A model for acoustic absorbent materials derived from coconut fiber

In the present paper, a methodology is proposed for obtaining empirical equations describing the sound absorption characteristics of an absorbing material obtained from natural fibers, specifically from coconut. The method, which was previously applied to other materials, requires performing measurements of air-flow resistivity and of acoustic impedance for samples of the material under study. The equations that govern the acoustic behavior of the material are then derived by means of a least-squares fit of the acoustic impedance and of the propagation constant. These results can be useful since they allow the empirically obtained analytical equations to be easily incorporated in prediction and simulation models of acoustic systems for noise control that incorporate the studied materials.

Active Visual Features Based on Events to Guide Robot Manipulators in Tracking Tasks

Traditional visual servoing systems do not deal with the topic of moving objects tracking. When these systems are employed to track a moving object, depending on the object velocity, visual features can go out of the image, causing the fail of the tracking task. This occurs specially when the object and the robot are both stopped and then the object starts the movement. In this work, we have employed a retina camera based on Address Event Representation (AER) in order to use events as input in the visual servoing system. The events launched by the camera indicate a pixel movement. Event visual information is processed only at the moment it occurs, reducing the response time of visual servoing systems when they are used to track moving objects.