Imagine a piece of technology that would let you control an apparatus simply by thinking about it. Lots of people, it turns out, have dreamed of just such a system, which for decades has fired the imaginations of scientists, engineers, and science fiction authors. It’s easy to see why: By transforming thought into action, a brain-machine interface could let paralyzed people control devices like wheelchairs, prosthetic limbs, or computers. Farther out in the future, in the realm of sci-fi writers, it’s possible to envision truly remarkable things, like brain implants that would allow people to augment their sensory, motor, and cognitive abilities.

That melding of mind and machine suddenly seemed a little less far-fetched in 1999, when John Chapin, Miguel Nicolelis, and their colleagues at the MCP Hahnemann School of Medicine, in Philadelphia, and Duke University, in Durham, N.C., reported that rats in their laboratory had controlled a simple robotic device using brain activity alone. Initially, when the animals were thirsty, they had to use their paws to press a lever, thus activating a robotic arm that brought a straw close to their mouths. But after receiving a brain implant that recorded and interpreted activity in their motor cortices, the animals could just think about pressing the lever and the robotic arm would instantly give them a sip of water.

Suddenly, a practical brain-machine interface, or BMI, seemed attainable. The implications were enormous for people who, because of paralysis caused by spinal-cord or brain damage, find it difficult or impossible to move their upper or lower limbs. In the United States alone, more than 5.5 million people suffer from such forms of paralysis, according to the Christopher and Dana Reeve Foundation.

The Hahnemann-Duke breakthrough energized the BMI field. Starting in 2000, researchers began unveiling proof-of-concept systems that demonstrated how rats, monkeys, and humans could control computer cursors and robotic prostheses in real time using brain signals. BMI systems have also revealed new ways of studying how the brain learns and adapts, which in turn have helped improve BMI design.

But despite all the advances, we are still a long way from a really dependable, sophisticated, and long-lasting BMI that could radically improve the lives of the physically disabled, let alone one that could let you see the infrared spectrum or download Wikipedia entries directly into your cerebral cortex. Researchers all over the world are still struggling to solve the most basic and critical problems, which include keeping the implants working reliably inside the brain and making them capable of controlling complex robotic prostheses that are useful for daily activities. At the risk of losing its credibility, the field now needs to transform BMI systems from one-of-a-kind prototypes into clinically proven technology, like pacemakers and cochlear implants.

It’s time for a fresh approach to BMI design. In my laboratory at the University of California, Berkeley, we have zeroed in on one crucial piece of the puzzle that we feel is missing in today’s standard approach: how to make the brain adapt to a prosthetic device, assimilating it as if it were a natural part of the body. Most current research focuses on implants that tap into specific neural circuits, known as cortical motor maps. With such a system, if you want to control a prosthetic arm, you try to tap the cortical map associated with the human arm. But is that really necessary?

Our research has suggested—counterintuitive though it may seem—that to operate a robotic arm you may not need to use the cortical map that controls a person’s arm. Why not? Because that person’s brain is apparently capable of developing a dedicated neural circuit, called a motor memory, for controlling a virtual device or robotic arm in a manner simi­lar to the way it creates such memories for countless other movements and activities in life. Much to our surprise, our experiments demonstrated that learning to control a disembodied device is, for your brain, not much different from learning to ski or to swing a tennis racket. It’s this extraordinary plasticity of the brain, we believe, that researchers should exploit to usher in a new wave of BMI discoveries that will finally deliver on the promises of this technology.

Illustration: Bryan Christie Design

Click on the image to enlarge.

The BMI field started more than 40 years ago at the University of Washington, in Seattle. In a pioneering experiment in 1969, Eberhard Fetz implanted electrodes in the brains of monkeys to monitor the activity of neurons in the motor cortex, the part of the brain that controls movement. When a neuron fired at a certain rate, a corresponding electrode would pick up a small electrical discharge, deflecting a needle and emitting a chirp in a monitoring device. Whenever that happened, the animals would receive a treat. Crucially, the animals could see and hear the monitoring device, which gave them feedback about their neural activity. Within minutes, the monkeys learned to intentionally fire specific neurons to make the needle move, so that they could get more treats. Fetz showed that it was possible to teach the brain to control a device external to the body.

Today, BMI systems vary greatly in their designs. A major distinction is the location of the electrodes. In some, the electrodes are implanted inside the brain, where they monitor the firing of individual neurons. Other researchers work with electro­corticography (ECoG) systems, which use electrodes placed on the surface of the brain just under the skull, or ­electroencephalography (EEG) systems, which use electrodes that sit on the scalp. ECoG and EEG monitor the rhythmic activity created by the collective behavior of large groups of neurons [see sidebar, “Invasive vs. Noninvasive”].

In the case of electrodes inside the brain, which are the ones I study and the focus of this article, the neural signals captured by the implant are fed into a computer program called a decoder. It consists of a mathematical model that transforms the neural activity into the movements of a computer cursor or robotic arm, typically. To measure neural activity, researchers usually count the number of times individual neurons fire in a certain time span, known as a bin, which is usually about 100 milliseconds. In a 100-ms bin, you might record zero to a few firings. That number is called a spike count. The mathematical model that translates the spike counts of a group of neurons into movement might be a simple linear relationship. Increasingly, however, researchers are using models that are more complex and nonlinear in their translation of spike counts to movement.

In the traditional approach to BMI, you create a decoder by monitoring the neural activity of the areas of the brain responsible for control of the natural arm. As a first step, you’d monitor those neurons while test subjects moved their arms in predetermined ways. Next you’d take the activity of the neurons and the motion of the arms recorded during that trial and compute the decoder’s parameters. The decoder would then be able to transform the firings of the neurons into movements of the prosthetic device.

The decoder’s function is in some sense like that of the spinal cord. The spinal cord is connected to hundreds of thousands of neurons in the brain. After these neurons fire, the spinal cord transforms that activity into a small number of signals that travel, in the case of the human arm, to about 15 muscle groups. So the spinal cord takes, say, 100 000 inputs and transforms them into about 15 outputs. The decoder does something similar, although on a much smaller scale. Current state-of-the-art BMI systems typically monitor the activity of a few dozen to a few hundred neurons and transform their firings into a small number of outputs, such as the position, velocity, and gripping force of a prosthetic device.

In the early 2000s, groups at Brown University, Arizona State University, and Duke reported more breakthroughs, demonstrating closed-loop BMI control for the first time. I was involved in the Duke study, conducted at Nicolelis’s lab, where I was a postdoctoral researcher from 2002 to 2005. In that study, we reported that two macaques were able to use brain activity alone to control a robotic arm with two articulations and a gripper (three degrees of freedom, in robotics parlance) to reach for and grasp objects. One of the key findings was that learning to control the BMI triggered plastic changes in different brain areas of the monkeys, suggesting that the brain had even greater flexibility than previously thought. Other groups at Caltech, Stanford, the University of Pittsburgh, the University of Washington, and elsewhere followed with their own studies, further advancing different aspects of BMI control. The team at Brown spun off a BMI start-up called Cyberkinetics and succeeded in getting approval from the U.S. Food and Drug Administration for clinical trials with five severely disabled patients. The trials of their device yielded promising results, and the effort continues as part of a large research consortium called BrainGate2.

