Abstract: In one embodiment, a method includes receiving a plurality of radio frequency chains at a wireless device in a block based modulation environment, recording subcarrier phases and differences between the subcarrier phases, and using the subcarrier phase differences to construct a feature vector for use in angle of arrival calculated positioning of a mobile device.

Abstract: A system, method, and apparatus for quantification of pre-determined particles in a soil or feces sample with certain automated steps. In one aspect it includes an input station for inputting a soil or feces sample; a sieving/filtering station for separating soil or feces from particles or particle carriers, and/or separating particle carriers from particles; and a collection station for receiving the extracted particles. It can include quantification of the collected sample with an imaging station to digitally image the particles and recognize and count the particles collected. A mechanism can mechanically move the filtered particles from the sieve/filter station to the collection station. A controller can be programmed to automatically control at least certain functions of the mechanism. An optional feature includes acquisition of chemical, biological, physical, or other parameters of the sample with one or more sensors positioned or positionable at the sample.

Abstract: In one embodiment, a method includes receiving a plurality of radio frequency chains at a wireless device in a block based modulation environment, recording subcarrier phases and differences between the subcarrier phases, and using the subcarrier phase differences to construct a feature vector for use in angle of arrival calculated positioning of a mobile device.

Abstract: In one embodiment, an apparatus includes a processor for processing a plurality of radio frequency chains at a wireless device in a block based modulation environment, recording subcarrier phases and differences between the subcarrier phases, and using the subcarrier phase differences to construct a feature vector for use in angle of arrival calculated positioning of a mobile device, and memory for storing the subcarrier phases and the feature vector.

Abstract: In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by client devices in the network. The service associates metrics regarding roaming delays to mobility paths in the mobility path graph. The service identifies a roaming boundary change that is predicted to reduce roaming delays between two or more wireless access points in the network, in part by assessing the metrics regarding roaming delays associated with the mobility paths in the mobility path graph. The service provides an indication of the identified roaming boundary change to a user interface.

Abstract: Apparatus, methods, and systems for automated liquid droplet manipulation include an open droplet supporting surface. An actuator can translate the surface in space with at least one degree freedom of movement to influence movement of one or more droplets on the surface. In one embodiment, the surface is patterned with areas that attract the droplets and interstitial areas that repel the droplets to enhance transport of droplets. For example, for water-based droplets the attracting areas can be hydrophilic and the repelling hydrophobic. In one embodiment, the repelling areas are superhydrophobic. Electromechanical movement of the surface avoids expensive and complex microfluidic fabrication and components, and avoids electrowetting requirements.

Abstract: Determining a location of a user device comprises a wireless computing system supported by an access point. The wireless computing system receives a signal from the user device. The system estimates a location of the user device based on RSSI and calculates a boundary around the estimated location. The wireless computing system selects a plurality of sections inside of the boundary and performs a coarse calculation of a location of the user device based on an angle of arrival of the received signal. The system determines sections of the plurality of sections that have results from the coarse calculation that are more likely to be a location of the user device. The system performs a fine calculation of the location based on the angle of arrival of the received signal within each of the sections. The system identifies a particular section as the location of the user device.

Abstract: In one embodiment, a service receives data indicative of roaming failures along mobility paths in a network. The mobility paths represent ordered series of wireless access points via which wireless clients have accessed the network over time. The service uses, based on the data indicative of the roaming failures, a machine learning-based model to associate mobility path failure metrics with portions of the mobility paths. The service identifies, for a first mobility path, an alternate mobility path that has a lower mobility path failure metric than that of the first mobility path. The service triggers a mobility path reroute for a particular client device in the network on the first mobility path to the alternate mobility path.

Abstract: An example method is provided in one example embodiment and may include gathering current wireless local area network (WLAN) data for a WLAN, wherein the WLAN data comprises network data, Radio Frequency (RF) data, and transmission data for a plurality of user equipment (UE) operating within the WLAN; generating a plurality of color maps; merging the plurality of color maps to generate a combined color map; and calculating a predicted application score for at least one UE operating within the WLAN based, at least in part, on application of the combined color map to a trained statistical model that represents linking relationships between the WLAN data gathered for the WLAN and a plurality of possible application scores for the plurality of UE. The plurality of color maps can include an RF color map, a transmission color map, and a Quality of Service color map.

Abstract: In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network. The service identifies, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam. The service performs, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam. The service sends handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.

Abstract: In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network. The service identifies, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam. The service performs, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam. The service sends handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.

