MIT Media Lab researchers have developed a low-cost color holographic video display powered by a $10 optical chip they created. The prototype display can update images fast enough—30 times per second—to make the image look like it is in motion. The device could lead to affordable color holographic-video displays and increase conventional 2D displays’ resolutions. The chip is the least expensive component in the system, but it is not the only newly-devised component. Typically, it is difficult to control the light waves to create a holographic video image. Existing technologies are too expensive and cumbersome. As a solution, the researchers used a lithium niobate crystal, smaller than other materials previously attempted, and a single waveguide for each pixel in their system. The waveguides confine the light traveling through them and each can be located in close proximity to each other. Each waveguide also contains a metal electrode able to create an acoustic wave, which is used to filter light. The images they made refreshed at a rate of five frames per second and were 420 × 420 pixels. The researchers published their findings in Nature. (Mashable)(Discovery News)(MIT)(Nature)
Monday, July 15, 2013
Friday, May 24, 2013
Canadian Researchers Create Unique Computerized Fabrics
A team of two Canadian researchers is creating fabrics able to change color and shape. Concordia University associate professor Joanna Berzowska and École Polytechnique de Montréal professor Maksim Skorobogatiy, developed different types of smart textiles with technology woven into the fiber and have created prototype garments able to change shape and color. One of their prototype garments is constructed with a pleated structure into which photonic band-gap fibers are woven. Custom electronics control how these fibers are lit, which creates different patterns and textures. The technology could also potentially capture energy from human movement that could, for example, charge a mobile telephone. (EurekAlert)(Concordia University)
Thursday, May 16, 2013
Researchers Create Powerful Microbatteries
A University of Illinois at Urbana-Champaign research team has successfully developed new microbatteries that are reportedly the most powerful ever documented. A microbattery is a solid state electrochemical miniaturized power source that could be used in small items such as medical devices or RFID tags. This new technology could be used to create new compact radio-communications and electronics applications such as lasers, sensors, and medical devices. The millimeter-sized batteries provide both high power and high energy, where, with conventional battery technologies, there is a tradeoff between the two. Typically, capacitors release energy very quickly but can only store a small amount of energy while fuel cells and batteries are able to store a great deal of energy, but release or recharge slowly. These high-performance batteries contain a fast-charging cathode with an equally high-performance, microscale anode. Researchers say they can tune the battery such that it has the optimal power and energy capabilities for the specific application. The new technology could be used in transmitters able to broadcast radio signals able 30 times farther than conventional technology, the researchers said. These small batteries could also reportedly recharge 1000 times faster than conventional technologies, they added. The scientists are now working on lowering their batteries’ cost and integrating them with other electronics components. They published their results in the journal Nature Communications. (EurekAlert)(University of Illinois at Urbana-Champaign)
Monday, October 15, 2012
Robots using tools: Researchers aim to create 'MacGyver' robot
A research team led by Professor Mike Stilman at the Georgia Institute of Technology hopes to change that by giving robots the ability to use objects in their environments to accomplish high-level tasks. The team recently received a three-year, $900,000 grant from the Office of Naval Research to work on this project.
"Our goal is to develop a robot that behaves like MacGyver, the television character from the 1980s who solved complex problems and escaped dangerous situations by using everyday objects and materials he found at hand," said Stilman, an assistant professor in the School of Interactive Computing at Georgia Tech. "We want to understand the basic cognitive processes that allow humans to take advantage of arbitrary objects in their environments as tools. We will achieve this by designing algorithms for robots that make tasks that are impossible for a robot alone possible for a robot with tools."
The research will build on Stilman's previous work on navigation among movable obstacles that enabled robots to autonomously recognize and move obstacles that were in the way of their getting from point A to point B.
"This project is challenging because there is a critical difference between moving objects out of the way and using objects to make a way," explained Stilman. "Researchers in the robot motion planning field have traditionally used computerized vision systems to locate objects in a cluttered environment to plan collision-free paths, but these systems have not provided any information about the objects' functions."
