Tuesday, July 31, 2012
Monday, July 30, 2012
Water Jumping Robots - SuperHydroPhobicity
Why Superhydrophobicity Is Crucial for a Water-Jumping Microrobot? Experimental and Theoretical Investigations
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†State Key Laboratory of Robotics and Systems and §School of Chemical Engineering and Technology, Harbin Institute of Technology, Harbin 150001, People's Republic of China
ACS Appl. Mater. Interfaces, 2012, 4 (7), pp 3706–3711
DOI: 10.1021/am300794z
Publication Date (Web): June 25, 2012
Copyright © 2012 American Chemical Society
*E-mail: panqm@hit.edu.cn.
‡ Author Contributions
These authors contributed equally to the work.
Section:Abstract
This
study reported for the first time a novel microrobot that could
continuously jump on the water surface without sinking, imitating the
excellent aquatic locomotive behaviors of a water strider. The robot
consisted of three supporting legs and two actuating legs made from
superhydrophobic nickel foam and a driving system that included a
miniature direct-current motor and a reduction gear unit. In spite of
weighing 11 g, the microrobot jumped 14 cm high and 35 cm long at each
leap. In order to better understand the jumping mechanism on the water
surface, the variation of forces exerted on the supporting legs was
carefully analyzed and calculated based on numerical models and
computational simulations. Results demonstrated that superhydrophobicity
was crucial for increasing the upward force of the supporting legs and
reducing the energy consumption in the process of jumping. Although
bionic microrobots mimicking the horizontal skating motions of aquatic
insects have been fabricated in the past years, few studies reported a
miniature robot capable of continuously jumping on the water surface as
agile as a real water strider. Therefore, the present finding not only
offers a possibility for vividly imitating and better understanding the
amazing water-jumping capability of aquatic insects but also extends the
application of porous and superhydrophobic materials to advanced
robotic systems.
Keywords:
water-jumping microrobot; bioinspired; mechanism; numerical model; superhydrophobicityTuesday, July 24, 2012
Friday, July 20, 2012
Friday, July 13, 2012
FLIP Rare ship
Saturday, May 19, 2012
RP FLIP, the Strangest Ship in the World
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The U.S. Office of Naval Research owns a very strange piece of oceanographic equipment. It’s called the FLoating Instrument Platform (FLIP), conceived and developed by the Marine Physical Laboratory (MPL) at the Scripps Institution of Oceanography, University of California. FLIP isn't a ship, even though researchers live and work on it for weeks at a time while they conduct scientific studies in the open ocean. It is actually a huge specialized buoy. The most unusual thing about this ship is it really flips.
FLIP is 355 feet (108 meters) long with small quarters at the front and a long hollow ballast at the end. When the tanks are filled with air, FLIP floats in its horizontal position. But when they are filled with seawater the lower 300 feet of FLIP sinks under the water and the lighter end rises. When flipped, most of the buoyancy for the platform is provided by water at depths below the influence of surface waves, hence FLIP is a stable platform mostly immune to wave action. At the end of a mission, compressed air is pumped into the ballast tanks in the flooded section and the vessel returns to its horizontal position so it can be towed to a new location.
During the flip, everyone stands on the outside decks. As FLIP flips, the decks slowly become bulkheads and the bulkhead becomes the deck. Most rooms on FLIP have two doors; one to use when horizontal, the other when FLIP is vertical. Some of FLIP's furnishings are built so they can rotate to a new position as FLIP flips. Other equipment must be unbolted and moved. Some things, like tables in the galley (kitchen) and sinks in the washroom, are built twice so one is always in the correct position. The entire flip operation takes twenty-eight minutes. When FLIP stands vertically, it rises more than five stories into the air.
FLIP was created 50 years ago, in 1962, by two Scripps scientists, Drs. Fred Fisher and Fred Spiess, because they needed a more quiet and stable place than a research ship to study how sound waves behave under water. Ships were unsuitable as they bob up and down and roll side to side.
FLIP is designed to study wave height, acoustic signals, water temperature and density, and for the collection of meteorological data. Because of the potential interference with the acoustic instruments, FLIP has no engines or other means of propulsion. It must be towed to open water, where it drifts freely or is anchored. When FLIP is in its vertical position it is both extremely stable and quiet.
Since Drs. Fisher and Spiess completed their first tests, many other important data have been gathered using FLIP. The way water circulates, how storm waves are formed, how seismic waves move, how heat is exchanged between the ocean and the atmosphere, and the sound made underwater by marine animals are just a few of the subjects studied using the amazing FLIP.
![flip-ship-10[4]](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8oY_8dnTY_PIT8M5yZMT7jZ8TYHW0lQpKucMl_shen-mWKcDelDl3ROClQOCdihtVXgHDrTzt2CFX5U-TgJZKNF4qDdatzSVAfg4-0DfoFxLpOekW9PKylT03NxWLsz444byVHAa65WW9/s1600-h/flip-ship-10%25255B4%25255D%25255B3%25255D.gif "flip-ship-10[4]")
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Tuesday, July 10, 2012
An algorithm can better predict your future movements by getting a little help from your friends.
http://www.technologyreview.com/news/428441/a-phone-that-knows-where-youre-going/
An algorithm can better predict your future movements by getting a little help from your friends.
Track to the future: Movement of three users around Switzerland’s Lake Geneva are indicated with different symbols. GPS positions at the same time are indicated with the same color.
Mirco Musolesi
Mirco Musolesi
Beyond merely tracking where you've been and where you are, your smartphone might soon actually know where you are going—in part by recording what your friends do.
Researchers in the U.K. have come up with an algorithm that follows your own mobility patterns and adjusts for anomalies by factoring in the patterns of people in your social group (defined as people who are mutual contacts on each other's smartphones).
The method is remarkably accurate. In a study on 200 people willing to be tracked, the system was, on average, less than 20 meters off when it predicted where any given person would be 24 hours later. The average error was 1,000 meters when the same system tried to predict a person's direction using only that person's past movements and not also those of his friends, says Mirco Musolesi, a computer scientist at the University of Birmingham who led the study.
He cautions that the 200 participants might not reflect the general population—they all lived within 30 miles of Lausanne, Switzerland, and were mainly "students, researchers, and people that are fairly predictable anyway." Even so, he says, the findings were noteworthy because "we are essentially exploiting the synchronized rhythm of the city" for greater predictive insights.
Although it is still a research prototype, the prediction algorithm, described in this paper, could be a boon to mobile network operators if it proves more widely applicable. These companies already possess such data and could use it to provide a sharper recommendations or ads for restaurants or shops near locations where you are likely to go. Musolesi's group is planning to build a developer platform based on the algorithm.
This paper was part of a Nokia-sponsored Mobile Data Challenge, at which the Birmingham group won 3,000 euros for their work. Other papers from the contest can be found here. All the projects drew on the same smartphone dataset from the 200 volunteers, who agreed to have their location, communication patterns, app usage, and other metrics tracked over an 18-month period ending in 2011.
To explain how your friends' patterns can be used to refine predictions about you, Musolesi gave an example. If Susan goes from home to the gym every Tuesday at 7 p.m. following a certain route, a prediction algorithm based only on her past movements might be thrown off on a certain Tuesday when she makes a side trip to the mall. But by noticing that her close friends Joe and Bob are in their usual hangouts that day, Musolesi's algorithm can determine that Susan is highly likely to go to the gym after finishing her mall errand. Habits and patterns of friends are highly correlated, meaning there will be enough noise-free information from the friends' mobility patterns to extrapolate from them. Naturally, the predictions can be refined even more when two people often spend time with each other, but such "mutual information" is not required for a friend's information to be useful.
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