Sharif Digital Repository

This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, and its complexity grows with the complexity of temporal patterns of data. Moreover, it utilizes a hierarchical dependent nonparametric approach to model marks of events. These capabilities allow the proposed model to adapt its temporal and topical complexity according to the complexity of data, which makes it a suitable candidate for real world scenarios. An online inference... 

An efficient method to automatically recognize basic human actions is proposed to improve the communication between a human and a computer. Human actions are considered as patterns generated by complex non-linear dynamical models. A non-linear dynamical model is used to represent human actions. Gaussian process dynamical models are used to capture the spatial and temporal behaviors of actions. To make the process more efficient a 7-dimensional feature is extracted for each action. Although the extracted feature vector is compact compared to a high-dimensional temporal pattern, it can efficiently discriminate among different actions. The tests run on CMU MoCap database with SVM show promising... 

Common spatial pattern (CSP) analysis and its extensions have been widely used as feature extraction approaches in the brain-computer interfaces (BCIs). However, most of the CSP-based approaches do not use any prior knowledge that might be available about the two conditions (classes) to be classified. Therefore, their applications are limited to datasets that contain enough variance information about the two conditions. For example, in some event-related potential (ERP) detection applications, such as P300 speller, the information is in the time domain but not in the variance of spatial components. To address this problem, first, we present a novel feature extraction method termed extended...