To create a robot capable of using objects in its environment to accomplish a task, Stilman plans to develop an algorithm that will allow a robot to identify an arbitrary object in a room, determine the object's potential function, and turn that object into a simple machine that can be used to complete an action. Actions could include using a chair to reach something high, bracing a ladder against a bookshelf, stacking boxes to climb over something, and building levers or bridges from random debris.
By providing the robot with basic knowledge of rigid body mechanics and simple machines, the robot should be able to autonomously determine the mechanical force properties of an object and construct motion plans for using the object to perform high-level tasks.
For example, exiting a burning room with a jammed door would require a robot to travel around any fire, use an object in the room to apply sufficient force to open the stuck door, and locate an object in the room that will support its weight while it moves to get out of the room.
Such skills could be extremely valuable in the future as robots work side-by-side with military personnel to accomplish challenging missions.
"The Navy prides itself on recruiting, training and deploying our country's most resourceful and intelligent men and women," said Paul Bello, director of the cognitive science program in the Office of Naval Research (ONR). "Now that robotic systems are becoming more pervasive as teammates for warfighters in military operations, we must ensure that they are both intelligent and resourceful. Professor Stilman's work on the 'MacGyver-bot' is the first of its kind, and is already beginning to deliver on the promise of mechanical teammates able to creatively perform in high-stakes situations."
To address the complexity of the human-like reasoning required for this type of scenario, Stilman is collaborating with researchers Pat Langley and Dongkyu Choi. Langley is the director of the Institute for the Study of Learning and Expertise (ISLE), and is recognized as a co-founder of the field of machine learning, where he championed both experimental studies of learning algorithms and their application to real-world problems. Choi is an assistant professor in the Department of Aerospace Engineering at the University of Kansas.
Langley and Choi will expand the cognitive architecture they developed, called ICARUS, which provides an infrastructure for modeling various human capabilities like perception, inference, performance and learning in robots.
"We believe a hybrid reasoning system that embeds our physics-based algorithms within a cognitive architecture will create a more general, efficient and structured control system for our robot that will accrue more benefits than if we used one approach alone," said Stilman.
After the researchers develop and optimize the hybrid reasoning system using computer simulations, they plan to test the software using Golem Krang, a humanoid robot designed and built in Stilman's laboratory to study whole-body robotic planning and control.
This research is sponsored by the Department of the Navy, Office of Naval Research, through grant number N00014-12-1-0143. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research.
Share this story on Facebook, Twitter, and Google:Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by Georgia Institute of Technology.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Tuesday, September 25, 2012
Scientists create chemical 'brain': Giant network links all known compounds and reactions
A decade in the making, the software optimizes syntheses of drug molecules and other important compounds, combines long (and expensive) syntheses of compounds into shorter and more economical routes and identifies suspicious chemical recipes that could lead to chemical weapons.
"I realized that if we could link all the known chemical compounds and reactions between them into one giant network, we could create not only a new repository of chemical methods but an entirely new knowledge platform where each chemical reaction ever performed and each compound ever made would give rise to a collective 'chemical brain,'" said Bartosz A. Grzybowski, who led the work. "The brain then could be searched and analyzed with algorithms akin to those used in Google or telecom networks."
Called Chematica, the network comprises some seven million chemicals connected by a similar number of reactions. A family of algorithms that searches and analyzes the network allows the chemist at his or her computer to easily tap into this vast compendium of chemical knowledge. And the system learns from experience, as more data and algorithms are added to its knowledge base.
Details and demonstrations of the system are published in three back-to-back papers in the Aug. 6 issue of the journal Angewandte Chemie.
Grzybowski is the senior author of all three papers. He is the Kenneth Burgess Professor of Physical Chemistry and Chemical Systems Engineering in the Weinberg College of Arts and Sciences and the McCormick School of Engineering and Applied Science.
