We know you love a good biohack as much as we do, so we thought you would like [Tony’s] brainwave-controlled RC truck. Instead of building his own electroencephalogram (EEG), he thought he would use NeuroSky’s MindWave. EEGs are pretty complex, multi-frequency waves that require some fairly sophisticated circuitry and even more sophisticated signal processing to interpret. So, [Tony] thought it would be nice to off-load a bit of that heavy-lifting, and luckily for him, the MindWave headset is fairly hacker-friendly.
EEGs are a very active area of research, so some of the finer details of the signal are still being debated. However, It appears that attention can be quantified by measuring alpha waves which are EEG content between 8-10 Hz. And it seems as though eye blinks can be picked from the EEG as well. Conveniently, the MindWave exports these energy levels to an accompanying smartphone application which [Tony] then links to his Arduino over Bluetooth using the ever-so-popular HC-05 module.
To control the car, he utilized the existing remote control instead of making his own. Like most people, [Tony] thought about hooking up the Arduino pins to the buttons on the remote control, thereby bypassing the physical buttons, but he noticed the buttons were a bit smaller than he was comfortable soldering to and he didn’t want to risk damaging the circuit board. [Tony’s] RC truck has a pistol grip transmitter, which inspired a slightly different approach. He mounted the servo onto the controller’s wheel mechanism, allowing him to control the direction of the truck by rotating the wheel using the servo. He then fashioned another servo onto the transmitter such that the servo could depress the throttle when it rotates. We thought that was a pretty nifty workaround.
Cool project, [Tony]! We’ve seen some cool EEG Hackaday Prize entries before. Maybe this could be the next big one.
Continue reading “Self-Driving Or Mind Control? Which Do You Prefer?”
If you’ve ever built a crystal radio, there’s something magical about being able to pull voices and music from far away out of thin air. If you haven’t built one, maybe you should while there’s still something on the AM band. Of course, nowadays the equivalent might be an SDR. But barring a computer solution, there are not many ways to convert radio waves into intelligence. From a pocket radio to advanced RADAR to a satellite in orbit, receiving a radio wave is accomplished in pretty much the same way.
There are, however, many ways to modulate and demodulate that radio wave. Of course, an AM radio works differently than an FM radio. A satellite data downlink works differently, too. But the process of capturing the radio wave from the air and getting them into a form ready for further processing hasn’t changed much over the years.
In this article, I’ll talk about the most common radio receiver architectures you may have seen in years past, and next week I’ll talk about modern architectures. Either way, understanding receiver architectures will help you design new radios or troubleshoot them.
Continue reading “How Early Radio Receivers Worked”
Making upgrades to a popular product line might sound like a good idea, but adding bigger/better/faster parts to an existing product can cause unforeseen problems. For example, dropping a more powerful engine in an existing car platform might seem to work at first until people start reporting that the increased torque is bending the frame. In the Raspberry Pi world, it seems that the “upgraded engine” in the Pi 4 is causing the WiFi to stop working under specific circumstances.
[Enrico Zini] noticed this issue and attempted to reproduce exactly what was causing the WiFi to drop out, and after testing various Pi 4 boards, power supplies, operating system version, and a plethora of other variables, the cause was isolated to the screen resolution. Apparently at the 2560×1440 setting using HDMI, the WiFi drops out. While you could think that an SoC might not be able to handle a high resolution, WiFi, and everything else this tiny computer has to do at once. But the actual cause seems to be a little more interesting than a simple system resources issue.
[Mike Walters] on a Twitter post about this issue probed around with a HackRF and discovered a radio frequency issue. It turns out that at this screen resolution, the Pi 4 emits some RF noise which is exactly in the range of WiFi channel 1. It seems that the Pi 4 is acting as a WiFi jammer on itself.
This story is pretty new, so hopefully the Raspberry Pi Foundation is aware of the issue and working on a correction. For now, though, it might be best to run a slightly lower resolution if you’re encountering this problem.
There was a time — not long ago — when radio and even wired communications depended solely upon Morse code with OOK (on off keying). Modulating RF signals led to practical commercial radio stations and even modern cell phones. Although there are many ways to modulate an RF carrier with voice AM or amplitude modulation is the oldest method. A recent video from [W2AEW] shows how this works and also how AM can be made more efficient by stripping the carrier and one sideband using SSB or single sideband modulation. You can see the video, below.
