Millimeter Wave RADAR Tracks Gestures

If we believe science fiction — from Minority Report to Iron Man, to TekWar — the future of computer interfaces belongs to gestures. There are many ways to read gestures, although often they require some sort of glove or IR emitter, which makes them less handy (no pun intended).

Some, like the Leap Motion, have not proved popular for a variety of reasons. Soli (From Google’s Advanced Technology and Projects group) is a gesture sensor that uses millimeter-wave RADAR. The device emits a broad radio beam and then collects information including return time, energy, and frequency shift to gain an understanding about the position and movement of objects in the field. You can see a video about the device, below.

You naturally think of using optical technology to look at hand gestures (the same way humans do). However, RADAR has some advantages. It is insensitive to light and can transmit through plastic materials, for example. The Soli system operates at 60 GHz, with sensors that use Frequency Modulated Continuous Wave (FMCW) and Direct-Sequence Spread Spectrum (DSSS). The inclusion of multiple beamforming antennas means the device has no moving parts.

Clearly, this is cutting-edge gear and not readily available yet. But the good news is that Infineon is slated to bring the sensors to market sometime this year. Planned early applications include a smart watch and a speaker that both respond to gestures using the technology.

Interestingly, the Soli processing stack is supposed to be RADAR agnostic. We haven’t investigated it, but we wonder if you could use the stack to process other kinds of sensor input that might be more hacker friendly? Barring that, we’d love to see what our community could come up with for solving the same problem.

We’ve seen Raspberry Pi daughter-boards (ok, hats) that recognize gestures used to control TVs. We’ve even built some crude gesture sensing using SONAR, if that gives you any ideas. Are you planning on using Soli? Or rolling your own super gesture sensor? Let us know and document your project for everyone over on Hackaday.io.

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Google Scrubs Brillo, Reveals Android Things

Another week goes by and another new IoT platform surfaces. Google has announced Android Things, a build of the mobile operating system designed for smart devices rather than the latest slab of mobile eye-candy. The idea is that the same Android tools, framework and APIs that will already be familiar to app developers can be used seamlessly on IoT Things as well as in the user’s palm.

Of course, if this is sounding familiar, it’s because you may have heard something of it before. Last year they announced their Project Brillo IoT platform, and this appears to be the fruit of those efforts.

So you may well be asking: what’s in it for us? Is this just another commercial IoT platform with an eye-watering barrier to entry somewhere, or can we join the fun? It turns out the news here is good, because as the project’s web site reveals, there is support for a variety of Intel, NXP, and Raspberry Pi development boards. If you have a Raspberry Pi 3 on your bench somewhere then getting started is as simple as flashing a disk image.

The Things team have produced a set of demonstration software in a GitHub repository for developers to get their teeth into. Never one to miss an opportunity, the British Raspberry Pi hardware developer Pimoroni has released an Android Things HAT laden with sensors and displays for it to run on.

The IoT-platform market feels rather crowded at times, but it is inevitable that Android Things will gain significant traction because of its tight connections with the rest of the Android world, and its backing by Google. From this OS will no doubt come a rash of devices that will become ubiquitous, and because of its low barrier to entry there is every chance that one or two of them could come from one of you. Good luck!

Interesting Switch Autopsy

We put a lot of trust into some amazingly cheap components, sometimes that trust is very undeserved. Long gone are the days when every electronic component was a beautifully constructed precision lab instrument.  As [Rupert Hirst] shows, this can be a hard lesson to learn for even the biggest companies.

[Rupert]’s Nexus 5 was suffering from a well known reboot issue. He traced it to the phone’s power switch. It was always shorting to ground, even though it clicked like it was supposed to.

He desoldered the switch and pried the delicate sheet metal casing apart. Inside were four components. A soft membrane with a hard nub on the bottom, presumably engineered to give the switch that quality feeling. Next were two metal buckles that produced the click and made contact with the circuit board, which is the final component.

He noticed something odd and  busted out his USB microscope. The company had placed a blob of solder on the bottom buckle. We think this is because steel on copper contact would lead to premature failure of the substrate, especially with the high impact involved during each switching event.

The fault lay in the imprecise placement of the solder blob. If it had been perfectly in the middle, and likely many phones that never showed the issue had it there, the issue would have never shown up. Since it was off-center, the impact of each switching event slowly deposited thin layers of solder onto the copper and fiberglass. Finally it built up enough to completely short the switch.

Interestingly, this exact problem shows up across different phone manufacturers, somewhere there’s a switch company with a killer sales team out there.

Google’s New OS Will Run on Your Raspberry Pi

According to reports from Android Police and ZDNet, you may soon have a new operating system from Google to run on your Raspberry Pi. Details are still extremely sparse, the only description on the GitHub page is “Pink + Purple == Fuchsia (a new Operating System)”. But, here’s what we do know:

The new OS, called Fuchsia, will be based on Magenta, which is in turn built on LittleKernel. That means that, surprisingly, Google will not be using a Linux kernel for the new OS but something more like an embedded RTOS. Although Google is targeting embedded systems, the possibility of being able to run it on a desktop has been mentioned, so it may not be too minimalistic.

