A portrait of Earth’s dirty atmosphere

Here’s a mind-blowing view of the Earth that you’ve probably never seen — or even thought of — before. Dubbed “Portrait of Global Aerosols” by NASA, this is the kind of imagery that climate scientists use to analyze the Earth’s atmosphere, the weather, and trends such as global climate change.

Now, first things first: The Earth doesn’t actually look like this from space (alas). Rather, this is an image output by the Goddard Earth Observation System Model, version 5 (GEOS-5). GEOS-5 is an almighty piece of software that runs on a supercomputer at NASA’s Center for Climate Simulation in Maryland.

In the case of this image, GEOS-5 is modeling the presence of aerosols (solid or liquid particles suspended in gas) across the Earth’s atmosphere. Each of the colors represents a different aerosol: Red is dust (swept up from deserts, like the Sahara); Blue is sea salt, swirling inside cyclones; Green is smoke from forest fires; and white is sulfates, which bubble forth from volcanoes — and from burning fossil fuels. The full-size version of the image is particularly mesmerizing, with beautiful swirls of Saharan sand in the Atlantic, and perhaps the tail end of the Gulf Stream circling around Iceland.

It’s hard to be certain, but it seems like the US east coast, central Europe, and east Asia are burning a lot of fossil fuels. Japan, of course, sits on the edge of the Pacific Ring of Fire, so the sulfates there could be from volcanoes. The smoke in Australia is probably from forest fires — but the large volume of smoke from the Amazon rain forest and sub-Saharan Africa is curious. Are these forest fires, or the large-scale burning of wood for heat and power?

As you can imagine, the amount of raw data required to produce such imagery is immense. Weather modeling is still one of the primary uses of supercomputers. To create the Portrait of Global Aerosols, GEOS-5 will have aggregated the measurements from hundreds of weather stations across Earth, along with data from the four NASA/NOAA GOES weather satellites. So you have some idea of the complexity of the GEOS-5 model, the resolution of this image is 10 kilometers (6 miles) — meaning the Earth has been split into regions (“pixels”) of 10km², and then the atmospheric conditions are simulated for each region. The surface area of the Earth is 510,072,000km², which means the total number of regions is around 5 million.

Each of these 5 million pixels might have megabytes or gigabytes of weather data associated with it — and of course, in any given area, the weather in each pixel interacts with those around it. This gives you some idea of how much data needs to be processed and moved around — and it only becomes exponentially more complex as sensors improve (producing more data) and as you increase the depth of your analysis. In the case of climate change, for example, scientists are modeling decades or even centuries of data to try and divine some kind of pattern — a task that taxes even the most powerful supercomputers. If you’ve ever wondered why we keep building faster and faster supercomputers, now you know why.

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Fusion power at home, or, how small science will defeat big science

Fusion research is known for its huge projects — and its huge lack of tangible success. Big machines like the Princeton tokamak and theLivermore laser have indeed managed to fuse a few nuclei, but have required too much energy to get too little in return. A Brooklyn web developer named Mark Suppes recently created fusion in in his own home, using a much simpler device called a Farnsworth fusor. Accessing declassified experiments, and using open-source software, open-source hardware and crowdsourced funding, he has turned the traditional approach to scientific research on its head — and he makes it look easy.

In his early teenage years, Philo Farnsworth presented a concept for the all-electronic “image dissector,” and soon developed it into the first functioning television set. He successfully defended his rights to the design against larger corporations like RCA, which tried to claim it in a patent, and in the process became a legend and inspiration for private inventors and DIYers everywhere. Farnsworth’s skill at controlling electrons with electric fields later led him to develop a small nuclear fusion device. The device used inertial electrostatic confinement, as opposed to magnetic confinement which is used to fuse charged particles in the larger and more complex machines.