A common problem in this first batch of studies was that the BMI system had to be recalibrated for every session. Subjects were able to control the computer cursor or robotic arm to perform a task, but they weren’t able to retain the skills from one session to the next. In other words, the BMI systems weren’t “plug and play.” And this limitation would become even more prominent as the complexity of the task increased. Another major obstacle soon became apparent, too: The electrodes’ ability to read brain activity would degrade over time, and the device had to be removed from the subject’s brain. With such shortcomings, a BMI system could never be practical.

Back to square one. At Berkeley, my group set out to investigate an intriguing hypothesis based on a simple question: If we’re trying to control a prosthetic device that’s completely different from a natural arm, why are we relying on brain signals related to a natural arm? If we want to control an artificial arm, we’d ideally use brain activity tailored to that specific arm. But could the brain learn to produce such activity for something that’s not even part of the body?

The answer is a resounding yes, according to a series of experiments and trials we’ve done over the past five years. In this new view of BMI design, the focus isn’t on using the existing nervous system to control an artificial device but rather on creating a new, hybrid nervous system that spans the biological and artificial components. This approach grew out of a series of experiments I carried out with Karunesh Ganguly, a postdoc in my lab. We implanted arrays of 128 tiny electrodes into the motor cortices of macaque monkeys. But unlike in previous studies, we chose to use only a subset of about 40 electrodes that provided reliable readings for several days. Another difference was that, rather than recalibrating the decoder every session, we kept it exactly the same during the whole time. Those differences would prove crucial for the results we would achieve.

In the experiment, the monkeys had to move a computer cursor to the center of a monitor screen and, upon seeing a circle elsewhere on the screen change color, move the cursor there. The reward was a sip of fruit juice. In an initial phase, the animals used their actual arms to operate a robotic exoskeleton and move the cursor. At the same time, we recorded brain activity from the subset of neurons. In a subsequent phase, we removed the exoskeleton and switched the experiment from manual to BMI control. In this case, the monkeys had to learn how to move the cursor with brain activity alone, irrespective of natural arm movement. And learn they did. Within a week, the monkeys had mastered the task and could repeat it proficiently day after day.

What was new here was the discovery that an animal’s brain could develop a motor map representing how to control a disembodied device—in this case, the computer cursor. In past studies, researchers didn’t keep track of a stable neuron group, and they inevitably had to recalibrate the decoder to adapt it to the new activity the neurons were producing in every session. But that recalibration resulted in a serious problem: It meant that the brain wasn’t being allowed to retain the skill learned in the previous sessions and thus wasn’t able to develop a cortical map of the prosthetic device. In our study we left the decoder unchanged, allowing the brain to assimilate the prosthetic device as if it were a new part of the body.

After two weeks, we performed two follow-up experiments with one of the monkeys. In the first, we started with a decoder that the animal had never used for BMI control. Within a short period of time, the monkey had mastered this new decoder, just as before. The interesting thing this time was that we could switch between this new decoder and an earlier decoder and the monkey’s brain would correctly choose the motor map that corresponded to whichever decoder was active. In the second experiment, we went further and created a shuffled decoder by completely scrambling its parameters. To our surprise, the animal was again able to learn this decoder and operate the computer cursor just as skillfully as before.

These findings highlight the brain’s remarkable plastic properties and suggest that a newly formed prosthetic motor map meets the essential properties of memory. Namely, the map remains unchanged over time and can be readily recalled: Every day the monkeys could promptly control the device; it seems to be a BMI that’s truly plug and play. The map was also resistant to interference from other maps comprising the same set of neurons—just as learning to play tennis doesn’t erase your ability to ride a bicycle.

And in our most recent study, with my students Aaron Koralek and John Long, and my colleagues Xin Jin, of the U.S. National Institutes of Health, and Rui Costa, of the Champalimaud Centre for the Unknown, in Lisbon, we went one step further. Our results showed that other brain areas that had not been explored in a BMI context—including neural circuits between the cortex and deep-brain structures such as the basal ganglia—are actually key to the learning of prosthetic skills. This means that in principle, learning how to control a prosthetic device using a BMI may feel completely natural to a person, because this learning uses the brain’s existing built-in circuits for natural motor control.

So is a truly practical BMI system at hand? Not quite. There are many challenges and obstacles, some obvious, others less so. In the obvious category are the basic parameters of the implants: They need to be tiny, use very little power, and work wirelessly. Another conspicuous challenge is the reliability of all the subsystems of a BMI, including the biophysical interface, the decoder, and also the feedback loop that allows the brain’s error-correcting mechanisms to kick in and improve performance. Each component would have to work for the user’s entire lifetime, of course.

Even after those challenges are met, there is a whole category of other obstacles involved with making the BMI system more sophisticated and capable. Ultimately, we want to build a BMI that can control not only primitive systems but also complex bionic prostheses with multiple degrees of freedom to perform dexterous tasks. And we want the BMI to be able to transmit signals from the brain to the prosthesis as well as from the prosthesis to the brain. That’s BMI’s holy grail: a system that’s part of your body in the sense that you not only control it but also feel it.

Most BMI studies today rely only on visual feedback: A monkey’s brain can form a motor memory for a given task because the animal can see how it is performing during the task and adjust its brain activity to improve its performance. But when we learn a task—riding a bicycle, playing tennis, typing on a keyboard—we’re not using just visual feedback; we’re also relying on our tactile senses and our proprioceptive system, which uses receptors on muscles and joints to tell you where a certain part of your body is in space. Could BMI systems do something similar? Could you use tactile and position information from a robotic gripper and stimulate the brain to allow a user to find a glass of water on a nightstand in the dark, for example?

We’ve begun trying to answer that question in our lab, and we’ve had some promising results already. We’re using a technique known as intracortical electrical microstimu­lation to attempt to induce tactile sensations. In a recent study, Subramaniam Venkatraman, a graduate student in my lab, used rats with electrodes implanted in their sensory cortices. He placed the rats in a cage and used a motion-tracking system to precisely monitor the position of one of their whiskers in real time. When the whisker hit a virtual ­target—a line located at one of various possible positions—the BMI system delivered a precise pulse of stimulation into the rat’s cortex, giving the animal the illusion of touching an object. Whenever the animal was able to consecutively hit the correct target four times, it received a drop of fruit juice as a reward.

The study demonstrated that the rats were able to combine natural signals from their proprioceptive system with artificially delivered tactile stimuli to encode object location. This result suggests that in addition to readout functions, we can also perform write-in operations to the brain, a capability that will be useful in providing feedback for future users of neuroprostheses. In fact, Nicolelis and his team at Duke recently described an experiment in which monkeys could control a computer with their minds and also apparently “feel” the texture of virtual objects. The interface was both extracting and sending signals to the brain—a bidirectional BMI.

Finally, another research area that BMI needs to advance in is that of the prostheses themselves. Advances in biomechatronics toward exploiting compact sensor, actuator, and energy-storage technologies will play a big role in the development of these systems. The good news is we’re already seeing major progress in this realm: Take Dean Kamen’s Deka Research and Development Corp., which built a robotic arm so advanced that it was nicknamed the “Luke arm,” after the remarkably lifelike prosthetic worn by Luke Skywalker in Star Wars.

BMI research is entering a new phase—call it BMI 2.0—thanks to work at universities, companies, and medical centers all over the world. (These include a recently launched Center for Neural Engineering and Prostheses, which I ­codirect, based at Berkeley and at the University of California, San Francisco, and which will focus on motor and speech prostheses.) There is a palpable sense that we are quite close to cracking several of the fundamental problems standing in the way of clinical and commercial use of BMI systems.

