The Open, Hackable Electronic Conference Badge

Electronic conference badges have been around for at least a decade now, and they all have the same faults. They’re really only meant to be used for a few days, conference organizers and attendees expect the badge to be cheap, and because of the nature of a conference badge, the code just works, and documentation is sparse.  Surely there’s a better way.

Enter the Hackable Electronic Badge. Ever since Parallax started building electronic conference badges for DEF CON, they’ve gotten a lot of requests to build badges for other conventions. Producing tens of thousands of badges makes Parallax the go-to people for your conference badge needs, but the requests for badges are always constrained by schedules that are too short, price expectations that are too low, and volumes that are unknown.

There’s a market out there for electronic conference badges, and this is Parallax’s solution to a recurring problem. They’re building a badge for all conferences, and a platform that can be (relatively) easily modified while still retaining all its core functionality.

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Let Skynet Become Self-Aware!

Not so long ago, it was hard to fly. Forget actual manned aircraft and pilots licenses; even flying model aircraft required hours of practice, often under the tutelage of a master at a flying field. But along with that training came an education in the rules of safe flight, including flying at a designated airfield and watching out for obstacles.

We accidentally messed that up. We in the drone industry made aircraft super easy to fly — perhaps too easy to fly. Thanks to smart autopilots and GPS, you can open a box, download an app and press “take off”. The copter will dutifully rise into the air and wait there for further instructions — no skill required. And it will do this even if you happen to be in an NFL football stadium in the middle of a big game. Or near an airport. Or in the midst of a forest fire.

The problem is that along with taking training out of the process of flying a drone, we inadvertently also took out the education process of learning about safe and responsible flight. Sure, we drone manufacturers include all sorts of warning and advisories in our instructions manual (which people don’t read) and our apps (which they swipe past), and companies such as DJI and my own 3DR include basic “geofencing” restrictions to try to keep operators below 400 feet and within “visual line of sight”. But it’s not enough.

Every day there are more reports of drone operators getting past these restrictions and flying near jetliners, crashing into stadiums, and interfering with first responders. So far it hasn’t ended in tragedy, but the way things are going it eventually will. And in the meantime, it’s making drones increasingly controversial and even feared. I call this epidemic of (mostly inadvertent) bad behavior “mass jackassery”. As drones go mass market, the odds of people doing dumb things with them reach the singularity of certainty.

We’ve got to do something about this before governments do it for us, with restrictions that catch the many good uses of drones in the crossfire. The reality is that most drone operators who get in trouble aren’t malicious and may not even know that what they’re doing is irresponsible or even illegal. Who can blame them? It’s devilishly hard to understand the patchwork quilt of federal, state and local regulations and guidelines, which change by the day and even the hour based on “airspace deconfliction” rules and FAA alerts written for licensed pilots and air traffic control. Many drone owners don’t even know that such rules exist.

Drones Themselves Should Know Rules of Each Area

Fortunately, they don’t have to. Our drones can be even smarter — smart enough to know where they should and shouldn’t fly. Because modern drones are connected to phones, they’re also connected to the cloud. Every time you open their app, that app can check online to find appropriate rules for flight where you are, right then and there.

Here’s how it works. The app sends four data fields to a cloud service: Who (operator identifier), What (aircraft identifier), Where (GPS and altitude position) and When (either right now or a scheduled time in the case of autonomous missions). The cloud service then returns a “red light” (flight not allowed), a “green light” (flight allowed, with basic restrictions such as a 400 feet altitude ceiling), or “yellow light” (additional restrictions or warnings, which can be explained to the operator in context and at the point of use).

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Right now industry groups such as the Dronecode Foundation, the Small UAV Coalition (I help lead both of them, but this essay just reflects my own personal views) and individual manufacturers such as 3DR and DJI are working on these “safe flight” standards and APIs. Meanwhile, a number of companies such as Airmap and Skyward are building the cloud services to provide the up-to-date third-party data layer that any manufacturer can use. It will start with static no-fly zone data such as proximity to airports, US national parks and other banned airspace such as Washington DC. But it will quickly add dynamic data, too, such as forest fires, public events, and proximity to other aircraft.

(For more on this, you can read a white paper from one of the Dronecode working groups here and higher level description here.)

There’s Always a Catch

Of course, this system isn’t perfect. It’s only as good as the data it uses, which is still pretty patchy worldwide, and the ways that the manufacturers implement those restrictions. Some drone makers may choose to treat any area five miles from an airport as a hard ban and prohibit all flight in that zone, even at the cost of furious customers who had no idea they were five miles from an airport when they bought that toy at Wal-mart (nor do they think it should matter, since it’s just a “toy”). Other manufacturers may choose to make a more graduated restriction for the sake of user friendliness, adding a level of nuance that is not in the FAA regulation. They might ban, say, flight one mile from an airport, but only limit flight beyond that to something like 150ft of altitude (essentially backyard-level flying).

