A low complexity system based on multiple weighted decision trees for indoor localization

[EN] Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity.

Radon in Groundwater of the Northeastern Gran Canaria Aquifer

[EN]Rn has been detected in 28 groundwater samples from the northeast of Gran Canaria (Canary Islands, Spain) utilizing a closed loop system consisting of an AlphaGUARD monitor that measures radon activity concentration in the air by means of an ionization chamber, and an AquaKIT set that transfers dissolved radon in the water samples to the air within the circuit. Radon concentration in the water samples studied varies between 0.3 and 76.9 Bq/L. Spanish radiological protection regulations limit the concentration of 222Rn for drinking water to 100 Bq/L, therefore the values obtained for all the analyzed samples are below this threshold. The hydrogeological study reveals a significant correspondence between the radon activity concentration and the material characteristics of the aquifer.