Google’s Travis Geiselbrecht has named the Raspberry Pi 3 specifically as one system it will run on, and said that it’ll be available soon. But, it seems Google is aiming to make it run on a variety of ARM devices (both 32 bit and 64 bit), as well as 64 bit PCs. This is a direct effort to compete against other commercial embedded operating systems that are currently available, and especially on IoT devices.

If you’re eager to see what this is all about, you can follow Google’s quick start recipes and see what you can come up with, although details are still sketchy enough that we’re just going to wait a bit.

Google Unveils Their Experimental Plan For Wireless Broadband Service

Two years ago, the FCC, with interested parties in Microsoft, Google, and many startups, created the Citizens Band Radio Service (CBRS), a rule that would open up the 3550-3650 MHz band  to anyone, or any company, to create their own wireless backbone between WiFi access points. It is the wireless solution to the last-mile problem, and last year the FCC enthusiastically endorsed the creation of the CBRS.

In a recently released FCC filing, Google has announced their experimental protocol for testing the new CBRS. This isn’t fast Internet to a lamp pole on the corner of the street yet, but it lays the groundwork for how the CBRS will function, and how well it will perform.

Google will be testing the propagation and interference of transmissions in the 3.5 GHz band in places around the US. Most of the Bay Area will be covered in the tests, as well as Boulder, CO, Kansas City, Omaha, Raleigh, NC, Provo, UT, and Reston, VA. Tests will consist of a simple CW tone broadcast in the 3.5 GHz band.

The 3.5 GHz band is already allocated to shipborne navigation and military radar systems, posing an obvious problem to any wireless broadband system using this spectrum. To this end, the FCC is proposing a novel solution to the problem of coexistence between the CBRS and the military. Instead of simply banning transmissions in the spectrum, FCC Chairman Wheeler proposes, “computer systems can act like spectrum traffic cops.” A computer is able to direct the wireless traffic much more effectively than a blanket ban, and will allow better utilization of limited spectrum.

Google’s FCC filing is just for testing propagation and interference, and we have yet to hear anything about how a network built on 3.5 GHz spectrum will be laid out. One thing is for certain, though: you will not have a 3.5 GHz USB networking dongle for the same reason you don’t have a Google Fiber input on your desktop.

Red bricks: Alphabet to turn off Revolv’s lights

Revolv, the bright red smart home hub famous for its abundance of radio modules, has finally been declared dead by its founders. After a series of acquisitions, Google’s parent company Alphabet has gained control over Revolv’s cloud service – and they are shutting it down.

Customers who bought into Revolv’s vision of a truly connected and automated smart home hub featuring 7 different physical radio modules to connect all their devices will soon become owners of significantly less useful, red bricks due to the complete shutdown of the service on May 15, 2016.
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Ask Hackaday: Google Beat Go; Bellwether or Hype?

We wake up this morning to the news that Google’s deep-search neural network project called AlphaGo has beaten the second ranked world Go master (who happens to be a human being). This is the first of five matches between the two adversaries that will play out this week.

On one hand, this is a sign of maturing technology. It has been almost twenty years since Deep Blue beat Gary Kasparov, the reigning chess world champion at the time. Although there are still four games to play against Lee Sedol, it was recently reported that AlphaGo beat European Go champion Fan Hui in five games straight. Go is generally considered a more difficult game for machine minds to play than chess. This is because Go has a much larger pool of possible moves at any given time.

Does This Matter?

Okay, the news part of this event has been covered: machine beats man. Does it matter? Will this affect your life and how? We want to hear what you think in the comments below. But I’m going to keep going with some of my thoughts on the topic.

You're still better at Ms. Pacman [Source: DeepMind paper in Nature]
You’re still better at Ms. Pacman [Source: DeepMind paper in Nature]
Let’s look first at what AlphaGo did to win. At its core, the game of Go is won by figuring out where your opponent will likely make a low-percentage move and then capitalizing on that choice. Know Your Enemy has been a tenet of strategy for a few millennia now and it holds true in the digital age. In addition to the rules of the game, AlphaGo was fed a healthy diet of 30 million positions from expert games. This builds behavior recognition into the system. Not just what moves can be made, but what moves are most likely to be made.

DeepMind, the company behind AlphaGo which was acquired by Google in 2014, has published a paper in Nature about their approach. They were even nice enough to let us read without dealing with a paywall. The secret sauce is the learning process which at its core tries to mimic how living entities learn: observe repetitively while assigning values to outcomes. This is key as it leads past “intellect”, to “intelligence” (the “I” in AI that everyone seems to be waiting for). But this is a bastardized version of “intelligence”. AlphaGo is able to recognize and predict behavior, then make choices that lead to a desired outcome. This is more than intellect as it does value the purpose of an opponent’s decisions. But it falls short of intelligence as AlphaGo doesn’t consciously understand the purpose it has detected. In my mind this is exactly what we need. Truly successful machine learning will be able to make sense out of sometimes irrational input.

The paper from Nature doesn’t go into details about Go, but it explains the approach of the learning system applied to Atari 2600. The algorithm was given 210×160 color video at 60Hz as an input and then told it could use a joystick with one button. From there it taught itself to play 49 games. It was not told the purpose or the rules of the games, but it was given examples of scores from human performance and rewarded for its own quality performances. The chart above shows that it learned to play 29 of them at or above human skill levels.

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