Phased array antenna systems are at the cusp of ubiquity. We now see Multiple-Input Multiple-Output (MIMO) antenna systems on WiFi routers. Soon phased array weather radar systems will help to predict the weather and keep air travel safe, and phased array base stations will be the backbone of 5G which is the next generation of wireless data communication. But what is a phased array antenna system? How do they work? With the help of 1024 LEDs we’ll show you.
So far, humans have had the edge in the ability to identify objects by touch. but not for long. Using Google’s Project Soli, a miniature radar that detects the subtlest of gesture inputs, the [St. Andrews Computer Human Interaction group (SACHI)] at the University of St. Andrews have developed a new platform, named RadarCat, that uses the chip to identify materials, as if by touch.
Realizing that different materials return unique radar signals to the chip, the [SACHI] team combined it with their recognition software and machine learning processes that enables RadarCat to identify a range of materials with accuracy in real time! It can also display additional information about the object, such as nutritional information in the case of food, or product information for consumer electronics. The video displays how RadarCat has already learned an impressive range of materials, and even specific body parts. Can Skynet be far behind?
The folks at [Design I/O] have come up with a way for you to play the world’s tiniest violin by rubbing your fingers together and actually have it play a violin sound. For those who don’t know, when you want to express mock sympathy for someone’s complaints you can rub your thumb and index finger together and say “You hear that? It’s the world’s smallest violin and it’s playing just for you”, except that now they can actually hear the violin, while your gestures control the volume and playback.
[Design I/O] combined a few technologies to accomplish this. The first is Google’s Project Soli, a tiny radar on a chip. Project Soli’s goal is to do away with physical controls by using a miniature radar for doing touchless gesture interactions. Sliding your thumb across the side of your outstretched index finger, for example, can be interpreted as moving a slider to change the numerical value of something, perhaps turning up the air conditioner in your car. Check out Google’s cool demo video of their radar and gestures below.
Project Soli’s radar is the input side for this other intriguing technology: the Wekinator, a free open source machine learning software intended for artists and musicians. The examples on their website paint an exciting picture. You give Wekinator inputs and outputs and then tell it to train its model.
The output side in this case is violin music. The input is whatever the radar detects. Wekinator does the heavy lifting for you, just give it input like radar monitored finger movements, and it’ll learn your chosen gestures and perform the appropriately trained output.
[Design I/O] is likely doing more than just using Wekinator’s front end as they’re also using openFrameworks, an open source C++ toolkit. Also interesting with Wekinator is their use of the Open Sound Control (OSC) protocol for communicating over the network to get its inputs and outputs. You can see [Design I/O]’s end result demonstrated in the video below.
Last week we saw a lot of interest in faux visualization of wireless signals. It used a tablet as an interface device to show you what the wireless signals around you looked like and was kind of impressive if you squinted your eyes and didn’t think too much about it. But for me it was disappointing because I know it is actually possible to see what radio waves look like. In this post I will show you how to actually do it by modifying a coffee can radar which you can build at home.
The late great Prof. David Staelin from MIT once told me once that, ‘if you make a new instrument and point it at nature you will learn something new.’ Of all the things I’ve pointed Coffee Can Radars at, one of the most interesting thus far is the direct measurement and visualization of 2.4 GHz radiation which is in use in our WiFi, cordless phones (if you still have one) and many other consumer goods. There is no need to fool yourself with fake visualizations when you can do it for real.
We’ve all likely watched an episode of “Star Trek” and admired the level of integration on the sick bay diagnostic bed. With its suite of wireless sensors and flat panel display, even the 1960s imagining of the future blows away the decidedly wired experience of a modern day ICU stay. But we may be getting closer to [Dr. McCoy]’s experience with this radar-based respiration detector.
[Øyvind]’s build, which takes the origin of the term “breadboard” to heart, is based on a not-inexpensive Xethru module, which appears to be purpose-built for detecting respiration. The extra-thick PC board seems to house the waveguides internally, which is a neat trick but might limit how the module can be deployed. The module requires both a USB interface and level shifter to interface the 2.8V levels of the module to the 5V Arduino Uno. In the video below, [Øyvind]’s prototype simply lights an RGB LED in response to the chest movement it detects, but there’s plenty of potential for development here. We’ve seen a laser-based baby breathing monitor before; perhaps this systems could be used to the same end without the risk of blinding your tyke. Or perhaps better diagnostics for sleep apnea patients than an intrusive night in a sleep study lab.
Clocking in at $750USD for the sensor board and USB interface, this build is not exactly for the faint of heart or the light of wallet. But as an off-the-shelf solution to a specific need that also has a fair bit of hacking potential, it may be just the thing for someone. Of course if radar is your thing, you might rather go big and build something that can see through walls.
How do you fix a shorted cable ? Not just any cable. An underground, 3-phase, 230kV, 800 amp per phase, 10 mile long one, carrying power from a power station to a distribution centre. It costs $13,000 per hour in downtime, counting 1989 money, and takes 8 months to fix. That’s almost $75 million. The Los Angeles Department of Water and Power did this fix about 26 years ago on the cable going from the Scattergood Steam Plant in El Segundo to a distribution center near Bundy and S.M. Blvd. [Jamie Zawinski] posted details on his blog in 2002. [Jamie] a.k.a [jwz] may be familiar to many as one of the founders of Netscape and Mozilla.
To begin with, you need Liquid Nitrogen. Lots of it. As in truckloads. The cable is 16 inch diameter co-axial, filled with 100,000 gallons of oil dielectric pressurised to 200 psi. You can’t drain out all the oil for lots of very good reasons – time and cost being on top of the list. That’s where the LN2 comes in. They dig holes on both sides (20-30 feet each way) of the fault, wrap the pipe with giant blankets filled with all kind of tubes and wires, feed LN2 through the tubes, and *freeze* the oil. With the frozen oil acting as a plug, the faulty section is cut open, drained, the bad stuff removed, replaced, welded back together, topped off, and the plugs are thawed. To make sure the frozen plugs don’t blow out, the oil pressure is reduced to 80 psi during the repair process. They can’t lower it any further, again due to several compelling reasons. The cable was laid in 1972 and was designed to have a MTBF of 60 years.
Let’s start off with proof. Below is an animation of a measurement of airplanes and meteors I made using a radar system that I built with a few simple easily available pieces of hardware: two $8 RTL software defined radio dongles that I bought on eBay, and two log-periodic antennas. And get this, the radar system you’re going to build works by listening for existing transmissions that bounce off the targets being measured!
I wrote about this in a very brief blog posting a few years ago. It was mainly intended as a zany little side story for our radio telescope blog, but it ended up raising a lot of interest. Because this has been a topic that keeps attracting inquiries, I’m going to explain how I did the experiment in more detail.
It will take a few posts to show how to build a radar capable of performing these types of measurements. This first part is the overview. In later postings I will go through more detailed block diagrams of the different parts of a passive radar system, provide example data, and give some Python scripts that can be used to perform passive radar signal processing. I’ll also go through strategies to determine that everything is working as expected. All of this may sound like a lot of effort, but don’t worry, making a passive radar isn’t too complicated.
Let’s get started!