Suppes first heard about the Farnesworth fusor from Robert Brussard’s Google Tech Talk.With DARPA’s permission, Brussard described his work on Polywell reactors. The Polywell is a refinement of the Farnesworth fusor, but has the potential for significant net energy production. Suppes knew little of physics, but decided that with a little help from the open source community, he could make a fusor for himself. His blog
and Github repository show step-by-step exactly how he did it. In the video below, you can see a talk that Suppes gave at Wired 2012.

Can you really create fusion at home?

The biggest challenge to homebrew fusion is creating a spot where the conditions are just right. Typically a vacuum chamber that can tolerate some heat is needed. In university and industrial research labs a vacuum system is built using standard erector set pieces called“conflat flange” mounts. Prior to Ebay, the best way to get value out of an old vacuum system was to recycle it for the nickel and chrome in the steel. Today however, passing these systems on to someone who can use them is just a matter of a few clicks.

Another thing Suppes had going for him was the capability to design and 3D print heat resistant parts in the complex geometry needed for the Polywell device. The Polywell is basically a set of electromagnetic coils positioned in a precise geometry that enables charged particles to be confined. Ceramic is needed because other heat resistant materials, like metals, would perturb the field and let particles escape.

The most important a tool for Suppes was the willingness of skilled individuals to help him at every turn. As the 38th person to build a working fusor, there was a lot of technical know-how floating around. Suppes was able to collect that information into one place and package it in a way anyone can understand. His approach of publish first, then review, has been catching on as the new way to do science. Not every person cares about the research that their tax dollars fund, but those who do care have demanded access to it — and are getting it.

A cautionary note is perhaps in order. David Hahn, also known as the radioactive boy scout, was a child prodigy who built a subcritical fission reactor in his backyard using tiny amounts of radioactive material from many smoke detectors. He eventually became obsessed with his hobby and landed himself in the hospital for treatment of radiation injuries, and then in jail for larceny. The risks from radiation are not the same with fission as with fusion. High energy X-rays and neutrons are created in a fusor and need need to be respected accordingly.

The fire that Farnsworth lit years ago continues to burn bright. The untimely death of Brussard, just a year after his Google Talk and initial results with the Polywell device offered the torch, and Suppes and others have run with it. Big science concentrates all the money and knowledge on large projects that can’t fail, but it is slowly yielding to small science, where nimble, crowd-funded and -sourced projects can gracefully die if they don’t yield productive results. Not every scientist is compelled to fuse atoms, nor every layperson, but with enough people working on the problem and communicating their results and techniques openly, humankind will one day harness the power of the Sun (perhaps through a Sun-encompassing Dyson sphere, hm?)

Now read: Inside California’s star power fusion facility

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Father-daughter duo have the world’s first brain-to-brain ‘telepathic’ conversation

It should be fairly obvious why, all technological considerations aside, there has been much more research into letting machines extract our thoughts, rather than insert them. Mind reading is a scary-enough concept all on its own — but mindwriting? It calls to mind the hacker deities of cyber punk novels; skinny, trench-swathed Neos projecting e-thoughts into the skulls of passing civilians. With such basic issues of privacy on the line, it took the trusting relationship between UK scientist Christopher James and his adventurous young daughter to give us our first stab at developing real telepathic, brain-to-brain communication technology.

James’ process of telepathic communication is rough, its results shaky, but the principle of brain-to-brain (B2B) communication is unquestionably met. It begins with the by-now standard collection of mental information, achieved in this case with electrodes placed against the skull. “I only used scalp electrodes on my daughter, since my wife wouldn’t let me drill holes in my daughter’s head,” James told the Times of India.

In the experiment, the sender imagined a series of binary digits, broadcasting their choices by imagining movement in their right arm or their left. The resulting patterns of brain activity were recorded and expressed by an LED — one frequency to represent a one, another to represent a zero. The patterns are simply too arcane to be useful to the conscious mind, too quick and complex, but they’re not meant to be read like Morse code, in any case.

