Super-Small Transistor

A University of Pittsburgh-led team has created a single-electron transistor that provides a building block for new, more powerful computer memories, advanced electronic materials, and the basic components of quantum computers.The researchers report in Nature Nanotechnology that the transistor's central component -- an island only 1.5 nanometers in diameter -- operates with the addition of only one or two electrons. That capability would make the transistor important to a range of computational applications, from ultradense memories to quantum processors, powerful devices that promise to solve problems so complex that all of the world's computers working together for billions of years could not crack them.
In addition, the tiny central island could be used as an artificial atom for developing new classes of artificial electronic materials, such as exotic superconductors with properties not found in natural materials.Using the sharp conducting probe of an atomic force microscope, Levy can create such electronic devices as wires and transistors of nanometer dimensions at the interface of a crystal of strontium titanate and a 1.2 nanometer thick layer of lanthanum aluminate. The electronic devices can then be erased and the interface used anew.
The SketchSET -- which is the first single-electron transistor made entirely of oxide-based materials -- consists of an island formation that can house up to two electrons. The number of electrons on the island -- which can be only zero, one, or two -- results in distinct conductive properties. Wires extending from the transistor carry additional electrons across the island.
One virtue of a single-electron transistor is its extreme sensitivity to an electric charge.Another property of these oxide materials is ferroelectricity, which allows the transistor to act as a solid-state memory. The ferroelectric state can, in the absence of external power, control the number of electrons on the island, which in turn can be used to represent the 1 or 0 state of a memory element. A computer memory based on this property would be able to retain information even when the processor itself is powered down, Levy said. The ferroelectric state also is expected to be sensitive to small pressure changes at nanometer scales, making this device potentially useful as a nanoscale charge and force sensor.
The research in Nature Nanotechnology also was supported in part by grants from the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. Army Research Office, the National Science Foundation, and the Fine Foundation.

Mining Data from Electronic Records

Recruiting thousands of patients to collect health data for genetic clues to disease is expensive and time consuming. But that arduous process of collecting data for genetic studies could be faster and cheaper by instead mining patient data that already exists in electronic medical records, according to new Northwestern Medicine research.
In the study, researchers were able to cull patient information in electronic medical records from routine doctors' visits at five national sites that all used different brands of medical record software. The information allowed researchers to accurately identify patients with five kinds of diseases or health conditions -- type 2 diabetes, dementia, peripheral arterial disease, cataracts and cardiac conduction.
"The hard part of doing genetic studies has been identifying enough people to get meaningful results," said lead investigator Abel Kho, M.D., an assistant professor of medicine at Northwestern University Feinberg School of Medicine and a physician at Northwestern Memorial Hospital. "Now we've shown you can do it using data that's already been collected in electronic medical records and can rapidly generate large groups of patients."
To identify the diseases, Kho and colleagues searched the records using a series of criteria such as medications, diagnoses and laboratory tests. They then tested their results against the gold standard -- review by physicians. The physicians confirmed the results, Kho said. The electronic health records allowed researchers to identify patients' diseases with 73 to 98 percent accuracy.
The researchers also were able to reproduce previous genetic findings from prospective studies using the electronic medical records. The five institutions that participated in the study collected genetic samples for research. Patients agreed to the use of their records for studies.Sequencing individuals genomes is becoming faster and cheaper. It soon may be possible to include patients genomes in their medical records. This would create a bountiful resource for genetic research.
The larger the group of patients for genetic studies, the better the ability to detect rarer affects of the genes and the more detailed genetic sequences that cause a person to develop a disease.The study also showed across-the-board weaknesses in institutions' electronic medical records. The institutions didn't do a good job of capturing race and ethnicity, smoking status and family history, all which are important areas of study.
The institutions participating in the study are part of a consortium called the Electronic Medical Records and Genomics Network.The research was supported by the National Human Genome Research Institute with additional funding from the National Institute of General Medical Sciences.

