Object localization using wireless sensor networks (WSN) often requires data from many sensor nodes and different types of sensors for position estimation. This incurs a heavy communication load, which can cause packet loss, communication delay and much energy consumption, deteriorating the performance of object localization. Here we employ an event-driven Gaussian process in order to learn the position of an unknown object using WSN with multiple types of sensors. In the event-driven framework, each sensor node transmits data only when decision criteria are satisfied. We consider the error-bounded algorithm as the decision criteria based on the measurement history of each sensor node. The overall communication between sensor nodes is reduced, thus increasing energy-efficiency of the network and relieving the concentration of communication traffic at the base node. Experiments to track the position of a mobile robot are conducted using a multisensor WSN, and the comparison is made between the eventdriven framework and the conventional approach in which sensors transmit data at a constant sampling rate. Experimental results demonstrate the efficiency and accuracy of the proposed event-driven Gaussian process approach.

Extraction of complex data structures like vector field topologies in large-scale, unsteady flow field datasets for the interactive exploration in virtual environments cannot be carried out without parallelization strategies. We present an approach based on Nested OpenMP to find critical points, which are the essential parts of velocity field topologies. We evaluate our parallelization scheme on several multi-block datasets, and present the results for various thread counts and loop schedules on all parallelization levels. Our experience suggests that upcoming massively multi-threaded processor architectures can be very advantageously for large-scale feature extractions.