Abstract: In one embodiment, a service receives data indicative of roaming failures along mobility paths in a network. The mobility paths represent ordered series of wireless access points via which wireless clients have accessed the network over time. The service uses, based on the data indicative of the roaming failures, a machine learning-based model to associate mobility path failure metrics with portions of the mobility paths. The service identifies, for a first mobility path, an alternate mobility path that has a lower mobility path failure metric than that of the first mobility path. The service triggers a mobility path reroute for a particular client device in the network on the first mobility path to the alternate mobility path.

Abstract: In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by client devices in the network. The service associates metrics regarding roaming delays to mobility paths in the mobility path graph. The service identifies a roaming boundary change that is predicted to reduce roaming delays between two or more wireless access points in the network, in part by assessing the metrics regarding roaming delays associated with the mobility paths in the mobility path graph. The service provides an indication of the identified roaming boundary change to a user interface.

Abstract: Determining a location of a user device comprises a wireless system supported by wireless access points receiving signals from a user device. The wireless system estimates a location of the user device based on a coarse calculation based on an angle of arrival of the received signal and determines if the user device is in an area under an access point. The wireless system identifies received signal strength indicator values for the user device and uses the values and the calculated location of the user device to improve calibration of a received signal strength location model. If the system determines that the user is not in the area under the access point, the wireless system combines the coarse calculation location, a received signal strength determined location, and a previous received signal strength determined location, and estimates a location based on the combined calculations.

Abstract: Determining a location of a user device comprises a wireless computing system supported by an access point. The wireless computing system receives a signal from the user device. The system estimates a location of the user device based on RSSI and calculates a boundary around the estimated location. The wireless computing system selects a plurality of sections inside of the boundary and performs a coarse calculation of a location of the user device based on an angle of arrival of the received signal. The system determines sections of the plurality of sections that have results from the coarse calculation that are more likely to be a location of the user device. The system performs a fine calculation of the location based on the angle of arrival of the received signal within each of the sections. The system identifies a particular section as the location of the user device.

Abstract: Determining a location of a user device comprises a wireless system supported by wireless access points receiving signals from a user device. The wireless system estimates a location of the user device based on a coarse calculation based on an angle of arrival of the received signal and determines if the user device is in an area under a access point. If so, the wireless system performs a fine location calculation. The wireless system identifies received signal strength indicator values for the user device and uses the values and the calculated location of the user device to improve calibration of a received signal strength location model. If the system determines that the user is not in the area under the access point, the wireless system combines the coarse calculation location and a received signal strength determined location and estimates a location based on the combined calculations.

Abstract: Determining a location of a user device comprises a wireless computing system supported by an access point. The wireless computing system receives a signal from the user device. The system estimates a location of the user device based on RSSI and calculates a boundary around the estimated location. The wireless computing system selects a plurality of sections inside of the boundary and performs a coarse calculation of a location of the user device based on an angle of arrival of the received signal. The system determines sections of the plurality of sections that have results from the coarse calculation that are more likely to be a location of the user device. The system performs a fine calculation of the location based on the angle of arrival of the received signal within each of the sections. The system identifies a particular section as the location of the user device.

Abstract: In an example embodiment, the orientation of a wireless device, such as an access point (AP) can be determined based on the location of neighboring wireless devices and the observed angle of arrival of signals from the wireless device at the neighboring wireless devices. For example, the angle of orientation can be determined by comparing an observed angle of arrival with the known actual angle between wireless devices. If a plurality of wireless devices measures the signal, the mean or median of the difference between observed angle of arrival of a signal from the wireless device with the actual angle for the plurality of wireless devices may be employed to determine the angular orientation.

Abstract: An example method is provided in one example embodiment and may include gathering current wireless local area network (WLAN) data for a WLAN, wherein the WLAN data comprises network data, Radio Frequency (RF) data, and transmission data for a plurality of user equipment (UE) operating within the WLAN; generating a plurality of color maps; merging the plurality of color maps to generate a combined color map; and calculating a predicted application score for at least one UE operating within the WLAN based, at least in part, on application of the combined color map to a trained statistical model that represents linking relationships between the WLAN data gathered for the WLAN and a plurality of possible application scores for the plurality of UE. The plurality of color maps can include an RF color map, a transmission color map, and a Quality of Service color map.

Abstract: Determining a location of a user device comprises a wireless system supported by wireless access points receiving signals from a user device. The wireless system estimates a location of the user device based on a coarse calculation based on an angle of arrival of the received signal and determines if the user device is in an area under an access point. The wireless system identifies received signal strength indicator values for the user device and uses the values and the calculated location of the user device to improve calibration of a received signal strength location model. If the system determines that the user is not in the area under the access point, the wireless system combines the coarse calculation location, a received signal strength determined location, and a previous received signal strength determined location, and estimates a location based on the combined calculations.