This study examined whether the context immediately succeeding a heterophonic-homographic word (ht-homographic) plays a role in ambiguity resolution during voiced reading of Hebrew. A pretest was designed to find the preferred alternatives of 12 ht-homographic words: 20 adult subjects completed truncated sentences, each ending with a homographic word, preceded by a context allowing for both of its alternatives to be read. Following the pretest, each word was embedded in four research conditions determined by post-homographic context (keeping preceding context constant): two adjacent revealing contexts, one supporting the preferred alternative and the other the un-preferred alternative; and two distant revealing contexts, one supporting the preferred alternative and the other the un-preferred alternative. Four lists of 12 sentences, each including the four conditions, were then read aloud by four groups of 20 adults. Results from a generalized linear mixed-model analysis showed that the immediately succeeding context affected the deciphering of un-preferred alternatives in voiced reading. An item analysis further showed that highly preferred alternatives were less prone to the immediately succeeding context effect than slightly preferred alternatives. We conclude that the context immediately succeeding a ht-homographic word plays a role in ambiguity resolution during voiced reading, through interactions with the word’s lexical and syntactic characteristics.

This paper proposes a new path-planning algorithm which is close to the family of bug algorithms. Path planning is one of the challenging problems in the area of service robotics. In practical applications, traditional methods have some limitations with respect to cost, efficiency, security, flexibility, portability, etc. Our proposed algorithm offers a computationally inexpensive goal-oriented strategy by following a smooth and short trajectory. The paper also presents comparisons with other algorithms. In addition, the paper also presents a test bed which is created to test the algorithm. We have used a two-wheeled differential drive robot for the navigation and only a single camera is used as a feedback sensor. Using an extended Kalman filter, we localize the robot efficiently in the map. Furthermore, we compare the actual path, predicted path and planned path to check the effectiveness of the control system.

We consider the conventional techniques of vision robot system calibration where camera parameters and robot hand–eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand–eye calibration based on the calibrated parameters of cameras. In this paper we propose a joint algorithm that combines the camera calibration and the hand–eye calibration together. The proposed algorithm gives the solutions of the cameras' parameters and the hand–eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm. We also apply our algorithm in the real application of the robot-assisted surgical system, and very good results have been obtained.