The initial applications of BMI, in helping patients suffering from paralysis due to spinal cord injury or other neurological disorders, including amyotrophic lateral sclerosis and stroke, are still probably a decade or two away. But after this technology becomes mainstream in health care, other realms await in the augmentation of sensory, motor, and cognitive capabilities in healthy subjects—a fascinating possibility for sure, but one that promises to unleash a big ethical debate. The world where we’re able to do a Google search or drive a car just by thinking will be a very different place. But that’s a BMI 3.0 story.

This article originally appeared in print as "Becoming Bionic."

While the United States struggles to convert scientific and engineering prowess into commercial victories and robust employment, Korea Inc. is doing exactly that. South Korea’s unemployment rate is about a third that of the United States, and household income is about two-thirds of America’s—on track to equal the U.S. level in a generation. For a country of roughly 50 million people, lacking in oil and other commodity resources, newfound wealth flows almost entirely from technological advances.

South Korea’s ascent is often greeted with astonish­ment; in the 1960s, the country was poorer than Ghana. Hard work and a zeal for imitating and outdoing the high-tech triumphs of Japan—which occupied Korea from 1910 to 1945—explain some of Korea’s rise. So does proximity to China’s booming northeastern cities.

But location and Confucian culture is only part of the Korean story. Smart government policies have strengthened Korea’s high-tech juggernauts—Samsung, LG, Hyundai, Kia, and Daewoo to name a few—raising high-skill, high-wage employment and increasing Korea’s global competitiveness. The experience provides clear lessons for the United States.

Make stuff people need. The United States continues to lead the world in visionary, next-generation science and engineering. But jobs and profits flow from today’s products and services. While the U.S. government tends to shun existing technologies, the Korean government lavishes money on its national champions to improve existing systems on the margins. The approach helps Samsung in its rearguard attack on Apple in smartphones, for instance. The lesson is contrarian: Don’t get too visionary; avoid getting too far ahead of the market. “We were lucky to have Japan as our learning target,” says Keun Lee, an economist at Seoul National University who studies innovation and rapid economic growth. “We copied Japanese policies. We became a fast follower rather than a first mover.”

Change comes slower than we anticipate. The United States has directed tremendous sums at alternative energy technologies and futuristic autos. Rather than fund next-gen cars, Korea’s government and its favored domestic firms have focused on making incremental improvements in old-tech autos. The result: Hyundai and Kia have posted impressive gains in market share by providing attractive value for money. When approaching electric vehicles, Korea has tackled the critical yet prosaic problem of battery recharging with its on-line electric vehicle system, which uses a wireless power supply.

Glorify engineers and harvest the science of others. Korea puts engineers at the center of its educational universe; science is a relative sideshow. While some of the brightest young Koreans attend U.S. universities, increasingly they’re staying at home, fighting for places in such stellar engineering bastions as the Korea Advanced Institute of Science and Technology (KAIST), which is led by an MIT professor emeritus of engineering, Nam Pyo Suh. In areas where world-class science is required, Koreans falter: For instance, the government space agency has repeatedly failed to launch its own satellites, relying instead on Russian launchers.

Go with whom you’ve got. While the United States opens its doors to the world’s technoscience stars, Korea depends almost entirely on indigenous talent. Korea Inc.’s dependence on natives means that Korean families pour resources into education, knowing that their own sons and daughters will win top jobs at home.

The win-now emphasis of Korea’s government policies on technology isn’t without critics. Some Koreans worry intensely that they have mortgaged the future to win now. The tactic of dominating established technology sectors promises fewer dramatic gains simply because the ripest areas have been taken. Growth must slow.

“We have entered into a cul-de-sac. We are trapped,” says Jae-Yong Choung, a professor of management and innovation at KAIST. “How do we escape? How do we build our own unique innovation systems?”

For Korea and Americans concerned about technological vitality, the revelation is that uniqueness—the radical breakthroughs—may be overblown as a metric of advance. Winning victories now—­mastering everyday technologies that result in value, jobs, and growth—is more important than capturing the notional frontiers of tomorrow.

Robots and dinosaurs have not, historically, had the greatest of relationships, but that doesn't mean we don't all secretly (or not so secretly) want our own robot dinos to cuddle, ride around on, and provide security. Paleontologists have decided that they're going to try to build themselves anatomically correct and fully functional robot dinosaurs to investigate how the animals moved, and definitely not to enact any sort of evil world domination scheme. They promise.

Dinosaurs were big. Some of them were really, really big. Like, 75 metric tons big. This is about 15 times heavier than a large elephant, and we have no idea how animals of that size were able to move around effectively. We have some guesses, but in order to find out for sure, we'd have to either clone them (which we should totally do at some point because nothing could ever go wrong), or build an anatomically correct robotic replica.

Drexel University paleontologists are already hard at work making 3D scans of sauropod leg bones with the goal of having a working limb (complete with simulated tendons and muscle) running around by the end of 2012. The 3D scans will be fed into a 3D printer, which ought to be able to correct for millions of years worth of deformation caused by fossilization and compression when it prints out replica bones. With a complete musculoskeletal system to experiment on, the researchers hope to be able to figure out how giant dinosaurs were able to stand up, whether they could trot or canter or actually run, and also how they, you know, reproduced with each other.

Putting together a complete robotic dinosaur replica will take a couple years, and it's important to note that it'll just be a replica, and won't offer conclusive proof about how dinos did or didn't get around. But without a Jurassic Park to go visit, robot dinos seem like a good enough way to go, as long as we make sure not to outfit them with missiles and laser cannons and stuff.

[ Drexel ] via [ DVICE ]

From the ferocity of the opposition to the Vogtle nuclear power plant project in Georgia, the first U.S. reactor project to get a construction permit in decade, you might think that the future of U.S. nuclear energy depends on it. In fact, the project seems set to get a Federal loan guarantee amounting to more than $8 billion, but even if it goes forward on that basis--which is by no means assured--this does not mean that the gates will be opened to a flood of other new nuclear power plant projects.

M.V. Ramana, a specialist on nuclear energy at Princeton University’s Program on Science and Global Security, wonders whether nuclear power "will ever make it in states with deregulated [electricity] markets." That is, there seem to be hints of a nuclear renaissance only in states that have old-fashioned vertically integrated utilities like Georgia's Southern Company, which have a guaranteed base of customers and can persuade regulators to charge consumers up-front for very high construction costs.

South Carolina Electric and Gas is awaiting and probably soon will obtain a combined construction and operating license for two new Westinghouse AP-1000 reactors at its Summer site. But it probably is the only other new project close to a go-ahead. Everywhere, high construction costs, long construction times, and competition from dirt-cheap natural gas make nuclear projects a hard sell. And wherever a specific plant is proposed local considerations also come into play.

Plans to build a GE-type advanced boiling water reactor near San Antonio, though close to getting regulatory approval in principle, took a big hit from Fukushima because of high Japanese involvement in the project. (Toshiba is now its main funder.) In Iowa, citizens are taking umbrage at the notion their average electricity bill might rise by $8 per month for 12 years, to finance a nuclear plant MidAmerican Energy proposes to build at the Duane Arnold site in Linn Country--and that estimate seems to be based on a rather optimistic guess about what the plant will cost. A proposed nuclear plant in Utah would require taking 64 billion liters of water annually from the Green River, even though the facility would use a relatively sophisticated once-through cooling system.