Dr. James conducting a preceding experiment in 2009.

When the LED signal travels to the recipient, it flashes into a very specific part of the eye (which part doesn’t matter much) and so the resulting optical signal is sent to a predictable section of the visual cortex. Surface electrodes just like those that originally recorded the signal are much better than people at making sense of the quick-flash LED language, seeing in the recipient’s brain more data than does the recipient themselves.

Once the pattern has been reverse-engineered from LED back to arm-waving, the telepathic process is said to have concluded. “The key idea to grasp,” said Dr. James, “is that a person’s eyes cannot distinguish between the different frequencies of flashing lights but a part of his brain, [the] visual cortex, can.” For more serious results, the electrodes would have to be implanted on the surface of the brain, a procedure for which he had neither governmental nor spousal approval.

All in all, this advance will take some time to spawn any dystopian mind flayers or Inception-style dreamscapes. This advance has to do with the translation of thought to binary data, and the ability to technically induce that data in the brain of another person. The glaringly absent piece of the puzzle is any ability to induce much more sophisticated visual images; multi-pixel messages that appear in the mind’s eye, as opposed to the physical one.

That sort of sophistication could come through a better understanding of just how stimulation of the visual cortex influences images in the mind, or in teaching brains the language of light bulbs. With LED technology now finding its way into contact lenses, this technology seems well-suited to the (possibly) upcoming brain-machine revolution. It’s unclear was uses this tech might find in such a future, especially when it steps beyond the constraints of fatherly affection.

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Smartphones become capable of sensing human emotion

Smartphones are amazing. They tell us where we’re going, let us know if it’s going to rain, and even act like personal assistants. Now, a new research project out of the University of Rochester aims to make your phone capable of sensing your emotions just from measuring how you’re speaking — not based on what you’re saying.

This research, titled the Bridge Project, focuses on small changes in the human voice. Rather than using the traditional methodology of self-reporting and monitoring body language, this new method is based on automatic passive emotion detection. This technology can always be listening and monitoring a patient’s emotional state without any work on his or her part — providing a bigger picture on the patient’s entire status.

The basics of the system involve measuring twelve different aspects of speech, and then mapping the data onto six different emotions. Wendi Heinzelman, professor of electrical and computer engineering, said that the project analyzed completely emotionless phrases of speech, such as saying dates of the month. Impressively, they are able to reach an 81% accuracy rating with this model while previous attempts were only around 55%. By having actors read scripts with certain performances, the researchers are able to tweak their algorithm to associate certain pitches, volumes, and harmonics to a specific emotional state.

While this is undoubtedly an invaluable tool for psychologists and medical researchers, it also has huge potential for consumers. Take a look at Apple’s Siri. It’s designed to appear more human-like by offering humorous answers, apologizing, and using more realistic speech, like “Let’s hear some Beatles,” instead of something with less flare, like “Now playing: The Beatles, track one.” This gives us a better experience because it mimics human interaction. Now, think about this technology integrated into Siri. When you’re getting frustrated, it could offer simple hints on how to interact better. When you’re sad, it could throw in compliments.

In Dr. Oliver Sacks’s book The Man Who Mistook His Wife For A Hat, he tells a story about a group of patients suffering from aphasia — an inability to understand words. In the story, he details how very capable these patients are in detecting emotion through speech. In fact, they are able to use sound cues to effectively communicate with their loved ones and doctors despite not being able to understand the words directly. He even notes that it is extremely difficult to execute a lie in front of an aphasiac because they are so adept at picking up the hidden emotion. This story truly illustrates how much of our emotional states are expressed verbally, and just how useful this research really is.

[Image credit: Lara604 & William Gunn]

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MIT creates tiny, 22nm transistor without silicon

Researchers at MIT’s Microsystems Technology Lab (MTL) have created the smallest transistor fashioned from indium gallium arsenide, a material that is being positioned as an eventual successor to silicon. MIT’s indium gallium arsenide (InGaAs) transistor has a gate length of just 22nm — roughly the same size as the smallest features on Intel’s 22nm FinFET Ivy Bridge chips.