That’s a reasonable first step. But the ultimate safe flight system would go a lot further. It would essentially extend the international air traffic control system to millions of aircraft (there are already a million consumer drones in the air) flown by everything from children to Amazon. The only way to do that is to let the drones regulate themselves (yes, let Skynet become self-aware).

Peer-to-peer Air Traffic Control

There’s a precedent for such peer-to-peer air traffic control: WiFI. Back in the 1980s, the FCC released spectrum in the 2.4 Ghz band for unlicensed use. A decade later, the first 802.11 standards for Wifi were released, which was based on some principles that have application to drones, too.

  1. The airspace used is not otherwise occupied by commercial operators
  2. The potential for harm is low (in the case of WiFi, low transmission power. In the case of drones, low kinetic energy due to the weight restrictions of the “micro” category)
  3. The technology has the capability to self-”deconflict” the airspace by observing what else is using it and picking a channel/path that avoids collisions.

That “open spectrum” sandbox that the FCC created also created a massive new industry around WiFi. It put wireless in the hands of everyone and routed around the previous monopoly owners of the spectrum, cellphone carriers and media companies. The rest was history.

Quadcopter ThumbWe can do the same thing with drones. Let’s create an innovation “sandbox” with de minimus regulatory barriers for small UAVs flying within very constrained environments. The parameters of the sandbox could be almost anything, as long as they’re clear, but it should be kinetic energy and range based (a limit of 2kg and 20m/s at 100m altitude and 1,000m range within visual line of sight would be a good starting point).

As in the case of open spectrum, in relatively low risk applications, such as micro-drones, technology can be allowed to “self-deconflict the airspace” without the need for monopoly exclusions such as exclusive licences or regulatory permits. How? By letting the drones report their position using the same cellphone networks they used to get permission to fly in the first place. The FAA already has a standard for this, called ADS-B, which is based on transponders in each aircraft reporting their position. But those transponders are expensive and unnecessary for small drones, which already know their position and are connected to the cloud. Instead, they can use “virtual ADS-B” to report their position via their cell network connections, and that data can be injected into the same cloud data services they used to check if their flight was safe in the first place.

Once this works, we’ll have a revolution. What WiFi did the telecoms industry, autonomous, cloud-connected drones can do to the aerospace industry. We can occupy the skies, and do it safely. Technology can solve the problems it creates.


About the Author

judge-thumb-AndersonChris Anderson (@Chr1sa) is the CEO of 3D Robotics and founder of DIY Drones. From 2001 through 2012 he was the Editor in Chief of Wired Magazine. Before Wired he was with The Economist for seven years in London, Hong Kong and New York.

The author of the New York Times bestselling books The Long Tail and Free as well as the Makers: The New Industrial Revolution.

His background is in science, starting with studying physics and doing research at Los Alamos and culminating in six years at the two leading scientific journals, Nature and Science.

In his self-described misspent youth [Chris] was a bit player in the DC punk scene and amusingly, a band called REM. You can read more about that here.

Awards include: Editor of the Year by Ad Age (2005). Named to the “Time 100,” the newsmagazine’s list of the 100 most influential people in the world (2007). Loeb Award for Business Book of the Year (2007). Wired named Magazine of the Decade by AdWeek for his tenure (2009). Time Magazine’s Tech 40 — The Most Influential Minds In Technology (2013). Foreign Policy Magazine’s Top 100 Global Thinkers (2013).

FERMIAC: The Computer That Advanced Beyond The Manhattan Project

One of the keys to nuclear fission is sustaining a chain reaction. A slow chain reaction can provide clean power for a city, and a fast one can be used to create a weapon that will obliterate a city. These days, kids can learn about Uranium and Plutonium in high school. But just a few generations ago, the idea of splitting the atom was just a lofty goal for the brightest physicists and mathematicians who gathered at Los Alamos National Laboratory under the Manhattan Project.

Decoding the mysteries of nuclear fission required a great deal of experimentation and calculations. One bright physicist in particular made great strides on both fronts. That man was [Enrico Fermi], one of the fathers of the atomic bomb. Perhaps his greatest contribution to moving the research beyond the Manhattan Project was creating a handheld analog computer to do the math for him. This computational marvel is known as the FERMIAC.

What is Fission?

