摘要:
Embodiments respond to a position inference request from a computing device to determine a location of a computing device. The position inference request received from the computing device identifies a set of beacons observed by the computing device. A geographic area is estimated in which the computing device is located using the set of beacons. At least one location method is selected to identify a location of the computing device within the geographic area. In some cases two or more location methods may be employed and their results combined using, for example, a weighting function. The location of the computing device is determined within the geographic area using the set of beacons and the selected location method(s). The location that is determined is communicated to the computing device.
摘要:
Embodiments order observed beacons based on relative signal strength to create a correspondence between beacon sets and positions. A computing device such as a mobile device provides a positioned observation including a plurality of observed beacons and a position of the mobile device during observation. The observed beacons are ordered based on quality indicators such as signal strength relative to each other. A set of the beacons are selected based on the ordering (e.g., the beacons with the strongest signal strength are selected in order). The position of the observing mobile device is associated with the beacon set to enable location inference for other devices providing observations including the same beacon set.
摘要:
Embodiments order observed beacons based on relative signal strength to create a correspondence between beacon sets and positions. A computing device such as a mobile device provides a positioned observation including a plurality of observed beacons and a position of the mobile device during observation. The observed beacons are ordered based on quality indicators such as signal strength relative to each other. A set of the beacons are selected based on the ordering (e.g., the beacons with the strongest signal strength are selected in order). The position of the observing mobile device is associated with the beacon set to enable location inference for other devices providing observations including the same beacon set.
摘要:
Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data involving a particular beacon is filtered based on a cluster start time associated with the beacon. A clustering analysis groups the filtered crowd-sourced data for the beacon into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the moved beacon. The cluster start time for the beacon is adjusted based on the earliest timestamp associated with the positioned observations corresponding to the selected cluster. Adjusting the cluster start time removes from a subsequent analysis the positioned observations associated with one or more prior positions of the beacon.
摘要:
Training datasets and test datasets consisting of observations (i.e., RSS measurements) partitioned per a mapping tile system are used to evaluate possible RSS weighting functions for each such tile. The observations from the training dataset are used to determine an optimal weighting function based on the training dataset that minimizes the error for the test data, wherein the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data. The accuracy of the optimal weighted function for each tile is characterized to determine whether to use the weighted function or an alternative (such as a non-weighted function) for subsequent inquiries.
摘要:
Training datasets and test datasets consisting of observations (i.e., RSS measurements) partitioned per a mapping tile system are used to evaluate possible RSS weighting functions for each such tile. The observations from the training dataset are used to determine an optimal weighting function based on the training dataset that minimizes the error for the test data, wherein the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data. The accuracy of the optimal weighted function for each tile is characterized to determine whether to use the weighted function or an alternative (such as a non-weighted function) for subsequent inquiries.
摘要:
Embodiments respond to a position inference request from a computing device to determine a location of a computing device. The position inference request received from the computing device identifies a set of beacons observed by the computing device. A geographic area is estimated in which the computing device is located using the set of beacons. At least one location method is selected to identify a location of the computing device within the geographic area. In some cases two or more location methods may he employed and their results combined using, for example, a weighting function. The location of the computing device is determined within the geographic area using the set of beacons and the selected location method(s). The location that is determined is communicated to the computing device.
摘要:
Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data involving a particular beacon is filtered based on a cluster start time associated with the beacon. A clustering analysis groups the filtered crowd-sourced data for the beacon into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the moved beacon. The cluster start time for the beacon is adjusted based on the earliest timestamp associated with the positioned observations corresponding to the selected cluster. Adjusting the cluster start time removes from a subsequent analysis the positioned observations associated with one or more prior positions of the beacon.
摘要:
Architecture for seamless role switching in application sharing. A multipoint control unit (MCU) can be used for connecting multiple clients over a network. An application sharing component establishes an application sharing session over the MCU, and application content is exchanged from a sharer client to one or more viewer clients. Each client can communicate a media connection preference to the session. Client sharer and viewer roles are defined during creation of the application sharing session. A client can seamlessly switch roles during the session from a viewer to a sharer, and vice-versa, so that different application content can be shared to the session viewers. The session can be restricted to only one sharer so that the viewing clients view one sharer client at a time.
摘要:
Subscribers and endpoints of enhanced multimodal communication systems are enabled to direct call requests and escalations during an existing conversation based on capabilities of endpoints. A list of communication mode/endpoint identifiers are exchanged when a conversation is established, enabling the endpoints to direct requests to for particular communication modes to endpoints capable of facilitating the communication mode at any point during the conversation. Additional capabilities/endpoints are added to the list through updates during the conversation.