This tiny InGaAs transistor was mostly fashioned from normal semiconductor processes — molecular beam epitaxy, electron beam lithography, and so on. The breakthrough here is using an exotic, compound material, rather than straight-up silicon. In this case, the MIT researchers allow evaporated indium, gallium, and arsenic atoms to react, forming a very thin crystal of InGaAs that will become the transistor’s channel (the thin, lighter line at the tip of the inverted V). Molybdenum is then deposited at the source and drain, oxide is deposited at the gate (the inverted V) — and voila, a tiny, exotic transistor. MIT says it “performs well,” but its exact performance characteristics aren’t given.

It is fairly well understood at this point that silicon — the fundamental building block of almost every computer chip, and much of modern society — will eventually run out of steam. No one quite agrees when this will occur, but the general consensus is within 10-20 years. Basically, at some point in the future, as CMOS components continue to shrink, silicon simply won’t function as a semiconductor any more. When this happens, we’ll need to replace silicon with something else.

ITRS’s table for emerging silicon replacement technologies

As we’ve discussed before on ExtremeTech, the ITRS (International Technology Roadmap for Semiconductors) currently pegs III-V semiconductors such as gallium arsenide (GaAs) as one of the only short-term alternatives to silicon. “Short-term” is relative, though; we’re talking at least five to 10 years until GaAs (or MIT’s InGaAs) finds its way into commercial memory or logic chips. In MIT’s case, the researchers have managed to build a singleInGaAs transistor — scaling that up to the billions of transistors that will be in CPUs of the future will verge on the impossible.

The problem with GaAs, InGaAs, carbon nanotubes, graphene, and any number of exotic materials that we cover on ExtremeTech, is that they’re trying to replace the most advanced technology in the world. It is not hyperbolic to state that hundreds of billions of dollars have been poured into CMOS R&D; maybe trillions. For these silicon replacements to even stand a chance, a similar investment will need to be made — and put simply, there is probably only one group in the world who has the requisite time or resources: Intel. We don’t even have definitive proof that the new materials will scale much further than silicon — so we’d be plowing billions of dollars into something that might only get us another few years of Moore’s law.

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Intel unveils 22nm SoC transistors, while TSMC and GlobalFoundries plan risky process jumps

Transistor announcements aren’t the sexiest occasions on the block, but Intel’s 22nm SoC unveil is important for a host of reasons. As process nodes shrink and more components move on-die, the characteristics of each new node have become particularly important. 22nm isn’t a new node for Intel; it debuted the technology last year with Ivy Bridge, but SoCs are more complex than CPU designs and create their own set of challenges.

Like its 22nm Ivy Bridge CPUs, the upcoming 22nm SoCs rely on Intel’s Tri-Gate implementation of FinFET technology. According to Intel engineer Mark Bohr, the 3D transistor structure is the principle reason why the company’s 22nm technology is as strong as it is. Other evidence backs up this point. Earlier this year, we brought you news that Nvidia was deeply concerned about manufacturing economics and the relative strength of TSMC’s sub-28nm planar roadmap. Morris Chang, TSMC’s CEO, has since admitted thatsuch concerns are valid, given that performance and power are only expected to increase by 20-25% as compared to 28nm.

Intel, in contrast, is predicting record gains. The company claims that its 28nm SoC “employs high speed logic transistors, low standby power transistors, and high-voltage tolerant transistors in a single SoC chip to support a wide range of products, including premium smart phones, tablets, netbooks, embedded systems, wireless communications, and ASIC products.” The company reports enormous improvements in leakage currents and Intel plans to take full advantage of the improved performance.

