From the Labs: Information Technology
New publications, experiments, and breakthroughs in information technology–and what they mean.
Ultrahigh-Resolution Signal Analysis
An obscure algorithm could lead to more precise radar and a better understanding of human hearing
Source: “Sparse Time-Frequency Representations”
Timothy J. Gardner et al.
Proceedings of the National Academy of Sciences 103(16): 6094-6099
Results: Timothy Gardner of MIT and Marcelo Magnasco of Rockefeller University have proved that a method of high-resolution signal analysis produces faithful representations of sounds.
Why it matters: Signal analysis algorithms have been used for decades in speech recognition software, radar, and geological imaging. A method called “reassigned time-frequencyrepresentation” can capture sound data at a theoretically unlimited level of resolution. If used in radar, for example, it could help to measure the speed of a helicopter’s blades, whereas radar using traditional methods could identify only the basic shape of the helicopter. However, no one had mathematically demonstrated that the method, for all its precision, produced faithful representations–in part because it is relatively obscure in the signal-processing field. Its fidelity proven, the method could be incorporated into radar systems and signal-processing software.
Methods: To illustrate their proof, the researchers analyzed white noise, which is similar to the static of a radio tuned between stations. They represented the noise as a two-dimensional picture with time displayed on the horizontal axis and frequency on the vertical axis. They then compared their white-noise image with one produced using traditional methods. The traditional image was blurry, with black dots that represented the complete absence of sound. The researchers found that their algorithm faithfully represented the shape of the noise pattern, including the positions of the black dots, but at a higher resolution.
Next steps: The researchers plan to apply their understanding of reassigned time-frequency representation in an investigation of human hearing. By testing their algorithms against artificial neural networks that represent auditory nerves, they will try to create better neurological models of the way the brain makes sense of sound.
A new ion trap could make quantum computers possible
Source: “A Microfabricated Surface-Electrode Ion Trap for Scalable Quantum Information Processing”
S. Seidelin et al.
Physical Review Letters 96: 253003
Results: Researchers at the National Institute of Standards and Technology have developed a new device that electromagnetically traps ions. The chiplike trap is easy to make, can be mass produced, and can hold up to 12 ions to be used for quantum computation.
Why it matters: Quantum computers would be able to perform millions of operations simultaneously; in principle, they could break complex encryption codes or search vast databases, tasks that are prohibitively time consuming with current technology. Many researchers believe that a promising way to represent “quantum bits”–the basic units of quantum computation–is to use ions, or charged atoms. The ions are held in place with electromagnetic fields produced by devices called ion traps, and computations are then executed by lasers, which manipulate the behavior of the trapped ions. But most existing ion traps have drawbacks. Those that are easy to make can manipulate ions in only one trapping zone, which limits their computational power; others that allow for more trapping zones are difficult to mass produce. The NIST trap is the first that could potentially address both problems.
Methods: Using standard microfabrication techniques, the researchers built a trap that has a single layer of gold electrodes; other types of traps have two or three layers of electrodes, making them more difficult to mass produce. The electrodes create an electromagnetic field that isolates magnesium ions and holds them in place 40 micrometers above the trap, where they could be used to perform a computation.
Next steps: The team will continue to explore more-complex traps that hold more ions. Future traps will also have structures that allow the ions to be manipulated with lasers in order to perform logic functions, a key step toward making quantum computers.