Abstract-This paper focuses on a system of recognizing human's emotion from a detected human's face. The analyzed information is conveyed by the regions of the eye and the mouth into a merged new image in various facial expressions pertaining to six universal basic facial emotions. The output information obtained could be fed as an input to a machine capable to interact with social skills, in the context of building socially intelligent systems. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human's expression and that are particularly relevant for the decoding of emotional expressions. Finally we use the merged image as an input to a feed-forward neural network trained by back-propagation. Such analysis of merged images makes it possible, obtain relevant information through the combination of proper data in the same image and reduce the training set time while preserved classification rate. It is shown by experimental results that the proposed algorithm can detect emotion with good accuracy.

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Positioning and tracking control systems are an important component of autonomous robot applications. This paper presents the design method of tracking control systems based on H∞ preview control where the present and future desired positions of the robot are used to determine the control actions to be applied so that the robot describes the desired trajectory as close as possible. The performance improvements achieved with H∞ preview control have been examined in the frequency and time domains for different types of reference signals when applied to a one-dimensional positioning system. It was found that preview control improves the tracking performance by improving the phase response of the tracking system.

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Abstract-Since second-order probability distributions assign probabilities to probabilities there is uncertainty on two levels. Although different types of uncertainty have been distinguished before and corresponding measures suggested, the distinction made here between first-and second-order levels of uncertainty has not been considered before. In this paper previously existing measures are considered from the perspective of first-and second-order uncertainty and new measures are introduced. We conclude that the concepts of uncertainty and informativeness needs to be qualified if used in a second-order probability context and suggest that from a certain point of view information can not be minimized, just shifted from one level to another.