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Smart phones are widely used in indoor positioning and navigation systems. Thus, it is important to know the relative position of the smart phone on the user while calculating position with RSS based systems. The accuracy of position will change within this scope. In this study, an intermediary solution is proposed to find the position of the smart phone on the user. Several algorithms are evaluated for the feature selection process. The outputs of the process are estimated via three different classifiers, namely Naives Bayes, k-Nearest Neighborhood and C4.5 decision tree. k-Nearest Neighborhood algorithm achieves 96% accuracy with the output of Correlation Feature Selection.

Nowadays, robots have taken place of human beings in every area so the reliability of the robot has become important. Reliability analysis is applied to improve the reliability of imperfect robotic systems and to minimize the failures that may be occur in these systems. Overall reliability of the robot is obtained with applying the reliability analysis to all subsystems of robot. In this study, example of task completion study was made for robotic systems. As a starter task completion percentages are calculated with calculating failure rates under normal circumstances, after that with changing the ambient temperature and load carried task completion percentages are recalculating. As a result of study, effects of temperatures and loads to system reliability has been shown with tables and graphs.