You’ve probably seen the image above trotted out when Intel talks about process node improvements. In this case, it’s the length of the line that’s more improvement than its rightward shifts. The diagram shows leakage current dropping more quickly than clock speed. At 65nm, Intel’s transistor performance and minimum leakage levels dropped off more quickly, while minimum leakage was much higher.

Here’s 65nm, 32nm, and individual data sets for SRAM cells across multiple process nodes.

At 65nm and a maximum input voltage of 1V, Intel’s SRAMs had a narrow operating range. 800MHz was the maximum effective frequency at that voltage — below 0.8v, the chip stopped working at any frequency. At 32nm (Medfield, Clover Trail), the company’s processors have considerable more latitude. 22nm pushes the envelope still further.

The challenge for both TSMC and GlobalFoundries is going to be how to match the performance of Intel’s 22nm technology with their own 28nm products. 20nm looks like it won’t be able to do so, which is why both companies are emphasizing their plans to move to 16nm/14nm ahead of schedule. There’s some variation on which node comes next; both GlobalFoundries and Intel are talking up 14nm; TSMC is implying a quick jump to 16nm.

I don’t want to say too much on how the three companies’ future processes might compare; tech papers at IEDM may shed more light on the particulars of each solution. What’s clear is that both GF and TSMC are going to try to accelerate FinFET development. GF’s tech papers imply that the company will deploy a hybrid 22nm-14nm process to make the jump more quickly.

Will it work? Unknown. TSMC and GlobalFoundries both have excellent engineers, but FinFET is a difficult technology to deploy. Ramping it up more quickly than expected while simultaneously bringing up a new process may be more difficult than either company anticipates. Given the advantages Intel claims for the technology, it might’ve made more sense to ramp FinFET on an established node. One of the most significant demonstrations of what Intel thinks it’s getting out of 22nm FinFET is the company’s decision to revise Atom for an out-of-order architecture. Intel has resisted the call to overhaul the in-order CPU; the current core at the heart of Medfield and Clover Trail offers nearly identical performance to the design that debuted in 2008.

22nm Atom should close the gap with existing ARM CPUs and give Intel a substantial advantage. Overall, the situation looks like Intel holds the cards until GF and TSMC manage to revise their roadmaps for the sub-20nm market.

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The past, present, and future of bionic eyes

Next-generation bionic eyes are practically here today. Imagine a blind person’s real-world conundrum trying to shop for one — they could schedule surgery for Nano Retina’s implant today and see their daughter’s wedding in 576-pixel clarity, but it would cost them their life’s savings. The Nano Retina 5000-pixel device could be ready tomorrow, or in another six months… and would be much more affordable. When the procedure involves assimilation of an electrode pincushion into the ganglionic tentacles of your retina, hardware upgrades are not as simple as popping in more RAM. What kind of decision matrix could be offered under such critical circumstances?

Cochlear implants, used to restore hearing, work phenomenally well when properly tuned and fitted. Most are refinements of the basic piece of hardware one might have sitting on their bookshelf — the graphic equalizer. The implant processes a single audio stream into bins of various sizes according to frequency, and then applies current to the corresponding frequency location in the cochlea, typically with a 16-spot linear electrode. The main function of these devices is to capture speech formants — the peaks in the frequency spectrum of the voice. The toughest challenge for the cochlear implant is to provide sound localization and source separation in noisy environments like a cocktail party.

Vision implants are much more complex. As any practiced photographer knows, the eye is more than a camera. The optic nerve does not feed the brain pixels. If you imagine your camera responding to auto-selected targets several times a second, gathering the full spectrum of light through its entire range of settings at each pause, and compressing the data onto a bandwidth- and energy-limited channel ideally matched to its receiver, you have some idea of what the retina accomplishes routinely.

The reason cochlear implants work so well is that the brain is just that good at making sense out of virtually any kind of signal it is given. If presented only with noise, or with nothing at all, the brain will eventually begin to manufacture hallucinations. If the implant signal contains even some distorted fragment of the original signal, it can be made to work convincingly. This is also the reason why retina implants can work without incorporating any knowledge of what the retina actually does in the healthy state.

