Recently, methods of data collection and practical use of the collected data in sensor networks have been proposed. In this paper, we propose the method of collecting sensor data from two geographically different spots (Tokyo Denki University in Chiba and Hachinohe National College of Technology in Hachinohe Aomori), of monitoring and controlling the collected data. The result of the basic expe...

1. Introduction: Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k may be smaller, equal to or larger than m. Th...

A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model S. Kolberg, H. Rue, and L. Gottschalk SINTEF Energy Research, Sem Sælands vei 11, 7465 Trondheim, Norway Department of Mathematical Sciences, NTNU, 7491 Trondheim, Norway Department of Geosciences, University of Oslo. P.O. Box 1047 Blindern, 0316 Oslo, Norway Received: 24 June 2005 – Accepted: 6 July 2...

This study addresses seasonal predictability of South American rainfall during ENSO. The skill of empirical and coupled multi-model predictions is assessed and compared. The empirical model uses the previous season AugustSeptember-October Pacific and Atlantic sea surface temperatures as predictors for December-January-February rainfall. Coupled multi-model 1-month lead December-JanuaryFebruary ...

Human observations during behavioral studies are expensive, time-consuming, and error prone. For this reason, automatization of experiments is highly desirable, as it reduces the risk of human errors and workload. The robotic system we developed is simple and cheap to build and handles feeding and data collection automatically. The system was built using mostly off-the-shelf components and has ...