24 February 2012—Researchers at the University of California, San Diego, reported in the journal Nature earlier this month that they have invented a new kind of nanometer-size laser that requires much less power to generate a coherent beam than previous designs. This type of laser could finally make it practical to use light instead of electricity to send terabits of data between different parts of a computer processor, the researchers suggest.

Because of the nanolaser’s negligible power needs, it can be modulated much more quickly than existing lasers, allowing information to be encoded directly onto the beam, says Mercedeh Khajavikhan, a postdoctoral researcher who is a member of UCSD’s Ultrafast and Nanoscale Optics Group, which made the breakthrough. “[It] can also become a backbone for future communication devices,” she says. The UCSD group, which is working on integrating nanophotonics with CMOS electronics, envisions other applications for the nanolaser design: It could be used in ultrahigh-resolution imaging and for figuring out the chemical makeup of a material at a distance.

The new design addresses some of the problems inherent in today’s nanoscale lasers. The main issue is that as the size of the laser cavity decreases, the amount of light amplification, or “gain,” shrinks along with it. (The amount of energy lost as photons strike the metal parts of the cavity’s walls also diminishes with size, but it does so at a slower rate than gain does.) A laser can get only so small before its input energy requirement is impossibly large.

The UCSD team got the photons moving in the right direction by designing their device as a coaxial waveguide—in this case, a metallic rod surrounded by an indium gallium arsenide phosphide semiconductor ring that was then coated in metal. To form the lasing cavity, the waveguide was capped at both ends. One end cap, made of silicon dioxide coated in silver, formed a totally reflecting mirror. The other plug, which allowed the pump beam—the laser’s power source—to enter and the laser light to escape, was filled with air.

This setup helped to solve the gain problem, because the laser cavity was restricted to emitting energy only in a mode wherein the amount of light amplification was high enough to overcome energy losses from the cavity itself. The result: a device efficient enough to generate coherent beams even when powered by just 720 picowatts of light. What’s more, its performance at room temperature was comparable to that achieved when it was put through its paces at 4.5 kelvins. Nanolaser efficiency usually drops off dramatically without cryogenic cooling.

The nanolaser’s room-temperature abilities are in part due to the metallic coating around the semiconductor ring, which serves as a heat sink. “The direct contact of metal [coating] and semiconductor is a rather bold approach and required special fabrication recipes,” says Khajavikhan.

Still, the researchers were determined to make a working nanolaser using standard nanofabrication tools, and they wanted to ensure that the devices could be fabricated in batches. “We can claim that, despite many challenges, we did just that,” says Khajavikhan.

Making the semiconductor’s surfaces smoother will further increase the laser’s efficiency and allow fabrication at even smaller sizes. “We feel this is just the beginning for a new family of light emitters with superior characteristics,” adds Yeshaiahu (Shaya) Fainman, director of the Ultrafast and Nanoscale Optics Group. “Many advances in this new area are yet to come.”

Long ago, when I was a freshman in ­engineering school, there was a required course in mechanical drawing. “You had better learn this skill,” the instructor said, “because all engineers start their careers at the ­drafting table.”

This was an ominous beginning to my education, but as it turned out, he was wrong. Neither I nor, I suspect, any of my classmates began our careers at the drafting table.

These days, engineers aren’t routinely taught drawing, but they spend a lot of time learning another skill that may be similarly unnecessary: mathematics. I confess this thought hadn’t occurred to me until recently, when a friend who teaches at a leading university made an off-hand comment. “Is it ­possible,” he suggested, “that the era of math­ematics in electrical ­engineering is coming to an end?”

When I asked him about this disturbing idea, he said that he had only been ­trying to be provocative and that his graduate students were now writing theses that were more mathematical than ever. I felt reassured that the mathematical basis of engineering is strong. But still, I wonder to what extent—and for how long—today’s under­graduate engineering students will be using classical ­mathematics as their careers unfold.

There are several trends that might suggest a diminishing role for mathematics in engineering work. First, there is the rise of software engineering as a separate discipline. It just doesn’t take as much math to write an operating system as it does to design a printed circuit board. Programming is rigidly structured and, at the same time, an evolving art form—neither of which is especially amenable to mathematical analysis.

Another trend veering us away from classical math is the increasing dependence on programs such as Matlab and Maple. The pencil-and-paper calculations with which we evaluated the relative performance of variations in design are now more easily made by simulation software packages—which, with their vast libraries of pre­packaged functions and data, are often more powerful. A purist might ask: Is using Matlab doing math? And of course, the answer is that sometimes it is, and sometimes it isn’t.

A third trend is the growing importance of a class of problems termed “wicked,” which involve social, political, economic, and un­defined or unknown issues that make the application of mathematics very difficult. The world is seemingly full of such frus­trating but important problems.

These trends notwithstanding, we should recognize the role of mathematics in the discovery of fundamental properties and truth. Maxwell’s equations—which are inscribed in marble in the foyer of the National Academy of Engineering—foretold the possibility of radio. It took about half a ­century for those radios to reach Shannon’s limit—described by his equation for channel ­capacity—but at least we knew where we were headed.

Theoretical physicists have explained through math the workings of the universe and even predicted the existence of previously unknown fundamental particles. The iconic image I carry in my mind is of Einstein at a blackboard that’s covered with tensor-filled equations. It is remarkable that one person scribbling math can uncover such secrets. It is as if the universe itself understands and obeys the mathematics that we humans invented.

There have been many philosophical discussions through the years about this wonderful power of math. In a famous 1960 paper en­titled “The Unreasonable Effectiveness of Mathematics in the Natural Sciences,” the physicist Eugene Wigner wrote, “The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift [that] we neither understand nor deserve.” In a 1980 paper with a similar title, the computer science pioneer Richard Hamming tried to answer the question, “How can it be that simple mathematics suffices to predict so much?”

This “unreasonable effectiveness” of mathematics will continue to be at the heart of engineering, but perhaps the way we use math will change. Still, it’s hard to imagine Einstein running simulations on his laptop.

Images and analysis: Steve Haroz

Everyday Sparsity: Natural signals [top] are often “sparse,” which means they have relatively few frequency components of significance. A random image [bottom], however, contains significant components at all frequencies. The new fast Fourier transform algorithm accelerates calculations on sparse signals only.

Click on image for a larger view.

Gilbert Strang, author of the classic textbook Linear Algebra and Its Applications, once referred to the fast Fourier transform, or FFT, as “the most important numerical algorithm in our lifetime.” No wonder. The FFT is used to process data throughout today’s highly networked, digital world. It allows computers to efficiently calculate the different frequency components in time-varying signals—and also to reconstruct such signals from a set of frequency components. You couldn’t log on to a Wi-Fi network or make a call on your cellphone without it. So when some of Strang’s MIT colleagues announced in January at the ACM-SIAM Symposium on Discrete Algorithms that they had developed ways of substantially speeding up the calculation of the FFT, lots of people took notice. 


“FFTs are run billions of times a day,” says Richard Baraniuk, a professor of electrical and computer engineering at Rice University, in Houston, and an expert in the emerging field of compressive sensing, which has much in common with the approaches now being applied to speed up the calculation of FFTs.


