It’s not hard to detect meteors: go outside on a clear night in a dark place and you’re bound to see one eventually. But visible light detection is limiting, and knowing that meteors leave a trail of ions means radio detection is possible. That’s what’s behind this attempt to map meteor trails using broadcast signals, which so far hasn’t yielded great results.
The fact that meteor trails reflect radio signals is well-known; hams use “meteor bounce” to make long-distance contacts all the time. And using commercial FM broadcast signals to map meteor activity isn’t new, either — we’ve covered the “forward scattering” technique before. The technique requires tuning into a frequency used by a distant station but not a local one and waiting for a passing meteor to bounce the distant signal back to your SDR dongle. Capturing the waterfall display for later analysis should show characteristic patterns and give you an idea of where and when the meteor passed.
[Dave Venne] is an amateur astronomer who turns his eyes and ears to the heavens just to see what he can find. [Dave]’s problem is that the commercial FM band in the Minneapolis area that he calls home is crowded, to say the least. He hit upon the idea of using the National Weather Service weather radio broadcasts at around 160 MHz as a substitute. Sadly, all he managed to capture were passing airplanes with their characteristic Doppler shift; pretty cool in its own right, but not the desired result.
The comments in the RTL-SDR.com post on [Dave]’s attempt had a few ideas on where this went wrong and how to improve it, including the intriguing idea of using 60-meter ham band propagation beacons. Now it’s Hackaday’s turn: any ideas on how to fix [Dave]’s problem? Sound off in the comments below.
Early and low-cost detection of a Heart Failure is the proposal of [Jean Pierre Le Rouzic] for his entry for the 2017 Hackaday Prize. His device is based on a low-cost Doppler device, like those fetal Doppler devices used to listen an unborn baby heart, feeding a machine learning algorithm that could differentiate between a healthy and an unhealthy heart.
The theory behind it is that a regular, healthy heart tissue has a different acoustic impedance than degenerated tissue. Based on the acoustic impedance, the device would classify the tissue as: normal, degenerated, granulated or fibrous. Each category indicates specific problems mostly in connective tissues.
There are several advantages to have a working device like the one [Rouzic] is working on. To start, it would be possible to use it at home, without the intervention of a doctor or medical staff. It seems to us that would be as easy as using a blood pressure device or a fetal Doppler. It’s also relatively cheap (estimated under 150$) and it needs no gel to work. We covered similar projects that measure different heart signals, like Open Source electrocardiography, but ECG has the downfall that it requires attaching electrodes to the body.
One interesting proposed feature is that what is learn from a single case, is sent to every devices at their next update, so the devices get ‘smarter’ as they are used. Of course, there are a lot of ways for this to go wrong, but it’s a good idea to begin with.
The module in question is a CDM324 24-GHz board that’s currently listing for $12 on Amazon. It’s the K-band cousin of the X-band HB100 used by [Mathieu] in a project we covered a few years back, but thanks to the shorter wavelength the module is much smaller — just an inch square. [Mathieu] discovered that the new module suffered from the same misleading amplifier circuit in the datasheet. After making some adjustments, a two-stage amp was designed and executed on a board that piggybacks on the module with a 3D-printed bracket.
Frequency output is proportional to the velocity of the detected object; the maximum speed for the sensor is only 14.5 mph (22.7 km/h), so don’t expect to be tracking anything too fast. Nevertheless, this could be a handy sensor, and it’s definitely a solid lesson in design. Still, if your tastes run more toward using this module on the 1.25-cm ham band, have a look at this HB100-based 3-cm band radio.
A team at the University of Washington recently developed Allsee, a simple gesture recognition device composed of very few components. Contrary to conventional Doppler modules (like this one) that emit their own RF signal, Allsee uses already existing wireless signals (TV and RFID transmissions) to extract any movement that may occur in front of it.
Allsee’s receiver circuit uses a simple envelope detector to extract the amplitude information to feed it to a microcontroller Analog to Digital Converter (ADC). Each gesture will therefore produce a semi-unique footprint (see picture above). The footprint can be analyzed to launch a dedicated action on your computer/cellphone. The PDF article claims that the team achieved a 97% classification accuracy over a set of eight gestures.
Obviously the main advantage of this system is its low power consumption. A nice demonstration video is embedded after the break, and we’d like to think [Korbi] for tipping us about this story.
After a little poking around he’s able to get it connected to a 12V feed from his bench supply, and to monitor the output with an oscilloscope. He established that it draws about 0.5A in current he built a companion board which uses AA batteries for power, and provides an audio output which can be plugged into his laptop’s audio-in jack. This technique makes reading the device as easy as recording some audio. From there a bit of simple signal processing lets him graph the incoming measurement.
In the video after the break you’ll see his inspection of the hardware. After making his alterations he takes it into the field, measuring several cars, a few birds, and himself jogging.
What if you could add gesture recognition to your computer without making any hardware changes? This research project seeks to use computer microphone and speakers to recognize hand gestures. Audio is played over the speakers, with the input from the microphone processed to detect Doppler shift. In this way it can detect your hand movements (or movement of any object that reflects sound).
The sound output is in a range of 22-80 kHz which is not audible to our ears. It does make us wonder if widespread use of this will drive the pet population crazy, or reroute migration paths of wildlife, but that’s research for another day. The system can even be used while audible sounds are also being played, so you don’t lose the ability to listen to music or watch video.
The screen above shows the raw output of the application. But in the video after the break you can see some possible uses. It works for scrolling pages, double-clicking (or double-tapping as it were), and there’s a function that detects the user walking away from the computer and locks the screen automatically.