Nuclear fission occurs when a nucleus is split into fragments, a process that unleashes a great deal of energy.  As a handful of neutrons travel through a reactor pile or other fissionable material, a couple of outcomes are possible. Any one neutron collision might result in fission. This means there will be some number of new neutrons whose paths must be tracked. If fission does not occur, the neutrons may simply scatter about upon collision, which changes their speed and trajectory. Some of the neutrons might be absorbed by the material, and others will simply escape it. All of these possibilities depend on the makeup of the material being bombarded and the speed of the neutron.

Fission Diagram by Michalsmid

Every event that happens to a neutron comprises its genealogical history. If this history is recorded and analyzed, a statistical picture starts to emerge that provides an accurate depiction of the fissility of a given material. [Fermi]’s computer facilitated the creation of such a picture by performing mathematical grunt work of testing different materials. It identified which materials were most likely to sustain a reaction.

Before he left Italy and the looming threat of fascism, [Fermi] led a group of young scientists in Rome called the Via Panisperna boys. This group, which included future Los Alamos physicist [Emilio Segrè], ran many experiments in neutron transport. Their research proved that slow neutrons are much better candidates for fission than fast neutrons.

During these experiments, [Fermi] ran through the periodic table, determined to artificially irradiate every element until he got lucky. He never published anything regarding his methods for calculating the outcomes of neutron collisions. But when he got to Los Alamos, [Fermi] found that [Stanislaw Ulam] had also concluded that the same type of repeated random sampling was the key to building an atomic weapon.

The Monte Carlo Method: Shall We Play a Game?

Monte Carlo method applied to approximating the value of π. by CaitlinJo

[Ulam], a Polish-born mathematician who came to the US in 1935, developed his opinion about random sampling due to an illness. While recuperating from encephalitis he played game after game of solitaire. One day, he wondered at the probability of winning any one hand as laid out and how best to calculate this probability. He believed that if he ran through enough games and kept track of the wins, the data would form a suitable and representative sample for modeling his chances of winning. Almost immediately, [Ulam] began to mentally apply this method to problems in physics, and proposed his ideas (PDF) to physicist and fellow mathematician [John von Neumann].

This top-secret method needed a code name. Another Los Alamos player, [Nick Metropolis] suggested ‘Monte Carlo’ in a nod to games of chance. He knew that [Ulam] had an uncle with a propensity for gambling who would often borrow money from relatives, saying that he just had to go to Monte Carlo. The game was on.

The Tricky Math of Fission

Determination of the elements most suitable for fission required a lot of calculations. Fission itself had already been achieved before the start of the Manhattan Project. But the goal at Los Alamos was a controlled, high-energy type of fission suitable for weaponization. The math of fission is complicated largely because of the sheer number of neutrons that must be tracked in order to determine the likelihood and speed of a chain reaction. There are so many variables involved that the task is monumental for a human mathematician.

Stanislaw-Ulam-FERMIAC
[Stanislaw Ulam] and FERMIAC.

After [Ulam] and [von Neumann] had verified the legitimacy of the Monte Carlo method with regard to the creation of nuclear weaponry, they decided that these types of calculations would be a great job for ENIAC — a very early general purpose computer. This was a more intensive task than the one it was made to do: compute artillery firing tables all day and night. One problem was that the huge, lumbering machine was scheduled to be moved from Philadelphia to the Ballistics Research Lab in Maryland, which meant a long period of downtime.

While the boys at Los Alamos waited for ENIAC to be operational again, [Enrico Fermi] developed the idea forego ENIAC and create a small device that could run Monte Carlo simulations instead. He enlisted his colleague [Percy King] to build the machine. Their creation was built from joint Army-Navy cast off components, and in a nod to that great computer he dubbed it FERMIAC.

FERMIAC: Hacking Probabilities

FERMIAC was created to alleviate the necessity of tedious calculations required by the study of neutron transport. This is something of an end-run around brute force. It’s made mostly of brass and resembles a trolley car. In order to use it, several adjustable drums are set using pseudorandom numbers. One of these numbers represents the material being traversed. A random choice is made between fast and slow neutrons. A second digit is chosen to represent the direction of neutron travel, and a third number indicates the distance traveled to the next collision.

FERMIAC in use
FERMIAC in action.

Once these settings are dialed in, the device is physically driven across a 2-D scale drawing of the nuclear reactor or materials being tested. As it goes along, it plots the paths of neutrons through various materials by marking a line on the drawing. Whenever a material boundary is crossed, the appropriate drum is adjusted to represent a new pseudorandom digit.

FERMIAC was only used for about two years before it was completely supplanted by ENIAC. But it was an excellent stopgap that allowed the Manhattan Project to not only continue unabated, but with rapid progress. FERMIAC is currently on display at the Bradbury Science Museum in Los Alamos, New Mexico alongside replicas of Fat Man and Little Boy, the weapons it helped bring to fruition. [Fermi]’s legacy is cemented as one of the fathers of the atomic bomb. But creating FERMIAC cements his legacy as a hacker, too.

