Abstract:
Methods, systems, computer-readable media, and apparatuses for determining indoor/outdoor state of a mobile device are presented. In some embodiments, a mobile device may maintain an indoor/outdoor state. The mobile device may include at least one first sensor and at least one second sensor, the first sensor associated with higher power consumption than the second sensor. The mobile device may gate off the first sensor and using the second sensor to obtain a sensor reading, if the second sensor can generate a reading indicative of the indoor/outdoor state of the mobile device. The mobile device may use the first sensor to obtain a sensor reading, if the second sensor cannot generate a reading indicative of the indoor/outdoor state of the mobile device. The mobile device may update the indoor/outdoor state of the mobile device based on a reading received from one of the first and the second sensors.
Abstract:
Systems and methods for applying and using context labels for data clusters are provided herein. A method described herein for managing a context model associated with a mobile device includes obtaining first data points associated with a first data stream assigned to one or more first data sources; assigning ones of the first data points to respective clusters of a set of clusters such that each cluster is respectively assigned ones of the first data points that exhibit a threshold amount of similarity and are associated with times within a threshold amount of time of each other; compiling statistical features and inferences corresponding to the first data stream or one or more other data streams assigned to respective other data sources; assigning context labels to each of the set of clusters based on the statistical features and inferences.
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:
Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.
Abstract:
Disclosed is an apparatus and method for speaker equalization in a mobile device. In one embodiment, placement of a mobile device including a speaker is determined and speaker equalization is performed in accordance with the determined placement.
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:
The disclosure is directed to modifying the operation of one or more hardware subsystems when a new context awareness service begins. An aspect determines a power budget for a plurality of operating context awareness services including the new context awareness service, wherein the power budget is based on a power requirement for each of the plurality of context awareness services, and wherein the power requirement for each of the plurality of context awareness services is based on power utilizations of the one or more hardware subsystems corresponding to the plurality of context awareness services, and allocates power resources to the one or more hardware subsystems based on importances of the plurality of context awareness services and/or the one or more hardware subsystems, wherein the allocation of the power resources is performed within the power budget.
Abstract:
Transmit power for an access point is controlled based on information received by the access point. For example, an access point may employ one or more algorithms that use messages received from nearby access terminals to maintain an acceptable tradeoff between providing an adequate coverage area for access point transmissions and mitigating interference that these transmissions cause at nearby access terminals. Here, the access point may employ a network listen-based algorithm upon initialization of the access terminal to provide preliminary transmit power control until sufficient information is collected for another transmit power control algorithm (e.g., an access terminal assisted algorithm). Also, the access terminal may employ an active access terminal protection scheme to mitigate interference the access point may otherwise cause to a nearby access terminal that is in active communication with another access point.
Abstract:
Systems and methods for classification of audio environments are disclosed. In one embodiment, a method of classifying an audio environment comprises sampling the audio environment to obtain sampled audio data in accordance with a first time interval, computing features of the sampled audio data, inferring an audio cluster identifier from the features of the sampled audio data in accordance with a second time interval, and updating an audio environment model using the features of the sampled audio data in accordance with a third time interval.
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).