These days researchers are trying to do a little better than the grainy images provided through our current implants. Signal processing techniques were developed in the Cold War era to track and target incoming missiles by extracting signals from noisy radar data. These same techniques are now used to convert the activity of groups of neurons in the motor cortex into a set of commands for moving a cursor, prosthetic device, or de-enervated limb in brain machine interfaces (BCIs). These methods and derivations of them can also be applied to incoming sensory data and can approximate what the retina actually does, without doing it in the same way.

Unfortunately, videos and TED talks are not the places where this kind of knowledge is typically transmitted in much depth. For that, one needs to look back to the work of the founding father of cybernetics, Norbert Wiener, and his eminently practical inspiration, Vito Volterra. After suggesting that helium be used instead of hydrogen in airships, to great success, Volterra shifted gears and came up with some methods to characterize complex systems. Wiener simplified Volterra’s equations and they are now widely used today in statistical techniques like linear regression analysis, and analysis of spike trains from neurons.

A single neuron in the brain of a blow fly can read input from its photoreceptors and command a wing muscle to change its flight path within about 30 milliseconds. That’s just time enough for a few spikes on a one-neuron chain, so the temporal structure of those spikes contains real information relevant both to the stimulus and motor imperative. It is therefore not just a coarse pulse frequency code. These Wiener equations, or more precisely, kernels, have been used to accurately represent the information in these spike trains and replace the neuron in simulated systems. To do so — even for a few spikes — requires intensive computation using reiterative numerical methods.

To attempt such a process for the million or more axons (the long connections between neurons) that constitute the output of the retina would be prohibitive. To get around this, researchers have further simplified the equations and can now do a decent job of reconstructing a stimulus, as long as the number of pixels or other kind of input chosen is limited. Rather than directly representing pixels, the processed responses of the ganglion cells in the retina can better be understood in terms of standard image-processing concepts like edge detection, and center-surround inhibition. These filters are built into the physical structure of the neuron’s dendritic tree. A project to create a connectome for the retina, known as Eyewire, is now looking to create a rough map of these details through a crowdsourced, online gaming effort.

Ultimately, this kind of analysis is a top-down approach and has its limitations. For the present time, it is the best we have. Neuromorphic chips and artificial neural networkscould replace these methods in the interim time until actual biologic equivalents can be grown for replacement retinas. Research on stem cell replacements for the “hair” cells in the cochlea, which do the actual sound mechano-transduction into electrical nerve activity, is making astounding progress that will hopefully soon be transferred to visual and motor systems.

MIT Technology Review has reported on a couple projects still in the early stages of development. At the Society of Neuroscience this meeting this November, Massoud Khraiche proposed using silicon nanowires to replace damaged photoreceptors. These nanowires could allow for both light detection and neuron stimulation. Another group, atCarnegie Mellon, is also making inside-the-eye devices even smaller. Their device would provide detail comparable to that of the fovea, the part of the eye with the highest density of photoreceptors.

Under some conditions, photoreceptors, like a dark-adapted rod cell, can detect a single photon. More impressively, that single rod cell can inform its owner of the event with some statistical reliability. In other words, the person can guess whether they saw the photon or not with significance better than chance. Considering that the same cell can also function on the reflective sands of a sunny beach, that gives us some appreciation for the dynamic range through which the retina can operate. Capturing this full complement of skill with a prosthetic bionic eye will certainly take time.

As far as choosing a go-to implant manufacturer, is hard to know what technologies and algorithms various developers may eventually employ. If your implant allows new vision apps to be installed over-the-air, that might be a good sign. Operating system/firmware upgrades should be provided for as well. Ultimately, if your implant permits actual hardware upgradeability by including a spare FPGA, that would be preferable. People with disabilities are learning today to temper their expectations when news reports announce medical breakthroughs. The day will come soon enough when lack of technology won’t be the biggest problem, rather they might simply be too expensive for the mass market to acquire. Hopefully, equitable systems for the disbursement of these new products will be found, and they can be enjoyed in the spirit of the best for the most.

