Abstract: This disclosure provides methods, systems, and apparatuses, for a microphone. In particular, the microphone includes a housing having an external device interface with a plurality of contacts including a data contact. An electro-acoustic transducer is configured to generate an electrical signal in response to sound. An electrical circuit is coupled to contacts of the interface, the electrical circuit including an ADC having an input coupled to an output of the conditioning circuit and configured to convert the electrical signal to audio data after conditioning. A controller is configured to communicate data, other than the audio data, via the data contact of the external device interface during a start-up transition period of the microphone assembly, wherein the controller is configured to communicate the audio data via the data contact of the external device interface only after the start-up transition period is complete.

Abstract: This disclosure provides methods, systems, and apparatuses, for a microphone. In particular, the microphone includes a housing having an external device interface with a plurality of contacts including a data contact. An electro-acoustic transducer is configured to generate an electrical signal in response to sound. An electrical circuit is coupled to contacts of the interface, the electrical circuit including an ADC having an input coupled to an output of the conditioning circuit and configured to convert the electrical signal to audio data after conditioning. A controller is configured to communicate data, other than the audio data, via the data contact of the external device interface during a start-up transition period of the microphone assembly, wherein the controller is configured to communicate the audio data via the data contact of the external device interface only after the start-up transition period is complete.

Abstract: A system comprises a plurality of sensors, a sensor processor, and a sampling rate engine. The sensor processor is coupled to an output of each sensor of the plurality of sensors. The sensor processor estimates user dynamics in response to a first output signal of a first sensor of the plurality of sensors. The sampling rate engine is coupled to an output of the sensor processor. The sampling rate engine determines a sampling rate value of a second sensor of the plurality of sensors in response to a user dynamics value from the sensor processor. The second sensor comprises a selectable sampling rate. The selectable sampling rate is configured in response to the sampling rate value determined by the sampling rate engine.

Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate and/or support one or more operations and/or techniques for selective crowdsourcing for multi-level positioning, such as positioning in a multi-level structure, for example.

Abstract: A system comprises a plurality of sensors, a sensor processor, and a sampling rate engine. The sensor processor is coupled to an output of each sensor of the plurality of sensors. The sensor processor estimates user dynamics in response to a first output signal of a first sensor of the plurality of sensors. The sampling rate engine is coupled to an output of the sensor processor. The sampling rate engine determines a sampling rate value of a second sensor of the plurality of sensors in response to a user dynamics value from the sensor processor. The second sensor comprises a selectable sampling rate. The selectable sampling rate is configured in response to the sampling rate value determined by the sampling rate engine.

Abstract: A system comprises a plurality of sensors, a sensor processor, and a sampling rate engine. The sensor processor is coupled to an output of each sensor of the plurality of sensors. The sensor processor estimates user dynamics in response to a first output signal of a first sensor of the plurality of sensors. The sampling rate engine is coupled to an output of the sensor processor./ The sampling rate engine determines a sampling rate value of a second sensor of the plurality of sensors in response to a user dynamics value from the sensor processor. The second sensor comprises a selectable sampling rate. The selectable sampling rate is configured in response to the sampling rate value determined by the sampling rate engine.

Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate and/or support one or more operations and/or techniques for selective crowdsourcing for multi-level positioning, such as positioning in a multi-level structure, for example.

Abstract: Method including detecting low user dynamics by a first MEMS sensor is provided. A first sensor determines sampling rate value corresponding to the low user dynamics. The first sensor sampling rate value is less than a second sensor sampling rate value corresponding to high user dynamics. A sampling rate of a second MEMS sensor is adjusted to the first sensor sampling rate value.

Abstract: Several methods and systems for location estimation are disclosed. In an embodiment, the method includes performing a primary wireless scan to identify a first set of access points at a user location associated with a first user location estimate. A secondary wireless scan is performed at pre-defined time intervals subsequent to the primary wireless scan. A set of access points is identified corresponding to each secondary wireless scan. The method further comprises detecting a presence or an absence of user motion based on a number of shared access points between the first set of access points and a set of access points corresponding to each secondary wireless scan. A current user location is estimated to be the first user location estimate if the user motion is detected to be absent, or a second user location estimate computed based on geolocation signals if the user motion is detected to be present.

Abstract: Several methods and systems for location estimation are disclosed. In an embodiment, the method includes performing a primary wireless scan to identify a first set of access points at a user location associated with a first user location estimate. A secondary wireless scan is performed at pre-defined time intervals subsequent to the primary wireless scan. A set of access points is identified corresponding to each secondary wireless scan. The method further comprises detecting a presence or an absence of user motion based on a number of shared access points between the first set of access points and a set of access points corresponding to each secondary wireless scan. A current user location is estimated to be the first user location estimate if the user motion is detected to be absent, or a second user location estimate computed based on geolocation signals if the user motion is detected to be present.

Abstract: A system comprises a plurality of sensors, a sensor processor, and a sampling rate engine. The sensor processor is coupled to an output of each sensor of the plurality of sensors. The sensor processor estimates user dynamics in response to a first output signal of a first sensor of the plurality of sensors. The sampling rate engine is coupled to an output of the sensor processor./ The sampling rate engine determines a sampling rate value of a second sensor of the plurality of sensors in response to a user dynamics value from the sensor processor. The second sensor comprises a selectable sampling rate. The selectable sampling rate is configured in response to the sampling rate value determined by the sampling rate engine.

Abstract: Method including detecting low user dynamics by a first MEMS sensor is provided. A first sensor determines sampling rate value corresponding to the low user dynamics. The first sensor sampling rate value is less than a second sensor sampling rate value corresponding to high user dynamics. A sampling rate of a second MEMS sensor is adjusted to the first sensor sampling rate value.

Abstract: At least some of the embodiments are methods including detecting low user dynamics by a first MEMS sensor, determining a first sensor sampling rate value corresponding to the low user dynamics wherein the first sensor sampling rate value is less than a second sensor sampling rate value corresponding to high user dynamics, and adjusting a sampling rate of a second MEMS sensor to the first sensor sampling rate value.

Abstract: Apparatus and methods for scanning for access points (APs) for wireless local area network (WLAN) positioning. In one embodiment a wireless device includes a WLAN positioning system. The WLAN positioning system includes an AP scanner. The AP scanner is configured to determine which WLAN channels are being used by APs proximate to the wireless device. The AP scanner is also configured to scan for AP transmissions only the WLAN channels determined to be used by APs proximate to the wireless device. The AP scanner is further configured to extract signal strength and AP identification information for WLAN positioning from the AP transmissions on the scanned channels.

Abstract: At least some of the embodiments are methods including detecting low user dynamics by a first MEMS sensor, determining a first sensor sampling rate value corresponding to the low user dynamics wherein the first sensor sampling rate value is less than a second sensor sampling rate value corresponding to high user dynamics, and adjusting a sampling rate of a second MEMS sensor to the first sensor sampling rate value.

Abstract: Apparatus and methods for scanning for access points (APs) for wireless local area network (WLAN) positioning. In one embodiment a wireless device includes a WLAN positioning system. The WLAN positioning system includes an AP scanner. The AP scanner is configured to determine which WLAN channels are being used by APs proximate to the wireless device. The AP scanner is also configured to scan for AP transmissions only the WLAN channels determined to be used by APs proximate to the wireless device. The AP scanner is further configured to extract signal strength and AP identification information for WLAN positioning from the AP transmissions on the scanned channels.