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Microsoft Research who brought us some wonderful technologies such as the incredible Photosynth continue to impress with a much improved web mapping application integrated with the company's new Bing search engine. During the TED 2010 conference, Microsoft engineer Blaise Aguera y Arcas demoed the new Bing augmented reality maps showing real-time registration of video taken with a smart phone and street-view type maps. He showed how the live video can be overlayed over the static images and additional information about the area can be accessed via a Web interface. Much of this is made possible because of the advanced computer vision technology that has been developed in the past decade at Microsoft Research. The Seadragon technology is the back-end that makes it possible to manipulate such vast amounts of data in real-time. Microsoft has also integrated Photosynth and Worldwide telescope into their maps product. You are probably wondering what does this have to do with robotics other than the fact that it is a very impressive application? I can imagine robots using Bing maps to keep localized within a city. One of the most difficult and important problem in robotics is that of Simultaneous Localization and Mapping. Bing maps solve the mapping problem and the new vision techniques (with a bit of help from GPS) can be used to solve the localization problem. The registered video can be used by a robot to localized itself when it goes out to buy your weekly groceries.  You can watch the 10-minute demo below; I bet that it won't be long before Microsoft makes these new features available to us all for free.

It's a problem that has long annoyed virtual reality researchers: VR systems can create a good experience when users are observing or manipulating the virtual world (think Michael Douglas in "Disclosure") but walking is another story. Take a stroll in a virtual space and you might end up with your face against a real-world wall. The same problem is becoming apparent in teleoperated robots. Imagine you were teleoperating a humanoid robot by wearing a sensor suit that captures all your body movements. You want to make the robot walk across a room at the remote location -- but the room you're in is much smaller. Hmm. Researches have built a variety of contraptions to deal with the problem. Like a huge hamster ball for people, for example. Or a giant treadmill. The CyberWalk platform is a large-size 2D omni-directional platform that allows unconstrained locomotion, adjusting its speed and direction to keep the user always close to the center. With a side of 5 meters, it's the largest VR platform in the world.

Perhaps the most difficult control problem for a drive servo is that of going down a ramp. Any back drivable drive servo will exhibit a freewheeling velocity on a given ramp. This is the speed at which the robot will roll down the ramp in an unpowered state. At this speed, the surface drag and internal drag of the servo are equal to the gravitational force multiplied by the sine of the slope. The freewheeling speed is thus load dependent.

Earlier in this series, we touched on one problem that can arise when sampling an analog signal, namely the problem of aliasing. There are three other issues with signal sampling to which we now turn our attention: digitization effects, finite register length effects, and oversampling. So far, weve assumed that all of the signals were measuring are continuous analog values; i.e., our measurements are completely accurate. Even in the cases in which we have noise, the underlying assumption is that the measurement itself, for example the noisy sensor output voltage, is known precisely.

The design of a generator system requires many hours of detailed planning with the goal of creating an extremely reliable backup power source. Properly installed, the system will deliver the intended level of reliability. However, if incorrectly wired, the system can become a problem for both the owner and the manufacturer. While the generator installation can be handled by a range of people, from a trained technician to the typical homeowner, wiring mistakes can occur. Installation includes working with 120 VAC split phase, 240 V line voltage, along with low-voltage signals below 50 V. A small and easily made mistake, such as miswiring high voltage to low voltage, will destroy sensitive electronics quickly and may render the equipment inoperable. Thus, a resettable overcurrent and overvoltage solution capable of handling line voltage, electrostatic discharge (ESD), electrical fast transients (EFT), and current surge is required to protect low voltage interface circuits against this problem.

Have you ever written code that behaves correctly under a simulator only to have intermittent failures in the field? Or maybe your code no longer functions properly when you compile with a newer version of your tool chain. You review your test bench and verify 100 percent complete test coverage and that all tests have passed with no errors--yet the problem stubbornly remains. While designers understandably place great emphasis on coding and simulation, they often have only a nodding acquaintance with the internal workings of the silicon within an FPGA. As a result, incorrect logic synthesis and timing problems, rather than logic errors, are the cause of most logic failures. But writing FPGA code that creates predictable, reliable logic is simple if designers take the right steps. In FPGA design, logic synthesis and related timing closure occur during compilation. And many things, including I/O cell structure, asynchronous logic and timing constraints, can have a big impact on the compilation process, varying results with each pass through the tool chain. Let's take a closer look at ways to eliminate these variances to better and more quickly achieve timing closure.