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The first flexible, fiber-optic solar cell that can be woven into clothes

An international team of engineers, physicists, and chemists have created the first fiber-optic solar cell. These fibers are thinner than human hair, flexible, and yet they produce electricity, just like a normal solar cell. The US military is already interested in weaving these threads into clothing, to provide a wearable power source for soldiers.

In essence, the research team started with optical fibers made from glass — and then, using high-pressure chemical vapor deposition, injected n-, i-, and p-type silicon into the fiber, turning it into a solar cell. Functionally, these silicon-doped fiber-optic threads are identical to conventional solar cells, generating electricity from the photovoltaic effect. Whereas almost every solar cell on the market is crafted out of 2D, planar amorphous silicon on a rigid/brittle glass substrate, though, these fiber-optic solar cells have a 3D cross-section and retain the glass fiber’s intrinsic flexibility.

The lead researcher, John Badding of Penn State University, says the team has already produced “meters-long fiber,” and that their new technique could be used to create “bendable silicon solar-cell fibers of over 10 meters in length.” From there, it’s simply a matter of weaving the thread into a fabric. Badding says that the military is “interested in designing wearable power sources for soldiers in the field,” but unfortunately he falls short of actually demonstrating some woven fabric. As we can see in the picture above, the solar cell fiber certainly looks flexible — but we’ll have to take Badding’s word for it that it can turn right angles, and withstand everyday garment stresses, without shattering.

Moving forward, the potential for flexible, woven solar cells is enormous. On the most basic, immediate level, you can imagine a baseball cap or t-shirt that can recharge your smartphone. As we move towards bionic implants and other biomedical devices, though, there is a very pressing need to develop a wearable power source — and fiber-optic solar cells could certainly be it.

These fibers also have two other intriguing properties that still need to be investigated. Due to their three-dimensional cross-section, they can absorb sunlight from any direction — unlike their conventional, 2D siblings that lose much of their efficiency when the sun sinks below a certain angle. Further, according to Pier Sazio, another member of the research team, they used the same silicon injection method to embed photodetectors inside the fiber. Sazio doesn’t extrapolate on what this might lead to, but it’s fun to speculate: Awearable computer with built-in solar charging and high-speed networking? Neat.

Now read: LG produces the first flexible cable-type lithium-ion battery, or Creating cheap solar panels with an ion cannon

Research paper: DOI: 10.1002/adma.201203879 – “Silicon p-i-n Junction Fibers”

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Qubits and binary data successfully squirted down the same fiber, at the same time: Here comes quantum cryptography

Toshiba Research and Cambridge University in England are reporting that they have succeeded in building the first conventional fiber-optic network that’s capable of transmitting and receiving both quantum data (for encryption) and normal high-speed binary data. This breakthrough means that the world’s fiber networks can now be secured with theoretically unbreakable encryption.

The basic concept behind quantum cryptography is that Alice sends a crypto key to Bob via a stream of single photons (quantum key distribution, QKD). If a man-in-the-middle attacker somehow manages to intercept the photons, this interrupts the transmission in such a way that can be detected. In theory, quantum crypto should result in totally secure communications. (In reality, there are other attack vectors that bypass the inherent security provided by the photons.)

Now, quantum-secured networks aren’t particularly new, but until now the single photons (qubits) have required their own dedicated optical fiber for transmission. In conventional fiber-optic networks, the transmission of data is very intense, with over 1 million photons carrying a single bit of binary data. Sending single photons down the same fiber simply wasn’t feasible; it was impossible to extract that single photon at the other end. Until now!

Toshiba/Cambridge QKD + fiber data network diagram. A = the overall setup. B = the quantum transmitter. C = the quantum receiver.

