Abstract: A power load prediction method includes determining a relationship between power load and temperature during a selected time. A decomposition of the determined relationship is determined. The decomposition indicates a plurality of contributors to the determined power load. A predicted power load is estimated based on the plurality of contributors and a regression model.

Abstract: A system and method for position tagging within an environment includes at least one mobile electronic device adapted to be moved within the environment and a pedestrian dead reckoning module. The at least one mobile electronic device includes an inertial measurement unit and at least one of a near field communication chip, barcode scanner, global positioning system, Bluetooth receiver or WiFi receiver. The pedestrian dead reckoning module is adapted to query and record time stamped readings from the inertial measurement unit and the at least one of the near field communication chip, barcode scanner, global positioning system, Bluetooth receiver or WiFi receiver. The system and method may generate one or more spatial maps of the environment through the collection of radio frequency signal data along with the time stamped readings.

Abstract: A system and method for position tagging within an environment includes at least one mobile electronic device adapted to be moved within the environment and a pedestrian dead reckoning module. The at least one mobile electronic device includes an inertial measurement unit and at least one of a near field communication chip, barcode scanner, global positioning system, Bluetooth receiver or WiFi receiver. The pedestrian dead reckoning module is adapted to query and record time stamped readings from the inertial measurement unit and the at least one of the near field communication chip, barcode scanner, global positioning system, Bluetooth receiver or WiFi receiver. The system and method may generate one or more spatial maps of the environment through the collection of radio frequency signal data along with the time stamped readings.

Abstract: A system and method for providing multi-floor activity classification for a mobile device within a multi-floor environment includes an activity recognition module receiving inertial readings and pressure readings from the mobile device. The activity recognition module classifies activities for the mobile device from the inertial readings and the pressure readings.

Abstract: Embodiments are directed to mobile localization, and more specifically, but not exclusively, to tracking mobile devices. Embodiments include methods that consider probability kernels with distance-like metrics between distributions. Also described are probabilistic kernels that can be used for a regression of location, which can achieve up to about inn accuracy in an office environment.

Abstract: Embodiments are directed to mobile localization, and more specifically, but not exclusively, to tracking mobile devices. Embodiments include methods that consider probability kernels with distance-like metrics between distributions. Also described are probabilistic kernels that can be used for a regression of location, which can achieve up to about inn accuracy in an office environment.

Abstract: A computer-implemented method is provided that automatically characterizes and verifies stationarity of a training image for use in multipoint geostatistical analysis. The stationarity is preferably characterized by statistical measures of orientation stationarity, scale stationarity, and category distribution stationarity.

Abstract: A system for inferring geological classes from oilfield well input data is described using a neural network for inferring class probabilities and class sequencing knowledge and optimising the class probabilities according to the sequencing knowledge.

Abstract: A computer-implemented method is provided that automatically characterizes and verifies stationarity of a training image for use in multipoint geostatistical analysis. The stationarity is preferably characterized by statistical measures of orientation stationarity, scale stationarity, and category distribution stationarity.

Abstract: A system for inferring geological classes from oilfield well input data is described using a neural network for inferring class probabilities and class sequencing knowledge and optimising the class probabilities according to the sequencing knowledge.