A new form of propulsion that could allow microrobots to explore human bodies has been discovered. The technique would be used to power robots and other devices such as microfluidic pumps from a distance. Finding a propulsion mechanism that works on the microscopic scale is one of the key challenges for developing microrobots. Another is to find a way to supply such a device with energy because there is so little room to carry on-board fuel or batteries. Now a team lead by Orlin Velev at North Carolina State University in Raleigh, US, has found that a simple electronic diode could overcome both these problems. Velev and Vesselin Paunov from the University of Hull, UK, floated a diode in a tank of salt water and zapped the set-up with an alternating electric field.

A new form of propulsion that could allow microrobots to explore human bodies has been discovered. The technique would be used to power robots and other devices such as microfluidic pumps from a distance. Finding a propulsion mechanism that works on the microscopic scale is one of the key challenges for developing microrobots. Another is to find a way to supply such a device with energy because there is so little room to carry on-board fuel or batteries. Now a team lead by Orlin Velev at North Carolina State University in Raleigh, US, has found that a simple electronic diode could overcome both these problems. Velev and Vesselin Paunov from the University of Hull, UK, floated a diode in a tank of salt water and zapped the set-up with an alternating electric field.

In mathematics, computer science, and related subjects, an 'algorithm' is an effective method for solving a problem expressed as a finite sequence of instructions. Algorithms are used for calculation, data processing, and many other fields. (In more advanced or abstract settings, the instructions do not necessarily constitute a finite sequence, or even a sequence; see, for example, "nondeterministic algorithm".) Each algorithm is a list of well-defined instructions for completing a task. Starting from an initial state, the instructions describe a computation that proceeds through a well-defined series of successive states, eventually terminating in a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate randomness. [source = Bing Reference]

This year's annual New York press briefing by the Edison Electric Institute, the organization representing investor-owned utilities, naturally was devoted to the smart grid, the hot topic of the day. Most notable, actually, was the absence of anything really new to report, which confirmed expectations that the smart grid will begin to prove itself next year at the earliest--or not. This was not the first EEI briefing devoted to smart grid prospects. Last year's briefing was devoted almost entirely to the smart meter avalanche, and a year or two before that much was made of Xcel Energy's SmartGridCity experiment in Boulder, Colorado. I reminded EEI president Thomas Kuhn of the Boulder briefing and pointed out that the experiment appears now to have been a failure. Kuhn did not dispute that and said it appears the problem in Boulder was that the target population was just too affluent: Despite the known green-mindedness of Boulderites, a major factor in Xcel's selecting the small city for its smart grid test run, it seems most of them do not care all that much about the modest monetary savings they stand to make from paying attention to electricity usage signals.

Researchers at Purdue University have developed a new design employing carbon nanotubes and small copper spheres that wicks water passively towards hot electronics that could meet the challenges brought on by increasing frequency speeds in chips. The problem of overheating electronics is well-documented and in the past the issue has been addressed with bigger and bigger fans. But with chip features shrinking below 50 nanometers the fan solution is just not cutting it. The Purdue researchers, led by Suresh V. Garimella, came up with a design that uses water as the coolant liquid and transfers the water to an ultrathin thermal ground plane. The design naturally pushes the water through obviating the need for a pump and through the use of microfluidic design is able to boil the water fully, which allows the wicking away of more heat.

The MATLAB team at MathWorks tested performance scaling of the backslash ("\") matrix division operator to solve for x in the equation A*x = b. In their testing, matrix A occupies far more memory (290 GB) than is available in a single high-end desktop machine—typically a quad core processor with 4-8 GB of RAM, supplying approximately 20 Gigaflops. Therefore, they spread the calculation across machines. In order to solve linear systems of equations they need to be able to access all of the elements of the array even when the array is spread across multiple machines. This problem requires significant amounts of network communication, memory access, and CPU power. They scaled up to a cluster in EC2, giving them the ability to work with larger arrays and to perform calculations at up to 1.3 Teraflops, a 60X improvement. They were able to do this without making any changes to the application code. Here's a graph showing the near-linear scalability of an EC2 cluster across a range of matrix sizes with corresponding increases in cluster size for MATLAB's parallel backslash operator:

Nikki Graziano's intriguing integration of mathematical curves into her photography sparked a Radar discussion about the relationship between mathematics and the real world. Does her work give insight into the nature of mathematics? Or into the nature of the world? And if so, what kind of insight? Mathematically, matching one curve to another isn't a big deal. Given N points, it's trivial to write an N+1 degree equation that passes through all of them. There are many more subtle ways of solving the same problem, with more aesthetically pleasing results: you can use sine functions, wavelets, square waves, whatever you want. Take out a ruler, measure some points, plug them into Mathematica, and in seconds you can generate as many curves as you like. So finding an equation that matches the curve of an artfully trimmed hedge is easy. The question is whether that curve tells us anything, or whether it's just another stupid math trick.

Ever since graphene was first produced in a lab at the University of Manchester in 2004, researchers around the world have been fascinated with its potential in electronics applications. Graphene possessed all the benefits of carbon nanotubes (CNTs), namely its charged-carrier mobility, but it didn’t have any of the down sides, such as CNTs’ need for different processing techniques than silicon and the intrinsic difficulty of creating interconnects for CNTs. But all was not easy for applying graphene to electronics applications. One of the fundamental problems for graphene was its lack of a band gap, which left it with a very low on-off ratio measured at about 10 as compared to in the 100s for silicon. Now this fundamental hurdle has been overcome. Based on research led by Phaedon Avouris at IBM’s IBM T.J. Watson Research Center, Yorktown Heights, New York, IBM is reporting that they have created a significant band gap in graphene.

4 January 2010—Though flexible devices such as roll-up displays have been promised for several years, their commercialization has been stalled by a missing ingredient: a flexible form of flash memory. But researchers at the University of Tokyo have recently developed an organic, floating-gate nonvolatile memory that behaves like flash memory, which may solve that problem. While silicon-based flash memory is fine for the mass data storage found in cellphones, digital music players, and thumb drives, fabricating it requires high processing temperatures, thus ruling out its production on flexible substrates like plastic. Organic semiconductors, however, can be processed at temperatures well below the melting point of most plastics. What's more, "the cost of flash memory is too high to use in applications that require large arrays of memory," says Tsuyoshi Sekitani, an assistant professor in the University of Tokyo's department of electrical and electronic engineering and one of the researchers who developed the new memory. "But we can print our organic memory on flexible substrates and over large areas using inkjet printers. So costs will be low."

Verification of algorithm-intensive systems is a long, costly process. Studies show that the majority of flaws in embedded systems are introduced at the specification stage, but are not detected until late in the development process. These flaws are the dominant cause of project delays and a major contributor to engineering costs. For algorithm-intensive systems —including systems with communications, audio, video, imaging, and navigation functions— these delays and costs are exploding as system complexity increases. It doesn't have to be this way. Many designers of algorithm-intensive systems already have the tools they need to get verification under control. Engineers can use these same tools to build system models that help them find and correct problems earlier in the development process. This can not only reduce verification time, but also improves the performance of their designs. In this article, we'll explain three practical approaches to early verification that make this possible. First, let's examine why the current algorithm verification process is inefficient and error-prone. In a typical workflow, designs start with algorithm developers, who pass the design to hardware and software teams using specification documents.

The moving parts of micromechanical machines tend to seize up under the forces of sticking and friction that engineers call stiction. The problem yields to solid lubricants, notably graphite (sheets of carbon atoms called graphene stacked in layers), although for a long time no one understood exactly why this happens. Now nanotechnology researchers, led by Professor Robert Carpick at the University of Pennsylvania and Professor James Hone at Columbia University, in New York City, have shown that how effective the lubrication is depends on the number of layers of graphene in the graphite. In particular, more layers means better lubrication. Because the same relationship between layers and lubrication occurs in thin sheets of molybdenum disulfide, niobium diselenide, and boron nitride—materials of widely differing properties—the workers conclude that this behavior is a fundamental aspect of friction. They expect that the discovery will lead to better lubrication of tiny moving parts. The researchers published details of their experiments in a recent issue of Science.