In the Angewandte paper titled "Parallel Optimization of Synthetic Pathways Within the Network of Organic Chemistry," the researchers have demonstrated algorithms that find optimal syntheses leading to drug molecules and other industrially important chemicals.
"The way we coded our algorithms allows us to search within a fraction of a second billions of chemical syntheses leading to a desired molecule," Grzybowski said. "This is very important since within even a few synthetic steps from a desired target the number of possible syntheses is astronomical and clearly beyond the search capabilities of any human chemist."
Chematica can test and evaluate every possible synthesis that exists, not only the few a particular chemist might have an interest in. In this way, the algorithms find truly optimal ways of making desired chemicals.
The software already has been used in industrial settings, Grzybowski said, to design more economical syntheses of companies' products. Synthesis can be optimized with various constraints, such as avoiding reactions involving environmentally dangerous compounds. Using the Chematica software, such green chemistry optimizations are just one click away.
Another important area of application is the shortening of synthetic pathways into the so-called "one-pot" reactions. One of the holy grails of organic chemistry has been to design methods in which all the starting materials could be combined at the very beginning and then the process would proceed in one pot -- much like cooking a stew -- all the way to the final product.
The Northwestern researchers detail how this can be done in the Angewandte paper titled "Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry."
The chemists have taught their network some 86,000 chemical rules that check -- again, in a fraction of a second -- whether a sequence of individual reactions can be combined into a one-pot procedure. Thirty predictions of one-pot syntheses were tested and fully validated. Each synthesis proceeded as predicted and had excellent yields.
In one striking example, Grzybowski and his team synthesized an anti-asthma drug using the one-pot method. The drug typically would take four consecutive synthesis and purification steps.
"Our algorithms told us this sequence could be combined into just one step, and we were naturally curious to check it out in a flask," Grzybowski said. "We performed the one-pot reaction and obtained the drug in excellent yield and at a fraction of the cost the individual steps otherwise would have accrued."
The third area of application is the use of the Chematica network approach for predicting and monitoring syntheses leading to chemical weapons. This is reported in the Angewandte paper titled "Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats."
"Since we now have this unique ability to scrutinize all possible synthetic strategies, we also can identify the ones that a potential terrorist might use to make a nerve gas, an explosive or another toxic agent," Grzybowski said.
Algorithms known from game theory first are applied to identify the strategies that are hardest to detect by the federal government -- the use of substances, for example, such as kitchen salt, clarifiers, grain alcohol and a fertilizer, all freely available from a local convenience store. Characteristic combinations of seemingly innocuous chemicals, such as this example, are red flags.
This strategy is very different from the government's current approach of monitoring and regulating individual substances, Grzybowski said. Chematica can be used to monitor patterns of chemicals that together become suspicious, instead of monitoring individual compounds. Grzybowski is working with the federal government to implement the software.
Chematica now is being commercialized. "We chose this name," Grzybowski said, "because networks will do to chemistry what Mathematica did to scientific computing. Our approach will accelerate synthetic design and discovery and will optimize synthetic practice at large."
Share this story on Facebook, Twitter, and Google:Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by Northwestern University. The original article was written by Megan Fellman.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal References:
Mikolaj Kowalik, Chris M. Gothard, Aaron M. Drews, Nosheen A. Gothard, Alex Weckiewicz, Patrick E. Fuller, Bartosz A. Grzybowski, Kyle J. M. Bishop. Parallel Optimization of Synthetic Pathways within the Network of Organic Chemistry. Angewandte Chemie International Edition, 2012; 51 (32): 7928 DOI: 10.1002/anie.201202209Chris M. Gothard, Siowling Soh, Nosheen A. Gothard, Bartlomiej Kowalczyk, Yanhu Wei, Bilge Baytekin, Bartosz A. Grzybowski. Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry. Angewandte Chemie International Edition, 2012; 51 (32): 7922 DOI: 10.1002/anie.201202155Patrick E. Fuller, Chris M. Gothard, Nosheen A. Gothard, Alex Weckiewicz, Bartosz A. Grzybowski. Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats. Angewandte Chemie International Edition, 2012; 51 (32): 7933 DOI: 10.1002/anie.201202210Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Tuesday, September 18, 2012
Biologists create first predictive computational model of gene networks that control development of sea-urchin embryos
Biologists at the California Institute of Technology (Caltech) have spent the last decade or so detailing how these gene networks control development in sea-urchin embryos. Now, for the first time, they have built a computational model of one of these networks.
