Resampling methods for particle filtering: identical distribution, a new method, and comparable study

Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial （Li et al., 2015）. First, identical distribution （ID） is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent met- rics including the （symmetrical） Kullback-Leibler divergence, Kolmogorov-Smimov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive under- standing of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations

Collaborative learning via social computing

Educational innovation is a field that has been greatly enriched by using technology in its processes, resulting in a learning model where information comes from numerous sources and collaboration takes place among multiple students. One attractive challenge within educational innovation is the design of collaborative learning activities from the social computing point of view, where collaboration is not limited to student-to-student relationships, but includes student-to-machine interactions. At the same time, there is a great lack of tools that give support to the whole learning process and are not restricted to specific aspects of the educational task. In this paper, we present and evaluate context-aware framework for collaborative learning applications(CAFCLA) as a solution to these problems. CAFCLA is a flexible framework that covers the entire process of developing collaborative learning activities, taking advantage of contextual information and social interactions. Its application in the experimental case study of a collaborative WebQuest within a museum has shown that, among other benefits, the use of social computing improves the learning process, fosters collaboration, enhances relationships, and increases engagement.

Performance analysis of visual markers for indoor navigation systems

The massive diffusion of smartphones, the growing interest in wearable devices and the Internet of Things, and the exponential rise of location based services（LBSs） have made the problem of localization and navigation inside buildings one of the most important technological challenges of recent years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; in addition, they can be fundamental to increasing the quality of life for citizens if deployed inside public buildings such as hospitals, airports, and museums.Sometimes, in emergency situations, they can make the difference between life and death. Various approaches have been proposed in the literature. Recently, thanks to the high performance of smartphones＇ cameras, marker-less and marker-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor localization and navigation using both Bluetooth low energy（BLE） and a 2D visual marker system deployed into the floor. In this paper, we presented a qualitative performance evaluation of three 2D visual markers, Vuforia,Ar Uco marker, and AprilT ag, which are suitable for real-time applications. Our analysis focused on specific case study of visual markers placed onto the tiles, to improve the efficiency of our indoor localization and navigation approach by choosing the best visual marker system.