As is typical of a [W2AEW] video, there’s more than just theory. An Icom transmitter provides signals in the 40 meter band to demonstrate the real world case. There’s discussion about how to measure peak envelope power (PEP) and comparison to average power and other measurements, as well.
Continue reading “Understanding Modulated RF With [W2AEW]”
If you watch old science fiction or military movies — or if you were alive back in the 1960s — you probably know the cliche for a radar antenna is a spinning dish. Although the very first radar antennas were made from wire, as radar sets moved higher in frequency, antennas got smaller and rotating them meant you could “look” in different directions. When most people got their TV with an antenna, rotating those were pretty common, too. But these days you don’t see many moving antennas. Why? Because antennas these days move electrically rather than physically using multiple antennas in a phased array. These electronically scanned phased array antennas are the subject of Hunter Scott’s talk at 2018’s Supercon. Didn’t make it? No problem, you can watch the video below.
While this seems like new technology, it actually dates back to 1905. Karl Braun fed the output of a transmitter to three monopoles set up as a triangle. One antenna had a 90 degree phase shift. The two in-phase antennas caused a stronger signal in one direction, while the out-of-phase antenna canceled most of the signal and the resulting aggregate was a unidirectional beam. By changing the antenna fed with the delay, the beam could rotate in three 120 degree steps.
Today phased arrays are in all sorts of radio equipment from broadcast radio transmitters to WiFi routers and 5G phones. The technique even has uses in optics and acoustics.
Continue reading “No Moving Parts: Phased Array Antennas Move While Standing Still”
If you’ve never been a patient at a sleep laboratory, monitoring a person as they sleep is an involved process of wires, sensors, and discomfort. Seeking a better method, MIT researchers — led by [Dina Katabi] and in collaboration with Massachusetts General Hospital — have developed a device that can non-invasively identify the stages of sleep in a patient.
Approximately the size of a laptop and mounted on a wall near the patient, the device measures the minuscule changes in reflected low-power RF signals. The wireless signals are analyzed by a deep neural-network AI and predicts the various sleep stages — light, deep, and REM sleep — of the patient, negating the task of manually combing through the data. Despite the sensitivity of the device, it is able to filter out irrelevant motions and interference, focusing on the breathing and pulse of the patient.
What’s novel here isn’t so much the hardware as it is the processing methodology. The researchers use both convolutional and recurrent neural networks along with what they call an adversarial training regime:
Our training regime involves 3 players: the feature encoder (CNN-RNN), the sleep stage predictor, and the source discriminator. The encoder plays a cooperative game with the predictor to predict sleep stages, and a minimax game against the source discriminator. Our source discriminator deviates from the standard domain-adversarial discriminator in that it takes as input also the predicted distribution of sleep stages in addition to the encoded features. This dependence facilitates accounting for inherent correlations between stages and individuals, which cannot be removed without degrading the performance of the predictive task.
Anyone out there want to give this one a try at home? We’d love to see a HackRF and GNU Radio used to record RF data. The researchers compare the RF to WiFi so repurposing a 2.4 GHz radio to send out repeating uniformed transmissions is a good place to start. Dump it into TensorFlow and report back.
Continue reading “AI Watches You Sleep; Knows When You Dream”
How hard is it to create a synthesizer to generate frequencies between 35 MHz to 4.4 GHz? [OpenTechLab] noticed a rash of boards based on the ADF4351 that could do just that priced at under $30. He decided to get one and try it out and you can find his video results below.
At that price point, he didn’t expect much from it, but he did want to experiment with it to see if he could use it as an inexpensive piece of test gear. The video is quite comprehensive (and weighs in at nearly an hour and a half). It covers not just the device from a software and output perspective but also talks about the theory behind these devices. [OpenTechLab] even sniffed the USB connection to find the protocol used to talk to the device. He wasn’t overly impressed with the performance of the board but was happy enough with the results at the price and he plans to make some projects with it.
Continue reading “4.4 GHz Frequency Synthesis Made Easy”