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Trajectory similarity measure is an important issue for analyzing the behavior of moving objects. In this paper, a similarity measure method for network constrained trajectories is proposed. It considers spatial and temporal features simultaneously in calculating spatio-temporal distance. The crossing points of network and semantic information of trajectory are used to extract the characteristic points for trajectory partition. Experiment results show that the storage space is decreased after trajectory partition and the similarity measure method is valid and efficient for trajectory clustering.

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E-learning technology which effectively supports the learning methodologies between students and professors and which provides location and time benefits to students is being researched now a days. However, E-learning classes produce bad effects comparing with offline classes in learning procedures including scholastic achievements. Bad effects of E-learning system could be proxy attendance, lack of concentration, and bad attitude of students. These environmental problems must be solved first to achieve the advantages of E-learning technology. To get rid of these problems, in this paper, we proposed a mechanism which provides effective learning progress by using face authentication method. This mechanism supervise the student by using real time face recognition which prevents proxy attendance, illegal activities, and student's absences.