Efforts to improve the calculation of Fourier transforms have a long and generally overlooked history. While most engineers associate the FFT with the procedure James Cooley of IBM and John Tukey of Princeton described in 1965, specialists recognize that it has much deeper roots. Although he never published it, the renowned German mathematician Carl Friedrich Gauss had worked out the basic approach, probably as early as 1805—predating, remarkably enough, even Fourier’s own work on the topic.


Given that great mathematical minds have been thinking about how to speed up this particular calculation for more than two centuries, how is it that progress is still being made? The fundamental reason is that the newer methods are tailored to run fast for only some signals—ones that are termed “sparse” because they contain a relatively small number of frequency components of significant size. The traditional FFT takes the same amount of computational time for any signal.


“Certainly there are applications where you need to run the full FFT because the data are not sparse at all,” says Piotr Indyk of MIT’s Computer Science and Artificial Intelligence Laboratory, who developed the new algorithms in collaboration with his colleague Dina Katabi and two students, Haitham Hassanieh and Eric Price. Fortunately, many real-world signals satisfy this sparsity requirement. 


“Most signals are sparse,” says Katabi, who points out that when you send, say, a video file over wireless channels, transmitting only a few percent of the frequency content is typically sufficient—and in line with the sparsity levels that her group’s new algorithms handle well. Baraniuk adds that the frequency content of many natural signals, be they astronomical images or bird chirps, tends to be concentrated among the lower frequencies. “Sparsity is everywhere,” he says.


Source: MIT CSAIL

Speedy: A new algorithm computes quickly when the signal it’s working on has few important frequency components. These results are for an FFT with 2 ²² samples.

The newest MIT algorithm, which is described in a soon-to-be-published paper, beats the traditional FFT so long as the number of frequency components present is a single-digit percentage of the number of samples you take of the signal. It works for any signal, but it works faster than the FFT only under those conditions, says Indyk. 


Experts say the new algorithms coming from MIT represent considerable progress. “They’ve added some very smart ideas to the recipe,” says Mark Iwen of Duke University, in Durham, N.C., who had earlier worked on the same problem with Anna Gilbert and Martin Strauss at the University of Michigan. The time it takes Iwen’s best FFT algorithm to run increases in proportion to the fourth power of the logarithm of the number of samples, whereas the newest MIT algorithm has a run time that’s proportional to just the first power of that number. “Removing those log factors is incredibly difficult,” says Iwen, who also credits the MIT group for coming up with impressively fast computer implementations of their new algorithms.


Indyk points out that the library of code his group (and many engineers) uses to calculate the traditional FFT was released in the 1990s—three decades after Cooley and Tukey had worked out the basic computer procedures required. “We don’t plan to spend 30 years developing a library,” he says. “We plan to release our prototype code soon; researchers can just play with it and see what happens.” Then, in something like six months, the MIT group should be able to provide a well-tested, portable library. 


Why so long? “Developing decent code takes time,” Indyk says. But there’s another thing, he points out, that’s bound to delay their release of a well-tested software library: “Every month or two we have a new idea.” 


Special thanks to Steve Haroz for the images and their Fourier transform analysis.

Researchers at MIT have developed a method by which they can control the growth process of nanowires and thereby control the composition, structure, and even their resulting properties.

The MIT research team, led by Silvija Gradečak, assistant professor of materials science and engineering, followed the usual method of growing nanoparticles by using “seed” particles (metal nanoparticles), but in their experiments the researchers closely controlled the amount of gases used in the growth process.

The results, which were published in the journal Nano Letters , demonstrated that by controlling the gases interacting with the seed particles, the researchers were able to control the width and composition of the resulting nanowires.

Gradečak's team used an electron microscope to observe the effects that the gases were having on the growth process, and then the researchers adjusted the amount of gases to get the characteristics they wanted in terms of both structure and composition.

While the research team restricted their seed particles to indium nitride and indium gallium nitride, they say that the process will work with a variety of different materials.

Naturally, the goal of controlling the size and composition of nanowires is to change their properties. If you could fine tune the exact properties you wanted in a nanowire, you could use it in applications for which they are best suited.

The application that seems to be at the top of the list for the nanowires created by the MIT team is LED light bulbs. In this case, the nanowires would be used as a substrate replacing the expensive sapphire or silicon carbide typically used.Not only could the nanowires be a less expensive substrate, but they could also prove to be more efficient, according to Gradečak.

The varying diameters and structures could also make the nanowires useful in thermoelectric devices, in which waste heat can be turned into electricity. By changing the structure and thickness of the nanowires along their length, it’s possible to make them good conductors of electricity but bad conductors of heat, a much-desired property for thermoelectric power systems.

There were two stories this week that highlight the need for smartphone owners to look at security on their phones like they do on their other personal computing devices.

The first was from Reuters , which on Wednesday reported that about 7 percent of all smartphone owners were victims of identity fraud in 2011. The statistic came the research firm Javelin Strategy & Research, which also stated that its study indicated some 12 million Americans were victims of identity theft last year, a jump of 13 percent from 2011.

The Reuters story says that cyber thieves are increasingly targeting smartphones because more are being used and their owners tend to be less cautious about IT security. For instance, it states that, according to Javelin, some 62 percent of smartphone users don't use a password to protect their phones. In addition, free phone apps are increasingly loaded with malware (pdf). Downloading from well-known app stores reduces the risk, but does not eliminate it completely if there exists another route into the phone, as the next story indicates.

The other smartphone security-related story appeared in today's LA Times , which reports that well-known security analyst Dmitri Alperovitch has discovered a zero-day security hole in Android and Apple smartphone browsers that allowed him to plant "malware that can commandeer the device, record its calls, pinpoint its location, and access user texts and emails." Alperovitch is going to present his findings at the RSA conference next week.

According to the Times, Alperovitch, along with a team of other security specialists, started with Nickispy (a Trojan Horse remote access tool emanating from China that disguised itself as a Google+ app), reversed engineered it, and then were able to successfully get the malware to load on an Android-based smartphone through a self-created phishing email. Once loaded, the smartphone functions, including all voice communications, could be completely accessed by a malicious remote user. Alperovitch also says once loaded, "there is no security software that would thwart it."

The Times article also states that a Chinese web site last year was selling Nickispy for US $300 to $540 to customers who wished to "spy on smartphones that ran Symbian or Windows Mobile operating systems." You can watch how Nickispy works in this McAfee video.

There will no doubt be other IT security findings of interest coming out of the RSA conference next week; look for further updates here at the Risk Factor.

Photo: iStockphoto

The Danish island of Bornholm, a quiet farming and fishing community of 42 000 in the Baltic Sea, will soon be home to one of the world’s smartest smart grids. Through the four-year, €21 million (US $28 million) EcoGrid project, about 2000 households there will be connected to an island-spanning network that will enable homeowners to cut back their electricity usage at times of peak demand and sell that unused wattage back to the grid at market rates. Managing all of these thousands of discrete energy trades, as well as Bornholm’s other power resources—including 36 megawatts of wind power, a 16-MW biomass plant, and a new fleet of electric cars—will be a central control system that behaves very much like a traditional power generator. Only this generator will be created entirely through software—a virtual power plant.


As its name implies, a virtual power plant doesn’t exist in the concrete-and-turbine sense. Rather, it uses the smart-grid infrastructure to tie together small, disparate energy resources as if they were a single generator. Just about any energy source can be linked up. And energy that’s not used can also contribute to a virtual power plant’s capacity.


