Showing posts with label Advanced. Show all posts
Showing posts with label Advanced. Show all posts

Friday, March 27, 2015

Quantum Entaglement Light Photon Created to Contain Thousands of Atoms for Advanced Atomic Time Keeping

Physicists from MIT and the University of Belgrade have developed a new technique that can successfully entangle 3,000 atoms using only a single photon. The results, published today in the journal Nature, represent the largest number of particles that have ever been mutually entangled experimentally.

The researchers say the technique provides a realistic method to generate large ensembles of entangled atoms, which are key components for realizing more-precise atomic clocks.
“You can make the argument that a single photon cannot possibly change the state of 3,000 atoms, but this one photon does — it builds up correlations that you didn’t have before,” says Vladan Vuletic, the Lester Wolfe Professor in MIT’s Department of Physics, and the paper’s senior author. “We have basically opened up a new class of entangled states we can make, but there are many more new classes to be explored.”

Vuletic’s co-authors on the paper are Robert McConnell, Hao Zhang, and Jiazhong Hu of MIT, as well as Senka Cuk of the University of Belgrade.

Atomic entanglement and timekeeping

Entanglement is a curious phenomenon: As the theory goes, two or more particles may be correlated in such a way that any change to one will simultaneously change the other, no matter how far apart they may be. For instance, if one atom in an entangled pair were somehow made to spin clockwise, the other atom would instantly be known to spin counterclockwise, even though the two may be physically separated by thousands of miles.

The phenomenon of entanglement, which physicist Albert Einstein once famously dismissed as “spooky action at a distance,” is described not by the laws of classical physics, but by quantum mechanics, which explains the interactions of particles at the nanoscale. At such minuscule scales, particles such as atoms are known to behave differently from matter at the macroscale.
Scientists have been searching for ways to entangle not just pairs, but large numbers of atoms; such ensembles could be the basis for powerful quantum computers and more-precise atomic clocks. The latter is a motivation for Vuletic’s group.

Today’s best atomic clocks are based on the natural oscillations within a cloud of trapped atoms. As the atoms oscillate, they act as a pendulum, keeping steady time. A laser beam within the clock, directed through the cloud of atoms, can detect the atoms’ vibrations, which ultimately determine the length of a single second.

“Today’s clocks are really amazing,” Vuletic says. “They would be less than a minute off if they ran since the Big Bang — that’s the stability of the best clocks that exist today. We’re hoping to get even further.”

The accuracy of atomic clocks improves as more and more atoms oscillate in a cloud. Conventional atomic clocks’ precision is proportional to the square root of the number of atoms: For example, a clock with nine times more atoms would only be three times as accurate. If these same atoms were entangled, a clock’s precision could be directly proportional to the number of atoms — in this case, nine times as accurate. The larger the number of entangled particles, then, the better an atomic clock’s timekeeping.

Picking up quantum noise

Scientists have so far been able to entangle large groups of atoms, although most attempts have only generated entanglement between pairs in a group. Only one team has successfully entangled about 100 atoms — the largest mutual entanglement to date, and only a small fraction of the whole atomic ensemble.

Now Vuletic and his colleagues have successfully created a mutual entanglement among 3,000 atoms, virtually all the atoms in the ensemble, using very weak laser light — down to pulses containing a single photon. The weaker the light, the better, Vuletic says, as it is less likely to disrupt the cloud. “The system remains in a relatively clean quantum state,” he says.

Precise Time:





Thursday, January 22, 2015

Robots Use Youtube Learning Tutorials to Cook Food at Institute for Advanced Computer Studies




""When it comes to teaching robots how to do things, there are some very key differences. A human knows what you mean when you say "I need a cup". A robot needs to be taught that that means it has to turn around, go to the cupboard, open it, take out the cup, close the cupboard, turn back around, return to you, manoeuvre the cup over the bench, and release the cup.

This is one of the key parts of figuring out machine learning: How can you program a robot so that it can intuit that a plastic cup, a glass and a mug may all be classified under the general term "cup"? How can you design a robot that is able to teach itself?

One way, as researchers at the University of Maryland Institute for Advanced Computer Studies are finding out, is YouTube. More specifically, cooking tutorials on YouTube. By watching these videos, robots are able to learn the complicated series of grasping and manipulation motions required for cooking by observing what humans do on the Internet.



"We chose cooking videos because everyone has done it," said UMD professor of computer science and director of the UMIACS Computer Vision Lab Yiannis Aloimonos. "But cooking is complex in terms of manipulation, the steps involved and the tools you use. If you want to cut a cucumber, for example, you need to grab the knife, move it into place, make the cut and observe the results to make sure you did them properly."

The robot uses several key systems in order to learn from YouTube videos. Computer vision, with two different recognition systems, allows the robot to visually process how the presenter grabs something, artificial intelligence processes that information, and finally language parsing helps it understand spoken commands and translate it into an action.

In this way, the robot can gather individual steps from various videos and assign them rules according to its programming, putting them together in the correct order.""



Follow Link More Information 

Wednesday, January 14, 2015

Advanced Photographic Tools Unmanned Autonomous Underwater Robotic Vehicles Uncover Deep-sea Ecosystem Diversity at ‘Landscape’ Scales

""Scientists at the National Oceanography Centre (NOC) have used advanced photographic tools in an unmanned Autonomous Underwater Vehicle (AUV) to make major advancements in estimating deep-sea ecosystem diversity at 'landscape' scales.
By using a camera on the Autosub6000 AUV to take a continuous stream of high resolution photographs of life on the sea floor, this new method revealed a tenfold increase in the precision of deep sea ecosystem diversity estimates relative to the use of scientific trawling. Autosub6000 was developed by the NOC as part of an ongoing project. This research showed that Anemones were the most abundant animal on the sea floor, information that has been previously missed from trawling because they became damaged in the nets and rendered unrecognisable.
Dr Kirsty Morris, the lead author of this research, published in Limnology and Oceanography: Methods, said "This is an important step towards the automated imaging of the deep sea, which is essential for understanding the complexity of seafloor biodiversity and its future management"

Once the images were taken they were analysed to identify the types of creatures seen, calculate their relative abundances and estimate their size relative to the image pixels. This allows for an overall estimate of the biomass to be calculated and compared with later/earlier images in order to monitor how these ecosystems change over time.
As climate change and potential exploitive pressure of seafloor resources impact the ocean, it is increasingly important to better document deep sea marine biodiversity and ecosystems. Currently trawling is one of the most common methods of assessing what creatures live in these habitats; however trawling has drawbacks in terms of poor accuracy and a bias in sampling seafloor biota. The new method, developed at the NOC, will substantially improve the accuracy and efficiency of sea floor surveying.
This method was developed and tested as part of the NERC funded AESA (Autonomous Ecological Surveying of the Abyss) project. It is currently being used to map the proposed marine conservation zone, know as Haig-Fras, as well as to estimate the seafloor carbon dynamics within the vicinity. This project will involve evaluating the role of habitat and sediment type, as well as discovering which animals live there.
Dr Henry Ruhl, co-ordinator of the AESA project, said "We are creating photographic maps of animals on the seafloor, which can scale up to the size of cities. This allows us to understand links between ecology, ecological functioning, and human impact in a way that was not previously feasible.""


Source Content Material Site:

All My Verses Chemistry 4 Conscious Eggs ********* ALCHemYEGG AUMniVERSE