The Toshiba and Cambridge researchers have overcome this restriction by transmitting the quantum photons and data signals at different wavelengths, and by using a special photodetector at the receiving end that turns on for just 100 millionth of a microsecond (a few hundred femtoseconds). The different wavelengths mean that the signals don’t clash, and the photodetector only turns on when it expects to receive a single photon.

The end result is a fiber network that can transmit binary data at 1Gbps in both directions, and perform quantum key distribution at 500Kbps at the same time, over a 90km (56mi) length. This is apparently 50,000 times faster than the previous best QKD over a fiber network of this length. In theory, this means that the next generation of fiber networks — assuming this femtosecond photodetector can be implemented commercially — could be secured with quantum cryptography. Most importantly, we’re only talking about new routers — this method of QKD could be performed on existing (and very expensive) fiber networks.

The immediate benefit will be for military and police/security networks, and domestic infrastructure (think secure communications in smart cities). With fiber steadily rolling out to consumers, though, you and I might soon be downloading torrents and watching cat videos that have been encrypted with quantum cryptography.

Now read: Quantum teleportation lays the foundation for a global quantum internet — or check out our popular story on the secret world of submarine fiber-optic cables.

Research paper: 10.1103/PhysRevX.2.041010 – “Coexistence of High-Bit-Rate Quantum Key Distribution and Data on Optical Fiber”

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EU seeks to reduce world’s energy usage, and double data center speeds, by replacing copper with fiber

Data centers are big and costly. Engineers all over the world are working hard at making servers and networking more efficient. Processors are using less power, cooling is getting easier, and evenrouters are reducing their footprint. Sadly, data centers are still using a gigantic amount of power, so the European Union is funding a trend away from traditional electrical data connections. Headed by the Fraunhofer Institute in Germany, project PhoxTroT aims at reducing power consumption by using light-based data connections, while at the same time increasing transfer speeds to two terabits per second (Tbps).

An article from Fraunhofer explains that this four-year project isn’t about reinventing the wheel — optical data transfer is already used around the world. Instead, PhoxTroT will be focused on taking existing technologies, combining them, and refining them into a system that will save money and use less energy while doubling connection speeds. “They will realize the optical transmission on a printed circuit board (‘on-board’), ‘board-to-board’ and also ‘rack-to-rack’. By combining these interfaces, it will also be possible to bridge longer distances within the foreseeable future,” says the article. This isn’t just a dolled-up fiber optic cable — this is taking the technology to the next level by integrating light-based data transfer throughout entire data centers on the individual server level, while increasing the effective range to hundreds of kilometers.

Not only is optical networking more power efficient and faster than its copper counterpart, but it’s also more robust in the face of disaster. After Hurricane Sandy took out a non-trivial amount of communications on the east coast of the United States, telcos went through and replaced copper lines with fiber-optic cables to update their network speeds and reliability. Electrical data transfer like typical coaxial and Ethernet cables still have a place, but it is slowly being overtaken in usefulness by optical data transfer. If PhoxTroT is a success, copper wiring will become even more of a niche.

With a little under twelve million dollars invested by the European Union, and eighteen different companies working together over the next four years, PhoxTroT can transform the data center into a much more eco-friendly and cost effective endeavor. Google‘s data centers alone draw 260 million watts continuously. A single Amazon data center in 2011 drew eight million watts continuously. Worldwide, data centers account for around 30 billion watts — a few percent of the world’s total power usage.

If these engineers can double the data throughput while using a small fraction of the power traditional networking uses, we’re talking savings of tens of millions of dollars per data center. The EU should be applauded for its efforts, and other countries and organizations should take a page out of its handbook in this instance. We’re saving money and saving the planet one data center at a time.

Now read: Will 100Mbps internet connections destroy the web as we know it?

[Image credit: Adrienne Serra & Alex]

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