This model, the scientists say, does a remarkably good job of calculating what these networks do to control the fates of different cells in the early stages of sea-urchin development -- confirming that the interactions among a few dozen genes suffice to tell an embryo how to start the development of different body parts in their respective spatial locations. The model is also a powerful tool for understanding gene regulatory networks in a way not previously possible, allowing scientists to better study the genetic bases of both development and evolution.
"We have never had the opportunity to explore the significance of these networks before," says Eric Davidson, the Norman Chandler Professor of Cell Biology at Caltech. "The results are amazing to us."
The researchers described their computer model in a paper in the Proceedings of the National Academy of Sciences that appeared as an advance online publication on August 27.
The model encompasses the gene regulatory network that controls the first 30 hours of the development of endomesoderm cells, which eventually form the embryo's gut, skeleton, muscles, and immune system. This network -- so far the most extensively analyzed developmental gene regulatory network of any animal organism -- consists of about 50 regulatory genes that turn one another on and off.
To create the model, the researchers distilled everything they knew about the network into a series of logical statements that a computer could understand. "We translated all of our biological knowledge into very simple Boolean statements," explains Isabelle Peter, a senior research fellow and the first author of the paper. In other words, the researchers represented the network as a series of if-then statements that determine whether certain genes in different cells are on or off (i.e., if gene A is on, then genes B and C will turn off).
By computing the results of each sequence hour by hour, the model determines when and where in the embryo each gene is on and off. Comparing the computed results with experiments, the researchers found that the model reproduced the data almost exactly. "It works surprisingly well," Peter says.
Some details about the network may still be uncovered, the researchers say, but the fact that the model mirrors a real embryo so well shows that biologists have indeed identified almost all of the genes that are necessary to control these particular developmental processes. The model is accurate enough that the researchers can tweak specific parts -- for example, suppress a particular gene -- and get computed results that match those of previous experiments.
Allowing biologists to do these kinds of virtual experiments is precisely how computer models can be powerful tools, Peter says. Gene regulatory networks are so complex that it is almost impossible for a person to fully understand the role of each gene without the help of a computational model, which can reveal how the networks function in unprecedented detail.
Studying gene regulatory networks with models may also offer new insights into the evolutionary origins of species. By comparing the gene regulatory networks of different species, biologists can probe how they branched off from common ancestors at the genetic level.
So far, the researchers have only modeled one gene regulatory network, but their goal is to model the networks responsible for every part of a sea-urchin embryo, to build a model that covers not just the first 30 hours of a sea urchin's life but its entire embryonic development. Now that this modeling approach has been proven effective, Davidson says, creating a complete model is just a matter of time, effort, and resources.
The title of the PNAS paper is "Predictive computation of genomic logic processing functions in embryonic development." In addition to Peter and Davidson, the other author on the PNAS paper is Emmanuel Faure, a former Caltech postdoctoral scholar who is now at the École Polytechnique in France. This work was supported by the National Institute of Child Health and Human Development.
Share this story on Facebook, Twitter, and Google:Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by California Institute of Technology. The original article was written by Marcus Woo.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
I. S. Peter, E. Faure, E. H. Davidson. Predictive computation of genomic logic processing functions in embryonic development. Proceedings of the National Academy of Sciences, 2012; DOI: 10.1073/pnas.1207852109Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.