After Los Alamos, [Stanislaw Ulam] would continue to make history in the field of nuclear physics. [Enrico Fermi] was opposed to participating in the creation of the exponentially more powerful hydrogen bomb, but [Ulam] accepted the challenge. He proved that Manhattan Project leader [Edward Teller]’s original design was unfeasible. The two men worked together and by 1951 had designed the Teller-Ulam method. This design became the basis for modern thermonuclear weaponry.

Today, the Monte Carlo method is used across many fields to describe systems through randomness and statistics. Many applications for this type of statistical modeling present themselves in fields where probabilities are concerned, like finance, risk assessment, and modeling the universe. Wherever the calculation of all possibilities isn’t feasible, the Monte Carlo method can usually be found.

[Main Image Source: FERMIAC machine by Mark Pellegrini]

UPDATE: Commentor [lwatchdr] pointed out that the use of the FERMIAC began after the Manhattan Project had officially ended in 1946. Although many of the same people were involved, this analog computer wasn’t put into use until about a year later.

Want A Low-cost ARM Platform? Grab A Prepaid Android Phone!

What would you pay for a 1.2 Ghz dual-core ARM computer with 1 GB RAM, 4 GB onboard flash, 800×600 display, and 5 megapixel camera? Did we mention it also has WiFi, Bluetooth, and is a low power design, including a lithium battery which will run it for hours? Does $15 sound low enough? That’s what you can pay these days for an Android cell phone. The relentless march of economies of scale has finally given us cheap phones with great specs. These are prepaid “burner” phones, sold by carriers as a loss leader. Costs are recouped in the cellular plan, but that only happens if the buyer activates said plan. Unlike regular cell phones, you aren’t bound by a contract to activate the phone. That means you get all those features for $15-$20, depending on where you buy it.

android-logo-transparent-backgroundThe specs I’m quoting come from the LG Optimus Exceed 2, which is currently available from Amazon in the USA for $20. The same package has been available for as little as $10 from retail stores in recent weeks. The Exceed 2 is just one of several low-cost Android prepaid phones on the market now, and undoubtedly the list will change. How to keep up with the current deals? We found an unlikely place. Perk farmers. Perk is one of those “We pay you to watch advertisements” companies. We’re sure some people actually watch the ads, but most set up “farms” of drone phones which churn through the videos. The drones earn the farmer points which can be converted to cash. How does this all help us? In order to handle streaming video, Perk farmers want the most powerful phones they can get for the lowest investment. Subreddits like /r/perktv have weekly “best deals” posts covering prepaid phones. There are also tutorials on rooting and de-bloating current popular phones like the Whirl 2 and the Exceed 2.

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Sinterit Pulls SLS 3D Printer Entry Level Price Down To Just $8k

Almost exactly two years ago, news of a great revolution in 3D printing carried itself through blogs and tech columns. Patents were expiring, and soon the ‘squirting filament’ printers would be overtaken by a vastly better method: selective laser sintering. In the last two years, the market has been markedly silent on the possibilities of SLS technology, until now, at least. Today, Sinterit is launching their first printer. It’s an SLS printer that builds objects by fusing nylon powder with a laser, producing things with much better quality than filament-based printers.

The Sinterit Lisa is a true laser sintering printer, able to create objects by blasting nylon powder with a 5W laser diode. Inside this box that’s about the same size as a laser printer is a CoreXY mechanism to move the laser diode around, heated pistons, cylinders, feed bed and print bed for keeping the print volume at the right temperature and the top layer perfectly flat. The layer thickness of the printer goes down to 0.06 mm, and the maximum print size is 13 x 17 x 13 cm. Material choice is, for now, limited to black PA12 nylon but other materials are being tested.

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Exponential Growth In Linear Time: The End Of Moore’s Law

Moore’s Law states the number of transistors on an integrated circuit will double about every two years. This law, coined by Intel and Fairchild founder [Gordon Moore] has been a truism since it’s introduction in 1965. Since the introduction of the Intel 4004 in 1971, to the Pentiums of 1993, and the Skylake processors introduced last month, the law has mostly held true.

The law, however, promises exponential growth in linear time. This is a promise that is ultimately unsustainable. This is not an article that considers the future roadblocks that will end [Moore]’s observation, but an article that says the expectations of Moore’s Law have already ended. It ended quietly, sometime around 2005, and we will never again see the time when transistor density, or faster processors, more capable graphics cards, and higher density memories will double in capability biannually.

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See Actual Microwaves — No More Faking It

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.

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