3-D Underwater Imaging
Technology inspired by fish could help undersea vehicles find their way
Source: “Artificial lateral line with biomimetic neuromasts to emulate fish sensing”
Chang Liu, Douglas Jones, et al.
Bioinspiration & Biomimetics 5(1): 016001
Results: Drawing inspiration from sensory cells in fish, researchers at Northwestern University and the University of Illinois have created a new kind of underwater 3-D imaging device. The invention uses an array of sensors to detect objects–in this case, a crayfish–on the basis of their movements. It can pinpoint the location of objects within a distance equal to half the length of the array.
Why it matters: The automated underwater vehicles currently used for surveillance, research, surveying, and other applications navigate either with cameras, which don’t work well in murky water, or with sonar, which doesn’t work well at close range. This new sensor could allow for more accurate navigation, particularly in confined spaces and unclear water.
Methods: Fish detect obstacles, predators, and prey with the help of sensory organs made up of arrays of specialized cells. These cells use tiny hairlike projections to sense water movement. The researchers used microfabrication techniques to mimic these cells. Each artificial sensor consists of a vertical silicon “hair,” about 500 micrometers long, that is anchored to a piezoelectric device. As the hair moves, the piezoelectric material generates a voltage. The researchers developed an algorithm that interprets voltage signals from an array of the sensors to locate the source of moving water.
Next steps: The researchers will make larger sensor arrays and attach them to underwater vehicles for practical testing. They will also work to improve the resolution of the sensors so that they can detect objects farther away.
Faster Cloud Computing
Reliable software can now handle data on the fly rather than in batches
Source: “MapReduce Online”
Tyson Condie et al.
Proceedings of the seventh USENIX Symposium on Network Design and Implementation, April 28-30, 2010, San Jose, CA
Results: Researchers have modified Hadoop MapReduce, a software platform designed to reliably process large amounts of data on a cluster of computers (as is necessary in cloud computing). The changes decreased by several orders of magnitude the time the software takes to process data, without sacrificing the reliability the technology is known for.
Why it matters: The earlier version of Hadoop was too slow to handle applications that require real-time responsiveness, such as providing near-instant updates about the traffic or sales transactions on a website. The new version could expand the range of applications that can run on distributed computers. It could also make applications that are run in the cloud more reliable by allowing managers to catch abnormal behavior as soon as it happens.
Methods: The researchers reduced the time it took Hadoop MapReduce to complete jobs by adapting a technique called pipelining. Ordinarily, Hadoop waits until one task is complete before it will start a second; that makes it easier to handle the failure of a computer in the cluster. With pipelining, data can be sent and processed continuously before the first task is complete.
Next steps: One of the researchers would like to develop the system further so that it can be used to customize Web-page layouts in real time in response to user behavior.