Here’s how that will work: Households on Bornholm will be equipped with gateway controllers, which in response to spikes in electricity prices and the homeowners’ preferences will automatically be able to turn off appliances or adjust the thermostat. The unused electricity can then be aggregated by the virtual power plant, along with other actual energy resources, and sold to customers who need power during peak times. Without the virtual power plant, the utility’s only other option for meeting peak loads is to ramp up production, which can get very expensive, says Kim Behnke, head of R&D and smart grid development at the Danish utility Energinet.dk, which is overseeing the EcoGrid project. 


The first virtual power plants came online about 10 years ago, mainly as research projects, says Thomas Werner, a product manager in Siemens’s smart grid division who oversees the company’s virtual power plant projects. But in the last several years, he says, energy market players have come to accept the virtual power plant as a commercially viable alternative to adding new capacity, as well as a way to handle the variability of renewables.


“Rather than having all these 5-kilowatt photovoltaic sources, you have a 100‑MW virtual plant that for the utility is much more manageable,” says Peter Asmus, a senior analyst at the market research firm Pike Research. “And it’s temporary—you might stitch together those resources for just a half hour, to help meet peak demand.” Pike Research estimates that the worldwide capacity of virtual power plants could grow from 45 gigawatts last year to as much as 105 GW by 2017, with revenues of about $6.5 billion.


A virtual power plant also lets smaller energy producers take part in energy markets from which they might otherwise be excluded. One plant set up by Siemens aggregates 1450 MW of capacity from small generators installed in hospitals, industrial facilities, and commercial buildings throughout Germany. Ordinarily, each of these units would be used only during emergencies and only to power its particular site. Hooked up via the virtual power plant, they can now be fired up whenever market rates or grid conditions make it worthwhile. 


A remaining challenge, Werner says, is that there is no standard interface between the central control system that manages and optimizes the virtual power plant and the distributed energy resources out in the field, many of which may not have been designed to communicate with an IT network.


The virtual power plant concept is an obvious culmination of the decadelong push to deploy smart meters, sensors, and other infrastructure, says Amit Narayan, director of smart grid research in modeling and simulation at Stanford University. “If you think about the evolution of the Internet, it’s the same thing,” he says. “Somebody had to lay the wires and build the infrastructure, but once that’s in place, a lot more can be done in terms of creating new applications.” 


And in much the same way that Google, Facebook, and Amazon troll through user data to discern subtle patterns in people’s tastes and then try to influence their buying habits, Narayan says, virtual power plants give grid operators the means to study their customers’ electricity usage and then try to get them to modify their behavior in a way that increases the capacity of the virtual power plant. 


Indeed, changing people’s habits should be one of the chief outcomes of the Bornholm project, says Behnke, because when consumers use less electricity, they’ll not only reduce their electricity bill, they’ll also get a bonus based on the market price for the electricity at that time. “We hope to show that even these small customers can help balance the grid, based on actual need within the hour,” he adds. His company estimates that the virtual power plant should help reduce peak loads on the island by at least 20 percent.


While Denmark already has a number of virtual power plants, they’re all designed to allow large electricity customers to trade energy in the day-ahead market, Behnke says. “That’s what we call Smart Grid, version 1. Now we are going for Smart Grid, version 2.” 


Oil drilling has gone on for years on Alaska's remote North Slope, but it has been limited to conventional resource extraction. More recently, however, with the ongoing success (economically, at least; environmentally is another story) of unconventional extraction of shale oil and natural gas in the lower 48 states, there is increasing interest in expanding fossil fuel extraction in Alaska. And now the U.S. Geological Survey has released its first ever assessment of "continuous" oil and gas in the North Slope region.

Continuous oil and gas refers to fossil fuels that are still locked up in rocks like shale; conventional resources involve oil or gas that has migrated away from the source rocks. The new USGS report found technically recoverable oil resources of between zero and 2 billion barrels. Natural gas resources lie somewhere between zero and 80 trillion cubic feet, and liquid natural gas between zero and 500 million barrels.

Those zeroes obviously make the range enormous, but they're in there for good reason. No actual drilling for these types of resources has ever been attempted on the North Slope, and it is impossible to know for sure what could be extracted before the process actually begins. According to the USGS: "The shale formations assessed have generated oil and gas that migrated into conventional accumulations, including the super-giant Prudhoe Bay field. It is also probable that these shale source rocks likely retain oil and gas that did not migrate, but only drilling can concretely confirm this or not."

For a bit of comparison: The Willison Basin currently undergoing a huge extraction boom in North Dakota contains about 3.6 billion recoverable barrels of oil; the Marcellus Shale formation, home to another boom in Pennsylvania, Ohio, New York, and other states, has more than 88 trillion cubic feet of shale gas.

The USGS, of course, makes no statements on whether or not we should actually go after these potentially huge fossil fuel resources in Alaska. There are always risks associated with drilling, and many critics say that if major incidents occur in such remote areas they will be much harder to contain and clean up than elsewhere. And there is already precedent: earlier this month a natural gas well blew out on the North Slope near the mouth of the Colville River, spilling drilling mud and methane.

The Obama Administration has generally been friendly to drilling proponents, opening up Alaska's national petroleum reserve to extraction and promising an all-of-the-above energy strategy in his most recent State of the Union address. Whether the shale gas boom spreads all the way to Alaska's North Slope remains to be seen.

Image via USGS

Ever knelt behind a home-theater receiver, tangled in wires, trying to figure out why nothing’s coming out of the stereo speakers? Is the cable bad? Is the cable box even putting out the digital audio signal? Oh, if only you could whip out a scope from your pocket and check. As it turns out, you can. Here’s a look at three pint-size oscilloscopes.

Perhaps the most ambitious pocket scope yet attempted, the DSO Quad (left), from Seeed Studio, is smaller than a smartphone. It offers four input channels (two analog, two digital), a gorgeous, high-contrast, active-matrix color screen, and measurements galore. Peak-to-peak voltage, duty cycle, frequency—you name it and this scope will measure it. That is, once you calibrate the thing! The Quad comes completely uncalibrated, requiring you to adjust tiny trimmer capacitors and perform a multistep firmware voltage calibration as well.

All this power is wrapped up in one of the most convoluted user interfaces I’ve ever seen. Eventually, you’ll get the hang of its four push buttons and two multipurpose swivel controls, which move back and forth and also up and down. The interface will surely improve, because the Quad is an open-source project. Such goodies as audio frequency response plotting and fast Fourier transform analysis, as well as bug fixes, have been released or are in development.

Remarkable as it is, the Quad is still more of a hobbyist’s gadget than a serious instrument. Despite its admirably fast sampling rate, the op-amp–based analog front end exhibits quirky, inconsistent response, which limits the bandwidth and accuracy. Some of the displayed measurements are wildly wrong. The Quad is great for casual waveform viewing, though, especially if you need to see multiple signals. It even includes a crude but useful function generator that outputs sine, square, triangle, and sawtooth waves.

The Quad comes with two analog probes and a USB cable for charging its internal lithium-ion battery. You can download a manual at Seeedstudio.com.

Offered through RadioShack, the Velleman HPS140i (right) is a much more straightforward, finished product. With only four buttons, it is easy to use. There are only two menus, and functions are logically grouped. Some key settings, including voltage scale, time, and input coupling, don’t require the menus at all. The sole input channel has a standard BNC connector on top of the case, right where it should be for handheld operation. A rubber shell protects against drops and bumps.

