Abstract-Human action recognition has been one of the most active fields of research in computer vision over the last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method for action recognition which uses temporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition (recall) rate of 88.64% for eight different actions and up to 96.18% for classifying the class of all fall actions versus normal actions.Index Terms-Human action recognition, involuntary action recognition, Fisher, linear discriminant analysis (LDA), kinect, 3D skeleton data, hidden markov model (HMM).
Absfmct-A generalized matrix approach to noise analysis of multi-I (a) port networks with arbitrary topology is presented. The network may be composed of any number of passive components and active two-port devices. The method is direct and easy to follow and has been implemented in a computer program. The program is based on admittance matrix formulation and noise current sources of each device and their correlations. For MESFET devices, one can supply the noise parameters of each device or its physical and geometrical parameters for evaluation of the noise sources and their correlations.
ABSTRACT. Performance of Friction Stir Welding (FSW) as a solid-state process is approved in several engineering applications, especially aluminum industries. Identification of mechanical behavior of the associated welded zone is necessary due to these extensive applications of FSW. In this study, considering the effect of rotational and forward speed of welding tool on the mechanical properties of welded region, a hybrid optimization method based on combination of Genetic Algorithm (GA) and Response Surface Method (RSM) named here as GA-RSM is proposed to achieve maximum tensile and ultimate strength. The results of GA-RSM are validated by per-forming necessary experimental tests on two wide-used 2024 and 5050 aluminum alloys. The results show that GA-RSM could be an effective approach to achieve optimized process for FSW with minimum cost.
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