Black MIDI: There Is No Denser Music

November 12, 2015 by Elliot Williams 73 Comments

Imagine if you played all the keys on a piano at once. What would it sound like? Now imagine that you’d like to transcribe that music. What would it look like? So many notes that you could hardly see the paper underneath.

Which is why the people making such “impossible music” are calling themselves the Black MIDI Crew: if you wrote the music down, it’d look like a big black blob. Or at least, that’s the joke. Amazingly, though, it doesn’t sound like a big mess. Check out “Pi, The Song With 3.1415 Million Notes” below the break to see what we mean.

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Getting Started With GNU Radio

November 11, 2015 by Al Williams 25 Comments

Software Defined Radio (SDR)–the ability to process radio signals using software instead of electronics–is undeniably fascinating. However, there is a big gap from being able to use off-the-shelf SDR software and writing your own. After all, SDRs require lots of digital signal processing (DSP) at high speeds.

Not many people could build a modern PC from scratch, but nearly anyone can get a motherboard, some I/O cards, a power supply, and a case and put together a custom system. That’s the idea behind GNU Radio and SDR. GNU Radio provides a wealth of Python functions that you can use to create sophisticated SDR application (or, indeed, any DSP application).

If Python is still not up your alley (or even if it is), there’s an even easier way to use GNU Radio: The GNU Radio Companion (GRC). This is a mostly graphical approach, allowing you to thread together modules graphically and build simple GUIs to control you new radio.

Even though you usually think of GRC as being about radios, it is actually a good framework for building any kind of DSP application, and that’s what I’ll show you in the video below. GRC has a signal generator block and interfaces to your sound card. It even has the ability to read and write data to the file system, so you can use it to do many DSP applications or simulations with no additional hardware.

UPDATE: Don’t miss the follow-up post that uses SDRPlay to build a GNU Radio based receiver.

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What Do Bertlmann’s Socks Mean To The Nature Of Reality?

November 11, 2015 by Will Sweatman 41 Comments

One can be reasonably certain that when the title of an article includes the phrase “The Nature of Reality”, thought provoking words must surely lie ahead. But when that same title seems to inquire about a gentleman’s socks, coupled with an image of said gentleman’s socks which happen to be mismatched and reflect very loud colors , one might be moved in a direction which suggests the article is not of a serious nature. Perhaps even some sort of parody.

It is my hope that you will be pleasantly surprised with the subtle genius of Irish physicist [John Bell] and his use of socks, washing machines, and a little math to show how we can test one of quantum physic’s most fundamental properties. A property that does indeed reside in the very nature of the reality we are a part of. Few people can say they understand the Bell Inequality down to its most fundamental level. Give me a little of your time, and you will be counted among these few.

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Drones Are Getting A Lot Smarter

November 11, 2015 by James Hobson 49 Comments

[DJI], everyone’s favorite — but very expensive — drone company just announced the Manifold — an extremely capable high performance embedded computer for the future of aerial platforms. And guess what? It runs Ubuntu.

The unit features a quad-core ARM Cortex A-15 processor with an NVIDIA Keplar-based GPU and runs Canonical’s Ubuntu OS with support for CUDA, OpenCV and ROS. The best part is it is compatible with third-party sensors allowing developers to really expand a drone’s toolkit. The benefit of having such a powerful computer on board means you can collect and analyze data in one shot, rather than relaying the raw output down to your control hub.

And because of the added processing power and the zippy GPU, drones using this device will have new artificial intelligence applications available, like machine-learning and computer vision — Yeah, drones are going to be able to recognize and track people; it’s only a matter of time.

We wonder what this will mean for FAA regulations…

Nvidia Brings Computer Vision And Deep Learning To The Embedded World

November 10, 2015 by Brian Benchoff 101 Comments

Today, Nvidia announced their latest platform for advanced technology in autonomous machines. They’re calling it the Jetson TX1, and it puts modern GPU hardware in a small and power efficient module. Why would anyone want GPUs in an embedded format? It’s not about frames per second; instead, Nvidia is focusing on high performance computing tasks – specifically computer vision and classification – in a platform that uses under 10 Watts.

For the last several years, tiny credit card sized ARM computers have flooded the market. While these Raspberry Pis, BeagleBones, and router-based dev boards are great for running Linux, they’re not exactly very powerful. x86 boards also exist, but again, these are lowly Atoms and other Intel embedded processors. These aren’t the boards you want for computationally heavy tasks. There simply aren’t many options out there for high performance computing on low-power hardware.

Tiny ARM computers the size of a credit card have served us all well for general computing tasks, and this leads to the obvious question – what is the purpose of putting so much horsepower on such a small board. The answer, at least according to Nvidia, is drones, autonomous vehicles, and image classification.

Image classification is one of the most computationally intense tasks out there, but for autonomous robots, there’s no other way to tell the difference between a cyclist and a mailbox. To do this on an embedded platform, you either need to bring a powerful general purpose CPU that sucks down 60 or so Watts, or build a smaller, more efficient GPU-based solution that sips a meager 10 Watts.

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Your Unhashable Fingerprints Secure Nothing

November 10, 2015 by Elliot Williams 95 Comments

Passwords are crap. Nobody picks good ones, when they do they re-use them across sites, and if you use even a trustworthy password manager, they’ll get hacked too. But you know what’s worse than a password? A fingerprint. Fingerprints have enough problems with them that they should never be used anywhere a password would be.

Passwords are supposed to be secret, like the name of your childhood pet. In contrast, you carry your fingers around with you out in the open nearly everywhere you go. Passwords also need to be revocable. In the case that your password does get revealed, it’s great to be able to simply pick another one. You don’t want to have to revoke your fingers. Finally, and this is the kicker, you want your password to be hashable, in order to protect the password database itself from theft.

In the rest of the article, I’ll make each of these three cases, and hopefully convince you that using fingerprints in place of a password is even more broken than using a password in the first place. (You listening Apple and Google? No, I didn’t think you were.)

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The Most Powerful DIY Railgun

November 9, 2015 by Brian Benchoff 107 Comments

The US Navy is working on a few railgun projects that will eventually replace the largest guns on the fleet’s cruisers and destroyers. These rail guns will fire a projectile away from the ship at around Mach 7 on a ballistic trajectory to a target one hundred miles away. It’s an even more impressive piece of artillery than a gun with a nuclear warhead, and someday, it will be real.

Until then, we’ll have to settle with [Zebralemur]’s DIY mobile railgun. He built this railgun capable of firing aluminum projectiles through pumpkins, cellphones, and into car doors and blocks of ballistics gelatin.

All rail guns need a place to store energy, and in all cases this is a gigantic bank of capacitors. For this project, [Zebralemur] is using fifty-six, 400 Volt, 6000 microfarad caps. The MSRP for these caps would be about $50,000 total, but somehow – probably a surplus store – [Zebralemur] picked them up for $2,400.

These caps are just the power supply for the rail gun, and aren’t part of the structure of this already large, 250 pound gun. Luckily, with the seats down in [Zebralemur]’s car, they fit in the back of his hatchback.

These caps are charged by a bunch of 9V batteries stuck end to end. When the caps are charged, all the power is dumped into two copper bars in the gun, accelerating the aluminum projectile to speeds fast enough to kill. It’s an incredible build, but something that should not be attempted by anyone. Although this does seem to be the year that all danger seekers are busting out their electromagnetic projection flingers.

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