This unit has an unusually wide and very readable screen. Available measurements include peak-to-peak, dBm (decibels referenced to one milliwatt), and Wrms (weighted root mean square) for speaker impedances from 2 to 32 ohms. Clearly, this scope is aimed at audio work, but it has enough bandwidth for low-frequency pulse and RF applications too.

The HPS140i has one fantastic feature every scope should include: fully automatic setup. It’s the scope equivalent of autoranging on a digital multimeter, or DMM. Apply a signal and the unit quickly selects vertical and horizontal settings to show you a couple of cycles of the waveform. Nice! You can turn it off, of course, and you can save waveforms to memory, too.

This is a solid, no-frills package that slips effortlessly into a pocket or any bag of tools. It comes with a USB charging cable, a switchable 10x/1x probe, and a handy calibrator square wave terminal on the back for setting the probe compensation. The manual is brief, but there’s more info at Vellemanusa.com and Hps140.com.

Calling the third oscilloscope, the Uni-T UT81B (center), a pocket scope might be accurate if you’ve got very large pockets. While smaller than most handheld scopes of the past, it’s much bigger than the other two units. It looks like an overgrown multimeter. And that’s basically what it is—a digital multimeter that can display waveforms. The Uni-T offers both DMM and scope modes and can read voltage, current, resistance, diode voltage drop, frequency, and capacitance. True to its multimeter roots, it uses banana plugs for input. An optional BNC converter is mentioned in the manual but is not included. It isn’t shown on Uni-T’s website, either.

MS/s—megasamples per second; μs/div—microseconds per division; mV/div—millivolts per division.
Click on chart to enlarge.

The user interface is well considered. Like the Velleman, this scope offers auto setup, and it can save waveforms to memory. It even includes PC software and an opto-isolated USB cable for use as a real-time PC scope. One nice DMM feature is the “relative” mode, in which the displayed reading is subtracted from the values to be measured. It’s especially useful for canceling out residual capacitance when measuring capacitor values or for matching resistors. In scope mode, you can view measurements of current, something you can’t do on most scopes.

Alas, even with contrast and brightness cranked way up, the screen is dim and washed out, although it looks better under a lamp. Worse, the scope is always DC coupled, even when set to measure AC. This design flaw severely limits the instrument’s use as a scope, because AC coupling is a critical function for viewing signals riding on DC voltages. The only way around the problem is to build a probe with a switchable blocking capacitor in it.

The UT81B comes in a nice zippered case, with AC adapter, banana plug test leads, opto-isolated USB cable, a decent manual, and PC software on CD-ROM.

All three of these handheld scopes are intriguing, for different reasons. For pure sex appeal and as a tinkerer’s delight, the DSO Quad is something to behold. As a practical, basic instrument that offers ease of use, better bandwidth and accuracy, and durability in your pocket, the Velleman gets my vote. As a bench meter or for a full-size tool kit, the Uni-T combines several instruments into one and adds some unusual, useful features. It’s a viable option if you can live with its limitations.

More pocket scope models are appearing almost weekly. Check eBay for the latest ones. Measure on!

This article originally appeared in print as "Pocket Scope Roundup."

The chairs of 47 nuclear engineering departments in North America regularly discuss concerns about their academic programs. After the Fukushima Dai-ichi incident unfolded, one question was on everyone’s mind: Would nuclear engineering take a hit? E-mails were quickly exchanged among the group members, and the clear answer was no. Students were not dropping the major, and engineering freshmen were still just as interested in it.

“We’re now accepting applications for 2012, and they are on track to be equivalent to last year’s numbers,” says Kathryn Higley, head of the nuclear engineering and radiation health physics department at Oregon State University, in Corvallis.

It has been only a year since Fukushima, but the continuing student interest is an indication that the discipline is holding its ground. The industry, bolstered by the need for carbon-free energy, is on its way up, and nuclear engineering remains a solid career path, says Arthur Motta, chair of Pennsylvania State University’s nuclear engineering program. “Even if the United States doesn’t build any new plants right now, 20 percent of our power is from nuclear, and that’s not going away anytime soon,” Motta says.

And not just in the United States. Germany and Italy have backpedaled, but many other countries are forging ahead with nuclear power. And with the Fukushima incident highlighting the need for improved reactors and better safety measures, the demand for nuclear engineers will only increase.

The contrast with the 1980s is striking. After Chernobyl, the nuclear industry buckled, and academic programs in nuclear science and engineering languished around the world. U.S. enrollments plummeted, bottoming out in 2000. But over time, the industry’s reputation has healed. Concerned about both nuclear security and a diminishing workforce, the U.S. Nuclear Regulatory Commission and the U.S. Department of Energy have been supporting nuclear engineering programs through scholarships and internships.

The result is skyrocketing enrollments. In the freshman class of 2000 at North Carolina State University, in Raleigh, there were 37 nuclear engineering majors; this year there are 209. Other schools show similar trends.

This past December, the NRC approved Westinghouse Electric Co.’s new AP1000 nuclear reactor design, clearing the path for two utilities to build new plants. This has boosted confidence among academics and the industry, says Yousry Azmy, head of the nuclear engineering department at NCSU.

Nuclear engineering graduates work mostly for utility companies and for vendors such as Westinghouse, GE, and Areva. Some go to national laboratories, regulatory agencies, or into nuclear medicine. But nuclear engineers gain systems and engineering skills, along with a solid background that they can apply to other realms. “Even if the market shifts, students will have a versatile tool kit and abilities that will allow them to move around,” Higley says. During the nuclear power lull in the early 2000s, many graduates went to computer chip and software companies, she points out.

Besides, Azmy says, “The future of nuclear engineering education in the United States isn’t entirely held hostage to the utilities in this country.” China is building 27 new reactors and expects to have another 120 operating within the next two decades. Saudi Arabia, Turkey, the United Arab Emirates, and Vietnam are actively building new nuclear power programs. “Many jobs will materialize in the United States and Europe,” says Azmy.

Recognizing the need for a talented nuclear workforce, countries such as China, Poland, and the United Arab Emirates are building their own academic programs in nuclear engineering. Many U.S. universities are making concerted efforts to build connections with these countries through student exchanges and international design projects. This gives students the chance to work with people from different cultures, Higley points out. “The companies they will work for have an international footprint and will want their employees to work with people in other countries that are using their technology.”

This article originally appeared in print as "Going Nuclear."

Most DIY projects never go beyond what’s essentially a prototype stage—they’re one-off affairs that don’t have to be cost-effective or easy to use.

But after a bunch of your friends say, “Gee, I could use one of those,” it’s time to consider producing your project on a larger scale. It’s also time to rethink the entire project: What works for a single build often doesn’t when you have to do it hundreds or even dozens of times.

I got a sense of this recently when my son and I built a high-powered replacement for the Wii sensor bar. Nintendo’s own sensor bar, which is actually a pair of infrared emitters that the Wii controller uses to determine which way it’s pointed, isn’t much good beyond 3 meters. We created a bar that has nine high-powered IR emitters. (Actually, we built two bars, one for each of us.) We liked it so much we knew others would too, so we launched a Kickstarter project with the goal of producing at least 100 pairs. Kickstarter is a way to fund projects in which the money comes from potential customers instead of a bank (see “Getting a Kick Start” for more about it).

When you create a Kickstarter project, you need to figure out the pledge levels and exactly what the pledger gets for his or her money. Essentially, you’re figuring out the selling price, as you would with any product. It turns out there’s a lot to consider.

