Entries with tag brown university.

Scientists Create Implantable Brain Sensor

Brown University neuroengineers have developed a wireless, fully implantable brain sensor that functions as a brain-machine interface. The rechargeable sensor is a pill-shaped chip consisting of electrodes that are implanted on the cortex. It can communicate signals in real-time from 100 neurons to a titanium box that houses a signal processing system for the sensor system. Inside the box is a lithium ion battery, ultralow-power integrated circuits for signal processing and conversion, a wireless radio, infrared transmitters, and a copper coil for inductive recharging. The researchers have tested the sensor in six animal subjects for more than a year. Regulators haven’t approved it for use in humans yet, but researchers say they hope this will occur eventually for clinical trials involving people with movement-related disabilities. Brain-machine interfaces could help individuals with severe paralysis, for example, by letting them control devices—including assistive robotic arms—with their thoughts. The engineers are presenting their sensor at the 2013 International Workshop on Clinical Brain-Machine Interface Systems in Houston and publishing their work in the Journal of Neural Engineering. (EurekAlert)(Brown University)

Software Identifies Sketches

New software developed by researchers from Brown University and the Technical University of Berlin enables computers to recognize sketches drawn in real time. Computers have been able to match realistic sketches – such as police sketches with mug shots -- but typically have difficulty processing abstract sketches or doodles. The researchers make their point with the example of how people draw rabbits – with exaggerated features such as long ears and a fluffy tail. The images are representational. They don’t look real, but humans know through experience that a particular doodle is meant to represent a rabbit. The key to the technology is a large database of sketches that the researchers used to teach a computer how humans sketch objects. They started by creating a list of everyday objects, then used crowdsourcing to hire people to sketch objects from each category to create the database. The data was used to teach the system based on current recognition and machine learning algorithms. Next, they developed an interface that lets users  input new sketches, which the computer attempts to identify in real time. The system now IDs sketches with roughly 56 percent accuracy; in testing, humans identified the object in the sketch with a rate of roughly 73 percent accuracy. The researchers say the application could help improve sketch-based interface and search applications. They presented their work last month at SIGGRAPH. (PhysOrg)(Brown University)(“How Do Humans Sketch Objects” at Technical University of Berlin website)

Researchers Develop Markerless Motion-Capture Technique

Typically, 3D motion capture requires multiple cameras and actors who don specialized bodysuits laden with markers that help track their movements. However, Disney Research and Brown University scientists have developed a new technique that doesn’t use markers and that needs just one camera. Typically, motion capture is confined to specially-equipped indoor studios, which constrains its use. Eventually, the markerless system could capture motion from existing film or video, without using actors at all. It could also capture difficult forms of motions, such as those in outdoor settings, for use in games or other interactive applications. The technology could also be used for body-mechanics analysis. In addition to capturing motion, the new system also determines an actor’s biped controllers, which are programs that incorporate the physics of a person’s motion and uses the information to generate poses for animators. This will allow let artists more easily reproduce a person’s motions in animations and change the environments in which the action occurs. The researchers admit their method is computationally intensive and not yet as good as traditional motion capture. However, they said the results are promising. They said that human intervention could improve results and that markerless motion capture may eventually use multiple cameras. (PhysOrg)(The Disney Blog)(Brown University)

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