3-D Towers

Using well-known patterned media, a team of researchers in France has figured out a way to double the areal density of information by essentially cutting the magnetic media into small pieces and building a "3D tower" out of it.Using well-known patterned media, a team of researchers in France has figured out a way to double the areal density of information by essentially cutting the magnetic media into small pieces and building a "3D tower" out of it.
"Over the past 50 years, with the rise of multimedia devices, the worldwide Internet, and the general growth in demand for greater data storage capacity, the areal density of information in magnetic hard disk drives has exponentially increased by 7 orders of magnitude," says Jerome Moritz, a researcher at SPINTEC, in Grenoble. "This areal density is now about 500Gbit/in2, and the technology presently used involves writing the information on a granular magnetic material. This technology is now reaching some physical limits because the grains are becoming so small that their magnetization becomes unstable and the information written on them is gradually lost." Therefore, new approaches are needed for magnetic data storage densities exceeding 1Tbit/in2.
Our new approach involves using bit-patterned media, which are made of arrays of physically separated magnetic nanodots, with each nanodot carrying one bit of information. To further extend the storage density, it's possible to increase the number of bits per dots by stacking several magnetic layers to obtain a multilevel magnetic recording device.
The best way to achieve a 2-bit-per-dot media involves stacking in-plane and perpendicular-to-plane magnetic media atop each dot. The perpendicularly magnetized layer can be read right above the dot, whereas the in-plane magnetized layer can be read between dots. This enables doubling of the areal density for a given dot size by taking better advantage of the whole patterned media area.

Ultra-Fast Magnetic Reversal

A newly discovered magnetic phenomenon could accelerate data storage by several orders of magnitude.With a constantly growing flood of information, we are being inundated with increasing quantities of data, which we in turn want to process faster than ever. Oddly, the physical limit to the recording speed of magnetic storage media has remained largely unresearched. In experiments performed on the particle accelerator BESSY II of Helmholtz-Zentrum Berlin, Dutch researchers have now achieved ultrafast magnetic reversal and discovered a surprising phenomenon.
In magnetic memory, data is encoded by reversing the magnetization of tiny points. Such memory works using the so-called magnetic moments of atoms, which can be in either "parallel" or "antiparallel" alignment in the storage medium to represent to "0" and "1."The alignment is determined by a quantum mechanical effect called "exchange interaction." This is the strongest and therefore the fastest "force" in magnetism. It takes less than a hundred femtoseconds to restore magnetic order if it has been disturbed. One femtosecond is a millionth of a billionth of a second. Ilie Radu and his colleagues have now studied the hitherto unknown behaviour of magnetic alignment before the exchange interaction kicks in. Together with researchers from Berlin and York, they have published their results in Nature.
For their experiment, the researchers needed an ultra-short laser pulse to heat the material and thus induce magnetic reversal. They also needed an equally short X-ray pulse to observe how the magnetization changed. This unique combination of a femtosecond laser and circular polarized, femtosecond X-ray light is available in one place in the world: at the synchrotron radiation source BESSY II in Berlin, Germany.
In their experiment, the scientists studied an alloy of gadolinium, iron and cobalt (GdFeCo), in which the magnetic moments naturally align antiparallel. They fired a laser pulse lasting 60 femtoseconds at the GdFeCo and observed the reversal using the circular-polarized X-ray light, which also allowed them to distinguish the individual elements. What they observed came as a complete surprise: The Fe atoms already reversed their magnetization after 300 femtoseconds while the Gd atoms required five times as long to do so. That means the atoms were all briefly in parallel alignment, making the material strongly magnetized.This is as strange as finding the north pole of a magnet reversing slower than the south pole.
With their observation, the researchers have not only proven that magnetic reversal can take place in femtosecond timeframes, they have also derived a concrete technical application from it: Translated to magnetic data storage, this would signify a read/write rate in the terahertz range. That would be around 1000 times faster than present-day commercial computers.

3-D Gesture

Touch screens such as those found on the iPhone or iPad are the latest form of technology allowing interaction with smart phones, computers and other devices. However, scientists at Fraunhofer FIT has developed the next generation non-contact gesture and finger recognition system. The novel system detects hand and finger positions in real-time and translates these into appropriate interaction commands. Furthermore, the system does not require special gloves or markers and is capable of supporting multiple users.
Touch screens such as those found on the iPhone or iPad are the latest form of technology allowing interaction with smart phones, computers and other devices. However, scientists at Fraunhofer FIT has developed the next generation non-contact gesture and finger recognition system. The novel system detects hand and finger positions in real-time and translates these into appropriate interaction commands. Furthermore, the system does not require special gloves or markers and is capable of supporting multiple users.
With touch screens becoming increasingly popular, classic interaction techniques such as a mouse and keyboard are becoming less frequently used. One example of a breakthrough is the Apple iPhone which was released in summer 2007. Since then many other devices featuring touch screens and similar characteristics have been successfully launched -- with more advanced devices even supporting multiple users simultaneously, e.g. the Microsoft Surface table becoming available. This is an entire surface which can be used for input. However, this form of interaction is specifically designed for two-dimensional surfaces.
Fraunhofer FIT has developed the next generation of multi-touch environment, one that requires no physical contact and is entirely gesture-based. This system detects multiple fingers and hands at the same time and allows the user to interact with objects on a display. The users move their hands and fingers in the air and the system automatically recognizes and interprets the gestures accordingly.Cinemagoers will remember the science-fiction thriller Minority Report from 2002 which starred Tom Cruise. In this film Tom Cruise is in a 3-D software arena and is able to interact with numerous programs at unimaginable speed, however the system used special gloves and only three fingers from each hand.The FIT prototype provides the next generation of gesture-based interaction far in advance of the Minority Report system. The FIT prototype tracks the user's hand in front of a 3-D camera. The 3-D camera uses the time of flight principle, in this approach each pixel is tracked and the length of time it takes light to be filmed travelling to and from the tracked object is determined. This allows for the calculation of the distance between the camera and the tracked object.
A special image analysis algorithm was developed which filters out the positions of the hands and fingers. This is achieved in real-time through the use of intelligent filtering of the incoming data. The raw data can be viewed as a kind of 3-D mountain landscape, with the peak regions representing the hands or fingers. In addition plausibility criteria are used, these are based around: the size of a hand, finger length and the potential coordinates.A user study was conducted and found that the system both easy to use and fun. However, work remains to be done on removing elements which confuses the system, for example reflections caused by wristwatches and palms which are positioned orthogonal to the camera.

Artificial intelligence

Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times, and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chess player, and countless other feats never before possible. Find out how the military is applying AI logic to its hi-tech systems, and how in the near future Artificial Intelligence may impact our lives.
 To understand what exactly artificial intelligence is, we illustrate some common problems. Problems dealt with in artificial intelligence generally use a common term called 'state'. A state represents a status of the solution at a given step of the problem solving procedure. The solution of a problem, thus, is a collection of the problem states. The problem solving procedure applies an operator to a state to get the next state. Then it applies another operator to the resulting state to derive a new state. The process of applying an operator to a state and its subsequent transition to the next state, thus, is continued until the goal (desired) state is derived. Such a method of solving a problem is generally referred to as state space approach. We will first discuss the state-space approach for problem solving by a well-known problem, which most of us perhaps have solved in our childhood.
Researchers in artificial intelligence have segregated the AI problems from the non-AI problems. Generally, problems, forwhich straightforward mathematical / logical algorithms are not readily available and which can be solved by intuitive approach only, are called AI problems. The 4-puzzle problem, for instance, is an ideal AI Problem. There is no formal algorithm for its realization, i.e., given a starting and a goal state, one cannot say prior to execution of the tasks the sequence of steps required to get the goal from the starting state. Such problems are called the ideal AI problems. The well known water-jug problem, the Travelling Salesperson Problem (TSP) , and the n-Queen problem are typical examples of the classical AI problems. Among the non-classical AI problems, the diagnosis problems and the pattern classification problem need special mention. For solving an AI problem, one may employ both artificial intelligence and non-AI algorithms. An obvious question is: what is an AI algorithm? Formally speaking, an artificial intelligence algorithm generally means a non-conventional intuitive approach for problem solving. The key to artificial intelligence approach is intelligent search and matching. In an intelligent search problem / sub-problem, given a goal (or starting) state, one has to reach that state from one or more known starting (or goal) states.
The question that then naturally arises is: how to control the generation of states. This, in fact, can be achieved by suitably designing some control strategies, which would filter a few states only from a large number of legal states that could be generated from a given starting / intermediate state. As an example, consider the problem of proving a trigonometric identity that children are used to doing during their schooldays. What would they do at the beginning? They would start with one side of the identity, and attempt to apply a number of formulae there to find the possible resulting derivations. But they won't really apply all the formulae there. Rather, they identify the right candidate formula that fits there best, such that the other side of the identity seems to be closer in some sense (outlook). Ultimately, when the decision regarding the selection of the formula is over, they apply it to one side (say the L.H.S) of the identity and derive the new state. Thus they continue the process and go on generating new intermediate states until the R.H.S (goal) is reached. But do they always select the right candidate formula at a given state? From our experience, we know the answer is "not always". But what would we do if we find that after generation of a few states, the resulting expression seems to be far away from the R.H.S of the identity. Perhaps we would prefer to move to some old state, which is more promising, i.e., closer to the R.H.S of the identity. The above line of thinking has been realized in many intelligent search problems of AI. Some of these well-known search algorithms are:

    * Generate and Test
    * Hill Climbing
    * Heuristic Search
    * Means and Ends analysis

Generate and Test Approach: This approach concerns the generation of the state-space from a known starting state (root) of

the problem and continues expanding the reasoning space until the goal node or the terminal state is reached. In fact after

generation of each and every state, the generated node is compared with the known goal state. When the goal is found, the

algorithm terminates. In case there exist multiple paths leading to the goal, then the path having the smallest distance from

the root is preferred. The basic strategy used in this search is only generation of states and their testing for goals but it

does not allow filtering of states.

(b) Hill Climbing Approach: Under this approach, one has to first generate a starting state and measure the total cost for

reaching the goal from the given starting state. Let this cost be f. While f = a predefined utility value and the goal is not

reached, new nodes are generated as children of the current node. However, in case all the neighborhood nodes (states) yield

an identical value of f and the goal is not included in the set of these nodes, the search algorithm is trapped at a hillock

or local extrema. One way to overcome this problem is to select randomly a new starting state and then continue the above

search process. While proving trigonometric identities, we often use Hill Climbing, perhaps unknowingly.

(c) Heuristic Search: Classically heuristics means rule of thumb. In heuristic search, we generally use one or more heuristic

functions to determine the better candidate states among a set of legal states that could be generated from a known state.

The heuristic function, in other words, measures the fitness of the candidate states. The better the selection of the states,

the fewer will be the number of intermediate states for reaching the goal. However, the most difficult task in heuristic

search problems is the selection of the heuristic functions. One has to select them intuitively, so that in most cases

hopefully it would be able to prune the search space correctly. We will discuss many of these issues in a separate chapter on

Intelligent Search.

(d) Means and Ends Analysis: This method of search attempts to reduce the gap between the current state and the goal state.

One simple way to explore this method is to measure the distance between the current state and the goal, and then apply an

operator to the current state, so that the distance between the resulting state and the goal is reduced. In many mathematical

theorem- proving processes, we use Means and Ends Analysis.

IPoint 3D

The "iPoint 3D" allows people to communicate with a 3-D display through simple gestures – without touching it and without 3-D glasses or a data glove. What until now has only been seen in science fiction films will be presented at CeBIT from March 3-8 by experts from the Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, (HHI).The heart of iPoint 3D is a recognition device, not much larger than a keyboard, that can be suspended from the ceiling above the user or integrated in a coffee table. Its two built-in cameras detect hands and fingers in real time and transmit the information to a computer.
The system responds instantly, as soon as someone in front of the screen moves their hands. No physical contact or special markers are involved. The small device is equipped with two FireWire cameras – inexpensive, off-the-shelf video cameras that are easy to install.
In addition to its obvious appeal to video gamers, iPoint 3D can also be useful in a living room or office, or even in a hospital operating room, or as part of an interactive information system. Since the interaction is entirely contactless, the system is ideal for scenarios where contact between the user and the system is not possible or not allowed, such as in an operating room.
The HHI invention can thus be used not only to control a display but also as a means of controlling other devices or appliances. Someone kneading pastry in the kitchen, whose hands are covered in dough, can turn down the boiling potatoes by waving a finger without leaving sticky marks on the stove. In an office, for example, an architect can peruse the latest set of construction drawings and view them from all angles by gesture control. The finger is the remote control of the future.