First, there’s the matter of component costs. Large-scale production saves money on both unit costs and shipping. The printed circuit board for the MegaBar (as we called it) ran about US $13, using BatchPCB (which I described in an April 2010 article, “Build a Custom-Printed Circuit Board”). But for 200 (two boards per pair), the cost dipped below $2, an $11 savings. While things like switches and LEDs don’t go down as dramatically, savings of 25 to 50 percent aren’t uncommon.

By the way, for those off-the-shelf components, I avoided eBay as much as possible. All too often you find a bargain that’s not available the next time you need it. Industrial suppliers, such as Mouser Electronics, keep large stocks of components available and will tell you how many they have on hand. Mouser, with an easily searched website and online data sheets for pretty much everything it sells, is especially nice.

Once you know your component prices, consider the packaging. This is really just another component, but because it comes last, it’s often overlooked. We wanted to keep the bar small, but the device needed some extra room for thermal dissipation—the driver chips can get quite hot. We settled on cases normally used for pagers, which had the added benefit of having internal standoffs that could be used to secure the board.

Photos: James Turner

MISTAKES WERE MADE: The finished MegaBar [left] consists of two IR emitter arrays packaged in cases meant for pagers. A prototype printed circuit board [right] shows the notches designed to let the board fit properly in the case, as well as a few wires I inserted to correct some design errors. Click on image to enlarge.

It really is better to think about packaging sooner rather than later. By specifying screw holes on the PC boards that lined up with the standoffs, we were able to mount the boards directly, but we had to figure out exactly what the dimensions needed to be, which in turn required a careful study of the data sheet for the case. Typically, the one critical measure you need will not be directly available, and you’ll have to infer it from other measurements that are provided.

Now is a good time to think about labor costs. For our project, each case needed to have 12 precisely positioned holes drilled (10 through the front, and 2 on the side). The holes needed to line up perfectly with the LEDs, switches, and jacks on the PC board or things wouldn’t fit when assembled. Even with a template, drilling that many holes is time-consuming. Add in soldering, testing, and shipping, and I was looking at a significant cost, even at teenage-son labor rates—high enough to consider having a local machine shop drill my holes for me.

The last piece of the cost puzzle is the outbound shipping. Our unit was about 2 kilograms, which seems light but still came to $6 just to send it within the United States.

Once you have all your costs, add what you want to make as net profit, and then tack on another 10 to 25 percent for all the things that never work out the way you planned—wastage, defective units, and so on.

There’s one more thing to keep in mind—liability. I’m not a lawyer, but I know that if you make something that gets ridden, heated up, or plugged in, someone will find a way to get hurt. In our case, we overengineered for safety, using parts rated well above the actual current load. We also used a DC power jack, rather than hardwiring the power leads, and a source voltage just high enough to drive the LEDs, reducing the heat that’s shed by the driver chips. Even so, we were leaving ourselves open to potential trouble. It’s a calculated risk.

If you plan carefully and wisely, you can scale up your one-off into a successful and profitable product. Inventing the next great mousetrap is just the first step—now manufacture it!

This article originally appeared in print as "DIY Manufacturing."

In the next few months, a series of lawsuits will play out in the Chinese courts that could define the risks foreign companies take when they try to make money in China’s booming markets. The U.S. green energy company AMSC is suing its former customer Sinovel Wind Group Co., China’s biggest wind turbine manufacturer, for breach of contract, copyright infringement, and theft of trade secrets. In total, AMSC, based in Devens, Mass., is asking for about US $1.2 billion in damages, making this the largest intellectual property case to date in China.


AMSC appears to have strong evidence, including a full confession from an AMSC employee who said he sold trade secrets to Sinovel. Yet China has a reputation as a place where intellectual property laws are routinely flouted. So the legal outcome is uncertain—and is of great interest to foreign companies. 


“There are a lot of eyes on this case,” says Jason Fredette, AMSC’s vice president of communications and marketing. “Given all the evidence that we do have, and given that this would be an open-and-shut case in any Western court, it is important to see the Chinese courts do the right thing.”


Sinovel did not respond to requests for comments, but the company previously released a statement calling the charges “completely false.” The company has also filed a counterclaim regarding the contractual dispute.


As of press time, the first legal skirmish—a $200 000 copyright infringement case regarding AMSC’s software—had been won by Sinovel, which got a full dismissal of the case from a provincial court. But the much larger cases regarding trade secrets and contracts may fare better in the higher courts of Beijing.


AMSC (formerly known as American Superconductor Corp.) sells wind turbine designs and turbine electrical control systems through its Windtec Solutions division. AMSC and Sinovel once had a very close business relationship: Sales to Sinovel previously accounted for about 70 percent of AMSC’s revenues. But in March 2011, Sinovel refused a shipment of wind turbine components. “We first thought that this was an inventory issue, and we were understanding,” says Fredette. “Then in June we discovered this IP theft, and that changed things quite a bit.” 


The intellectual property in question is a new software package from AMSC that enables “low-voltage ride through,” which allows wind turbines to continue operating during grid outages. AMSC gave Sinovel the software to test, but it had an expiration date that should have shut it down after a few weeks. 


When an AMSC employee in China found a Sinovel turbine operating with “expired” software, AMSC quickly fingered an employee in Austria, Dejan Karabasevic. “We had a very limited number of employees who had access to the LVRT software and an even smaller number who had traveled in China,” says Fredette. Karabasevic pleaded guilty in an Austrian court and was sentenced to one year in prison and two of probation for stealing trade secrets. 


AMSC says it has further evidence, including signed employment contracts between Karabasevic and senior-level Sinovel employees that amount to more than $1.5 million.


The Chinese courts that will rule on the AMSC cases are aware of the world’s scrutiny, says Mark Wu, an assistant professor at Harvard Law School and an expert on intellectual property in China. He says that the Chinese government is trying to shift its economy away from manufacturing and toward research and innovation. “Stronger IP protections are necessary to achieve that policy goal,” says Wu. He also notes that more patent and copyright lawsuits are being brought in Chinese courts by Chinese litigants. “So it seems like progress, because there are more Chinese stakeholders,” he says.


But, Wu adds, foreign companies are still frustrated with IP theft and dissatisfied with China’s enforcement mechanisms. “Often foreign companies get some form of remedy from the courts, but they say that it’s insufficient compared to the value of what was stolen,” Wu says. “The question really is whether IP enforcement in China is effective from a business standpoint.” 


AMSC suffered a serious blow when it lost Sinovel’s business; since a year ago its stock price has plummeted about 80 percent, and the company has laid off nearly 40 percent of its employees. But as CEO Daniel McGahn said during an investor conference call in September, the company can’t afford to withdraw from China. “It is an economic reality that we must do business in China, and I believe we can do it securely and profitably.” 


If AMSC wins its lawsuits in the Chinese courts, a lot of other companies will suddenly feel more secure, too. 


We've been wondering wherefore art thou Romeo ever since Aldebaran Robotics promised us a March 2011 unveil of their adult-size bipedal humanoid. Now, not quite a year behind schedule, we've got the first video look at what's in store.

To be fair, Aldebaran has been busy doing stuff like coming out with a new version of Nao, so we'll cut them some slack with their (in both foresight and hindsight) entirely implausible original unveil date. And even now, Romeo is not yet all that we were promised, although he does seem to be coming along rather well: