Author: Madhu Reddy

Researchers have shown that a DC voltage applied to layers of graphene and boron nitride can be used to control light emission from a nearby atom. Here, graphene is represented by a maroon-colored top layer; boron nitride is represented by yellow-green lattices below the graphene; and the atom is represented by a grey circle. A low concentration of DC voltage (in blue) allows the light to propagate inside the boron nitride, forming a tightly confined waveguide for optical signals. Researchers have shown that a DC voltage applied to layers of graphene and boron nitride can be used to control light emission from a nearby atom. Here, graphene is represented by a maroon-colored top layer; boron nitride is represented by yellow-green lattices below the graphene; and the atom is represented by a grey circle. A low concentration of DC voltage (in blue) allows the light to propagate inside the boron nitride, forming a tightly confined waveguide for optical signals.

Researchers have found a way to couple the properties of different two-dimensional materials to provide an exceptional degree of control over light waves. They say this has the potential to lead to new kinds of light detection, thermal-management systems, and high-resolution imaging devices.

The new findings — using a layer of one-atom-thick graphene deposited on top of a similar 2-D layer of a material called hexagonal boron nitride (hBN) — are published in the journal Nano Letters. The work is co-authored by MIT associate professor of mechanical engineering Nicholas Fang and graduate student Anshuman Kumar, and their co-authors at IBM’s T.J. Watson Research Center, Hong Kong Polytechnic University, and the University of Minnesota.

Although the two materials are structurally similar — both composed of hexagonal arrays of atoms that form two-dimensional sheets — they each interact with light quite differently. But the researchers found that these interactions can be complementary, and can couple in ways that afford a great deal of control over the behavior of light.

The hybrid material blocks light when a particular voltage is applied to the graphene, while allowing a special kind of emission and propagation, called “hyperbolicity,” when a different voltage is applied — a phenomenon not seen before in optical systems, Fang says. One of the consequences of this unusual behavior is that an extremely thin sheet of material can interact strongly with light, allowing beams to be guided, funneled, and controlled by voltages applied to the sheet.

“This poses a new opportunity to send and receive light over a very confined space,” Fang says, and could lead to “unique optical material that has great potential for optical interconnects.” Many researchers see improved interconnection of optical and electronic components as a path to more efficient computation and imaging systems.

Light’s interaction with graphene produces particles called plasmons, while light interacting with hBN produces phonons. Fang and his colleagues found that when the materials are combined in a certain way, the plasmons and phonons can couple, producing a strong resonance.

The properties of the graphene allow precise control over light, while hBN provides very strong confinement and guidance of the light. Combining the two makes it possible to create new “metamaterials” that marry the advantages of both, the researchers say.

Phaedon Avouris, a researcher at IBM and co-author of the paper, says, “The combination of these two materials provides a unique system that allows the manipulation of optical processes.”

The combined materials create a tuned system that can be adjusted to allow light only of certain specific wavelengths or directions to propagate, they say. “We can start to selectively pick some frequencies [to let through], and reject some,” Kumar says.

These properties should make it possible, Fang says, to create tiny optical waveguides, about 20 nanometers in size — the same size range as the smallest features that can now be produced in microchips. This could lead to chips that combine optical and electronic components in a single device, with far lower losses than when such devices are made separately and then interconnected, they say.

Co-author Tony Low, a researcher at IBM and the University of Minnesota, says, “Our work paves the way for using 2-D material heterostructures for engineering new optical properties on demand.”

Another potential application, Fang says, comes from the ability to switch a light beam on and off at the material’s surface; because the material naturally works at near-infrared wavelengths, this could enable new avenues for infrared spectroscopy, he says. “It could even enable single-molecule resolution,” Fang says, of biomolecules placed on the hybrid material’s surface.

Sheng Shen, an assistant professor of mechanical engineering at Carnegie Mellon University who was not involved in this research, says, “This work represents significant progress on understanding tunable interactions of light in graphene-hBN.” The work is “pretty critical” for providing the understanding needed to develop optoelectronic or photonic devices based on graphene and hBN, he says, and “could provide direct theoretical guidance on designing such types of devices. … I am personally very excited about this novel theoretical work.”

The research team also included Kin Hung Fung of Hong Kong Polytechnic University. The work was supported by the National Science Foundation and the Air Force Office of Scientific Research.

References:http://www.sciencedaily.com/

A Kickstarter campaign is heating up over a device called Phree. It’s for writing on nearly any surface you want to and seeing your writing instantly appear on your screen. In a promotional video, a presenter says, “In 2015 we love our touchscreeens.” Only, there’s one problem: “They are not a perfect input device.” Users need better precision and more space, and so Phree was created, taking you way beyond the screen.

This is a high-resolution mobile input device, where you can sketch or jot down notes, thoughts, addresses, numbers, and such, without the added effort of having to unlock a phone, open an app and wait.
Phree will connect to a range of devices: phone, tablet, laptop, TV, anything with a Bluetooth connection. They said Phree is compatible with Office, OneNote, EverNote, Acrobat, and more. Also, they said Phree supports all major phone, tablet and PC operating systems – Android, iOS, Windows, OSX, Linux.
Opher Kinrot, Uri Kinrot, chief engineer and Gilad Lederer are co-founders of the Tel Aviv-based company OTM Technologies, which created Phree. Elisha Tal is the chief designer.
The design involves an oval cross-section for usefulness and comfort. Held in writing position, Phree’s touch display always faces the user. One can touch to change from pen to highlighter or from red to blue or from messaging to dialing. Phree prototypes are in black, graphite, silver and gold.
They have turned to Kickstarter to push Phree closer to market and delivery stages.
The key driver that enables Phree is “Optical Translation Measurement” (OTM) technology, which precisely tracks hand motion across a surface. They have engineered and built a compact optical sensor that fits at the tip of a pen-like device.

The OTM sensor is a 3D laser interferometer. It tracks the relative motion of a nearby surface, they said, by measuring the interference signal between a laser beam projected on the surface and reflections from the surface. The signal is translated to X-Y-Z motion information by their signal-processing algorithms running on a small, integrated electronics component

.

The battery will last around one week for typical usage. Full charging time is about one hour.
For developers, the good news is that this is an open platform. They said, “An open API allows developers to make use of the screen for specific interaction with their applications. The API provides access to additional sensor information such as vertical (Z) motion data, as well as access to the accelerometer.”
The Kickstarter page lists the range of prices and reward details. At the time of this writing, for example, a pledge of $316 would bring a twin pack of two Phree devices A pledge of $219 would get one Phree and case. Estimated delivery date for both price offers is April 2016

References:http://phys.org/

 

Conventional electroluminescent (EL) foils can be bent up to a certain degree only and can be applied easily onto flat surfaces. The new process developed by Karlsruhe Institute of Technology (KIT) in cooperation with the company of Franz Binder GmbH & Co. now allows for the direct printing of electroluminescent layers onto three-dimensional components. Such EL components might be used to enhance safety in buildings in case of power failures. Other potential applications are displays and watches or the creative design of rooms.

“By means of the innovative production process we developed together with our industry partner, any type of three-dimensional object can be provided with electroluminescent coatings at low costs,” Dr.-Ing. Rainer Kling of the Light Technology Institute of KIT says. Usually, the luminescent material is located between two plastic layers in EL carrier foils. By means of the new printing process, however, the electroluminescent layers are directly printed onto the object without any intermediate carrier. In this way, convex and concave surfaces of various materials, such as paper or plastic, can be made glow.
The different components of the coating, including the electroluminescent and the electrically conductive materials, are applied by a novel pad printing process. The pad printing machine is equipped with an elastic rubber pad that is easily deformable and, hence, excellently suited for the coating of curved surfaces.
“In this way, it is possible to provide surfaces and even spheres with homogeneous coatings at low costs,” says engineer Elodie Chardin, who works on this research project. “Homogeneity of the coating of about one tenth of a millimeter in thickness was one of the challenges of this project,” says the executive engineer of the industry partner, Elisabeth Warsitz. The process requires a few production steps only and, hence, is characterized by a low consumption of resources. By using various luminescent substances, various colors may be applied to the same surface.

The research and development project of KIT in cooperation with the Binder Connector Group, headquartered in the German town of Neckarsulm, took about two years and was funded with EUR 125,000 by the Deutsche Bundesstiftung Umwelt (German Foundation for the Environment).

References:http://phys.org/

As anyone who’s ever used a spreadsheet can attest, it’s often convenient to organize data into tables. But in the age of big data, those tables can be enormous, with millions or even hundreds of millions of rows.

