Google Inc.

Other

Brian John Julian

Brian Patrick Williams

Etienne Le Grand

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes modeling signals of wireless signal emitters. A computing device can determine first and second trained Gaussian processes. The respective first and second Gaussian processes can be based on first and second hyperparameter values related to first and second wireless signal emitters. The computing device can determine first and second sets of comparison hyperparameter values of the respective first and second hyperparameter values, and then determine whether the first and second sets of comparison hyperparameter values are within one or more threshold values. After determining that the first and second sets of comparison hyperparameter values are within the threshold(s), the computing device can determine the first and second Gaussian processes are dependent and then provide an estimated-location output based on a representative Gaussian process based on the first and the second Gaussian processes.

Gaussian process-based approach for identifying correlation between wireless signals

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes. A computing device can determine trained Gaussian processes related to wireless network signal strengths, where a particular trained Gaussian process is associated with one or more hyperparameters. The computing device can designate one or more hyperparameters. The computing device can determine a hyperparameter histogram for values of the designated hyperparameters of the trained Gaussian processes. The computing device can determine a candidate Gaussian process associated with one or more candidate hyperparameter value for the designated hyperparameters. The computing device can determine whether the candidate hyperparameter values are valid based on the hyperparameter histogram. The computing device can, after determining that the candidate hyperparameter values are valid, add the candidate Gaussian process to the trained Gaussian processes. The computing device can provide an estimated location output based on the trained Gaussian processes.

Data driven evaluation and rejection of trained Gaussian process-based wireless mean and standard deviation models

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on measurement bins (MBs) determined by a computing device. An MB can be associated with a wireless signal emitter (WSE), and can include a mean signal strength value (SSV) and a standard deviation of SSVs for each WSE associated with the MB. The computing device can designate a WSE. The computing device can determine a collection of the MBs associated with the designated WSE. The computing device can train a mean Gaussian process for the designated WSE based on the mean SSV and the standard deviation of SSVs of the collection of MBs. The mean Gaussian process can be associated with a covariance matrix having a diagonal entry based on a standard deviation of SSVs of an MB in the collection of MBs. The computing device can provide an estimated location based on the trained mean Gaussian process.

Calculating mean wireless signal strengths using a gaussian process approach incorporating predictive standard deviations

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on signal strength measurements. A computing device can receive a particular signal strength measurement, which can include a wireless-signal-emitter (WSE) identifier and a signal strength value and can be associated with a measurement location. The computing device can determine one or more bins; each bin including statistics for WSEs and associated with a bin location. The statistics can include mean and standard deviation values. The computing device can: determine a particular bin whose bin location is associated with the measurement location for the particular signal strength measurement, determine particular statistics of the particular bin associated with a wireless signal emitter identified by the WSE identifier of the particular signal strength measurement, and update the particular statistics based on the signal strength value. The computing device can provide an estimated location output based on the bins.

Computational complexity reduction of training wireless strength-based probabilistic models from big data

A device for mounting of a plumbing fixture such as a faucet or the like through a hole in a counter, a sink or the like and connection of the fixture to a supply line. A collar member of the device is slidingly retained on an inlet tube of the fixture by a connecting nut of the supply line. A rod, guided in its axial movement by a guide member that is connected to the collar member, has a flag member that is rotatable between a stowed position and a deployed position. In the stowed position, the device may be passed through the hole when connected to the plumbing fixture and the supply line. In the deployed position, the flag member increases the axial profile of the device and co-acts with the body of the plumbing fixture in retaining the plumbing fixture in the hole.

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Cupertino, CA, United States of America

Brian John Julian