Contrary to either expectation per se, there was no indication of an effect of hearing impairment or age on the sensitivity to intermodal asynchrony in audiovisual speech. It is possible that the negative effects of aging were balanced with the positive effects of increased sensitivity due to reliance on visual cues with hearing impairment. The listeners, normal hearing or hearing impaired, who were more sensitive to asynchrony (with narrower synchrony windows) tended to understand speech in noise better, with both audio-only and audiovisual speech. The practical implication of the results is that delays in audio or video signals of communication systems would affect hearing-impaired listeners in a manner similar to normal-hearing listeners, and due to the importance of visual cues for the hearing-impaired listeners, special attention should be given to limit these delays.

Abstract-Building on recent work in sensor-actuator networks and distributed manipulation, we consider the use of pure actuator networks for localization-free robotic navigation. We show how an actuator network can be used to guide an unobserved robot to a desired location in space and introduce an algorithm to calculate optimal actuation patterns for such a network. Sets of actuators are sequentially activated to induce a series of static potential fields that robustly drive the robot from a start to an end location under movement uncertainty. Our algorithm constructs a roadmap with probability-weighted edges based on motion uncertainty models and identifies an actuation pattern that maximizes the probability of successfully guiding the robot to its goal.Simulations of the algorithm show that an actuator network can robustly guide robots with various uncertainty models through a two-dimensional space. We experiment with additive Gaussian Cartesian motion uncertainty models and additive Gaussian polar models. Motion randomly chosen destinations within the convex hull of a 10-actuator network succeeds with with up to 93.4% probability. For n actuators, and m samples per transition edge in our roadmap, our runtime is O(mn 6 ).

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