One way to make big-data analysis computationally practical is to reduce the size of data tables—or matrices, to use the mathematical term—by leaving out a bunch of rows. The trick is that the remaining rows have to be in some sense representative of the ones that were omitted, in order for computations performed on them to yield approximately the right results.
At the ACM Symposium on Theory of Computing in June, MIT researchers will present a new algorithm that finds the smallest possible approximation of the original matrix that guarantees reliable computations. For a class of problems important in engineering and machine learning, this is a significant improvement over previous techniques. And for all classes of problems, the algorithm finds the approximation as quickly as possible.
In order to determine how well a given row of the condensed matrix represents a row of the original matrix, the algorithm needs to measure the “distance” between them. But there are different ways to define “distance.”
One common way is so-called “Euclidean distance.” In Euclidean distance, the differences between the entries at corresponding positions in the two rows are squared and added together, and the distance between rows is the square root of the resulting sum. The intuition is that of the Pythagorean theorem: The square root of the sum of the squares of the lengths of a right triangle’s legs gives the length of the hypotenuse.
Another measure of distance is less common but particularly useful in solving machine-learning and other optimization problems. It’s called “Manhattan distance,” and it’s simply the sum of the absolute differences between the corresponding entries in the two rows.
Inside the norm
In fact, both Manhattan distance and Euclidean distance are instances of what statisticians call “norms.” The Manhattan distance, or 1-norm, is the first root of the sum of differences raised to the first power, and the Euclidean distance, or 2-norm, is the square root of the sum of differences raised to the second power. The 3-norm is the cube root of the sum of differences raised to the third power, and so on to infinity.
In their paper, the MIT researchers—Richard Peng, a postdoc in applied mathematics, and Michael Cohen, a graduate student in electrical engineering and computer science—demonstrate that their algorithm is optimal for condensing matrices under any norm. But according to Peng, “The one we really cared about was the 1-norm.”
In matrix condensation—under any norm—the first step is to assign each row of the original matrix a “weight.” A row’s weight represents the number of other rows that it’s similar to, and it determines the likelihood that the row will be included in the condensed matrix. If it is, its values will be multiplied according to its weight. So, for instance, if 10 rows are good stand-ins for each other, but not for any other rows of the matrix, each will have a 10 percent chance of getting into the condensed matrix. If one of them does, its entries will all be multiplied by 10, so that it will reflect the contribution of the other nine rows it’s standing in for.
Although Manhattan distance is in some sense simpler than Euclidean distance, it makes calculating rows’ weights more difficult. Previously, the best algorithm for condensing matrices under the 1-norm would yield a matrix whose number of rows was proportional to the number of columns of the original matrix raised to the power of 2.5. The best algorithm for condensing matrices under the 2-norm, however, would yield a matrix whose number of rows was proportional to the number of columns of the original matrix times its own logarithm.
That means that if the matrix had 100 columns, under the 1-norm, the best possible condensation, before Peng and Cohen’s work, was a matrix with hundreds of thousands of rows. Under the 2-norm, it was a matrix with a couple of hundred rows. That discrepancy grows as the number of columns increases.
Taming recursion
Peng and Cohen’s algorithm condenses matrices under the 1-norm as well as it does under the 2-norm; under the 2-norm, it condenses matrices as well as its predecessors do. That’s because, for the 2-norm, it simply uses the best existing algorithm. For the 1-norm, it uses the same algorithm, but it uses it five or six times.
The paper’s real contribution is to mathematically prove that the 2-norm algorithm will yield reliable results under the 1-norm. As Peng explains, an equation for calculating 1-norm weights has been known for some time. But “the funny thing with that definition is that it’s recursive,” he says. “So the correct set of weights appears on both the left-hand side and the right-hand side.” That is, the weight for a given matrix row—call it w—is set equal to a mathematical expression that itself includes w.
“This definition was known to exist, but people in stats didn’t know what to do with it,” Peng says. “They look at it and think, ‘How do I ever compute anything with this?'”
What Peng and Cohen prove is that if you start by setting the w on the right side of the equation equal to 1, then evaluate the expression and plug the answer back into the right-hand w, then do the same thing again, and again, you’ll quickly converge on a good approximation of the correct value of w.
“It’s highly elegant mathematics, and it gives a significant advance over previous results,” says Richard Karp, a professor of computer science at the University of California at Berkeley and a winner of the National Medal of Science and of the Turing Award, the highest honor in computer science. “It boils the original problem down to a very simple-to-understand one. I admire the mathematical development that went into it.”

References:http://phys.org/