Abstract:
Techniques are disclosed for using the hardware and/or software of the mobile device to obscure speech in the audio data before a context determination is made by a context awareness application using the audio data. In particular, a subset of a continuous audio stream is captured such that speech (words, phrases and sentences) cannot be reliably reconstructed from the gathered audio. The subset is analyzed for audio characteristics, and a determination can be made regarding the ambient environment.
Abstract:
Systems and methods for monitoring the proximity of a personal item are provided. Systems and methods as provided herein allow a computing device to monitor the proximity of a personal item by generating an alert when wireless communications between the computing device and the personal item are lost in an unsafe zone to remind a user of the computing device about the personal item in an attempt to prevent leaving the personal item in the unsafe zone, where it may be susceptible to theft or loss. The provided systems and methods also automatically assign safe zones by analyzing clusters of location data points obtained by the computing device over time to determine a home location and an office location, assigning the home location and the office location as safe zones, and assigning all other locations as unsafe. A user may further manually designate locations as safe zones.
Abstract:
Systems and methods for providing application-controlled, power-efficient context (state) classification are described herein. An apparatus for performing context classification with adjustable granularity as described herein includes a classifier controller configured to receive a request for a context classification and a granularity input associated with the request; and a context classifier communicatively coupled to the classifier controller and configured to receive the request and the granularity input from the classifier controller, to select a resource usage level for the context classification based on the granularity input, wherein a granularity input indicating a higher granularity level is associated with a higher resource usage level and a granularity input indicating a lower granularity level is associated with a lower resource usage level, and to perform the context classification at the selected resource usage level.
Abstract:
System and methods for performing context inference in a computing device are disclosed. In one embodiment, a method of performing context inference includes: determining, at a computing device, a first context class using context-related data from at least one data source associated with a mobile device; and determining, at the mobile device, a fusion class based on the first context class, the fusion class being associated with at least one characteristic that is common to the first context class and a second context class that is different from the first context class.
Abstract:
Systems and methodologies are described that facilitate identifying peers based upon encoded signals during peer discovery in a peer to peer network. For example, direct signaling that partitions a time-frequency resource into a number of segments can be utilized to communicate an identifier within a peer discovery interval; thus, a particular segment selected for transmission can signal a portion of the identifier, while a remainder can be signaled based upon tones communicated within the selected segment. Moreover, a subset of symbols within the resource can be reserved (e.g., unused) to enable identifying and/or correcting timing offset. Further, signaling can be effectuated over a plurality of peer discovery intervals such that partial identifiers communicated during each of the peer discovery intervals can be linked (e.g., based upon overlapping bits and/or bloom filter information).
Abstract:
Methods, systems, computer-readable media, and apparatuses for inferring context are provided. In one potential implementation, first context information associated with a first duration is identified, second context information is accessed to determine a context segmentation boundary; and the first context information and the second context information is then aggregated to generate an inferred segmented aggregated context. In a further implementation, the first context information is used to average inferred contexts, and the context segmentation boundary is used to reset a start time for averaging the first context information.
Abstract:
In a multi-level power transmission scheme, an access point transmits at one power level, while repeatedly transmitting at a burst power level for short periods of time. For example, a femto cell may transmit a beacon with periodic high power bursts of short duration, while the femto cell transmit power also undergoes high power bursts aligned with the beacon bursts. In a network listen-based power control scheme, an access point listens for one or more parameters sent over-the-air by the network and then defines transmit power based on the received parameter(s). In some aspects, beacon transmit power may be set based on a defined outage radius parameter and the total received signal power on a channel. In some aspects, access point transmit power may be set based on a defined coverage parameter and the received energy associated with signals from access points of a certain type.
Abstract:
Disclosed is a system, apparatus, computer readable storage medium, and method to perform a context inference for a mobile device. In one embodiment, a data processing system includes a processor and a storage device configurable to store instructions to perform a context inference for the data processing system. Data may be received from at least a first sensor, and a first classification of the data from the sensor may be performed. Confidence for the first classification can be determined and a second sensor can be activated based on a determination that the confidence fails to meet a confidence threshold. A data sample classification from the activated second sensor may be classified jointly with the data from first sensor.
Abstract:
Systems and methodologies are described that facilitate identifying peers based upon encoded signals during peer discovery in a peer to peer network. For example, direct signaling that partitions a time-frequency resource into a number of segments can be utilized to communicate an identifier within a peer discovery interval; thus, a particular segment selected for transmission can signal a portion of the identifier, while a remainder can be signaled based upon tones communicated within the selected segment. Moreover, a subset of symbols within the resource can be reserved (e.g., unused) to enable identifying and/or correcting timing offset. Further, signaling can be effectuated over a plurality of peer discovery intervals such that partial identifiers communicated during each of the peer discovery intervals can be linked (e.g., based upon overlapping bits and/or bloom filter information).
Abstract:
Systems and methods for providing application-controlled, power-efficient context (state) classification are described herein. An apparatus for performing context classification with adjustable granularity as described herein includes a classifier controller configured to receive a request for a context classification and a granularity input associated with the request; and a context classifier communicatively coupled to the classifier controller and configured to receive the request and the granularity input from the classifier controller, to select a resource usage level for the context classification based on the granularity input, wherein a granularity input indicating a higher granularity level is associated with a higher resource usage level and a granularity input indicating a lower granularity level is associated with a lower resource usage level, and to perform the context classification at the selected resource usage level.