t really a 6502 CPU.
Unlike the contemporaneous MOS 6581 SID, which is basically an analog synthesizer on a chip, the APU in the NES is extraordinarily spartan. There are two pulse wave channels, a triangle wave channel, a random noise channel, and the very rarely used delta modulation channel (DMC) used to play very low quality audio samples. This is a re-implementation of the NES APU for a university lab; it is very understandable that [Andre] and [Scott] didn’t implement the rarely used DMC.
Everything about the circuitry of the NES is well documented, so [Andre] and [Scott] had a great wiki for their research. At the highest level, the APU runs on a 894kHz clock and controls three channels through dedicated registers. These outputs are fed through a mixer, which the guys scaled and combined into a 16-bit output played through a Wolfson WM8731 audio codec.
After implementing the NES APU, [Andre] and [Scott] added an SD card reader that can read the Nintendo Sound Format – the standard distribution format for NES chiptunes – and emulated a 6502 to control the registers. The result is a relatively simple device that plays NES chiptunes with amazing accuracy. The sound files on the project report sound like the real thing, but this is entirely emulated on modern hardware.
Usually, when you think of driving a VGA–in software or hardware–you think of using a frame buffer. The frame buffer is usually dual port RAM. One hardware or software process fills in the RAM and another process pulls the data out at the right rate and sends it to the VGA display (usually through a digital to analog converter).
[Connor Archard] and [Noah Levy] wanted to do some music processing with a DE2-115 FPGA board. To drive the VGA display, they took a novel approach. Instead of a frame buffer, they use the FPGA to compute each pixel’s data in real-time.