Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An indication that a wireless computing device (WCD) is moving toward a physical setting may be received. The physical setting may include a particular topography. There may be at least (i) location-determination information of a first type and (ii) location-determination information of a second type. The location-determination information of the first type may facilitate low-resolution location determinations in the physical setting and the location-determination information of the second type may facilitate high-resolution location determinations in the physical setting. Based on the physical setting and the particular topography, location-determination information may be selected from at least (i) the location-determination information of the first type or (ii) the location-determination information of the second type. At least some of the selected location-determination information may be used to estimate a location of the WCD.

Abstract: Described is a compact focal plane shutter system adapted for large-format exposure shutters. The focal plane shutter includes at least one multi-segmented screen having multiple rigid blades. Each blade can be moved at a different velocity. By using multiple blades, the rotational inertia about a drive axis can be reduced compared to a conventional single screen shutter system, thus a greater acceleration can be achieved with the same amount of torque about the drive axis. Using smaller blades allows for greater acceleration, resulting in faster shutter speeds to support a wider range of applications. Additionally, the overlap between blades of a multi-segmented screen in an open position is greater than the overlap of the blades in a closed position, providing a compact system. A drive mechanism linearly translates the blades of the multi-segmented screen between the open and closed positions.

Abstract: Described is a compact focal plane shutter system adapted for large-format exposure shutters. The focal plane shutter includes at least one multi-segmented screen having multiple rigid blades. Each blade can be moved at a different velocity. By using multiple blades, the rotational inertia about a drive axis can be reduced compared to a conventional single screen shutter system, thus a greater acceleration can be achieved with the same amount of torque about the drive axis. Using smaller blades allows for greater acceleration, resulting in faster shutter speeds to support a wider range of applications. Additionally, the overlap between blades of a multi-segmented screen in an open position is greater than the overlap of the blades in a closed position, providing a compact system. A drive mechanism linearly translates the blades of the multi-segmented screen between the open and closed positions.