Abstract:
A home cloud computing system employs a virtualization system to virtualize data of a device and adaptively transform type or format of the virtualized data for one or more other devices, thus leveraging resources of the device for the one or more other devices. Through data virtualization and adaptive transformation, devices of heterogeneous types are seamlessly connected to one another and can act as input or output devices for each other to create a home cloud network of devices.
Abstract:
The use of data from multiple data source provides inferred air quality indices with respect to a particular pollutant for multiple areas without the addition of air quality monitor stations to those areas. Labeled air quality index data for a pollutant in a region may be obtained from one or more air quality monitor stations. Spatial features for the region may be extracted from spatially-related data for the region. The spatially-related data may include information on fixed infrastructures in the region. Likewise, temporal features for the region may be extracted from temporally-related data for the region that changes over time. A co-training based learning framework may be further applied to co-train a spatial classifier and a temporal classifier based at least on the labeled air quality index data, the spatial features for the region, and the temporal features for the region.
Abstract:
A home cloud computing system employs a virtualization system to virtualize data of a device and adaptively transform type or format of the virtualized data for one or more other devices, thus leveraging resources of the device for the one or more other devices. Through data virtualization and adaptive transformation, devices of heterogeneous types are seamlessly connected to one another and can act as input or output devices for each other to create a home cloud network of devices.